case study of young adults

Adolescence

The transition from adolescence to adulthood, emerging adulthood involves two key components of autonomy..

Posted July 26, 2021 | Reviewed by Ekua Hagan

• Emotional autonomy refers to becoming free of childish emotional dependence on adults.
• Behavioral autonomy refers to youth becoming more skilled in their own self-governing behavior and independent enough to make decisions.
• From the onset of puberty through age 25, the adolescent brain undergoes profound changes in structure and function.
• The developmental period of emerging adulthood offers incredible opportunities for growth and change.

The transition to adulthood is critical but often misunderstood. As societal and economic changes have created new demands and challenges for young people, particularly those in the 18- to 25-year-old range, we now recognize emerging adulthood as a distinct period separate from adolescence and older adulthood (Arnett, 2004). During this period, emerging adults experience new life roles.

Research by Arnett (2004) and others (Kroger, Martinussen, & Marcia, 2010) has shown the length of time for young people to actually create a personal identity has increased to the mid-to-late 20s. Emerging adulthood in Western culture is still a time of shifting identities. There is a continued risk of experimentation with unhealthy behaviors, posing perhaps an even greater risk for the young people in this later emerging adulthood stage. They are no longer minors and are faced with two additional life challenges: increased adult responsibilities and decreased familial support.

Autonomy: Emotional and Behavioral Components

A core element in the journey to adulthood involves the attainment of autonomy (Rice & Dolgin, 2008). During this time period, young people establish their uniqueness from others, and new interests, values, goals , and worldviews divergent from close others may emerge (Rice & Dolgin, 2008). As a normal developmental process, autonomy has been described as having two components: emotional and behavioral.

Emotional autonomy refers to becoming free of childish emotional dependence on adults (Rice & Dolgin, 2008). Parents can either foster an overdependence on the developing young person or provide the opposite, a lack of guidance and support. Clearly, a balance of both is the most preferred course of action (Rice & Dolgin, 2008). Behavioral autonomy refers to youth learning to become more skilled in their own self-governing behavior and independent enough to make decisions on their own accord (Holmbeck et al., 2006; Rice & Dolgin, 2008). Young persons are faced with the ultimate developmental conundrum: On the one hand, they are met with the task of exploring alternative behaviors and roles that smack of adultlike decisions, and on the other hand, they bear the new, yet daunting role of no longer being confined by parental and once-perceived societal regulations.

From the onset of puberty through age 25, the adolescent brain undergoes profound changes in structure and function (Wetherill & Tapert, 2013). Advances in developmental neuroscience and neuroimaging demonstrate regions of the brain develop at different rates—from birth to emerging adulthood (Mills, Goddings, Clasen, Giedd, & Blakemore, 2014). Recognizing how many adolescent behaviors can be attributed to a developmental mismatch between structural/functional imbalances in certain brain regions is a key to MI spirit. Recent research about two key brain regions has evidenced how structural changes affect functional behavioral outputs in youth (Feldstein Ewing, Tapert, & Molina, 2016; Luciana & Feldstein Ewing, 2015). Specifically, evidence is emerging on how the limbic regions are associated with reward and emotional regulation , and how regions such as the prefrontal cortex are associated with cognitive control. Other brain regions, associated with the activation and processing of social information, can actually enhance the development of adolescent cognitive executive functions , as compared to other developmental periods (Steinberg, 2008). For example, while impulsive behaviors may be seen as a lack of “cognitive control,” we concur some degree of risk-taking behavior may be necessary and important for youth to gain important life experiences required to assume adult roles.

This developmental period offers incredible opportunities. In contrast to older adults whose brains are no longer in a formative stage of development, the neural networks of youth are being reshaped with each learning experience. Understanding these processes can further help you to understand how to turn challenges into opportunities for growth.

Arnett JJ. Emerging adulthood: The winding road from the late teens through the twenties. Oxford University Press, USA; 2004.

Ewing SWF, Tapert SF, Molina BS. Uniting adolescent neuroimaging and treatment research: Recommendations in pursuit of improved integration. Neuroscience & Biobehavioral Reviews. 2016;62:109-114.

Holmbeck GN, O’Mahar K, Abad M, Colder C, Updegrove A. Cognitive-behavioral therapy with adolescents. Child and adolescent therapy: Cognitive-behavioral procedures. 2006:419-464.

Kroger J, Martinussen M, Marcia JE. Identity status change during adolescence and young adulthood: A meta-analysis. Journal of adolescence. 2010;33(5):683-698.

Luciana M, Ewing SWF. Introduction to the special issue: Substance use and the adolescent brain: Developmental impacts, interventions, and longitudinal outcomes. Elsevier; 2015.

Mills KL, Goddings A-L, Clasen LS, Giedd JN, Blakemore S-J. The developmental mismatch in structural brain maturation during adolescence. Developmental neuroscience. 2014;36(3-4):147-160.

Rice PF, Dolgin KG. The Adolescent: Development, relationships, and culture. 12th ed. Allyn & Bacon; 2008.

Steinberg L. A social neuroscience perspective on adolescent risk-taking. Developmental review. 2008;28(1):78-106.

Wetherill R, Tapert SF. Adolescent brain development, substance use, and psychotherapeutic change. Psychology of Addictive Behaviors. 2013;27(2):393.

Sylvie Naar, Ph.D., is the Distinguished Endowed Professor in the College of Medicine’s department of Behavioral Sciences and Social Medicine at Florida State University, where she is the founding director of the Center for Translational Behavioral Science.

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Most in the u.s. say young adults today face more challenges than their parents’ generation in some key areas.

About seven-in-ten Americans think young adults today have a harder time than their parents’ generation when it comes to saving for the future (72%), paying for college (71%) and buying a home (70%), according to a Pew Research Center survey conducted in October 2021. These findings come at a time when younger Americans are more likely than previous generations to have taken on student debt with tuition costs steadily rising, and to face an affordable housing crisis as rent and housing prices have grown markedly faster than incomes in the last decade.

To learn more about how Americans view the circumstances young adults face across various life measures compared with their parents’ generation, Pew Research Center surveyed 9,676 U.S. adults between Oct. 18-24, 2021. Everyone who took part is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way, nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the  ATP’s methodology .

Here are the questions used for this analysis , along with responses, and  its methodology .

There’s less consensus when it comes to assessing labor market outcomes for young people today compared with their parents’ generation. Similar shares say finding a job is easier (40%) as say it is harder (39%) for young adults today. A smaller share of U.S. adults (21%) say it’s about the same.

When it comes to finding a spouse or partner, Americans are more than twice as likely to say younger adults today have it harder than their parents’ generation (46%) than to say they have it easier (21%). Around a third (32%) say it’s about the same.

On some other measures, Americans are more positive in their assessments of young adults’ circumstances. A significant majority of U.S. adults (74%) say it is easier for younger generations today to stay in touch with family and friends. Only 14% say this is harder for young adults compared with their parents’ generation. A plurality (41%) says getting into college is easier for young adults today compared with their parents’ generation; 33% say it’s harder for young adults today and 26% say it’s about the same.

There are notable age differences when it comes to assessing the circumstances of young adults today.

While majorities across all age groups say young adults have it harder when it comes to buying a home, saving for the future and paying for college, Americans ages 18 to 29 are more likely than older age groups to say this. More than eight-in-ten adults younger than 30 (84%) say buying a home is harder for young adults today, while 80% say the same about saving for the future and paying for college. Among those ages 30 to 49, 72% say buying a home and paying for college is harder for young adults today, and 74% say this about saving for the future. Those 50 and older are the least likely to say these measures are harder for younger generations to reach, with 63% saying this about buying a home, 67% saying this about saving for the future, and 66% saying this about paying for college.

When it comes to finding a job, younger Americans are again the most likely to say this is harder for young adults today. Overall, 55% of 18- to 29-year-olds say finding a job is harder for young adults today than it was for their parents’ generation. About four-in-ten or less of those ages 30 to 49 and those 50 and older say this about young adults (39% and 33%, respectively). There are also double-digit differences between the views of adults younger than 30 and those ages 50 or older when it comes to finding a spouse or partner (52% of 18- to 29-year-olds say this is harder for young adults today vs. 42% in the older group) and getting into college (45% vs. 27%, respectively). In fact, a plurality of adults 50 and older say getting into college is easier today (44%). There are no large differences by age on the measure of staying in touch with family and friends.

Generally, these views differ only modestly by gender, with one exception. On finding a spouse or partner, about half of women (51%) – compared with 40% of men – say this is harder for young adults today than it was for their parents’ generation. This gap is only present among those ages 30 and older; roughly equal shares of women (53%) and men (52%) younger than 30 say this is harder for young adults today. Notably, women in older age groups give similar answers as younger women, while older men are less likely than their younger counterparts to say finding a spouse or partner is harder for young adults today (42% of men 30 to 49 and 34% of men 50 and older say this).

Finally, on most of these measures, there are no significant differences between adults who are parents of children ages 18 to 29 and those who are not. On a few items where such differences exist, they tend to disappear when looking at adults 50 and older. The only item where such differences persist among older adults is on assessments of finding a job. Interestingly, those 50 and older who are parents of adult children ages 18 to 29 are more likely than those in the same age group who do not have young adult children to say young adults today have it easier when it comes to finding a job (47% vs. 42%, respectively).

Note: Here are the questions used for this analysis , along with responses, and  its methodology .

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About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .

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• Open access
• Published: 05 October 2022

The impact of the initial COVID-19 outbreak on young adults’ mental health: a longitudinal study of risk and resilience factors

• Anna Wiedemann 1 , 2 , 3 ,
• Jan Stochl 1 , 3 , 4 ,
• Sharon A. S. Neufeld 1 ,
• Jessica Fritz 1 , 5 ,
• Junaid Bhatti 1 ,
• Roxanne W. Hook 1 ,
• NSPN Consortium ,
• Ian M. Goodyer 1 ,
• Raymond J. Dolan 6 ,
• Edward T. Bullmore 1 ,
• Samuel R. Chamberlain 7 , 8 ,
• Peter Fonagy 9 ,
• Jesus Perez 1 , 2 , 3 , 10 , 11 &
• Peter B. Jones 1 , 2 , 3  

Scientific Reports volume  12 , Article number:  16659 ( 2022 ) Cite this article

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• Epidemiology
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Risk factors

Few studies assessing the effects of COVID-19 on mental health include prospective markers of risk and resilience necessary to understand and mitigate the combined impacts of the pandemic, lockdowns, and other societal responses. This population-based study of young adults includes individuals from the Neuroscience in Psychiatry Network ( n  = 2403) recruited from English primary care services and schools in 2012–2013 when aged 14–24. Participants were followed up three times thereafter, most recently during the initial outbreak of the COVID-19 outbreak when they were aged between 19 and 34. Repeated measures of psychological distress (K6) and mental wellbeing (SWEMWBS) were supplemented at the latest assessment by clinical measures of depression (PHQ-9) and anxiety (GAD-7). A total of 1000 participants, 42% of the original cohort, returned to take part in the COVID-19 follow-up; 737 completed all four assessments [mean age (SD), 25.6 (3.2) years; 65.4% female; 79.1% White]. Our findings show that the pandemic led to pronounced deviations from existing mental health-related trajectories compared to expected levels over approximately seven years. About three-in-ten young adults reported clinically significant depression (28.8%) or anxiety (27.6%) under current NHS guidelines; two-in-ten met clinical cut-offs for both. About 9% reported levels of psychological distress likely to be associated with serious functional impairments that substantially interfere with major life activities; an increase by 3% compared to pre-pandemic levels. Deviations from personal trajectories were not necessarily restricted to conventional risk factors; however, individuals with pre-existing health conditions suffered disproportionately during the initial outbreak of the COVID-19 pandemic. Resilience factors known to support mental health, particularly in response to adverse events, were at best mildly protective of individual psychological responses to the pandemic. Our findings underline the importance of monitoring the long-term effects of the ongoing pandemic on young adults’ mental health, an age group at particular risk for the emergence of psychopathologies. Our findings further suggest that maintaining access to mental health care services during future waves, or potential new pandemics, is particularly crucial for those with pre-existing health conditions. Even though resilience factors known to support mental health were only mildly protective during the initial outbreak of the COVID-19 pandemic, it remains to be seen whether these factors facilitate mental health in the long term.

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Introduction

It has been suggested that the ongoing COVID-19 pandemic may be fuelling a mental health crisis, particularly in adolescents and young adults 1 , 2 . Even though severe disease and any direct neuropsychiatric effects caused by SARS-CoV-2 are uncommon in this age group, the psychological, social, educational, and economic effects of repeated stay-at-home orders, i.e., lockdowns , and the emerging concern about the long-term effects of COVID-19 might be expected to exert a toll that could endure long after the pandemic has receded. Despite the need for a rigorous, coordinated response from researchers, few studies on the mental health-related effects of the COVID-19 pandemic within young adults have been longitudinal, or incorporated unbiased measures of pre-pandemic risk and resilience factors. Most have used cross-sectional designs, convenience samples without pre-pandemic comparisons, and have used modified and unvalidated measures 3 . Amongst those which did include population-based data and measures before January 2020, the month the World Health Organisation declared the COVID-19 outbreak a public health emergency of international concern, the overwhelming majority suggests that the initial outbreak of COVID-19 has had a substantial impact on young adults’ mental health, showing a marked increase in symptoms of common mental health disorders such as depression and anxiety compared to pre-pandemic levels 4 , 5 , 6 , 7 . A very small number of studies, however, have found no changes in the prevalence of anxiety and depression symptoms 8 , 9 .

Studies assessing the impact of COVID-19 on mental health have predominately focused on risk factors associated with poorer mental health outcomes, finding that younger age, being female, and having pre-existing health conditions are most commonly related to increased distress and anxiety levels during the pandemic 10 , 11 , 12 , 13 , 14 . Only a few studies considered data from more than one pre-pandemic assessment, limiting the evidence about how the pandemic has impacted mental health-related trajectories, particularly in adolescence and emerging adulthood 15 , 16 , 17 . Even though some studies assessed coping strategies and potential protective factors 16 , 17 , 18 , we identified no study which tested a range of prospectively-measured resilience factors in this age group.

What is the added value of this study?

This population-based study addresses several limitations within the current literature by examining both risk and resilience in a representative cohort of young adults first assessed at age 14–24 years in 2012–2013 and followed up three times thereafter, most recently during the first lockdown in the United Kingdom in Spring 2020. Surprisingly little research to date has focused on understanding what facilitates mental health and wellbeing during the COVID-19 pandemic. We used latent growth curve modelling of individual trajectories for measurements of psychological distress and mental wellbeing over the first three pre-pandemic assessments (2012–2017) to predict expected mental health at the fourth assessment during the initial outbreak of the COVID-19 pandemic (May 2020). We then quantified pandemic-related effects on mental health as the difference between these expected mental health outcomes and those actually observed (we call these differences ‘extended residuals’ throughout the article).

We hypothesised that observed psychological distress would be higher than expected, and observed mental wellbeing lower than expected, and that this would be more prominent in those at higher risk for common mental disorders such as depression and anxiety. We subsequently assessed the role of prospectively-measured and evidence-based resilience factors at individual, family, and community level. We hypothesised that those who endorsed higher levels across these domains prior to the COVID-19 outbreak, for instance, those reporting high family or friendship support, would deviate less from their expected mental health trajectory compared with those reporting lower levels across these domains.

Study design and participants

Participants were part of the Neuroscience in Psychiatry Network 2400 cohort (NSPN; n  = 2403) established in 2012 to study the emergence of psychopathology and psychiatric disorders across adolescence and young adulthood. NSPN 2400 is a British cohort that was recruited through primary care services and schools in Greater London, Cambridgeshire, and Peterborough. A total of 2,403 participants were recruited into an age and sex-stratified sample with roughly equal numbers of males and females across five age groups 14–15, 16–17, 18–19, 20–21, and 22–24. The cohort is broadly representative of the youth population across England and Wales when compared to census data of ethnicity, country of birth, parental education, sex, and deprivation. Participants were followed up three times after the baseline, most recently when they were aged between 19 and 34 during the first national lockdown in May 2020.

This work follows the STROBE guidelines for cohort studies 19 . For further details on recruitment, measures, and representativeness of the cohort, we refer the reader to the published cohort profile by Kiddle and colleagues 20 , an update is currently in preparation. The STROBE diagram, which includes the most recent follow-up, is shown in Fig.  1 .

STROBE diagram illustrating the recruitment stages of the NSPN 2400 cohort; adapted from Kiddle et al. (2018). Eol   expression of interest, HQP   home questionnaire pack; superscript a: 36 practices in Cambridgeshire and Peterborough Primary Care Trust (PCT), 8 in Barnet PCT, 3 in Camden PCT and 3 in Islington PCT; superscript b: schools in Barnet (2), Camden (4), Islington, Tower Hamlets, Haringey, Lambeth and Redbridge (all 1 each), and colleges in Cambridgeshire and Peterborough (6) and Islington (1); superscript c: excluded due to current age beyond scope; superscript d: Assessment 4 was designed as an online survey for which all baseline participants who (a) had a valid email address, and (b) had not withdrawn consent in previous assessments, have been invited (note that for previous assessments only participants who took part in the preceding assessment were invited); superscript e: excluded due to uncertainty of survey responder identity.

Ethical standards

Ethical approval for this study was granted by the Cambridge East Research Ethics Committee under REC 12/EE/0250 for the first three assessments and REC 16/EE/0260 for the fourth assessment. Informed consent was obtained from all participants and/or their legal guardian(s). The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008.

Primary outcome measures

We used the Kessler Psychological Distress Scale (K6) 21 , 22 and the short version of the Warwick-Edinburgh Mental Wellbeing Scale (SWEMWBS) 23 to assess changes in psychological distress and wellbeing across time. Both of these self-report measures were designed for use in epidemiological surveys and have shown good ability to monitor population prevalence and trends in psychological distress and wellbeing.

The K6 consists of a 6-item subset of its longer 10-item version assessing non-specific distress relating to depressive and anxious phenomena over the past four weeks. It shows superior total classification accuracy and precision in discrimination between individuals with and without mental illness compared with its longer 10-item version 21 . Its psychometric properties have been extensively validated in adults and increasingly in adolescence and emerging adults, showing that it accurately detects mood and anxiety disorders in this age group , 24 , 25 . However, it has been suggested that due to the high rates of behaviour disorders in adolescence, the K6 should include indicators of such symptoms to adequately screen for serious mental illness 26 . To date, the scale covers symptoms of depression and anxiety such as feeling nervous, hopeless, restless, or worthless. Participants rate each statement on a 5-point Likert scale ranging from 'none of the time' to 'all of the time'. Scores range from 0 to 24. A score of 13 or higher is used to identify individuals with non-specific serious psychological distress, i.e., they have a high likelihood of having a diagnosable mental health condition severe enough to cause major functional limitations which require treatment 21 . It is estimated that serious mental illness affects about 3–4% of the adult population 27 , prevalence in adolescence is estimated to be slightly higher 28 .

The SWEMWBS is a 7-item shortened version of the 14-item WEMWBS measuring aspects of psychological wellbeing over the past two weeks. It is widely used in population studies due to its robust measurement properties, brevity, and well-established population norms in adults and adolescents 29 , 30 . Items relate to thoughts and feelings of mental wellbeing such as feeling close to other people or feeling relaxed. Participants rate each item on a 5-point Likert scale ranging from ‘none of the time’ to ‘all of the time’. Scores range from 7 to 35 with higher scores indicating better mental wellbeing. Total raw scores are converted to metric scores for parametric statistical procedures using the conversion table published by Stewart-Brown and colleagues 23 .

We assessed the factor structure of both our primary outcome measures. Confirmatory factor analysis within our study sample showed good fit for a one-factor solution and good reliability for either measure. For further details, see Supplementary Materials 1 .

Secondary outcome measures

We further assessed depression and anxiety using the Patient Health Questionnaire (PHQ-9) 31 and the Generalised Anxiety Disorder Questionnaire (GAD-7) 32 . Both measures have been added to the most recent NSPN follow-up due to their common use across primary and secondary mental health care services in the UK. The Improving Access to Psychological Therapies (IAPT) services, for instance, routinely use both the PHQ-9 and the GAD-7 to define clinical cases and monitor recovery rates. We were particularly interested to assess IAPT-defined clinical cases during the initial outbreak of the COVID-19 pandemic as a secondary outcome and as an additional validation procedure of our primary outcome approach. Items for both scales are answered on a 4-point Likert scale ranging from ‘not at all’ to ‘nearly every day’ with total scores ranging from 0 to 27 and 0 to 21 for the PHQ-9 and GAD-7 respectively; IAPT-defined clinical cut-offs are ≥ 10 for depression and ≥ 8 for anxiety 33 .

Risk factors for psychological distress (higher K6 scores), and poor mental wellbeing (lower SWEMWBS scores), were divided into two major categories covering commonly reported sociodemographic characteristics associated with common mental illnesses such as depression or anxiety as well as specific pandemic-related risk factors from a questionnaire included in the fourth assessment.

Sociodemographic risk factors comprised younger age, female sex, lower educational attainments, non-white ethnicity, and socio-economic deprivation. Qualifications levels were assessed as in the 2011 Census for England and Wales where low educational attainments are defined as no qualification, or any qualifications below Level 4 which is any qualifications below a first degree (such as, e.g., BSc, BA) or equivalent. Ethnicity was obtained at baseline and first follow-up following the Office for National Statistics (ONS) guidelines for the same census. Index of Multiple Deprivation (IMD) was collected at baseline only and was calculated based on the 2015 English Indices of Deprivation. For our analysis, we used deprivation deciles, calculated by ranking all areas from most deprived to least deprived and then dividing them into ten equal groups. The lowest decile refers to the most deprived and the highest to the least deprived areas across England. The pandemic-related questionnaire obtained information on pre-existing health conditions, living situation, self-isolation status, major childcare commitments, and pandemic-related adverse experiences such as job loss, a major cut in household income, hospitalisation, or death due to COVID-19 or another cause. A detailed list of pandemic-related questionnaire items is provided in Supplementary Materials 2 .

Resilience factors

All resilience factors were chosen based upon a pre-registered systematic review 34 and subsequent investigations by Fritz and colleagues, using exactly the same measures wherever possible 35 , 36 , 37 . We included seven amendable resilience factors at individual, family, and community levels, all assessed through self-report at baseline. Further details on each scale as well as psychometric properties can be found in Supplementary Materials 3 .

At individual level:

High self-esteem as assessed using the Rosenberg Self-Esteem Scale 38 , a widely-used 10-item scale measuring positive and negative feelings about the self.

Low aggression as assessed through four items of the Antisocial Behavioural Checklist 39 , originally derived from DSM-IV clinical criteria for conduct disorder behaviour.

Low expressive suppression as measured by the relevant item on the Antisocial Process Screening Device 40 .

Low ruminative brooding as assessed by the relevant item in the Short Leyton Obsessional Inventory 41 .

At family and community level:

Positive and involved parenting assessed through six items from the Alabama Parenting Questionnaire 42 measuring retrospectively perceived parenting practices and parental involvement.

High family support and high family cohesion measured through the 12-item general functioning subscale of the McMaster Family Assessment Device 43 , a widely used instrument based on the McMaster’s model of general family functioning.

High friendship support as measured by six items of the Cambridge Friendship Questionnaire 44 .

Statistical analysis

We used latent growth curve modelling, a longitudinal analysis technique to estimate growth over time, to fit individual pre -pandemic mental health trajectories for total scores of K6 and SWEMWBS. As the NSPN cohort was assessed three times before the initial outbreak of the COVID-19 pandemic, we were limited to estimate linear trends as non-linear estimations require at least four time points. We then used the obtained latent intercept and slope to predict expected total scores at the time point of the fourth assessment. A similar approach has been used previously to estimate the effect of the COVID-19 outbreak on mental health in a community sample of Canadian adolescents 15 . We quantified individual pandemic-related effects by subtracting expected mental health outcomes from those actually observed; we refer to these outcomes as pandemic-related extended residuals throughout the article (cf. Fig.  2 ). The closer an individual scored to zero, the closer their observed mental health score during the pandemic matched their expected mental health score based on their pre-pandemic trajectory through assessments 1–3. An individual with a higher-than-expected K6 score during the pandemic, for instance, would have a positive K6 extended residual score reflecting higher-than-expected psychological distress. Similarly, an individual with a lower-than-expected SWEMWBS score during the pandemic would have a negative SWEMWBS extended residual score reflecting lower-than-expected mental wellbeing. Thus, the pandemic-related extended residuals do not measure cross-sectional mental health at the fourth assessment; rather, they are an indication of the psychological response to lockdown, or the initial outbreak of the COVID-19 pandemic in more general. Pandemic-related extended residuals were computed for participants who completed either of the primary outcome measures at all four time points. The analytical sample might therefore differ for K6 and SWEMWBS as participants could skip questions or questionnaires if they wished to. These individuals were still included in the cohort to maximise participation and to recognise that they may have valuable data to offer. More details on primary outcome measure availability are provided in Supplementary Materials 1 . A comparison of participants who took part in all four assessments with those who dropped out is presented in Supplementary Materials 4 .

Illustration of latent growth curve modelling and computation of extended residuals; these have been computed for both primary outcome measures separately. Please note we used the exact time point of each assessment as latent slope loadings to account for the length of individual trajectories (cf. Fig. 1 for assessment periods).

We assessed mental health-related risk during the pandemic cross-sectionally as well as longitudinally by comparing linear regression models for both observed scores and extended residual scores separately for the K6 and SWEMWBS. Categorical risk factors were binarised as present or absent. We computed both unadjusted as well as adjusted models; the latter adjusted for sociodemographic and pandemic-related risk factors separately. Any p -values were corrected for multiple comparisons using the Holm-Bonferroni method 45 . Resilience factors for subsequent analyses were calculated using confirmatory factor analysis, treating item-level data as categorical, analysing polychoric correlation matrices, and using mean- and variance-adjusted weighted least squares as estimator. Further details are provided in Supplementary Materials 3 .

The relationship between mental health-related resilience factors and extended residuals was assessed via two regularised partial correlation networks, one for the pandemic-related extended residuals of psychological distress (K6), and another for the extended residuals of mental wellbeing (SWEMWBS). The use of such model approaches has increased substantially over the last decade as they have shown to be a useful tool to explore psychopathology 46 . Results are visualised as network graphs (cf. Fig.  6 ) where “nodes” (circles or squares) represent variables, in our case, the extended residuals of interest as well as resilience factors, and the “edges” (lines) represent conditional dependencies between two respective variables. Technical details on the model regularisation are provided in Supplementary Materials 6 . Network estimation was based on the full information sample , i.e., including all possible pairwise associations which includes participants with some missing data. Sensitivity analyses correlating the adjacency matrix, that is the matrix describing the finite graph structure, with its corresponding adjacency matrix for the complete information sample (only including participants with no missing data) for both the K6 and SWEMWBS network, showed these matrices were perfectly correlated, supporting the feasibility of this approach (K6: n  = 632 (full) vs. n  = 579 (complete); SWEMWBS: n  = 620 (full) vs. = 566 (complete); r  = 1.00 for both). We further computed node predictability, here operationalised as R 2 , for the extended residuals to assess the practical relevance of these particular edges. Additional analyses, aimed at evaluating the robustness of the estimated networks are discussed in Supplementary Materials 6 rather than in the article itself.

All data were managed using REDCap 47 , 48 electronic data capture tools hosted at the University of Cambridge. REDCap is a secure, web-based application designed to support data capture for research studies, providing (1) an intuitive interface for validated data entry; (2) audit trails for tracking data manipulation and export procedures; (3) automated export procedures for seamless data downloads to common statistical packages; and (4) procedures for importing data from external sources. Analyses were conducted in R [Version 4.0.4] 49 as well as MPLUS [Version 8.5] 50 . Details on R-packages used are provided in the relevant supplementary materials.

Of the 2403 members of the NSPN 2400 cohort, 1000 (42%) responded to the fourth assessment during the first national lockdown. Of these, 737 had taken part in all four assessments; their sociodemographic characteristics are described in Table 1 . Data on primary outcome measures were not available for all of them as outlined in “ Statistical analysis ”; the analytical sample for our longitudinal analyses included 632 participants for the K6 and 620 for the SWEMWBS, with an overlap of 598 participants. The corresponding tables of sample characteristics for both sub-samples are presented in Supplementary Materials 4 a and 4b. Overall, more male than female participants dropped out, but other key characteristics such as ethnicity, country of birth, parental education, and deprivation remained relatively stable (for further details, see Supplementary Materials 4 , Table 6). Baseline mental health as assessed by the primary outcome measures did not differ between the analysed sub-samples when compared with the rest of the cohort.

We observed an overall shift of score distributions in both primary outcome measures such that the whole population scored higher on psychological distress and lower on mental wellbeing during the initial outbreak of the COVID-19 pandemic and the first national lockdown when compared with pre-pandemic assessments. This is reflected in Fig.  3 which shows density distributions of both primary outcome measures across all four assessments. The density function of both outcome measures obtained at baseline is significantly different from the density distribution obtained from these measures during the initial outbreak of the COVID-19 pandemic (Kolmogorov–Smirnov test: D K6  = 0.11, p  = 0.001, n  = 632; D SWEMWBS  = 0.09, p  = 0.02, n  = 620), reflecting a shift from lower-to-higher psychological distress and higher-to-lower mental wellbeing during the initial outbreak of the COVID-19 pandemic. This is also the case when comparing the latest pre-pandemic assessment in 2015–17 with the most recent follow-up during the COVID-19 pandemic (Kolmogorov–Smirnov test: D K6  = 0.22, p  < 0.001, n  = 632; D SWEMWBS  = 0.17, p  < 0.001, n  = 620). We further observed that the prevalence of serious mental illness as detected with a K6 scale score ≥ 13 [K6 score range: 0–24] decreased over the first three assessments from 7.4%, 7.0%, to 6.0%, but then increased to 9.0% at the fourth assessment.

Density distributions for pre-pandemic data collection periods and data collected mid-pandemic for both primary outcome measures; the Kessler Psychological Distress Scale ( A ) and the Short Warwick-Edinburgh Mental Wellbeing Scale ( B ).

Considering the complete sample of the most recent, fourth assessment ( n  = 1000), about three in ten individuals reported depression ( n  = 274 for PHQ-9 ≥ 10) or anxiety ( n  = 263 for GAD-7 ≥ 8) scores that would be considered as clinical cases eligible for treatment under current IAPT guidelines 33 . About two in ten met clinical cut-offs for both ( n  = 190). Data for PHQ-9 and GAD-7 were missing for 48 participants.

