INTRODUCTION
Common mental disorders (CMDs) encompassing neurotic and nonpsychotic affective disorder refers to two major symptom dimensions – depression and anxiety and[1] are “common” because they are widely prevalent in the community and primary care.[1] CMDs are regarded as “invisible disorders” as they are often overlooked by patients, caregivers, health professionals, and policymakers yet cause significant health burdens. International Classification of Diseases 10th Edition (ICD-10)[2] classifies CMDs as: “neurotic, stress-related and somatoform disorders” and “mood disorders.” Due to the high degree of comorbidity between sub-categories and the similarity in epidemiological profiles and treatment responsiveness,[3] the CMDs construct is a more practical and valid concern for public health interventions. Fortunately, there has been a growing recognition of the disability caused by CMDs globally and of late in India also. Depressive and anxiety disorders contributed to 7·5% and 3·4% of years lived with disability (YLD).[4]
A World Health Organization (WHO) analysis emphasized that for every $1 invested in the scaling-up treatment of depressive and anxiety disorders, the return is $4 in terms of health and economic benefits.[5] This return-on-investment underscores the strong recommendation for greater investment in mental health services for CMDs in every country.[6]
India hosts 17.7% of the global population,[7] but very few epidemiological studies have estimated the prevalence of CMDs or their burden. The World Mental Health Survey (WMHS) India component observed the past 12-month prevalence of CMDs to be 5.5%,[8] and this included anxiety disorders, depressive disorders, and substance use disorders (SUDs) and three sub-types (specific phobia, panic disorder, generalized anxiety disorder [GAD]) were estimated among anxiety disorders. However, researchers did not include agoraphobia, social phobia, obsessive-compulsive disorder (OCD), and posttraumatic stress disorder (PTSD). There are no other existing nationally representative studies on CMDs in India. However, the prevalence of CMDs among the geriatric population from NMHS 2016 is already published.[9]
This analysis from the National Mental Health Survey (NMHS) of India (2015-2016) focuses on the current prevalence of CMDs among all adults (18 years and above), its sociodemographic correlations, disability burden, treatment gap, and its socioeconomic impact with an attempt to identify missed opportunities and list priorities for planning services.
METHODOLOGY
A detailed description of the methodology is available online (http://indianmhs.nimhans.ac.in/nmhs-reports.php) and elsewhere.[1011] In summary, NMHS 2016 was a large population-based study conducted across 12 states of India using a multi-stage, stratified, random cluster sampling technique based on probability proportionate to size at each stage (MSRS-PPS) and sampled nearly two-thirds of the population of India. As per India’s census 2011, each inhabited village constituted the rural cluster, and each ward in the urban area formed the metro/nonmetro urban cluster.
All adults 18 years and older were included. All eligible adult respondents within the identified household were interviewed. Persons who could not be interviewed even after three visits were considered nonresponders. A rigorous attempt was made to include all eligible people in the study.
We obtained ethical clearance from the Institute Ethics Committee of “National Institute of Mental Health and Neurosciences,” Bengaluru, India, and corresponding IECs of partner institutions in each state. Obtained informed consent from the respondents before conducting the interview.
The Mini-International Neuropsychiatric Interview (MINI) version 6.0.0[12] was used to diagnose CMDs. The MINI is a structured diagnostic tool for screening and diagnosing mental disorders as per ICD-10 in multiple Indian languages. Other instruments used were the Sheehan Disability Scale,[13] a specially designed questionnaire exclusively for this NMHS survey to assess treatment, and healthcare-seeking pattern to evaluate the treatment gap and socioeconomic impact. The NMHS field team conducted diagnostic interviews at each state with a psychology/social work/communications and rural development background. The training for the data collection team conceptually relied on SEE–PRACTICE–CONDUCT–REFINE principle and was conducted over 7–8 weeks in a uniform training schedule. Further complete detail is available elsewhere.[10]
For the present communication, depressive disorder (without psychotic symptoms) and anxiety disorders were included as CMDs. Anxiety disorders comprised GAD, panic disorder, social anxiety disorder, agoraphobia, PTSD, and OCD. Diagnosis of all the above disorders was as per ICD-10 - Diagnostic Criteria for Research. The current prevalence refers to the last month for social phobia, agoraphobia, panic disorder, PTSD, 6 months for GAD, and 2 weeks for depressive disorder.
