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Each chapter in "Research Methods for English Studies" focuses on one particular method and offers concrete, practical advice on how to utilize it. Approaches discussed include textual analysis, biographical interpretation, discourse analysis, interviewing, visual methodologies, archival methods, quantitative analysis, ethnographic methods, oral history, and creative writing....

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MA English literature EL7308 RESEARCH METHODS MODULE HANDBOOK

2020, MA English literature EL7308 RESEARCH METHODS MODULE HANDBOOK

This module will enable students to hone the skills required to undertake research in literary studies and which are necessary to present the results of such research through writing and oral presentation. Students are encouraged to think about how to select appropriate methodologies from a range of possible choices, and consider how these methodologies can be used to shape the forms of research undertaken.

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• Acknowledgments
• 1. Introduction (Gabriele Griffin)
• 2. Archival Methods (Carolyn Steedman)
• 3. Auto/biographical Methods (Mary Evans)
• 4. Oral History (Penny Summerfield)
• 5. Visual Methodologies (Gillian Rose)
• 6. Discourse Analysis (Gabriele Griffin)
• 7. The Uses of Ethnographic Methods in English Studies (Rachel Alsop)
• 8. Numbers and Words: Quantitative Methods for Scholars of Texts (Pat Hudson)
• 9. Textual Analysis (Catherine Belsey)
• 10. Interviewing (Gabriele Griffin)
• 11. Creative Writing as a Research Method (Jon Cook)
• 12. English Research Methods and the Digital Humanities (Marilyn Deegan)
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9. Textual Analysis as a Research Method

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Meditation: a simple, fast way to reduce stress.

Meditation can wipe away the day's stress, bringing with it inner peace. See how you can easily learn to practice meditation whenever you need it most.

If stress has you anxious, tense and worried, you might try meditation. Spending even a few minutes in meditation can help restore your calm and inner peace.

Anyone can practice meditation. It's simple and doesn't cost much. And you don't need any special equipment.

You can practice meditation wherever you are. You can meditate when you're out for a walk, riding the bus, waiting at the doctor's office or even in the middle of a business meeting.

Understanding meditation

Meditation has been around for thousands of years. Early meditation was meant to help deepen understanding of the sacred and mystical forces of life. These days, meditation is most often used to relax and lower stress.

Meditation is a type of mind-body complementary medicine. Meditation can help you relax deeply and calm your mind.

During meditation, you focus on one thing. You get rid of the stream of thoughts that may be crowding your mind and causing stress. This process can lead to better physical and emotional well-being.

Benefits of meditation

Meditation can give you a sense of calm, peace and balance that can benefit your emotional well-being and your overall health. You also can use it to relax and cope with stress by focusing on something that calms you. Meditation can help you learn to stay centered and keep inner peace.

These benefits don't end when your meditation session ends. Meditation can help take you more calmly through your day. And meditation may help you manage symptoms of some medical conditions.

Meditation and emotional and physical well-being

When you meditate, you may clear away the information overload that builds up every day and contributes to your stress.

The emotional and physical benefits of meditation can include:

• Giving you a new way to look at things that cause stress.
• Building skills to manage your stress.
• Making you more self-aware.
• Focusing on the present.
• Reducing negative feelings.
• Helping you be more creative.
• Helping you be more patient.
• Lowering resting heart rate.
• Lowering resting blood pressure.
• Helping you sleep better.

Meditation and illness

Meditation also might help if you have a medical condition. This is most often true if you have a condition that stress makes worse.

A lot of research shows that meditation is good for health. But some experts believe there's not enough research to prove that meditation helps.

With that in mind, some research suggests that meditation may help people manage symptoms of conditions such as:

• Chronic pain.
• Depression.
• Heart disease.
• High blood pressure.
• Irritable bowel syndrome.
• Sleep problems.
• Tension headaches.

Be sure to talk to your healthcare professional about the pros and cons of using meditation if you have any of these or other health conditions. Sometimes, meditation might worsen symptoms linked to some mental health conditions.

Meditation doesn't replace medical treatment. But it may help to add it to other treatments.

Types of meditation

Meditation is an umbrella term for the many ways to get to a relaxed state. There are many types of meditation and ways to relax that use parts of meditation. All share the same goal of gaining inner peace.

Ways to meditate can include:

Guided meditation. This is sometimes called guided imagery or visualization. With this method of meditation, you form mental images of places or things that help you relax.

You try to use as many senses as you can. These include things you can smell, see, hear and feel. You may be led through this process by a guide or teacher.

• Mantra meditation. In this type of meditation, you repeat a calming word, thought or phrase to keep out unwanted thoughts.

Mindfulness meditation. This type of meditation is based on being mindful. This means being more aware of the present.

In mindfulness meditation, you focus on one thing, such as the flow of your breath. You can notice your thoughts and feelings. But let them pass without judging them.

• Qigong. This practice most often combines meditation, relaxation, movement and breathing exercises to restore and maintain balance. Qigong (CHEE-gung) is part of Chinese medicine.
• Tai chi. This is a form of gentle Chinese martial arts training. In tai chi (TIE-CHEE), you do a series of postures or movements in a slow, graceful way. And you do deep breathing with the movements.
• Yoga. You do a series of postures with controlled breathing. This helps give you a more flexible body and a calm mind. To do the poses, you need to balance and focus. That helps you to focus less on your busy day and more on the moment.

