Journal of Mental Health and Human Behaviour

REVIEW ARTICLE
Year : 2021  |  Volume : 26  |  Issue : 1  |  Page : 5--16

Prevalence and diagnostic tools predictability of common mental disorders among Indian children and adolescent population: A systematic review and meta-analysis

G Radhika1, R Sankar1, R Rajendran2,  
1 Department of Psychology, Annamalai University, Chidambaram, Tamil Nadu, India
2 Department of Educational Management with Applied Psychology with Centre for Educational Management and Applied Psychology, National Institute of Technical Teachers Training and Research (NITTR), Chennai, Tamil Nadu, India

Correspondence Address:
G Radhika
Department of Psychology, Annamalai University, Annamalainagar, Chidambaram - 608 002, Tamil Nadu
India

Abstract

The objective of this systematic review was to examine the pooled prevalence of common mental disorder (CMD) and to evaluate the predictability of screening instruments to detect CMD in the children and adolescent population in India. Data sources included the MEDLINE, PubMed, PyschEXTRA, and PyschINFO up to 2020, with additional studies identified from a search of reference lists to examine the diagnostic utility of tools carried out in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analyses guidelines, PRISMA within parentheses after the Preferred Reporting Items for Systematic Review and Meta-Analyses guidelines (PRISMA). Only studies involving children and adolescents with an independent measure of depression and anxiety in India were included. Random effects meta-analyses were employed to calculate a pooled estimate of depression prevalence. Twenty studies met all inclusion and exclusion criteria for the systematic review. The analysis showed that several tools were used in different regions of the nation to measure CMD such as the beck depression inventory (BDI), Children's Depression Rating Scale-Revised (CDRS-R), and Depression Anxiety Stress Scale. The pooled prevalence of depression was 19% (95% confidence interval (CI) = 12.57–27.12), 15% (95% CI = 4.67–30.90) for anxiety, and 11% (95% CI = 4.37–19.77) for any depressive disorder. In terms of BDI had the highest sensitivity (61%) while CDRS-R had the highest specificity (75%). Given the high heterogeneity of the studies, there is insufficient evidence that any tool accurately screens for CMD and likely to underestimate the true prevalence.


How to cite this article:
Radhika G, Sankar R, Rajendran R. Prevalence and diagnostic tools predictability of common mental disorders among Indian children and adolescent population: A systematic review and meta-analysis.J Mental Health Hum Behav 2021;26:5-16


How to cite this URL:
Radhika G, Sankar R, Rajendran R. Prevalence and diagnostic tools predictability of common mental disorders among Indian children and adolescent population: A systematic review and meta-analysis. J Mental Health Hum Behav [serial online] 2021 [cited 2023 Jun 9 ];26:5-16
Available from: https://www.jmhhb.org/text.asp?2021/26/1/5/322814


Full Text



 Background



According to the World Health Organization (WHO),[1] “mental health” is defined as “a state of well-being in which every individual realizes his or her own potential, can cope with the normal stresses of life, can work productively and fruitfully, and is able to make a contribution to her or his community” (p. 1). This also means mental health is the state of complete mental and social well-being and is not a mere absence of “infirmity or disease” (p. 1).[1] However, there is an increasingly alarming rate of common mental disorder (CMD) cases all around the world with >350 million people affected by mental disorders,[2] wherein anxiety and depression disorder are deemed to be the most CMDs.[3] CMDs is referred as a disorder of mental condition meeting nosology conditions of the Diagnostic and Statistical Manual (DSM) of Mental Disorders and International Classification of Diseases-tenth (ICD-10) revision for the maximum prevailing conditions such as depression, anxiety further as well as substance abuse conditions particularly alcohol in mild or average forms. There are several causes for functional incapacities wherein the same is seen in psychiatric conditions that are well established. Furthermore, such a condition is not just a burden to the individual but also considerable distress to the socio-economic status of nations which is associated with absenteeism, increased health-care service demands and concern to public health.[4] It is predicted that by 2030, depression will be the most likely cause for disease burden in both low- and middle-income (LAMI) nations.[5]

Numerous researches have concluded that pharmacological and behavioral interventions are significant in mental health symptoms of numerous disorders. However, there is a wide treatment gap exists between normal and the individuals with mental health disorders which ranged from as lower than 10% and >90% through LAMI nations.[6] In developed nations, population-based researches revealed the frequency of CMD ranges was between 7% and 30% with an average of 17%, where 20% were women, and 12.5% were of men. When compared to Africa, Latin America and India, it is stated that occurrence in public has increased to 30% whereas roughly around 50% in primary care set up.[7]

