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. Author manuscript; available in PMC: 2023 Aug 1.
Published in final edited form as: Lancet Psychiatry. 2022 Aug;9(8):645–659. doi: 10.1016/S2215-0366(22)00186-9

Sub-national Patterns and Correlates of Depression among Adults 45 years or Older: Findings from wave 1 of the Longitudinal Aging Study in India

Perianayagam Arokiasamy 1,2, Matthew Prina 3, Y Selvamani 1, Dipika Gudekar 1, Supriya Salvi 1, Mathew Varghese 4, Rakhi Dandona 5,6
PMCID: PMC9375859  NIHMSID: NIHMS1825530  PMID: 35843255

Abstract

Background:

Depression is a major public health challenge linked with several poor health outcomes and disabilities among adults 45 years or older in India. In this report, we describe the prevalence of depression and its associations with a variety of sociodemographic correlates and co-existing health conditions for this age group for India and its states.

Methods:

Data from wave 1 of the Longitudinal Ageing Study in India (LASI) were used to estimate national and subnational state level age-standardized prevalence of depression (major depressive episodes-MDEs), using the internationally validated Composite International Diagnostic Interview - Short Form (CIDI-SF) scale. Hierarchical mixed effect multivariate logistic regression models were used to study the sociodemographic correlates and co-existing health conditions of MDEs among the nationally representative sample of 72,250 adults aged 45 years or older from 35 states/union territories except the state of Sikkim. Associations between depression and self-rated health, co-morbid conditions, functional health, and life satisfaction measures were also examined.

Findings:

A total of 40,335 (58·3%) females and 29,407 (41·7%) males aged 45 to116 years (median age 58 years) participated. The overall age standardized prevalence of depression based on CIDI-SF scale was 5·7% (95% CI 5·5, 5·8) compared with 0·5% (95% CI 0·46, 0·57) self-reported prevalence of depression among adults 45 years or older in India (Table 1). Wide sub-national variations were seen in the depression prevalence, ranging from 0·77% (95% CI 0·28, 1·27) in Mizoram state to 12·9% (95% CI 11·6, 14·2) in Madhya Pradesh. Prevalence was higher in females (6·1% 95% CI 5·8, 6·3) than males (4·8%, 95% CI 4·6, 5·1) for India, which was more pronounced in few of the northern states. The risk of depression was significantly higher in those residing in rural areas, widowed, with no or low education, and in the poorest. Depression showed strong positive association with poor self-rated health and, ADL and IADL limitations.

Interpretation:

Despite significant burden, depression remain undiagnosed and strongly linked with poor health and wellbeing outcomes in adults 45 years or older in India population. The ageing population of India and the subnational variations further amplify the implications of this new evidence for prevention and treatment of depression in India.

Funding:

LASI was funded by the Ministry of Health and Family Welfare, Government of India, the National Institute of Ageing, USA and the United Nations Population Fund, India.

Introduction

Mental health disorders – in various forms and intensities - affect large proportion of populations in their lifetime and are associated with overall rise in disability, and healthcare costs 1–2. Mental ill health accounts for a fifth of the overall disability burden and mortality globally, with major depressive disorder being a leading cause of disease burden. 2–3 Rapid demographic ageing transition is driving the rise in the proportion of older people living in low and middle-income countries (LMICs) and consequently the rise in chronic disease prevalence including mental health disorders in low income settings3–4. Globally, in 2017 LMICs accounted for three quarters of mental health burden, with India alone accounting for 15% of the global mental health burden second after China (17%) 3,5–6. While late life depression is common in LMICs, its prevalence varies widely across countries.7–8

The burden of depressive disorders in India is significant. The Global Burden of Disease (GBD) Study India estimated that 46 million people >10 years of age in India had depressive disorders in 2017, with the prevalence increasing with age and with the highest prevalence rate of around 6·5% among population aged 60+ years9. The GBD study documented wide heterogeneity in the prevalence of depressive disorders across the Indian states and highlighted the paucity of risk factors for depressive disorders in India.9 Likewise, the National Mental Health Survey (NMHS 2015–16), conducted among adults aged 18 years and more in twelve Indian states, reported a lifetime and current prevalence of depressive disorders at 6·9% and 3·5% respectively for people aged 60 years and above10. The NMHS 2015–16 also documented a significant treatment gap for these disorders.10 However, these studies lacked an exclusive focus on adults 45 years or older. Little is known about the correlates and risk factors of depressive disorders and, its association with quality of life for older population in India that can be used for effective policy making for this age group. In this paper, we present national and subnational estimates of the prevalence of depression and its correlates for population aged 45 years or older from the Longitudinal Ageing Study in India (LASI). In addition, we report the association of depression with chronic health conditions, measures of functional abilities, poor self-rated health, and life satisfaction.

Methodology

Data and study design

LASI is a nation-wide comprehensive study of the health, social, and economic wellbeing of adults aged 45 years or older in India, harmonized with the worldwide Health and Retirement Study and its sister studies.11 Ethics approval for LASI was granted by all collaborating institutions and the Indian Council for Medical Research. Detailed description of study design, tools, and novel survey protocols adopted in LASI are provided in appendix pp 3–4 and available elsewhere12,13.

In this paper, we used data from the recent wave 1 of LASI, 2017–18, a study of 72,250 adults aged 45 years or older (including spouses aged less than 45 years) representing India and all its 35 states/union territories except the state of Sikkim. LASI adopted a multistage stratified area probability cluster sampling design. Within each state, a three-stage sampling design in rural areas and a four-stage sampling design in urban areas was followed to select households for the study. All age-eligible persons from the sampled households were eligible to participate. Of the 65,584 households which consented to participate in the LASI survey in 35 states and union territories, 27% of households had one age-eligible participant, 41% had two and more age-eligible participants and the remaining 32% had no age eligible participant. The overall household response rate was 96%, and the individual response rate was 87%.

In brief, LASI collected data at three levels – household, individual, and community-using computer assisted face-to-face personal interviews and direct health measurements of a range of biomarkers of health. Trained interviewers administered the face-to-face interviews. LASI implemented standardized translation, training, survey protocols ensuring that each investigator across India had a similar level of understanding of question-by-question specifications, and correct recording of responses with multi-layered supervision, and rigorous quality control protocols. In addition, trained health investigators conducted tests for physical functioning markers, performance-based markers, and anthropometric measurements, cognitive measures, besides collecting dried blood spots on filter paper cards for testing for select health markers for chronic health conditions. Separate written informed consent was administered for household and individual surveys, and dried blood spot collection. Detailed description of data collection protocols is provided in appendix pp 3–4 and elsewhere12,13.

