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• What Is Peer Review? | Types & Examples

What Is Peer Review? | Types & Examples

Published on December 17, 2021 by Tegan George . Revised on June 22, 2023.

Peer review, sometimes referred to as refereeing , is the process of evaluating submissions to an academic journal. Using strict criteria, a panel of reviewers in the same subject area decides whether to accept each submission for publication.

Peer-reviewed articles are considered a highly credible source due to the stringent process they go through before publication.

There are various types of peer review. The main difference between them is to what extent the authors, reviewers, and editors know each other’s identities. The most common types are:

• Single-blind review
• Double-blind review
• Triple-blind review

Collaborative review

Open review.

Relatedly, peer assessment is a process where your peers provide you with feedback on something you’ve written, based on a set of criteria or benchmarks from an instructor. They then give constructive feedback, compliments, or guidance to help you improve your draft.

Table of contents

What is the purpose of peer review, types of peer review, the peer review process, providing feedback to your peers, peer review example, advantages of peer review, criticisms of peer review, other interesting articles, frequently asked questions about peer reviews.

Many academic fields use peer review, largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the manuscript. For this reason, academic journals are among the most credible sources you can refer to.

However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure.

Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.

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Depending on the journal, there are several types of peer review.

Single-blind peer review

The most common type of peer review is single-blind (or single anonymized) review . Here, the names of the reviewers are not known by the author.

While this gives the reviewers the ability to give feedback without the possibility of interference from the author, there has been substantial criticism of this method in the last few years. Many argue that single-blind reviewing can lead to poaching or intellectual theft or that anonymized comments cause reviewers to be too harsh.

Double-blind peer review

In double-blind (or double anonymized) review , both the author and the reviewers are anonymous.

Arguments for double-blind review highlight that this mitigates any risk of prejudice on the side of the reviewer, while protecting the nature of the process. In theory, it also leads to manuscripts being published on merit rather than on the reputation of the author.

Triple-blind peer review

While triple-blind (or triple anonymized) review —where the identities of the author, reviewers, and editors are all anonymized—does exist, it is difficult to carry out in practice.

Proponents of adopting triple-blind review for journal submissions argue that it minimizes potential conflicts of interest and biases. However, ensuring anonymity is logistically challenging, and current editing software is not always able to fully anonymize everyone involved in the process.

In collaborative review , authors and reviewers interact with each other directly throughout the process. However, the identity of the reviewer is not known to the author. This gives all parties the opportunity to resolve any inconsistencies or contradictions in real time, and provides them a rich forum for discussion. It can mitigate the need for multiple rounds of editing and minimize back-and-forth.

Collaborative review can be time- and resource-intensive for the journal, however. For these collaborations to occur, there has to be a set system in place, often a technological platform, with staff monitoring and fixing any bugs or glitches.

Lastly, in open review , all parties know each other’s identities throughout the process. Often, open review can also include feedback from a larger audience, such as an online forum, or reviewer feedback included as part of the final published product.

While many argue that greater transparency prevents plagiarism or unnecessary harshness, there is also concern about the quality of future scholarship if reviewers feel they have to censor their comments.

In general, the peer review process includes the following steps:

• First, the author submits the manuscript to the editor.
• Reject the manuscript and send it back to the author, or
• Send it onward to the selected peer reviewer(s)
• Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made.
• Lastly, the edited manuscript is sent back to the author. They input the edits and resubmit it to the editor for publication.

In an effort to be transparent, many journals are now disclosing who reviewed each article in the published product. There are also increasing opportunities for collaboration and feedback, with some journals allowing open communication between reviewers and authors.

It can seem daunting at first to conduct a peer review or peer assessment. If you’re not sure where to start, there are several best practices you can use.

Summarize the argument in your own words

Summarizing the main argument helps the author see how their argument is interpreted by readers, and gives you a jumping-off point for providing feedback. If you’re having trouble doing this, it’s a sign that the argument needs to be clearer, more concise, or worded differently.

If the author sees that you’ve interpreted their argument differently than they intended, they have an opportunity to address any misunderstandings when they get the manuscript back.

Separate your feedback into major and minor issues

It can be challenging to keep feedback organized. One strategy is to start out with any major issues and then flow into the more minor points. It’s often helpful to keep your feedback in a numbered list, so the author has concrete points to refer back to.

Major issues typically consist of any problems with the style, flow, or key points of the manuscript. Minor issues include spelling errors, citation errors, or other smaller, easy-to-apply feedback.

Tip: Try not to focus too much on the minor issues. If the manuscript has a lot of typos, consider making a note that the author should address spelling and grammar issues, rather than going through and fixing each one.

The best feedback you can provide is anything that helps them strengthen their argument or resolve major stylistic issues.

Give the type of feedback that you would like to receive

No one likes being criticized, and it can be difficult to give honest feedback without sounding overly harsh or critical. One strategy you can use here is the “compliment sandwich,” where you “sandwich” your constructive criticism between two compliments.

Be sure you are giving concrete, actionable feedback that will help the author submit a successful final draft. While you shouldn’t tell them exactly what they should do, your feedback should help them resolve any issues they may have overlooked.

As a rule of thumb, your feedback should be:

• Easy to understand
• Constructive

Prevent plagiarism. Run a free check.

Below is a brief annotated research example. You can view examples of peer feedback by hovering over the highlighted sections.

Influence of phone use on sleep

Studies show that teens from the US are getting less sleep than they were a decade ago (Johnson, 2019) . On average, teens only slept for 6 hours a night in 2021, compared to 8 hours a night in 2011. Johnson mentions several potential causes, such as increased anxiety, changed diets, and increased phone use.

The current study focuses on the effect phone use before bedtime has on the number of hours of sleep teens are getting.

For this study, a sample of 300 teens was recruited using social media, such as Facebook, Instagram, and Snapchat. The first week, all teens were allowed to use their phone the way they normally would, in order to obtain a baseline.

The sample was then divided into 3 groups:

• Group 1 was not allowed to use their phone before bedtime.
• Group 2 used their phone for 1 hour before bedtime.
• Group 3 used their phone for 3 hours before bedtime.

All participants were asked to go to sleep around 10 p.m. to control for variation in bedtime . In the morning, their Fitbit showed the number of hours they’d slept. They kept track of these numbers themselves for 1 week.

Two independent t tests were used in order to compare Group 1 and Group 2, and Group 1 and Group 3. The first t test showed no significant difference ( p > .05) between the number of hours for Group 1 ( M = 7.8, SD = 0.6) and Group 2 ( M = 7.0, SD = 0.8). The second t test showed a significant difference ( p < .01) between the average difference for Group 1 ( M = 7.8, SD = 0.6) and Group 3 ( M = 6.1, SD = 1.5).

This shows that teens sleep fewer hours a night if they use their phone for over an hour before bedtime, compared to teens who use their phone for 0 to 1 hours.

Peer review is an established and hallowed process in academia, dating back hundreds of years. It provides various fields of study with metrics, expectations, and guidance to ensure published work is consistent with predetermined standards.

• Protects the quality of published research

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. Any content that raises red flags for reviewers can be closely examined in the review stage, preventing plagiarized or duplicated research from being published.

• Gives you access to feedback from experts in your field

Peer review represents an excellent opportunity to get feedback from renowned experts in your field and to improve your writing through their feedback and guidance. Experts with knowledge about your subject matter can give you feedback on both style and content, and they may also suggest avenues for further research that you hadn’t yet considered.

• Helps you identify any weaknesses in your argument

Peer review acts as a first defense, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process. This way, you’ll end up with a more robust, more cohesive article.

While peer review is a widely accepted metric for credibility, it’s not without its drawbacks.

• Reviewer bias

The more transparent double-blind system is not yet very common, which can lead to bias in reviewing. A common criticism is that an excellent paper by a new researcher may be declined, while an objectively lower-quality submission by an established researcher would be accepted.

• Delays in publication

The thoroughness of the peer review process can lead to significant delays in publishing time. Research that was current at the time of submission may not be as current by the time it’s published. There is also high risk of publication bias , where journals are more likely to publish studies with positive findings than studies with negative findings.

• Risk of human error

By its very nature, peer review carries a risk of human error. In particular, falsification often cannot be detected, given that reviewers would have to replicate entire experiments to ensure the validity of results.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Measures of central tendency
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Thematic analysis
• Discourse analysis
• Cohort study
• Ethnography

Research bias

• Implicit bias
• Cognitive bias
• Conformity bias
• Hawthorne effect
• Availability heuristic
• Attrition bias
• Social desirability bias

Peer review is a process of evaluating submissions to an academic journal. Utilizing rigorous criteria, a panel of reviewers in the same subject area decide whether to accept each submission for publication. For this reason, academic journals are often considered among the most credible sources you can use in a research project– provided that the journal itself is trustworthy and well-regarded.

In general, the peer review process follows the following steps: 

• Reject the manuscript and send it back to author, or 
• Send it onward to the selected peer reviewer(s) 
• Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made. 
• Lastly, the edited manuscript is sent back to the author. They input the edits, and resubmit it to the editor for publication.

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field. It acts as a first defense, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.

Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.

Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.

However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure. 

A credible source should pass the CRAAP test  and follow these guidelines:

• The information should be up to date and current.
• The author and publication should be a trusted authority on the subject you are researching.
• The sources the author cited should be easy to find, clear, and unbiased.
• For a web source, the URL and layout should signify that it is trustworthy.

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George, T. (2023, June 22). What Is Peer Review? | Types & Examples. Scribbr. Retrieved October 15, 2024, from https://www.scribbr.com/methodology/peer-review/

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The top 10 journal articles

Vol. 53 No. 1 Print version: page 26

1. COVID-19 disruption on college students: Academic and socioemotional implications

Tasso, A. F., Hisli Sahin, N., San Roman, G. J.

This study in Psychological Trauma: Theory, Research, Practice, and Policy (Vol. 13, No. 1) reveals that college students experienced emotional distress on many levels during the COVID-19 pandemic. Researchers surveyed 257 students at a U.S. college who all participated in remote learning off campus during the spring of 2020 because of the pandemic. Students reported being afraid of contracting COVID-19 and even more afraid of people within their social network contracting the virus. They also reported worrying about themselves or loved ones becoming severely ill, academic-related distress following the transition to remote learning, and COVID-19-related mental health distress, including interpersonal disengagement, struggles with motivation, and boredom, as well as anxiety, depression, and sleep disturbances. DOI: 10.1037/tra0000996

2. COVID-19 and the workplace: Implications, issues, and insights for future research and action

Kniffin, K. M., Narayanan, J., Anseel, F., Antonakis, J., Ashford, S. P., Bakker, A. B., Bamberger, P., Bapuji, H. Bhave, D. P., Choi, V. K., Creary, S. J., Demerouti, E., Flynn, F. J., Gelfand, M. J., Greer, L. L., Johns, G., Kesebir, S., Klein, P. G., Lee, S. Y., Ozcelik, H., Petriglieri, J. L., Rothbard, N. P., Rudolph, C. W., Shaw, J. D., Sirola, N., Wanberg, C. R., Whillans, A., Wilmot, M. P., Vugt, M.

This article in American Psychologist (Vol. 76, No. 1) presents possible workplace trends resulting from COVID-19, including remote work, virtual teamwork and management, social distancing, and unemployment. The analysis suggests that working from home will continue and expand post-pandemic. As for effects on workers, the authors predict increases in economic inequality, loneliness, stress, burnout, and addiction. Other workplace changes the authors forecast include virtual work arrangements that may foster more participatory relationships, new performance management and evaluation systems for remote workers, and new modes of surveillance by companies to check in on employees working remotely. DOI: 10.1037/amp0000716

3. A closer look at appearance and social media: Measuring activity, self-presentation, and social comparison and their associations with emotional adjustment

Zimmer-Gembeck, M. J., Hawes, T., Pariz, J.