Parts of meditation

Each type of meditation may include certain features to help you meditate. These may vary depending on whose guidance you follow or who's teaching a class. Some of the most common features in meditation include:

Focused attention. Focusing your attention is one of the most important elements of meditation.

Focusing your attention is what helps free your mind from the many things that cause stress and worry. You can focus your attention on things such as a certain object, an image, a mantra or even your breathing.

• Relaxed breathing. This technique involves deep, even-paced breathing using the muscle between your chest and your belly, called the diaphragm muscle, to expand your lungs. The purpose is to slow your breathing, take in more oxygen, and reduce the use of shoulder, neck and upper chest muscles while breathing so that you breathe better.

A quiet setting. If you're a beginner, meditation may be easier if you're in a quiet spot. Aim to have fewer things that can distract you, including no television, computers or cellphones.

As you get more skilled at meditation, you may be able to do it anywhere. This includes high-stress places, such as a traffic jam, a stressful work meeting or a long line at the grocery store. This is when you can get the most out of meditation.

• A comfortable position. You can practice meditation whether you're sitting, lying down, walking, or in other positions or activities. Just try to be comfortable so that you can get the most out of your meditation. Aim to keep good posture during meditation.
• Open attitude. Let thoughts pass through your mind without judging them.

Everyday ways to practice meditation

Don't let the thought of meditating the "right" way add to your stress. If you choose to, you can attend special meditation centers or group classes led by trained instructors. But you also can practice meditation easily on your own. There are apps to use too.

And you can make meditation as formal or informal as you like. Some people build meditation into their daily routine. For example, they may start and end each day with an hour of meditation. But all you really need is a few minutes a day for meditation.

Here are some ways you can practice meditation on your own, whenever you choose:

Breathe deeply. This is good for beginners because breathing is a natural function.

Focus all your attention on your breathing. Feel your breath and listen to it as you inhale and exhale through your nostrils. Breathe deeply and slowly. When your mind wanders, gently return your focus to your breathing.

Scan your body. When using this technique, focus attention on each part of your body. Become aware of how your body feels. That might be pain, tension, warmth or relaxation.

Mix body scanning with breathing exercises and think about breathing heat or relaxation into and out of the parts of your body.

• Repeat a mantra. You can create your own mantra. It can be religious or not. Examples of religious mantras include the Jesus Prayer in the Christian tradition, the holy name of God in Judaism, or the om mantra of Hinduism, Buddhism and other Eastern religions.

Walk and meditate. Meditating while walking is a good and healthy way to relax. You can use this technique anywhere you're walking, such as in a forest, on a city sidewalk or at the mall.

When you use this method, slow your walking pace so that you can focus on each movement of your legs or feet. Don't focus on where you're going. Focus on your legs and feet. Repeat action words in your mind such as "lifting," "moving" and "placing" as you lift each foot, move your leg forward and place your foot on the ground. Focus on the sights, sounds and smells around you.

Pray. Prayer is the best known and most widely used type of meditation. Spoken and written prayers are found in most faith traditions.

You can pray using your own words or read prayers written by others. Check the self-help section of your local bookstore for examples. Talk with your rabbi, priest, pastor or other spiritual leader about possible resources.

Read and reflect. Many people report that they benefit from reading poems or sacred texts and taking a few moments to think about their meaning.

You also can listen to sacred music, spoken words, or any music that relaxes or inspires you. You may want to write your thoughts in a journal or discuss them with a friend or spiritual leader.

• Focus your love and kindness. In this type of meditation, you think of others with feelings of love, compassion and kindness. This can help increase how connected you feel to others.

Building your meditation skills

Don't judge how you meditate. That can increase your stress. Meditation takes practice.

It's common for your mind to wander during meditation, no matter how long you've been practicing meditation. If you're meditating to calm your mind and your mind wanders, slowly return to what you're focusing on.

Try out ways to meditate to find out what types of meditation work best for you and what you enjoy doing. Adapt meditation to your needs as you go. Remember, there's no right way or wrong way to meditate. What matters is that meditation helps you reduce your stress and feel better overall.

Related information

• Relaxation techniques: Try these steps to lower stress - Related information Relaxation techniques: Try these steps to lower stress
• Stress relievers: Tips to tame stress - Related information Stress relievers: Tips to tame stress
• Video: Need to relax? Take a break for meditation - Related information Video: Need to relax? Take a break for meditation

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• Meditation: In depth. National Center for Complementary and Integrative Health. https://nccih.nih.gov/health/meditation/overview.htm. Accessed Dec. 23, 2021.
• Mindfulness meditation: A research-proven way to reduce stress. American Psychological Association. https://www.apa.org/topics/mindfulness/meditation. Accessed Dec. 23, 2021.
• AskMayoExpert. Meditation. Mayo Clinic. 2021.
• Papadakis MA, et al., eds. Meditation. In: Current Medical Diagnosis & Treatment 2022. 61st ed. McGraw Hill; 2022. https://accessmedicine.mhmedical.com. Accessed Dec. 23, 2021.
• Hilton L, et al. Mindfulness meditation for chronic pain: Systematic review and meta-analysis. Annals of Behavioral Medicine. 2017; doi:10.1007/s12160-016-9844-2.
• Seaward BL. Meditation. In: Essentials of Managing Stress. 5th ed. Jones & Bartlett Learning; 2021.
• Seaward BL. Managing Stress: Principles and Strategies for Health and Well-Being. 9th ed. Burlington, Mass.: Jones & Bartlett Learning; 2018.