Choice of primary measure of depression

In LASI, mental health of participants was assessed in two domains: depression and cognition. We used the Composite International Diagnostic Interview - Short Form (CIDI-SF) scale, a brief structured interview designed for assessing participants with diagnosable (major depressive episodes (MDEs)14. The CIDI-SF provides a valid diagnosis of depression and is a reliable structured tool for use with trained interviewers. It is cost-effective, robust, easy to administer, non-clinical, and diagnostic survey instrument comparable to the original CIDI scale15–18 and the Diagnostic and Statistical Manual of Mental Disorders (DSM) –III.19 It was developed as a non-clinical diagnostic tool where clinical assessment and rating of mental disorders by clinicians is not possible.20 The CIDI-SF tool is used worldwide in general-purpose large-scale epidemiological studies that cannot invest more time needed to administer detailed psychiatric diagnostic interviews but nonetheless want to evaluate prevalence of depressive disorders.14 Validation studies have shown diagnostic classifications for depressive episodes made in the full CIDI can be reproduced with excellent accuracy with the CIDI-SF scale.20 It is widely used in national large-scale surveys such as the US National Health Interview Survey and the Canadian National Population Health Survey15–16, and also in the World Mental Health Surveys;18 and using the CIDI-SF in LASI allows for comparability with the HRS family of ageing studies in around 40 countries.11 The validity of translated CIDI-SF is documented across various languages,21 including in India.17,18,22

For this study, the entire LASI instrument including CIDI-SF was translated into 16 major regional languages following the WHO standard translation protocols (Appendix pp 3).23 The translated LASI instrument was pretested in each of the regional languages as part of field practice following 5 weeks of survey and health investigators training. However, the psychometric properties of the CIDI-SF were not directly assessed as part of this study, as it was beyond the scope of this large-scale project. More detailed description of field survey implementation protocols is provided in Appendix pp 4 and elsewhere in the LASI national report.12,13

In LASI, the participants who reported yes to three or more of the seven items of depressive symptoms on CIDI-SF scale were considered as having probable depression (MDEs) (Appendix pp 5).24 In LASI, participants were also asked if they ever had been diagnosed with depression by any health professional. The self-reported prevalence of depression based on this question is also presented for comparison.

Measures of sociodemographic characteristics

In addition, we included a set of sociodemographic measures, health behaviour factors, chronic health conditions and health and wellbeing measures as important correlates of depression guided by a conceptual framework presented in (Appendix pp 6). LASI collected data on sociodemographic characteristics age, place of residence (rural, urban), marital status (married, widowed, divorced/separated/others), work status, and educational attainment, and annual household per capita income (PCI). Education was categorized as less than 5 years complete, 5–9 years complete, and 10 or more years of schooling. PCI was divided into five quintiles from the lowest to highest. Work status was categorized as currently working and not working (both those had worked previously and who had never worked).

Health behavioral factors

LASI documented use of tobacco and alcohol, and whether the participant was physically active and practiced yoga. We generated a combination measure of tobacco and alcohol use – as none, only tobacco use, only alcohol use, and use of both. Physical inactivity was defined as those who did not engage in any type of moderate or vigorous physical activity according to WHO criteria 25 or yoga for a given time throughout the week.

Chronic health conditions

LASI collected self-reported information on chronic diseases including cardiovascular diseases (CVDs), diabetes, lung diseases (asthma, chronic bronchitis, and chronic obstructive pulmonary disorder (COPD), bone diseases (arthritis, rheumatism, and osteoporosis), cancer, urogenital disorders (kidney failure, urinary stone, incontinence, and benign prostatic hyperplasia (BPH) in male), injuries and falls and sense organ-related diseases (oral health, eye problems and ear problems). These were considered as co-existing health conditions of depression.

Health, functional abilities, and wellbeing measures

LASI included measures of functional ability, self-rated health, and life satisfaction. Functional abilities were measured by activities of daily living (ADL) and instrumental ADL measures.26 We generated a single measure of ADL by combing all six ADL items categorizing participants with no ADL limitations and those with at least one ADL limitation. Instrumental activities of daily living (IADL) measured difficulties across six domains. A single measure of IADL was generated categorizing participants who reported no IADL limitations and those who reported at least one IADL limitation. Participants who reported their overall health as poor or very poor were considered as having poor self-rated health (SRH). We also included a five-item scale to measure global cognitive judgments of one’s life satisfaction, as a measure of wellbeing.27 These five items include a) In most ways my life is close to my ideal b) The conditions of my life are excellent c) I am satisfied with my life. d) So far, I have gotten the important things I want in life. e) If I could live my life over, I would change almost nothing. The responses were captured in a seven-point scale ranging from strongly disagree to strongly agree (1–7). In the analysis, we summed up these fives item scale scores ranging from 5–35 and categorized as 5–20 (low), 21–25 (Medium), and 26–35 (high), such as a higher score indicated a higher life satisfaction.

Statistical analysis

We estimated age-standardized prevalence of depression and self-reported depression with diagnosis among adults 45 years of age or older for India and the 35 states and union territories, with 95% confidence intervals. We also adjusted for Secondary Sampling Units (SSU)-wise outliers. Age-standardized prevalence rates for states by gender, and rural and urban populations are also reported. We further report weighted means and proportions of depression, and self-reported depression with diagnosis according to sociodemographic and co-existing chronic health conditions.

We further estimated two-level (states and union territories at the second level; individuals at the first level) hierarchical mixed effect multivariate logistic regression models to assess the association of depression with sociodemographic correlates, SES determinants, health behaviour factors, co-existing chronic health conditions and self-reported health, functional abilities, and life satisfaction measure. Guided by conceptual framework (appendix pp 6), we considered four categories of predictors in the regression model: demographic correlates, socioeconomic determinants (SES), health behaviour factors, coexisting chronic health conditions, and wellbeing measures (covariates). Stage-wise inclusion of predictors was considered to understand how interrelationship among predictors affect the significance and outcome of each predictor. Model 1 included demographic correlates and SES determinants; model 2 with the addition of health behaviour factors and coexisting health conditions and model 3 with wellbeing measures.

Before conducting regression analyses, we evaluated multi-collinearity associations among predictors and conducted regression diagnostics to test model assumptions (appendix pp 7-9). For all predictors in the final regression models, we found variance inflation factor (VIF) was less than 2 suggesting independence among variables except marital state and living arrangement. Living arrangement showed significant association with marital status with VIF of >2 for living arrangement and therefor living arrangement was not considered for the final models. All analyses are weighted, regression estimates are presented with 95% CI and all statistical analysis were performed using Stata 14·1 version with maps drawn using arc–GIS.