This Psychology of Popular Media (Vol. 10, No. 1) study presents a tool to assess youth’s preoccupation with their physical appearance on social media. Researchers administered a 21-item survey about social media to 281 Australian high school students. They identified 18 items with strong inter-item correlation centered on three categories of social media behavior: online self-presentation, appearance-related online activity, and appearance comparison. In a second study with 327 Australian university students, scores on the 18-item survey were found to be associated with measures of social anxiety and depressive symptoms, appearance-related support from others, general interpersonal stress, coping flexibility, sexual harassment, disordered eating, and other issues. The researchers also found that young women engaged in more appearance-related social media activity and appearance comparison than did young men. DOI: 10.1037/ppm0000277

4. When social isolation is nothing new: A longitudinal study on psychological distress during COVID-19 among university students with and without preexisting mental health concerns

Hamza, C. A., Ewing, L., Heath, N. L., Goldstein, A. L.

In this study in Canadian Psychology (Vol. 62, No. 1), researchers examined the psychological impacts of COVID-19 on the mental health of postsecondary students with and without preexisting mental health concerns prior to the pandemic. The researchers surveyed 773 college students in Canada in May 2019 and again in May 2020 about recent stressful experiences and their mental health status. They found that students with preexisting mental health concerns showed improving or similar mental health during the early pandemic compared with 1 year prior. By contrast, students without preexisting mental health concerns were more likely to exhibit declining mental health during the pandemic, perhaps because they had less experience with social isolation than did students with preexisting mental health issues, the researchers suggest. DOI: 10.1037/cap0000255

5. Trauma-focused cognitive-behavioral therapy (TF-CBT) for interpersonal trauma in transitional-aged youth

Peters, W., Rice, S., Cohen, J., Murray, L., Schley, C., Alvarez-Jimenez, M., Bendall, S.

This pilot study in Psychological Trauma: Theory, Research, Practice, and Policy (Vol. 13, No. 3) indicates that trauma-focused cognitive behavioral therapy (TF-CBT) is an effective treatment for young people who have experienced post-traumatic stress disorder (PTSD) following interpersonal trauma such as child physical or sexual abuse, maltreatment, or neglect. Researchers delivered 15 TF-CBT sessions over 25 weeks to 20 youth ages 15 to 25 (­transitional-aged) in Australia, 16 of whom had a PTSD diagnosis. They found that following treatment, 15 of 16 participants no longer met criteria for a PTSD diagnosis, and self-report measures of PTSD, depression, and anxiety showed improvement, though some participants reported transient increases in symptoms. The researchers plan to conduct a larger randomized clinical trial to examine the effectiveness of TF-CBT for PTSD and other frequently co-occurring symptoms, including anxiety, depression, and substance use. DOI: 10.1037/tra0001016

6. Social media use and friendship closeness in adolescents’ daily lives: An experience sampling study

Pouwels, J. L., Valkenburg, P. M., Beyens, I., van Driel, I. I., Keijsers, L.

Adolescents who use social media apps such as Instagram more frequently than their peers feel closer to their friends, suggests this study in Developmental Psychology (Vol. 57, No. 2). Researchers asked 387 adolescents ages 13 to 15 in the Netherlands to report six times per day for 3 weeks their Instagram, WhatsApp, and Snapchat use in the previous hour, as well as their momentary experiences of friendship closeness. They found that participants who used WhatsApp and Instagram with close friends with whom they felt a sense of trust, support, and intimacy more frequently throughout the 3 weeks experienced higher levels of friendship closeness during the study than their peers. However, participants felt less close to their friends after they had used Instagram or WhatsApp in the previous hour, perhaps, the researchers suggest, resulting from unmet expectations that friends would immediately provide feedback on their posts. Neither association was found with Snapchat. DOI: 10.1037/dev0001148

7. Every (Insta)gram counts? Applying cultivation theory to explore the effects of Instagram on young users’ body image

Stein, J.-P., Krause, E., Ohler, P.

This study in Psychology of Popular Media (Vol. 10, No. 1) suggests that young people who frequently browse Instagram in a highly engaged way are more critical of strangers’ bodies and indulge more often in disordered eating—even if their own body image is unaffected. Researchers asked 228 participants ages 18 to 34 in Germany about changes in weight-related knowledge, attitudes, and self-reported dietary restraint. They found that participants, especially women, who browsed Instagram’s content more actively than their peers formed harsher views about the weight of strangers as well as an increased risk for disordered eating, but not a reduction in satisfaction with their own bodies. DOI: 10.1037/ppm0000268

8. Nonverbal overload: A theoretical argument for the causes of Zoom fatigue

Bailenson, J. N.

This review article in Technology, Mind, and Behavior (Vol. 2, No. 1) combines theory and prior research to derive four explanations for “Zoom fatigue,” the feeling of exhaustion brought on by video calls: excessive close-up eye contact with speakers, constant self-evaluation of one’s own image on the screen, remaining in a fixed position in view of the camera, and the increased cognitive load of sending and receiving nonverbal communication. The author offers the following solutions: reduce the size of the Zoom window to minimize face size, hide “self-view,” position the camera further away to allow for moving beyond a fixed sitting position without disrupting the call, and take “audio-only” breaks by both turning the camera off and turning away from the screen. DOI: 10.1037/tmb0000030

9. Coping during the COVID-19 pandemic: Relations with mental health and quality of life

Shamblaw, A. L., Rumas, R. L., Best, M. W. 

During the COVID-19 pandemic, people using avoidance coping strategies experienced increased depression and anxiety, while those using approach coping strategies, such as positive reframing, received the largest mental health boost, suggests this study in Canadian Psychology (Vol. 62, No. 1). In April 2020, researchers surveyed 797 online participants in the United States and Canada about 14 different approach or avoidance coping strategies as well as symptoms of depression, anxiety, and quality of life. One month later, 395 of the participants took the survey again. The researchers found that avoidance coping was associated with higher depression, higher anxiety, and lower quality of life at baseline and increased depression and anxiety 1 month later. Approach coping was associated with lower depression and better quality of life at baseline but not over the 1-month period. Of the specific coping strategies examined, reframing negative aspects of the pandemic was the most beneficial. DOI: 10.1037/cap0000263

10. Integrating responsive motivational interviewing with cognitive-behavioral therapy for generalized anxiety disorder: Direct and indirect effects on interpersonal outcomes

Muir, H. J., Constantino, M. J., Coyne, A. E., Westra, H. A., Antony, M. M.

This study in the Journal of Psychotherapy Integration (Vol. 31, No. 1) indicates that adding motivational interviewing (MI)—a psychotherapy module that helps people resolve feelings of ambivalence—to cognitive behavioral therapy (CBT) to treat generalized anxiety disorder (GAD) can bring about long-term changes in nonassertiveness and overaccommodation. In other words, the combination treatment helps people better assert themselves and not give in to others’ demands. Researchers randomly assigned 85 Canadian patients with GAD to a brief treatment of CBT or MI-CBT. Patients completed measures of nonassertiveness and overaccommodation throughout the treatment and across a 12-month follow-up. The researchers found that both MI-CBT and CBT reduced nonassertiveness and overaccommodation, but at 12 months, MI-CBT had helped patients more than CBT alone. This effect was explained by MI-CBT therapists’ ability to help patients overcome midtreatment resistance. DOI: 10.1037/int0000194

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• Introduction
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• Article Information

Linear regression model of mean time in days to return review and variables associated with mean time in days to return reviews, by manuscript.

Changes in aspects of peer reviewer behavior over time, before and during the COVID-19 pandemic, with number of global daily deaths from COVID-19 as an indicator of pandemic intensity.

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Perlis RH , Kendall-Taylor J , Hart K, et al. Peer Review in a General Medical Research Journal Before and During the COVID-19 Pandemic. JAMA Netw Open. 2023;6(1):e2253296. doi:10.1001/jamanetworkopen.2022.53296

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Peer Review in a General Medical Research Journal Before and During the COVID-19 Pandemic

• 1 Massachusetts General Hospital, Harvard Medical School, Boston
• 2 JAMA Network, Chicago, Illinois
• 3 Harvard Medical School, Boston, Massachusetts
• 4 Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
• 5 Rutgers School of Public Health, Piscataway, New Jersey
• 6 Minneapolis Heart Institute, Minneapolis Heart Institute Foundation, Minneapolis, Minnesota
• 7 Harvard T.H. Chan School of Public Health, Boston, Massachusetts
• 8 Hebrew SeniorLife and Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts
• 9 MaineHealth and Maine Medical Center Research Institute, Scarborough
• 10 Stanford University School of Medicine, Stanford, California
• 11 NYU Langone Health, New York, New York
• 12 Carver College of Medicine, University of Iowa, Iowa City
• 13 Abramson Cancer Center, University of Pennsylvania, Philadelphia
• 14 Department of Emergency Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
• 15 JAMA Network Open , Chicago, Illinois
• 16 University of Washington School of Medicine, Seattle

Question   How did peer review change from before to during the first year of the COVID-19 pandemic at a large open-access general medical journal?

Findings   In this cohort study of 5013 manuscripts reviewed and peer reviews invited by an open-access general medical journal, peer review turnaround time was slightly shorter and editor-reported review quality was modestly increased during the first year of the pandemic.

Meaning   This study found that peer review at a large, open-access general medical journal remained resilient during the first 15 months of the pandemic despite shifts in in the dynamics of the review process.

Importance   Although peer review is an important component of publication for new research, the viability of this process has been questioned, particularly with the added stressors of the COVID-19 pandemic.

Objective   To characterize rates of peer reviewer acceptance of invitations to review manuscripts, reviewer turnaround times, and editor-assessed quality of reviews before and after the start of the COVID-19 pandemic at a large, open-access general medical journal.

Design, Setting, and Participants   This retrospective, pre-post cohort study examined all research manuscripts submitted to JAMA Network Open between January 1, 2019, and June 29, 2021, either directly or via transfer from other JAMA Network journals, for which at least 1 peer review of manuscript content was solicited. Measures were compared between the period before the World Health Organization declaration of a COVID-19 pandemic on March 11, 2020 (14.3 months), and the period during the pandemic (15.6 months) among all reviewed manuscripts and between pandemic-period manuscripts that did or did not address COVID-19.

Main Outcomes and Measures   For each reviewed manuscript, the number of invitations sent to reviewers, proportions of reviewers accepting invitations, time in days to return reviews, and editor-assessed quality ratings of reviews were determined.

Results   In total, the journal sought review for 5013 manuscripts, including 4295 Original Investigations (85.7%) and 718 Research Letters (14.3%); 1860 manuscripts were submitted during the prepandemic period and 3153 during the pandemic period. Comparing the prepandemic with the pandemic period, the mean (SD) number of reviews rated as high quality (very good or excellent) per manuscript increased slightly from 1.3 (0.7) to 1.5 (0.7) ( P  < .001), and the mean (SD) time for reviewers to return reviews was modestly shorter (from 15.8 [7.6] days to 14.4 [7.0] days; P  < .001), a difference that persisted in linear regression models accounting for manuscript type, study design, and whether the manuscript addressed COVID-19.

Conclusions and Relevance   In this cohort study, the speed and editor-reported quality of peer reviews in an open-access general medical journal improved modestly during the initial year of the pandemic. Additional study will be necessary to understand how the pandemic has affected reviewer burden and fatigue.

The effect of COVID-19 on academic publishing has been the subject of substantial discussion. In particular, the pandemic has reinvigorated conversations about the growing role and variable quality of preprints that do not undergo peer review, 1 the burden of peer review on the academic community, 2 concerns about reviewer fatigue, 3 and how best to ensure the rigor and value of peer-reviewed medical literature. 4 , 5 For medical publishing specifically, increasing volumes of manuscripts related to COVID-19 6 - 8 and expectations for rapid publication and dissemination have further stressed a system that some in medicine already believed was broken. 9

Few empirical observations about the ways in which the peer review process may have changed during the pandemic have been reported. A recent study 7 of manuscripts and reviews submitted to 2329 journals before and during the pandemic found a modest decrease in rates of reviewer acceptance of invitations to review among health and medical journals between February and May 2020, with a more pronounced decrease among potential reviewers who were women, but not men.