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Health outcome level: individual, health outcome level: family and organizational, health outcome level: community, health outcome level: population, toward better data, toward better analysis and interpretation, conclusions, acknowledgments, understanding disparities in the pediatric icu: a scoping review.

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Erica Andrist , Rachel G. Clarke , Kayla B. Phelps , Alyssa L. Dews , Anna Rodenbough , Jerri A. Rose , Adrian D. Zurca , Nurah Lawal , Christina Maratta , Katherine N. Slain; Understanding Disparities in the Pediatric ICU: A Scoping Review. Pediatrics May 2024; 153 (5): e2023063415. 10.1542/peds.2023-063415

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Health disparities are pervasive in pediatrics. We aimed to describe disparities among patients who are likely to be cared for in the PICU and delineate how sociodemographic data are collected and categorized.

Using MEDLINE as a data source, we identified studies which included an objective to assess sociodemographic disparities among PICU patients in the United States. We created a review rubric, which included methods of sociodemographic data collection and analysis, outcome and exposure variables assessed, and study findings. Two authors reviewed every study. We used the National Institute on Minority Health and Health Disparities Research Framework to organize outcome and exposure variables.

The 136 studies included used variable methods of sociodemographic data collection and analysis. A total of 30 of 124 studies (24%) assessing racial disparities used self- or parent-identified race. More than half of the studies (52%) dichotomized race as white and “nonwhite” or “other” in some analyses. Socioeconomic status (SES) indicators also varied; only insurance status was used in a majority of studies (72%) evaluating SES. Consistent, although not uniform, disadvantages existed for racial minority populations and patients with indicators of lower SES. The authors of only 1 study evaluated an intervention intended to mitigate health disparities. Requiring a stated objective to evaluate disparities aimed to increase the methodologic rigor of included studies but excluded some available literature.

Variable, flawed methodologies diminish our understanding of disparities in the PICU. Meaningfully understanding and addressing health inequity requires refining how we collect, analyze, and interpret relevant data.

Health disparities are “systematic, plausibly avoidable health differences…associated with discrimination or marginalization” 1   and are pervasive in pediatics. 2   , 3   Factors implicated in health disparities are sometimes considered social determinants of health (SDoH), “the conditions in [which] people are born, live, learn, work, play, worship, and age that affect…health, functioning, and quality-of-life” 4   .

Less is known about disparities among critically ill and injured children compared with general pediatrics, 5   – 9   other pediatric subspecialties, 10   – 14   or critically ill adults, 15   – 18   although attention is increasing. 19   The authors of studies often focus on specific conditions versus general PICU populations, which reflects the multidisciplinary nature of pediatric critical care medicine (PCCM) and multiple potential vulnerabilities to systemic influences. Additionally, authors inconsistently define sociodemographic characteristics, which may produce variable results or reinforce discriminatory narratives. 20   – 24   Knowledge of disparities in PCCM is, therefore, fragmented.

To address these gaps, we conducted a scoping review 25   , 26   of the literature addressing health disparities among patients likely to receive PICU care. Our objectives were to advance existing work 19   by (1) assessing predictors and outcomes relevant to disparities in PCCM, (2) delineating how sociodemographic data are collected, and (3) identifying conceptual flaws and knowledge gaps. This review parallels the National Institute on Minority Health and Health Disparities (NIMHD) Research Framework ( Fig 1 ), which is designed to illustrate “the complex and multifaceted nature of minority health and health disparities” 27   .

NIMHD research framework emphasizing the multiple intersecting potential loci of health disparities. Figure via the NIMHD (2017); available at https://nimhd.nih.gov/researchFramework .

Ovid MEDLINE was searched in consultation with a biomedical librarian ( Fig 2 ). Medical Subject Headings search terms encompassed disparities, pediatric critical care, and indications for critical care (eg, diabetic ketoacidosis [DKA]). Mortality was an included term because children who die are critically ill by definition, and most children who die in the hospital receive PICU care. 28   – 30   Congenital heart disease (CHD) was included given its relevance to PCCM training and practice. Our search was initially conducted in October 2020 and updated through June 2023.

Flow diagram of the search strategy and its multiple updates.

Eligible studies included an explicit objective to assess sociodemographic disparities among patients 0 to 21 years old. Studies including children and adults were eligible if dedicated pediatric analyses were presented. We excluded studies published before 2000 because older data may not reflect current standards of care or evolving policy. International data were excluded because disparities are influenced by historical and sociopolitical contexts, which vary in other countries. Neonatal ICU literature was excluded because of differing influences of maternal health. At least 2 reviewers evaluated each citation.

Identified outcome and exposure variables were mapped to the NIMHD Research Framework. 27   Outcomes were categorized by health outcome levels and exposures by the applicable domain(s), although overlap existed, particularly among community-level exposures. Variables were mapped by author consensus and could be mapped to multiple cells. The authors of some studies hypothesized etiologic contributions from domain-relevant exposures, occasionally without directly assessing the exposure or citing additional evidence. Therefore, the final concept map ( Table 1 ) included the outcome and exposure variables examined and additional hypothesized predictors and denoted any support for hypothesized predictors. Our results include descriptors used in the original literature (eg, “Hispanic” vs “Latino”), although our discussion follows published guidelines for reporting race and ethnicity. 31  

Final Concept Map, Including the Outcome and Exposure Variables Relevant to Health Disparities Examined in the Pediatric Critical Care Literature

AED, automatic external defibrillator; DNR, do-not-resuscitate; EOL, end of life; HLA, human leukocyte antigen; IHCA, in-hospital cardiac arrest; IPR, inpatient rehabilitation; LOS, length of stay; PPC, pediatric palliative care; RRT, rapid response team; SCI, spinal cord injury; SOI, severity of illness; TBI, traumatic brain injury.