Results

Appendix (pp 10–11) presents the sample characteristics of the study population, which included 40,335(58·3%) females and 29,407 (41·7%) males. A total of 41% of study participants were adults aged less than 55 years, 27·2% were aged 55–64 years, and 31·9% were aged 65 years or more; 68% were from rural areas, 76% were currently married, 46% were currently working, 60% had less than five years of schooling, and 21% were in the poorest PCI quintile. Forty-four percent of participants either smoked or consumed smokeless tobacco and/or alcohol, 32% were physically inactive, about 15% had at least one ADL limitation, 36% had at least one IADL limitation and 32% reported low levels of cognitive life satisfaction. Appendix pp 13 further provides sample characteristics of the study population by states, rural and urban and, males and females.

Depression

The overall age standardized prevalence of depression based on CIDI-SF scale in India was 5·7% (95% CI 5·5, 5·8) compared to the prevalence of 0·5% (95% CI 0·46, 0·57) for self-reported diagnosis of depression among adults 45 years or older (Table 1). Substantial state-level variations were observed in the age standardized prevalence of depression for this age group as shown in Table 1 and Figure 1. The age standardized prevalence of depression ranged from 0·77% (95% CI 0·28, 1·27) in Mizoram state to 12·9% (95% CI 11·6, 14·2) in Madhya Pradesh, with Uttar Pradesh documenting a higher prevalence of >10%. On the other hand, the highest adjusted prevalence of self-reported diagnosis of depression was 2·85% (95% CI 2·18, 3·53) for Telangana state (Table 1). Though the adjusted prevalence of depression was higher in rural areas than those in urban areas for India overall, the rural-urban difference was notable in the states of Chandigarh, Delhi, Madhya Pradesh, Uttar Pradesh, West Bengal, and Maharashtra (Table 1 and Figure 1).

Table 1:

Age-standardized prevalence (%) of depression [major depressive episodes (MDE and self-reported prevalence of depression with diagnosis among adults 45 years and older by states, India, 2017–18.

States/UTs Self-reported diagnosed depression (%, 95% CI) Prevalence based on CIDI (SF)
Depression (%, 95% CI) Rural (%) (95% CI) Urban (%) (95% CI) Male (%) (95% CI) Female (%) (95% CI)
Andaman Nicobar Islands 0.75 (0.28, 1.23) 1.33 (0.70, 1.97) 1.6 (0.72, 2.5) 0.85 (0.0, 1.7) 1.3 (0.43, 2.2) 1.18 (0.37, 2.0)
Andhra Pradesh 0.65 (0.34, 0.97) 3.63 (2.90, 4.36) 3.3 (2.5, 4.1) 4.2 (2.8, 5.7) 3.2 (2.1, 4.2) 3.9 (2.9, 4.8)
Arunachal Pradesh 0 (0, 0) 1.33 (0.68, 1.98) 1.5 (0.76, 2.3) 0.33 (0.0, 0.8) 1.7 (0.56, 2.9) 1.1 (0.31, 1.9)
Assam 0.29 (0.08, 0.51) 5.60 (4.66, 6.53) 5.3 (4.3, 6.2) 7.2 (4.3, 10.1) 4.7 (3.3, 6.0) 6.3 (5.0, 7.6)
Bihar 0.07 (0, 0.15) 6.7 (5.8, 7.7) 6.2 (5.2, 7.2) 9.9 (6.7, 13.0) 5.4 (4.1, 6.7) 7.4 (6.1, 8.7)
Chandigarh 0.94 (0.32, 1.56) 7.87 (6.17, 9.57) - 7.9 (6.2, 9.6) 5.8 (3.7, 7.8) 8.6 (6.3, 10.9)
Chhattisgarh 0.14 (0, 0.29) 4.55 (3.62, 5.49) 4.5 (3.5, 5.6) 4.3 (2.3, 6.3) 3.0 (1.9, 4.1) 5.5 (4.2, 6.9)
Dadra Nagar Haveli 0.08 (0, 0.23) 2.58 (1.62, 3.54) 1.8 (0.81, 2.8) 3.8 (1.9, 5.8) 2.9 (1.4, 4.3) 2.2 (1.0, 3.4)
Daman Diu 0.12 (0, 0.35) 3.83 (2.58, 5.07) 2.8 (1.0, 4.6) 4.2 (2.6, 5.8) 3.7 (1.8, 5.5) 3.8 (2.2, 5.5)
Delhi 0.23 (0, 0.49) 6.49 (5.14, 7.84) - 6.5 (5.1, 7.8) 6.0 (4.0, 7.9) 5.3 (3.6, 7.1)
Goa 0.63 (0.24, 1.03) 9.08 (7.55, 10.6) 13.2 (10.4, 16.1) 6.1 (4.5, 7.7) 6.5 (4.4, 8.5) 10.4 (8.3, 12.4)
Gujarat 0.45 (0.17, 0.73) 3.26 (2.52, 3.99) 3.5 (2.5, 4.4) 2.7 (1.6, 3.7) 2.3 (1.4, 3.2) 3.7 (2.7, 4.7)
Haryana 0.34 (0.08, 0.6) 6.15 (5.04, 7.27) 5.7 (4.4, 7.1) 6.8 (4.8, 8.8) 5.3 (3.7, 6.9) 6.7 (5.1, 8.2)
Himachal Pradesh 0.46 (0.12, 0.8) 4.94 (3.76, 6.12) 4.7 (3.4, 5.9) 6.0 (2.4, 9.6) 2.3 (3.7, 6.9) 6.4 (4.7, 8.1)
Jammu Kashmir 0.83 (0.4, 1.27) 4.41 (3.38, 5.44) 5.0 (3.7, 6.2) 2.6 (1.1, 4.0) 3.7 (2.3, 5.1) 4.8 (3.4, 6.3)
Jharkhand 0.08 (0, 0.2) 5.89 (4.92, 6.86) 6.3 (5.2, 7.5) 3.8 (2.1, 5.5) 6.1 (4.5, 7.6) 5.7 (4.4, 6.9)
Karnataka 0.35 (0.12, 0.59) 7.98 (6.87, 9.08) 7.7 (6.4, 9.1) 8.1 (6.1, 10.0) 6.9 (5.3, 8.6) 8.3 (6.9, 9.7)
Kerala 0.67 (0.32, 1.02) 5.99 (5.01, 6.97) 7.2 (5.7, 8.8) 4.5 (3.3, 5.7) 3.9 (2.5, 5.2) 7.0 (5.7, 8.3)
Lakshadweep 0.26 (0, 0.55) 2.31 (1.46, 3.15) 1.6 (0.24, 2.9) 2.5 (1.5, 3.5) 1.3 (0.33, 2.3) 2.7 (1.6, 3.8)
Madhya Pradesh 0.85 (0.51, 1.19) 12.9 (11.6, 14.2) 13.6 (12.0, 15.2) 10.8 (8.6, 13.0) 11.7 (9.9, 13.6) 13.6 (11.8, 15.3)
Maharashtra 0.18 (0.05, 0.32) 7.36 (6.54, 8.19) 10.0 (8.7, 11.4) 4.5 (3.6, 5.4) 6.1 (4.9, 7.3) 7.9 (6.8, 9.0)
Manipur 0.14 (0, 0.33) 1.28 (0.65, 1.91) 1.11 (0.42, 1.8) 1.5 (0.31, 2.7) 1.5 (0.48, 2.6) 1.1 (0.33, 1.9)
Meghalaya 0.21 (0, 0.5) 1.0 (0.38, 1.61) 1.1 (0.37, 1.8) 0.6 (0.0, 1.8) 0.42 (0, 0.99) 1.2 (0.37, 2.1)
Mizoram 0.11 (0, 0.32) 0.77 (0.28, 1.27) 0.65 (0.04, 1.3) 0.83 (0.09, 1.6) 0 (0, 0) 1.3 (1.6, 4.1)
Nagaland 0 (0, 0) 2.53 (1.63, 3.43) 2.4 (1.4, 3.4) 2.6 (0.93, 4.3) 2.1 (0.87, 3.3) 2.8 (1.6, 4.1)
Odisha 0.21 (0.04, 0.38) 4.59 (3.82, 5.37) 4.4 (3.6, 5.2) 4.8 (2.9, 6.8) 3.7 (2.6, 4.8) 5.1 (4.0, 6.2)
Puducherry 0.64 (0.21, 1.07) 4.24 (3.22, 5.26) 5.4 (3.4, 7.4) 3.7 (2.5, 4.8) 4.2 (2.6, 5.8) 4.0 (2.8, 5.2)
Punjab 1.1 (0.62, 1.59) 8.66 (7.4, 9.92) 9.4 (7.9, 11.0) 6.1 (4.2, 8.1) 6.3 (4.7, 7.9) 9.9 (8.2, 11.7)
Rajasthan 0.37 (0.07, 0.67) 6.77 (5.62, 7.92) 7.0 (5.7, 8.3) 5.9 (3.6, 8.1) 7.0 (5.3, 8.7) 6.7 (2.5, 4.0)
Tamil Nadu 0.62 (0.36, 0.87) 3.05 (2.48, 3.62) 3.7 (2.8, 4.7) 2.5 (1.8, 3.2) 2.7 (1.8, 3.5) 3.3 (2.5, 4.0)
Telangana 2.85 (2.18, 3.52) 4.53 (3.69, 5.37) 5.4 (4.3, 6.5) 2.6 (1.5, 3.7) 4.8 (3.5, 6.2) 4.1 (3.0, 5.1)
Tripura 0.16 (0, 0.39) 3.69 (2.62, 4.77) 4.0 (2.7, 5.3) 2.67 (0.85, 4.5) 2.7 (1.3, 4.0) 4.3 (2.8, 5.8)
Uttar Pradesh 0.42 (0.22, 0.63) 11.0 (10.0, 12.0) 11.9 (10.7, 13.1) 7.1 (5.4, 8.8) 8.9 (7.6, 10.3) 12.1 (10.7, 13.5)
Uttarakhand 0.13 (0, 0.31) 5.75 (4.51, 7.00) 6.6 (5.1, 8.2) 3.1 (1.3, 4.9) 5.4 (3.5, 7.3) 5.7 (4.1, 7.3)
West Bengal 1.09 (0.77, 1.4) 5.9 (5.16, 6.64) 8.0 (6.8, 9.3) 3.5 (2.7, 4.3) 5.3 (4.3, 6.4) 6.1 (5.1, 7.1)
India 0.51 (0.46,0.57) 5.7 (5.5, 5.8) 6.3 (6.0, 6.5) 4.6 (4.4, 4.9) 4.3 (4.1, 4.6) 6.3 (6.1, 6.6)
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Figure 1:

Figure 1:

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Age-standardized prevalence (%) of depression among adults 45 years or older in states and union territories of India by residence and sex, LASI Wave 1, 2017–18

For India overall, the weighted prevalence of depression was higher in the age group 60 years and older (7·6%, 95% CI 7·3, 7·9) as compared with those in less than 60 years age group (6·5%, 95% CI 6·3, 6·8) as shown in Appendix pp 14. The prevalence of depression was similar across these two age groups for all the states except for Madhya Pradesh, Delhi, Lakshadweep, Tamil Nadu, Puducherry, and West Bengal where it was much higher among those aged 60+ years (appendix pp 14). Females had a higher age-standardized prevalence rate of depression than males for India overall but more pronounced in the states of Himachal Pradesh, Punjab, Chhattisgarh, Uttar Pradesh, Bihar, Kerala, Assam (Table 1 and Figure 1). The age-wise pattern of depression was different for males and females, with the former documenting an increase between 45–49 years and 50–54 years, and then the increase was seen only for 75+ years men (Figure 2). For women, a gradual increase was seen from 45 years to 69 years, after which there was a sharper increase as shown in Figure 2. Table 2 further provides bivariate assessment of the weighted proportions of depression, all 7 items of CIDI-SF, and self-reported depression with diagnosis by background characteristics (Appendix pp. The weighted proportion ranged from 4·2% (CI 4·02%, 4·32%) for thoughts of deaths to 6·49% (CI 6·31%, 6·67%) for anhedonia and 6·59% (CI 6·4%, 6·77%) for low energy. Appendix pp 15 provides the weighted proportions of all 7 items of CIDI-SF by states and the weighted proportion of depression by background characteristics across states/UTs are provided in appendix pp 16–17.

Figure 2:

Figure 2:

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Age patterns in the prevalence (%) of self-reported depression with diagnosis and depression based on CIDI-SF by sex, India, LASI Wave 1, 2017–18

Table 2:

Weighted proportion (%) of self-reported depression with diagnosis, depression based on CIDI-SF, and their 7 subdomains among adults aged 45 years or more by background characteristics, India, 2017–18.