To understand how peer review changed with the onset of the COVID-19 pandemic, we examined peer review data from JAMA Network Open , an open-access general medical journal launched in 2018 with a 2020 impact factor of 8.5. 10 Specifically, we aimed to quantify changes in rates of peer reviewer acceptance of invitations to review manuscripts and review quality from the period before to the period during the COVID-19 pandemic.

Manuscripts submitted to JAMA Network Open first undergo technical quality assessment, then close evaluation by an editor. For manuscripts that are deemed of sufficient quality and priority to undergo peer review, editors seek review from 1 or more content reviewers and 1 statistical reviewer. We extracted data from databases used by the JAMA Network to track manuscript submissions and peer reviews. We included all manuscripts received at JAMA Network Open from January 1, 2019, through June 30, 2021, that were categorized as Original Investigations or Research Letters for which at least 1 content review was sought. These manuscripts could be submitted directly to the journal or transferred from other journals within the JAMA Network. The study used deidentified administrative data with no participant contact and followed the journal’s policy for such research, which indicates that information may be systematically collected and analyzed as part of research to improve the quality of the editorial or peer review process. This study was reviewed by the Massachusetts General–Brigham Institutional Review Board and considered to be exempt from informed consent because it uses deidentified data and posed minimal risk. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline for cohort studies. 11

For each manuscript, we determined the total number of individuals who were invited to review the manuscript and the number and proportion of reviewers accepting, declining, or failing to respond to invitations. For the accepted invitations, we determined the mean time (across reviewers) to return the review and the number and proportion of reviews rated by editors as very good or excellent on a 5-point anchored scale: poor, fair, good, very good, or excellent. This process was completed for all manuscripts as part of routine editorial practice. For purposes of analysis, the last 2 categories (very good and excellent) were aggregated to reflect high-quality reviews. For descriptive purposes and because some individuals served as peer reviewers for multiple manuscripts, we also report acceptance rate for reviewer invitations, time to return review, and quality of review at the level of individual reviews.

Additional manuscript features collected at submission included study design 12 (eg, randomized trial, cohort study, or economic evaluation), whether a study was submitted directly or transferred from another JAMA Network journal, and whether a study referenced or addressed COVID-19. The last of these is determined via an automated process implemented to identify any manuscript with any of the following terms in the manuscript text: acute respiratory virus, personal protective equipment, N95, COVID, COVID-19, coronavirus, SARS, or novel virus.

We compared peer review characteristics for submitted manuscripts with an index date before or after March 11, 2020, the date that the World Health Organization declared a COVID-19 pandemic. 13 Thus, data were divided into 2 groups: the prepandemic period from January 1, 2019, to March 10, 2020 (14.3 months), and the pandemic period from March 11, 2020, to June 29, 2021 (15.6 months). We similarly compared characteristics of reviews provided for manuscripts submitted after March 11, 2020, that did or did not address COVID-19. We used multivariable linear regression to calculate effect sizes and 95% CIs for the association between prepandemic and pandemic status for reviewer turnaround time, adjusted for other manuscript characteristics (study design, direct submission vs transfer from other JAMA Network journals, article type, and whether the manuscript addressed COVID-19). (Incorporating clustering by subject area, as a proxy for a handling editor, did not meaningfully change results.)

To visualize changes in peer review characteristics over time—and, in particular, whether secular trends in these characteristics might have preceded the pandemic—we also described manuscript and review features on a weekly basis, with each manuscript assigned to the date of first reviewer invitation. For graphic presentation, we applied a 3-week rolling mean using the rollmean function in R’s zoo library, version 1.8-9.

All analyses used R software, version 4.1.2 14 ; the threshold for statistical significance was considered to be a 2-tailed P  < .05, without adjustment for multiple comparisons.

Between January 1, 2019, and June 30, 2021, the journal sought reviews for 5013 manuscripts (mean [SD], 38.3 [13.3] per week), including 4295 Original Investigations (85.7%) and 718 Research Letters (14.3%). Of these manuscripts, 1860 and 3153 manuscripts were submitted during the prepandemic and pandemic periods, respectively.

Characteristics of manuscripts received before March 11, 2020, or on or after that date are summarized in Table 1 . These manuscripts included 376 clinical trials (7.5%), 1939 cohort studies (38.7%), and 1148 cross-sectional studies (22.9%); among the 5013 manuscripts reviewed, 932 (18.6%) addressed COVID-19. Table 1 also includes univariate comparisons of these and other characteristics between the prepandemic and pandemic periods. The overall mean (SD) volume of manuscripts reviewed per week increased from 30.3 (8.6) to 46.4 (12.2) ( P  < .001), and the mean (SD) number of reviewers invited per manuscript to achieve the minimum number of required reviews increased from 6.0 (3.6) to 7.0 (4.5) ( P  < .001). The mean (SD) proportion of reviewers per manuscript who accepted invitations did not change significantly (39.5% [28.6%] vs 38.4% [28.3%]; P  = .21). However, the mean (SD) number of reviews returned per manuscript also increased from 1.6 (0.6) to 1.7 (0.5) ( P  < .001), as did the mean (SD) number of reviews rated as high quality (ie, very good or excellent) per manuscript (from 1.3 [0.7] to 1.5 [0.7]; P  < .001). Mean (SD) time to return reviews also decreased from 15.8 (7.6) to 14.4 (7.0) days ( P  < .001).

In multivariable linear regression adjusting for baseline manuscript features (article type, study design, and direct submission vs transfer), differences in mean time to return review persisted. Time to return of review was decreased in the pandemic period compared with the prepandemic period (adjusted mean difference of −1.2 days; 95% CI, −0.7 to −1.6) ( Figure 1 ).

In a complementary analysis of 33 615 reviewers invited before (n = 13 208 [39.3%]) or after (n = 20 407 [60.7%]) pandemic onset who returned reviews, the proportion of reviewers accepting invitations was similar (3337 [25.3%] vs 5229 [25.6%]; P  = .46), whereas the mean (SD) time to return reviews decreased from 15.2 (9.2) in the prepandemic period to 14.8 (8.6) during the pandemic ( P  = .02).

A similar pattern of differences to those observed comparing the prepandemic vs pandemic periods was identified in comparisons of manuscripts that did not address COVID-19 (n = 2238) and those that did address COVID-19 (n = 915) ( Table 2 ). COVID-19–related manuscripts required fewer reviewer invitations (mean [SD], 6.4 [4.4] vs 7.2 [4.6]; P  < .001), and the proportion of reviewers who declined invitations to review was lower (mean [SD], 32.8% [22.6%] vs 35.2% [22.3%]; P  = .006). The mean (SD) number of very good or excellent reviews was greater for COVID-19–related manuscripts (1.5 [0.7] vs those not related to COVID-19 (1.4 [0.7]; P  < .001). Mean (SD) time to return reviews was also lower: 14.6 (7.0) days for those not COVID related vs 13.7 (6.8) days for those that were COVID related ( P  = .002).

Figure 2 illustrates changes in peer reviews over time, with the global number of COVID-19 deaths at the bottom for reference. 15 In general, the volume of manuscript submissions increased over time, as did the number of reviewers invited, both continuing qualitative trends beginning before the pandemic. Conversely, a decrease in time to return reviews appears to have followed pandemic onset.

In this cohort study of 5013 manuscripts with reviews solicited by an open-access general medical journal before and during the COVID-19 pandemic, we did not identify evidence of deterioration in the peer review process. The overall rate of reviewer acceptance of review requests remained stable after pandemic onset. However, the time required for reviewers to complete reviews was modestly shorter during the pandemic, and the mean number of high-quality reviews per manuscript was greater compared with the period before the pandemic. Although the association with time to return reviews persisted after adjustment for potential confounding features, such as study design and manuscript type, we cannot exclude unobserved changes in how the editors invited reviewers—for example, making a greater effort to identify interested reviewers or sending personal notes—that may have coincided with the pandemic.

Similar patterns were observed for COVID-19–focused manuscripts. In this case, rates of reviewer acceptance were significantly greater for such manuscripts. Time to return reviews was slightly but statistically significantly shorter, whereas the number of high-quality reviews received per manuscript was slightly greater.

Our results complement those of a recent investigation 7 of peer review during the pandemic. That study found lower rates of review invitation acceptance from health and medical journals among women but not men. 7

This study has multiple limitations. First, we did not have access to reviewer-level characteristics, such as gender, age, race and ethnicity, or academic seniority, that would allow us to quantify the differential effect of the pandemic on some reviewers or compare across demographic characteristics, because these measures are not collected by the journal. Second, we cannot necessarily attribute the changes we observed to the pandemic itself. While JAMA Network Open was publishing for 2 years before the pandemic, submissions increased rapidly, particularly in the first year, as journal recognition and reputation increased. Likewise, reviewers’ willingness to participate in peer review may have changed over time as they became more aware of the journal. As such, there are likely secular trends that may explain some of the changes we observed; the generalizability of our results to other journals will require further study, and we hope this work will encourage such efforts. Third, editors’ estimates of review quality are entirely subjective, so it is possible that editors took extenuating circumstances into account; for example, editors may have “graded on a curve” during the pandemic, and in reality quality could have remained unchanged or diminished. Finally, the automated flagging of submissions as COVID-19 related may have missed relevant submissions that did not include specific keywords and, likewise, may have flagged submissions that included pandemic-related keywords in the introduction or discussion but were not truly related to COVID-19.

In contextualizing these findings, it is also important to consider other changes in the peer review process occurring during the pandemic. For example, reviewers may have been more or less likely to accept invitations to review manuscripts based on eagerness to review new science about COVID-19, availability due to reduced clinical commitments, unavailability due to intensified clinical commitments, concerns about COVID-related misinformation, or reviewer fatigue associated with increase in volume of manuscripts submitted during the pandemic. Moreover, the editors’ behavior may have changed with the increase in number of submissions, such as making more personal requests or opting to proceed with a decision with fewer reviewers for a given manuscript.

Despite these limitations, our results may help inform ongoing conversations about quality and burden of peer review during the COVID-19 pandemic. The findings suggest that the pandemic modestly affected the review process in terms of turnaround time and review quality. However, this apparent stability does not address the extent to which reviewer sentiment toward the peer review process may have shifted or the pandemic’s effect on the ability of invited reviewers to complete other tasks. Other lines of investigation, including surveys, suggest that the pandemic has negatively affected researchers’ quality of life, more so for women than men. 16 , 17

The findings of this pre-post cohort study suggest that the peer review process at a large, open-access journal has continued to function during the COVID-19 pandemic despite changes in both the volume of submissions and the work and home environments of many peer reviewers. Most encouragingly, during the pandemic, review quality did not appear to have diminished. Still, in light of abundant evidence that COVID-19 has negatively impacted researchers, 16 , 17 continued efforts to study and improve the peer review process are needed.

Accepted for Publication: December 9, 2022.

Published: January 27, 2023. doi:10.1001/jamanetworkopen.2022.53296

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2023 Perlis RH et al. JAMA Network Open .

Corresponding Author: Roy H. Perlis, MD, MSc, Massachusetts General Hospital, Harvard Medical School, 185 Cambridge St, Simches Research Bldg, Sixth Floor, Boston, MA 02114 ( [email protected] ).

Author Contributions: Dr Perlis had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Perlis, Berlin, Inouye, Jacobs, Morris, Ogedegbe, Perencevich, Shulman, Fihn, Rivara, Flanagin.

Acquisition, analysis, or interpretation of data: Perlis, Kendall-Taylor, Ganguli, Hart, Berlin, Bradley, Haneuse, Inouye, Trueger, Flanagin.

Drafting of the manuscript: Perlis, Hart, Berlin, Haneuse, Shulman.

Critical revision of the manuscript for important intellectual content: Perlis, Kendall-Taylor, Ganguli, Berlin, Bradley, Haneuse, Inouye, Jacobs, Morris, Ogedegbe, Perencevich, Shulman, Trueger, Fihn, Rivara, Flanagin.

Statistical analysis: Perlis, Hart, Haneuse.