From the information acquired from the special education programs, providers of health insurance,[8] national registers of health services,[9] it was identified that there had been an increase in the occurrence rate of mental disorders over the past 50 years in children and adolescents. Studies which have acquired data from the national indices of drug treatments have identified that the rate of medication treatment in mental disorders has amplified[10] particularly in children and adolescents. There has been less number of screening tools accessible in recognizing CMD in LMIC, although there are numerous choices in screening tools accessible that were developed for high-income nations. Furthermore, these tools are developed for high-income nations wherein LAMI instances were generally missed as a clinical and epidemiological presentation of mental health issues tend to differ between sets.[11] Hence, the adoption of appropriate instruments for screening is imperative to integrate CMD care into existing primary health-care services, especially in LAMI countries.

Studies for valuing CMD screening tools are available in a number of LMIC; however, there is an apparent gap in the literature on CMD screening tools. Previous studies limited in the analysis of mental health disorders detection, especially among children and adolescents. Developed countries such as the US and the UK come in the top list of tool development for mental health.[11] Therefore, there is a need to examine the application of these tools adoption to LMIC like India since it involves the aspects of cultural and increasing urbanization. There have been no consolidated researches or resources which implementers or researchers could recognize as the tools that could best perform based on the requisites. Several researchers have examined and validated various screening tools for mental disorders especially in specific populations or settings; however, to the best of our knowledge, none had examined the validity of the CMD screening tools especially for adolescents and children and in India using systematic review and meta-analysis.

Through a comprehensive systematic review of researches which examines the CMD screening tools for use in LAMI nations, the present paper will provide researches, health-care providers, and policymakers with an evidence-based and comprehensive summary of the most suitable CMD tools for use in Indian children and adolescent populations. Since there is little research done on the selection of appropriate mental health screening tool for children and adolescent population, the researcher conducted a systematic review of existing research in the area wherein the Indian scenario is considered. The present review was guided by two research questions: (1) to summarize the evidence and provide a pooled estimate of the prevalence of CMD (depression, anxiety, and other depression-related disorders) in Children and adolescents in India (2) to identify the various screening tools used to identify the prevalence of CMDs (a) depression, (b) anxiety and (c) any depression-related disorders in children and adolescents and (3) to examine the predictability of diagnostic tools at detecting CMD.

 Methods



The present systematic review follows the guidelines for reporting of Preferred Reporting Items for Systematic Review and Meta-Analyses guidelines.

Search strategy

For the present research, the search terms used are provided in [Figure 1], wherein databases such as Global Health, MEDLINE, EMBASE, and PsychInfo were searched for relevant articles for the systematic review. The initial search was initiated on 31st January 2020, and the results were not limited by language or date of publication. However, based on the papers acquired, the inclusion time frame for the inclusion of studies was set.{Figure 1}

Search terms

((“psychometrics”[MeSH Terms] OR “psychometrics”[All Fields] OR (“psychometric”[All Fields] AND “test”[All Fields]) OR “psychometric test”[All Fields]) AND (“weights and measures”[MeSH Terms] OR (“weights”[All Fields] AND “measures”[All Fields]) OR “weights and measures”[All Fields] OR “scale”[All Fields]) AND (“mental disorders”[MeSH Terms] OR (“mental”[All Fields] AND “disorders”[All Fields]) OR “mental disorders”[All Fields] OR (“mental”[All Fields] AND “disorder”[All Fields]) OR “mental disorder”[All Fields]) AND (“child”[MeSH Terms] OR “child”[All Fields] OR “children”[All Fields]) AND (“India”[MeSH Terms] OR “India”[All Fields])) AND (“2012/01/01”[PDAT] : “2020/01/31”[PDAT]).

Study selection

All the abstracts returned after the search of databases were reviewed for inclusion. The researcher retrieved full-text articles which were recognized as relevant and were assessed using the inclusion criteria set. The list of papers which met all the inclusion criteria was then used for the identification of additional studies in the reference list. For the reduction of human error and bias, the researcher repeated the selection of studies for 10% of the papers considered once again, wherein the rates of the agreement were compared. [Figure 1] depicts the selection strategy used by the researcher for the selection of research papers.

Inclusion criteria

Study design

We included observational studies that utilized one or more tools for screening patients with CMD (including depression and anxiety disorders).