Latent growth curve modelling of individual pre -pandemic mental health trajectories showed that psychological distress as measured by the K6 decreased on average by 0.45 points ( y K6  = 5.89—0.45x) at each subsequent assessment; mental wellbeing as measured by the SWEMWBS increased on average by increments of 0.34 ( y SWEMWBS  = 22.14 + 0.34x). This is in line with the trend observed in the density distributions across the first three assessments as displayed in Fig.  3 , i.e., overall distress is decreasing, and overall mental wellbeing is increasing over the first three assessments. Fitting individual trends over the first three assessments, subsequently predicting the expected total score at the time point of their fourth assessment and subtracting the expected mental health outcomes from those actually observed (i.e. , extended residuals; cf . Fig .4 ), shows that about 8-in-10 individuals score higher-than-expected on the K6 ( n  = 510/632) and lower-than-expected on the SWEMWBS (n  = 491/620). Please note that this includes individuals close to zero and is therefore solely a descriptive cut-off. The average pandemic-related extended residual score for the K6 was 3.99 ( SD  = 5.67), suggesting that individuals scored about four points higher during the most recent follow-up when compared with expected levels over approximately seven years. Similarly, the average pandemic-related extended residual score for the SWEMWBS was −2.99 ( SD  = 4.25), suggesting that during the first lockdown individuals scored about three SWEMWBS points lower than expected.

Extended residual scores for both primary outcome measures; the Kessler Psychological Distress Scale ( A ; n  = 632) and the Short Warwick-Edinburgh Mental Wellbeing Scale ( B ; n  = 620).

Mental-health related risk

Linear modelling of risk factors for psychological distress and lower mental wellbeing during the initial outbreak of the COVID-19 pandemic are reported as unstandardised beta coefficient and their 95% confidence intervals (cf. Fig.  5 ). As unadjusted models did not differ meaningfully from adjusted models (i.e. , adjusted for any risk factors within the same category, see Methods 2.3), we report only the latter within the text to ease readability. Detailed results in table format are provided in Supplementary Materials 5. Linear models showed that women scored higher on the K6 compared to men ( b [unstd]  = 1.28, SE  = 0.37, 95% CI [0.55, 2.02], p adj = 0.01). Greater socioeconomic deprivation at baseline (lower IMD decile) was associated with higher self-reported psychological distress, although unstandardised beta coefficients were small ( b [unstd]  = −0.21, SE  = 0.07, 95% CI [−0.35, −0.08], p adj  = 0.04). Psychological distress was also significantly higher in individuals who reported any pandemic-related adverse experience ( b [unstd]  = 1.79, SE  = 0.36, 95% CI [1.08, 2.49], p adj  < 0.001). Considering individual pandemic-related psychological responses via the extended residuals, however, demonstrated that none of these risk factors were significant predictors.

Unstandardised beta coefficients and their 95% confidence intervals for linear models assessing sociodemographic as well as pandemic-related risk factors for both primary outcome measures; the Kessler Psychological Distress Scale ( A ) and the Short Warwick-Edinburgh Mental Wellbeing Scale ( B ). The left panel shows coefficients for the observed scores at Assessment 4 whilst the right panel shows extended residual scores which take into account individual pre-pandemic trajectories.

Pre-existing health conditions (reported during the pandemic) were associated with higher observed K6 scores ( b [unstd]  = 3.11, SE  = 0.42, 95% CI [2.28, 3.95], p adj  < 0.001) and higher extended residuals, reflecting higher-than-expected psychological distress ( b [unstd]  = 1.69, SE  = 0.56, 95% CI [0.59, 2.80], p adj  = 0.04). Nonetheless, the explanatory power (proportion of the variance explained) in either model was low (R 2  = 0.12, F(5,617) = 18.26 , p  < 0.001 for observed K6 scores and R 2  = 0.02, F(5,617) = 3.42 , p  < 0.01 for corresponding extended residuals). Pre-existing health conditions within this cohort were predominately clinically diagnosed mental health rather than physical conditions. For a detailed breakdown, see Supplementary Materials 4 c.

None of the sociodemographic risk factors were a statistically significant predictor of mental wellbeing (SWEMWBS) in linear regression models. Two pandemic-related risk factors significantly predicted observed mental wellbeing scores, i.e., participants with pre-existing health conditions and those who reported pandemic-related adverse experiences, but none of the factors predicted individual pandemic-related psychological responses considering individual pre-pandemic SWEMWBS trajectories (i.e., the extended residuals).

Mental health-related resilience

The two network graphs for resilience factors in relation to extended residuals of psychological distress and mental wellbeing are presented in Fig.  6 . All resilience factors were positively related and coded so that higher scores represent higher resilience. Higher-than-expected psychological distress (dst) during lockdown was related to higher aggression (agg; r  = −0.07), higher expressive suppression (exp; r  = −0.03), lower friendship support (fri; r  = −0.02), lower self-esteem (slf; r  = −0.08), and lower general family functioning (fam; r  = −0.06) as assessed at baseline (or all vice versa). The proportion of variance explained by these edges was low (R 2  = 0.04).

Resilience networks including extended residuals for both primary outcome measures, the Kessler Psychological Distress Scale ( A ; n  = 632) and the Short Warwick-Edinburgh Mental Wellbeing Scale ( B ; n  = 620). Extended residuals take into account individual pre-pandemic trajectories, reflecting the deviation from expected mental health whereas greater extended residuals of psychological distress (dst) relate to higher-than-expected distress, and lower extended residuals of mental wellbeing (wlb) relate to lower-than-expected wellbeing . Please note that the network layout has been averaged to ease comparison.

Lower-than-expected mental wellbeing (wlb) during lockdown, on the other hand, was related to higher self-esteem (slf; r  = −0.25), higher general family functioning (fam; r  = −0.03), and higher positive and involved parenting (pip; r  = 0.01) as assessed at baseline (or vice versa). These edges explained about one tenth of the extended residuals’ variance (R 2  = 0.12). Post-hoc analyses looking at the association between observed mental wellbeing mid-pandemic with the above-mentioned resilience factors, however, showed that all were positively correlated (cf. Supplementary Materials 7 ). Further details assessing the robustness of the estimated networks are provided in Supplementary Materials 6 .

This study examined the pandemic-related impact on young adults’ mental health using longitudinal data from a representative cohort of individuals first assessed in 2012–13 at the age of 14–24, followed up three times, most recently at the age of 19–34 during the initial outbreak of the COVID-19 pandemic and the first national lockdown in the United Kingdom. Our results indicate a notable increase in psychological distress and decrease in mental wellbeing compared with expected levels over approximately seven years. During the first COVID-19 wave, about three-in-ten young adults reported depression or anxiety scores classified as clinical cases eligible for psychological talking therapy under current NHS guidelines, about two-in-ten met clinical cut-offs for both. The prevalence of serious mental illness increased from 7.4% at baseline, or 6.0% at the most recent pre-pandemic follow-up completed in 2017, to 9.0% during the initial outbreak of the COVID-19 pandemic. This means that during the initial outbreak of the COVID-19 pandemic nearly 1-in-10 young adults reported levels of psychological distress likely associated with serious functional impairments that substantially interfere with major life activities.

We found that the general increase in psychological distress was ubiquitous and not restricted to those with conventional risk factors. Individuals with pre-existing health conditions, however, were nonetheless disproportionally affected, reporting worse psychological distress, and deviating most strongly from their trajectories compared to their peers. Pre-pandemic self-reported factors which commonly facilitate mental health resilience after adversity were at best mildly protective of individuals' pandemic-related psychological distress response.

The density distributions of both primary outcome measures as well as the pre-pandemic mental health-related trajectories show that psychological distress continuously decreased, and mental wellbeing continuously increased over the first three assessments. These findings are in line with previous longitudinal studies among adolescents and emerging adults, characterising an improvement of general psychological wellbeing during the transition from adolescence to early adulthood 51 . Our data indicate that the initial outbreak of the COVID-19 pandemic led to a notable deterioration from existing mental health-related trajectories, resulting in an overall increase in psychological distress and decrease in mental wellbeing. About 8-in-10 individuals scored higher-than-expected on the K6 and lower-than-expected on the SWEMWBS. Even though this includes some individuals closer to zero where this change is less meaningful, it reflects a clear shift towards worse overall mental health within this sample of young adults. Deviations from mental health trajectories—contrary to expectations—did not differ across age. This could be due to the limited age-range included as previous studies reporting increased risk of mental health problems in young adults often include data across the lifespan 10 , 52 . The prevalence of individuals with non-specific serious psychological distress increased by 3% compared to the most recent pre-pandemic follow-up. Other measures assessing the prevalence of common mental health disorders such as anxiety or depression potentially requiring treatment, were included only in the most recent follow-up. Hence, we are unable to gain an even more nuanced understanding about the change in psychopathological severity during the pandemic. Nonetheless, the prevalence of both anxiety and depression seems high when compared to existing pre-pandemic epidemiological data within this age group 53 , 54 , 55 .

We observed significantly worse psychological distress during the initial outbreak of the COVID-19 pandemic in female and economically deprived participants. However, these factors did not influence pandemic-driven mental health change from existing trajectories. Similarly, we found that individuals who reported pandemic-related adverse events, such as job loss, or a major cut in household income during the first COVID-19 wave, reported worse psychological distress and mental wellbeing, but much to our surprise, this was not the case when considering pre-pandemic mental health trajectories suggesting that these events may not be independent of pre-existing risk for mental distress and wellbeing. These findings highlight the importance of the availability of pre-pandemic data to properly assess pandemic-driven change in mental health. Results from the UCL COVID-19 Social Study, which started weekly data collection at the end of March 2020, show that even though mental health improved over the course of the pandemic, inequalities were still evident 20 weeks after the start of lockdown 56 . Overall, this suggests that the pandemic has the potential to contribute to a widening of pre-existing mental health-related inequalities.

In line with previous research, individuals with pre-existing health conditions, in this cohort largely driven by an existing diagnosis of depression or anxiety, were at risk of increased psychological distress even after considering individual pre-pandemic trajectories; their vulnerability was magnified 13 , 52 . Reporting pre-existing health conditions was also related to lower mental wellbeing, however, it did not influence pandemic-driven change from existing trajectories. This supports the importance of assessing both psychological distress and mental wellbeing as they may measure distinct constructs and should not be considered uncritically as being at different ends of a single mental health continuum. Despite the need for future research to address any potential long-term effects of pandemic-related stress within individuals with pre-existing health conditions, our findings highlight the importance of maintaining access to mental health-care services during any new COVID-19 waves, or when preparing for future pandemics.

Overall, none of the resilience factors investigated were protective of pandemic-driven psychological responses, although some factors did show small effects. This suggests that environmental factors that enhance resilience and support mental wellbeing, particularly in response to adverse events, were at best mildly protective of individual psychological responses to the initial outbreak of the COVID-19 pandemic. However, we found a robust and comparatively strong relationship between high pre-pandemic self-esteem and lower-than-expected mental wellbeing during the first COVID-19 lockdown (and vice versa). Nonetheless, individuals with higher pre-pandemic self-esteem displayed higher levels of mental wellbeing compared to their peers with lower pre-pandemic self-esteem. Whilst the way people value and perceive themselves should ideally be independent of others, research has shown that this is often not the case. In fact, the pursuit of self-esteem often relies on external sources, such as approval from others 57 . A possible explanation of our finding may be that the pandemic-induced lack of social contact may have minimised opportunities to reinforce self-esteem at the cost of mental wellbeing. This is further supported by our finding that positive and involved parenting as well as better general family functioning before the pandemic relates, even if only weakly, to lower-than-expected wellbeing during the pandemic. It has previously been hypothesised that that strong social relationships might be a protective factor during the pandemic 58 . This might still be the case; however, our findings show it is not as straight forward and warrants further research. It is possible that other, more direct coping mechanisms, or traits not measured in our study, might support pandemic-related resilience, particularly during the initial outbreak of this pandemic. One study, for instance, has shown that coping strategies such as sticking to a daily routine, regular exercise, and positive reappraisal were associated with less distress and better mental wellbeing in young adults 18 . It remains to be seen whether resilience factors that are known to facilitate mental health after adversity, help to prevent mental health problems in the long-term. In other words, future work will hopefully shed light onto the factors which helped individuals to cope and adapt to this pandemic over the last two years.

Strengths and limitations

Our study has important strengths, most notably the inclusion of a range of prospectively-measured resilience factors, supporting the approach that such factors should not be studied in isolation 59 . We provide novel insight into markers of risk and resilience necessary to understand and mitigate the combined impacts of the pandemic, lockdowns, and other societal responses. The NSPN cohort is further broadly representative of the youth population across England and Wales when compared to census data of this age group (for further details, we refer the reader to the cohort profile) 20 . Attrition is a limitation, although one shared by other cohorts that include a similar phase of the life course (e.g., ALSPAC) 60 . High attrition rates can lead to bias, as those who drop out of the study may differ from those who remain. For example, the most recent follow-up during the initial COVID-19 outbreak saw 42% of baseline participants return. Although many key characteristics of the cohort, such as ethnicity, country of birth, parental education, and deprivation, remain stable, the disproportionate loss of male participants over time is a concern. However, the homogeneity of the pandemic-related responses across different classifications of participants, such as their sex, for instance, implies that extrapolation of those responses is unlikely to affect the conclusions. The inclusion of pre- and mid-pandemic data is an important strength of this study, however, the availability of ‘only’ three pre-pandemic assessments limits our modelling approach to linear trends within in the framework of latent growth curve models. We explored this limitation by restricting expected scores of primary outcome measures at assessment four to their respective questionnaire boundaries, i.e., if an individual presents with a particular steep slope, we restricted their predicted scores to the minimum or maximum of what the questionnaire allowed (results not reported). Our conclusions remained the same when we used this approach, and we therefore report only the results of the simpler, unrestricted model.

Conclusions

The initial outbreak of the COVID-19 pandemic has had a significant impact on young adults’ mental health. The effects coincident with lockdown and the early stages of the pandemic were ubiquitous and not necessarily restricted to conventional risk factors. Our findings highlight the importance of the availability of pre-pandemic data to properly assess pandemic-driven change in mental health. The increased risk of poorer mental health outcomes in young adults with pre-existing health conditions, in this cohort largely driven by clinically diagnosed depression or anxiety, further underscores the importance of maintaining access to inclusive mental health-care services during any new COVID-19 waves, or potential future pandemics. In contrast to our predictions, resilience factors known to support mental health, particularly in response to adverse events, had little protective effects on individual psychological responses to the pandemic. It remains to be seen, however, whether such resilience factors facilitate mental health in the long term.

Data availabilty

Data relating to assessments 1–3 analysed during the current study can be requested and downloaded through the portal for the Neuroscience in Psychiatry Network— NSPN:Open . Data relating to assessment 4 are not yet publicly available but are available from the corresponding author on reasonable request. NSPN:Open provides access to clinical, cognitive, structural and functional MRI data from a study of healthy adolescent brain development conducted as part of the Neuroscience in Psychiatry Network, a Wellcome Trust-funded collaboration between the University of Cambridge and University College London. These anonymised research data are released to the global scientific research community in accordance with the informed consent of the participants.

Tsamakis, K. et al. COVID-19 and its consequences on mental health (review). Exp. Ther. Med. 21 , 1–7 (2021).

Article   Google Scholar  

Torjesen, I. Covid-19: Mental health services must be boosted to deal with ‘tsunami’ of cases after lockdown. BMJ 369 , m1994 (2020).

Article   PubMed   Google Scholar  

Pierce, M. et al. Says who? The significance of sampling in mental health surveys during COVID-19. Lancet Psychiatry 7 , 567–568 (2020).

Article   PubMed   PubMed Central   Google Scholar  

Breslau, J. et al. A longitudinal study of psychological distress in the United States before and during the COVID-19 pandemic. Prevent. Med. (Baltim.) 143 , 106362 (2021).

Peters, A., Rospleszcz, S., Dallavalle, M. & Berger, K. The impact of the COVID-19 pandemic on self-reported health. Dtsch. Arztebl. Int. 117 , 861–867 (2020).

PubMed   PubMed Central   Google Scholar  

Pierce, M. et al. Mental health before and during the COVID-19 pandemic: A longitudinal probability sample survey of the UK population. Lancet Psychiatry 7 , 883–892 (2020).

Ramiz, L. et al. A longitudinal study of mental health before and during COVID-19 lockdown in the French population. Glob. Health 17 , 29 (2021).

Rimfeld, K. et al. Genetic correlates of psychological responses to the COVID-19 crisis in young adult twins in Great Britain. Behav. Genet. 51 , 110–124 (2021).

van der Velden, P. G., Contino, C., Das, M., Loon, P. V. & Bosmans, M. W. G. Anxiety and depression symptoms, and lack of emotional support among the general population before and during the COVID-19 pandemic: A prospective national study on prevalence and risk factors. J. Affect. Disord. 277 , 540–548 (2020).

Daly, M. & Robinson, E. Longitudinal changes in psychological distress in the UK from 2019 to September 2020 during the COVID-19 pandemic: Evidence from a large nationally representative study. Psychiatry Res. 300 , 113920 (2021).

Article   CAS   PubMed   Google Scholar  

Emery, R. L. et al. Understanding the impact of the COVID-19 pandemic on stress, mood, and substance use among young adults in the greater Minneapolis-St. Paul area: Findings from project EAT. Soc. Sci. Med. 276 , 113826 (2021).

Essau, C. A. & de la Torre-Luque, A. Adolescent psychopathological profiles and the outcome of the COVID-19 pandemic: Longitudinal findings from the UK Millennium Cohort Study. Prog. Neuropsychopharmacol. Biol. Psychiatry 110 , 110330 (2021).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Pan, K.-Y.Y. et al. The mental health impact of the COVID-19 pandemic on people with and without depressive, anxiety, or obsessive-compulsive disorders: A longitudinal study of three Dutch case-control cohorts. Lancet Psychiatry 8 , 121–129 (2021).

Warne, N. et al. Disordered eating and self-harm as risk factors for poorer mental health during the COVID-19 pandemic: A population-based cohort study. medRxiv https://doi.org/10.1101/2021.04.30.21256377 (2021).

De France, K., Hancock, G. R., Stack, D. M., Serbin, L. A. & Hollenstein, T. The mental health implications of COVID-19 for adolescents: Follow-up of a four-wave longitudinal study during the pandemic. Am. Psychol. https://doi.org/10.1037/amp0000838 (2021).

Hussong, A. M., Midgette, A. J., Thomas, T. E., Coffman, J. L. & Cho, S. Coping and mental health in early adolescence during COVID-19. Res. Child Adolesc. Psychopathol. 49 , 1113–1123 (2021).

Porter, C. et al. Impact of the COVID-19 pandemic on anxiety and depression symptoms of young people in the global south: Evidence from a four-country cohort study. BMJ Open 11 , 1–14 (2021).

Shanahan, L. et al. Emotional distress in young adults during the COVID-19 pandemic: Evidence of risk and resilience from a longitudinal cohort study. Psychol. Med. https://doi.org/10.1017/S003329172000241X (2020).

Von Elm, E. et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: Guidelines for reporting observational studies. Bull. World Health Organ. 85 , 867–872 (2007).

Kiddle, B. et al. Cohort profile: The NSPN 2400 Cohort: A developmental sample supporting the Wellcome Trust Neuro Science in Psychiatry Network. Int. J. Epidemiol. 47 , 18–19g (2018).

Kessler, R. C. et al. Screening for serious mental illness in the general population. Arch. Gen. Psychiatry 60 , 184–189 (2003).

Kessler, R. C. et al. Short screening scales to monitor population prevalences and trends in non-specific psychological distress. Psychol. Med. 32 , 959–976 (2002).

Stewart-Brown, S. et al. Internal construct validity of the Warwick-Edinburgh Mental Well-Being Scale (WEMWBS): A Rasch analysis using data from the Scottish Health Education Population Survey. Health Qual. Life Outcomes 7 , 1–8 (2009).

Mewton, L. et al. The psychometric properties of the Kessler Psychological Distress Scale (K6) in a general population sample of adolescents. Psychol. Assess. 28 , 1232–1242 (2016).

Bessaha, M. L. Factor structure of the Kessler Psychological Distress Scale (K6) among emerging adults. Res. Soc. Work Pract. 27 , 616–624 (2017).

Green, J. G., Gruber, M. J., Sampson, N. A., Zaslavsky, A. M. & Kessler, R. C. Improving the K6 short scale to predict serious emotional disturbance in adolescents in the USA. Int. J. Methods Psychiatr. Res. 19 (Suppl 1), 23–35 (2010).

Tomitaka, S. et al. Distribution of psychological distress is stable in recent decades and follows an exponential pattern in the US population. Sci. Rep. 9 , 1–10 (2019).

U.S. Department of Health and Human Services. Key Substance Use and Mental Health Indicators in the United States: Results from the 2020 National Survey on Drug Use and Health . HHS Publication No. PEP21-07-01-003 2021. https://www.nimh.nih.gov/health/statistics/mental-illness (2021).

Ng Fat, L. et al. Evaluating and establishing national norms for mental wellbeing using the short Warwick—Edinburgh Mental Well-being Scale (SWEMWBS): Findings from the Health Survey for England. Qual. Life Res. 26 , 1129–1144 (2017).

Melendez-Torres, G. J. et al. Measurement invariance properties and external construct validity of the short Warwick-Edinburgh mental wellbeing scale in a large national sample of secondary school students in Wales. Health Qual. Life Outcomes 17 , 1–3 (2019).

Kroenke, K., Spitzer, R. L. & Williams, J. B. W. The PHQ-9: Validity of a brief depression severity measure. J. Gen. Intern. Med. 16 , 606–613 (2001).

Spitzer, R. L., Kroenke, K., Williams, J. B. W. & Löwe, B. A brief measure for assessing generalized anxiety disorder: The GAD-7. Arch. Intern. Med. 166 , 1092–1097 (2006).

National Collaborating Centre for Mental Health. The Improving Access to Psychological Therapies Manual . https://www.england.nhs.uk/wp-content/uploads/2018/06/the-iapt-manual.pdf (2021).

Fritz, J., de Graaff, A. M., Caisley, H., van Harmelen, A.-L. & Wilkinson, P. O. A systematic review of amenable resilience factors that moderate and/or mediate the relationship between childhood adversity and mental health in young people. Front. Psychiatry 9 , 230 (2018).

Fritz, J. et al. A network model of resilience factors for adolescents with and without exposure to childhood adversity. Front. Psychiatry 9 , 1–13 (2018).

ADS   Google Scholar  

Fritz, J. et al. Unravelling the complex nature of resilience factors and their changes between early and later adolescence. BMC Med. 17 , 1–16 (2019).

Fritz, J., Stochl, J., Goodyer, I. M., van Harmelen, A. L. & Wilkinson, P. O. Embracing the positive: An examination of how well resilience factors at age 14 can predict distress at age 17. Transl. Psychiatry 10 , 1–14 (2020).

Rosenberg, M. Self esteem and the adolescent. Science (80-) 148 , 804 (1965).

Goodyer, I. M. et al. Improving mood with psychoanalytic and cognitive therapies (IMPACT): A pragmatic effectiveness superiority trial to investigate whether specialised psychological treatment reduces the risk for relapse in adolescents with moderate to severe unipolar depr. Trials 12 , 175 (2011).

Poythress, N. G. et al. Internal consistency reliability of the self-report antisocial process screening device. Assessment 13 , 107–113 (2006).

Bamber, D., Tamplin, A., Park, R. J., Kyte, Z. A. & Goodyer, I. M. Development of a short Leyton obsessional inventory for children and adolescents. J. Am. Acad. Child Adolesc. Psychiatry 41 , 1246–1252 (2002).

Frick, P. J., Christian, R. E. & Wootton, J. M. Age trends in the association between parenting practices and conduct problems. Behav. Modif. 23 , 106–128 (1999).

Epstein, N. B., Baldwin, L. M. & Bishop, D. S. The McMaster family assessment device. J. Marital Fam. Ther. 9 , 171–180 (1983).

Goodyer, I. M., Wright, C. & Altham, P. M. Recent friendships in anxious and depressed school age children. Psychol. Med. 19 , 165–174 (1989).

Holm, S. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6 , 65–70 (1979).

MathSciNet   MATH   Google Scholar  

Epskamp, S. & Fried, E. I. A tutorial on regularized partial correlation networks. Psychol. Methods 23 , 617–634 (2018).

Harris, P. A. et al. The REDCap consortium: Building an international community of software platform partners. J. Biomed. Inform. 95 , 103208 (2019).

Harris, P. A. et al. Research electronic data capture (REDCap)-A metadata-driven methodology and workflow process for providing translational research informatics support. J. Biomed. Inform. 42 , 377–381 (2009).

R Core Team. R: A Language and Environment for Statistical Computing . (2021).

Muthén, L. K. & Muthén, B. O. Mplus User’s Guide . (2017).

Dion, J. et al. A prospective study of the impact of child maltreatment and friend support on psychological distress trajectory: From adolescence to emerging adulthood. J. Affect. Disord. 189 , 336–343 (2016).

Kwong, A. S. F. et al. Mental health before and during the COVID-19 pandemic in two longitudinal UK population cohorts. Br. J. Psychiatry https://doi.org/10.1192/bjp.2020.242 (2020).

Article   PubMed Central   Google Scholar  

Remes, O., Brayne, C., van der Linde, R. & Lafortune, L. A systematic review of reviews on the prevalence of anxiety disorders in adult populations. Brain Behav. 6 , e00497 (2016).

Mojtabai, R., Olfson, M. & Han, B. National trends in the prevalence and treatment of depression in adolescents and young adults. Pediatrics 138 , 6 (2016).

Niedzwiedz, C. L. et al. Mental health and health behaviours before and during the initial phase of the COVID-19 lockdown: Longitudinal analyses of the UK Household Longitudinal Study. J. Epidemiol. Commun. Health https://doi.org/10.1136/jech-2020-215060 (2020).

Fancourt, D., Steptoe, A. & Bu, F. Trajectories of anxiety and depressive symptoms during enforced isolation due to COVID-19 in England: A longitudinal observational study. Lancet Psychiatry 0366 , 1–9 (2020).

Google Scholar  

Crocker, J. The costs of seeking self-esteem. J. Soc. Issues 58 , 597–615 (2007).

Tetreault, E. et al. Perceived changes in mood and anxiety among male youth during the Covid-19 pandemic: Findings from a mixed-methods study. J. Adolesc. Heal. https://doi.org/10.1016/j.jadohealth.2021.05.004 (2021).

Fritz, J., Fried, E. I., Goodyer, I. M., Wilkinson, P. O. & van Harmelen, A. L. A network model of resilience factors for adolescents with and without exposure to childhood adversity. Sci. Rep. 8 , 1–13 (2018).

Boyd, A. et al. Cohort profile: The ‘Children of the 90s’—The index offspring of the Avon Longitudinal Study of Parents and Children. Int. J. Epidemiol. 42 , 111–127 (2013).

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Data collection was supported by a strategic award from the Wellcome Trust (095844/Z/11/Z) to the University of Cambridge (IMG, ETB, PBJ) and University College London (RJD, PF). Data management was supported by the NIHR Cambridge Bioresource and data analysis by the NIHR ARC East of England. These funders had no role in determining our study design, hypotheses, interpretation, or the writing of this report. AW, JP, and PBJ were supported by the NIHR ARC East of England at Cambridgeshire and Peterborough NHS Foundation Trust. JF was supported by the NIHR Cambridge Biomedical Research Centre; ETB by an NIHR Senior Investigator award. SRC role in this study was funded by a Wellcome Trust Clinical Fellowship (110049/Z/15/Z & 110049/Z/15/A) which also co-supported data collection for the fourth assessment.

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Department of Psychiatry, University of Cambridge, Douglas House, 18B Trumpington Road, Cambridge, CB2 8AH, UK

Anna Wiedemann, Jan Stochl, Sharon A. S. Neufeld, Jessica Fritz, Junaid Bhatti, Roxanne W. Hook, Edward Bullmore, Ian Goodyer, Peter Jones, Sharon Neufeld, Rafael Romero-Garcia, Michelle St. Clair, Petra Vértes, Kirstie Whitaker, Becky Inkster, Cinly Ooi, Umar Toseeb, Barry Widmer, Junaid Bhatti, Laura Villis, Ayesha Alrumaithi, Sarah Birt, Emma Davies, Ashlyn Firkins, Christina Maurice, Cleo McIntosh, Jessica Memarzia, Ciara O’Donnell, Jenny Scott, Beatrice Kiddle, Ela Polek, John Suckling, Anne-Laura van Harmelen, Sam Chamberlain, Richard A. I. Bethlehem, Ian M. Goodyer, Edward T. Bullmore, Jesus Perez & Peter B. Jones

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Anna Wiedemann, Jesus Perez & Peter B. Jones

National Institute for Health Research, Applied Research Collaboration, East of England, Cambridge, UK

Anna Wiedemann, Jan Stochl, Jesus Perez & Peter B. Jones

Department of Kinanthropology and Humanities, Charles University, Prague, Czechia

Department of Clinical Psychology, Philipps University of Marburg, Marburg, Germany

• Jessica Fritz

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Raymond Dolan, Michael Moutoussis, Tobias Hauser, Gita Prabhu, Alexandra Hopkins, Rogier Kievit & Raymond J. Dolan

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Samuel R. Chamberlain

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Peter Fonagy, Danae Kokorikou, Pasco Fearon & Peter Fonagy

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Contributions

P.B.J. and S.R.C. led the design and protocol for the most recent follow-up during the COVID-19 pandemic. J.B. led the fourth assessment administration together with R.W.H. A.W. and J.B. managed the data at the latest, fourth assessment; J.B. and S.A.S.N. for assessments 1–3. A.W., J.S., J.P. and P.B.J. planned the analysis; A.W. carried it out and produced the first draft of the manuscript. J.F. advised on resilience factor definition and computation. I.M.G. and E.T.B. were the chief investigators on the original NSPN award with additional investigators including R.J.D., P.F., and P.B.J. S.R.C. was chief investigator for the latest follow-up study. All investigators were involved in conceptualising and planning the NSPN cohort design. All authors had input on the interpretation of results and approved the final manuscript.

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ETB serves on the scientific advisory board of Sosei Heptares and as a consultant for GlaxoSmithKline and Monument Therapeutics. SRC receives honoraria from Elsevier for editorial work outside of the submitted work. Previously, he also consulted for Promentis. All other authors declare no competing interests.