Administrative, technical, or material support: Perlis, Kendall-Taylor, Hart.

Supervision: Perlis.

Conflict of Interest Disclosures: Dr Perlis reported receiving personal fees for scientific advisory board service from Belle Artificial Intelligence, Burrage Capital, Psy Therapeutics, Genomind, Circular Genomics, and RID Ventures outside the submitted work. Ms Flanagin reported being affiliated with the JAMA Network and employed by the American Medical Association, which publishes JAMA Network Open , during the conduct of the study. No other disclosures were reported.

Disclaimer: Dr Rivara is editor in chief; Dr Fihn is deputy editor; Drs Perlis, Ganguli, Bradley, Inouye, Jacobs, Morris, Ogedegbe, Perencevich, and Shulman are associate editors; Drs Berlin and Haneuse are statistical editors; and Dr Trueger is digital media editor of JAMA Network Open , but they were not involved in any of the decisions regarding review of the manuscript or its acceptance.

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The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Introduction

The extant study was conducted over a cross-sectional period and aimed to assess the effect of intrapersonal on the interpersonal dimensions of Emotional Intelligence among University Students.

A literature survey was carried out, and the study’s hypotheses were framed. Utilising a standardised Emotional Intelligence Scale, a widely accepted and validated measurement tool in the field, for measurement, the survey was disseminated in digital and physical formats. The researchers employed the snowball sampling technique to distribute the questionnaires and recruit volunteers for the study. The data collection period spanned from August 2023 through September 2023. The demographic information of the individuals was described using the SPSS 25 software, while the dataset for the personal and social competencies was analysed using the SmartPLS software.

Results and discussion

The research reveals a statistically significant association between the variables under investigation. Specifically, there exists a negative correlation between Motivation and Social Skills, as well as between Self-regulation and Social Awareness. These findings open up exciting opportunities for future research, inspiring further exploration into the development of intrapersonal and interpersonal competencies among students.

A critical aspect that influences a human’s ability to make decisions, control emotions and manage behaviour is emotional intelligence. It is a concept that gained popularity for its significance in practical applications and academic research in various fields after Daniel Goleman published his book on Emotional Intelligence in 1995. It is “the ability to recognise, understand, manage and utilise emotions effectively in oneself and others” ( Mayer et al., 1999 ). The competencies of Emotional intelligence are widely divided into intrapersonal and interpersonal competence. Though they are widely distinct, these competencies are interconnected and help improve the emotional and social functioning of society. There are two perspectives of emotional intelligence: ability emotional intelligence ( Mayer et al., 2008 ) and trait emotional intelligence ( Petrides et al., 2007 ). Several models of emotional intelligence, such as those proposed by Goleman (1995) , Mayer and Salovey (1997) , and Bar-On (2006) , were developed to understand the concept of emotional intelligence with sub-competencies. Previous studies, including those by Petrides and Furnham (2000) and Schutte et al. (2001) , have shown that the development of intrapersonal skills and interpersonal skills impacts various outcome variables. This raises the question: Can the intrapersonal skills of emotional intelligence, as discussed in Goleman (2001) , have an influence on the interpersonal skills of emotional intelligence? To answer the above question, the article’s key objective is to find if the different intrapersonal skills of emotional intelligence impact the interpersonal skills of emotional intelligence, as these skills are some of the components that contribute to the psychological well-being of individuals. The article provides a comprehensive overview of the two broad components of EI and emphasises the implications of EI, which is essential for the psychological well-being of Students.

Intrapersonal competence of emotional intelligence

Intrapersonal Competence makes an individual introspect and manage one’s emotions. It is the heart of emotional intelligence. The three components that are the core of intrapersonal competence are self-awareness, self-regulation and self-motivation. These are important not only for emotional well-being but also for the personal development of individuals ( Ștefan, 2022 ). It helps deal with the everyday challenges of life ( Bar-On, 2006 ). It has lower-order emotional intelligence abilities, which are emotional perception, emotional understanding and emotional expression. Emotional perception is a skill that espies and interprets the body language, the volume and pitch of voice, and facial expressions in oneself and in others ( Krumhuber et al., 2023 ). Emotional expression is the ability to convey emotional information through verbal and non-verbal cues, such as recognising emotional triggers, understanding emotional dynamics, the awareness of emotional complexity and cognitive empathy aligning with different contexts ( Seidel et al., 2010 ). Comprehending the causes and the consequences of the emotions expressed is emotional understanding. These basic abilities are required for the emotional functioning of an individual. Apart from the fact that the above abilities stipulate the individual to reflect and grow by managing their emotions, communicating effectively with empathetic interactions, and building relationships, it also leads to better professional development by contributing to better leadership ( Saha et al., 2023 ; Lee et al., 2023 ) and conflict resolution skills ( Mercader-Rubio et al., 2023 ). Accurate emotional reasoning and emotional information processing are integral for intrapersonal skills. Self-awareness has three sub-competencies, emotional self-awareness which allows individuals to understand their feelings and have a direct impact on their decisions and actions. Accurate self-assessment helps one understand their strengths and weaknesses. An individual can evaluate oneself and recognise areas of improvement on their own, which would help one develop personally as well. A strong sense of self-worth helps stem self-confidence, which is the realistic perception of one’s ability to express oneself and make decisions. Daniel Goleman, in his book on Emotional Intelligence, identifies that self, awareness is the foundation for developing the other components of EI, which include social awareness and social skills ( Goleman, 1995 ) and that self-reflection, which is a key aspect of self-awareness is linked to social cognition, which involves understanding the emotions of others during social interactions ( Silvia and Duval, 2001 ). Mayer and Salovey (1997) highlighted that self-awareness develops social competence, which includes both social awareness and social skills. This theory aligns with the idea that self-awareness, or the understanding of one’s own mental states, is essential for understanding others’ perspectives ( Woodruff and Premack, 1978 ), which is a fundamental aspect of social awareness and social skills. Hence, the following hypothesis was framed to test in the current study.

H1 : Self-awareness influences the social awareness of students.

H2 : Self-awareness influences the social skills of students.

Self-regulation is defined as a person’s ability to manage one’s internal impulses. It maintains integrity by disrupting negative emotions and helps adapt to various environments and situations. It is a combination of the subskills of self-control, conscientiousness, adaptability, trustworthiness, and innovation. Self-control is the key component of self-regulation, which keeps the impulsive nature in check, helps manage stress and behaves acceptably socially ( Wenzel et al., 2024 ). Trustworthiness and integrity help maintain standards of ethical behaviour and be consistent in one’s values and principles ( Christison and Murray, 2023 ). Taking responsibility by being reliable, diligent and organised and setting high standards for oneself is conscientiousness. The ability to adjust to new environments and be flexible and open to new ideas is adaptability ( Boyar et al., 2023 ). When an individual is comfortable with new approaches and takes risks in exploring creative ideas and solutions in novel situations is innovative ( Winton and Sabol, 2024 ). A research highlighted that individuals who regulate their emotions can understand and respond to the emotions of others better ( Gross, 2002 ). Another study showed that emotional regulation had an impact on the quality of social interaction ( Lopes et al., 2005 ). Eisenberg and Spinrad (2004) also emphasised how self-regulation impacted empathy, which is the key element of social awareness. They also further found that self-regulated individuals were better at listening and responding to information ( Eisenberg et al., 2010 ), which is essential for effective social interactions. Students with better self-regulation also exhibited better social skills, which included cooperation and responsibility ( McClelland et al., 2007 ). Hence, the following hypothesis was framed to test in the current study.

H3 : Self-regulation influences the social awareness of students.

H4 : Self-regulation influences the social skills of students.

Self-motivation helps one to be energetic and optimistic. These include achievement drive, the desire of an individual to attain a standard of excellence in life by setting challenging goals and being determined to achieve them. Commitment is a skill that helps one align with the group or organisational goals and be dedicated to achieving the objectives ( Rahiman et al., 2020 ). When an individual is ready to take proactive steps and is a self-starter to prompt actions on opportunities, it shows that the individual takes initiative, which is a component of self-motivation ( Kugbey et al., 2018 ; Venkatesh and Balaji, 2013 ). Motivation also includes being optimistic, a positive outlook on a person’s ability to overcome setbacks and obstacles and be resilient ( Christie et al., 2007 ).

Although there is evidence that intrapersonal emotional intelligence improves both emotional and physical health ( Resurreccion et al., 2014 ), some research indicates that having high EI, especially emotional awareness, may negatively affect the individual’s ability to handle emotionally significant situations ( Martins et al., 2010 ). Self-Determination Theory emphasises that motivation is linked to better social understanding and higher levels of empathy ( Ryan and Deci, 2000 ). They also argued that this empathy developed into strong social skills. This was further supported by another study that suggested that intrinsic motivation promoted prosocial behaviour ( Gagné and Deci, 2005 ). Another study discussed how motivated individuals are more socially aware ( Sansone and Thoman, 2005 ), and it enhanced empathy and social skills ( Grant and Berry, 2011 ). Hence, based on the above literature, the following hypotheses were formulated to be tested.

H5 : Motivation influences social awareness.

H6 : Motivation influences social skills.

Interpersonal competence of emotional intelligence

In the emotional intelligence context, interpersonal competence is the ability to manage social interactions by understanding and regulations one’s own emotions and others. This competence is momentous in education ( Lindsey and Rice, 2015 ; Willmot and Colman, 2016 ), showing evidence for effective classroom management ( Valente et al., 2019 ) and academic achievement ( Mohzan et al., 2013 ; Costa and Faria, 2015 ; Parker et al., 2004 ). In psychology, emotional intelligence is linked to better communicative adequacy ( Raeissi et al., 2023 ) and relationship-building ( Schutte et al., 2001 ). In healthcare, there are studies showing that emotional intelligence directly influences better performance in their roles ( Govindaraju, 2021 ).

The interpersonal competence of emotional intelligence has social awareness and social skills, which have multiple sub-competencies. Social awareness is being able to understand and empathise with the feelings and perspectives of others. In a social context, it is being aware of the cultural differences and the effect of one’s behaviour on other individuals and social groups by having empathy which requires a person to take an active interest in others and read the emotional cues, interests and desires of others and be compassionate and supportive toward them ( Imperato and Strano-Paul, 2021 ; Austin et al., 2007 ). Organisational awareness is a social awareness that helps one understand social networking and power dynamics within an institution. It helps with effective collaboration and influences acceptable behaviour in formal and informal structures ( Aamir, 2023 ). Offering service and support to others with the proactive ability to anticipate and meet the needs of others to build a positive culture within an institution is service orientation ( Shafait and Huang, 2024 ).

H7 : Social awareness influences the social skills of students.

Managing social interactions in an effective manner is a social skill. Influencing others by persuading and guiding them to achieve common goals is an integral part of social skills ( Singh and Ryhal, 2023 ). It also includes being able to communicate actively, understand others’ emotional tones and express ideas clearly to others ( Raeissi et al., 2023 ; Behera and Pani, 2014 ). Resolving disagreements and managing conflicts to find beneficial solutions to problems ( Schlaerth et al., 2013 ) and being authentic in leading groups and motivating them to thrive is a skill of leadership ( Miao et al., 2018 ). Social skills also require catalysing change for adaptability and building bonds ( Goleman, 2001 ) for long-term maintenance of relationships to induce collaboration ( Cox, 2011 ) and cooperation ( Fernández-Berrocal et al., 2014 ) among individuals, which also helps enhance the team capabilities ( Shafique and Naz, 2023 ) by creating group synergy and creates a more cohesive group.

The gap from the previous literature shows that though emotional intelligence as a whole has an impact on other variables, there is a lack of studies that show the impact of self-competence on the social competence of emotional intelligence, which holistically builds the psychological well-being of students. Figure 1 shows the proposed model aligning with the hypotheses formulated for the current study.

Proposed model.