The map is based on the NIMHD Health Disparities Research Framework, and variables were mapped by author consensus to the appropriate outcome levels and domains. Plain text indicates the variable was directly evaluated by the authors of a study in the review. 

Domain-relevant exposure hypothesized by an author group, which was not directly evaluated by the study but included a reference(s) to evidence.

Domain-relevant exposure variable which was neither directly evaluated nor supported by related evidence.

The initial search yielded 1355 results, and 136 studies were included. Of these, 122 were retrospective and 86 interrogated databases ( Table 2 ). The authors of 27 studies examined general medical (22) or general surgical (5) populations; other studies were condition specific.

Methodological Characteristics of Studies Included

COVID-19, coronavirus disease 2019.

Most studies were retrospective and used state or national databases as their data source. The authors of included studies assessed a range of demographic variables, the most common of which was race. Outcomes of interest included PICU utilization metrics, overall mortality, hospital mortality, post-PICU metrics, and measures of family-centered care. The studies encompassed several general and specialty medical and surgical populations in inpatient and outpatient settings.

Percentages are rounded to the nearest percentage point and may not sum to precisely 100% because of rounding.

The authors of some studies assessed multiple demographic variables or outcomes and, therefore, percentages do not sum to 100%.

SDoH is defined as “the conditions in the environments where people are born, live, learn, work, play, worship, and age” that impact health outcomes, which includes a potentially huge range of variables. Some examples of SDoH included in this category include secondhand smoke exposure, single-parent households, and parental religiosity.

Race and Ethnicity

Racial and ethnic data were reported in 124 studies ( Table 2 ), and classification and analytic schemata varied ( Table 3 ). The authors of more than half of the studies (64/124, 52%) dichotomized race as white and “nonwhite” or collectively analyzed multiple racial minority groups as “Other” ( Table 3 ). Although the US Census denotes ethnicity independent of race, 32   108/124 studies (87%) treated Hispanic ethnicity as a discrete racial classification. The authors of 9 studies (7%) included “two or more races” or “mixed” categories.

Specification of Demographic Variables Assessed

The authors of the included studies assessed a range of demographic variables. The most common was race, but the classification schemata and descriptors used were variable.

Percentages are rounded to the nearest percentage point and may not sum to precisely 100% due to rounding. The denominator used for each percentage calculation was the total number of studies in which the authors examined the relevant characteristic (ie, all studies in which the authors examined race or SES metrics).

The authors of some studies that collected racial data excluded some groups from analyses or combined groups; these numbers reflect the percentage of studies in which the authors presented specific analyses. Percentages do not sum to 100% because study authors presented data for multiple races.

Linguistic descriptors of racial categories varied among studies. Although some categories are combined for ease of display, multiple descriptors are reported to minimize assumptions of overlap.

Hispanic ethnicity was treated as a discrete racial category by the authors of most studies, although some authors presented analyses of Hispanic ethnicity regardless of race. Both methodologies are reported in this category.

This category uses language as it was used in the original literature and often reflects the combination of multiple racial groups, as described in footnote b.

The authors of some studies reported multiple methods and, therefore, percentages do not sum to precisely 100%.

This category includes racial data from the electronic health record or a database without further information about how the source obtained the data.

Definitions of “neighborhood” of residence varied but usually described either a zip code or census tract.

Examples of composite indices include the Social Vulnerability Index or the Child Deprivation Index.

Racial data were obtained from health records or a source database by 100 studies (81%); 10 of these 100 described how the source obtained the data. Self- or parent-identified race was reported in 30 of 124 studies (24%). White patients were the referent in all but 3 analyses; 33   – 35   thus, white patients are the presumed referent unless stated otherwise.

Socioeconomic Status

Socioeconomic status (SES) indicators were examined in 102 studies ( Table 2 ). Insurance status was the most common indicator, with the authors of 73 studies (72%) assessing differences between commercially, publicly, and uninsured children ( Table 3 ). Area-level indicators of income or poverty were reported in 48 studies (47%), whereas the authors of 6 (6%) studies used individual measures. The median income of residential zip code was the most common area-level indicator (37 studies). The authors of 17 studies (17%) used existing indices (eg, the Social Vulnerability Index) or combinations of SES measures in study-specific indices.

Regional Disparities and Other SDoH

The authors of 25 studies (18%) examined disparities between rural and urban children or between regions of the country ( Table 2 ). Regions were not uniformly defined.

The authors of 1 study assessed the relationship between homelessness and PICU utilization after suicide attempts. 36   SDoH were not otherwise examined as primary exposure variables but were sometimes assessed as secondary exposure variables or as SES proxies ( Table 2 ).