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Wiedemann, A., Stochl, J., Neufeld, S.A.S. et al. The impact of the initial COVID-19 outbreak on young adults’ mental health: a longitudinal study of risk and resilience factors. Sci Rep 12 , 16659 (2022). https://doi.org/10.1038/s41598-022-21053-2

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Research Article

Transition approaches for autistic young adults: A case series study

Contributed equally to this work with: Yosheen Pillay, Charlotte Brownlow, Sonja March

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation School of Education, Centre for Health Research, University of Southern Queensland, Springfield, Queensland, Australia

Roles Supervision, Writing – original draft, Writing – review & editing

Affiliation Centre for Health Research, University of Southern Queensland, Toowoomba, Queensland, Australia

Roles Data curation, Formal analysis, Investigation, Methodology, Supervision, Writing – original draft, Writing – review & editing

Affiliation Centre for Health Research, School of Psychology and Wellbeing, University of Southern Queensland, Springfield, Queensland, Australia

• Yosheen Pillay, 
• Charlotte Brownlow, 
• Sonja March

• Published: May 5, 2022
• https://doi.org/10.1371/journal.pone.0267942
• Peer Review
• Reader Comments

The aim of this study was to evaluate the experience of autistic young adults aged 18 to 25 years old over a 12-month transition period from 2016 to 2017. Data was collected through a longitudinal repeated measures case series design with assessments conducted at 2 time points, at baseline then 12 months later. Assessments included self-report evaluations of transition planning and intervention received at high school, engagement in post-secondary education and access to employment, living circumstances, and social support. Examination of 9 cases showed family and social support was an important facilitator of successful transition whilst low independence was a risk factor associated with unsuccessful transition. In-depth analysis of cases showed a lack of engagement in post-secondary education and unemployment were associated with poor quality of life whilst skills development, work experience placements, and support from service providers were associated with improved quality of life. Implications of the findings highlight the need for educational and socially inclusive interventions to support the heterogeneity in individual, social, communication, and behavioural challenges in autistic young adults.

Citation: Pillay Y, Brownlow C, March S (2022) Transition approaches for autistic young adults: A case series study. PLoS ONE 17(5): e0267942. https://doi.org/10.1371/journal.pone.0267942

Editor: Amanda A. Webster, University of Wollongong, AUSTRALIA

Received: December 9, 2021; Accepted: April 19, 2022; Published: May 5, 2022

Copyright: © 2022 Pillay et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data underlying the results presented in the study are available from https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/SOY24P&version=DRAFT .

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Autism is estimated to affect 1 in 52 Australian adolescents aged 13 to 15 years old, with an overall increase of 42% in the number of Australians diagnosed since 2012 [ 1 , 2 ]. It is reported that autism is more prevalent in males than females estimated at 4 males to every one female [ 3 ], but studies have questioned whether the ratio may be closer than this [ 4 ]. Autistic individuals experience variability in social functioning and communication with each person demonstrating different strengths and challenges [ 5 ]. The majority experience challenges in education, employment, independent living, friendships, and romantic relationships throughout their adult lives [ 6 , 7 ]. The growing prevalence of autistic young adults would suggest that a significant group within the community faces a lifetime of disadvantage because of lack of support for their autism. Difficulty coping with change and adjusting to new environments is widely considered a hallmark of autism. As such, independent living and access to employment associated with the transition to adulthood may be compounded with multiple difficulties unique to autism. Autistic females may experience increased risk as they have lower employment rates and earnings, work fewer hours, and have an increased dependence on disability support when compared to males [ 8 ].

Transition indicates a shift in role status for young adults aged 18 to 25 years from an adolescent to undertaking adult responsibilities within society [ 9 ]. Markers of a successful transition to adulthood include high school completion, post-secondary education, employment, independence, integrating with the community, and good personal and social relationships [ 10 – 12 ]. Successfully transitioning from high school to post-secondary education can improve future employment options, increase financial independence, and improve quality of life (QoL) in adulthood [ 13 – 15 ]. Several studies have documented poor post-secondary outcomes for autistic young adults [ 13 , 16 ]. Compared to adults with other disabilities, post-school outcomes are the poorest for autistic young adults who have the lowest employment rates and highest rate of no activities after school [ 11 , 17 ]. These findings suggest that targeted transition planning for autistic young adults is needed to address barriers to participation in post-secondary education and employment. Transition planning is recognised as a collaborative effort across interagency providers, schools, individuals, and families [ 18 ]. However, recent findings indicate insufficient interagency involvement, limited skill building and community access restricted efforts to gain employment [ 19 ].

An individual is more likely to experience a high QoL if important needs in major life settings of education, work, home, and community are fulfilled [ 20 , 21 ]. Involvement in social networks and having people to talk to, is closely linked to physical and mental wellbeing and can contribute to improved QoL during the transition to adulthood [ 22 ]. However, social interaction difficulties as experienced by autistic young adults may lead to withdrawal and social isolation [ 23 ]. Such individuals may be prone to social anxiety due to struggles in communication and limited ability to socialise with others [ 24 ].

QoL is defined as an individual’s position in relation to their culture, value system, goals, standards, expectations, and concerns and incorporates eight core domains of wellbeing which are emotional wellbeing, interpersonal relations, material wellbeing, personal development, physical wellbeing, self-determination, social inclusion, and human rights [ 25 , 26 ]. Many autistic individuals experience poor QoL in early adulthood as they remain without appropriate support services, are employed in low paying menial jobs, and are dependent on their families, the state medical, and welfare system [ 27 , 28 ]. Several studies highlight factors associated with positive outcomes for autistic young adults which include a supportive social network, having employment, living independently, and access to support services [ 29 , 30 ]. Nevertheless, despite studies demonstrating positive change, many autistic young adults remain disadvantaged and are unable to integrate into society regardless of intellectual ability [ 31 ]. Therefore, there is a need to better understand the factors associated with successful and unsuccessful transition to employment, post-secondary education, independent living, and relationships in early adulthood for autistic young adults to inform program development and improve support for these individuals [ 32 ]. Given the link between positive outcomes in adulthood and overall personal wellbeing, QoL was used as the key outcome variable of interest and a proxy for successful transition in the current study.

Due to the evolving and lengthy nature of the transition process, longitudinal studies provide an opportunity to examine this process at repeated time points to examine specific changes made by individuals and determine what factors might be associated with successful or unsuccessful transition. A longitudinal design allows for the evaluation of transition experiences through observation of changes in lifestyle and environmental factors such as post-secondary education, employment, and attainment of independence over a 12-month transition period [ 33 ]. Such approaches can assist in identifying those individuals most at risk for poor transition as well as potential targets for intervention that could assist at different intervention stages.

The aim of the present study was firstly to understand the transition journey over time for autistic young adults, and secondly, to examine the potential risk and protective factors associated with both successful and unsuccessful transition during this time. We did this by conducting a longitudinal case series examination with 9 autistic young adults. A case series design is particularly useful in that it allows for empirical inquiry and contextual analysis of unique features, events, and their relationships [ 34 ]. Further, as autism is experienced differently by individuals, a case series approach allows for an in-depth individual analysis of transition experiences by an individual in their real-world [ 34 ]. While the advantages of this case series are its prospective nature, allowing data to be collected over a 12-month period, this approach provides mainly descriptive data and allows only for statistical comparisons within subjects. Two research questions guided the present study. First, we were interested in how levels of social support and QoL change over a 12-month period for a group of 9 autistic young adults. Second, this study examined what factors are evident during the transition period that are related to a successful or unsuccessful transition to adulthood. This was addressed through examination of 9 case studies with two presented in detail to illustrate the complexities and challenges involved during the transition period, and to highlight how risk and protective factors influence individual outcomes.

This study implemented a longitudinal case study design to conduct an examination of the journey of 9 autistic participants during a transition period. The transition period referred to within the present study is a point within the life journey where young adults between the ages of 18 to 25 years old exit the school system and undertake emergent adult roles and responsibilities within society [ 9 , 35 ]. Specifically, this study included a repeated measures case series, with online assessments conducted at 2 time points at baseline and 12-months later. Assessments included self-report evaluations of transition planning and intervention received at high school, engagement in post-secondary education, access to employment, living circumstances, and social support.

Participants

Participants were young adults diagnosed with autism living in Australia who completed the online survey between 1 April 2016 and 30 June 2016 at baseline, then a second time between 1 April 2017 and 30 June 2017 at follow-up. Inclusion criteria were that participants were autistic young adults without intellectual disability, currently experiencing transitions (e.g., to post-secondary education, employment), aged 18 to 25 years old, living in Australia, and completed the survey at both time points. The final sample for analysis comprised of 9 participants who were overwhelmingly female ( n = 7) ranging from 19 to 25 years of age ( M = 20, SD = 2.22). All 9 participants received a diagnosis of ASD with 4 participants diagnosed between the ages of 5 and 11 years and 5 participants diagnosed between the ages of 19 to 21 years. Details of the specific diagnosis and length of diagnosis is included within each of the 9 cases.

The data from all 9 cases are presented here to demonstrate overall changes in QoL and social support over the transition period. All 9 cases were examined in detail to provide case studies and identify factors associated with successful and unsuccessful transition. Given that we were interested in identifying risk and protective factors associated with successful and unsuccessful transition, we wanted to select cases who showed positive change in QoL and cases that showed negative change in QoL. That is, we wanted contrasting cases. Out of the 9 cases, 2 cases were identified who showed the lowest and highest QoL scores at time 1. Both also showed increases or decreases in their scores at the second assessment time point. Therefore, variability in their scores allowed examination of factors associated with positive and negative change and made them good candidates for further in-depth exploration. Both participants were male, Mr. Keith aged 23 years old who showed reliable improvement in QoL, and Mr. Reggie aged 24 years old who showed reliable deterioration in QoL. Case studies were analysed for all participants and the trends within the overall group of 9 cases are detailed below (see analytic strategy section).

Supporting information [ S1 File ] 7 cases

The Supporting information [ S1 File ] 7 Cases contains an additional 7 cases including tables as further support and analysis of the risk and protective factors associated with transitions for autistic young adults in the study.

An online survey was designed to measure potential indicators of successful and unsuccessful transition, as well as individual and clinical characteristics that might act as risk or protective factors to successful or unsuccessful transition outcomes. Specifically, the baseline online survey comprised of three components: (a) The author developed About You Survey was included to assess for demographics (e.g., age, gender, location) and individual characteristics (e.g., age at diagnosis, receipt of transition planning); (b) The Quality of Life Questionnaire [ 36 , 37 ] to assess QoL as a proxy for successful transitions; and (c) The Multidimensional Scale of Perceived Social Support [ 38 ] to assess perceived social support as a possible protective factor.

About you survey.

The author developed About You Survey was used to collect baseline demographic information including age and identified gender. Employment status was measured by asking participants if they were in paid employment, in tertiary study, or unemployed. Living arrangements was measured by asking participants if they were living with a partner, roommate, or parents. Relationship status was measured by asking participants if they were in a relationship, single, or married. Individual characteristics were measured by asking participants age of diagnosis and whether intervention was received in behaviour support, social skills training, life skills training, and independent living skills. Participants also answered questions on transition planning at school, and whether there was individual and family involvement in transition planning, as well as whether participants received work experience placements at school and disability support. All participants completed the survey comprising 36 questions.

In order to establish credibility and trustworthiness with participants, the survey was peer reviewed by a group of four autistic young adults, who were also peer researchers at Autism Co-operative Research Centre for Living with Autism [ 39 , 40 ]. Feedback from the peer review process on the author developed About You component of the survey suggested gathering further information on participants’ daily occupation. Specifically, this led to the inclusion of an additional section asking about the nature and quality of daily activities conducted by the participant (see sections 1.18 Daily activities, question 1.19 ‘What is your daily activity’, and question 1.20 ‘Please give a brief description of activities you engage in whilst at home’ in the survey). In addition, the online survey was piloted with 2 autistic young adults. The 2 pilot group participants were asked to complete the draft survey and provide feedback on the visual layout of the questions and response format, wording of questions, and clarity of instructions. Feedback from the pilot review was positive with minor suggestions in the wording of 3 questions. Further, feedback from one community support organisation, prior to advertising on their website, prompted the inclusion of 4 open-ended questions in the About You Survey which allowed participants to provide a text response as described below.

• Can you describe how you feel about your life at this point in time?
• Can you describe how your daily activities/employment make you feel?
• Can you describe the extent to which you are able to function independently on a daily basis, for example, making your own meals, getting yourself to work?
• How would you describe your involvement socially with friends, family, and the community?

At follow-up, the same online survey was administered. In order to gain individual insights into transition experiences over a 12-month time period, 2 open-ended qualitative questions were included to provide additional information about potential changes in QoL. These were not used to measure QoL, rather to inform the case descriptions and identification of factors related to positive experiences and challenges faced during the transition period. These were as follows:

• Over the last year what were some of the positive experiences you have had?
• Over the last year what were some of the main challenges you have experienced?

The quality of life questionnaire.

The QoLQ [ 36 ] is a 40-item self-report scale designed to measure the QoL of autistic individuals and those with a disability. The QoLQ consists of 4 subscales: (a) Satisfaction (SAT) as a measure of overall wellbeing with life; (b) Competence and Productivity (CP) as a measure of skills and experiences associated with access to employment; (c) Empowerment and Independence (EI) as a measure of functional independence in daily living skills; and (d) Social Belonging and Community Integration (SB) as a measure of community integration. Each subscale has 10 items that are rated on a 3-point likert scale from 1 (low) to 3 (high). Each subscale has a potential total score ranging from 10 to 30 with higher subscale scores representing higher levels of satisfaction, competence and productivity, empowerment and independence, social belonging, and overall higher QoL. Total QoL is computed by adding the 4 subscale scores with an overall potential range of 40 to 120 [ 37 ].

The instrument possesses good psychometric properties. The authors [ 37 ] report coefficient alphas for the total QoL score as .90 and for each subscale as follows: (a) Satisfaction .78; (b) Competence/Productivity .90; (c) Empowerment/Independence .82; and (d) Social Belonging .67. Test-retest reliability for the total QoL score has been reported as .87 and for each subscale as follows: (a) Satisfaction .80; (b) Competence/Productivity .96; (c) Empowerment/Independence .83; and (d) Social Belonging .82 [ 37 ].

There are no published clinical cut-offs for the QoLQ. However, individuals with disability in semi-independent or independent living, engaged in employment, with increased community integration, with a total QoL score of 80 and above are identified as having a high QoL [ 37 ]. Those individuals with disability in supervised accommodation, unemployed, with low levels of satisfaction, and a total QoL score of 79 and below are identified as having a low QoL [ 37 ]. In order to examine differences during the transition period for participants who showed deterioration and improvement in levels of QoL, a median split of the sample based on total QoL scores was conducted at follow-up. Individuals with a total QoL score of 80 and above are identified as having a high QoL, and those with a total QoL score of 79 and below are identified as having a low QoL [ 37 ]. Subscale scores of 22 and above would likely reflect higher levels of Satisfaction, higher Competence/Productivity, greater Empowerment/Independence, and greater Social Belonging, whilst subscale scores of 21 and below would likely reflect lower levels of Satisfaction, lower Competence/Productivity, less Empowerment/Independence, and, less Social Belonging [ 36 ]. The QoLQ was used as a measure in this study as it has been validated for individuals with intellectual and developmental disabilities in Australia [ 32 ], the US, and other countries [ 41 – 44 ].

The multidimensional scale of perceived social support.

The MSPSS is a 12 item self-report scale designed to measure perceived adequacy of social support from family, friends, and significant others [ 38 ]. The MSPSS has 3 subscales with 4 items per subscale corresponding with Support from Significant Other (SSO), Support from Family (SF), and Support from Friends (SFr). Participants are asked to rate items on a 7-point Likert-type scale ranging from very strongly disagree (1) to very strongly agree (7). Total subscale scores are calculated by adding responses to each of the 4 items, then calculating the average score for each subscale. The MSPSS total score is calculated by adding subscale total scores, then computing the average score. Mean scale scores ranging from 1 to 2.9 are considered low support, a score of 3 to 5 is considered moderate support, and a score from 5.1 to 7 is considered high support [ 26 ]. Higher total scores indicate increased perceptions of social support.

The psychometric properties of the MSPSS have been established. The authors [ 38 ] report Cronbach’s alpha for the total MSPSS score as .88 and for each subscale as follows: (a) Support from Significant Other .91; (b) Support from Family .87; and (c) Support from Friends .85. Test-retest reliability for the total MSPSS score is reported as .85 and for each subscale as follows: (a) Support from Significant Other .72; (b) Support from Family .85; and (c) Support from Friends .75. The MSPSS has been used in research with mothers of autistic children [ 44 ] and with autistic adult populations [ 45 , 46 ].

The study was approved by the Human Research Ethics Committee (H16REA039). A letter of invitation including an information sheet about the study was e-mailed to autism support organisations and online forums. Informed consent to participate in the survey at both time points was tacit. That is, participants were asked to read the information sheet and tick a consent box in the information sheet in order to proceed. Tacit consent was assumed by subsequent progression, completion, and submission of the survey. Participants were asked to confirm that they were aged between 18 to 25 years, had a diagnosis of autism without intellectual disability, were in the transition period, and were living in Australia. Participants who met the selection criteria were eligible to participate at both time points of the survey and were asked to provide an e-mail contact address at time 1 (T1) for participation in the survey 12-months later at time 2 (T2). Once the survey was completed, the online responses were saved to a secure server that required security password access, until the completion of data analysis. Participants were provided with a pseudonym to provide anonymity in the final study write up.

At baseline a total of 16 participants,10 who were female and 6 who were male, completed the online survey and gave consent to participate in the survey at both time points. Three male participants were excluded due to being outside of the inclusion age criteria of 18 to 25 years old. As a result, 13 participants were eligible to participate in the survey at baseline and follow-up as part of the larger study. At follow-up, a total of 10 participants with 7 females and 3 males responded and completed the assessment Therefore 3 participants were lost to attrition. No significant differences were identified in the 3 participants lost to attrition to the 10 who completed the follow-up assessment. Further, one of the 10 participants being male, who completed the follow-up assessment was excluded from analysis as full data for the QoLQ and MSPSS was missing in their entirety. The final sample for analysis comprised of 9 participants, 7 who were female and 2 who were male, with data at both time points. Fig 1 provides a visual representation of the selection process for the inclusion and exclusion of data, and the final sample in the larger study.

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pone.0267942.g001

Analytic strategy

Change in social support and qol..

To address the first research question and to examine whether QoL and social support changed over the 12-month transition period for the sample of autistic young adults, the data was analysed in several ways. First, changes in subscale and total scores were descriptively examined at baseline and follow-up time points to determine whether measured QoL and MSPSS changed over this period. Second, the Reliable Change Index (RCI) was utilised to determine if individual participants showed reliable change on QoL from baseline to follow-up [ 47 ].

RCI’s provide a measure of statistical significance regarding individual change in scores that takes into account the scale reliability and is beneficial with individual participants or small samples. Positive RCI’s reflect increases and negative RCI’s reflect decreases in the target score, and an RCI with a magnitude of 1.96 or greater in either direction is considered statistically reliable at the p < .05 level [ 47 ]. RCI’s were calculated for all participants for total QoL and the 4 subscale scores to assess for statistically reliable change longitudinally from baseline to follow-up. Each participant’s RCI was then categorised into, showed statistically reliable improvement, showed statistically reliable deterioration, or showed no reliable change categories to enable reporting of cases showing each type of change during the follow-up period. Individual cases were then categorised into reliable change categories and grouped according to those who showed statistically reliable improvement or statistically reliable deterioration at follow-up in terms of QoL, the primary outcome variable.

Individual case analysis and factors associated with transition.

To address the second research question and examine risk and protective factors that were evident and potentially related to QoL during transition, a series of steps were taken. First, we conducted an inductive content analysis to examine the risk and protective factors associated with QoL [ 48 ]. Survey data was grouped into categories of factors contributing to improvement in QoL and those contributing to deterioration in QoL as reported by participants. This analysis was achieved by an in-depth coding process of each participant’s response to the survey questions. The second author conducted the analysis independently to reach consensus on identified risk and protective factors [ 48 ]. Factors identified included important needs met in education, work, home, and community, as well as receipt of support services, intervention, and dependence on families and medical systems. Cases were then organised within the QoL change categories as described above (reliable improvement, reliable deterioration).

Two contrasting case studies are presented to provide further in-depth qualitative insight into how these factors were potentially associated with improvements and those with deterioration during the transition period. The process for case selection is described in the Participants section, however the remaining cases are also presented in the Supporting Information [ S1 File ] 7 Cases, according to reliable change category. Each case study is described in terms of demographic and individual factors, education, support, intervention, QoL, positive experiences, social support, and autism impact and how these changed over a 12-month time period using RCI’s for the key variables of QoL and social support. Descriptive data was supplemented with participant written responses to open-ended questions within the surveys at follow-up. Relevant quotes were extracted from participant responses to support their reported experiences. Common themes across cases were then summarised and contrasted.

Demographic and individual factors

The final sample comprised 9 participants, 7 (77.8%) who were female and 2 (22.2%) who were male. The majority of participants (55.6%) lived with parents in the family home and were single. Results show that one (11.1%) lived with a partner, 3 (33.3%) lived with friends or roommates, and 4 (44.4%) were in a relationship. In addition, 5 (55.6%) received a diagnosis between the ages of 5 to 11 years whilst 4 (44.4%) were between 19 to 21 years of age, and 6 (66.7%) received fortnightly disability pension payments and 3 (33.3%) did not. Responses show that 5 (55.6%) were engaged in post-secondary education, 2 (22.2%) in paid employment, one (11.1%) stayed at home, and one (11.1%) did not report. Weekly wages for the 2 participants (22.2%) who were in employment was over $200 per week, with average hours worked between 20 to 30 hours per week. A detailed summary of baseline demographic information can be found in Table 1 .

https://doi.org/10.1371/journal.pone.0267942.t001

Changes in social support and QoL

Participants were grouped according to their RCI category of change, that is, those who improved in QoL from baseline to follow-up (Improvement in Quality of Life), those who deteriorated from baseline to follow-up (Deterioration in Quality of Life), and those who showed no change in QoL from baseline to follow-up (No Change in Quality of Life). An equal number of participants 4 (44.4%) showed improvement in QoL as did those showing deterioration. No change in QoL was evident for only one participant (11.1%). Six (66%) participants received moderate social support, 2 (22%) participants received high social support and one (11%) participant demonstrated an increase from moderate to high social support over the transition period. QoL groupings were subsequently utilised in the following case series analysis, with participant results discussed according to their QoL status and social support over the 12-month follow-up period. A summary of these findings is presented in Table 2 in addition to descriptions of the level of QoL at baseline and follow-up.

https://doi.org/10.1371/journal.pone.0267942.t002

For the Satisfaction subscale, clinically reliable improvement was evident for 4 (44.4%) participants with the remaining participants showing reliable deterioration. For the Competence/Productivity subscale, clinically reliable improvement was evident for 4 (44.4%) of participants, with 4 (44.4%) participants showing no change and one (11.1%) participant showing reliable deterioration. For the Empowerment/Independence subscale, clinically reliable improvement was only evident for 2 (22.2%) participants, with the majority, (66.6%) showing no reliable change. Finally, for the Social Belonging subscale, equal numbers of participants (one third) showed reliable improvement, reliable deterioration, and no change. In sum, 4 participants showed improvement on total QoL and the Satisfaction subscale, whilst 3 out of the same 4 participants showed improvement on the Empowerment and Independence, and Social Belonging subscales, and half showing improvement on the Competence and Productivity subscale. Therefore, those participants who showed improvement in total QoL also tended to improve in Empowerment and Independence, Competence and Productivity, overall Satisfaction, and Social Belonging. A summary of these findings is presented in Tables 3 and 4 .

https://doi.org/10.1371/journal.pone.0267942.t003

https://doi.org/10.1371/journal.pone.0267942.t004

Factors associated with transition

There was a great deal of similarity in protective factors identified across cases, though greater variation in the risk factors identified for poorer transition. Social support in particular, appeared as a common protective factor for those participants who were unemployed during transition. With regards to risk factors associated with poorer transition, functional independence and unemployment were evident for many as was mental health problems, however, the type of mental health problem differed between participants. Table 5 presents a summary of the risk and protective factors associated with successful and unsuccessful transition, identified through the case study analysis for all 9 cases.

https://doi.org/10.1371/journal.pone.0267942.t005

Individual case analysis

Two case illustrations are presented below in detail to further illustrate how the risk and protective factors described in Table 3 influenced QoL during the transition process. The 2 case studies were drawn from the 2 categories of showing ‘reliable improvement in QoL’ and ‘reliable deterioration in QoL’ in order to differentiate between those participants who transitioned successfully and those who did not.

Mr Keith: Improvement in quality of life

Demographic characteristics..

Mr. Keith is a 23-year-old single male who lives with his parents, is employed as a kitchen hand for 20 to 30 hours a week, earns an income of $200 per week, and receives the disability pension. Mr. Keith received an autism diagnosis at the age of 5. He therefore has had his diagnosis for a period of 18 years.

Education, support, and intervention.

Mr. Keith attended a state high school, received support through the special education program, and completed year 12. At school Mr. Keith received a transition plan with parent involvement in transition planning, and received the following interventions: behaviour support, social skills training, independent living skills, and life skills training. Additionally, he received support through disability employment services and had two work experience placements whilst at school. Mr. Keith has a Technical and Further Education College (TAFE) qualification and reported having received life skills training and independent living skills training post high school. Thus, Mr. Keith has completed his education, obtained additional post-secondary education qualifications, and specific skills to enhance his transition.

Quality of life.

Mr. Keith showed clinically reliable improvement in scores on the total QoL, Satisfaction, and Social Belonging subscales from baseline to follow-up. Scores on the Competence/Productivity remained consistently high from baseline to follow-up, thus indicating that he is confident with the skills and experience required for his employment, whilst scores on the Empowerment/Independence subscale remained low for Mr. Keith over time. Notably, Mr. Keith did report receiving comprehensive support at baseline, which may have contributed to his high QoL score at baseline. RCI scores for total QoL and subscales from baseline to follow-up are presented in Table 6 .

https://doi.org/10.1371/journal.pone.0267942.t006

Social support.

Perceived social support scores show an increase on the total Multidimensional Scale of Perceived Social Support score, Support from Significant Other, and Support from Friends subscales over time, whilst Support from Family for Mr. Keith remained high at both time points. Specifically, at 12-month follow-up, Mr. Keith reported that he enjoyed interacting with friends and family on a daily basis and at work he enjoyed meeting customers. In his words, ‘I participate fully with friends and family’. Thus, social support appeared to be a particularly positive feature in Mr. Keith’s transition, especially support from his family.

Challenges.

Scores on the Empowerment/Independence subscale showed no clinically reliable change for Mr. Keith over time, thus indicating Mr. Keith’s independence in daily living activities remained low. Mr. Keith reported no major challenges; however, he did note feelings of inadequacy in not being able to cook meals for himself. Further, Mr. Keith reported difficulty in accessing transport to visit his Grandmother at the nursing home, which was important to him. In addition, he wished that he could attend more community events, and specifically mentioned music concerts. Thus, it would appear that access to transport and mobility may present some difficulty in achieving functional independence for Mr. Keith.

Positive experiences.

At follow-up, scores on the Satisfaction and Social Belonging subscales showed clinically reliable improvement for Mr. Keith. Indeed, Mr. Keith reported being a volunteer at a day-care centre for children with disabilities. In his words, ‘I feel a sense of fulfilment in helping kids, who are having difficulties understanding why they are different because of the ASD’. Thus, it appeared that Mr. Keith experienced meaningful and positive experiences by interacting with and supporting autistic children during the follow-up period.

Autism impact.

For Mr. Keith whilst he reported improvement in QoL overall, and several positive experiences, there were still notable ways in which his ASD impacted his life. Specifically, Mr. Keith reported difficulty accessing transport and attending community events. However, in particular, Mr. Keith reported being happy with his life, satisfied with his work and in his words, he reported that he, ‘Enjoys doing the same things every day’. Thus, maintaining a routine was important and beneficial to Mr. Keith’s overall QoL.

Mr Reggie: Deterioration in quality of life

Mr. Reggie is a 24-year-old-male, is in a relationship, is unemployed, and lives with his parents. He is engaged in part-time study at university and receives the disability pension. Mr. Reggie’s daily activity includes online gaming, online social interaction with his girlfriend, and sleeping. Mr. Reggie received an autism diagnosis at age 11. He therefore has had his diagnosis for a period of 13 years.

Mr. Reggie attended a state high school and completed Year 12. At high school, Mr. Reggie did not receive support or interventions, did not access work experience placements, and did not receive a transition plan.

Mr. Reggie showed clinically reliable deterioration in scores on the total QoL, Satisfaction, Competence/Productivity, and Empowerment/Independence subscales from baseline to follow-up. Scores on the Social Belonging subscale remained low for Mr. Reggie at both baseline and follow-up, thus indicating ongoing difficulty in community integration throughout the transition period. RCI scores for total QoL and subscales from baseline to follow-up are presented in Table 7 .

https://doi.org/10.1371/journal.pone.0267942.t007

Whilst Mr. Reggie’s reported perceived social support scores remained high on the Support from Significant Other and Support from Family subscales over time, and his perceived Support from Friends remained moderate, with notable difficulties in this area noted. Specifically, at 12-month follow-up, Mr. Reggie reported that his girlfriend, a key source of support, moved interstate, and that he lost contact with his friends over the follow-up period, due to his inability to socialise with them. In his words he reported that, ‘I will go out with friends about two times a year’ indicating that he did not access support from friends regularly. In contrast, Mr. Reggie reported that his family provided him with encouragement by helping him socialise. For example, he stated, ‘They push me do things that I don’t like to do, but I have to, like going to my Grandma’s birthday.’ Thus, support from family appeared to be encouraging for Mr. Reggie.

Low scores on the Satisfaction, Competence/Productivity, and Social Belonging subscales indicated that overall, Mr. Reggie was unhappy with his life situation, and experienced difficulty accessing employment. Specifically, he reported feeling incompetent in enrolling at university and seeking employment. Indeed, Mr. Reggie reported major challenges relating to study and employment. Further, Mr. Reggie stated that, ‘I want to move forward but find it difficult to engage with people and businesses.’ It may also be that Mr. Reggie’s mental health challenges could be contributing to his difficulties with socialising and finding employment. In his words, Mr. Reggie also reported that he was, ‘ depressed and unmotivated’ which were additional challenges for him during the transition period. Thus, Mr. Reggie’s challenges in communicating and interacting with people, and feeling unproductive in his life appeared to impact his overall mental health and wellbeing during the transition process.