Considering the unequal probability of selection and nonresponse rates, the weighted prevalence estimates were derived for CMDs. All estimates are presented with 95% CIs. Multiple logistic regression was done considering CMDs as the dependent variable and sociodemographic characteristics (i.e., gender, age, education, occupation, marital status, and place of residence) as independent variables considered necessary from a clinical and public health implication for identifying risk factors associated with CMDs. The adjusted odds ratio was calculated. IBM SPSS (Statistical Package for Social Sciences) version 27.0 from International Business Machines Corporation, New York, USA[14] was used for all analyses.
RESULTS
Across the 12 states of India, identified 10,610 households for the survey, and among these, 9666 households were surveyed (household response rate of 91·1%). Thirty-nine thousand five hundred and thirty-two eligible adults 18 years and above were contacted, and 34,802 were finally interviewed (individual response rate 88.0%). The age and gender composition of the surveyed population were similar to the population of India as per Census 2011: 52% of the sample were females and 48% there was a more significant proportion of the elderly; 34% belonged to the 18–29 age group, while 16% were above 60 years. Nearly 75% of the study samples were married, and 68% were from rural areas. Around 51% of respondents were unemployed, and 24% were “not literate.”
The overall weighted prevalence of current CMDs was 5·1% (95% confidence interval [CI]: 5·06–5·13) [Table 1]. The prevalence of CMDs was highest at age 50–59 years compared with other age groups (5.71% in 40–49, 6·03% in 50–59, and 5.87% in those above 60). Higher prevalence was amongst females (5.79% 95 CI 5.46–6.14), those with lesser education (illiterate [6.03%, 95% CI 5.54–6.55] and primary education [5.68%, 95% CI 5.13–6.28]), being unemployed (5.09%, 95% CI 4.78–5.42), married (4.93%, 95% CI 4.67–5.20) and those residing in metro urban areas (8.11%, 95% CI 7.32–8.96).
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Table 1: The current prevalence of common mental disorders (n=34,802)
The current prevalence of depressive and anxiety disorders (including PTSD and OCD) is 2·68% and 2·94%, respectively. The current prevalence was higher among females in both depressive (female: 3.01% vs. male: 2.4%) and anxiety disorders (female: 3·69% vs. male: 2·14%) [Table 1].
Table 2 shows the multiple logistic regression analysis for various sociodemographic factors associated with CMDs. Among the sociodemographic variables, gender, age and residence were found to be significantly associated with CMDs. The risk of CMDs among females was found to be 1.53 times higher than males. Furthermore, people residing in metro urban cities had 1.86 times higher risk than those living in rural areas.
Table 2: Multiple logistic regression analysis for factors associated with current common mental disorders (n=1683)
Nearly two-third of individuals had reported disability either at work or family or social life due to CMDs. Of which, almost 50% had moderate or more disability due to CMDs [Table 3].
Table 3: Disability in various domains in patients with common mental disorders (n=1509)
Socioeconomic impact assessment [Table 4] showed that nearly one out of 2 patients had difficulties with activities of daily life and thus “could not do as usual” and quantified that they had either inability/reduced ability to work at least 10 days in a month. Family members, on average, 2 days/month, had to forgo work to take care of patients with CMDs, which points out the social impact of CMDs. Median monthly expenditure incurred due to health and treatment-related costs were estimated at around
1500/month.
Table 4: Socioeconomic impact of current common mental disorders
Overall the treatment gap of CMD was estimated to be around 80·4% and was slightly greater amongst females 81·5%. The treatment gap at urban nonmetro (87·4%) is surprisingly higher than rural (79·7%) and urban metro (77·7%) populations [Table 5].