Background characteristics Weighted proportion of self-reported diagnosed depression (%) (95% CI) Weighted proportion based on CIDI (SF)
Loss of Interest (Anhedonia) (%) (95% CI) Thoughts of death (%) (95% CI) Low energy (%) (95% CI) Loss of appetite (%) (95% CI) Trouble in concentration (%) (95% CI) Feeling of worthless (%) (95% CI) Difficulty in falling asleep (%) (95% CI) Depression (MDE) (%) (95% CI)
Demographic factors
a) Age groups (years)
45–54 0.44 (0.37, 0.52) 5.94 (5.67, 6.21) 3.73 (3.51, 3.94) 6.13 (5.86, 6.41) 5.52 (5.26, 5.78) 5.87 (5.61, 6.14) 4.93 (4.69, 5.18) 5.26 (5, 5.51) 6.51 (6.23, 6.79)
55–64 0.57 (0.47, 0.68) 6.7 (6.35, 7.05) 4.24 (3.95, 4.52) 6.63 (6.28, 6.98) 6.04 (5.71, 6.38) 6.29 (5.95, 6.63) 5.49 (5.17, 5.81) 6 (5.67, 6.34) 7.15 (6.79, 7.51)
65+ 0.83 (0.7, 0.95) 7.03 (6.68, 7.39) 4.7 (4.41, 4.99) 7.14 (6.79, 7.5) 6.82 (6.47, 7.16) 6.75 (6.41, 7.1) 5.76 (5.44, 6.08) 6.35 (6.01, 6.69) 7.54 (7.18, 7.9)
b) Residence
Rural 0.65 (0.57, 0.72) 7.02 (6.78, 7.25) 4.48 (4.29, 4.68) 7.17 (6.93, 7.41) 6.9 (6.67, 7.14) 6.9 (6.67, 7.14) 5.86 (5.64, 6.08) 6.62 (6.39, 6.86) 7.63 (7.38, 7.88)
Urban 0.5 (0.41, 0.59) 5.39 (5.11, 5.67) 3.51 (3.29, 3.74) 5.36 (5.09, 5.64) 4.32 (4.07, 4.57) 4.92 (4.66, 5.19) 4.27 (4.02, 4.52) 4.09 (3.84, 4.33) 5.71 (5.42, 6)
c) Sex
Male 0.51 (0.43, 0.59) 5.98 (5.7, 6.25) 3.83 (3.61, 4.05) 6.09 (5.82, 6.37) 5.21 (4.96, 5.47) 5.81 (5.54, 6.08) 5.09 (4.84, 5.34) 4.95 (4.7, 5.2) 6.43 (6.15, 6.71)
Female 0.66 (0.58, 0.74) 6.86 (6.62, 7.11) 4.42 (4.22, 4.62) 6.94 (6.69, 7.19) 6.68 (6.44, 6.93) 6.59 (6.35, 6.83) 5.53 (5.31, 5.76) 6.42 (6.18, 6.66) 7.43 (7.17, 7.68)
d) Marital status
Currently married 0.52 (0.45, 0.58) 5.82 (5.62, 6.02) 3.53 (3.37, 3.68) 5.95 (5.75, 6.15) 5.36 (5.17, 5.55) 5.58 (5.39, 5.78) 4.75 (4.57, 4.93) 5.18 (4.99, 5.37) 6.3 (6.09, 6.5)
Widowed 0.85 (0.7, 1) 9.06 (8.58, 9.53) 6.56 (6.15, 6.97) 9.09 (8.61, 9.57) 8.73 (8.26, 9.2) 8.85 (8.38, 9.33) 7.6 (7.16, 8.04) 8.19 (7.73, 8.64) 9.73 (9.24, 10.23)
Divorced/Separated/others 0.97 (0.56, 1.38) 4.92 (4.01, 5.84) 3.32 (2.56, 4.08) 4.74 (3.84, 5.64) 4.85 (3.93, 5.76) 4.85 (3.94, 5.76) 4.15 (3.31, 5) 4.39 (3.53, 5.26) 5.28 (4.33, 6.22)
Socioeconomic factors
a) Education
Less than 5 years 0.69 (0.61, 0.77) 7.48 (7.22, 7.74) 4.96 (4.75, 5.18) 7.62 (7.35, 7.88) 6.98 (6.73, 7.23) 7.22 (6.96, 7.47) 6.29 (6.05, 6.53) 6.62 (6.38, 6.87) 8.14 (7.87, 8.41)
5–9 years 0.5 (0.4, 0.61) 5.64 (5.29, 5.99) 3.46 (3.18, 3.73) 5.84 (5.48, 6.19) 5.44 (5.1, 5.79) 5.59 (5.23, 5.94) 4.46 (4.15, 4.78) 5.33 (4.99, 5.68) 6.07 (5.7, 6.43)
10 or more 0.42 (0.32, 0.53) 4.29 (3.95, 4.63) 2.44 (2.18, 2.7) 4.14 (3.81, 4.48) 3.86 (3.53, 4.18) 3.98 (3.66, 4.31) 3.33 (3.02, 3.63) 3.72 (3.4, 4.03) 4.44 (4.1, 4.79)
b) Work status
Currently working 0.27 (0.22, 0.33) 5.37 (5.13, 5.62) 3.22 (3.03, 3.42) 5.52 (5.27, 5.77) 5.28 (5.03, 5.52) 5.35 (5.11, 5.6) 4.47 (4.24, 4.69) 5.07 (4.83, 5.31) 5.91 (5.66, 6.17)
Worked in past but currently not working or never worked 0.88 (0.79, 0.98) 7.48 (7.21, 7.74) 5.01 (4.79, 5.23) 7.52 (7.26, 7.79) 6.77 (6.52, 7.02) 7.07 (6.81, 7.33) 6.12 (5.88, 6.37) 6.45 (6.2, 6.7) 7.97 (7.7, 8.25)
c) Household income quintile
Poorest 0.76 (0.62, 0.9) 7.89 (7.44, 8.34) 5.26 (4.89, 5.63) 8.1 (7.65, 8.56) 7.64 (7.2, 8.08) 7.68 (7.24, 8.12) 6.33 (5.92, 6.73) 7.36 (6.92, 7.79) 8.46 (8, 8.92)
Poorer 0.6 (0.47, 0.73) 6.97 (6.53, 7.4) 4.26 (3.92, 4.6) 7.03 (6.6, 7.47) 6.62 (6.2, 7.04) 6.56 (6.14, 6.98) 5.66 (5.27, 6.05) 6.29 (5.88, 6.7) 7.42 (6.98, 7.87)