Mr. Reggie was able to identify some positive experiences during the 12 months. For example, he reported getting his driver’s licence as a positive experience, however in his words he also reported, ‘I don’t like going out, so I get mum to do errands for me.’ Thus, while he achieved something positive, he was unable to integrate this fully into his daily routine. As he was able to book flights and accommodation to visit his partner interstate, Mr. Reggie viewed these abilities as a positive experience.

For Mr. Reggie, the impacts of his autism and associated challenges appeared to have a considerable effect on his daily activities. Overall, Mr. Reggie reported ongoing feelings of inadequacy in communicating with people. Notably, communication difficulties are a hallmark of autism [ 49 ]. Thus, communication difficulties remained a challenge for him. Further, Mr. Reggie appeared to experience some of the negative stigma attached to autism during the transition period, as he reported, ‘People see the diagnosis and think I can’t do things.’ He reported that this affected his behaviour, and often meant that he asked his mother to contact people at places of importance on his behalf. For example, he reported, ‘I get mum to contact places, like the university for enrolment, but then people treat me weird after.’ Thus, it would appear that in this case, family advocacy in communicating for Mr. Reggie as a young adult, presented a barrier for him in later social interactions.

This study examined the transition experiences of a target group of participants over a 12-month period to evaluate potential risk and protective factors associated with successful or unsuccessful transition during this time. Nine cases illustrated challenges and complexities during this period and 2 were presented in further detail to represent those who demonstrated improved QoL and those who deteriorated in QoL from baseline to follow-up and to demonstrate how risk and protective factors might influence transition. The final number of participants represented in the study were 2 males and 7 females. Whilst females appear to be over-represented in the study, there is ongoing discussion in the literature of the increase in prevalence estimates of autism in females [ 4 ].

Overall, at baseline, only a few participants showed high QoL, with some showing high satisfaction with life, high functional independence, and high social belonging. At baseline, most participants showed low QoL and experienced low levels of satisfaction, low confidence and productivity in skills and experience required to access employment, low levels of functional independence in daily activities, and low levels of social belonging. Therefore, at a crucial point in their lives, most of the young adults in this sample are attempting to navigate challenges associated with transitions with a relatively low starting point of confidence and skills. These findings support previous research with autistic individuals entering young adulthood, where the majority reported low QoL, were dependent on family, required support in daily activities, with limited social interaction [ 6 , 7 ]. Thus, these findings highlight that many autistic individuals transitioning to adulthood experience overall poor social and psychological wellbeing and concurs with previous research [ 32 ].

Overall, from baseline to follow-up, the young adult group as a whole showed some improvement on competence and productivity in access to employment, independence in daily activities, and total QoL, whilst overall satisfaction with life, integration with the community, and social belonging remained consistently low. Autism specific planning and interventions, skills development, parental involvement in transition planning, work experience placements, and support from service providers appeared to be protective factors for Mr. Keith and all participants who experienced improved QoL and a successful transition. These findings are consistent with recent research which indicates with focused skills training, access to work experience placements, as well as parental involvement, autistic young adults can be successful in transitioning to post-secondary education, employment, and experience an improved QoL [ 12 , 31 ].

A lack of engagement in post-secondary education and unemployment were associated with deterioration in QoL for Mr. Reggie and all cases who experienced an unsuccessful transition. Further, lack of interventions in high school with no parental involvement, or support from a disability service provider, with no transition focus on educational and functional outcomes, had a negative impact on successful engagement in post-secondary education and employment. This finding provides further support for the importance of targeted transition planning, parental and interagency support as discussed in previous research [ 16 , 17 ]. Co-occurring depression and anxiety, limited social skills, and communication challenges appeared to be risk factors for unsuccessful transition. It is therefore imperative that disability support services are equipped to facilitate mental health intervention for these individuals.

Outcomes across all 9 cases suggest that social support from family was an important protective factor for this group, whilst lower independence appeared to be a risk factor for both successful and unsuccessful transition. Quite unexpectedly, parental support was also perceived as a potential barrier for Mr. Reggie who experienced unsuccessful transition. One possible explanation for this, is that parents of autistic individuals can be perceived as overly protective, likely due to the perception that their child is incapable of self-managing challenges associated with the transition process [ 21 ]. As such, in the present study, the appearance of Mr. Reggie being a young adult attending university and the associated social expectation of being independent, combined with having a parent advocate for him at enrolment, presented a confusing situation for university staff. This finding helps to illustrate the point that regardless of intellectual ability autistic young adults still experience difficulties engaging in the community [ 31 ]. Interestingly, the importance of an autism diagnosis and associated challenges contributed to identity formation, self-awareness, and self-efficacy, potentially as a protective factor for successful transition, in navigating and managing transition challenges for both Mr. Keith and Ms. Katherine.

Overall, across all cases, consistent with current research, social and communication difficulties, co-occurring depression and anxiety, and challenges in adaptive behaviour associated with autism emerged as risk factors to a successful transition to post-secondary education, employment, independent living, and friendship formation [ 23 ]. Support from friends, families, and significant others appeared to be protective factors and provides further support for research in this area [ 29 – 31 ]. Further, employment and earning a wage increased self-esteem and led to success in adulthood.

Implications of study findings

Study findings show that young adults appeared to be limited not by their individual difficulties, but by the very systems charged with supporting them through their schooling, post high school activities, and transition to key adult roles within the community. Although some autistic young adults showed success in transition and improvements in QoL over the 12-month transition period, all young adults in this study noted some level of difficulty with skills required to access paid employment, challenges in social and community integration, and organisational skills in daily living activities.

Challenges associated with poor outcomes for young adults during transitions were due to first, insufficient professional attention to their abilities at the school and post school systemic level, second, limited knowledge of the developmental nature of autism, and third, limited understanding of the implementation of individualised interventions that will facilitate successful outcomes in this population, particularly as they transition to adulthood. The implication of these findings suggests a comprehensive understanding of the nature of autism as a lifelong developmental condition by all individuals who interact with the young autistic adults, including family, teachers, peers, disability support staff, and workplace colleagues is crucial. Therefore, ongoing autism specific intervention both at school and post-school may be beneficial.

At the school level, intervention promoting social communication with peers, developing skills required in organisation, time management and budgeting, are specific skills that may help to foster success in the social, vocational, and post-secondary education domains during the transition period. The evidence from our study suggests that knowledge of autism specific skills intervention is important in informing autism policy development and practice within disability support infrastructure and suggests that a transition focused education is important. These findings are of critical importance to a wide audience including educational institutions at a school and post-school level, policy developers, and families.

Strengths and limitations

The present study utilised a longitudinal case series design incorporating 2 assessment time points to determine how QoL changed over this period. The case series allowed for an in-depth contextual analysis of unique features and individual transition experiences of autistic young adults in their real-world. Such case illustrations can inform focused goal planning at the school and post-school level, to inform program development for agencies supporting these individuals, and to contribute specialised knowledge to strategic policy development. The case study methodology was rigorous, with measures used to assess changes in QoL and social support. Further, the use of RCI’s provided statistical rigour to the examination of effects. Nevertheless, there are several limitations.

Given the heterogeneity of an autism diagnosis, the young adults in the present study cannot necessarily be considered representative of the broader autistic population. Despite the common risk and protective factors identified, it is difficult to understand the magnitude of effect due to the small sample size. At the time of the surveys, data on socio-economic status of the family of origin and race was not collected which may have influenced outcomes. It is important to note that despite research which suggests autism is more common in males, our sample was predominantly female. The 2 male cases presented in detail in this manuscript were deliberately chosen as they represented similar stages of transition, age, gender and clearly represented an example of deterioration and improvement. Whilst our overall group outcomes suggest similarities across the sample, and the other 7 female cases presented in the Supporting Information [ S1 File ] 7 Cases do provide some insights into transition processes and QoL outcomes for females compared to males, the lack of direct gender comparisons must be acknowledged as a limitation of this research. Data collection via the survey was restricted to participants who had access to a computer. Only having access to a computer is an example of selection bias. Multiple approaches to data collection could have potentially increased the sample size. In conjunction to using the online survey, distributing and collecting hard copies of surveys from participants through autism associations may have yielded more responses.

Finally, whilst a longitudinal case series design captured rich data from 9 cases, it is difficult to generalise findings from the present study given the heterogeneity of an autism diagnosis in specific behaviour traits, social skills, and cognitive ability ascribed to an individual. Future studies should examine the impacts in a large cross-sectional population and compare to the existing evidence base. Research with parents and teachers would enrich the study findings by contributing social validity.

This case series highlighted specific demographic, individual, and group characteristics that facilitate a successful transition to adulthood for autistic young adults. At a systems level, this is important in informing family, disability organisations, and key stakeholders on strategic policy in tailored interventions to ensure a seamless transition to post-secondary education, employment, independence, social inclusion, and overall life satisfaction for autistic young adults.

Supporting information

https://doi.org/10.1371/journal.pone.0267942.s001

• 1. Autism Aspergers Advocacy Australia. Autism prevalence in Australia 2015. 2016. Avaliable from: http://a4.org.au/prevalence2015 .
• 2. Australian Bureau of Statistics. Survey of disability, ageing and carers 2015 (cat. no. 4430.0). 2016. Available from: http://www.abs.gov.au/ausstats/[email protected]/Latestproducts/4430.0Main%20Features762015?opendocument&tabname .
• View Article
• Google Scholar
• PubMed/NCBI
• 5. Autism Spectrum Australia (ASPECT). We Belong Report on adults with ASD. 2013. Avaliable from: https://www.autismspectrum.org.au/uploads/documents/Research/Autism_Spectrum_WE_BELONG_Research_Report-FINAL_LR_R.pdf .
• 9. Arnett JJ. Adolescence and emerging adulthood (5 th ed .). Pearson; 2014. https://doi.org/10.1177/2167696814561999 pmid:27308184
• 36. Schalock R, Keith KD. Quality of life questionnaire. Worthington, OH:IDS Publishing Corporation; 1993.
• 37. Schalock R, Keith KD. Quality of life questionnaire manual revision. Worthington, OH: IDS Publishing Corporation; 2004.
• 39. Creswell JW. Cresswell JD. Research design: Qualitative, quantitative and mixed methods approaches.Theory into Practice. US: SAGE; 2017.
• 48. Krippendorff K. Content Analysis an introduction to its methodology. London: SAGE; 2004.
• 49. American Psychiatric Association. Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: American Psychiatric Association; 2013.

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Mental and Physical Health, Psychosocial Maturity, and Desistance in Young Adulthood

• ORIGINAL ARTICLE
• Published: 20 February 2023
• Volume 9 , pages 331–352, ( 2023 )

Cite this article

• Jeffrey T. Ward   ORCID: orcid.org/0000-0003-1607-5392 1 ,
• Nathan W. Link 2 &
• Megan Forney 3  

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Recent theoretical and empirical work has drawn increased attention to the role that mental and physical health can play in promoting life-course success and desistance from crime. This study integrates literature on youth development with the health-based desistance framework to investigate a key developmental pathway through which health influences desistance among system-involved youth. Using multiple waves of data from the Pathways to Desistance Study, the current study uses generalized structural equation modeling to examine whether and to what extent mental and physical health influence offending and substance use directly and indirectly through psychosocial maturity. Findings indicate that both depression and poor health stall the development of psychosocial maturity, and that those with higher psychosocial maturity are less likely to engage in offending and substance use. The model provides general support for the health-based desistance framework, finding an indirect process linking better health states to normative developmental desistance processes. Results hold important implications for the development of age-graded policies and programs geared toward promoting desistance among serious adolescent offenders both within correctional and community settings.

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Introduction 

Young adulthood, the period from 18 to 25 in which individuals are freed from childhood dependencies but have not yet fully entrenched themselves with normative responsibilities of adulthood, is a crucial developmental period in the life course (Bonnie et al., 2015 ) where greater attention should be paid to desistance processes (e.g., see Laub & Sampson, 2001 , p. 55). This life stage is especially critical in cultures that enable long periods of role exploration (Arnett, 2000 ). While youth transition to adulthood with effectively full cognitive capacities, their psychosocial capacities are still developing well into their twenties (Icenogle et al., 2019 ), and have a direct relationship with desistance (Monahan et al., 2009 , 2013 ; Steinberg, 2014 ). Although psychosocial maturity may be part of a complex maturation process (Rocque, 2015 ), it is evident that the ability to make rational decisions is compromised by immaturity. Furthermore, youth who are more immature may be less oriented towards adult roles, such as work (McCuish et al., 2020 ). Piotrowski et al. ( 2014 ) contend that “when undertaking adult roles does not coincide with achieving a certain level of psychosocial maturity, it is not conducive to progressive changes in identity, i.e., an increase in a sense of adulthood, and an increase in the number/strength of commitments and identification with them” (p. 60). Recognizing the importance of normative psychosocial development for aging out of crime, Monahan and colleagues ( 2013 ) have called for the identification of “risk factors for delayed development of psychosocial maturity across adolescence and adulthood” (p. 1103). More recently, a similar call has been made by Ozkan and Worrall ( 2017 ) who argue for more research on the “forces that shape psychosocial maturity directly” (p. 837). Footnote 1

Mental and physical health issues among young adults are a serious concern and have the potential to adversely affect psychosocial maturity development and offending. For example, recent research suggests that nearly half of young adults struggle with mental health issues and more than one-third report unmet needs (Adams et al., 2022 ). Regarding physical health, for example, well over half of young adults are either obese or overweight (Ellison-Barnes et al., 2021 ). Adding to a small body of research on possible sources of stunted psychosocial development, such as incarceration (Dmitrieva et al., 2012 ), the present research draws on health-related criminological perspectives (Fahmy & Mitchell, 2022 ; Jackson & Vaughn, 2018 ; Link et al., 2019 ; Link et al., 2020 ; Mallik-Kane & Visher, 2008 ; Wallace & Wang, 2020 ) and multidisciplinary research on psychosocial development, health, and health behaviors (Galambos et al., 2008 ; Kuiper et al., 2018 ; Reysen et al., 2020 ; Westenberg et al., 1999 ; Pailing & Reiners, 2018 ) to examine whether and to what extent poor mental and physical health stall normative development and contribute to persistence in offending among a sample of justice-involved adolescent offenders. We begin by reviewing core ideas in health-based desistance and then explicate their relevance for the development of psychosocial maturity. We then test these ideas using a sample of young adults who have been convicted of a serious or violent offense.

The Health-based Desistance Framework

Complementing important research examining the role of physical health as a facilitator of crime (Stogner & Gibson, 2010 ; Stogner et al., 2014 ), scholars have begun to unpack the effects of both mental and physical health for reentry and reintegration success and desistance (Fahmy & Mitchell, 2022 ; Link et al., 2019 ; Mallik-Kane & Visher, 2008 ; Wallace & Wang, 2020 ). For example, drawing widely on research in criminology and other disciplines, Link et al. ( 2019 ) advanced a “health-based model of desistance” that draws attention to the roles that mental and physical health may play in a successful exit from crime. The model hypothesizes that the mental and physical health states that returning prisoners have at the time of release affect employment, positive family relations, and financial stability, which, in turn, influence the likelihood of desistance. An initial test of the model using data from US adults indicated several significant indirect pathways through which poor health increased recidivism and reincarceration through problems with employment, family relationships, and/or finances (Link et al., 2019 ). Focusing on direct (or total) effects of health, Wallace and Wang ( 2020 ) found that better in-prison mental health and changes post-release were related to a lower likelihood of recidivism, whereas better in-prison physical health and changes post-release were related to higher recidivism. Thomas and colleagues ( 2015 ) also note evidence that poor physical health is associated with lower recidivism risk. It has been suggested that good physical health may be a necessary condition for both engaging in crime and rejoining society (Wallace & Wang, 2020 ). Therefore, direct and indirect processes linking physical health to recidivism may differ (see Link et al., 2019 ) and possibly offset.

Compared to studies examining physical health on recidivism, there is a large literature on the association between mental health and offending (Blumenthal & Lavender, 2000 ; Link et al., 2016 ; Silver, 2006 ); studies have documented significant, yet modest, associations between crime and clinical factors including substance abuse (Steadman et al., 1998 ), treatment non-compliance (Swanson et al., 1997 ), and psychotic symptoms related to the disorders themselves (Link et al., 1999 ). At the same time, mental illness as a risk factor for violence and other recidivism is low (Bonta et al., 2014 ), especially in comparison with basic demographic factors such as age and sex (Link et al., 1992 ). While much of this research has centered on the relationship between serious mental illnesses such as schizophrenia, schizoaffective, and bipolar disorders, it is also necessary to account for and assess the impact of more ubiquitous mental health conditions such as depression. In one longitudinal analysis of a sample of serious adolescent offenders, depression was found to be a risk factor for aggressive and income-generating offenses (Ozkan et al., 2019 ). Another recent study examined cross-lagged associations between depression (and anxiety) and offending and found that depression did not significantly influence offending over time; however, it should be noted that this study was unique in that there was more than 10 years between each wave of data collection (Huesmann et al., 2019 ). Taken together, the effects of depression on offending may operate over shorter developmental time spans, perhaps over the course of months to years. Notably, while Ozkan and colleagues ( 2019 ) found depression to be linked to offending in their longitudinal models, they noted more inconsistent results when examining the contemporaneous associations between depression and offending in their cross-sectional models. These findings hint that a developmental process may connect depression and offending. Footnote 2

It is noteworthy that the concept of “health” is widening to include more holistic considerations (see Fahmy & Mitchell, 2022 ), which is consistent with arguments that correctional programming for adult offenders with narrow focuses on physical health alone may be insufficient for meeting often complex and multi-faceted health needs (Wallace & Wang, 2020 ). At a minimum, frameworks for health-based desistance should seek to consider the impact of both physical and mental health (Link et al., 2019 ; Wallace & Wang, 2020 ) as well as consider possible indirect developmental processes by which health states may promote desistance (Link et al., 2019 ). Several recent studies have examined both mental and physical health impacts on reintegration and desistance among adults returning home from prison (Link et al., 2019 ; Wallace & Wang, 2020 ) as well as on the ability of adults with mental illness to make progress toward improving their relationship and employment situations (Link et al., 2020 ). Yet, the health-based desistance framework may be useful for understanding desistance across different periods of the life-course—including during young adulthood. Beyond health’s contributions to enabling social forces of desistance (e.g., employment) to take deeper root in adulthood, there is a need to consider whether mental and physical health also affects the more subjective forces of desistance (e.g., positive identity formation) (Link et al., 2019 , p. 566), which may be particularly the case during the transitional period to adulthood. To date, left largely unexamined is whether and to what extent poor health states disrupt or slow normative developmental processes.

Psychosocial Maturity Development and Health

The aggregate relationship between age and crime is curvilinear, with crime increasing through adolescence and declining as youth enter young adulthood (Hirschi & Gottfredson, 1983 ; Laub & Sampson, 2003 ; Sampson & Laub, 2005 ). In viewing desistance as a developmental process (Bushway et al., 2003 ), psychosocial maturity is associated with a reduction in antisocial behavior (Monahan et al., 2009 ; Monahan et al., 2013 ; Ozkan & Worrall, 2017 ) and substance use (Fischer et al., 2007 ; Ozkan & Worrall, 2017 ; Riggs Romaine, 2019 ). Psychosocial maturation is a crucial developmental process that involves attaining a functional level of autonomy and social responsibility (Greenberger, 1984 ). Greenberger and Sorensen’s ( 1974 ) model of psychosocial maturity posits that individuals acquire autonomy when they have feelings of control, take initiative, and lack the need to depend on others (self-reliance); have a stronger clarity of oneself, consider life goals, internalize values, and obtain better self-esteem (identity); and develop work skills and experience more work-related aspirations and joy (work orientation).

Psychosocial maturity continues to develop beyond the teenage years—extending through age 25 (Monahan et al., 2013 ). In other words, this developmental facet continues to influence crime well into young adulthood. As prior research has demonstrated that youth with higher levels of psychosocial maturity are more likely to desist from crime irrespective of race/ethnicity (Rocque et al., 2019 ) and that psychosocial maturity may be a better predictor of desistance than age (Monahan et al., 2013 ; Pailing & Reniers, 2018 ), there is an ongoing need for research to identify factors that may contribute to the development of this capacity (Monahan et al., 2013 ; Ozkan & Worrall, 2017 ). Recent research finds that social factors, such as school experiences (Abeling-Judge, 2021 ) and parent relationships (Abeling-Judge, 2020 ) are positively associated with psychosocial maturity. These findings echo McConochie et al.’s ( 1974 ) contention made decades ago that individual characteristics intersect with socialization experiences and “maturing mental and physical capabilities to produce unique individual levels of psychosocial maturity” (p. 3, emphasis added ).

While research utilizing the health-based desistance framework to understand developmental processes and desistance among troubled youth is scarce, there is some evidence consistent with theoretical expectations. For instance, prior research has demonstrated a correlation between psychosocial maturity and depression (Galambos et al., 2008 ; Morales-Vives & Dueñas, 2018 ; Pailing & Reniers, 2018 ). Galambos and colleagues ( 2008 ) found depression to be negatively associated with psychosocial maturity among individuals with and without motor disabilities; they suggest that those who are dealing with depression may have lower energy to complete even basic tasks and thus may have reduced psychosocial maturation given inability to deal with challenges associated with the transition to adulthood. In addition to this potential mechanism, there may be a range of other dynamics that theoretically link the two, including that those with greater depression may, as a result, suffer from lower self-esteem, issues with self-concept and self-worth, and may derive less pleasure from success in conventional activities such as employment or other critical bonds to society. While even less research has examined the potential links between physical health and maturity (for an important exception, see Reysen et al., 2020 ), it is similarly plausible that poor physical health could also affect psychosocial development. For example, regarding work orientation, youth who are unhealthy may be more likely to miss work, and certain health conditions may hinder the ability to perform the job itself. More generally, being in good physical shape likely confers a range of psychological benefits including an improved self-esteem, a stronger sense of mastery, and internalizing the notion that a goal-focused initiative is a worthwhile pursuit that can achieve real benefits. That is, those in better health are likely to be more self-reliant and to have a stronger sense of identity. In short, we suggest that symptoms associated with health conditions and poor health states likely delay the development of psychosocial maturity.

Despite the limited research, scholars have hinted at the importance of investigating the interconnections of health, psychosocial maturity, and behavior more carefully, including in the absence of direct effects between health and crime. For example, Pailing & Reniers ( 2018 ) have suggested that “an effect between depression and risk-taking behavior could be indirect, through psychosocial maturity” (p. 10). As delinquent youth with mental health needs are especially likely to experience difficulties adjusting to adult roles (Steinberg et al., 2004 ), it is critical to understand how health states indirectly influence offending during the transition to adulthood. While there is a growing literature on the role of health for life-course achievements and behavior change (e.g., Fahmy & Mitchell, 2022 ; Link et al., 2019 , 2020 ; Mallik-Kane & Visher, 2008 ; Wallace & Wang, 2020 ) and strong evidence linking psychosocial maturity to offending (Monahan et al., 2009 ; Monahan et al., 2013 ; Ozkan & Worrall, 2017 ) and substance use (Fischer et al., 2007 ; Ozkan & Worrall, 2017 ; Riggs Romaine, 2019 ), it remains empirically unclear whether experiencing poor health may diminish individuals’ development of psychosocial maturity and thus stall normative desistance processes.

Drawing on the health-based desistance framework and the extant literature, we hypothesize that depression will be inversely related to psychosocial maturity, and that better self-rated physical health will be positively related to psychosocial maturity. Further, we hypothesize that psychosocial maturity will be inversely related to offending and substance use. Finally, as consistent with the aforementioned direct effects, we hypothesize significant indirect effects linking mental and physical health states to behavior outcomes through their effects on the development of psychosocial maturity.

Methodology

Pathways to Desistance is a multi-site prospective 11-wave, 7-year study of 1354 serious juvenile offenders (1180 male, 184 female). Pathways took place in Maricopa County (Phoenix), Arizona ( n  = 654) and Philadelphia County, Pennsylvania ( n  = 700). Participants were between the ages of 14 and 17 years at baseline and adjudicated delinquent (or found guilty in criminal court) of a serious offense. Offenses included felonies and more serious misdemeanor offenses (e.g., sex or weapons offenses). Drug offenses were capped at 15% of the male sample and all youth whose cases were transferred to adult court were considered eligible for the study (Schubert et al., 2004 ). Pathways had a 67% enrollment rate, and prior investigations with these data have revealed that significant differences exist between youth who did and did not enroll in the study. Non-enrolled youth had fewer prior arrests that led to formal charges than enrolled youth (1.5 vs 2.1, respectively), were older at their first arrest (14.2 vs 13.9, respectively) and adjudication (16.1 vs 15.9, respectively), and were less likely to be non-Hispanic Caucasian (20% vs. 25%, respectively; see Schubert et al., 2004 for further sample description). The present study focuses on young adulthood using data from Waves 7 to 10, along with some baseline (Wave 1) data. Footnote 3 In addition to a theoretical focus on youth making the transition to adulthood, pragmatically, as we focus on both multiple health-based factors, the present study uses data from these later waves—when both measures have sufficiently valid data. Descriptive statistics are available in Table 1 , and a bivariate correlation matrix between focal variables is available in Appendix Table 3 .

The Self-Reported Offending inventory (see Huizinga et al., 1991 ) is used to create a variety measure of general offending. Respondents were asked whether they had engaged in the following delinquent acts: destroyed or damaged property; set fires; entered building to steal; shoplifted; bought, received, or sold stolen property; used checks or credit cards illegally; stole car or motorcycle; sold marijuana; sold other illegal drugs; carjacked someone; drove drunk or high; been paid for sex; shot someone; shot at someone; robbed someone with a weapon; robbed someone without a weapon; beaten up someone badly; beaten up someone as part of a gang; been in a fight; and carried a gun. Higher scores indicate a greater participation in a variety of delinquent acts. It is measured at both Waves 10 and 7, where it serves as an outcome and covariate, respectively.

• Substance Use

The Substance Use/Abuse Inventory (see Chassin et al., 1991 ) is used to form a variety measure of substance use. Respondents were asked to indicate whether they had used: alcohol; marijuana or hashish; sedative or tranquilizers; stimulants or amphetamines; cocaine; opiates; ecstasy; hallucinogens; inhalants; or nitrates, odorizers, or rush. Higher scores indicate use of a greater variety of substances during the recall period. As with offending variety, substance use variety is measured at Wave 10 and 7, serving as an outcome and covariate, respectively.

Physical Health

Physical health is a subjective indicator of one’s overall self-rated health status (see Furstenberg, 2000 ). Self-rated health measures have been found to correlate with objective health statuses and serve as a proxy for global health (Wu et al., 2013 ). Respondents reported whether their overall health was “poor”, “fair”, “good”, or “excellent”. This question was part of a short healthcare inventory that asked respondents about their health insurance status and where they go when they need to see a doctor. Self-rated health is measured at Wave 8. Footnote 4

Depression is measured using the depression subscale from the Brief Symptom Inventory (Derogatis & Meslisarots, 1983 ). The BSI is a 53-item self-report measure assessing the extent that individuals are bothered in the previous week by symptoms related to nine domains of psychological distress such as somatization, anxiety, phobia, and—of central interest here—depression (e.g., “feeling no interest in things”, “feeling blue”, “feeling lonely”, “feelings of worthlessness”). Response options were coded not at all (0), a little bit (1), moderately (2), quite a bit (3), and extremely (4). Items were averaged to obtain the scale score. It should be noted that BSI scoring resulted in some respondents being coded as having an invalid test (see Mulvey, n.d.); the implication is that missing data on this variable is notable. Depression is measured at Wave 8.

• Psychosocial Maturity

Psychosocial maturity is measured using the Psychosocial Maturity Inventory (Greenberger et al., 1975 ), which taps into developmental autonomy. The PSMI is a 30-item measure that assesses three sub-dimensions of psychosocial maturity including identity (e.g., “I change the way I feel and act so often that I sometimes wonder who the ‘real’ me is”), self-reliance (e.g., “Luck decides most things that happen to me”), and work orientation (e.g., “I hate to admit it, but I give up on my work when things go wrong”). Footnote 5 Respondents reported their level of agreement on a four-point Likert scale and all items were coded such that higher scores indicate greater psychosocial maturity, and an average of the items forms the scale. Psychosocial maturity is used at Wave 9 as a mediator and is also assessed at Wave 7 as a covariate.

All covariates are measured antecedent to the developmental process under investigation. We control for key demographic factors including age, race, gender, and socio-economic status. Age is a truncated measure of age at Wave 7, and age-squared was included to capture any curvilinear associations. Footnote 6 Race is measured using a series of dummy variables (Black, Hispanic, Other), where White serves as the reference group. Sex is a dummy indicator of male. Finally, SES is measured at baseline using the parent index of social position, which is based upon occupational and educational scores (see Hollingshead, 1971 ); higher scores indicate greater SES. Peer antisocial behavior is measured by asking youth to report the proportion of their friends that engaged in 12 antisocial behaviors on a five-point Likert scale spanning from “none of them” to “all of them” (Thornberry et al., 1994 ). To measure youth’s perceptions of success, a six-item measure of expectations for work, family, and law-abiding behavior adapted from the NYS prediction of adult success scale is used (see Menard & Elliott, 1996 ); higher scores indicate more predicted success. To control for differences across locations, we include a dummy indicator for site (1 = Maricopa County, AZ; 0 = Philadelphia County, PA). Street time is measured as the proportion of time on the street (i.e., not in a secure facility) and is included as a covariate where appropriate. Footnote 7 Finally, as noted above, we account for psychosocial maturity to examine whether health predicts changes in psychosocial maturity. Likewise, we control for substance use and offending when modeling those outcomes as well to examine whether health states and/or psychosocial maturity levels influence changes in behavioral outcomes.

Analytic Strategy

The present study utilizes generalized structural equation models to estimate a path analysis between self-rated health, depression, psychosocial maturity, substance use and general delinquency. Specifically, generalized structural equation modeling permits assessment of direct and indirect pathways among a blend of categorical (i.e., self-rated health), continuous (i.e., depression, psychosocial maturity), and variety count (i.e., substance use and general delinquency) variables. For variety count outcomes, we display incidence rate ratios (IRRs) to improve interpretation of these associations. Footnote 8 As structural equation modeling cannot yield causal estimates from associations alone (Bollen & Pearl, 2013 ), we include covariates in the model to estimate paths while adjusting for prior levels of mediators/outcomes and several potential confounders discussed above.