PICU Utilization Measures and Disparities in the Therapeutic Course

Racial minority status was associated with disparate PICU utilization (eg, admissions, length of stay [LOS]) in 16 of 27 studies (59%); the authors of 15 of these 16 studies found evidence of higher PICU utilization among Black and/or Hispanic patients compared with white or non-Black patients. 33   , 37   – 50   Other racial minority children were infrequently assessed, but higher PICU utilization was seen among young Asian and Pacific Islander (AAPI) and American Indian and Alaska Native (AIAN) children with influenza 40   and coronavirus disease 2019. 48   The authors of studies who did not observe racial disparities in PICU utilization examined bronchiolitis, 51   , 52   coronavirus disease 2019, 53   , 54   multisystem inflammatory syndrome in children, 55   traumatic brain injury (TBI), 56   and cancer. 57   Conflicting associations between race and PICU utilization for asthma were reported. 41   , 46   , 58   A retrospective single-center study of Ohio children with traumatic injury was the only one to reveal a lower likelihood of PICU admission for Black children. 59   Relationships between race and PICU LOS were inconsistent. 41   , 46  

Few individualized SES indicators were evaluated ( Table 3 ), and variable associations with PICU utilization were found. One database study revealed that publicly insured children had more postoperative PICU admissions than commercially insured children, and mean LOS was longer 60   ; however, public insurance was not associated with PICU utilization for asthma in a national database study, 61   and a prospective Ohio single-center study revealed no relationships between PICU utilization for asthma and self-reported parental education or financial hardship. 58   Homelessness was associated with increased hospital utilization but not PICU admission after suicide attempts in a New York state database study. 36  

Throughout the therapeutic course, no racial differences were observed in adjunctive asthma therapies, 46   sedation management, 62   or time to Pediatric Advanced Life Support initiation. 63   All racial minority children on Medicaid were likelier to require mechanical ventilation for respiratory syncytial virus, 64   and Black and Hispanic children were ventilated longer after cardiac surgery. 65   Extracorporeal membrane oxygenation usage varied by race, but whether cannulation was more or less likely for racial minority children depended on the indication. 65   – 68   Asian 69   and nonwhite 70   children were likelier to develop “complications” after a traumatic injury, as were Hispanic children after CHD repair. 71   Finally, a national database study revealed increased catheter-associated bloodstream infections among Black and Hispanic children; although catheter-associated bloodstream infection rates decreased over the 5-year study period, racial disparities did not. 72  

PICU readmissions were more common for technology-dependent children who were Black or publicly insured 73   and occurred more quickly for Black children with asthma. 74   Black and Hispanic children with cancer were likelier to have multiple PICU admissions. 75   Routine discharge from the hospital was less common for Black children after cerebrospinal fluid shunting procedures, 76   and conflicting associations were seen between race, SES metrics, and inpatient rehabilitation after TBI or spinal cord injury. 77   – 79   No data regarding disparities in post-intensive care syndrome pediatrics or other post-PICU admission sequelae were found.

Genetic polymorphisms in β-agonist receptors, 34   , 46   , 74   “biologic vulnerabilities,” 43   and “gene variants” 44   in fatty acid metabolism were hypothesized to explain increased PICU utilization among Black children with asthma, lupus, and pancreatitis. Broad genetic predisposition was also suggested: “it could be possible that the Asian ethnicity is more genetically susceptible to complications” after trauma. 69   However, no authors directly assessed predictors of PICU utilization that mapped to the biological domain ( Table 1 ). Within the behavioral domain, no associations between PICU utilization and medication adherence were found, 46   , 58   , 80   although authors made this “purely speculative” claim. 81   Other behavioral predictors included lower automobile restraint use observed among African American, Hispanic, and Native American children 69   and lower bicycle helmet use among Hispanic, 82   publicly insured, and uninsured children. 70  

Features of the physical/built environment were directly assessed in relation to PICU asthma admissions by the authors of an Ohio prospective single-center study, who did not find associations between smoke or allergen exposures and PICU admissions after multivariate analyses. 58   Finally, the health care systems domain figured into the individual level by way of insurance status ( Table 1 ), and a national database study of children with diabetes revealed that Black and Hispanic children were less likely to receive insulin via a pump, which potentially influenced higher rates of DKA among Black patients. 50  

No racial disparities in PICU mortality were found in a national database study of a general PICU population. 83   However, disparities were observed among Black and Hispanic children with cancer 75   or for Black, 67   , 71   , 84   , 85   Hispanic, 85   and nonwhite 67   , 86   children after CHD repair. The authors of a multicenter study in Texas also found that Hispanic and multiracial children were likelier to die in the PICU after a rapid response call. 49   However, disparities were observed in hospital and global mortality for racial minority children among most conditions studied. 33   , 40   , 42   , 81   , 87   – 116   Dedicated analyses of AAPI or AIAN children were less common compared with Black and Hispanic children ( Table 3 ), but mortality disparities were observed in influenza 40   and cancer, 75   , 100   as well as among AIAN children with sepsis. 88   The authors of 1 national database study found CHD mortality disparities for children of “other” race, which in their analysis included children listed as Asian, American Indian, and “other” by the data source. 113  

Six studies revealed racial disparities in trauma mortality, 47   , 87   , 89   , 91   , 115   , 116   contrasting with 8 studies that did not. 56   , 69   , 70   , 117   – 121   Several authors argued that the stronger predictor of trauma mortality was SES, 69   , 70   , 79   , 89   , 115   – 119   , 121   which was usually indicated by insurance status. Six non-trauma studies revealed no racial disparities in mortality, 63   , 83   , 122   – 125   and 2 studies revealed survival advantages for some racial minority children. 115   , 126  