Methodology

The study adopted the cross-sectional research design; hence, the data was collected from the participants at one point. A comprehensive literature research was undertaken, and a questionnaire tailored to the Indian participants was adapted from Dr. Shailendra Singh’s Emotional Intelligence Scale for the present study ( Singh, 2004 ). A total of 352 students participated in the study using snowball sampling from the province of Vellore. Next, the hypotheses for the investigation were formulated. The data that was gathered was subsequently subjected to analysis utilising the SPSS and SMARTPLS tools. The subjects’ demographic information was analysed using SPSS 25 for descriptive statistics. Partial Least Squares Structural Equation Modelling (PLS-SEM) was employed to analyse and investigate the collected data. The main objective of this study was to assess the psychometric properties of the measuring instrument and to statistically examine the assumptions of the study model ( Hair et al., 2019a , b ). Given the innovative nature of this work in developing new structural pathways, the use of Partial Least Squares Structural Equation Modelling (PLS-SEM) was deemed appropriate. The measurement model was first verified, and afterwards, the structural model was validated using Smart-PLS version 4.0. The demographic information of the participants in the current study is shown elaborately in Table 1 . The demographic information included were gender, age, religion, community, stream of education and family type to ensure that students from different socio-demographic backgrounds were included in the study and to get comprehensive data that is not monotonous in nature.

Demographic classification of the participants.

The PLS technique requires a measurement model evaluation to identify inaccurate indicator variables before assessing the structural model, according to Hair et al. (2019a , b) . The reflecting measurement models’ composite reliability, Cronbach’s alpha, individual indicator reliability, commonality, and average variance extracted (AVE) are used to assess internal consistency, convergent validity, and commonality. The Fornell-Larcker criteria and HTMT tests assessed discriminant validity ( Hair et al., 2019a , b ).

Figure 2 shows the measurement model obtained using smart PLS. The inner model consists of the path coefficients and p values. The constructs consist of Cronbach’s alpha values. The outer model consists of the outer weights/loadings and p values.

Measurement model using SMARTPLS.

In the individual item level, the loadings of the indicators and their commonality surpassed the threshold values of 0.7 and 0.5, respectively. The outer loading of SOA6 did not significantly contribute to its respective construct; therefore, it was removed. Furthermore, all constructs demonstrated AVE values above 0.50, confirming convergent validity. The composite reliability values for all the constructs were above the threshold of 0.70, indicating adequate internal consistency ( Table 2 ).

Convergent validity of the measurement model.

Discriminant validity was assessed using the Fornell-Larcker criteria and the Heterotrait-Monotrait ratios of correlations. The Fornell-Larcker analysis reveals that the square root of the average variance extracted (AVE) for each construct surpasses the correlations with other constructs, as presented in Table 3 . All constructs exhibited discriminant validity.

Fornell-Larker criterion.

The HTMT values for all variables were found to be below the threshold of 0.85, providing evidence in support of discriminant validity (refer to Table 4 ) ( Voorhees et al., 2016 ).

Discriminant validity—HTMT.

Discriminant validity is evaluated and reported when the loading on a certain construct surpasses the loadings on other constructs while considering the cross-loading values. The values of the constructs for all indicators were found to be greater than the values of their corresponding cross-loadings (see Table 5 ). After ensuring the fulfilment of convergent and discriminant validity, the structural model was evaluated.

Cross loadings.

The cross loadings of items against their factors have been marked in bold.

The output of the structural model, obtained by the application of bootstrapping with 5,000 samples, is presented in Table 6 . The table displays seven hypotheses (H1, H2, H3, H4, H5, H6, H7) along with their corresponding T statistics and p values. The hypotheses H1, H2, H4, H5, and H7 have been accepted based on their p -values being less than 0.05. This suggests that there is a statistically significant relationship between self-awareness and social awareness ( β value = 0.117, p value = 0.026), self-awareness and social skills ( β value = 0.480, p value = 0), self-regulation and social skills ( β value = 0.392, p value = 0), motivation and social awareness ( β value = 0.618, p value = 0), and social awareness and social skills ( β value = 0.146, p value = 0.002). However, the H3 and H6 hypotheses are deemed invalid as their respective p values (0.084 and 0.869) are above the significance level of 0.05. This suggests that there is insufficient statistical evidence to support the existence of a relationship between the variables.

Path coefficients of the variables.

The hypothesis that have been rejected are marked in bold.

From the analysed data, it can be observed that self-awareness impacts social awareness. Though a similar study showed a negative correlation between the two ( Merlin and Prabakar, 2024 ), the current study supports the theory and previous study results ( Bratton et al., 2011 ). Self-awareness had a significant impact on social skills, showing the importance of self-awareness in the development of social skills among individuals. Self-awareness plays a pivotal role in one’s behaviour in the social setting. Self-awareness acts as a bridge to understanding others and how the social environment works and helps individuals to enhance their social self to receive better social support and build bonds. Self-regulation and Motivation negatively impacted the social skills of the individuals in the current study. This could be because self-regulation and motivation are skills that are focused on the self and do not necessarily contribute to the social self of a person. Previous studies support the result, showing that self-regulation did not impact the social skills of individuals ( Merlin and Prabakar, 2024 ). Further in-depth research needs to be done to fathom why these self-competences do not impact social competencies of emotional intelligence. Different perspectives on why the intrapersonal competencies did not contribute to interpersonal competence development must also be considered. Self-regulation and social awareness influenced the social skills of the students. Previous studies also support the current findings on the importance of self-regulation ( Pelco and Reed-Victor, 2007 ) and social awareness ( Eisenberg et al., 2010 ; Urrila and Mäkelä, 2024 ) for social skills. The results also highlight the importance of the development of self-awareness, self-regulation and motivation. It is possible that the students are low in these competencies. Hence, these competencies do not have an impact on the interpersonal competencies.

EI is increasingly recognised as an important factor in influencing the psychological well-being of students. It boosts students’ academic performance ( Bilimale et al., 2024 ; Swetha et al., 2023 ), interpersonal relationships ( Bechter et al., 2023 ; Parker et al., 2021 ) and overall mental health ( Xu and Choi, 2023 ; Augusto-Landa et al., 2024 ). Emotional Intelligence also serves as a crucial tool for managing stress ( Ullah et al., 2023 ; Cajachagua Castro et al., 2023 ), have resilience ( Sk and Halder, 2024 ; Nguyen et al., 2023 ; Hwang and Kim, 2023 ) which helps relieve from psychological distress ( Abualruz et al., 2024 ). Some of the practical implications to enhance both intrapersonal and interpersonal emotional intelligence are introducing emotional intelligence integrated curriculum design in the institutions instead of treating it as an extracurricular subject. Personalised emotional learning plans that can help support the development of EI skills and mindful technology use programs, which is a novel approach to leveraging EI for psychological well-being. Peer support networks can also be created to empower each other’s emotional and psychological well-being.

The study had certain limitations. The data collected and analysed are only based on the responses from the self-report questionnaire. Given the fact that the concept of the study is exploratory within the emotional intelligence domain, further investigations can be done with support from qualitative data or observational methods to comprehensively understand the relationship between the intrapersonal and interpersonal competencies of emotional intelligence.

The results from the present study show that certain intrapersonal competencies do not contribute to the development of interpersonal competence of emotional intelligence among students. Despite empirical evidence from various studies showing that interpersonal skills play a significant role in the development of interpersonal skills, the current study shows the negative impact of self-regulation on social awareness and motivation on social skills. This shows the need for further investigations on the dimensions of emotional intelligence used in the study. This could also be due to the overemphasis on the emotional competencies, that the other competencies are overlooked and not given importance. However, the results of the current study cannot be universally applicable because of the novelty of the findings. Further investigations should be done to check if similar results are obtained from the duplication of the concepts in the study.

Emotional intelligence has far-reaching implications for the psychological well-being of students. Learning to develop emotional intelligence not only enhances mental health but also provides the tools required for success in many spheres of life. Therefore, encouraging the psychological well-being of students and enabling them to realise their best potential depends on developing EI in learning environments.

Funding Statement

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Data availability statement

Ethics statement.

The studies involving humans were approved by Institutional Ethical Committee for Studies on Human Subjects (IECH) of Vellore Institute of Technology, Vellore. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

IM: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Writing – original draft. PS: Supervision, Writing – review & editing.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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• Open access
• Published: 15 October 2024

Estimation of burden of cancer incidence and mortality in India: based on global burden of disease study 1990–2021

• Diptismita Jena 1 , 2   na1 ,
• Bijaya K. Padhi 3   na1 ,
• Quazi Syed Zahiruddin 4   na1 ,
• Suhas Ballal 5 ,
• Sanjay Kumar 6 ,
• Mahakshit Bhat 7 ,
• Shilpa Sharma 8 ,
• M. Ravi Kumar 9 ,
• Sarvesh Rustagi 10 ,
• Abhay M. Gaidhane 11 ,
• Ashish Gaur 12 , 13 ,
• Sanjit Sah 14 , 15 , 16 &
• Prakasini Satapathy 17 , 18  

BMC Cancer volume  24 , Article number:  1278 ( 2024 ) Cite this article

Metrics details

Cancer poses a significant public health challenge in India, making it crucial to predict its future impact for effective healthcare planning. This study forecast cancer incidence, mortality, and disability-adjusted life years (DALYs) in India from 2022 to 2031.

We extracted age-standardized data on incidence, prevalence, DALYs, and mortality from 1990 to 2021 from the Global Burden of Disease (GBD) study. We used Decadal Average Percentage Change techniques to identify trends in cancer burden over decades and the Autoregressive Integrated Moving Average (ARIMA) method were used for forecasting. The ARIMA (2,2,2) model was identified as the best for predicting cancer incidence, ARIMA (0,3,3) for DALYs, and ARIMA (0,2,2) for mortality.

The cancer incidence rate is expected to rise from 529.40 (95% CI: 525.41-533.38) in 2022 to 549.17 (95% CI: 487.43-610.92) per 100,000 population in 2031. The DALYs rate is projected to decrease from 2001.53 (95% CI: 1964.24-2038.82) in 2022 to 1842.08 (95% CI: 1273.57-2410.60) per 100,000 population in 2031, indicating improvements in cancer burden management. Mortality rates are forecasted to increase slightly, from 71.52 (95% CI: 69.91–73.12) in 2022 to 73.00 (95% CI: 60.88–85.11) per 100,000 population in 2031. Overall, while incidence and mortality rates show a slight upward trend, the DALYs rate is projected to decrease, reflecting potential advancements in cancer management and treatment over the forecast period.

Conclusions

Over the next decade, cancer incidence and mortality are expected to increase in India, highlighting the need for enhanced prevention, early detection, and proper treatment strategies. Despite these increases, the anticipated decrease in DALYs suggests potential advancements in cancer management, warranting further investigation into the drivers of this positive trend and measures to sustain it.

Peer Review reports

Introduction

Cancer continues to be a leading cause of morbidity and mortality worldwide. Cancer presents a significant global health challenge with a projected surge in cases. In 2022, nearly 20 million people were detected 10 million succumbed to the diseas. This burden is expected to worsen, with estimates suggesting a 77% increase in new cases by 2050. Lung cancer currently reigns as the most common andmost frequently diagnosed cancer, followed by breast and colorectal cancers. These sobering statistics emphasize the urgent need for effective prevention, diagnosis, and treatment strategies to combat this growing global threat [ 1 ]. Recent years have seen significant changes in the global burden of cancer (GBD), with variations in incidence, mortality, and disability-adjusted life years (DALYs) across different regions and cancer types. A new GBD report 2021 reveals a worrying rise in global cancer burden, with incidence rates jumping 29% from 651.98 per 100,000 in 1990 to 842.43 in 2021, while mortality rates increased by 16% from 108.45 per 100,000 in 1990 to 125.31in 2021 [ 2 ].