As both health measures were assessed for all active study participants for the first time in Wave 8, our study assesses the impact of young adults’ health states (average age = 20) on psychosocial maturity development 1 year later and examines the implications for desistance from offending and substance use 2 years later (average age = 22). Footnote 9 We follow the approach taken by Link and colleagues ( 2019 ) and regress depression on physical health but recognize the possibility that mental and physical health may influence one another over time. Consistent with theoretical expectations, we regress psychosocial maturity on depression and self-rated health, and we regress substance use and general delinquency on psychosocial maturity. We also regress these two outcomes on both health states. To assess whether there are indirect effects of health states on offending and substance use, we first infer mediation through joint significance test procedures (see Taylor et al., 2008 ); following this preliminary assessment, we then explicitly test whether self-rated health and/or depression indirectly affect desistance through psychosocial maturity uses bootstrapping which has been shown to be the best approach to testing indirect effects (Hayes, 2009 ; Hayes & Scharkow, 2013 ; MacKinnon et al., 2004 ; Preacher & Hayes, 2008 ); specifically, tests for significant indirect effects employ biased-corrected bootstrap standard errors with 2500 bootstrap replications. Our generalized structural equation model with its blend of count, continuous, and ordered categorical endogenous variables necessitated the use of numerical integration via montecarlo (see Muthen & Muthen, 1998–2021). We used full information robust maximum likelihood estimation to handle missing data. Footnote 10

Table 2  provides comprehensive results from the generalized structural equation model, including all direct effects on the five endogenous variables. Complementing this table, Fig.  1 provides a concise summary of statistically significant direct effects between mental and physical health, psychosocial maturity, and the two behavioral outcomes that are the focus of the current study. Net of covariates, physical health is significantly associated with depression; those with better self-rated health report less depression (b =  − 0.11, p  = 0.01). Footnote 11 No other variables had a significant partial association with depression under the conventional alpha level of significance. Several covariates significantly predicted physical health states in young adulthood. Specifically, males are significantly more likely to self-report better physical health ( b  = 0.35, p  = 0.001). Individuals with greater expectations for success ( b  = 0.22, p  < 0.001) and those with higher psychosocial maturity ( b  = 0.19, p  = 0.03) reported better physical health. Using a greater variety of substances was associated with lower physical health ( b  =  − 0.10, p  = 0.01), whereas those who engaged in a greater variety of offending reported slightly better health ( b  = 0.04, p  = 0.02). Age had a significant curvilinear association with self-reported physical health (b Age  = 1.78, p  = 0.04; b Age 2  =  − 0.05, p  = 0.05).

Summary of significant direct effects. Unstandardized effects. Solid lines ( *** p  ≤ 0.001, ** p  ≤ 0.01, * p  ≤ 0.05); Dashed lines ( + p  ≤ 0.10). IRR = Incidence rate ratio

Youth with better physical and better mental health states reported higher levels of psychosocial maturity. A one unit increase in self-rated physical health is associated with 0.04 unit average increase in psychosocial maturity ( p  = 0.02, b stdy  = 0.10), whereas a one unit increase in depression is associated with a 0.09 unit average decrease in psychosocial maturity ( p  = 0.001, b stdxy  =  − 0.12). The effects of health states on psychosocial maturity development 1 year later are relatively small, though statistically significant and hold when controlling for earlier levels of psychosocial maturity, demographics, and other covariates. Coupled with findings above, this suggests that self-rated health and depression may each directly impede the normative development of psychosocial maturity, but that some of the potential stunting effects of physical health may operate through its impact on worsened mental health (see Fig.  1 ). Supporting a moderate level of relative stability across development, youth with higher levels of psychosocial maturity at Wave 7 have significantly higher levels of psychosocial maturity at Wave 9 ( b  = 0.37, p  < 0.001). Notably, controlling for covariates and prior levels of psychosocial maturity, youth who held higher expectations for success reported significantly higher levels of psychosocial maturity ( b  = 0.06, p  < 0.001).

Net of covariates, psychosocial maturity significantly influences both substance use and offending. Specifically, a one unit increase in psychosocial maturity is associated with a 26% decrease in the expected variety count of substance use ( b  =  − 0.30, p  = 0.005, IRR = 0.74) and a 32% decrease in the expected variety count of offending ( b  =  − 0.39, p  = 0.002, IRR = 0.68). Physical and mental health states do not have a direct effect on offending; however, while not significant under the conventional two-sided alpha level, depression ( b  = 0.17, p  = 0.08) did have a direct partial association with substance use that would have reached significance under a one-sided test. Finally, several covariates were significant predictors of behavioral outcomes. On average, males engaged in 67% greater variety of substance use ( b  = 0.51, p  < 0.001, IRR = 1.67) and a nearly two-hundred percent greater variety of offending ( b  = 1.08, p  < 0.001, IRR = 2.95). Compared to White individuals, Black ( b  =  − 0.40, p  = 0.003, IRR = 0.67) and Hispanic ( b  = -0.34, p  = 0.02, IRR = 0.71) young adults report a lower variety of substance use. Not surprisingly, individuals with a higher variety of substance use earlier in adolescence report a higher variety of substance use in young adulthood ( b  = 0.29, p  < 0.001), and a similar pattern holds for offending ( b  = 0.16, p  < 0.001). Age exhibits a significant curvilinear partial association with substance use (b Age  = 3.45, p  = 0.01; b Age 2  =  − 0.09, p  = 0.01). The proportion of time on the street is significantly associated with substance use variety ( b  = 0.40, p  = 0.002) but not offending. Finally, while only reaching significance under a one-sided test in these models, those with a greater proportion of delinquent peers in Wave 7 have higher general offending variety ( b  = 0.16, p  = 0.06) and substance use variety ( b  = 0.11, p  = 0.07) roughly 3 years later.

The joint significance test—a method of inferring significant indirect effects from pathways where each direct effect along the pathway of interest is significant—supports several significant indirect avenues linking health to changes in behavioral outcomes years later (see Fig.  1 ). Regarding theorized pathways, the joint significance test identifies four significant two-path indirect effects including (1) Health → Psychosocial Maturity → Substance Use , (2) Health → Psychosocial Maturity → Offending , (3) Depression → Psychosocial Maturity → Substance Use , and (4) Depression → Psychosocial Maturity → Offending and two significant three-path indirect effects including (1) Health → Depression → Psychosocial Maturity → Substance Use , and (2) Health → Depression → Pyschosocial Maturity → Offending . Importantly, bias-corrected and accelerated bootstrapped confidence intervals with 2500 replications support these conclusions, finding relatively modest but statistically significant indirect effects (Appendix Table 4 ). In sum, findings from these analyses suggest that poor health states—including depression and self-rated health—contribute to a delay in normal desistance processes in young adulthood through the stunting of psychosocial maturity development.

Discussion and Conclusions

Criminological scholarship has little considered the role of physical and mental health states in the life course and how these may interact with or influence the stages in a criminal career. Mental health issues are a major concern among juveniles and emerging adults (World Health Organization,  2021 ) and recent data suggests that the COVID-19 global pandemic has exacerbated adolescent depression (Barendse et al., 2022 ). In terms of physical health, well over half of young adults have an unhealthy weight (Ellison-Barnes et al., 2021 ). How health states shape normative development is an important area of study. Utilizing a developmental and life-course perspective focused on persons making the transition to adulthood, we applied a health-focused model of desistance only previously applied to older adults and asked specifically how health influence offending and substance abuse over time. To do so, we took an age-graded approach to the health-based desistance framework by considering multiple theoretical literatures, speculating that health conditions would have an adverse impact on the achievement of psychosocial maturity. Furthermore, we advance recent work (e.g., see Pailing & Reniers, 2018 ) by modeling psychosocial maturity explicitly as a mediator between mental health (depression) and offending. And because physical health is linked indirectly with desistance among adults (Link et al., 2019 ), we simultaneously assess whether self-rated health affects offending via psychosocial maturity. Findings revealed support for both pathways. In this way, the results provide insight as to how health can affect criminal behavior through an indirect, developmental process.

Psychosocial maturity is a critical part of development and strongly predicts adolescent offending (Monahan et al., 2009 ; Monahan et al., 2013 ; Ozkan & Worrall, 2017 ) and other analogous behaviors (Fischer et al., 2007 ; Ozkan & Worrall, 2017 ; Riggs Romaine, 2019 ). While there exists some literature on the correlates of developing psychosocial maturity, only a small amount of studies have examined whether health states impact this achievement, often focusing on general psychosocial factors such as feelings of control over health behaviors (Cotter & Lachman, 2010 ), perceptions of physical activity (Kaasalainen et al., 2013 ), and general emotions and behavior (Kuiper et al., 2018 ), rather than psychosocial maturity as defined in developmental psychology (Greenberger & Sorensen, 1974 ; Steinberg & Cauffman, 1996 ). The current findings address this gap in the literature by establishing that health problems correlate with lower levels of psychosocial maturity later in time, and by linking health states with desistance outcomes indirectly through psychosocial maturity. With a potential exception being the direct pathway from depression to substance use 2 years later, it is noteworthy that health states were not directly associated with behavioral outcomes. It is possible that any effect of health on offending/substance use that would be direct would manifest itself in a more contemporaneous fashion.

Symptoms of mental disorders, such as depression, can interact with family problems (Sheeber et al., 2001 ), peers, and other factors, and can lead to poor attendance and performance in schools (Finning et al., 2019 ). Likewise, physical health problems may impede one’s ability to be self-reliant and lower one’s self-perception that they are oriented toward prosocial activities, such as work. In these ways among others, poor health states stall the natural progression of this critical psychosocial construct, in turn increasing the likelihood that they will succumb to pressures toward deviance. Moreover, poor health states increase substance abuse via less-developed psychosocial maturity. This finding complements existing literature that establishes the reverse: elevated use of alcohol and marijuana suppresses growth in psychosocial maturity (Chassin et al., 2010 ). Taken together, this implies a potentially complex web of development; damaged health may indirectly lead to offending and substance abuse, which consequently further suppresses the growth of core psychological and social traits and characteristics. Delayed or stunted growth in psychosocial maturity can also lead to further depression (Benson, 2014 ). Our findings point to the role that mental and physical health plays in these complex developmental processes among youth who have been involved in serious offending behaviors as adolescents. For both system-involved and general populations, how health and psychosocial maturity influence one another across adolescence and young adulthood, and the potential consequences of these developmental processes for entry into and success of life-course trajectories, is an area ripe for research.

Alongside interventions aimed at increasing psychosocial maturity (Riggs Romaine et al., 2018 ), the findings presented here point to the importance of improving health and well-being as a precursor to healthy maturation. In terms of psychological care, increased resources and effort should be diverted into attending to the mental health needs of young adults. This is paramount given the prevalence of mental health problems among these populations and the current level of unmet needs (Adams et al., 2022 ).

In terms of physical health, enhanced community-based recreational programs that incorporate physical activity provide a range of both physical and developmental benefits for adolescence and young adults (Piko & Keresztes, 2006 ), in addition to potential benefits related to structuring routine activities in prosocial ways. Decades of research now support this notion (Kapsal et al., 2019 ). Moreover, research demonstrates that physical activity reduces depression (Cooney et al., 2014 ). As a result, exercise interventions may sever at least two pathways in which stalled psychosocial maturity can lead to crime and substance abuse. A complementary angle is to support healthy nutrition in communities, which may be especially critical in certain urban and rural areas that lack supermarkets with fresh produce. Good nutrition has obvious benefits to physical health, but newer findings suggest that it also has salubrious impacts on mental health via the microbiome, specifically for depression (Winter et al., 2019 ). These core ideas related to promoting better health relevance for policies and programming in correctional settings (see Link et al. 2019 ).

Health insurance is another critical aspect to this equation, as having access to health care allows trained professionals to intervene in both psychological and physical care. Unfortunately, many Americans remain uninsured; for example, 4.3 million children under the age of 19 did not have health coverage for the entire year (Bunch & Bandekar, 2021 ) but it is young adults who have the highest rates of being uninsured (Centers for Medicare and Medicaid Services, n.d.). Reforms as part of the Affordable Care Act (ACA) hold the capacity to in part ameliorate insurance undercoverage for young adults through the provision that allows individuals to stay under their parent(s)’ insurance coverage until age 26. Still, access to quality health insurance and care could be improved.

While we focused on depression as it is a common mental health issue troubling young adults, some have contended that the BSI is best used in its entirety as a global psychological distress measure (see Skeem et al., 2006 for a brief discussion of this issue). We repeated the analysis (not shown) using the global index comprised of all nine domains (somatization, obsessive–compulsive, interpersonal sensitivity, depression, anxiety, hostility, phobia, paranoia, psychoticism) and the results were substantively similar. This supplemental finding is important for two reasons. It reinforces the importance of having strong global mental health for promoting development of psychosocial maturity. In addition, it also supports the importance of self-rated physical health for promoting psychosocial maturity—as it remained significant even when controlling for this more comprehensive, global indicator of psychological distress. A second possible limitation related to measurement is the fact that an alternative global “maturity of judgment” construct has been advanced by Steinberg and Cauffman ( 1996 ), which considers three components (temperance, perspective, and responsibility) formed by six subscales, of which our measure of psychosocial maturity is one of these subscales. Footnote 12 More research is needed to improve understanding of when and how different forms of health contribute to alternative dimensions of mature judgment. Footnote 13 Data limitations precluded us from assessing specific aspects of physical health, and investigating self-rated health over longer periods of time, including in adolescence. We hope future research will consider these and other fundamental measurement problems when advancing work in this area.

While the present study was able to assess the relationships between health, psychosocial maturity, and offending and substance abuse over nearly 3 years during young adulthood, there are several critical avenues for future research examining health-based desistance processes during the early life course. First, future work should determine whether these findings replicate in other samples of serious youth offending populations outside of Philadelphia and Phoenix. Further, as most delinquents may engage in relatively minor deviance (Moffitt, 1993 ), research should clarify the degree to which mental and physical health matter for psychosocial maturation in the general population. Second, there is a need to understand dynamic within-individual change processes and possible reciprocal effects from early adolescence through late adulthood. It is also important to identify whether there are combined influences of multiple sources of health problems on psychosocial maturity. Third, although the present research identified connections between health and psychosocial maturity, future work should empirically examine why exactly physical assnd mental health problems stall psychosocial maturation. For instance, health problems could stall psychosocial development because of a lack of energy to engage in basic tasks (e.g., see Galambos et al., 2008 ), thereby reducing opportunities to become self-reliant and form a strong sense of self. More research is needed to understand the specific mechanisms at play—a mix of quantitative and qualitative research would be especially informative—as a better understanding of these processes would hold much value for policy and programming. Finally, there is some evidence that certain types of experiences might only induce temporary delays in psychosocial maturity development (see Dmitrieva et al., 2012 ). Future research should seek to examine the implications of both acute and chronic poor health in young adulthood (and adolescence) on psychosocial maturity development and attainment, documenting any adverse developmental consequences on life successes and criminal behavior as individuals complete the transition to adulthood and beyond.

Related, there is almost no applied research to date examining whether maturity can be elevated through interventions (Riggs Romaine et al., 2018 ; see also Mulvey et al., 2014 ).

There is some evidence to suggest that self-control may explain the association between depression and delinquency (Remster, 2014 ), but not all studies find support for self-control as a mediator of this relationship (Ozkan et al., 2019 ).

For clarity, we refer to the ‘baseline’ interview as Wave 1, and sixth follow-up interview as Wave 7, and so on.

We repeated the analysis using a dichotomous measure of health where we compared those with the best health (excellent) to all those reporting less than ideal health (good, fair, or poor) and found substantially similar findings.

We anticipate that health states affect each dimension of individual adequacy of psychosocial maturity from the Greenberger model in largely similar ways, and thus utilize the composite measure. Work orientation, self-reliance, and identity were highly intercorrelated with correlations ranging from 0.70 to 0.80.

We also repeated the analysis using baseline age and age-squared, in which there was no missing data and the results were substantively similar.

McCuish ( 2020 ) recently draws attention to the importance of considering exposure time in desistance research (and especially in group-based trajectory modeling that can lead to false desistance conclusions for a nontrivial proportion of the sample). Roughly 55%, 35%, and 10% of respondents were on the street the entire time Wave 10 recall period, some of the recall period, or none of the recall period, respectively.

Scholars have recently recommended using binomial regression for bounded count data such as variety offending variables (see Britt et al., 2018). We estimated bivariate associations between the significant focal predictors from Table 2 of each variety count outcome using both negative binomial and binomial regressions for bounded count data. In sum, associations were substantively similar across model types and thus significant associations of focal variables with the two bounded count outcomes appear robust in this context.

The self-rated health item was phased beginning in Wave 5 but was not fully implemented into the protocol for all subjects until Wave 8. Specifically, there were only 5, 188, and 604 valid cases on the item in Waves 5, 6, and 7, respectively. In Wave 8, there were 1213 valid cases.

It should be noted that Mplus estimates models conditional on covariates and will invoke listwise deletion; exogenous variables must be explicitly brought into the model to estimate their effects using maximum likelihood. To do so for focal variables, we regressed health states on covariates that were measured antecedent to the assessment of mental and physical health.

As previously noted, we followed Link and colleagues’ ( 2019 ) approach to regress depression on physical health. To assess direction of association here, we regressed Wave 9 physical health (using OLS as well as ordinal logistic regression) on Wave 8 physical health and depression and found only prior physical health to be significant. We also regressed Wave 9 depression on Wave 8 physical health and depression and found both variables to be significant.

Ozkan and Worrall ( 2017 ) note that the Steinberg and Cauffman ( 1996 ) definition of psychosocial maturity includes temperance and efforts should be made to distinguish between ‘self-control’ (see Gottfredson and Hirschi, 1990) and psychosocial maturity.

Supplemental analyses (not shown) found that physical health and depression had significant effects on responsibility net of covariates—which is not surprising as the same psychosocial maturity inventory makes up part of this responsibility component (alongside resistance to peer influence). Both health states only had a marginally significant effect on temperance ( p  < 0.10) and no significant effect on perspective. Furthermore, when both outcomes were regressed on health states alongside all three of these dimensions from the Steinberg and Cauffman ( 1996 ) model and covariates, temperance (a composite measure of impulse control and suppression of aggression) was the only component that significantly predicted both substance use and offending. Thus, when temperance and perspective are controlled, responsibility did not independently contribute to desistance processes at this developmental period. In sum, direct health effects may be confined to the responsibility component of psychosocial maturity and possible indirect links from health to offending may be more complex with the Steinberg and Cauffman model. Future research should assess how physical and mental health, responsibility, temperance, perspective, and offending each exhibit within-individual change processes over time.

References 

Abeling-Judge, D. (2020). Facilitating maturation through social bonds among delinquent youth in the transition to adulthood. Journal of Developmental and Life-Course Criminology, 6 (4), 448–476.

Article   Google Scholar  

Abeling-Judge, D. (2021). The value of school: Educational experiences and maturational growth among delinquent youth. Journal of Developmental and Life-Course Criminology, 7 (3), 385–419.

Adams, S. H., Schaub, J. P., Nagata, J. M., Park, M. J., Brindis, C. D., & Irwin, C. E., Jr. (2022). Young adult anxiety or depressive symptoms and mental health service utilization during the COVID-19 pandemic. Journal of Adolescent Health, 70 (6), 985–988.

Arnett, J. J. (2000). A theory of development from the late teens through the twenties. American Psychologist, 55 , 469–480.

Barendse, M., Flannery, J., Cavanagh, C., Aristizabal, M., Becker, S. P., Berger, E., ... & Pfeifer, J. (2022). Longitudinal change in adolescent depression and anxiety symptoms from before to during the COVID-19 pandemic: A collaborative of 12 samples from 3 countries. Journal of Research on Adolescence. https://doi.org/10.1111/jora.12781

Benson, J. E. (2014). Reevaluating the “subjective weathering” hypothesis: Subjective aging, coping resources, and the stress process. Journal of Health and Social Behavior, 55 (1), 73–90.

Blumenthal, S., & Lavender, T. (2000). Violence and mental disorder. London, England: The Zito Trust.

Bollen, K. A., & Pearl, J. (2013). Eight myths about causality and structural equation models. In S. L. Morgan (Ed.), Handbook of causal analysis for social research (pp. 301–328). New York, NY: Springer.

Chapter   Google Scholar  

Bonnie, R. J., Stroud, C., & Breiner, H. (Eds.). (2015). Investing in the health and well-being of young adults . National Academies Press.

Bonta, J., Blais, J., & Wilson, H. A. (2014). A theoretically informed meta-analysis of the risk for general and violent recidivism for mentally disordered offenders. Aggression and Violent Behavior, 19 , 278–287.

Britt, C. L., Rocque, M., & Zimmerman, G. M. (2018). The analysis of bounded count data in criminology. Journal of Quantitative Criminology, 34 , 591–607.

Bunch, L. N., & Bandekar, A. U. (2021). Changes in children’s health coverage varied by poverty status from 2018 to 2020. U.S. Census Bureau. Retrieved from: https://www.census.gov/library/stories/2021/09/uninsured-rates-for-children-in-poverty-increased-2018-2020.html . Accessed 1 Sept 2022.

Bushway, S. D., Thornberry, T. P., & Krohn, M. D. (2003). Desistance as a developmental process: A comparison of static and dynamic approaches. Journal of Quantitative Criminology, 19 , 129–153.

Centers for Medicare and Medicaid Services. (n.d.). Young adults and the affordable care act: Protecting young adults and eliminating burdens on families and businesses [Fact Sheet].  https://www.cms.gov/CCIIO/Resources/Files/adult_child_fact_sheet . Accessed 1 Sept 2022.

Chassin, L., Rogosch, F., & Barrera, M. (1991). Substance use and symptomatology among adolescent children of alcoholics. Journal of Abnormal Psychology, 100 (4), 449–463.

Chassin, L., Dmitrieva, J., Modecki, K., Steinberg, L., Cauffman, E., Piquero, A. R., Knight, G. P., & Losoya, S. H. (2010). Does adolescent alcohol and marijuana use predict suppressed growth in psychosocial maturity among male juvenile offenders? Psychology of Addictive Behaviors, 24 (1), 48–60.

Cooney, G., Dwan, K., & Mead, G. (2014). Exercise for depression. Journal of the American Medical Association, 311 , 2432–2433.

Cotter, K. A., & Lachman, M. E. (2010). Psychosocial and behavioural contributors to health: Age-related increases in physical disability are reduced by physical fitness. Psychology & Health, 25 (7), 805–820.

Derogatis, L., & Melisaratos, N. (1983). The Brief Symptom Inventory: An introductory report. Psychological Medicine, 13 , 595–605.

Dmitrieva, J., Monahan, K. C., Cauffman, E., & Steinberg, L. (2012). Arrested development: The effects of incarceration on the development of psychosocial maturity. Development and Psychopathology, 34 , 1073–1090.

Ellison-Barnes, A., Johnson, S., & Gudzune, K. (2021). Trends in obesity prevalence among adults aged 18 through 25 years, 1976–2018. Journal of the American Medical Association, 326 (20), 2073–2074.

Fahmy, C., & Mitchell, M. M. (2022). Examining recidivism during reentry: Proposing a holistic model of health and wellbeing. Journal of Criminal Justice . https://doi.org/10.1016/j.jcrimjus.2022.101958

Finning, K., Ukoumunne, O. C., Ford, T., Danielsson-Waters, E., Shaw, L., De Jager, I. R., ... & Moore, D. A. (2019). The association between child and adolescent depression and poor attendance at school: A systematic review and meta-analysis. Journal of Affective Disorders , 245 , 928–938

Fischer, J. L., Forthun, L. F., Pidcock, B. W., & Dowd, D. A. (2007). Parent relationships, emotion regulation, psychosocial maturity and college student alcohol use problems. Journal of Youth Adolescence, 36 , 912–926.

Furstenberg, Frank F. (2000) Selected items from ongoing research from the MacArthur Network on Transitions to Adulthood.

Galambos, N. L., Magill-Evans, J., & Darrah, J. (2008). Psychosocial maturity in the transition to adulthood for people with and without motor disabilities. Rehabilitation Psychology, 53 (4), 498–504.

Greenberger, E. (1984). Defining psychosocial maturity in adolescence. Advances in Child Behavioral Analysis and Therapy, 3 , 1–37.

Google Scholar  

Greenberger, E., & Sorensen, A. B. (1974). Toward a concept of psychosocial maturity. Journal of Youth and Adolescence, 3 (4), 329–358.

Greenberger, E., Josselson, R., Knerr, C., & Knerr, B. (1975). The measurement and structure of psychosocial maturity. Journal of Youth and Adolescence, 4 , 127–143.

Hayes, A. F. (2009). Beyond Baron and Kenny: Statistical mediation analysis in the new millennium. Communication Monographs, 76 , 408–420.

Hayes, A. F., & Scharkow, M. (2013). The relative trustworthiness of inferential tests of the indirect effect in statistical mediation analysis: Does method really matter? Psychological Science, 24 , 1918–1927.

Hirschi, T., & Gottfredson, M. (1983). Age and the explanation of crime. American Journal of Sociology, 89 (3), 552–584.

Hollingshead, A. B. (1971). Commentary on the indiscriminate state of social class measurement. Social Forces, 49 , 563–567.

Huesmann, L. R., Boxer, P., Dubow, E. F., & Smith, C. (2019). Anxiety, depression, and offending in the Columbia County longitudinal study: A prospective analysis from late adolescence to middle adulthood. Journal of Criminal Justice, 62 , 35–41.

Huizinga, D., Esbensen, F. A., & Weiher, A. W. (1991). Are there multiple paths to delinquency? The Journal of Criminal Law and Criminology, 82 , 83–118.

Icenogle, G., Steinberg, L., Duell, N., Chein, J., Chang, L., Chaudhary, N., … Bacchini, D. (2019). Adolescents’ cognitive capacity reaches adult levels prior to their psychosocial maturity: Evidence for a “maturity gap” in a multinational, cross-sectional sample. Law and Human Behavior , 43 , 69–85.

Jackson, D. B., & Vaughn, M. G. (2018). Promoting health equity to prevent crime. Preventive Medicine, 113 , 91–94.

Kaasalainen, K. S., Kasila, K., Villberg, J., Komulainen, J., & Poskiparta, M. (2013). A cross-sectional study of low physical fitness, self-rated fitness and psychosocial factors in a sample of Finnish 18- to 64-year-old men. BMC Public Health, 13 , 1–10.

Kapsal, N. J., Dicke, T., Morin, A. J., Vasconcellos, D., Maïano, C., Lee, J., & Lonsdale, C. (2019). Effects of physical activity on the physical and psychosocial health of youth with intellectual disabilities: A systematic review and meta-analysis. Journal of Physical Activity and Health, 16 (12), 1187–2119.

Kuiper, J., Broer, J., & van der Wouden, J. C. (2018). Association between physical exercise and psychosocial problems in 96617 Dutch adolescents in secondary education: A cross-sectional study. European Journal of Public Health, 28 (3), 468–473.

Laub, J. H., & Sampson, R. J. (2001). Understanding desistance from crime. Crime and Justice, 28 , 1–69.

Laub, J. H., & Sampson, R. J. (2003). Shared beginnings, divergent lives: Delinquent boys to age 70 . Harvard University Press.

Link, B. G., Andrews, H., & Cullen, F. T. (1992). The violent and illegal behavior of mental patients reconsidered. American Sociological Review, 57 , 275–292.

Link, B. G., Monahan, J., Stueve, A., & Cullen, F. (1999). Real in their consequences: A sociological approach to understanding the association between psychotic symptoms and violence. American Sociological Review, 64 (2), 316–332. https://doi.org/10.2307/2657535

Link, N. W., Cullen, F. T., Agnew, R., & Link, B. G. (2016). Can general strain theory help us understand violent behaviors among people with mental illnesses? Justice Quarterly, 33 (4), 729–754.

Link, N. W., Ward, J. T., & Stansfield, R. (2019). Consequences of mental and physical health for reentry and recidivism: Toward a health-based model of desistance. Criminology, 57 (3), 544–573.

Link, N. W., Ward, J. T., & Link, B. G. (2020). Getting people with serious mental illnesses on track: Insights from the health-based model of desistance. Canadian Journal of Criminology and Criminal Justice, 62 (3), 71–95.

MacKinnon, D. P., Lockwood, C. M., & Williams, J. (2004). Confidence limits for the indirect effect: Distribution of the product and resampling methods. Multivariate Behavioral Research, 39 , 99.

Mallik-Kane, K., & Visher, C. A. (2008). Health and prisoner reentry: How physical, mental, and substance abuse conditions shape the process of reintegration . The Urban Institute.

McConochie, D. (1974). An exploratory examination of individual, family, and school influences on psychosocial maturity. Center for Social Organization of Schools.

McCuish, E. C. (2020). Seductions of exposure time: The mismeasurement of desistance among persons involved in frequent and serious offending. Canadian Journal of Criminology and Criminal Justice, 62 , 29–50.

McCuish, E., Lussier, P., & Rocque, M. (2020). Maturation beyond age: Interrelationships among psychosocial, adult role, and identity maturation and their implications for desistance from crime. Journal of Youth and Adolescence, 49 , 479–493.

Menard, S. and Elliott, D. S. (1996). Prediction of adult success using stepwise logistic regression analysis. A report prepared for the MacArthur Foundation by the MacArthur Chicago-Denver Neighborhood Project.

Moffitt, T. E. (1993). Adolescence-limited and life-course-persistent antisocial behavior: A developmental taxonomy. Psychological Review, 100 , 674–701.

Monahan, K. C., Steinberg, L., Cauffman, E., & Mulvey, E. (2009). Trajectories of antisocial behavior and psychosocial maturity from adolescence to young adulthood. Developmental Psychology, 45 , 1654–1668.

Monahan, K. C., Steinberg, L., Cauffman, E., & Mulvey, E. P. (2013). Psychosocial (im)maturity from adolescence to early adulthood: Distinguishing between adolescence-limited and persisting antisocial behavior. Development and Psychopathology, 25 , 1093–1105.

Morales-Vives, F., & Dueñas, J. M. (2018). Predicting suicidal ideation in adolescent boys and girls: The role of psychosocial maturity, personality traits, depression and life satisfaction. The Spanish Journal of Psychology, 21 (10), 1–12.

Mulvey, E.P., Schubert, C.A., & Piquero, A. (2014). Pathways to desistance—Final technical report. Retrieved from: https://www.ojp.gov/pdffiles1/nij/grants/244689.pdf . Accessed 1 Sept 2022.

Ozkan, T., Rocque, M., & Posick, C. (2019). Reconsidering the link between depression and crime: A longitudinal assessment. Criminal Justice and Behavior, 46 (7), 961–979.

Ozkan, T., & Worrall, J. L. (2017). A psychosocial test of the maturity gap thesis. Criminal Justice and Behavior, 44 (6), 815–842.

Pailing, A. N., & Reniers, R. L. E. P. (2018). Depressive and socially anxious symptoms, psychosocial maturity, and risk perception: Associations with risk-taking behaviour. PLoS One, 13 (8), 1–16.