Middle 0.65 (0.51, 0.78) 5.87 (5.48, 6.27) 3.57 (3.26, 3.88) 5.77 (5.38, 6.16) 5.73 (5.34, 6.12) 5.83 (5.44, 6.22) 5.11 (4.74, 5.48) 5.54 (5.16, 5.92) 6.4 (5.99, 6.81)
Richer 0.62 (0.49, 0.75) 6.35 (5.94, 6.76) 3.88 (3.56, 4.21) 6.45 (6.04, 6.86) 6.21 (5.81, 6.61) 6.21 (5.8, 6.61) 5.17 (4.8, 5.54) 5.71 (5.32, 6.1) 6.88 (6.46, 7.3)
Richest 0.38 (0.28, 0.48) 5.49 (5.11, 5.87) 3.84 (3.52, 4.16) 5.71 (5.32, 6.1) 4.51 (4.16, 4.86) 5.39 (5.01, 5.77) 4.52 (4.17, 4.87) 4.43 (4.09, 4.77) 6.01 (5.61, 6.41)
Behavioural health risk factors
a) Tobacco and alcohol
No risk 0.57 (0.5, 0.64) 6.25 (6.03, 6.48) 3.98 (3.8, 4.16) 6.3 (6.08, 6.53) 5.82 (5.61, 6.04) 6.06 (5.84, 6.28) 5.11 (4.91, 5.32) 5.53 (5.32, 5.74) 6.75 (6.52, 6.98)
Using tobacco only 0.8 (0.65, 0.95) 7.45 (7.01, 7.9) 4.82 (4.46, 5.19) 7.5 (7.05, 7.94) 7.09 (6.66, 7.52) 7.19 (6.76, 7.63) 6.14 (5.73, 6.55) 6.86 (6.44, 7.29) 7.96 (7.5, 8.42)
Using alcohol only 0.63 (0.4, 0.86) 6.97 (6.22, 7.71) 4.95 (4.31, 5.58) 7.32 (6.55, 8.08) 5.15 (4.51, 5.8) 5.99 (5.3, 6.69) 6.09 (5.39, 6.79) 5.22 (4.57, 5.87) 7.51 (6.74, 8.29)
Using both 0.4 (0.25, 0.55) 5.75 (5.2, 6.3) 3.69 (3.24, 4.13) 6.17 (5.6, 6.73) 5.85 (5.3, 6.4) 5.73 (5.18, 6.27) 4.81 (4.31, 5.32) 5.53 (4.99, 6.06) 6.43 (5.85, 7)
b) Physical activity and yoga
Both No 0.99 (0.87, 1.12) 7.43 (7.09, 7.77) 4.79 (4.51, 5.07) 7.55 (7.21, 7.89) 6.98 (6.65, 7.31) 6.86 (6.53, 7.18) 5.98 (5.68, 6.29) 6.53 (6.21, 6.85) 7.92 (7.57, 8.27)
Only Physically active 0.41 (0.35, 0.47) 5.77 (5.54, 6) 3.75 (3.56, 3.94) 5.91 (5.68, 6.15) 5.38 (5.15, 5.61) 5.75 (5.52, 5.99) 4.78 (4.57, 4.99) 5.2 (4.98, 5.42) 6.3 (6.05, 6.54)
Only Practicing yoga 0.55 (0.26, 0.85) 8.66 (7.54, 9.78) 5.83 (4.89, 6.76) 8.53 (7.41, 9.64) 7.94 (6.86, 9.02) 8.05 (6.96, 9.13) 7.15 (6.13, 8.18) 7.95 (6.87, 9.03) 9.48 (8.31, 10.65)
Physically active and practicing yoga 0.4 (0.24, 0.56) 7.1 (6.46, 7.74) 4.02 (3.53, 4.51) 6.83 (6.2, 7.46) 6.66 (6.04, 7.29) 6.89 (6.25, 7.52) 6.17 (5.57, 6.77) 6.4 (5.79, 7.01) 7.54 (6.88, 8.2)
Co-existing chronic health conditions
c) Diabetes
No 0.59 (0.53, 0.65) 6.29 (6.09, 6.48) 3.92 (3.77, 4.08) 6.38 (6.19, 6.57) 6.07 (5.88, 6.26) 6.07 (5.88, 6.26) 5.19 (5.02, 5.37) 5.77 (5.59, 5.96) 6.82 (6.62, 7.02)
Yes 0.68 (0.51, 0.86) 8.01 (7.43, 8.59) 6.04 (5.53, 6.55) 8.11 (7.53, 8.69) 6.04 (5.53, 6.54) 7.73 (7.16, 8.3) 6.53 (6, 7.05) 6.03 (5.52, 6.54) 8.42 (7.83, 9.01)
d) Cardiovascular diseases (CVDs)
No 0.45 (0.4, 0.51) 5.57 (5.37, 5.78) 3.43 (3.27, 3.59) 5.66 (5.46, 5.87) 5.44 (5.24, 5.64) 5.37 (5.17, 5.56) 4.52 (4.34, 4.7) 5.18 (4.99, 5.38) 6.06 (5.85, 6.27)
Yes 0.98 (0.84, 1.11) 8.85 (8.46, 9.24) 6.1 (5.76, 6.43) 8.96 (8.56, 9.35) 7.68 (7.31, 8.05) 8.59 (8.2, 8.98) 7.49 (7.13, 7.86) 7.4 (7.04, 7.77) 9.45 (9.04, 9.85)
e) Bone diseases
No 0.37 (0.32, 0.41) 6.08 (5.89, 6.27) 3.94 (3.78, 4.09) 6.18 (5.99, 6.37) 5.68 (5.5, 5.87) 5.89 (5.71, 6.08) 5.07 (4.9, 5.25) 5.4 (5.22, 5.58) 6.62 (6.42, 6.82)
Yes 1.92 (1.64, 2.2) 8.8 (8.23, 9.37) 5.47 (5.01, 5.93) 8.87 (8.3, 9.45) 8.25 (7.69, 8.8) 8.34 (7.78, 8.9) 6.87 (6.36, 7.39) 8.07 (7.52, 8.62) 9.2 (8.61, 9.78)
f) Lung diseases
No 0.57 (0.51, 0.62) 6.38 (6.19, 6.57) 4.07 (3.92, 4.22) 6.47 (6.28, 6.66) 5.93 (5.75, 6.11) 6.14 (5.96, 6.33) 5.25 (5.08, 5.42) 5.68 (5.5, 5.86) 6.9 (6.7, 7.09)
Yes 1.12 (0.79, 1.46) 8.15 (7.26, 9.04) 5.73 (4.98, 6.49) 8.33 (7.44, 9.23) 8.21 (7.32, 9.1) 8.08 (7.2, 8.97) 6.76 (5.95, 7.58) 7.64 (6.78, 8.5) 8.67 (7.76, 9.59)
g) Cancer 0 (0, 0)