Cancer, a formidable foe in the global health arena, casts a long shadow across nations. Millions are diagnosed annually, straining healthcare systems and societies. South Asia, a region experiencing rapid demographic shifts and lifestyle changes, faces a particularly concerning rise in cancer cases [ 3 ]. India, the most populous nation, this translates to a harsh reality – a growing cancer burden with projected increases in both incidence and mortality rates. This rise in India can be attributed to several factors such as rapid urbanization, an aging population, increasingly sedentary lifestyles, and unhealthy dietary choices. Additionally, exposure to both indoor and outdoor air pollution is a growing concern [ 4 ]. Population aging plays a significant role, as the risk of cancer increases with age [ 5 ]. Alcohol and smoking are the leading risk factors for laryngeal cancer (LC), with global trends indicating a decrease in age-standardized mortality rates but an increase in the absolute number of deaths, emphasizing the critical need for effective smoking control and alcohol consumption reduction strategies [ 6 ]. Additionally, changing lifestyles, including increased tobacco use, unhealthy diets, and lack of physical activity, contribute to the problem. However, a crucial caveat emerges – an estimated 80% of these projected cases are believed to be preventable. By focusing on modifiable risk factors like tobacco use, lifestyle changes and certain infections, India can potentially mitigate a significant portion of this future burden. This emphasizes the critical role of preventive strategies. Public health initiatives promoting healthy lifestyles, tobacco control measures, and vaccinations against cancer-causing viruses are crucial steps in combating this growing challenge [ 7 ].

Prior research has explored the national picture of cancer burden and its variations across India. These studies have also identified key areas for improving cancer control efforts in the country [ 8 , 9 , 10 ]. GBD data paints a concerning picture, with millions succumbing to this disease annually. The mortality rate in 1990 was 41.39 per 100,000 population and 60.44 per 100,000 in 2021. This represents a 46.02% increase in three decades. The incidence rate and DALYs rate of cancer increased by 34.94% and 22.48% between 1990 and 2021 respectively. A previous study reported a substantial increase in cancer burden in India between 1990 and 2021, emphasizing the importance of preventive and early detection strategies [ 11 ]. A significant shift has occurred in the leading cause of cancer death in India, according to the 2021 GBD report. Breast cancer has overtaken stomach cancer, which held the top spot in 1990. This highlights the changing landscape of cancer burden in the country. In India, breast cancer stands out as the most frequent cause of both new cancer diagnoses and cancer deaths among women. It was responsible for over 13.5% of all new female cancers and 10% of cancer deaths in women in 2020 [ 12 ].

This overwhelming burden has resulted in characterizations of India’s cancer situation as an epidemic or a tsunami [ 13 , 14 , 15 ]. Fighting cancer is a global priority, with the United Nation Sustainable Development Goal (SDG) aiming to reduce cancer deaths by a third by 2030. Data from the National Cancer Registry Programme (NCRP) paints a worrying picture, projecting a significant increase in total cancer cases in India. From nearly 1 million in 2010, the number is projected to surpass 1.1 million by 2020. India initiated its NCRP in 1982. Since then, the program has steadily grown, incorporating population-based cancer registries (PBCRs) in various urban centres and expanding to include some rural areas. Effective cancer control in India requires a multipronged approach that includes enhancing healthcare infrastructure, promoting education and awareness about cancer prevention, and implementing state-specific cancer control programs [ 16 ].

Extensive research has been carried out to measure the scope of the cancer problem in India [ 8 , 16 , 17 , 18 , 19 ]. The GBD study’s metrics offer a comprehensive overview of cancer’s impact, facilitating a better understanding of its future trajectory and aiding in the formulation of targeted interventions. A confluence of factors is driving the rise in cancer cases worldwide, particularly in middle- and low-income countries like India. Thus, understanding the current landscape through the GBD study’s metrics is crucial for policymakers, healthcare providers, and researchers to develop effective strategies aimed at mitigating the future impact of cancer in India. The use of statistical modelling approaches such as the Autoregressive Integrated Moving Average (ARIMA) models has become increasingly prevalent in epidemiological studies for forecasting disease trends. ARIMA models are particularly useful due to their ability to handle time series data with trends and seasonality, making them ideal for predicting cancer incidence and mortality rates. These models help in understanding the past and current trends and provide projections that can guide policy-making and resource allocation in the healthcare sector [ 20 ]. Extensive utilization of ARIMA models in cancer studies, including breast cancer and oral cancer, underscores their effectiveness in capturing the complexities of disease progression, making them ideal for projecting future cancer incidence and mortality rates [ 21 , 22 ].In the context of cancer forecasting, ARIMA models have been employed to predict future trends based on historical data. This approach considers the autoregressive nature of cancer incidence and mortality, integrates the differences in the data to stabilize the mean, and uses moving averages to smooth out short-term fluctuations [ 21 , 23 ].

This study delves into the multifaceted challenge of cancer in India by analysing trends, decadal changes, gender disparities, and spatial variations. By incorporating future predictions for cancer incidence, DALYs and mortality, we aim to provide a nuanced understanding of this public health concern. This knowledge will be instrumental in informing targeted interventions and resource allocation strategies to not only mitigate the rising cancer burden but also improve health outcomes across the nation. Therefore, leveraging advanced modelling techniques to project cancer trends is a vital component of public health planning in India.

Data sources

This study utilized age-standardized data on cancer incidence, prevalence, DALYs, and mortality for India from the GBD study for the period 1990 to 2021 ( https://vizhub.healthdata.org/gbd-results/ ) [ 2 ]. This analysis utilizes cancer data estimates provided by the Institute for Health Metrics and Evaluation (IHME) in collaboration with the Indian Council of Medical Research (ICMR) and the Public Health Foundation of India (PHFI). High-quality data on cancer incidence and survival are collected by Population-Based Cancer Registries (PBCRs), which cover various geographic regions and time periods. Hospital discharge records provide information on cancer diagnosis, treatment, and outcomes, particularly in countries with extensive healthcare systems and electronic health records. Autopsy reports contribute data on cause of death, particularly in regions where other data sources may be limited. Through a systematic and transparent statistical modelling process, the GBD study generates estimates that account for variations in data quality and availability across different locations. This modelling process involves the use of advanced statistical techniques to integrate and harmonize data from disparate sources, ensuring robust and comparable estimates of cancer burden.

The data and estimates generated by the GBD study are essential for understanding the burden of cancer in India and for informing public health policies and cancer control strategies. By leveraging the comprehensive data and methodological rigor of the GBD study, this research provides a detailed and accurate assessment of cancer trends in India over the past three decades.

Statistical analysis

To elucidate the evolving landscape of cancer burden in India, this study adopted a two-pronged analytical approach utilizing R software (version 4.0.1). First, we investigated historical trends (decadal) in cancer incidence, prevalence, DALYs and mortality across various demographics. This analysis provided insights into the past patterns of cancer burden within different population subgroups. Second, we leveraged time series models to forecast these indicators for the next decade. This approach aligns with the widely used time series forecasting methodology, which meticulously analyses past observations to construct a model that captures the underlying structure of the data. Similar to how the ARIMA model is a popular choice for stochastic time series analysis, this study utilizes time series models to predict the future trajectory of cancer burden in India. This forecasting component allows us to anticipate potential changes in cancer burden and inform future resource allocation, preventative measures, and healthcare infrastructure development.

The ARIMA model emerges as a powerful tool for analyzing cancer burden data due to its ability to capture various aspects of time series. Unlike simpler models, ARIMA accounts for changing trends, seasonal fluctuations, and random variations within the data. This makes it suitable for forecasting cancer incidence, prevalence, and mortality, which can exhibit complex patterns over time [ 23 ]. This initial stage involves exploring different ARIMA configurations (p, d, q) based on statistical tests and visual inspection of data characteristics The ARIMA model, denoted as ARIMA (p, d, q), captures the influence of past observations (p), removes trends through differencing (d), and accounts for random errors with a moving average term (q). These parameters (p, d, q) are determined by analyzing the data’s autocorrelation and partial autocorrelation patterns to identify significant lags. The model is then fine-tuned by estimating the optimal values for p, d, and q. Finally, the model’s adequacy is ensured by verifying its residuals resemble random noise, a characteristic of a good fit. Once this rigorous process is complete, the chosen ARIMA model with its estimated parameters can be used to forecast future cancer burden trends.

The ARIMA model itself is a combination of two sub-models: the Autoregressive (AR) model and the Moving Average (MA) model. The integration step (denoted by d) removes non-stationarity, a common challenge in time series data. This allows for more accurate forecasting. While the specifics of AR and MA models involve mathematical formulas, the key takeaway is that ARIMA effectively captures trends and variations within cancer burden data, enabling researchers to predict its future trajectory. The general formula of AR ( p ) and MA (q ) models can be expressed in Eqs. ( 1 ) and ( 2 ), respectively.

An autoregressive AR(p) model of order p can be written as:

Where \(\:c\) is a constant, \(\:{e}_{t}\) is a white noise \({e_t} \sim N(0,{\sigma ^2}),\) \(\:\varphi\:=({\varphi\:}_{1},\:\:{\varphi\:}_{2},\dots\:,{\varphi\:}_{p})\) is the vector of model coefficients & p is a non-negative integer.

A moving average MA(q) model of order q uses past forecast errors in a regression model as:

Where \(\:c\) is a constant, \(\:{e}_{t}\) is a white noise \({e_t} \sim N(0,{\sigma ^2}),\) , \(\:\varphi\:=({\theta\:}_{1},\:\:{\theta\:}_{2},\dots\:,{\theta\:}_{q})\) is the vector of model coefficients & p is a non-negative integer.

ARIMA (p, q) model can be written as:

ARIMA (p, d, q) model can be written as:

Where, p autoregressive terms, d is the non-seasonal differences, q is the number of lagged forecast errors.

To identify the most optimal model for forecasting, we employ two key criteria: Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The model with the lowest AIC and BIC values is considered the most suitable for predicting future trends in cancer burden data.

Where \(\:k(=p+q+1)\) is the number of parameters in the statistical model and \(\:L\) is the maximizes value of the likelihood function for the estimated model.

To ensure the data’s suitability for time series analysis of cancer burden, we conducted the Dickey-Fuller test for stationarity. This test revealed that stationarity was achieved only after differencing the data a specific number of times (indicated by the differencing order). Subsequently, we proceeded with ARIMA model application. To evaluate the model’s forecasting accuracy, we employed established metrics like Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE).

To understand the historical trends in cancer burden, we first present data from the GBD study for the period 1990–2021.

Based on the data in the Table  1 it is possible to draw some conclusions about changes in deaths and DALYs in India between two decades from 1990 to 2021. The table stratifies the data by gender, age groups, and specific risk factors. Females generally fared better than males in terms of deaths and DALYs. From 1990 to 2000, deaths for females decreased slightly (-0.06%) and DALYs also decreased (-0.07%). In contrast, males males saw no change in deaths (0.00%) but a small decrease in DALYs (-0.01%). The data suggests an association between age and mortality rates. The youngest age group (0–14 years) showed a significant decrease in both deaths and DALYs (-0.27%) from 2000 to 2010. Similarly, the50–74 age group experienced a decline in both deaths and DALYs (-0.06%) during this decade. However, the trend reversed for the elderly population (75 + years) who witnessed an increase in deaths (0.16%) and DALYs (0.17%). In the decade from 2010 to 2021, the 85 + age group experienced an increase in deaths by 0.21% and in DALYs by 0.22%. The analysis of specific risk factors like tobacco use displayed a decrease in deaths (-0.02%) and DALYs (-0.04%) suggesting potential improvements in tobacco control efforts in the decade 2010 to 2021. Conversely, metabolic risks emerged as a growing concern, with an increase in deaths (0.39%) and DALYs (0.38%). Other environmental risks remained relatively constant, with a minimal increase in deaths (0.02%) 2000 to 2010, followed by an increase of 0.15% from 2010 to 2021 and a slight rise in DALYs from 0.01% to (0.02%) over the decade from 2010 to 2021.

Overall, the data highlights subtle changes in deaths and DALYs across different demographics and risk factors over two decades between 1990 and 2021. From 2010 to 2021, a positive trend was observed in the reduction of both deaths and DALYs among younger age groups and individuals impacted by tobacco use. However, the data also suggest s a need to address the growing concern of metabolic risks and the increasing burden of deaths and DALYs among the elderly population.