Piko, B. F., & Keresztes, N. (2006). Physical activity, psychosocial health and life goals among youth. Journal of Community Health, 31 (2), 136–145.

Piotrowski, K., Brzezińska, A. I., & Pietrzak, J. (2014). Four statuses of adulthood: Adult roles, psychosocial maturity and identity formation in emerging adulthood. Health Psychology Report, 1 , 52–62.

Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40 , 879–891.

Remster, B. (2014). Self-control and the depression–delinquency link. Deviant Behavior, 35 , 66–84.

Reysen, S., Plante, C. N., Lam, T. Q., Kamble, S. V., Katzarska-Miller, I., Assis, N., Packard, G., & Moretti, E. G. (2020). Maturity and well-being: Consistent associations across samples and measures. Journal of Wellness, 2 (2), 1–8.

Riggs Romaine, C. L. (2019). Psychosocial maturity and risk-taking in emerging adults: Extending our understanding beyond delinquency. Emerging Adulthood, 7 (4), 243–257.

Riggs Romaine, C. L., Kemp, K., Giallella, C. L., Goldstein, N. E. S., Serico, J., & Kelley, S. (2018). Can we hasten development? Effects of treatment on psychosocial maturity. International Journal of Offender Therapy and Comparative Criminology, 62 (9), 2857–2876.

Rocque, M. (2015). The lost concept: The (re)emerging link between maturation and desistance from crime. Criminology & Criminal Justice, 15 , 340–360.

Rocque, M., Beckley, A. L., & Piquero, A. R. (2019). Psychosocial maturation, race, and desistance from crime. Journal of Youth and Adolescence, 48 , 1403–1417.

Sampson, R. J., & Laub, J. H. (2005). A life-course view of the development of crime. Annals of the American Academy of Political and Social Science, 602 (12), 12–45.

Schubert, C. A., Mulvey, E. P., Steinberg, L., Cauffman, E., Losoya, S. H., Hecker, T., Chassin, L., & Knight, G. P. (2004). Operational lessons from the pathways to desistance project. Youth Violence and Juvenile Justice, 2 (3), 237–255.

Sheeber, L., Hops, H., & Davis, B. (2001). Family processes in adolescent depression. Clinical Child and Family Psychology Review, 4 (1), 19–35.

Silver, E. (2006). Understanding the relationship between mental disorder and violence: The need for a criminological perspective. Law and Human Behavior, 30 , 685–706.

Skeem, J. L., Schubert, C., Odgers, C., Mulvey, E. P., Gardner, W., & Lidz, C. (2006). Psychiatric symptoms and community violence among high-risk patients: A test of the relationship at the weekly level. Journal of Consulting and Clinical Psychology, 74 (5), 967.

Steadman, H. J., Mulvey, E. P., Monahan, J., Robbins, P. C., Appelbaum, P. S., Grisso, T., Roth, L., & Silver, E. (1998). Violence by people discharged from acute psychiatric inpatient facilities and by others in the same neighborhoods. Archives of General Psychiatry, 55 , 393–401.

Steinberg, L. (2014). Age of opportunity. Lessons from the new science of adolescence . Houghton Mifflin Harcourt.

Steinberg, L., & Cauffman, L. (1996). Maturity of judgment in adolescence: Psychosocial factors in adolescent decision making. Law and Human Behavior, 20 (3), 249–272.

Steinberg, L., Chung, H. L., & Little, M. (2004). Reentry of young offenders from the justice system: A developmental perspective. Youth Violence and Juvenile Justice, 2 , 21–38.

Stogner, J., & Gibson, C. L. (2010). Healthy, wealthy, and wise: Incorporating health issues as a source of strain in Agnew’s general strain theory. Journal of Criminal Justice, 38 , 1150–1159.

Stogner, J., Gibson, C. L., & Miller, J. M. (2014). Examining the reciprocal nature of the health-violence relationship: Results from a nationally representative sample. Justice Quarterly, 31 , 473–499.

Swanson, J. W., Estroff, S., Swartz, M., Borum, R., Lachicotte, W., Zimmer, C., & Wagner, R. (1997). Violence and severe mental disorder in clinical and community populations: The effects of psychotic symptoms, comorbidity, and lack of treatment. Psychiatry, 60 , 1–22.

Taylor, A. B., MacKinnon, D. P., & Tein, J.-Y. (2008). Tests of the three-path mediated effect. Organizational Research Methods, 11 , 241–269.

Thomas, E. G., Spittal, M. J., Taxman, F. S., & Kinner, S. A. (2015). Health-related factors predict return to custody in a large cohort of ex-prisoners: New approaches to predicting re-incarceration. Health & Justice, 3 (10), 1–13.

Thornberry, T. P., Lizotte, A. J., Krohn, M. D., Farnworth, M., & Jang, S. J. (1994). Delinquent peers, beliefs, and delinquent behavior: A longitudinal test of interactional theory. Criminology, 32 , 47–83.

Wallace, D., & Wang, X. (2020). Does in-prison physical and mental health impact recidivism? SSM-Population Health, 11 , 100569.

Westenberg, P. M., Siebelink, B. M., Warmenhoven, N. J. C., & Treffers, P. D. A. (1999). Separation anxiety and overanxious disorders: Relations to age and level of psychosocial maturity. Journal of the American Academy of Child Adolescent Psychology, 38 (8), 1000–1007.

Winter, G., Hart, R. A., Charlesworth, R. P., & Sharpley, C. F. (2019). Gut microbiome and depression: What we know and what we need to know. Reviews in the Neurosciences, 29 (6), 629–643.

World Health Organization (2021). Adolescent mental health [Fact Sheet]. Retrieved from,  https://www.who.int/news-room/fact-sheets/detail/adolescent-mental-health . Accessed 1 Sept 2022.

Wu, S., Wang, R., Zhao, Y., Ma, X., Wu, M., Yan, X., & He, J. (2013). The relationship between self-rated health and objective health status: A population-based study. BMC Public Health, 13 , 1–9.

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Ward, J.T., Link, N.W. & Forney, M. Mental and Physical Health, Psychosocial Maturity, and Desistance in Young Adulthood. J Dev Life Course Criminology 9 , 331–352 (2023). https://doi.org/10.1007/s40865-023-00224-3

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Stroke in young adults, stroke types and risk factors: a case control study

• Priscilla Namaganda 1 ,
• Jane Nakibuuka 2 ,
• Mark Kaddumukasa 3 &
• Elly Katabira 4  

BMC Neurology volume  22 , Article number:  335 ( 2022 ) Cite this article

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Stroke is the second leading cause of death above the age of 60 years, and the fifth leading cause in people aged 15 to 59 years old as reported by the World Health Organization global burden of diseases. Stroke in the young is particularly tragic because of the potential to create long-term disability, burden on the victims, their families, and the community at large. Despite this, there is limited data on stroke in young adults, and its risk factors in Uganda. Therefore, we determined the frequency and risk factors for stroke among young adults at Mulago hospital.

A case control study was conducted among patients presenting consecutively to the general medical wards with stroke during the study period September 2015 to March 2016. A brain Computerized Tomography scan was performed to confirm stroke and classify the stroke subtype. Controls were patients that presented to the surgical outpatient clinic with minor surgical conditions, matched for age and sex. Social demographic, clinical and laboratory characteristics were assessed for both cases and controls. Descriptive statistics including frequencies, percentages, means, and standard deviation were used to describe the social demographics of case and controls as well as the stroke types for cases. To determine risk factors for stroke, a conditional logistic regression, which accounts for matching (e.g., age and sex), was applied. Odds ratio (with 95% confidence interval) was used as a measure for associations.

Among 51 patients with stroke, 39(76.5%) had ischemic stroke and 12(23.5%) had hemorrhagic stroke. The mean age was 36.8 years (SD 7.4) for stroke patients (cases) and 36.8 years (SD 6.9) for controls. Female patients predominated in both groups 56.9% in cases and 52.9% in controls. Risk factors noted were HIV infection, OR 3.57 (95% CI 1.16–10.96), elevated waist to hip ratio, OR 11.59(95% CI 1.98–68.24) and sickle cell disease, OR 4.68 (95% CI 1.11–19.70). This study found a protective effect of oral contraceptive use for stroke OR 0.27 95% CI 0.08–0.87. There was no association between stroke and hypertension, diabetes, and hyperlipidemia.

Among young adults with stroke, ischemic stroke predominated over hemorrhagic stroke. Risk factors for stroke were HIV infection, elevated waist to hip ratio and sickle cell disease.

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Stroke is the second leading cause of death above the age of 60 years, and the fifth leading cause in people aged 15 to 59 years old as reported by the World Health Organization (WHO) global burden of diseases [ 1 ]. The severity of stroke in the young is relatively low in developed countries ranging from 2 -7% in Italy and USA respectively [ 2 , 3 ]. In Africa, on the other hand the prevalence of stroke among young adults is 12.9% in Nigeria [ 4 ], 31% in South Africa [ 5 ], 28.9% in Morocco [ 6 ]. The incidence of ischemic stroke in the young has been increasing globally over the last 2–3 decades. From the Danish National Patient Register, the incidence rates of first‐time hospitalizations for ischemic stroke and transient ischemic attack (TIA) in young adults have increased substantially since the mid 1990s while the incidences of hospitalizations for intracerebral hemorrhage and subarachnoid hemorrhage remained stable during the study period [ 7 ].

In Uganda, literature on stroke in young adults is limited however results of a study done among acute stroke patients admitted to the national referral hospital (Mulago hospital) showed a 30-day mortality of 43.8%. Out of 133 patients, 32 patients (25%) were less than 51 years old. Out of the 56 patients that died, 13 patients (23%) were less than 51 years [ 8 ].

Rapid western cultural adaption (sedentary lifestyle, deleterious health behavior like consumption of tobacco and alcohol and high fat/cholesterol diet) and Human immunodeficiency syndrome/ Acquired immunodeficiency syndrome (HIV/AIDS) that is highly prevalent in Africa has accelerated risk factors and increased the burden of stroke [ 9 ].

Most literature indicates that the traditional risk factors i.e., hypertension, diabetes mellitus and dyslipidemia are still the commonest risk factors with hypertension having the highest frequency. Other risk factors common to the young include smoking, excessive alcohol intake, illicit drug use, oral contraceptive use and migraine [ 10 ].

Although stroke is predominantly a disease of the middle age and the elderly, its occurrence in younger age groups is not rare. Stroke in young adults seems to be increasing and is particularly tragic because of the potential to create long-term disability, burden on the victims, their families, and the community at large such as Uganda. Despite the huge socioeconomic impact of stroke in this age group, there is a scarcity of data regarding stroke in young adults in sub-Saharan Africa including Uganda. Effective stroke prevention strategies in the young require comprehensive information on risk factors and possible causes. Although case reports and etiologic investigations of possible causes of stroke in the young have been identified especially in developed countries, there is limited data on risk factors in Africa Uganda inclusive. Information obtained from this study will fill the knowledge gap in this area of stroke in the young which will inform institutional strategies on prevention and management of stroke in this age group. This study, therefore, seeks to determine the frequency of stroke types and risk factors for this population.

The aims of the study were:

To determine the frequency of stroke types among young adults on the general medical wards in Mulago hospital between September 2015 and March 2016.

To determine the risk factors for stroke (i.e., ischemic, and hemorrhagic stroke) among young adults on the general medical wards in Mulago hospital between September and March 2016.

This was a case control study. Cases were defined as patients with a confirmed diagnosis of stroke by brain computerized tomography (CT) scan that met the inclusion criteria. Controls were defined as patients with minor surgical conditions that met the inclusion criteria. The study was carried out in Mulago hospital which is the national referral hospital in Uganda as well as the teaching hospital of Makerere University College of health sciences. It has a bed capacity of 1500 beds and has both inpatient wards, outpatient departments both for medical and surgical specialties. It has a radiological department with CT scan and highly trained personnel and a well-equipped laboratory. Cases were recruited consecutively from the medical wards specifically on the neurology ward of Mulago hospital. Patients on the neurology ward are managed by physicians that have had additional training in the management of neurological conditions.

Controls were recruited from general surgical outpatient departments from Mulago hospital. They were matched for age and sex. Eligible patients were patients aged 15–45 years, confirmed diagnosis of stroke on brain CT scan and with a written informed consent or assent for patients less than 18 years. These included patients with intracranial hemorrhages and ischemic stroke, none had subarachnoid hemorrhage. Patients were excluded if they were unconscious and with no valid surrogate (next of kin) and HIV positive with opportunistic infections. Patients eligible as control were, patient aged 15–45 years, minor surgical condition, written informed consent or assent for patients less than 18 years. Patients with features of stroke secondary to non-vascular causes like trauma, tumors were excluded as controls. For controls, we chose patients with minor surgical conditions because we wanted controls to be hospital patients but with non-medical conditions that could confound our findings. Such conditions included lacerations, hernias, lipomas, ingrown toenails, circumcision.

Based on the catchment area of Mulago, patients with minor surgical conditions are likely to have similar social economic status and come from similar neighborhoods as would health controls living in the catchment areas as patients with stroke.

The best alternative would have been healthy controls from the neighborhoods of the patients with stroke, but this would have been resource consuming.

The sample size was calculated assuming a prevalence of 62.2% of hypertension among the stroke patients as was indicated in a similar study among the young Thai adults in Bangkok, Thailand (Bandasak et al., 2011) [ 11 ]. We also assumed that the risk for stroke is higher among the hypertensive with an OR of 3. With this sample size, we were powered to detect associations with other risk factors like smoking (OR 2.6) [ 12 ], diabetes (OR 13.2 for black men and 22.1 for black women) [ 13 ].

With these assumptions, a sample size of 51 cases and 51 controls was found sufficient with 80% power and 0.05 level of significance.

Sampling procedure

All young patients admitted on the general medical wards suspected of having stroke were screened and brain CT scan done. Once a diagnosis of stroke was confirmed on CT scan, participants who consented to participate in the study were recruited consecutively, a standardized questionnaire administered by the research team for those patients able to communicate. For patients not able to communicate, consent and information were obtained through the care givers. Controls were selected from the general surgical outpatient clinic using consecutive sampling method. This was done after we had obtained all the cases. These were matched for age and sex until the sample size was accrued.

Information was collected on:

Social demographic characteristics i.e., age, sex, level of education, occupation, religion, history of smoking and alcohol consumption, history of illicit drug use, history of oral contraceptive use.

Clinical examination included general physical examination, blood pressure using a digital blood pressure machine. For patients who were too weak to sit up, blood pressure measurement was taken in supine position. For those able to sit, it was taken in the sitting position. The two blood pressure measurements were taken at an interval of 5 min and the average blood pressure recorded as the final blood pressure.

Physical measurements for the weight and hip were taken using a stretchable tape measure. Waist measurements were taken at the narrowest point-umbilicus and hip measurements at the widest point- buttocks. A waist to hip ratio was obtained and recorded on the questionnaire.

Blood was drawn for laboratory tests; high density lipoprotein, low density lipoprotein (HDL/LDL), fasting blood sugar, full blood count, Hb electrophoresis, prothrombin time/ international normalization ratio (PT/INR), HIV serology, Treponema pallidum hemagglutination (TPHA).

Other information obtained was history and family history of diabetes and hypertension.

The general surgical outpatient clinic runs every Tuesday, and Thursday in Old Mulago hospital Participants were identified at the surgical outpatient clinic. Those matching the age and sex of the cases were recruited, written consent/assent obtained, and questionnaire was administered by the PI. The procedure as explained above was followed for the controls.

Data collection

A pre-tested and standardized questionnaire was used as a data collection tool. The principal investigator administered the questionnaire to the participants in data collection. Data on socio demographics and past medical history was collected.

Results from imaging and laboratory investigations were also recorded into the questionnaire.

Data collected was double entered into the computer using EPI-DATA (version 3.1) software to minimize data entry errors. Data was then backed up and archived in both soft and hard copy to avoid losses. Confidentiality was ensured using code numbers instead of patients’ names. Questionnaires were stored in a lockable cabinet for safety.

Data analysis

Data was analyzed using STATA Version 12 (StataCorp. 2011.  Stata Statistical Software: Release 12 . College Station, TX: StataCorp LP). Descriptive statistics were used to describe characteristics of the study participants and the stroke subtypes which included frequencies, percentages, means and standard deviation. To determine factors associated with stroke, a conditional logistic regression, which accounts for matching (e.g., age and sex), was applied. Odds ratio (with 95% confidence interval) was used as a measure for associations. Factors with p -values < 0.2 at a bi-variable analysis were entered into a multiple conditional logistic regression to obtain the adjusted estimates. Factors whose 95% confidence interval for the odds ratio that excludes a 1 or whose p -value < 0.05, were considered statistically significant at the adjusted level. Post-hoc power calculation was performed for the adjusted analysis to check if there was enough power to detect a difference between cases and controls.

Quality control

To ensure quality of results several measures were undertaken, these included:

The questionnaires were pre-tested and standardized before study commenced.

The research team administered the structured, pre- coded and pre-tested questionnaire to enrolled participants on a face-to-face basis and brain CT scans were done by competent and well-trained radiology technicians and interpretation done by a specialist radiologist at the Radiology Department of Mulago hospital.

The questionnaires were checked for completeness at the end of every interview. The two files were compared, and any discordance corrected against data recorded with the questionnaire. The data were then backed up.

Ethical consideration

Written informed consent/ assent was obtained from all participants or their parent/guardian or legal authorized representative to participate in the study. Ethical approval was obtained from Makerere University, school of medicine research and ethics committee (SOMREC) (reference number #REC REF 2015–105).

Confidentiality was ensured using code numbers instead of patients’ names. Questionnaires were stored in a lockable cabinet for safety.

Profile of the study

Enrollment of study participants was carried out between September 2015 to March 2016 in Mulago hospital. The patient flow diagram for cases and controls is as shown in Fig.  1 .

Patient flow diagram

Social demographic characteristics of the study population

A total of 51 cases aged 18 to 45 years and the same number of hospital control matched for age and sex were identified. The mean age of cases was 36.8 years (standard deviation (SD) 7.4) and the control was 36.8 years (SD 6.9). Females predominated in both groups with 56.9% in cases and 52.9% in controls. There was no significant difference in other baseline characteristics between cases and controls except in oral contraceptive use, waist to hip ratio, HIV status and sickle cell disease. Details of the social demographic characteristics are shown in Table 1 .

Clinical characteristics of the study participants

The mean fasting blood sugar was 6.6 (SD 3.9) for cases and 5.3 (SD 0.7) for controls. This was statistically significant with a p value of 0.015. Waist to hip ratio was also statistically significant with a p value of 0.007. Cases with an elevated wait to hip ratio were 14 (27.5%) and controls were 3 (5.9%). Table 2 shows the baseline clinical characteristics of the study participants.

Laboratory characteristics of the study participants

HIV serology and Hb electrophoresis were statistically significant with a p value of 0.076 and 0.023 respectively. 18 patients (35.3%) were reactive for HIV among cases and controls 10 (19.6%). 12 patients (23.5%) had abnormal Hb electrophoresis among cases controls 3 (5.9%). Table 3 shows the laboratory characteristics of the study participants.

Stroke types

Stroke types by social demographic characteristics of cases.

Among 62 patients, who had brain CT scan done, 11 patients had non stroke pathologies (4 had brain abscesses, 7 patients had ring enhancing lesions suggestive of toxoplasmosis). Among 51 patients with stroke confirmed on CT scan, the frequency of ischemic stroke was 76.5% and hemorrhagic stroke was 23.5%.

Most participants with ischemic or hemorrhagic stroke were in the age group 36–45 years. Females predominated in both ischemic and hemorrhagic stroke. Details of the social demographic characteristics by stroke types are shown in Table 4 .

Clinical and laboratory characteristics by stroke types

Majority of patients with hemorrhagic stroke were hypertensive (91.7%) compared to only 25.6% among patients with ischemic stroke. Details of the clinical and laboratory characteristics of the study participants by stroke subtypes are shown in Table 5 .

Risk factors for stroke at univariate analysis

Social demographic characteristics at univariate analysis.

Oral contraceptive use showed a significant difference with an unadjusted OR of 0.27 (95% CI 0.08–0.87) case subjects 23.3% and control subjects 56.5%. Belonging to other religion (seventh day advent, Pentecostal) was statistically significant with a p value of 0.009, OR 0.17. These findings are detailed in Table 6 below.

Clinical characteristics at univariate analysis

There was a significant difference in waist to hip ratio between cases (27.5%) and controls (5.9%), with unadjusted OR 6.85 (CI 1.70–27.62). HIV serology with an unadjusted OR of 2.64 (95% CI 1.03–6.82). Hb electrophoresis with an unadjusted OR of 4.31 (95% CI- 1.15–16.17). Fasting blood sugar with an unadjusted OR of 1.64 (95% CI 1.02–2.62). Details of the above findings are shown in Table 7 below.

Risk factors for stroke at multivariate analysis

At multivariate analysis, HIV serology (OR 3.72, 95% CI 1.16–10.96), waist to hip ratio (OR 11.26 95% CI 1.98–68.24) and sickle cell disease OR 4.78 95% CI 1.11–19.70) were independent risk factors for stroke in young adults. Table 8 shows risk factors at multivariate analysis. None of the patients with HIV met the definition of AIDS as defined by the occurrence of any of the more than 20 life-threatening cancers or “opportunistic infections”, by WHO.

This case–control study showed that the frequency of ischemic stroke was higher than that of hemorrhagic stroke in young Ugandan population. We showed that positive HIV serology, elevated waist to hip ratio and sickle cell disease were independent risk factors for stroke in this population.

This is consistent with several studies that have been done and found ischemic stroke to be more prevalent than hemorrhagic stroke. Studies done in Africa, in Libya reported 77% ischemic stroke and 23% hemorrhagic stroke (these included both intracerebral and subarachnoid hemorrhagic stroke) [ 14 ], in Morocco, 87.3% ischemic stroke and 12.7% hemorrhagic (study did not specify on the subtypes of hemorrhagic stroke) [ 6 ]. In a study from Bosnia and Herzegovina, Subarachnoid hemorrhage was more frequent in young adults compared with older patients (> 45 years of age) (22% vs. 3.5%), intracerebral hemorrhage (ICH) was similar in both groups (16.9% vs. 15.8%), but ischemic stroke (IS) was predominant stroke type in the older group (61% vs. 74%) [ 15 ]. On the other hand, studies focusing on all young stroke patients and including also subarachnoid hemorrhages have found much higher proportion of hemorrhagic strokes in younger vs. older individuals. Population-based studies have reported as low as 57% prevalence for ischemic stroke in those aged > 45, as reported by a recent narrative review [ 16 ]. This difference in occurrence of stroke subtypes could be due to the low prevalence of hypertension in this population in our setting given that hypertension has been reported to be the commonest risk factor for hemorrhagic stroke.

Most previous studies of HIV and stroke have been retrospective, but the prospective studies in Africa and East Africa have reported the importance of HIV as a risk factor for stroke [ 17 ]. A recently published study done in Malawi, with defined cases and population controls and 99% ascertainment of HIV status, reported HIV infection as an independent risk factor for stroke. This study further found that patients who had started standard HIV treatment in the previous six months had a higher risk of stroke (OR 15.6 95% CI 4.21–46.6). This was probably due to an immune reconstitution inflammatory syndrome (IRIS) like process [ 18 ]. A variety of mechanisms have been implicated in the association of HIV and stroke, these include HIV associated vasculopathy, vasculitis which causes abnormality of the intracranial or extracranial cerebral blood vessels and neoplastic involvement. Indirectly through cardioembolic, coagulopathy in association with protein C and protein S deficiency. Some infections are well established causes of stroke, such as Mycobacterium tuberculosi s , syphilis, and varicella zoster virus through increased susceptibility to acquisition or reactivation of these infections [ 19 , 20 ]. Combined antiretroviral therapy (cART) might unmask occult opportunistic infections that subsequently cause a stroke. This possibility should be considered in all patients who have had an acute stroke or have worsening of stroke symptoms after initiation of cART [ 21 ].

An elevated waist to hip ratio (WHR) was associated with 12 times increased risk of stroke among young adults in Mulago hospital compared to individuals with a normal waist to hip ratio. Abdominal obesity (measured as waist–hip ratio) is associated with an increased risk of myocardial infarction, stroke, and premature death [ 22 ]. This agrees with a few studies that have assessed the association of stroke with waist to hip ratio. Aaron et al. 1990, assessed the relation between body fat distribution, and the 2-year incidences of hypertension and stroke in a cohort of 41,837 women aged 55–69 years. Women who developed stroke were 2.1 (95% CI 1.5–2.9) times more likely to have an elevated ratio than those who did not [ 23 ]. Md Habib et al. 2011 assessed high waist to hip ratio as a risk factor for ischemic stroke for overall stroke and he found 64% of the ischemic stroke patient had abnormal WHR in Bangladesh [ 24 ]. Abdominal obesity measured with WHR was an independent risk factor for cryptogenic ischemic stroke (CIS) in young adults after rigorous adjustment for concomitant risk factors in the Revealing the Etiology, Triggers, and Outcome (SECRETO; NCT01934725) study, a prospective case–control study that included patients aged 18–49 years with a first ever CIS at 19 European university centers [ 25 ].

Sickle cell disease was also associated with increased risk of stroke among young adults in Mulago hospital. This agrees with several studies that have associated sickle cell disease with stroke. Ohene et al. 1998 assessed cerebrovascular accidents (CVA) in sickle cell disease, found the highest rates of prevalence of 4.01% and incidence of 0.61 per 100 patient-years. The incidence of hemorrhagic stroke was highest among patients aged 20 to 29 years [ 26 ].

In our study, the unadjusted OR for oral contraceptive use was 0.26 95% CI 0.08–0.87 with a p value of 0.028. This observation at the unadjusted level is significant but could be due to another variable which is a confounder to OC use such as higher socioeconomic status and better control of other possible risk factors.

In our study, we found no association between hypertension and stroke in young adults though it’s an independent risk factor for stroke in the older population. This finding is different from the multinational interstroke study which attributed most strokes among young adults in low- and middle-income countries to hypertension. In that study, only one fifth of the patients were from wealthier African countries where hypertension, diabetes and hypercholesterolemia are likely to occur with higher prevalence than in Mulago hospital [ 27 ]. Other studies have also reported the role of hypertension as a risk factor for stroke in young adults, low physical activity and hypertension were the most important risk factors, accounting for 59.7% and 27.1% of all strokes, respectively among a German nationwide case–control study based on patients enrolled in the SIFAP1 study (Stroke in Young Fabry Patients) 2007 to 2010 and controls from the population-based GEDA study (German Health Update) 2009 to 2010 [ 28 ]. A study that used population-based controls for hospitalized young patients with ischemic stroke demonstrated that independent risk factors for stroke were atrial fibrillation (OR 10.43; cardiovascular disease (OR, 8.01; type 1 diabetes mellitus (OR, 6.72; type 2 diabetes mellitus (OR, 2.31, low high‐density lipoprotein cholesterol (OR, 1.81; current smoking status (OR, 1.81; hypertension (OR, 1.43, and a family history of stroke (OR, 1.37) [ 29 ].

This finding could be explained by the high prevalence of hypertension in the general peri urban Ugandan population among young adults as reported by Kayima et al. 2015. He found a prevalence of 15% (95% CI 14.2 – 19.6%) % for young adults aged 18–44 years [ 30 ].

The study was conducted at Mulago hospital which is a national referral hospital in Uganda situated in central Uganda. Mulago hospital received patients both referred patients from all over Uganda and those from its catchment area. This is generally representative of the whole Ugandan population.

Uganda has a young population and with an HIV prevalence comparable to most countries in Sub-Saharan Africa, so the findings of this study are generalizable to other Sub-Saharan African populations.

Ischemic stroke is more prevalent than hemorrhagic stroke among young adults in Mulago hospital. Independent risk factors for stroke among young adults in Mulago hospital were HIV infection, elevated waist to hip ratio and sickle cell disease. Oral contraceptive use was found to be protective of stroke among young adults in Mulago hospital. There was no significant association between stroke among young adults and hypertension, diabetes, hyperlipidemia, smoking, alcohol use and illicit use.

Study limitations

The sample size was too small to detect all but the strongest associations with common exposures. When designing the study, we based on hypertension as a significant driver for strokes in this population based on other studies done to calculate the sample size, however based on our findings, hypertension was not a big driver of stroke in this population. Secondly the nature of stroke type associated with hypertension is hemorrhagic which were less common in this study. This was an unexpected finding and needs more evaluation.

Consecutive sampling methods has selection bias in which a variable that is associated with the outcome under investigation may occur more frequently or less in those sampled in this period as compared to the general population.

The use of a combined ischemic stroke and intracerebral hemorrhage group may have obscured relationships specific to one group, i.e., the risk factors for stroke were not stratified for type of stroke.

The best alternative for controls would have been healthy controls from the neighborhoods of the patients with stroke, but this would have been resource consuming hence the choice of hospital controls with different medical conditions from cases.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Go AS, Mozaffarian D, Roger VL, Benjamin EJ, Berry JD, Borden WB, Turner MB. Heart disease and stroke statistics—2013 update: a report from the American Heart Association. Circulation. 2013;127(1):e6–245.

PubMed   Google Scholar  

Griffiths D, Sturm J. Epidemiology and etiology of young stroke. Stroke research and treatment, 2011. 2011.

Google Scholar  

Bevan HS, Sharma K, Bradley W. Stroke in young adults. Stroke. 1990;21(3):382–6.

Article   CAS   Google Scholar  

Mustapha AF, Sanya EO, Bello TO. Stroke among young adults at the LAUTECH Teaching Hospital, Osogbo, Nigeria. Nig Q J Hosp Med. 2012;22(3):177–80.

CAS   PubMed   Google Scholar  

Hoffmann M. Stroke in the young: the multiethnic prospective Durban stroke data bank results. J Stroke Cerebrovasc Dis. 1998;7(6):404–13.

Chraa M, Louhab N, Kissani N. Stroke in young adults: about 128 cases. Pan Afr Med J. 2014;17(37).

Tibæk M, Dehlendorff C, Jørgensen HS, Forchhammer HB, Johnsen SP, Kammersgaard LP. Increasing incidence of hospitalization for stroke and transient ischemic attack in young adults: a registry-based study. J Am Heart Assoc. 2016;5(5):e003158.  https://doi.org/10.1161/JAHA.115.003158 .