Age group

Studies that had the research population as “children and adolescents.”

Disorder

All validation studies that used psychometric validated screening tools for CMD (e.g., beck depression inventory [BDI], Patient Health Questionnaire-9 [PHQ-9]).

National setting

Studies conducted in India.

Period

Studies that were conducted and published within the time frame of 2005–2020.

Exclusion criteria

Since the researcher attempts to maximize the breadth of the review, research articles that met the inclusion criteria set by the researcher were considered irrespective of the quality of the methodology used. Such use of all papers that met the inclusion criteria supports the researcher to maximize the use of the findings of the previous studies. However, the researcher restricted papers that were written in languages other than English since there are chances of misinterpreting the findings that were in other languages. In addition, opinion papers, posters and letters, qualitative studies, and literature reviews.

Data extraction

References were screened and stored in Mendeley reference management software (Elsevier, Amsterdam, the Netherlands, v. 1.18). Data were extracted from the included studies on screening tools and questionnaires, tool administrators, the population of the study (+region, state), type of study, setting, the sample size, type of prevalence estimate, and the psychometric characteristics of the screening tools. The measures used to assess the performance of the screening tool were true positive (TP), true negative (TN), false positive (FP), and false negative (FN). However, data were acquired by two authors (RG and RS) and any disagreement was discussed and solved by a third author (RR).

Quality appraisal

To appraise the quality of studies considered for the systematic review, the researcher selected the quality assessment checklist developed by Hoy et al.,[12] which is specifically developed for the assessment of prevalence studies. The checklist is used to assess the overall risk of bias in each prevalence study considered, and the tool is found to have a high inter-rater agreement and addresses internal and external validity.[12] The overall summary of bias risk is calculated wherein studies with low risks were considered for the research. The checklist is added in [Appendix 1].[INLINE:1]

Statistical analysis

In order to analyze the diagnostic performance, a 2 × 2 tables were constructed, and the TP, FP, TN, and FN were analyzed to calculate the specificity and sensitivity. A random-effect meta-analysis of proportional data was adopted to reflect the disparate study, with statistical significance set at P < 0.05. Despite anticipated heterogeneity in the results included in this present meta-analysis, we have synthesized quantitative estimates of results. As acknowledged by Ioannidis et al.,[13] a quantitative summary can provide more information and allow for an investigation of diversity in results than qualitative interpretation. To estimate heterogeneity, the I2 statistics were calculated and in the event of high heterogeneity as specified by Cochran's Q test (P < 0.05) or I2 (inconsistency) value >50%, the meta-analysis was repeated following exclusion of data from low-quality studies by conducting sensitivity analyses. As anticipated, we observed considerable heterogeneity in the prevalence of CMD and therefore, we employed a random effect. Publication bias was estimated using a funnel plot. All statistical analyses were performed using MedCalc Statistical software version 14.8.1 (MedCalc Software, Ostend, Belgium; http://www.medcalc.org; 2014).

 Results



Selection of studies

For the systematic review and meta-analysis, the initial search of databases retrieved around 1049 original research articles wherein the researcher identified 453 studies to be relevant based on abstracts and title. Around 124 papers that are full-text articles were retrieved wherein only 18 articles were found to satisfy the inclusion criteria set by the researcher. Reference lists of articles were also examined for the papers that met the inclusion criteria wherein one systematic review was found which had ample papers to support the researcher to add up the studies further for the present systematic review and meta-analysis. Hence, the total number of studies considered for the review is 20. [Figure 2] depicts the flow of the selection process wherein the full list of studies included is provided as Supplementary Material.{Figure 2}

Appraisal of quality

From the selected 20 studies, the set of nine quality standards as ascertained by Hoy et al.[12] was employed by the researcher wherein the total sum of risk of bias was calculated. Of all the researches appraised, the score for all 20 studies came to “low risk” in the quality appraisal. Hence, all the 20 studies were selected for the systematic review and meta-analysis. The critical appraisal checklist for all the papers considered is attached as Supplementary material.