Authors explored the biological domain in racial mortality disparities. 42   , 81   , 95   , 96   , 113   The authors of 1 study directly evaluated the distribution of potentially contributory genes by self-identified race. Although racial variation was observed among 22 of 33 alleles associated with poor outcomes after heart transplantation, Black race remained independently associated with mortality. 81   Influenza vaccination was lower among racial minority children, 40   which also encompasses sociocultural and health systems domains. Several authors explicitly cautioned against biological explanations for racial disparities, 55   , 56   , 75   , 102   noting “the artificial nature of racial and ethnic groups as social and political constructs.” 55  

Finally, the health care systems domain was relevant to individual mortality disparities. In addition to its association with trauma mortality, 70   , 79   , 89   , 91   , 116   , 117   , 119   , 121   public insurance or lack of insurance was associated with increased mortality in CHD 106   and cancer, 42   , 90   , 127   as well as among children undergoing surgery, 60   including liver transplantation. 102   Authors also proposed contributions from racism or implicit bias experienced before hospital arrival and within the health care system. 55   , 67   , 68   , 77   , 93   , 121   , 128  

Subspecialty Care Received in the PICU

Subspecialty care received in the PICU is a component of critical care access and delivery. Black 66   and nonwhite 68   children were less likely to undergo heart or kidney 99   transplants, and nonprivate insurance also reduced access to kidney transplants. 99   The biological domain may be relevant to these disparities because of smaller matched donor pools available for Black patients. 99  

Pediatric palliative care (PPC) services for chronically and critically ill children are becoming standard of care. 129   One national database study revealed lower rates of PPC consultation among Hispanic inpatients after bone marrow transplantation. 130   The intensity of end-of-life (EOL) care varied by race and ethnicity, with 2 California state database reviews revealing more aggressive life-sustaining therapy among Black, Hispanic, and Asian children 131   and Black, Hispanic, and “other” adolescents 132   with advanced cancer. These findings were corroborated by a single-center study in Tennessee, which revealed that Black children with cancer were >4 times likelier to receive cardiopulmonary resuscitation before death, 133   but contrasted with a Texas single-center study revealing an association between Latino ethnicity and increased hospice enrollment. 134   Within the hospital setting, families of Black patients were less likely to adopt do-not-resuscitate orders after cardiac arrest. 135  

The sociocultural domain figured prominently in EOL care, including the debate over whether religious or cultural preferences influenced the differences seen. 131   , 132   , 134   – 136   The health care systems domain was also relevant, with reduced frequency or quality of conversations regarding poor prognoses with Black and Hispanic families observed. 135   – 137  

No racial differences in invitations to or participation in family-centered rounds were observed, 138   , 139   but Black and Latino families were less likely to receive communication in their preferred settings or feel that staff spent enough time speaking with them. 138   Black families also reported lower measures of therapeutic alliance with providers. 140   Families with limited English proficiency were less likely to be invited to family-centered rounds or to feel knowledgeable about their child’s care plan. 141   Black parents were less likely to be approached for participation in clinical research 142   or to consent to participate in a biorepository. 143   Despite these findings, racial disparities in parental satisfaction with care were not observed. 144   Sociocultural and health care systems domains relevant to the family and organizational level included provider–patient communication 136   – 139   , 143   , 145   and language barriers. 94   , 128   , 138   , 139   , 141   , 144   , 145  

Other behavioral and sociocultural factors included racial differences in self-reported parental coping strategies. African American mothers indicated higher usage of “avoidance” techniques, such as distraction, compared with Hispanic and white mothers. 35   Racial minority parents were also likelier to report religious coping. 35   , 137   Black and Hispanic mothers were likelier to report posttraumatic stress disorder after a child’s death in intensive care, 146   and bereaved siblings of Black and Hispanic children reported higher anxiety. 147  

Children in different communities experienced disparate PICU utilization, 148   – 151   severity of illness (SOI), 149   , 152   and readmissions. 150   In-hospital mortality rates were inversely associated with median income of residential zip code in 1 national database study, 29   and neighborhood SES measures were inversely correlated with mortality rates in TBI, 115   , 152   cancer, 90   , 92   , 93   and CHD, 84   , 108   , 153   but not cardiomyopathy or myocarditis. 68   Conflicting relationships between neighborhood SES metrics and sepsis mortality were reported. 154   – 156   One national database study did not find relationships between median zip code income quartile and CHD mortality, 123   and neighborhood poverty was not associated with PICU utilization after traumatic injury in 1 Ohio single-center study. 59  

The physical/built environment was often considered at the neighborhood level. A geospatial analysis in Atlanta revealed that the highest rates of PICU asthma admissions in census tracts occurred near major roadways or airports. 150   Authors also proposed contributions from substandard housing 148   , 149   , 151   , 157   and environmental pollution or toxin exposure. 65   , 75   , 148   , 156   The biological domain was also invoked within a community framework. To explain higher PICU utilization for bronchiolitis among children from higher-income neighborhoods, one author group suggested, “Infants living in…higher-income ZIP codes [may be] immunocompromised by overly sterile home and community environments.” 158   The authors of a California retrospective single-center study argued that health literacy has community sociocultural influences: “Low collective social capital…creates a negative synergy in which local friends and neighbors are less able to help and advise each other.” 159   Within the health systems domain, the authors of an Ohio single-center study, who did not find associations between neighborhood SES measures and DKA, argued that protocolizing therapies may protect against inequality. 160  