Table 5: Treatment gap and care characteristics of adult common mental disorders
DISCUSSION
This NMHS is the first nationwide epidemiological survey with a large population sample and rigorous methodology which estimated the prevalence, disability, and treatment gap of psychiatric disorders across different states in India from the representative general population. Of the 39,532 individuals surveyed, the response rate was 88%. The sample size was large and representative compared to the earlier survey (24,371 individuals in Sagar et al., 2017) with a similar response rate. The inclusion of agoraphobia, social phobia, OCD, and PTSD in the estimation of CMDs is a value addition.
While earlier Indian data estimated the prevalence of neurotic disorders to be in the range of 5.8%–7.3%,[1516] the overall weighted current prevalence of CMDs in the present study was 5.1% (95% CI: 5.06–5.13). These numbers translate to 70 million Indian adults suffering from CMDs. Consistent with the literature, CMDs were higher in females[1718] and those above 40 years. This result brings the need for more research into the impact of CMDs on work productivity and absenteeism.[19] That metro had a higher prevalence of CMDs adds to the growing evidence of the impact of urbanization in the development of mental disorders.[20]
The overall treatment gap of CMDs was 80.4%, more significant in nonmetros and amongst females. This is the first time that disability has been enquired in India in a community-based survey. The present study found that around two-third (60%) reported disability, underscoring the urgent need for planning services. Substantive disability compounded with a huge treatment gap increases disability-adjusted life years and YLD and adds to higher economic costs.[21] As most CMDs affect one’s productive years, it can lead to absenteeism leading to less productivity and increased job strain, etc.[22]
The Portugal-WMHS estimated disability among CMDs to be only 14.6%.[23] The reason for this vast difference between Portugal-WMHS and NMHS samples has to be interpreted carefully. WMHS study has used WHODAS 2.0 to measure disability, including cognition and self-care components, which are less likely to be affected in CMDs. Indian sites WMHS in 2005 showed that the treatment gap for CMDs was around 95%.[8] This change in the treatment gap can be taken on a positive note that the treatment gap has improved over a decade, which could be due to improved healthcare services over time. However, caution is the inclusion of SUDs in this considerable treatment gap. Conventionally, a large treatment gap is attributed to stigma, lack of human and financial resources, accessibility, awareness among the public, etc.[24] The treatment gap for CMDs in NMHS was higher in the urban area (87·7% in nonmetro urban). Although nonintuitive, huge treatment gaps are also due to unevenly distributed resources, leaving the transition location of urban areas “orphans” compared to metro and rural areas.[25]
Are the prevalence of the CMDs on the rise in last decade? Sagar et al.[8] reported the 12-month prevalence of CMDs was 5·52% which included SUDs but excluded PTSD, OCD, social phobia, and agoraphobia. However, our study, undertaken almost a decade later (2003–2005 vs. 2015–2016), has observed an increase in the prevalence of CMDs. Similar to our findings, WMHS estimated the 12-month prevalence of mood and anxiety disorders to be in the range of 4.1%–5.7%[26] in other low- and middle-income countries (LAMIC). However, the 12-month prevalence of mood and anxiety disorders in higher-income countries was estimated to be around 9.6%–27.8%. In a large meta-analysis, the 12-month global prevalence of CMDs was estimated to be 17.6%.[27] Hence, our findings are comparable to other LAMIC countries and are much lesser than estimates from higher-income countries.
Although the lower prevalence of CMDs in India is comparable with LAMIC, it is lower than in Western countries. This lower prevalence rate of CMDs in the Indian sample is not exclusive to CMDs; it is also observed in schizophrenia.[28] Both International Pilot Study on Schizophrenia[29] and Determinants of Outcome of Severe Mental Disorders (DOSMED)[30] studies reported that the course and outcome of schizophrenia in India were more favorable than in developed countries. This lower prevalence of CMDs in India (though comparable with LAMIC) can be attributable to varying degrees of psychosocial, cultural, and economic reasons. In addition, the contribution of individual variabilities such as resilience, alexithymia, and “trait” markers of individuals towards the threshold of mental illness needs to be considered.[3132]
Limitations
The current prevalence in this study is a more reliable point estimate and avoids potential recall bias. This study is not ruling out the possibilities of under-estimation of CMDs in the country because of under-reporting due to stigma, exclusion of somatoform disorders (under detection as depressive symptoms may have subsumed under somatic symptoms), specific phobia and dysthymia, and missing of more disabled sub-threshold or subsyndromal disorders.