Table  2 highlights the percentage changes in prevalence and incidence rates across different states and union territories (UTs) in India over three decades: 1992–2001, 2002–2011, and 2012–2021. From the first to the last two decades, Arunachal Pradesh’s cancer prevalence rates increased by 10.77% and 20.05%, while Bihar saw rises of 7.73% and 19.04%. Delhi and Kerala experienced significant increases, with Delhi’s prevalence growing by 19.62% and 28.17%, and Kerala’s by 21.57% and 31.89%. Other notable increases include Gujarat (16.02% and 25.47%), Maharashtra (13.56% and 26.13%), and West Bengal (15.43% and 26.33%). Overall, the data indicates a substantial rise in cancer prevalence across various Indian states over the past two decades. Kerala had the highest percentage change in prevalence rates at 31.89% in the last two decades, while Bihar had the lowest at 7.73% in the first two decades. Similarly, over the past two decades, cancer incidence rates have notably increased across Indian states. Kerala saw the highest rise at 14.30% and 19.58%, while Bihar experienced the lowest increase at 5.54% and 13.12%. Other significant increases include Delhi (13.89% and 19.07%), Gujarat (11.53% and 17.10%), and Maharashtra (9.72% and 17.40%). Overall, both prevalence and incidence rates have generally increased across the states and UTs over the three decades. The percentage changes indicate notable increases, particularly in the last two decades, suggesting a growing public health challenge.

Decadal changes in cancer burden incidence rate (incidence, prevalence, DALYs, and mortality) across Indian states (1990–2021) for both ( a ), male ( b ) and female ( c )

Figure  1 illustrates the decadal changes in cancer burden (incidence, prevalence, DALYs, and mortality) across Indian states for both sexes from 1990 to 2021. The data reveals a moderate increase in cancer prevalence and incidence rates from 1990 to 2000, followed by a significant rise in incidence and DALYs from 2000 to 2010, with a slight decline in mortality. From 2010 to 2021, all parameters showed moderate increases, with a notable rise in mortality rates, suggesting an overall growing cancer burden over the last three decades. Specifically, for males, minimal increases were observed from 1990 to 2000, substantial rises in incidence and DALYs from 2000 to 2010, and a moderate increase in prevalence and DALYs from 2010 to 2021, with incidence rates stabilizing and mortality rates rising. For females, the first decade exhibited stable prevalence and minimal changes in other parameters, significant increases in incidence and DALYs from 2000 to 2010, and a moderate rise in all parameters from 2010 to 2021, with notable increases in DALYs and mortality rates. This overall trend indicates that while cancer incidence has risen significantly, the impact on DALYs and mortality has become more pronounced in recent years, reflecting a growing cancer burden in India.

Cancer prevalence rate male (left) and female (right) in India for the year 2021

Cancer prevalence in India exhibits marked disparities between genders and regions. As depicted in Fig.  2 , males and females display distinct geographical patterns of cancer prevalence rate in 2021. The maps reveal regional disparities in cancer prevalence between genders. For males, the prevalence rates range from 651 to 908 per 100,000 population, with higher prevalence observed in states like Karnataka, Tamil Nadu, and West Bengal. Conversely, for females, prevalence rates are significantly higher, ranging from 1317 to 1898 per 100,000 population, with the highest rates found in states such as Karnataka, Tamil Nadu, and Uttar Pradesh. The data indicates that females have a higher cancer prevalence rate compared to males across most regions.

Cancer incidence rate male (left) and female (right) in India for the year 2021

Figure  3 displays the geographic distribution of cancer incidence rates in India for the year 2021. The figure presents data for both males (left side) and females (right side), allowing for a gender-based comparison of cancer prevalence across Indian states. The maps indicate that for males, the incidence rates range from 358 to 495 per 100,000 population, with higher incidence rates observed in states such as Karnataka, Andhra Pradesh, and West Bengal. For females, the incidence rates are higher, ranging from 628 to 762 per 100,000 population, with the highest rates found in Karnataka, Tamil Nadu, and Uttar Pradesh. These maps highlight regional variations in cancer incidence, with certain states exhibiting higher rates for both genders. Notably, females exhibit significantly higher incidence rates compared to males across most regions.

Cancer DALYs rate male (left) and female (right) in India for the year 2021

Figure  4 paints a concerning picture of the geographical distribution of cancer burden in India for the year 2021. The maps, divided by gender, reveal significant regional variations in DALYs caused by cancer. The maps show considerable regional variations in DALYs, reflecting the overall burden of cancer. For males, the DALYs rates range from 1193 to 4679 per 100,000 population, with the highest rates observed in states like Uttar Pradesh, Arunachal Pradesh, and West Bengal. For females, DALYs rates range from 1297 to 3022 per 100,000 population, with the highest rates in Uttar Pradesh, Madhya Pradesh, and Arunachal Pradesh. The maps highlight those certain states, particularly in the northern and North-eastern regions, experience a higher cancer burden for both genders. Additionally, while the overall range of DALYs is higher for males, certain states show comparable or even higher rates for females, indicating significant gender-specific impacts of cancer in these regions.

Cancer mortality rate male (left) and female (right) in India for the year 2021

Figure  5 maps the stark regional variations in cancer mortality rates across Indian states for 2021. The data is presented for both males (left) and females (right), enabling a gender-specific analysis. For males, mortality rates range from 47 to 176 per 100,000 population, with states like Arunachal Pradesh, Assam, and Uttar Pradesh showing the highest burden. Females exhibit a similar trend, with rates varying between 45 and 113 per 100,000 population and the highest mortality concentrated in Arunachal Pradesh, Assam, and Uttar Pradesh. This concerning geographic pattern highlights the North-eastern states and Uttar Pradesh as areas with a significantly higher cancer mortality burden for both genders. While males generally experience higher mortality rates, the consistency in high-mortality regions across genders underscores critical public health challenges related to cancer mortality in these parts of India.

Trend of Prevalence, incidence, DALYs and mortality Rate in India from the year 1990 to 2021

Figure  6 takes a deep dive into in prevalence, incidence, DALYs, and mortality rates of cancer trends in India from 1990 to 2021, disaggregated by total population, males, and females. The prevalence and incidence rates show a gradual increase over the years, with a marked rise starting around 2007, particularly among females, who exhibit higher rates compared to males and the total population. DALYs rates, which reflect the overall burden of cancer, show a slight decrease from 1990 to around 2005, followed by a gradual increase, stabilizing after 2010. Mortality rates remain relatively stable across the years for all groups, with a slight increase observed after 2010. Notably, females consistently exhibit higher prevalence and incidence rates, while DALYs and mortality rates show less pronounced gender differences. This data indicates an increasing cancer burden in India over the past three decades, with significant gender-specific variations, particularly in prevalence and incidence. The rising cancer burden in Fig.  6 highlights the need for time series models like ARIMA with differencing to identify underlying patterns for better forecasting and resource allocation.

Second order difference of incidence rate ( a ), third order difference of DALYs ( b ) and second order difference of mortality rate ( c ) of cancer in India

As shown in Fig.  7 , we implemented a differencing process. This technique involves subtracting a previous value in the time series from the current value The incidence rate of cancer also underwent examination for stationarity. The Dickey-Fuller test results indicated a potential lack of stationarity with a test statistic (t = -3.6) and a p-value of 0.04 (significant at the 5% level). To address this, we incorporated a 2-period lag (lag order = 2) in the data. The analysis of the DALYs the effectiveness of this approach is confirmed by the Dickey-Fuller test. After applying the 3rd differencing, the test statistic (t = -4.74) is significant at a 5% level with a p-value of 0.01. Similar to the DALYs rate, the mortality rate required adjustments for stationarity. The Dickey-Fuller test statistic (t = -3.59) with non-stationarity (p-value = 0.04). We implemented a 2-period lag to account for past values, achieving stationarity for ARIMA modelling.

By implementing these differencing approaches, we ensured that the cancer incidence, mortality, and DALYs data became stationary, satisfying a key assumption for statistical analysis like ARIMA modelling. Stationary data ensures that the mean and variance remain constant over time, allowing for more reliable modelling and forecasting.

ACF and PACF plot of cancer incidence ( a ), DALYs ( b ) and mortality rate ( c ) in India

Informed by the Autocorrelation function (ACF) and Partial Autocorrelation Function (PACF) plots in Fig.  8 , a battery of ARIMA (p, d, q) models were evaluated to identify the optimal fit for cancer incidence, DALYs, and mortality rates. Cancer incidence data was best captured by ARIMA (2,2,2). This selection likely reflects the model’s ability to account for both short-term dependencies (order 2 autoregressive) and longer-term influences (order 2 moving average) inherent in the incidence time series. Conversely, DALYs (ARIMA (0,3,3)) exhibited minimal autoregressive patterns (order 0), suggesting past values have a weaker influence on future DALYs. The higher-order moving average term (order 3) in the DALY model implies that past shocks or innovations may have a more persistent effect on future DALY values. Mortality rates (ARIMA (0,2,2)) also displayed minimal autoregressive patterns (order 0). The order 2 moving average term in the mortality model suggests that past trends or fluctuations in mortality rates may have a lasting impact on future values. The final selection of these specific ARIMA models likely involved rigorous comparison of statistical criteria, such as AIC and BIC, across all candidate models. The chosen models presumably demonstrated the lowest values for these criteria, signifying a superior fit to their respective cancer data series.

Table  4 presents the AIC and BIC values for different ARIMA models suggested for cancer incidence, DALYs, and mortality rates in India. The AIC and BIC values help determine the best-fitting model, with lower values indicating a better fit. For cancer incidence, the ARIMA (2,2,2) model has the lowest AIC value of 139.80 and BIC value of 146.46, indicating it is the best model among those considered. For DALYs, the ARIMA (0,3,3) model shows the lowest AIC value of 266 and BIC value of 271.18, making it the most suitable model. Similarly, for mortality rates, the ARIMA (0,2,2) model has the lowest AIC value of 91.96 and BIC value of 95.95, suggesting it is the best fit for the data. The Box-Pierce tests provided positive results for all three cancer rate models. The incidence rate (ARIMA (2,2,2)) showed a strong fit (χ² = 0.06, p-value = 0.79), while the mortality rate (ARIMA (0,2,2)) also indicated good fit (χ² = 1.70, p-value = 0.19). The DALY rate (ARIMA (0,3,3)) test result (χ² = 2.60, p-value = 0.10) was inconclusive, requiring further investigation to ensure the model’s suitability.

Plot for residual and p-value of Ljung-Box statistics of best fitted ARIMA models of cancer incidence ARIMA (2,2,2) ( a ), DALYs ARIMA (0,3,3) ( b ) and mortality rate ARIMA (0,2,2) ( c )

From Fig.  9 we assessed the adequacy of the chosen ARIMA models (incidence rate: ARIMA (2,2,2), DALY rate: ARIMA (0,3,3), and mortality rate: ARIMA (0,2,2)) by examining the residuals’ properties. This figure displays plots for standardized residuals, the autocorrelation function (ACF) of residuals, and the p-value of the Ljung-Box statistic [ 20 ]. Ideally, these plots and the p-value should not indicate any significant patterns in the residuals. Table  5 complements this analysis by presenting the estimated parameters of each model, along with their corresponding residuals and significance levels.

Table  6 provides a forecast of cancer incidence rates, DALYs rates, and mortality rates in India from 2022 to 2031, using ARIMA models with 95% confidence intervals. For the incidence rate, which follows the ARIMA (2,2,2) model, there is a steady increase from 529.40 with 95% CI (525.41-533.38) in 2022 to 549.17 with 95% CI (487.43-610.92) in 2031, widening over time. The DALYs rate, modelled by ARIMA (0,3,3), shows a decreasing trend from 2001.53 with 95% CI (1964.24-2038.82) in 2022 to 1842.08 with 95% CI (1273.57-2410.60) in 2031, suggesting an improvement in the overall cancer burden, although the confidence intervals remain substantial. The mortality rate, forecasted with the ARIMA (0,2,2) model, exhibits a slight increase from 71.52 with 95% CI (69.91–73.12) in 2022 to 73.00 with 95% CI (60.88–85.11) in 2031, indicating increasing uncertainty over time. Overall, while incidence and mortality rates show a slight upward trend, the DALYs rate is projected to decrease, reflecting potential advancements in cancer management and treatment over the forecast period.