Neighborhood characteristics were also associated with prehospital care relevant to critical illness. The likelihood that a child had an automatic external defibrillator applied during an out-of-hospital cardiac arrest was directly correlated with measures of neighborhood SES. 161   , 162   Gun violence was not borne equally among communities. Black children from urban or low-income zip codes were likelier to experience gun-related injuries and deaths. 47   , 87   , 91   , 120   Non-firearm injuries were more severe and more often fatal among children from low-income neighborhoods. 91   , 152   , 163   Patients living further from a hospital were less likely to use emergency medical services, even for high-acuity conditions. 164  

Trauma mortality was higher for rural compared with urban children in 3 studies, 165   – 167   and motor vehicle collisions in rural areas more commonly involved intoxicated drivers. 166   Rural residence or increased distance from a specialty center impacted access to trauma and specialty pediatric services; 153   , 165   – 168   this did not consistently translate to mortality disparities, but need for interhospital transfer was associated with increased mortality. 118  

The authors of 9 studies analyzed disparities by state or region of the country. In 5 studies, children in the South (variably defined) experienced greater racial disparities in or were likelier to die of sepsis, 98   cancer, 75   , 96   traumatic injury, 115   , 166   and kidney transplantation 99   compared with other regions. Health care systems issues, including state variability in insurance access, 74   , 90   racism or implicit bias, 55   , 67   , 68   , 93   , 121   , 128   , 130   attention to patient care coordination, 73   , 91   , 93   and variable clinical research participation 142   were cited as potential loci of these population-based disparities.

The authors of a large body of literature describe health disparities in PCCM. Data sources vary and authors use inconsistent methods of sociodemographic data collection and analysis. It is, therefore, difficult to draw broad conclusions from the full body of literature.

A foundational problem in health disparities research is the translation of evolving social markers and identities into consistent, quantifiable data points. 22   – 24   Our review confirms the absence of common standards, which clouds our understanding of disparities and may partially explain conflicting findings.

SES measures also provide insufficient nuance. 23   Although insurance status is an easily accessible marker of SES, eligibility for public insurance varies by state, and private insurance provides variable coverage. 23   , 169   In addition, public insurance is often available to children with complex or chronic conditions, regardless of income, which potentially confounds the associations between public insurance and mortality or EOL outcomes.

Area-level SES indicators can broadly encompass aspects of an individual’s daily life and experience; 23   however, these also have limits. “Neighborhoods” defined by zip codes or census tracts may be socioeconomically heterogenous or unrelated to social communities. The authors of national studies evaluating the role of income do not account for regionally variable costs of living. Composite indices can incorporate multiple, diverse SES variables, but condensing these variables into a single score risks obscuring relationships between them. 23  

Health equity research requires a more integrated, reflective, collaborative approach than many clinicians are accustomed to taking to research and analysis. 22   , 24   , 170   – 172   For example, increasing statistical power, which is a common goal in biomedical research, may produce a “statistical genocide” 172   in health equity research if data from some racial groups are combined or eliminated, diminishing the assessment of many children and communities. However, ignoring race or SES in analyses because of flawed or unstandardized data undermines attempts to evaluate disparities and promote equity. We must, therefore, rigorously assess (and, when possible, improve) our data sources, interrogate the influence of bias in our questions and conclusions, and consider how medical care synchronizes with social, legal, and political factors to produce health disparities. 1   , 22   , 170   – 172  

“Confounders” of Racial Disparities

Fifteen studies revealed racial disparities in outcomes that were attenuated after multivariate analyses, leading most authors to conclude that they did not observe racial disparities. However, some “confounders” may be usefully explored as potential mechanisms of racial disparities. 171   , 173   , 174   For example, if univariate analyses reveal associations between Black race and TBI mortality that disappear after adjustment for SOI, we argue that prioritizing health equity should lead us to conclude that racial disparities in TBI mortality exist, a potential mechanism is injury severity, and mitigation strategies could target injury prevention.

Moreover, racism may operate within “confounders”. Although it may be statistically possible to separate variables such as living in a high-poverty neighborhood from race, this ignores decades of structurally racist policies linking these variables. 1   , 22   , 24   , 56   , 170   , 171   , 173   – 175   Even if it is not explicitly named, we clearly recognize this linkage when we use neighborhood racial composition as a socioeconomic indicator ( Table 3 ). The interpretation of data along multiple sociodemographic axes is as essential as isolating specific relationships. 22   , 24   , 170   , 171  

Responsibly Using the Biological Domain

Race is a social construct. 21   – 23   , 25   , 55   , 56   , 170   , 172   , 175   , 176   Transitioning the dominant medical conceptualization of race from biological to social shifts racial health disparities from biologically inevitable to sociopolitically modifiable. 22   , 24   , 172   , 175   , 176   Such a framework does not disallow the possibility that disease-modifying genes could be linked to specific racialized phenotypes; however, this is distinct from suggesting a genetic basis of race or uniform overlap between genes and socially constructed racial categories. Moreover, disease often results from interactions between genes and environmental factors, and several environmental exposures (eg, substandard housing or environmental pollution) are products of structural racism. This interaction must be explored in discussions of any genetic contribution to racial health disparities. 172   , 177   The NIMHD research framework recognizes this by situating biology among environmental, cultural, and systems factors.