Description of included studies

Twenty (n = 20) studies were selected for the systematic review wherein the general characteristics of all the papers are depicted in [Table 1] and [Table 2]. For the systematic review, the researcher considered depression, anxiety, and stress to be CMD. The researcher identified studies that were associated with either a specific type of CMD (Depression or anxiety) or studies that considered a combination of various CMDs (Depression, anxiety, stress and so on). 9 studies focused on depression-related issues and their prevalence,[14],[19],[20],[23],[24],[26],[27],[29],[32] wherein only one study considered anxiety only.[14] However, any depressive CMDs were focused by 11 studies Cholakottil et al.,[15] Basker et al.,[16] Basker et al.,[33] Ganguly et al.,[30] Sarda et al.,[31] Sandal et al.,[25] Kumar and Akoijam,[28] Russell et al.,[18] Chhabra and Sodh.[21] One study considered academic stress, mental pressure, and parental pressure, wherein one research considered both depression and anxiety.[34]{Table 1}{Table 2}

Four studies considered the BDI for the screening of depression and depression-related disorders in Indian children and adolescents. Two studies used the Children's Depression Rating Scale-Revised (CDRS-R) tool, and two papers used the Depression Anxiety Stress Scale tool. Three papers used the PHQ-9. Other studies (n = 10) utilized tools such as Child Adolescent Psychiatric Screening, Diagnostic interview schedule for children – child informant, children depression inventory (CDI), Kutcher adolescent depression scale, ICD-10, Mini-International Neuropsychiatric Interview for Children and Adolescents (KID), self-report semi-structured questionnaire, and specially designed semi-structured questionnaire [Table 1].

Of all the studies considered, there were few studies which had the questionnaire translated to the native languages other than English. A maximum of 9 studies had screening tools administered in English, whereas 4 studies had questionnaires translated to Hindi. Two studies had screening tools translated to Tamil and two studies in Bengali and Gujarati. However, there was missing information about the translation of screening tools in 6 studies. Thirteen studies had sample population from both rural and urban backgrounds wherein six studies were conducted with children and adolescents in urban regions. However, one research corresponded to the children and adolescents from rural regions and three papers did not reveal any information regarding the region of participants.

The overall pooled estimates of depression, anxiety, and any other depressive disorders are depicted in [Figure 3]. The researcher categorized CMDs into sub-categories as “depression,” “anxiety,” and “any depressive disorders.” A meta-analysis of the studies considered in the research revealed the overall estimate for the pooled prevalence of depression is 19.3% (95% confidence interval [CI] 12.57–27.11) wherein the significant between-study heterogeneity is found to be extremely high (“I2 = 98.41%” P < 0.0001) with prevalence rates ranging between 1% and 53%. Similarly, the overall estimate for the pooled prevalence of anxiety is found to be around 15% (95% CI for 4.67–30.91) wherein the significant between-study heterogeneity is found to be high (“I2 = 98.89%” P < 0.0001). In addition, the overall estimate for the prevalence of “any other depressive disorders” is found to be around 11% (95% CI for 2.37–19.77) wherein the significant between-study heterogeneity also found to be high (“I2 = 97.95%” P < 0.0001).{Figure 3}

Visual inspection of the funnel plot reveals that a relatively large number of studies found to lie outside the plot and those are a few very large studies, the lack of medium-sized samples and relatively small studies. However, as studies were found to have outliers in the funnel plots [Figure 3], the outliers were removed, and the included studies for all the categories are depicted in [Figure 4]. The overall estimates for the prevalence of depression after outlier removal is found to be around 12% (95% CI 8.97–16.04) wherein the significant between-study heterogeneity is found to be still high (“I2 = 91.45%” with P < 0.0001). Similarly, the overall estimate for the pooled prevalence of anxiety after outlier removal is found to be around 3% (95% CI for 1.51–3.67, I2 = 0%', P = 0.466). In addition, the overall estimate for the pooled prevalence of “any other depressive disorders” after outlier removal is found to be around 11% (95% CI for 4.73–19.77: “I2 = 97.95%” with P < 0.0001).{Figure 4}

Only for the CDRS-R and the BDI, there were at least two and three studies available for meta-analytic procedure, respectively [Table 3]. The CDRS-R had a low pooled sensitivity of 47% (95% CI: 0.41–0.54) yet slightly higher specificity of 75% (95% CI: 0.66–0.83); however, heterogeneity was moderate (I2 = 88.9% and 85.7%, respectively). PPV values were higher overall, with values of 86.5% (Basker et al.),[16] 72.6% (Russell et al.)[18] with an accuracy of 55.3% and 55.8% respectively. On the other hand, BDI had a sensitivity of 61% (95% CI: 0.55–0.66) and a specificity of 59% (95% CI: 0.53–0.66) to identify depression. Similarly, slightly moderate heterogeneity was observed (I2 = 88.9% and 85.7%, respectively). PPV values were higher overall, with values of 77.8% (Basker et al.),[16] and 73.9% (Russell et al.)[28] with an accuracy of 54.5% and 66.1%, respectively.{Table 3}