Article   PubMed   PubMed Central   Google Scholar  

Kwarisima L, Mukisa R, Nakibuuka J, Matovu S, Katabira E. Thirty-day stroke mortality and associated clinical and laboratory factors among adult stroke patients admitted at Mulago Hospital (Uganda). Afr J Neurol Sci. 2014;33(1):79–86.

Feigin VL, Lawes CM, Bennett DA, Anderson CS. Stroke epidemiology: a review of population-based studies of incidence, prevalence, and case-fatality in the late 20th century. Lancet Neurol. 2003;2(1):43–53.

Article   Google Scholar  

Katsnelson MJ, Della-Morte D, Rundek T. Stroke in the young. Period Biol. 2012;114(3):347–53.

Bandasak R, Narksawat K, Tangkanakul C, Chinvarun Y, Siri S. Association between hypertension and stroke among young Thai adults in Bangkok, Thailand. Southeast Asian J Trop Med Public Health. 2011;42(5):1241–8 PMID: 22299451.

Bhat VM, Cole JW, Sorkin JD, Wozniak MA, Malarcher AM, Giles WH, Stern BJ, Kittner SJ. Dose-response relationship between cigarette smoking and risk of ischemic stroke in young women. Stroke. 2008;39(9):2439–43. https://doi.org/10.1161/STROKEAHA.107.510073 . Epub 2008 Aug 14. PMID: 18703815; PMCID: PMC3564048.

Rohr J, Kittner S, Feeser B, Hebel JR, Whyte MG, Weinstein A, Sherwin R. Traditional risk factors and ischemic stroke in young adults: the Baltimore-Washington Cooperative Young Stroke Study. Arch Neurol. 1996;53(7):603–7.

Radhakrishnan K, Ashok PP, Sridharan R, Mousa ME. Stroke in the young: incidence and pattern in Benghazi. Libya. 1986;73(4):434–8.

CAS   Google Scholar  

Smajlović D, Salihović D, Ibrahimagić OĆ, Sinanović O. Characteristics of stroke in young adults in Tuzla Canton Bosnia and Herzegovina. Coll Antropol. 2013;37(2):515–9.

Tatlisumak, et al. Nontraumatic intracerebral haemorrhage in young adults. Nat Rev Neurol. 2018;14:237–50. https://doi.org/10.1038/nrneurol.2018.17 .

Article   PubMed   Google Scholar  

O’Donnell M, Yusuf S. Risk factors for stroke in Tanzania. Lancet Glob Health. 2013;1:e241–2.

Benjamin LA, Corbett EL, Connor MD, et al. HIV, antiretroviral treatment, hypertension, and stroke in Malawian adults, a case control study. Neurology. 2016;86:324–33. https://doi.org/10.1212/WNL.0000000000002278 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Benjamin LA, Bryer A, Emsley HCA, Khoo S, Solomon T, Connor MD. HIV infection and stroke: current perspectives and future directions. Lancet Neurol. 2012;11(10):878–90. https://doi.org/10.1016/S1474-4422(12)70205-3 .

Lammie GA, Hewlett RH, Schoeman JF, Donald PR. Tuberculous cerebrovascular disease: a review. J Infect. 2009;59:156–66.

Berger JR. AIDS and stroke risk. Lancet Neurol. 2004;3:206–7 [PubMed: 15039031].

Nejtek V, Talari D. Novel Risk Factors for TIA and Stroke in Young Adults. 2016.

Folsom AR, Prineas RJ, Kaye SA, Munger RG. Incidence of hypertension and stroke in relation to body fat distribution and other risk factors in older women. Stroke. 1990;21:701–6.

Khan MH, Chakraborty SK, Biswas RSR. High Waist to Hip Ratio as a Risk Factor for Ischemic Stroke Patients Admitted in a Tertiary Care Hospital. Bangladesh J Anat. 2011;9(1):30–4.

JJaakonmäki N, Zedde M, Sarkanen T, Martinez-Majander N, Tuohinen S, Sinisalo J, SECRETO Study Group. Obesity and the risk of cryptogenic ischemic stroke in young adults. J Stroke Cerebrovasc Dis. 2022;31(5):106380.

Rothman SM, Fulling KH, Nelson JS. Sickle cell anemia and central nervous system infarction: a neuropathological study. Ann Neurol. 1986;20:684–90.

O’Donnell MJ, Xavier D, Liu L, et al. Risk factors for ischemic and intracerebral hemorrhagic stroke in 22 countries (the INTERSTROKE study): a case-control study. Lancet. 2010;376:112–23.

Aigner A, Grittner U, Rolfs A, Norrving B, Siegerink B, Busch MA. Contribution of established stroke risk factors to the burden of stroke in young adults. Stroke. 2017;48(7):1744–51.

Kivioja R, Pietilä A, Martinez-Majander N, Gordin D, Havulinna AS, Salomaa V, Putaala J. Risk factors for Early-Onset ischemic stroke: a Case-Control study. J Am Heart Assoc. 2018;7(21):e009774.

Kayima J, Nankabirwa J, Sinabulya I, Nakibuuka J, Zhu X, Rahman M, Kamya MR. Determinants of hypertension in a young adult Ugandan population in epidemiological transition—the MEPI-CVD survey. BMC Public Health. 2015;15(1):1–9.

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Acknowledgements

We acknowledge the patients of Mulago hospital who gave us consent to obtain this information.

This study was funded with funds from the MEPI-Neurology program under Makerere University. The funding project had no role in the design of the study and collection, analysis, and interpretation of data and no role in writing the manuscript.

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PN– conception, design of work, acquisition, analysis, interpretation of data, drafted and substantively revised the manuscript, JN– analysis, interpretation of data, drafted and substantively revised the manuscript, MK – analysis, interpretation of data, drafted and substantively revised the manuscript, EK– design of work, acquisition, analysis, interpretation of data, drafted and substantively revised the manuscript. All authors read and approved the final manuscript.

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Written informed consent/ assent was obtained from all participants or their parent/guardian or legal authorized representative to participate in the study. Ethical approval was obtained from Makerere University, school of medicine research and ethics committee (SOMREC) (reference number #REC REF 2015–105). All methods and procedures were carried out in accordance with relevant guidelines and regulations.

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Namaganda, P., Nakibuuka, J., Kaddumukasa, M. et al. Stroke in young adults, stroke types and risk factors: a case control study. BMC Neurol 22 , 335 (2022). https://doi.org/10.1186/s12883-022-02853-5

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She proceeded to fail to do her homework in class. She claimed not to know why she stopped trying. Her teachers have never been able to find a way to motivate her. Nor have we, her parents. We have tried asking if she needs help, as in a tutor, we’ve grounded her, taken her phone away, and more recently, at the OK of a counselor, paid her for grades. Even that is now not helping. She wants to handle things herself, but then does not. She gets angry when others try to help her, provide motivation for her, or set her up on a schedule of any type.

She has so much going for her. I don’t want to see her waste her life and her potential. She doesn’t appear to be anxious or stressed out. But she seems to fear success and the pressure she would feel to keep it up. That’s what it seems like to me.

Any words of wisdom you have would be most welcome. Thank you.”

15 Minute Consultation:

We determined that her daughter was a good candidate for Real Life Coaching. Her daughter and I spoke and we found common ground through music and the arts. We booked a first session.

First Session Work:

Please state three things you wish to work on:

Goal 1: Getting things done and organizing my life.

Goal 2: Not being pushed around to do things I don’t want to do (communication).

Goal 3: Finding something to do with my life.

What would you say are some of the challenges you have to making these things be as you want them to be?

Goal 1: I’m lazy. Too much video stuff. FB.

Goal 2: I don’t like confrontation.

Goal 3: I get bored easily. Everyone else does things better than me.

What would be a good indicator that you have made progress in each of these things?

Goal 1: When I can keep to a written schedule.

Goal 2: When I can stop saying yes to things just to get out of a lecture.

Goal 3: When I stick to something for a whole year.

Six Months Later:

X has been making great progress in self-esteem and communication. She has learned to say “Let me think about it” when she is asked to things she doesn’t want to do and then her and I come up with an alternate plan that meets her parents needs and still stays within X’s goals.

X is also keeping to her daily schedule. We found the secret to daily inspiration for her and it is working.

Son Did Well in University But Is Slacking and Jobless

The Parent’s Contact Email:

“My son, “M” just turned 27 years old is a good guy, but a slacker. (No drugs / No ETOH…just poorly self motivated!!) Things recently went from bad to worse…quit his min wage job without having another lined up. Has a certificate in graphic arts, but it got him nowhere. Real love is game programming. I am encouraging him to go back to school for it. A great opp. Need help motivating him for it. I am not trying to define his life, but instead get him on a path! Please help. I have your book…but need to present it to him in a kind way!”

After trying several positive ways to encourage “M” to meet with me, we went to “plan D”. All funds cut off unless he was willing to talk to me one time. It worked! “M” was stuck and ashamed of not having figured out how to make it “out there”.

Goal 1: Quitting weed.

Goal 2: Figuring out what job I could do well with.

Goal 3: Getting organized.

Goal 1: I really like the ritual of it all. Anywhere I go I can easily make friends ‘cuz there are always stoners around.

Goal 2: I’ve lost confidence in my ability to choose a good future.

Goal 3: I tend to start gaming and lose track of time.

Goal 1: Not smoking on weekdays.

Goal 2: Doing a simple job I like preparing for something even better.

Goal 3: Getting something good done each day.

“M” succeeded in not smoking during the weekdays for a month but eventually started smoking every day again. He decided to stop completely for a month and see what happens. He is weed-free for 21 weeks and counting! He got a job at an upscale restaurant that trains their staff on business/restaurant skills. He’s been voted “most promising” three months in a row.

“M” is finding the schedule is making it easy to get at least two good things done every day, even with his work schedule. I know what he is going to pick as his long term vocation but we’ll wait and see if that is his “hero’s journey”.

Take the Next Step!

Discover how our mentoring program can help you!

Complete the following steps to receive a free consultation:

Step 1: Please read: How it Works - Mentoring Teens and Young Adults .

Step 2: Please read the Pricing Info .

We are looking forward to showing you how our Mentoring Program can help young adults succeed when nothing else has.

Copyright 2016 World Wide Youth Mentoring Inc.

Interested in becoming a Professional Mentor? Click here

Case Studies for Parents of Adults

As children grow, a parent’s role evolves—from caregiver to choreographer to coach. When children hit young adulthood and finish their college years, parents function primarily as consultants. But this promotion is no cushy retirement. It’s a challenging gig: even the most well-adjusted young adult can run into roadblocks, and parents have less control over kids’ decisions than before.

Want to hone your parental consulting skills? We’ll use the ultimate b-school teaching tool, the case study, to explore how you can offer advice for the most perplexing young-adulthood dilemmas and help your grown-up kids grow into leaders in their careers, families, and communities.

Case Study 1: Making Room for Mistakes

The dilemma:

Your twenty-three-year-old son, Curtis, attended community college for a few years and received his associate’s degree but couldn’t decide on a major or career that interested him. Your family values education, and you hoped he would transfer to a state college, but he decided to take a break from school. He spent a few months traveling, then got a job at a local grocery store where a friend of his worked. He’s worked there contentedly for the past year or so.

This summer, his friend invited him to help make a web video series. Curtis gushed to you about how much he enjoyed working on it—plus he’s thrilled that the series gained a significant amount of views and subscribers. Curtis and his friend just started a crowdfunding campaign to create a second series. Now he’d like to quit his job to devote more time to producing videos and seeking out sponsors, and he hopes to translate his online success into a sweet Hollywood screenwriting deal. You don’t want to crush Curtis’s dreams, but you’re concerned that his expectations are unrealistic.

The response:

The consultant role gets especially hard when you feel that your child is taking a wrong turn or destined for disappointment. How do you balance your desire to prevent harm with the need to grant your child space to pursue his dreams—and learn from mistakes?

Ask first. In this situation, your first instinct is to call it like you see it. But with young adults, it’s important to first ask if they’re open to advice. “Unsolicited advice can come off as critical rather than supportive,” says Julie de Azevedo Hanks, PhD, a licensed clinical social worker and psychotherapist, owner of Wasatch Family Therapy in Salt Lake City, and author of The Burnout Cure and The Assertiveness Guide for Women. “When it’s unwelcome, your advice-sharing is going to negatively impact the relationship, and the relationship is more important than being right.” Hanks suggests this script: “I’ve been thinking about you and your career plans. I wonder if you’re open to feedback or ideas.”

Reality check. Hanks suggests saying, “I believe you’re capable, but you still need to eat and have a place to live while you’re making your dreams come true. What are your plans for that?” You may discover that Curtis is expecting you to serve as a backup checking account or lodging. If he hasn’t thought through practical matters, offer to help brainstorm strategies, such as keeping his current job part-time or waiting until he’s saved up money to cover a few months’ expenses. You don’t need to tell him to shrink his dreams, but emphasize he is more likely to succeed if he breaks it down into smaller, specific steps.

Stay on your side. Sometimes parents feel that guilt trips are their only tool for persuasion. But for a healthy relationship, Hanks says, you need to “stay on your side of the court.” You’re not allowed to jump to the other side and hit for the other person; you must volley the ball from your own side, with phrases like “This is how I feel” or “This is how it looks from my perspective.” Rather than resorting to passive-aggressive comments, Hanks says, own any concerns and express them as your concerns. The unhealthy response to Curtis might be “Oh, is that really what you want to do with your life?” or “You really shouldn’t do something like this until you finish school.” A better response is to use “I” statements: “I’m really glad you’ve found something you love doing, but there’s a part of me that’s a little nervous because I want you to be financially secure. I think that getting a degree first or staying on with your current job for a while would be a wiser choice. I just wanted to let you know how I felt, and you can take it or leave it.”

Case Study 2: Enforcing House Rules

You have a new—er, old—resident in your house: your twenty-five-year-old son, Seth. He moved back home when he was no longer able to live off the part-time job he held in his college town. He isn’t sure what kind of career he wants to pursue and is taking time to explore his interests.

Seth isn’t a troublemaker, but his nocturnal schedule is driving you crazy. Sometimes he stays out late with friends and doesn’t come home until one in the morning, or he has a friend over to play video games even further into the wee hours. Then he sleeps in late and eats leftovers, conveniently missing any meal prep or cleanup. He mows the lawn and takes out the trash when asked, and you enjoy his company. But you’re beginning to resent plucking dirty laundry off the bathroom floor—and fear this arrangement has no end in sight.

When adult children return home, the parenting relationship is murkier than when they were younger. Where does their independence end and your authority begin? You want to help out, but you don’t want to be taken advantage of.

Set the record straight. It is increasingly common for young adults in the United States to remain or return home. According to the Pew Research Center, between 2010 and 2015 the number of young adults living with parents rose from 24 to 26 percent—despite an improving job market. In your case, Seth seems to view his living arrangement as an upgraded version of his teen years, with all the comforts of home, minus the rules and responsibilities. But here’s the real deal: living at home is a privilege, not an entitlement; it’s a stepping stone on the path to independence.

Communicate expectations. Discuss specific expectations for Seth’s responsibilities (and ideally, get it on paper). Possible discussion points:

• How long does he expect to stay? Set a move-out date or, at the very least, a date to reevaluate.
• Do you expect him to pay rent? If so, how much?
• How much should he contribute to utilities, groceries, and other household expenses?
• What housework will he be responsible for (e.g., laundry, dinner prep, dishwashing)?
• Can he use the family car? If so, how often? Does he need to pay for gas?
• Do you expect him to spend a certain number of hours each day applying for jobs?
• In cases where there are grandchildren, how will the household divide responsibilities for their care?
• What house rules, especially in regard to curfew, guests, or activities you don’t want in the home (like drinking), do you expect your adult child to follow? What or how many infractions are grounds for eviction?

Maintain boundaries. This is about more than knocking before you enter a bedroom. You need emotional boundaries too—and time away from your kid could help pave the way to his independence. “It’s really important for parents to have peer friends,” Hanks says. “It’s okay to do things with your adult child, but your child should not be your only or best friend. I’ve heard so many young-adult clients say, ‘I can’t find my own apartment or get married or move for this job opportunity because my mom or dad would be so lonely.’ That’s too much pressure on your child.”

Stand your ground. What if your young-adult resident is a troublemaker—or lawbreaker? “A common pitfall for parents is not allowing a young adult to face consequences for their choices, in the name of love,” Hanks says. “But I define love as doing what’s in their best interest, not what seems ‘nice’ or reduces conflict. I’ve told parents, ‘Don’t bail your kid out of jail or pay their overdue cell phone bill. They need to learn.’ The most loving response is sometimes the hardest.”

Case Study 3: Understanding Singlehood

Your thirty-two-year-old daughter, Laura, earned an MBA (at the Marriott School, naturally) and works in finance in the heart of a big city. She is a doting aunt to your three grandkids. She ran in her second marathon this fall. She is also single.

You were disappointed when she told you she wouldn’t be able to make it home for Thanksgiving this year because of work, and it hurt when your other daughter passed along what Laura told her: she is tired of being assigned to the “kids’ table” with younger cousins and weary of constant reminders from you and other family members about her marital status.

Everything you’ve said about her singleness has felt well-intentioned: You’ve told her that she’s a great catch. You’ve reassured her of the LDS belief that, at the very least, she’ll be able to find Mr. Right in the next life. You’ve offered dating advice, asked her about men she’s interested in, and encouraged her to continue attending LDS singles events (though she would prefer to take a break, complaining that women always outnumber men two-to-one). You have told Laura that you’re proud of her career accomplishments and are grateful for her financial stability and success, but you also emphasize the importance of raising a family. You just want her to be happy. You’re hurt that Laura didn’t express her concerns to you directly, but you don’t want this rift to further damage your relationship with her.

How do you mend the relationship when you realize your well-intentioned efforts to support your adult child have fallen short?

Understand what went wrong. “Just because you made decisions with your child’s best interests in mind, doesn’t mean that they were experienced the way you intended,” writes Joshua Coleman, PhD, psychologist and author of When Parents Hurt. In this case, however well intended, what Laura needed was not more reminders of her singleness. Frequently focusing on her marriage prospects, her dating appeal, or the importance of marriage in LDS doctrine sends the message that her worth is dependent on her marital status. At worst, emphasizing the potential for marriage in the afterlife can imply that her current life lacks real value.

Apologize. Reach out to Laura to apologize and avoid being defensive. You could re-extend an invite to Thanksgiving—sans kids’ table—but don’t expect or pressure her to come. Of course, it may take time to mend the fences. “Don’t give up too soon,” Coleman urges. “You may need to reach out for a long time before you see an improvement in the relationship.” In the meantime, along with nixing the marriage guilt trips, look at other ways you can be more considerate or supportive. For example, don’t assume that her schedule is more flexible or less important than that of your children with spouses and kids. Occasionally visit her rather than always expecting her to come to your home or a married sibling’s home.

Affirm her value. “All a single adult really wants is for her parents and those around her to accept her as a whole person—married or not,” says Naomi Watkins, founder of Aspiring Mormon Women, a nonprofit that supports the professional and educational goals of LDS women. Express interest in your daughter’s pursuits in all areas of life, and be an uplifting emotional support when she needs it. Kristen M. Oaks, author of A Single Voice, was single until her fifties, when she married Dallin H. Oaks of the Quorum of the Twelve Apostles. She admonishes young adults, “Be the best you can be. I also advise you, from experience, to worry less about marriage than about becoming a disciple of Christ.”

Case Study 4: Surviving Office Conflicts

Your twenty-four-year-old son, Mitchell, has worked as a designer at an ad agency for a few years, and you love showing off his work to friends and strangers alike. Last month, he was promoted to the position of art director.

His new role is a dream come true for Mitchell—except for one thing. One of his team members, a slightly older staffer named Bill, was supposed to send Mitchell some project documents but “forgot,” so Mitchell showed up to a major client meeting unprepared. After the meeting, Bill apologized, and they chatted about some of their ideas. Later, at a team meeting, Bill championed one of the ideas Mitchell had mentioned—but presented it as his own. Mitchell was too stunned to say anything.

You talk on the phone fairly frequently, and Mitchell explained that he likes his new job but doesn’t think he can handle much more of this undermining coworker and has no idea how to confront him.

When Mitchell was in elementary school, you could report bullying to his teacher—but now that he’s an adult, you can’t call his boss to complain. It can make you feel helpless when you’re reminded you can’t protect your kids from everything.

Listen and ask. It’s great that Mitchell feels comfortable sharing his struggles with you. You may quickly assume you know how he’s feeling because you have been in similar situations, but make sure you listen to his full story before jumping in with advice. Then, Hanks suggests, ask, “Are you open to ideas? I’ve had my share of difficult coworkers, so maybe we could talk about how to defuse the situation.”

Teach about workplace dynamics. Since Mitchell is early in his career, this may be his first time dealing with a workplace conflict. You could point out that office politics and team tension are natural; it’s simply what happens when stakes are high and people have different opinions and personalities. But when it gets personal, it’s damaging. Mitchell could approach Bill directly (a good resource is Crucial Conversations by Kerry Patterson, et al.). Mitchell could also bring the issue to HR, but remind him that he should come prepared with proposed solutions—treating it as a venting session signals immaturity. If the situation becomes extreme, you could suggest that Mitchell visit www.workplacebullying.org for guidelines.

Brainstorm strategies. To deal with brazen idea-poaching, Mitchell could say in a meeting, “Bill, thanks for recognizing my idea.” He could also send his manager an email saying, “I’m so glad Bill picked up on this idea I discussed with him earlier. Let me know how I can help.” It’s likely that Bill feels threatened by Mitchell, a quickly advancing, younger colleague. A 2009 study in the Journal of Experimental Social Psychology offers one short-term strategy for subordinates: express gratitude. Sending the antagonizer a thank-you note or offering a compliment decreases aggressive behavior.

Case Study 5: Coping with a Faith Crisis

Your daughter, Caroline, recently graduated from college. She has always been actively involved in the LDS Church; however, at your most recent family get-together, she revealed that she hasn’t attended church for a few months.

The feelings your daughter describes sound similar to a story relayed by Rosemary M. Wixom, former Primary general president, in a 2015 LDS general conference talk: “With the spirit of inquiry, this [woman] continued to ask questions. But as the questions grew harder, so did the answers. And sometimes there were no answers—or no answers that brought peace. Eventually, as she sought to find answers, more and more questions arose, and she began to question some of the very foundations of her faith.” The woman told Wixom, “I did not separate myself from the Church because of bad behavior, spiritual apathy, looking for an excuse not to live the commandments, or searching for an easy out. I felt I needed the answer to the question ‘What do I really believe?’”

Caroline says she needs to take a break from attending church to sort out her feelings and beliefs. You feel afraid for her spiritual welfare, and you would be heartbroken if she chose to leave your family’s faith.

When your faith is such an important part of your life, it’s hard not to take a child’s faith crisis personally. You know your child has agency but wish she would choose the path you believe is best.

Don’t make it about you. It’s common for parents to react, “How can you do this to me?” But it’s not about you, says Hanks: “It’s about your adult child figuring out what she wants for her life.”

Know her heart. In President Wixom’s story, the woman struggling with her faith said, “My parents knew my heart and allowed me space. They chose to love me while I was trying to figure it out for myself.” Hanks notes that the biggest complaint from young adults who leave their family’s faith is that their family no longer considers their opinions valid. Even if you disagree with her conclusions, express confidence in your daughter’s integrity and continue to listen to her and treat her with respect.

Allow her space. It may be tempting to try to “fix” the situation by offering point-by-point rebuttals to Caroline’s concerns or pointing out ways she could live more righteously. If she is clear that she doesn’t want to be a part of the Church, don’t send conference talks and scriptures, cautions Hanks. “It’s like telling your family you’re cutting out sugar from your diet, and then they hand you your favorite candy,” she says. “That damages the relationship.”

See the good. First, consider how this experience could benefit Caroline. In his book Navigating Mormon Faith Crisis, Thomas Wirthlin McConkie proposes, “What if we understood faith crisis as part of a natural cycle of spiritual growth, a breaking open to make room for new life and new faith?” Second, consider how this experience could deepen your relationship with Caroline. “Your relationship doesn’t need to depend on shared spiritual beliefs,” Hanks says. “There are so many other ways to relate with people.”

Love her. Whether Caroline returns to church or not, it’s essential to show love and preserve the relationship—which means you shouldn’t shun her or make her feel less included. “There are no eternal families without relationships first,” Hanks says. As President Dieter F. Uchtdorf, Second Counselor in the First Presidency of the LDSChurch, points out, “In this Church that honors personal agency so strongly, that was restored by a young man who asked questions and sought answers, we respect those who honestly search for truth.”

Mythbusting Millennial Stereotypes

The phrase “kids these days” is almost never followed with something positive. The older generation often sees more vices than virtues in the younger generation. But of course, reality is not quite that simple. Here are some stereotypes and facts about the millennial generation.

Stereotype: Millennials are doomed.

Reality: According to the Pew Research Center, millennials are the first in the modern era to have higher levels of student debt, poverty, and unemployment, and lower levels of wealth than their parents and grandparents had at the same age. Yet more than 80 percent of millennials say they are optimistic about their financial futures.

Stereotype: Millennials just want to move back home.

Reality: Indeed, young adults remaining or returning home is increasingly common in the United States. According to the Pew Research Center, between 2010 and 2015 the number of young adults living with parents rose from 24 to 26 percent—despite an improving job market.

Stereotype: Millennials expect constant praise.

Reality: A comprehensive survey of 13,150 PwC employees from across the world showed that millennials do value praise—41 percent prefer to be rewarded or recognized for their work at least monthly, but so did 30 percent of non-millennials. Millennials in the survey said they wanted a work environment that emphasizes teamwork, transparency, and a sense of community.

Stereotype: Millennials aren’t willing to pay their dues.

Reality: Millennials place the highest value on flexibility—64 percent in the PwC survey said they would like to occasionally work from home, and 66 percent wanted to shift their work hours. The PwC report explained, “Millennials do not believe that productivity should be measured by the number of hours worked at the office, but by the output of work performed. They view work as a ‘thing’ and not a ‘place.’” They’re not alone, though—18 percent of employees across generations would be willing to give up pay or delay promotions in exchange for fewer work hours.

Stereotype: Millennials are not as hardworking as older generations.

Reality: Research by Angela Duckworth, a psychology professor at the University of Pennsylvania, shows that a person’s level of grit—a combination of passion and perseverance—rises with age. Does that mean cultural forces have made millennials inherently less gritty and virtuous? No, says Duckworth—it’s simply a reflection of the maturity principle. Longitudinal studies offer hopeful evidence that we do, after all, learn, grow, and become better people as we age. So the only thing “wrong” with millennials, says Duckworth, is that “they just haven’t grown up—yet.”

Article written by: Holly Munson Illustrations by Scotty Reifsnyder

About the Author Holly Munson is a freelance writer, editor, and content strategist. She graduated from BYU with a degree in journalism and lives in Philadelphia with her family.

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April 10, 2024

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With cancer cases rising in young people, could earlier screenings help save lives?

by Erin Kayata, Northeastern University

Cancer cases are increasing among young people, with rising instances of colorectal cancer in people under age 55 and cervical cancer diagnoses ticking up for women ages 33 to 44, according to the American Cancer Society .

A recent ACS report shows that at least four cancers with cases on the rise are screenable, as is the case with colorectal and cervical cancers. And yet, the recommended screening age for some cancers is usually over 40.

As cases go up, we may see a shift in when people begin getting screened, according to Gary Young, director of Northeastern University's Center for Health Policy and Healthcare Research. In fact, he's said there's already calls to lower the age of colorectal cancer screenings as cases increase in young people .

"As we see an increased prevalence of cancer in people at a young age, that can lead to recommendations from some to lower the age," Young said. "(The rise in cases) is a troubling consideration and maybe at least part of the response needs to be to lower the age for screening."

But the decision to begin screenings earlier isn't as simple as it seems.

These screening guidelines come from many different sources, including government agencies and professional associations, Young said. In general, screenings before 40 are mostly limited to cervical cancer . Guidelines generally recommend most women get screened every three to five years. Women in their twenties and thirties should also check their breasts for irregularities that may indicate breast cancer while people with a family history of colon cancer may also be recommended for screening before they're 40.

By age 40 to 45, most people are recommended to begin colon cancer screening regardless of family history. The same is true for breast cancer: most women should get mammograms every year starting at 45.

The guidelines weigh factors such as the effectiveness of the screening method and whether it can be useful in catching disease in the early stages.

Professionals consider whether the diagnostic technology for a cancer can actually identify the disease in the early stages and whether the testing might yield false positives.

There can be some variety on when cancer screenings should begin. However, many indicate most screenings should begin when a person is in their 40s or 50s.

"There's no one place that is responsible for guidelines," Young said. "The general framework to the guidelines is to compare the harms and benefits that screening can result in for people at a particular age group."

This is why some are hesitant to begin cancer screenings earlier.

"We may be sending people off to more invasive diagnostic procedures that may have a non-negligible risk and can certainly provoke anxiety and worry for people," Young said. "And ultimately, there's usually some cost-effectiveness considerations that some people may think seem bloodless or cold, but the idea is thinking about the cost per saved life. We live in a world of finite resources and so … economic considerations come into that."

Young added that an increase in younger diagnoses in colorectal cancer is one where a lowering of the screening age might be beneficial, given how many more people are being diagnosed. But a critical factor is whether early diagnosis can lead to increased chance of survival.

Some young people might be able to get screenings sooner based on their family history and personal preferences. But even then, getting to appointments and affording them pose additional barriers.

For starters, Young said many insurance companies won't pay for certain screenings. And even if they do, the out-of-pocket costs can add up, said Tiffany Joseph, associate professor of sociology and international affairs at Northeastern University.

Since the Affordable Care Act was passed in 2009, insurances have covered preventative screenings for certain types of cancers, said Joseph, whose research explores the consequences of public policy on individuals. But additional screenings might not be covered the same way, placing out-of-pocket costs on the patient.

"If you have a family history and your provider is receptive to you getting those preventive screenings, you can, but there might be additional screenings that go beyond what the insurance company is going to cover," she said. "They are covering it, but the out-of-pocket cost could still be too much for some people."

Patients often need referrals for many specialists who screen for different cancers. These referrals often come from primary care physicians and there is a shortage of these types of doctors. Specialists often require months of wait time.

"There is so much demand for various types of medical services and people are just having to wait really long times to be able to get in for these screenings," Joseph said. "If it's a case where the cancer is very slow developing, that's not going to be as much of an issue. But if it's something that's a lot more aggressive, you're having to wait longer amounts of time before you can even get in for the screening. That puts you at a larger risk."

This story is republished courtesy of Northeastern Global News news.northeastern.edu .