“Genetics” alone additionally fails to satisfactorily account for racial disparities because racial disparities are observed among conditions with no plausible genetic component. 102   , 177   In addition, biologic explanations cannot account for regional variations in racial disparities. Finally, “genetics” is invoked to explain racial disparities observed between children from multiple racial minority groups versus white children, including when analyses only examine white and “non-white” children. Although likely inadvertent, this is fundamentally a white supremacist argument for the genetic superiority of white children and should no longer be acceptable as a mechanistic claim.

The Panacea of “Access to Care”

Authors posited differential “access to care” as a contributor to disparities in 44 studies but seldom specified what “access to care” encompasses. Although “access to care” recognizes the sociopolitical factors fundamental to health inequity, “access” is too imprecise to be actionable.

Access to health care includes 4 components: coverage that facilitates entry into health systems, a source of health care services, timeliness of needed care, and availability of an appropriately skilled health care workforce. 178   All 4 components are relevant to health disparities, but insurance coverage was by far the most commonly evaluated ( Table 3 ). Authors concluded that without (or with inferior) insurance coverage, children are less likely to receive preventive care or present to care in the early stages of illness; increasing insurance coverage might, therefore, decrease disparities.

Insurance coverage matters, but how it may be implicated in PCCM is complex. Robust insurance coverage could increase certain types of PICU utilization by expanding access to diagnostic or surgical procedures requiring subsequent PICU care. Additionally, insurance coverage may not translate to timely access or even financial access to preventive, emergency, or subspecialty care. Relationships between PICU utilization and other components of access, such as pharmacy availability, have not been assessed. Finally, the contributions of health literacy and language barriers to epistemic access to care and decision-making also warrant evaluation.

Mitigation and the Role of PCCM in Health Equity

The detection of disparities is a necessary first step in health equity research, 179   and systems that have produced inequity require long-term investments to improve. 172   Nonetheless, the PCCM literature includes little advancement beyond detection for nearly 25 years. The authors of a single study in this review evaluated an intervention aimed at mitigating racial disparities. 128   We argue that, as pediatric practitioners, we have a clinical and an ethical duty to begin addressing health disparities.

Pediatric practitioners are often at the forefront of advocacy efforts. 180   , 181   PCCM providers have unique perspectives that can enrich pediatric advocacy. 152   , 182   In addition, by reconceptualizing a PICU admission as an opportunity to connect families with resources, promote individual and family resilience, and foster trustworthiness of health care providers and systems, the PICU becomes an asset to advance child health, not merely the last line of defense after all other systems have failed.

Limitations

We excluded studies without an explicit aim to evaluate health disparities, which attempted to maximize rigor (ie, we anticipated that scholars aiming to investigate health equity would have a more sophisticated approach than those who incidentally found disparities). However, we, nonetheless, observed methodologic problems, and this may have excluded excellent, relevant studies. We only searched MEDLINE, given our focus on the United States, and the condition-specific nature of PCCM disparities research ( Table 2 ) likely means that some relevant literature escaped our search. Finally, although we maintain that sociopolitical context matters to disparities, we also acknowledge that racism, poverty, and discrimination are not problems unique to the United States. Moreover, the study of global inequity may provide additional insight into structural mechanisms and mitigation.

There is evidence of sociodemographic disparities in PCCM, but drawing firm conclusions is hindered by inconsistent definitions and variable approaches to analysis in the literature to date. Rectifying these problems will require major overhauls in approaches to data collection and description. Focused research into the biological, behavioral, environmental, and health care system domains that influence health disparities is necessary, as well as research aimed to identify and evaluate institutional, community, and political interventions to improve health equity across the continuum of pediatric care.

Heidi R. Flori, MD, Jocelyn R. Grunwell, MD, PhD, and Marion Kruse, PhD provided critical review and revision of this manuscript. Marisa Conte, MLIS formulated and executed the literature search. Errin Mitchell, MD assisted with the literature review and data abstraction. Gary L. Freed, MD, MPH and Julie Sturza, MPH participated in the initial project that gave rise to the idea for this literature review.

Dr Andrist conceptualized the manuscript, reviewed the relevant literature, participated in the conceptual mapping, and wrote and revised the manuscript; Drs Clarke, Phelps, Rodenbough, and Maratta and Ms Dews reviewed the relevant literature, participated in the conceptual mapping, and critically reviewed and revised the manuscript; Drs Rose, Zurca, and Lawal participated in the conceptual mapping and critically reviewed and revised the manuscript; Dr Slain conceptualized the manuscript, participated in the conceptual mapping, and critically reviewed and revised the manuscript; and all authors approve the final manuscript as submitted and agree to be accountable for all aspects of the work.

FUNDING: No external funding.

CONFLICT OF INTEREST DISCLOSURES: The authors have indicated they have no potential conflicts of interest relevant to this article to disclose.

Asian and Pacific Islander

American Indian and Alaska Native

congenital heart disease

diabetic ketoacidosis

end of life

length of stay

National Institute on Minority Health and Health Disparities

pediatric critical care medicine

pediatric palliative care

social determinants of health

socioeconomic status

severity of illness

traumatic brain injury

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