 Discussion



The researcher in the present paper performed a systematic review of the literature and a meta-analysis to identify the prevalence of CMD and the efficacy of diagnostic tools in the Indian children and adolescent population. Since there were only few studies have been carried out on the choice of a suitable mental health screening tool for children and adolescent population, the researcher conducted a systematic review of existing research based on the CMD screening tools, wherein the studies in the Indian context were considered. In the Indian scenario, it is deemed that there are limitations when it comes to the provisioning of child and adolescent mental health services as most services are restricted to the urban regions; rural areas are left unnoticed.[35] It is hence imperative that screening of psychological CMDs is necessary for children and adolescents in the nation, which prevents further health and economic burden to the nation's health-care system.

For the systematic review and meta-analysis, totally 23 studies were selected for validation. While the researcher was intended to validate the screening tool used, it was revealed that only 4 studies[16],[18],[30],[33] were conducted on the premise of examining the validity and reliability of the screening tools whereas the rest of the studies were completely prevalence-based researches. Most tools used by researches were adapted to the local settings as the screening tools were translated and checked by an expert. Furthermore, the translated screening tools were retranslated into English to ensure the face and content validity of the locally adapted questionnaire.

The researcher identified 9 studies that exclusively examined depression in Indian children and adolescents. The results of the study characteristics based on disorder and screening tool used indicate that the screening of depression and depression-related disorders in Indian children and adolescents majorly utilized the BDI as a screening tool. This has been used in four studies, wherein both modified and unmodified versions were used. PHQ-9 is used in three papers; however, when compared to BDI and PHQ-9, the utilization of BDI is greater. Of all the studies considered, the screening tools used were translated and administered. BDI is a proven tool for screening depression and possess excellent psychometric properties. BDI could be applied in LAMI nations and also supports in assessing depression symptoms within patients with conditions such as HIV; hence, the purview of utilizing BDI in India is broad. However, the validity of the screening tool in the Indian setting needs further researches.

Only one research[14] considered anxiety disorder exclusively in the Indian children and adolescent population, wherein the self-report semi-structured questionnaire, a standardized psychological test, and the State-Trait Anxiety Inventory were used. However, it is not clear from the studies how far the tools have been customized to fit with the current cultural setting and their reliability and validity. A total of 13 studies[15],[16],[21],[25],[28],[30],[31],[33],[34],[36] examined depressive disorders such as anxiety, depression, stress, pressure, and altogether wherein different screening tools were used. In line with the findings of the present review, the researcher has also utilized BDI and PHQ as screening tools in other national settings as well. Lee et al.[37] examined the validity and reliability of BDI-II and correlated it with PHQ, wherein the results showed a strong correlation with PHQ. BDI-II was concluded as a reliable tool for measuring the severity of depressive symptoms in Korean adolescents. Siu et al.[38] presented an article that describes the update of the 2009 US Preventive Services Task Force recommendation on screening for major depressive disorder in children and adolescents which also indicates that BDI and PHQ are the most studies instruments. Hence, it could be concluded that BDI is the better and satisfactory screening tool used in LMIC such as India among children and adolescents.

There is a large variation in the different screening tools used in an Indian setting, which could be associated with the fact that no screening tool has been appropriately validated and tested in the Indian scenario. The main findings of this systematic review were that there are only four studies tested the predictability of the screening tools used in the research wherein BDI, CDRS-R, and PHQ-9 were validated and tested against gold standard values. In this meta-analysis, out of 20 studies, only 5 were included in the review that reported sensitivity and specificity. Therefore, their accuracy seems to have been taken for granted. In this review, the BDI had the slightly highest sensitivity (61%) and the CDRS-R (75%) had the highest specificity. The sensitivity for BDI instrument ranged from 28% to 91% and the specificity ranged from 18% to 89%. In general, for a screening instrument, high sensitivity is an important factor than high specificity due to the fact that after screening, a diagnostic interview performed by training clinicians, during which FPs will be detected. In this review, BDI showed a slightly better predictive ability for depression than CDRS-E. There is no agreement in the literature on the minimal requirements (“benchmark”) for diagnostic accuracy of instruments for depression, although an earlier systematic review suggested sensitivity of at least 85% and a specificity of at least 75% for case-finding instruments. The sensitivity of BDI in the present review is lower than reported[39] due to an artifact, as the studies had large variation in the studies in terms of setting an “optimal” cut off score, that might, on occasion, have inflated the tools accuracy. In addition, the number of studies per questionnaire in the present meta-analysis was low (maximum 2 for CDRS-R and 3 for BDI) and other methodological variation such as the difference in the sample size, and usage of different screening tools used across studies require cautious interpretation. Hence, there is a need for future researches that could validate other screening tools in Indian scenario against gold standards (clinical diagnostic interview).