No 0.59 (0.53, 0.65) 6.47 (6.29, 6.66) 4.15 (4, 4.3) 6.57 (6.38, 6.75) 6.05 (5.87, 6.22) 6.24 (6.06, 6.42) 5.33 (5.16, 5.49) 5.78 (5.61, 5.95) 6.99 (6.8, 7.18)
Yes 2.13 (0.81, 3.45) 9.08 (6.41, 11.75) 7.43 (4.99, 9.87) 9.22 (6.53, 11.91) 9.18 (6.5, 11.86) 9.54 (6.81, 12.27) 8.22 (5.67, 10.78) 9.06 (6.39, 11.73) 9.79 (7.03, 12.55)
h) Urogenital Disorders
No 0.54 (0.48, 0.59) 6.3 (6.12, 6.49) 4.06 (3.91, 4.21) 6.41 (6.22, 6.6) 5.9 (5.72, 6.08) 6.11 (5.93, 6.29) 5.18 (5.01, 5.35) 5.61 (5.44, 5.79) 6.83 (6.63, 7.02)
Yes 1.52 (1.16, 1.87) 9.2 (8.35, 10.06) 5.81 (5.12, 6.5) 9.15 (8.29, 10) 8.62 (7.79, 9.45) 8.48 (7.65, 9.3) 7.71 (6.92, 8.5) 8.55 (7.72, 9.38) 9.61 (8.74, 10.48)
i) Falls/injury
No 0.49 (0.44, 0.55) 5.54 (5.35, 5.72) 3.6 (3.45, 3.76) 5.54 (5.35, 5.72) 5.05 (4.87, 5.23) 5.3 (5.11, 5.48) 4.54 (4.37, 4.71) 4.79 (4.61, 4.96) 5.95 (5.75, 6.14)
Yes 1 (0.83, 1.17) 10.13 (9.61, 10.64) 6.34 (5.92, 6.75) 10.58 (10.06, 11.11) 9.96 (9.44, 10.47) 9.94 (9.43, 10.45) 8.43 (7.95, 8.91) 9.68 (9.17, 10.18) 11.04 (10.51, 11.58)
j) Organ related diseases
No 0.28 ( 0.21,0.35) 4.98 (46.84, 52.83) 3.2 (2.95, 3.44) 5 (4.7, 53) 4.7 (4.41, 4.99) 4.63 (4.34, 49.15) 4.17 (3.9, 4.45) 4.37 (4.09, 4.66) 5.4 (5.09, 5.71)
yes 0.73 (0.66, 0.81 7.11 (68.83, 73.36) 4.57 (43.88, 47.56) 7.24 (7.01, 74.66) 6.63 (64.11, 68.49) 6.94 (67.12, 71.6) 5.83 (5.62, 6.04) 6.39 (6.17, 6.61) 7.67 (7.43, 7.9)
General health and wellbeing
a) Poor self-Rated Health
No 0.31 (0.27, 0.36) 4.57 (4.4, 4.74) 2.86 (2.72, 3) 4.61 (4.44, 4.78) 4.44 (4.27, 4.61) 4.47 (4.3, 4.64) 3.7 (3.54, 3.85) 4.17 (4.01, 4.34) 4.98 (4.81, 5.16)
Yes 1.74 (1.5, 1.98) 15.53 (14.86, 16.19) 10.35 (9.79, 10.92) 15.91 (15.24, 16.59) 13.75 (13.12, 14.39) 14.7 (14.05, 15.36) 13.13 (12.51, 13.75) 13.48 (12.84, 14.11) 16.54 (15.85, 17.22)
b) ADL
No ADL limitation 0.38 (0.33, 0.42) 5.07 (4.89, 5.24) 3.24 (3.1, 3.38) 5.14 (4.96, 5.32) 4.9 (4.73, 5.08) 4.92 (4.75, 5.09) 4.17 (4.01, 4.33) 4.7 (4.53, 4.87) 5.55 (5.37, 5.73)
1+ADL limitation 1.8 (1.54, 2.07) 14.38 (13.66, 15.1) 9.34 (8.74, 9.93) 14.6 (13.87, 15.32) 12.52 (11.84, 13.2) 13.72 (13.01, 14.43) 11.86 (11.19, 12.52) 11.93 (11.27, 12.6) 15.09 (14.36, 15.83)
c) IADL
No IADL limitation 0.28 (0.23, 0.33) 4.49 (4.3, 4.67) 2.72 (2.57, 2.86) 4.45 (4.27, 4.64) 4.2 (4.02, 4.38) 4.3 (4.11, 4.48) 3.59 (3.43, 3.76) 4.08 (3.91, 4.26) 4.84 (4.65, 5.03)
1+IADL limitation 1.17 (1.03, 1.32) 10.17 (9.76, 10.57) 6.83 (6.5, 7.17) 10.5 (10.09, 10.9) 9.49 (9.1, 9.88) 9.87 (9.48, 10.27) 8.56 (8.19, 8.93) 8.96 (8.58, 9.34) 10.98 (10.57, 11.4)
Cognitive judgment of life satisfaction scale
Low 0.81 (0.68, 0.93) 9.38 (8.98, 9.78) 6.33 (6, 6.66) 9.81 (9.4, 10.22) 8.78 (8.39, 9.17) 9.37 (8.97, 9.77) 8.05 (7.68, 8.43) 8.57 (8.19, 8.96) 10.36 (9.94, 10.77)
Medium 0.64 (0.52, 0.76) 6.22 (5.85, 6.58) 3.85 (3.57, 4.14) 6.42 (6.05, 6.79) 5.84 (5.49, 6.19) 5.69 (5.34, 6.04) 5.01 (4.68, 5.33) 5.54 (5.19, 5.88) 6.7 (6.33, 7.08)
High 0.37 (0.3, 0.43) 4.56 (4.33, 4.79) 2.79 (2.61, 2.97) 4.38 (4.15, 4.6) 4.25 (4.03, 4.48) 4.33 (4.11, 4.56) 3.58 (3.37, 3.78) 3.96 (3.74, 4.17) 4.77 (4.54, 5.01)
Total 0.6 (0.54, 0.66)
 6.49 (6.31, 6.67) 4.17 (4.02, 4.32) 6.59 (6.4, 6.77) 6.07 (5.89, 6.25) 6.27 (6.09, 6.45) 5.35 (5.18, 5.51) 5.8 (5.63, 5.98) 7.01 (6.82, 7.2)
Number 70,880 69,742 69,742 69,742 69,742 69,742 69,742 69,742 69,742
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Note: ADL: Activities of Daily Living, IADL: Instrumental activities of daily living,