Implications
The current study was conducted in the largest representative sample with a uniform methodology that provides critical insights to planners and practitioners. We require a DOSMED type study[33] which includes higher income and LAMIC countries, to study the differences in prevalence and its correlations of CMDs (including sub-threshold) across the countries.
Given the higher sufferings of CMDs compounded with its higher treatment gap and lower accessibility to mental health care and services, there is an urgent need to improve psychiatric care in India through its National Mental Health Programme[34] NMHPS operational arm, the District Mental Health Programme[35] should focus more on CMDs apart from traditional SMDs.[36] The need is also on the task-shifting to primary care in general healthcare settings with impactful, innovative telemedicine on-consultation training methods.[3738] There is also a need for national guidelines of the management of CMDs in primary, secondary, and tertiary healthcare delivery systems.
From a larger public health point of view, the present estimates of 1 in 20 persons with CMDs in India need to be seen in context within the spectrum of the noncommunicable disease.[39] Given the higher prevalence of CMDs among NCDs.[40] It is recommended to include CMDs in the management of NCDs for a better course and prognosis. Future epidemiological studies should address culturally sensitive psychiatric illnesses, i.e., somatization and dysthymic disorder. The bigger takeaway from this study is that mental health planners should now target risk factors such as urbanization, vulnerable populations (women and elderly), etc.
CONCLUSIONS
The NMHS data on the current prevalence estimate of CMD provides ample evidence to recognize CMDs as a significant public health problem in India. Considering its impact on disability and potential economic loss, a major shift should focus on task-shifting. Interventions should focus on early detection in primary care, appropriate pharmacological and nonpharmacological interventions, greater accessibility to the community, and lesser health care costs to the public.
Financial support and sponsorship
The data used for analysis in this publication is from the National Mental Health Survey (NMHS) funded by the Ministry of Health and Family Welfare, Government of India and was implemented and co-ordinated by National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, INDIA in collaboration with state partners. NMHS phase 1 (2015-16) was undertaken in 12 states of India across the 6 regions and interviewed 39,532 individuals (http://indianmhs.nimhans.ac.in). Funder had no role in implementation, data acquisition, data analysis and interpretation and write up of the paper.
Conflicts of interest
There are no conflicts of interest.
Acknowledgments
“NMHS India National Collaborators Group include Pathak K., Singh L. K., Mehta R. Y., Ram D., Shibukumar T. M., Kokane A., Lenin Singh R. K., Chavan B. S., Sharma P., Ramasubramanian C., Dalal P. K., Saha P. K., Deuri S. P., Giri A. K., Kavishvar A. B., Sinha V. K., Thavody J., Chatterji R., Akoijam B. S., Das S., Kashyap A., Ragavan V. S., Singh S. K., Misra R. and investigators as listed in the report: National Mental Health Survey of India, 2015-2016: Prevalence, Patterns, and Outcomes.”
The data used for analysis in this publication is from the National Mental Health Survey (NMHS) funded by the Ministry of Health and Family Welfare, Government of India, and was implemented and coordinated by NIMHANS, Bengaluru, INDIA in collaboration with state partners. NMHS phase 1 (2015-2016) was undertaken in 12 states of India across the six regions and interviewed 39,532 individuals (http://indianmhs.nimhans.ac.in). The funder had no role in implementation, data acquisition, data analysis, and interpretation and write-up of the paper.
The authors would also like to sincerely thank Professor David V. Sheehan, Distinguished University Health Professor Emeritus at College of Medicine, University of South Florida, USA, for his guidance and valuable inputs for the smooth, scientific, and efficient conduct of the survey.
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