Totally, 17 studies were identified to examine the prevalence of depression in children and adolescents. The pooled estimate of depression 19%. The result indicates that the prevalence of depression is less among children and adolescents in India. However, Jha et al.[26] the study indicated that the prevalence of depression rate is 49.2% among adolescents. Furthermore, Sandal et al.[25] show that the depression was identified to be 65.53% among adolescents. Chauhan et al.[19] resulted in the prevalence of depression among children is 38%. These results indicate that the prevalence of depression among adolescents is greater than in children in India. Totally, 9 studies were selected to test the prevalence of anxiety among children and adolescents. The study results indicate that the prevalence of anxiety is more among Indian children and adolescents. Studies of Kumar and Akoijam[28] and Sandal et al.[25] results that anxiety prevalence level is greater than depression and stress.

The prevalence of “any other depression” is examined in 9 studies. The funnel plot analysis shows that the prevalence of other psychological problems among children and adolescents is less. Analyzed the emotional and behavioral difficulties of adults, and the results show that 46% of adults having total difficulty levels in the abnormal range. Chhabra and Sodhi[40] results stated that one-third (39.6%) of adolescents were having psychological problems in India. The studies were done by Chhabra and Sodhi[40] and Deb et al.[34] are signifying that the reason for psychological problems was having significantly more academic problems, middle class, adolescents with working mothers, family disputes, domestic violence, lesser number of close friends, and greater substance abuse. However, these findings have to be interpreted with caution due to the high heterogeneity between the studies both in the estimation of prevalence and screening tool predictability. This is probably due to the methodological variations in terms of varying characteristics of studies been included such as a wide range of setting, difference in study place, sample size, use of different screening, and diagnostic tools to estimate prevalence. Therefore, it is likely that the heterogeneity and overall quality of the included studies affect the findings reliability.

Limitations of the meta-analysis

Limitations should be taken into account when construing the results of this study. The study's aim was to estimate the prevalence of CMDs and the applicability of diagnostic tools in Indian adolescents and children. The studies included had limited descriptions of the method and characteristics of the participants such as response rate. The study did not consider all the mental illnesses; only those concerning children and adolescents with specifically an independent degree of anxiety and depression. Second, few studies excluded patients who had a history of depression or anxiety or receiving medications and therefore, true prevalence and screening tool accuracy were likely to be underestimated. Third, several tools have been used, such as BDI, CDRS-R, PHQ-9, and GHQ-12 while some used multiple tools, and however, not all studies reported validation of the tools. Fourth, authors, in general, conducted their studies in a wide variety of settings such as in schools, hospitals, and community settings. Therefore, this further limits the generalizability of the findings. Fifth, the prevalence of CMD may vary according to the cultural region such as the southern region of India versus east or north and therefore, this can result from the actual difference in prevalence and discrepancies in the measurements. Finally, although we searched a wide range of database to identify relevant studies and our stringent exclusion criteria on not including gray literature is likely to reduce the prevalence estimates.

Implications for research and practice

Depression tends to be a prominent and underestimated complication in the Indian scenario. This study attempted to make a systematic review and meta-analysis on the prevalence of CMD and the applicability of diagnostic tools in Indian adolescents and children. Therefore, this fills the gap in a pooled estimate for CMDs in India. To be noted, while several tools and techniques were used, it is revealed that in the studies pertaining to the Indian context, only a few studies validated the tools used for screening of CMDs. Hence, a need for future researches exists to validate screening tools in Indian scenario against gold standards. Keeping in mind the need of giving evidence and an all-inclusive outline, the present review would be of use to health-care providers and policymakers who want to recognize the occurrence of CMD and the usefulness of diagnostic tools in Indian children and adolescent population. Besides, the findings from this review would be useful for them in understanding these aspects for future studies. It should be noted that there is a scarcity of research on the choice of a pertinent mental health screening tool for children and adolescent populations. Though the tools used in this study prove to be effective in certain countries other than India, its validity is something to be questioned. To come to unanimity on screening children with CMD, there should be an easy perception of the tools by children and adolescents.