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Hilary Cass’s proposals are mostly common sense. She must reject anti-trans bias with the same clarity

By failing to take on clinicians who doubt the very existence of trans children and young people, the review lets down those it seeks to support

T he long-awaited Cass review of gender identity services (Gids) for children and young people is finally here, and people with a wide variety of views appear to be welcoming it. In more civil, fact-based times, in which transness was accepted as just another example of human variation, this outcome could be to its credit: appropriate for a review of clinical services by an expert clinician.

However, we do not live in such times. Instead, in recent years, the UK has fallen to 15th in European LGBT+ equality rankings (in 2016, the UK ranked third ) and was highlighted by the Council of Europe alongside Hungary, Turkey and Russia as a state where LGBT+ rights are under attack from political figures, including governments. We are also experiencing a steep rise in transgender hate crimes, which a UN report directly attributes to “the toxic nature of the public debate surrounding sexual orientation and gender identity”.

This context is important for understanding the Cass review’s rather confounding reception. Based on the coverage in the UK’s rightwing media, where equality for trans people is most loudly and regularly opposed, you might have been convinced that Hilary Cass agrees with them and them alone (The Daily Mail’s front page hailed her as “a voice of sanity”; the Times claimed the report “rejected” the use of puberty blockers outright). However, this is not the case.

Cass criticises Gids’ long waiting lists. My involuntary immersion in this topic for almost a decade enables me to report that the trans community fervently agrees with this, and has been sounding the alarm for years. Cass criticises the lack of broader mental health care provision, including treatment for eating disorders. The community agrees, as would anyone who knows the first thing about NHS mental health provision. Cass cites the lack of autism awareness and assessment. Again, the community – well aware and unafraid of our propensity for neurodivergence – agrees. Cass calls for more and more local Gids service provision. Unsurprisingly, the trans community agrees. Cass bemoans the lack of a peer-reviewed evidence base for trans healthcare. Right there with you, Doc (although there is plenty of research you decided to exclude).

I could go on, but you get the idea. The key words – read plainly and in good faith – can hardly be disagreed with either. Care for trans young people that is “unhurried, holistic, therapeutic, safe and effective”? What’s to dislike? This is only what prospective patients, patients and their parents and caregivers have been calling for all along.

In reality, the problem has never been disagreement about how to care for trans children and young people. Rather, individuals genuinely motivated to create such services have been effectively sidelined by an overwhelmingly more powerful coalition of politicians, journalists and, indeed, healthcare workers who are motivated by an anti-trans ideology – a need to assert and somehow “prove”, to exclusion of all other possibilities, that trans people like me do not, in fact, exist. And, therefore, that we do not spend the first 18 years of ours lives as children. What many trans adults like me fear is that Cass has fallen into the trap of reflecting and therefore given credence to anti-trans bias.

Take puberty blockers, for example. Young people hoping to be prescribed this previously-uncontroversial puberty delayer, including those I’ve been directly in contact with, usually have to wait so long for appointments that they age out of Gids before the conversation even starts. In 2022, 378 children and young people were eligible to be prescribed blockers on the NHS, a relatively small number by any measure. Likewise with masculinising or feminising hormones for under-18s. The review makes this sound like previously common practice. In reality, such a step would only be considered for someone aged 16-18 and is even rarer.

There are more insidious examples too. Cass makes reference to clinicians feeling unable to raise concerns about the slow and inadequate services being provided by Gids. Justin Webb on the Today programme asked whether this by all accounts legitimate criticism was stifled because clinicians feared being accused of “transphobia”. Cass goes some way to agreeing, but then focuses on conciliation, saying, that “whatever the reason” for clinicians’ concerns, she believes everyone was sincerely trying to do their best for their patients.

That failure to add context reflects a lack of context in the report itself: in which a picture is painted of clinicians who all want the best for their young patients, and have been let down by a lack of evidence. That is not a complete picture. Take Dr David Bell, the psychiatrist behind a critical report of the Tavistock centre , and who has welcomed the Cass review. Bell is often presented as a moderate critic of Gids and yet has argued that trans children do not exist in nature but have been invented , and that cases of gender dysphoria in children can be explained by confusion caused by sexuality, confusion caused by neurodiversity, confusion caused by abuse, trauma or mental health conditions but, crucially, never by that child being, either solely or in addition to other factors, transgender. He has described “top surgery” – shorthand that trans men use for a masculinising double-mastectomy – as “bizarre Orwellian newspeak”.

He has described gender-affirming surgeries for adults in Frankenstein terms, bemoaning people like me as “sterile and lifelong patients, many facing catastrophic complications”. I don’t really want to dignify this claim with a serious rebuttal, so suffice to say that regret rates for gender-affirming surgeries consistently hover around a whopping 1% .

Bell’s views are echoed by Julie Bindel, who, reacting to the review, says the idea of trans children is a “crazy fallacy”, calls people like me who believe in gender-affirming healthcare for trans children “fanatics in the grip of a demented doctrine” and likens us to Jimmy Savile, and thanks Cass for the “validation” her report provides. Last month, Bindel attended the conference of the Clinical Advisory Network on Sex and Gender, a gender-critical pressure group of which Bell is a member. In Bindel’s view, the group heroically opposes “the cruelty of inflicting a mass sexual experiment on children”.

These views fundamentally undermine trans people’s identities and the legal basis on which our rights to things like dignity, privacy and medical care are also protected. Failing to identify such extreme opinions and push back on them in a review focused on improving care for gender-questioning children and young people is unforgivable. Giving Cass the benefit of the doubt, perhaps simply stating that trans children and adults exist seemed too basic – but in the clinical and cultural context we’re operating in, it remains vital.

If the Cass review was held under a black light, we would see the fingerprints of anti-trans ideology. I don’t believe Cass shares this way of thinking, I think she believes in evidence-based healthcare and that trans children exist. However, allowing her review to be so heavily influenced by bias is a critical failure that is hers to own.

As her work is used, as it will be, to perpetuate a broader hostile environment towards trans people in the UK, the young people she has tried to help will, understandably, feel betrayed. I take this opportunity to implore her team to keep this in mind as she calls for a similar review of services for 17- to 25 - year-olds and potentially beyond. Trans adults also need holistic, safe care (doesn’t everyone?) but our clinics are in a dire state too, with up to five years to wait for a first appointment. Now, reviews hang over us too – about us but no one knows to what extent with us – that may become Trojan horses for those who would roll back or perhaps eradicate affirming trans healthcare from the NHS altogether.

Dr Cass, appeasement might get you through this short-term discomfort in the media spotlight, but please remember: it isn’t your healthcare, your rights or your everyday dignity they are trying to take away.

Freddy McConnell is a freelance journalist

Do you have an opinion on the issues raised in this article? If you would like to submit a response of up to 300 words by email to be considered for publication in our letters section, please click here .

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Case studies

UCL and Islington Council collaborate to empower young people with better mental health support

Empower Islington, a project co-led by UCL and Islington Council, has helped young Islington residents co-create more effective and sustained mental health support.

12 April 2024

The pandemic may be a few years behind us, but the effects of it are still being felt, particularly on young people living with existing inequalities. 

“With 52% of Islington residents identifying as being from black, Asian minority and ethnic backgrounds, many local young people were disproportionately affected by the pandemic in terms of poverty, health disparity, racism and health access. 

“Because of this, we could see many young people were in clear need of employment, education, and mental health support,” explains Dr Keri Wong Associate Professor of Developmental Psychology at the IOE, UCL’s Faculty of Education and Society.

Dr Wong and her team have been listening to what young residents wanted in terms of support, and co-creating tailored workshops to meet their needs. The five sessions they’ve developed (with templates freely accessible online) have covered everything from university life and improving sleep, to rethinking body image. Other sessions focused on social media use, and using storytelling and art to improve mental health, all topics chosen to address the areas young people wanted help with most.

The project builds on a previous collaboration, the CopeWell Study, run with the Jamal Edwards Delve charity in West London.

Support from UCL Innovation & Enterprise

This knowledge exchange project has been supported by the UKRI Higher Education Innovation Funding (HEIF), managed by the Knowledge Exchange Funding team in UCL Innovation & Enterprise.

The Business and Innovation Partnerships team also helped with the partnership process, including the funding application and introducing the team to relevant contacts in the Council.

Working together to create a more equitable future 

Over 30 young people have now benefitted from the training, and the Council are looking to offer it as a regular part of their support through their Youth Progression Team.

Siobhan Scantlebury, Head of Youth Progression at Islington Council, said: “This is an exciting collaboration with UCL that's helping us to shape our employment, education and training services for young people based on their needs and aspirations. Working with an expert in this area like UCL, and amplifying the voices of young people locally, will mean we can better respond to what our young residents need with clearer insights and more targeted support.”

This project is just one example of how UCL and the London Borough of Islington are working in partnership to shape a more equitable future for the borough. The two organisations signed a Memorandum of Understanding in November 2023 to underline their shared commitment to working more closely together.

Translating research into policy and practice

Dr Wong adds: “Completing the CopeWell and Empower Islington projects helped me put my research into practice, to see what was working in the community, what were the gaps in the system, and what more needs to be done. 

“These experiences have pushed me to thinking more deeply about the importance of connecting my research, and others, to policy. I want to make sure the communities I hear from, particularly the voices of minority ethnic groups and those living with existing inequalities, are also heard in policy spaces. 

“I’m now working as an ESRC Policy Fellow in the Home Office to see how best to translate research into policy and practice and to get policymakers into academic spaces to join in on our discussions.”

Read more about Dr Keri Wong’s experience of co-creating mental health support with young people .

Watch Dr Wong share the study’s findings on Independent Sage .

Watch a video about the Empower Islington Project

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• Empower Islington
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• Academic profile: Dr Keri Wong
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https://educationhub.blog.gov.uk/2024/01/29/disposable-vape-ban-and-what-it-means-for-young-people/

Disposable vape ban and what it means for young people

The number of children using vapes has tripled in the last three years and there is strong evidence to suggest that cheap and easy-to-use disposable vapes are partly to blame.

Our research shows that in 2023, around 69 per cent of vapers aged 11 to 17 in Great Britain were using disposable vapes, up from 7.7 percent in 2021. This is extremely worrying given the unknown long-term health impacts and the addictive nature of the nicotine in vapes.

While vaping can play a role in helping adult smokers to quit, the NHS advises that you shouldn’t take it up if you don’t already smoke– and children should never vape.

Here’s what we’re doing to prevent children from vaping and smoking to protect their health, both in school and out.

Are disposable vapes being banned?

Yes, the sale and supply of disposable vapes is being banned in England, Scotland and Wales because of their appeal to young people. Northern Ireland will also consider introducing this in future.

Alongside this, to make vapes less attractive to children, we're strengthening the regulation of vape flavours, packaging and how they are displayed in shops.

To crack down on underage sales, trading standards officers will have the power to issue an ‘on the spot’ fine of up to £100 when they spot the sale of tobacco and vapes to children in England and Wales.

The ban is being introduced after a public consultation on smoking and vaping showed nearly 70 percent of respondents including parents, teachers, healthcare professionals were in favour of the measure.

Adults will still have access to non-disposable vapes to help them to stop smoking.

When will the disposable vape ban begin?

We aim to bring in legislation to ban disposable vapes as soon as possible.

Any legislation will allow for a buffer period of at least 6 months, to allow businesses to adapt.

What action are you taking to stop young people smoking?

It will soon be illegal to sell tobacco products to anyone born on or after 1 January 2009.

The measures, which we announced in October, means that children turning 15 this year or younger can never legally be sold tobacco.

Stopping young people from ever starting to smoke will protect an entire generation from smoking harms as they grow up.

What are you doing to prevent vaping in schools?

Schools are legally required to have a behaviour policy that sets out what is expected of pupils, including what items are banned from school premises. Some schools have already banned vapes.

In Relationships, Sex and Health Education (RSHE), pupils in primary and secondary school are taught the facts about legal and illegal harmful substances and their risks, including smoking, alcohol and drugs.

We are currently reviewing the RSHE curriculum, including looking at strengthening content around smoking and vaping, and will launch a public consultation on a revised version as soon as possible.

We have also published training resources for teachers, including one on drugs, alcohol and tobacco, which makes specific reference to e-cigarettes and vaping.

You may also be interested in:

• What is RSHE and can parents access curriculum materials?
• 5 ways we support schools to deal with bullying
• How we’re taking action to keep young people and children safe in our schools

Tags: Ban on disposable vapes , Vaping ban , vaping rules at school

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Young people behind Channel 4 streaming growth

April 12, 2024 10.47 Europe/London By Julian Clover

Data from BARB shows 18% of all Channel 4 viewing now comes from streaming. There has also been an 18% growth in viewer minutes among 16 to 34-year-olds in Q1 2024 compared to Q1 2023. Channel 4 remains the youngest profiling PSB streaming player, with 27% of viewing from young audiences. Amongst 16-34s, 38% of all Channel 4 viewing is now streamed, compared to 29% in Q1 2023.

This is a welcome relief for the broadcaster and its Fast Forward strategy to become a public service streamer

Documentaries and ‘constructed factual’ shows touching on real crime stories featured prominently in the quarter, with three new shows – The Jury: Murder Trial, To Catch a Copper and The Push: Murder on the Cliff – entering the the top 10 streaming viewer minutes for both 16-34s and all individuals, within a 28-day consolidation window.

Channel 4’s Chief Content Officer Ian Katz said: “I’m delighted to see our streaming-first strategy delivering across genres. In last the few months we’ve had a hugely successful run of factual programming combining purpose and scale.

“This data shows a really strong start to what will be a transformative year, after we laid out our Fast Forward strategy and began reengineering our business to become a genuinely digital-first public service streamer.

March was Channel 4’s biggest streaming month since BARB started recording in November 2021 and beat the channel’s previous record from October 2023. The March 2024 total of 6.9 billion viewer minutes compares to March 2023 total of 4.9 billion viewer minutes. The 18% growth in viewer minutes among 16 to 34-year-olds represents an increase from 3.2bn in Q1 2023 Q1 to 3.8bn in Q1 2024.

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About Julian Clover

Julian Clover is a Media and Technology journalist based in Cambridge, UK. He works in online and printed media. Julian is also a voice on local radio. You can talk to Julian on Twitter @julianclover , on Facebook or by email at [email protected] .

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• v.14(11); 2022 Nov

Substance Abuse Amongst Adolescents: An Issue of Public Health Significance

1 School of Epidemiology and Public Health, Jawaharlal Nehru Medical College, Datta Meghe Institute of Medical Sciences, Wardha, IND

Sonali G Choudhari

2 School of Epidemiology and Public Health; Community Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Medical Sciences, Wardha, IND

Sarika U Dakhode

3 Department of Community Medicine, Dr. Panjabrao Deshmukh Memorial Medical College, Amravati, IND

Asmita Rannaware

Abhay m gaidhane.

Adolescence is a crucial time for biological, psychological, and social development. It is also a time when substance addiction and its adverse effects are more likely to occur. Adolescents are particularly susceptible to the negative long-term effects of substance use, including mental health illnesses, sub-par academic performance, substance use disorders, and higher chances of getting addicted to alcohol and marijuana. Over the past few decades, there have been substantial changes in the types of illegal narcotics people consume. The present article deals with the review of substance abuse as a public health problem, its determinants, and implications seen among adolescents. A systematic literature search using databases such as PubMed and Google Scholar was undertaken to search all relevant literature on teenage stimulant use. The findings have been organized into categories to cover essential aspects like epidemiology, neurobiology, prevention, and treatment. The review showed that substance addiction among adolescents between 12 to 19 years is widespread, though national initiatives exist to support young employment and their development. Research on psychological risk factors for teenage substance abuse is vast, wherein conduct disorders, including aggression, impulsivity, and attention deficit hyperactivity disorder, have been mentioned as risk factors for substance use. Parents' attitudes toward drugs, alcohol, academic and peer pressure, stress, and physical outlook are key determinants. Teenage drug usage has a significant negative impact on users, families, and society as a whole. It was found that a lot has been done to provide correct intervention to those in need with the constant development of programs and rehabilitative centers to safeguard the delicate minds of youths and prevent them from using intoxicants. Still, there is much need for stringent policy and program guidelines to curb this societal menace. 

Introduction and background

Drug misuse is a widespread issue; in 2016, 5.6% of people aged 15 to 26 reported using drugs at least once [ 1 ]. Because alcohol and illegal drugs represent significant issues for public health and urgent care, children and adolescents frequently visit emergency rooms [ 2 ]. It is well known that younger people take drugs more often than older adults for most drugs. Drug usage is on the rise in many Association of Southeast Asian Nations, particularly among young males between the ages of 15 and 30 years [ 3 ]. According to the 2013 Global Burden of Disease report, drug addiction is a growing problem among teenagers and young people. Early substance use increases the likelihood of future physical, behavioral, social, and health issues [ 4 ]. Furthermore, recreational drug use is a neglected contributor to childhood morbidity and mortality [ 5 ]. One of the adverse outcomes of adolescent substance use is the increased risk of addiction in those who start smoking, drinking, and taking drugs before they are of 18 years. Moreover, most individuals with Substance Use Disorders begin using substances when they are young [ 6 ]. Substance use disorders amongst adolescents have long-term adverse health effects but can be mitigated with efficient treatment [ 7 ].

Childhood abuse is linked to suicidal thoughts and attempts. The particular mental behavior that mediates the link between childhood trauma and adult suicidal ideation and attempts is yet unknown. Recent studies show teens experiencing suicidal thoughts, psychiatric illness symptoms like anxiety, mood, and conduct disorders, and various types of child maltreatment like sexual abuse, corporal punishment, and emotional neglect that further leads to children inclining toward intoxicants [ 8 ]. Although teen substance use has generally decreased over the past five years, prolonged opioid, marijuana, and binge drinking use are still common among adolescents and young adults [ 9 ]. Drug-using students are more prone to commit crimes, including bullying and violent behavior. It has also been connected to various mental conditions, depending on the substance used. On the other hand, it has been linked to social disorder, abnormal behavior, and association with hostile groups [ 10 ]. Adolescent substance users suffer risks and consequences on the psychological, sociocultural, or behavioral levels that may manifest physiologically [ 11 ]. About 3 million deaths worldwide were caused by alcohol consumption alone. The majority of the 273,000 preventable fatalities linked to alcohol consumption are in India [ 12 ], which is the leading contributor. The United Nations Office on Drug and Crime conducted a national survey on the extent, patterns, and trends of drug abuse in India in 2003, which found that there were 2 million opiate users, 8.7 million cannabis users, and 62.5 million alcohol users in India, of whom 17% to 20% are dependent [ 13 ]. According to prevalence studies, 13.1% of drug users in India are under the age of 20 [ 14 ].

In India, alcohol and tobacco are legal drugs frequently abused and pose significant health risks, mainly when the general populace consumes them. States like Punjab and Uttar Pradesh have the highest rates of drug abuse, and the Indian government works hard to provide them with helpful services that educate and mentor them. This increases the burden of non-communicable illnesses too [ 15 ]. In addition, several substances/drugs are Narcotic and Psychotropic and used despite the act named ‘Narcotic Drugs and Psychotropic Substances Act, 1985. 

This review article sheds light on ‘substance abuse’ amongst adolescents as an issue of public health significance, its determinants, and its implications on the health and well-being of adolescents.

Methodology

The present article deals with the narrative review of substance abuse as a public health problem, its determinants, and implications seen among adolescents. A systematic literature search using databases such as PubMed and Google Scholar was undertaken to search all relevant literature on teenage stimulant use. The findings have been organized into categories to cover essential aspects like epidemiology, neurobiology, prevention, and treatment. Various keywords used under TiAb of PubMed advanced search were Stimulants, "Drug abuse", "Psychotropic substance", "Substance abuse", addiction, and Adolescents, teenage, children, students, youth, etc., including MeSH terms. Figure ​ Figure1 1 shows the key substances used by youth.

Reasons for abuse

People may initially choose to take drugs for psychological and physical reasons. Psychological issues, including mental illness, traumatic experiences, or even general attitudes and ideas, might contribute to drug usage. Several factors can contribute to emotional and psychosocial stress, compelling one to practice drug abuse. It can be brought on by a loss of a job because of certain reasons, the death of a loved one, a parent's divorce, or financial problems. Even medical diseases and health problems can have a devastating emotional impact. Many take medicines to increase their physical stamina, sharpen their focus, or improve their looks.

Students are particularly prone to get indulged in substance abuse due to various reasons, like academic and peer pressure, the appeal of popularity and identification, readily available pocket money, and relatively easy accessibility of several substances, especially in industrial, urban elite areas, including nicotine (cigarettes) [ 16 , 17 ]. In addition, a relationship breakup, mental illness, environmental factors, self-medication, financial concerns, downtime, constraints of work and school, family obligations, societal pressure, abuse, trauma, boredom, curiosity, experimentation, rebellion, to be in control, enhanced performance, isolation, misinformation, ignorance, instant gratification, wide availability can be one of the reasons why one chooses this path [ 18 ].

The brain grows rapidly during adolescence and continues to do so until early adulthood, as is well documented. According to studies using structural magnetic resonance imaging, changes in cortical grey matter volume and thickness during development include linear and nonlinear transformations and increases in white matter volume and integrity. This delays the maturation of grey and white matter, resulting in poorer sustained attention [ 19 ]. Alcohol drinking excessively increases the likelihood of accidents and other harmful effects by impairing cognitive functions like impulse control and decision-making and motor functions like balance and hand-eye coordination [ 20 ]. Lower-order sensory motor regions of the brain mature first, followed by limbic areas crucial for processing rewards. The development of different brain regions follows different time-varying trajectories. Alcohol exposure has adversely affected various emotional, mental, and social functions in the frontal areas linked to higher-order cognitive functioning that emerge later in adolescence and young adulthood [ 21 ].

Smoking/e-cigarettes

The use of tobacco frequently begins before adulthood. A worryingly high percentage of schoolchildren between 13 and 15 have tried or are currently using tobacco, according to the global youth tobacco survey [ 22 ]. It is more likely that early adolescent cigarette usage will lead to nicotine dependence and adult cigarette use. Teenage smoking has been associated with traumatic stress, anxiety, and mood problems [ 23 ]. Nicotine usage has been associated with a variety of adolescent problems, including sexual risk behaviors, aggressiveness, and the use of alcohol and illegal drugs. High levels of impulsivity have been identified in adolescent smokers.

Additionally, compared to non-smokers, smoking is associated with a higher prevalence of anxiety and mood disorders in teenagers. Smoking is positively associated with suicidal thoughts and attempts [ 24 ]. Peer pressure, attempting something new, and stress management ranked top for current and former smokers [ 25 ]. Most teenagers say that when they start to feel down, they smoke to make themselves feel better and return to their usual, upbeat selves. Smoking may have varying effects on people's moods [ 26 ]. Teenagers who smoke seem more reckless, less able to control their impulses, and less attentive than non-smokers [ 27 ].

Cannabis/Marijuana

Marijuana is among the most often used illegal psychotropic substances in India and internationally. The prevalence of marijuana usage and hospitalizations related to marijuana are rising, especially among young people, according to current trends. Cannabis usage has been connected to learning, working memory, and attention problems. Cannabis has been shown to alleviate stress in small doses, but more significant amounts can cause anxiety, emotional symptoms, and dependence [ 28 ]. Myelination and synaptic pruning are two maturational brain processes that take place during adolescence and the early stages of adulthood. According to reports, these remodeling mechanisms are linked to efficient neural processing. They are assumed to provide the specialized cognitive processing needed for the highest neurocognitive performance. On a prolonged attentional processing test, marijuana usage before age 16 was linked to a shorter reaction time [ 29 ]. Cannabis use alters the endocannabinoid system, impacting executive function, reward function, and affective functions. It is believed that these disturbances are what lead to mental health problems [ 30 ].

MDMA (Ecstasy/Molly)

MDMA (3,4-methylenedioxy-methamphetamine) was a synthetic drug used legally in psychotherapy treatment throughout the 1970s, despite the lack of data demonstrating its efficacy. Molly, or the phrase "molecular," is typically utilized in powder form. Serotonin, dopamine, and norepinephrine are produced more significantly when MDMA is used. In the brain, these neurotransmitters affect mood, sleep, and appetite. Serotonin also causes the release of other hormones that may cause emotions of intimacy and attraction. Because of this, users might be more affectionate than usual and possibly develop ties with total strangers. The effects wear off three to six hours later, while a moderate dose may cause withdrawal symptoms to continue for a week. These symptoms include a decline in sex interest, a drop in appetite, problems sleeping, confusion, impatience, anxiety, sorrow, Impulsivity and violence, issues with memory and concentration, and insomnia are a few of them. Unsettlingly, it is rising in popularity in India, particularly among teenagers [ 31 ].

Opium 

In addition to being a top producer of illicit opium, India is a significant drug consumer. In India, opium has a long history. The most common behavioral changes are a lack of motivation, depression, hyperactivity, a lack of interest or concentration, mood swings or abrupt behavior changes, confusion or disorientation, depression, anxiety, distortion of reality perception, social isolation, slurred or slow-moving speech, reduced coordination, a loss of interest in once-enjoyed activities, taking from family members or engaging in other illegal activity [ 32 ]. Except for the chemical produced for medicinal purposes, it is imperative to prohibit both production and usage since if a relatively well-governed nation like India cannot stop the drug from leaking, the problem must be huge in scope [ 33 ].

Cocaine is a highly addictive drug that causes various psychiatric syndromes, illnesses, and symptoms. Some symptoms include agitation, paranoia, hallucinations, delusions, violence, and thoughts of suicide and murder. They may be caused by the substance directly or indirectly through the aggravation of co-occurring psychiatric conditions. More frequent and severe symptoms are frequently linked to the usage of cocaine in "crack" form. Cocaine can potentially worsen numerous mental diseases and cause various psychiatric symptoms.

Table ​ Table1 1 discusses the short- and long-term effects of substance abuse.

Other cheap substances ( sasta nasha ) used in India

India is notorious for phenomena that defy comprehension. People in need may turn to readily available items like Iodex sandwiches, fevibond, sanitizer, whitener, etc., for comfort due to poverty and other circumstances to stop additional behavioral and other changes in youth discouragement is necessary [ 42 - 44 ]. 

Curbing drug abuse amongst youth

Seventy-five percent of Indian households contain at least one addict. The majority of them are fathers who act in this way due to boredom, stress from their jobs, emotional discomfort, problems with their families, or problems with their spouses. Due to exposure to such risky behaviors, children may try such intoxicants [ 45 ]. These behaviors need to be discouraged because they may affect the child's academic performance, physical growth, etc. The youngster starts to feel depressed, lonely, agitated and disturbed. Because they primarily revolve around educating students about the dangers and long-term impacts of substance abuse, previous attempts at prevention have all been ineffective. To highlight the risks of drug use and scare viewers into abstaining, some programs stoked terror. The theoretical underpinning of these early attempts was lacking, and they failed to consider the understanding of the developmental, social, and other etiologic factors that affect teenage substance use. These tactics are based on a simple cognitive conceptual paradigm that says that people's decisions to use or abuse substances depend on how well they are aware of the risks involved. More effective contemporary techniques are used over time [ 46 ]. School-based substance abuse prevention is a recent innovation utilized to execute changes, including social resistance skills training, normative education, and competence enhancement skills training.

Peer pressure makes a teenager vulnerable to such intoxicants. Teenagers are often exposed to alcohol, drugs, and smoking either because of pressure from their friends or because of being lonely. Social resistance training skills are used to achieve this. The pupils are instructed in the best ways to steer clear of or manage these harmful situations. The best method to respond to direct pressure to take drugs or alcohol is to know what to say (i.e., the specific content of a refusal message) and how to say it. These skills must be taught as a separate curriculum in every school to lower risk. Standard instructional methods include lessons and exercises to dispel misconceptions regarding drug usage's widespread use. 

Teenagers typically exaggerate how common it is to smoke, drink, and use particular substances, which could give off the impression that substance usage is acceptable. We can lessen young people's perceptions of the social acceptability of drug use by educating them that actual rates of drug usage are almost always lower than perceived rates of use. Data from surveys that were conducted in the classroom, school, or local community that demonstrate the prevalence of substance use in the immediate social setting may be used to support this information. If not, this can be taught using statistics from national surveys, which usually show prevalence rates that are far lower than what kids describe.

The role social learning processes have in teen drug use is recognized by competency-improvement programs, and there is awareness about how adolescents who lack interpersonal and social skills are more likely to succumb to peer pressure to use drugs. These young people might also be more inclined to turn to drug usage instead of healthier coping mechanisms. Most competency enhancement strategies include instruction in many of the following life skills: general problem-solving and decision-making skills, general cognitive abilities for fending off peer or media pressure, skills for enhancing self-control, adaptive coping mechanisms for reducing stress and anxiety through the use of cognitive coping mechanisms or be behavioral relaxation techniques, and general social and assertive skills [ 46 ].

Programs formulated to combat the growing risk of substance abuse

The Ministry of Health and Family Welfare developed Rashtriya Kishor Swasthya Karyakram for teenagers aged 10 to 19, with a focus on improving nutrition, sexual and reproductive health, mental health, preventing injuries and violence, and preventing substance abuse. By enabling them to make informed and responsible decisions about their health and well-being and ensuring that they have access to the tools and assistance they need, the program seeks to enable all adolescents in India in realizing their full potential [ 47 ].

For the past six years, ‘Nasha Mukti Kendra’ in India and rehabilitation have worked to improve lives and provide treatment for those who abuse alcohol and other drugs. They provide cost-effective and dedicated therapy programs for all parts of society. Patients come to them from all around the nation. Despite having appropriate programs and therapies that can effectively treat the disorder, they do not employ medication to treat addiction.

Conclusions

Around the world, adolescent drug and alcohol addiction has significantly increased morbidity and mortality. The menace of drugs and alcohol has been woven deep into the fabric of society. As its effects reach our youth, India's current generation is at high stake for the risk associated with the abuse of drugs like cannabis, alcohol, and tobacco. Even though the issue of substance abuse is complicated and pervasive, various stakeholders like healthcare professionals, community leaders, and educational institutions have access to a wealth of evidence-based research that can assist them to adopt interventions that can lower rates of teenage substance misuse. It is realized that while this problem is not specific to any one country or culture, individual remedies might not always be beneficial. Due to the unacceptably high rate of drug abuse that is wreaking havoc on humanity, a strategy for addressing modifiable risk factors is crucial. Because human psychology and mental health influence the choices the youth make related to their indulgence in drug misuse, it is the need of the hour to give serious consideration to measures like generating awareness, counseling, student guidance cells, positive parenting, etc., across the world. It will take time to change this substance misuse behavior, but the more effort we put into it, the greater the reward we will reap.

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The authors have declared that no competing interests exist.