By analysing this table, we can see the predicted trajectory of each cancer measure over the next decade. The increasing values for incidence and mortality would be reflected by rising figures, while the decreasing DALYs would be represented by falling values. It’s important to remember that the confidence interval provides a margin of error for these forecasts, indicating the potential range of actual outcomes.

Forecasted plot of the cancer incidence ( a ), DALYs ( b ) and mortality rate ( c ) for India with 95% and 80% CI

Figure  10 , represents forecast for cancer in India between 2022 and 2031 paints a concerning picture for incidence and mortality rates. The ARIMA models predict an upward trend in both these areas, suggesting a potential increase in cancer cases and deaths over the next decade.

However, there is a glimmer of hope in the forecast for DALYs. The DALYs model suggests a decline, which could be due to factors like improved treatment and early detection leading to better management of the disease and potentially lower long-term impact. It’s important to note that these are forecasts based on statistical models, and actual trends may vary depending on advancements in cancer prevention, screening, and treatment.

The study reveals a growing cancer burden in India from 1990 to 2021, with significant increases in incidence, DALYs, and mortality, particularly from 2000 to 2021, with males experiencing substantial rises in incidence and DALYs from 2000 to 2010 and moderate increases thereafter, while females showed stable prevalence initially, followed by significant increases in incidence and DALYs from 2000 to 2010 and moderate rises in all parameters from 2010 to 2021.

Cancer looms large as a public health threat in India. A deep dive into three decades of data from the GBD (1990–2021) reveals concerning trends. We analysed the overall cancer burden, focusing on gender and regional disparities. Males experienced substantial rises in incidence and DALYs from 2000 to 2010 and moderate increases thereafter, while females showed stable prevalence initially, followed by significant increases in incidence and DALYs from 2000 to 2010 and moderate rises in all parameters from 2010 to 2021. Our findings revealed concerning significant regional variations exist in cancer prevalence, incidence, burden (DALYs), and mortality across India. Some states consistently exhibit higher rates for both genders. Females generally have higher prevalence and incidence rates compared to males. However, the overall burden (DALYs) can be comparable or even higher for females in specific regions. The North-eastern states and Uttar Pradesh emerge as areas with a considerably higher burden of cancer for both genders, as evidenced by prevalence, incidence, DALYs, and mortality data. Although males typically have higher cancer mortality rates, females experience a significant cancer burden in some regions. Using the ARIMA model we forecast the cancer burden in India, for next decade 2022–2031 and we identified the best fit models based on minimum AIC and BIC criteria. For the incidence rate, which follows the ARIMA (2,2,2) model, there is a steady increase from 529.40 in 2022 to 549.17 in 2031, with confidence intervals widening over time. The DALYs rate, modelled by ARIMA (0,3,3), shows a decreasing trend from 2001.53 in 2022 to 1842.08 in 2031, suggesting an improvement in the overall cancer burden, although the confidence intervals remain substantial. The mortality rate, forecasted with the ARIMA (0,2,2) model, exhibits a slight increase from 71.52 in 2022 to 73.00 in 2031, with a widening confidence interval indicating increasing uncertainty over time. Overall, while incidence and mortality rates show a slight upward trend, the DALYs rate is projected to decrease, reflecting potential advancements in cancer management and treatment over the forecast period.

Our analysis, utilizing data from the GBD (1990–2021), projects a significant increase in cancer incidence and mortality rates in India. Other Studies indicate a concerning rise in cancer incidence across India, with projections estimating a jump from 1.45 million cases in 2016 to 1.75 million by 2020. Importantly, around 70% of these cases are potentially preventable through lifestyle changes and addressing modifiable risk factors like tobacco use and infections. This emphasizes the critical need for prioritizing preventive strategies to effectively manage this growing public health burden [ 15 ]. Using data from NCRP and other sources, this study projected India’s cancer burden to rise from 26.7 million DALYs in 2021 to 29.8 million by 2025, with the highest burden in northern and North-eastern regions. Employing the negative binomial regression model, it identified lung, breast, and oesophagus cancers as major contributors to the burden. Using Linear Regression, NCRP (ICMR) projected that India’s total cancer cases will rise from 979,786 in 2010 to 1,148,757 in 2020, with significant increases in tobacco-related, digestive system, head and neck, lymphoid, hematopoietic, and gynaecological cancers. Breast cancer alone is expected to surpass 100,000 cases by 2020 [ 10 ].

While our study projects a concerning rise in cancer cases, other studies the projected increase in total DALYs due to breast cancer in India from 2016 to 2026 underscores the urgent need for effective primary and secondary prevention measures [ 24 , 25 ]. Colorectal cancer (CRC) incidence and mortality in China significantly increased from 1990 to 2019, with males experiencing a higher burden than females. Predictions indicate this upward trend will continue over the next decade [ 26 ]. The study developed an ARIMA (2,1,0) model using Box-Jenkins methodology to accurately forecast cancer case admissions in Kenya, showing an increasing trend in incidents from 2015 to 16 onwards, aiding health facilities in decision-making [ 23 ]. Pancreatic cancer incidence and death rates in China have risen significantly from 1990 to 2019 and are projected to continue increasing through 2029, according to ARIMA model predictions. Preventive measures are necessary to address this growing disease burden [ 27 ]. Breast cancer incidence in Taiwan has doubled from 1997 to 2016 and is projected to plateau by 2031 using age-period-cohort models. The majority of future cases will involve women over 55 ages, highlighting the need for targeted prevention and screening [ 28 ].

Advancements in treatment and early detection likely play a role in the potential decline of the disease’s long-term impact, underlining their importance in mitigating India’s growing cancer burden. Despite the forecasted increase in cancer incidence and mortality over the next decade (2022–2031), addressing gender, age differential and regional disparities, combined with continued improvements in treatment and early detection, can help India navigate a brighter future in the fight against cancer. Understanding these scientific aspects of cancer forecasts allows India to develop effective strategies to combat these growing health challenges. Research on cancer genomics specific to the Indian population can further refine forecasts and guide targeted interventions. Public health initiatives promoting healthy lifestyles and early detection can potentially mitigate the projected rise in cases.

Consequently, by neglecting to major of risk factors, cancer types, and age groups, the study fails to pinpoint specific areas for targeted public health initiatives in India. Future research incorporating more granular, primary data from specific Indian regions would be valuable to refine our understanding of the nuanced variations in cancer burden across this diverse population.

Cancer poses a significant public health burden in India. This study provides the complex challenge of cancer in India by examining trends, decadal shifts, gender disparities, and regional variations. By incorporating future forecasts for cancer incidence, DALYs, and mortality for the period 2022–2031, it provides a comprehensive understanding of the projected cancer burden and the need for effective interventions. This suggests that while new cases and deaths are expected to rise, improvements in treatment and early detection might reduce the long-term impact of cancer, reflected in the decreasing DALY rates. The findings underscore the pressing need for enhanced preventive measures, early detection, and improved treatment strategies to effectively manage and mitigate the growing cancer burden in India. However, a potential decline in the long-term impact, as suggested by the DALYs rate, could be attributed to improved treatment and early detection strategies. These advancements not only enhance patient outcomes but also contribute significantly to achieving India’s SDG 3: Ensure healthy lives and promote well-being for all ages. Continued efforts in prevention, early diagnosis, and enhanced treatment strategies are crucial not only to manage and mitigate the growing cancer burden but also to ensure India reaches its SDG target for cancer control.

Data availability

This study utilized age-standardized data on cancer incidence, prevalence, DALYs, and mortality for India from the GBD study for the period 1990 to 2021. The dataset is available in the public domain for download: https://vizhub.healthdata.org/gbd-results/.

Abbreviations

Autocorrelation function

Akaike Information Criterion

Autoregressive

Autoregressive Integrated Moving Average

Bayesian Information Criterion

Confidence Interval

• Disability-adjusted life years

Global Burden of Disease

Indian Council of Medical Research

Institute for Health Metrics and Evaluation

Laryngeal cancer

Moving Average

Mean Absolute Error

Mean Absolute Percent Error

Mean Absolute Percentage Error

Mean Absolute Scaled Error

National Cancer Registry Programme

Partial Autocorrelation Function

Population-based cancer registries

Public Health Foundation of India

Root Mean Square Error

Root Mean Squared Error

Sustainable Development Goal

Union territories

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Diptismita Jena, Bijaya K Padhi and Quazi Syed Zahiruddin contributed equally to this work.

Authors and Affiliations

Center for Global Health Research, Saveetha Institute of Medical and Technical Sciences, Saveetha Medical College and Hospital, Saveetha University, Chennai, India

Diptismita Jena

Global Center for Evidence Synthesis, Chandigarh, India

Department of Community Medicine, School of Public Health, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India

Bijaya K. Padhi

South Asia Infant Feeding Research Network, Division of Evidence Synthesis, Global Consortium of Public Health and Research, Datta Meghe Institute of Higher Education, Wardha, India

Quazi Syed Zahiruddin

Department of Chemistry and Biochemistry, School of Sciences, JAIN (Deemed to be University), Bangalore, Karnataka, India

Suhas Ballal

Department of Allied Healthcare and Sciences, Vivekananda Global University, Jaipur, Rajasthan, 303012, India

Sanjay Kumar

Department of Medicine, National Institute of Medical Sciences, NIMS University Rajasthan, Jaipur, India

Mahakshit Bhat

Chandigarh Pharmacy College, Chandigarh Group of Colleges-Jhanjeri, Mohali, Punjab, 140307, India

Shilpa Sharma

Department of Chemistry, Raghu Engineering College, Visakhapatnam, Andhra Pradesh, 531162, India

M. Ravi Kumar

School of Applied and Life Sciences, Uttaranchal University, Dehradun, Uttarakhand, India

Sarvesh Rustagi

Global Health Academy, School of Epidemiology and Public Health, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education, Wardha, India

Abhay M. Gaidhane

Department of Biotechnology, Graphic Era, Deemed to be University, Clement Town Dehradun, Dehradun, 248002, India

Ashish Gaur

Department of Allied Sciences, Graphic Era Hill University Clement Town Dehradun, Dehradun, 248002, India

SR Sanjeevani Hospital, Kalyanpur, Siraha, 56517, Nepal

Department of Paediatrics, Hospital and Research Centre, Dr. D. Y. Patil Medical College, Dr. D. Y. Patil Vidyapeeth, Pune, Maharashtra, 411018, India

Department of Public Health Dentistry, Dr. D.Y. Patil Dental College and Hospital, Dr. D.Y. Patil Vidyapeeth, Pune, Maharashtra, 411018, India

University Center for Research and Development, Chandigarh University, Mohali, Punjab, 140413, India

Prakasini Satapathy

Medical Laboratories Techniques Department, AL-Mustaqbal University, Hillah, Babil, 51001, Iraq

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Conceptualization: BKP, DJ, SASDesigned the study: BKP, DJ, QSZ, SBFormal analysis: DJ, SK, MBMethodology: DJ, BKP, SHS, MRKData extraction: SR, AMGSoftware: DJ Supervision: SAS, PS, BKPValidation: BKP, QSZVisualization: AG, MRK, SRWriting—original draft: DJ, BKP, SAS, PSWriting—review & editing: PS, BKP, SR, AMG, AG, SB, SK, MB, SHSAll authors revised and approved the final manuscript.

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Jena, D., Padhi, B.K., Zahiruddin, Q.S. et al. Estimation of burden of cancer incidence and mortality in India: based on global burden of disease study 1990–2021. BMC Cancer 24 , 1278 (2024). https://doi.org/10.1186/s12885-024-13035-6

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DOI : https://doi.org/10.1186/s12885-024-13035-6

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