 Conclusion



In India, CMDs remain a challenge and screening of children and adolescents with CMDs has become a topic of great interest owing to the advantages in reducing the economic and financial burden to the healthcare system. A systematic review and meta-analysis were performed, which revealed the various tools used for screening and assessment of CMDs in Indian children and adolescents. However, due to its wide variation in the pooled estimates of the prevalence of CMDs limits our ability to draw a definite conclusion but it clear that the prevalence of CMD is high among adolescents and children. This poses a huge burden of mental ill-health among this population for which there is little attention in either research, practice or policy perspective in India on issues of children and adolescents.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.

References

1World Health Organization. Mental Health: A State of Well-Being. World Health Organization; 2018.
2Ferrari AJ, Charlson FJ, Norman RE, Patten SB, Freedman G, et al. Burden of Depressive Disorders by Country, Sex, Age, and Year: Findings from the Global Burden of Disease Study 2010. PLoS Med 2013;10:e1001547.
3Hardy S. Common mental health disorders in general practice. Pract Nurs 2018;29:63-9.
4Whiteford HA, Degenhardt L, Rehm J, Baxter AJ, Ferrari AJ, Erskine HE, et al. Global burden of disease attributable to mental and substance use disorders: Findings from the Global Burden of Disease Study 2010. Lancet 2013;382:1575-86.
5Rathod S, Pinninti N, Irfan M, Gorczynski P, Rathod P, Gega L, et al. Mental health service provision in low – And middle-income countries. Health Serv Insights 2017;10:1-7.
6Lora A, Kohn R, Levav I, McBain R, Morris J, Saxena S. Accessibility, patterns of care and the treatment gap in schizophrenic disorders: A WHO-AIMS survey in 50 low – And middle-income countries. Bull World Heal Organ 2012;90:47-54.
7Barreto do Carmo MB, Santos LM dos, Feitosa CA, Fiaccone RL, Silva NB da, Santos DN dos, et al. Screening for common mental disorders using the SRQ-20 in Brazil: What are the alternative strategies for analysis? Rev Bras Psiquiatr 2017;40:115-22.
8Grobe T, Blitzer E, Schwartz F. Barmer GEK Arztreport 2013: Script for health analysis. BARMER GEK die gesund experten, 2013;18:160-73.
9Morrow RL, Garland EJ, Wright JM, Maclure M, Taylor S, Dormuth CR. Influence of relative age on diagnosis and treatment of attention-deficit/hyperactivity disorder in children. CMAJ 2012;184:755-62.
10Merten EC, Cwik JC, Margraf J, Schneider S. Overdiagnosis of mental disorders in children and adolescents (in developed countries). Child Adolesc Psychiatry Ment Health 2017;11:5.
11Endsley P, Weobong B, Nadkarni A. The psychometric properties of GHQ for detecting common mental disorder among community dwelling men in Goa, India. Asian J Psychiatr 2017;28:106-10.
12Hoy D, Brooks P, Woolf A, Blyth F, March L, Bain C, et al. Assessing risk of bias in prevalence studies: Modification of an existing tool and evidence of interrater agreement. J Clin Epidemiol 2012;65:934-9.
13Ioannidis JP, Patsopoulos NA, Rothstein HR. Reasons or excuses for avoiding meta-analysis in forest plots. BMJ 2008;336:1413-5.
14Deb S, Chatterjee P, Walsh K. Anxiety among high school students in India: Comparisons across gender, school type, social strata and perceptions of quality time with parents. Aust J Educ Dev Psychol 2010;10:18-31.
15Cholakottil A, Kazhungil F, Koyamu AM. Prevalence and Pattern of Psychiatric Disorders in School Going Adolescents. The International Journal of Indian Psychology 2017;4:124-8.
16Basker MM, Russell PS, Russell S, Moses PD. Validation of the children's depression rating scalerevised for adolescents in primary-care pediatric use in India. Indian J Med Sci 2010;64:72-80.
17Bansal V, Goyal S, Srivastava K. Study of prevalence of depression in adolescent students of a public school. Ind Psychiatry J 2009;18:43-6.
18Russell PS, Basker M, Russell S, Moses PD, Nair MK, Minju KA. Comparison of a self-rated and a clinician-rated measure for identifying depression among adolescents in a primary-care setting. Indian J Pediatr 2012;79 Suppl 1:S45-51.
19Chauhan S, Lal P, Nayak H. Prevalence of depression among school children aged 15 years and above in a public school in Noida, Uttar Pradesh. J Acad Ind Res 2014;3:269-73.
20Shukla NK, Shukla M, Ahmad S, Shukla R, Khan Z. A cross-sectional study on depression among school going adolescent girls in Barabanki district, Uttar Pradesh, India. Int J Contemp Pediatr 2016;4:178.
21Chhabra GS, Sodh MK. Factors contributing to psycho-social Ill-health in male adolescents. Online J Heal Allied Sci 2011;10:1-4.
22Bhola P, Sathyanarayanan V, Rekha DP, Daniel S, Thomas T. Assessment of self-reported emotional and behavioral difficulties among pre-university college students in Bangalore, India. Indian J Community Med 2016;41:146-50.
23Chopra R, Punia S, Sangwan S. Personal variables – A predictor of depression among adolescents. J Educ Soc Behav Sci 2017;22:1-7.
24Jayanthi P, Thirunavukarasu M. Prevalence of depression among school going adolescents in South India. Int J Pharm Clin Res 2015;7:61-3.
25Sandal RK, Goel NK, Sharma MK, Bakshi RK, Singh N, Kumar D. Prevalence of depression, anxiety and stress among school going adolescent in Chandigarh. J Family Med Prim Care 2017;6:405-10.
26Jha KK, Singh SK, Nirala SK, Kumar C, Kumar P, Aggrawal N. Prevalence of depression among school-going adolescents in an Urban Area of Bihar, India. Indian J Psychol Med 2017;39:287-92.
27Singh M, Gupta M, Grover S. Prevalence & factors associated with depression among schoolgoing adolescents in Chandigarh, north India. Indian J Med Res 2017;146:205.
28Kumar KS, Akoijam BS. Depression, anxiety and stress among higher secondary school students of Imphal, Manipur. Indian J Community Med 2017;42:94-6.
29Malik M, Khanna P, Rohilla R, Mehta B, Goyal A. Prevalence of depression among school going adolescents in an urban area of Haryana, India. Int J Community Med Public Heal 2015;2:624-6.
30Ganguly S, Samanta M, Roy P, Chatterjee S, Kaplan DW, Basu B. Patient health questionnaire-9 as an effective tool for screening of depression among Indian adolescents. J Adolesc Health 2013;52:546-51.
31Sarda R, Kimmatkar N, Hemnani JT, Hemnani TJ, Mishra P, Jain SK. Prevalence of psychiatric disorders in western U.P. Region – A school based study. Int J Sci Study 2013;1:1-7.
32Nisha RS, Krishnaveni A. Screening for depression among adolescents using Child Adolescent Psychiatric Screening (CAPS) Tool. J Med Sci Clin Res 2017;05:22142-6.
33Basker M, Moses PD, Russell S, Russell PS. The psychometric properties of Beck Depression Inventory for adolescent depression in a primary-care paediatric setting in India. Child Adolesc Psychiatry Ment Health 2007;1:8.
34Deb S, Strodl E, Sun J. Academic stress, parental pressure, anxiety and mental health among Indian high school students. Int J Psychol Behav Sci 2015;5:26-34.
35Ronad SV, Babu RB, Patali CS, Shrinivas K, Ugargol SS, Gondabal HS, et al. Child and adolescent mental health in Indian context. Child Adolesc Psychiatr Clin N Am 2015;24:731-49.
36Hochschild AR. The Managed Heart Commercialization of Human Feeling, Updated with a New Preface. 3rd ed.: Califonia, University of California Press; 2012. p. 1-352.
37Lee EH, Lee SJ, Hwang ST, Hong SH, Kim JH. Reliability and validity of the beck depression inventory-II among Korean adolescents. Psychiatry Investig 2017;14:30-6.
38Siu AL, US Preventive Services Task Force (USPSTF), Bibbins-Domingo K, Grossman DC, Baumann LC, Davidson KW, et al. Screening for depression in adults: US preventive services task force recommendation statement. JAMA 2016;315:380-7.
39US Preventive Services Task Force. Screening and treatment for major depressive disorder in children and adolescents: US Preventive Services Task Force Recommendation Statement. Pediatrics 2009;123:1223-8.
40Chhabra GS, Sodhi MK. Factors contributing to psycho-social ill-health in male adolescents. Online J Heal Allied Sci 2011;10:7-10.

 

 
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