Table 3 presents results of two-level hierarchical mixed effects multivariate logistic regression analysis of depression by SES correlates, behavioural health, co-existing chronic health conditions, and wellbeing measures. Results show significantly higher odds of depression for those in rural areas, with being widowed, those with less than 5 years of schooling, in poorest income quintile, and with a variety of co-existing chronic health conditions. Interestingly, the odds of depression were statistically significantly lower with increasing age and increasing education levels. Use of tobacco showed significant risk association with depression whereas, those physically active and practicing yoga had significantly higher odds of reporting depression but lower odds of self-reported diagnosed depression.

Table 3:

Two level (state at the second and individual at the first level) hierarchical mixed effects multivariate logistic regression analysis of sociodemographic correlates, associated risk factors and pre-existing chronic health conditions on depression based on CIDI-SF, LASI Wave 1, 2017–18.

Model 1 Model 2 Model 3
OR (95 % CI) P value OR (95 % CI) P value OR (95 % CI) P value
Demographic factors
a) Age groups (years)
<55 1 1 1
55–64 0.98 (0.90 1.06) 0.633 0.86 (0.79 0.94) 0.001 0.82(0.75 0.89) <0 000
65+ 0.83(0.76 0.91) <0 000 0.69 (0.63 0.76) <0 000 0.57 (0.51 0.63) <0 000
b) Residence
Rural 1.33(1.23 1.45) <0 000 1.36 (1.25 1.47) <0 000 1.26 (1.16 1.37) <0 001
Urban 1 1 1
c) Sex
Male 1 1 1
Female 1.01 (0.94 1.10) 0.751 1.14 (1.04 1.25) 0.006 1.10(1.00 1.20) 0.044
d) Marital status
Currently married 1 1 1
Widowed 1.71(1.57 1.86) <0 000 1.62 (1.49 1.77) <0 000 1.53 (1.40 1.66) <0 000
Divorced/Separated/Deserted/others 1.35(1.11 1.64) <0 003 1.36 (1.12 1.65) 0.002 1.15 (0.94 1.41) 0.172
Socioeconomic factors
a) Education
Less than 5 years 1.34(1.21 1.49) <0 000 1.33 (1.19 1.48) <0 000 1.04 (0.93 1.16) 0.499
5–9 years 1.23(1.10 1.38) <0 000 1.19 (1.06 1.33) 0.003 1.06 (0.94 1.19) 0.348
10 or more years 1 1 1
b) Work status
Currently working 1 1 1
Worked in past but currently not working or never 1.21(1.12 1.31) <0 000 1.13 (1.04 1.22) 0.003 1.00 (0.92 1.08) 0.93
c) Household income quintiles
Poorest 1.32 (1.18 1.47) <0 000 1.38 (1.24 1.54) <0 000 1.22(1.09 1.37) <0 001
Poorer 1.16 (1.04 1.30) 0.008 1.20 (1.07 1.34) 0.001 1.09 (0.97 1.22) 0.147
Middle 1.09 (0.98 1.22) 0.111 1.14 (1.02 1.28) 0.021 1.07 (0.95 1.20) 0.249
Richer 1.13(1.01 1.26) 0.031 1.15 (1.02 1.28) 0.017 1.09 (0.97 1.22) 0.144
Richest 1 1 1
Behavioural health risk factors
a) Tobacco
No 1 1
Yes 1.24 (1.15 1.35) <0 000 1.21 (1.11 1.31) <0 000
b) alcohol
No 1
Yes 1.17 (1.05 1.30) 0.003 1.11 (1.00 1.24) 0.047
c) Physical activity and yoga
Both No 1 1
Only Physically active 0.90 (0.84 0.98) 0.011 1.02 (0.94 1.10) 0.715
Practicing yoga 1.19 (1.01 1.41) 0.043 1.32 (1.11 1.57) 0.000
Physically active and practicing yoga 1.09 (0.96 1.23) 0.187 1.31 (1.16 1.50) 0.000
Co-existing chronic health conditions
Diabetes 1.09 (0.99 1.21) 0.094 1.00 (0.91 1.12) 0.08
CVD 1.34 (1.24 1.44) <0 000 1.21(1.12 1.31) <0 000
Lung diseases 1.26 (1.11 1.44) <0 000 1.02 (0.89 1.16) 0.879
Bone diseases 1.39 (1.27 1.52) <0 000 1.17 (1.07 1.28) <0 001
Cancer 1.84 (1.34 2.54) <0 000 1.43(1.03 1.98) 0.035
Urogenital Disorders 1.50 (1.34 1.69) <0 000 1.26(1.12 1.42) <0 000
Falls/injury 1.73 (1.61 1.86) <0 000 1.50 (1.39 1.62) <0 000
Organ related diseases 1.43 (1.32 1.56) <0 000 1.29 (1.18 1.40) <0 000
General health and wellbeing
Poor self-rated health 2.39 (2.21 2.59) <0 000
1+ ADL 1.60 (1.46 1.75) <0 000
1+ IADL 1.51 (1.40 1.64) <0 000
Cognitive judgment of life satisfaction scale
Low 1.94(1.78 2.10) <0 000
Middle 1.29 (1.18 1.42) <0 000
High 1
Random part
Sd(_cons) 0.67 (0.51.87) 0.64 (0.49, 0.83) 0.59 (0.46 0.77)
Observations 68,505 68,321 67,715
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Note: Framework of models: Model 1 included demographic correlates and SES determinants; model 2 with the additions of health behaviour factors, and model 3 with addition of health conditions and wellbeing measures. ADL: Activities of Daily Living, IADL: Instrumental activities of daily living, OR= Adjusted Odds Ratio CI = confidence interval

In addition, Table 3 shows that depression was significantly associated with poor self-rated health, ADL and IADL limitations and low cognitive judgment of life satisfaction. Those with depression were 2·39 times (95% CI 2·21, 2·59; p < ·001) more likely to report poor self-rated health. Similarly, the association of depression with limitations in ADL (OR=1·6; 95% CI 1·46,1·75; p <0·001) and IADL (OR=1·51; 95% CI 1·40, 1·64; p <0·001) was significant and positive. Those with depression were more likely to report low cognitive life satisfaction (OR=1·94; 95% CI 1·78, 2·10). Figure 3 presents odds ratios of self-reported depression by SES correlates, health behaviour factors, chronic health conditions, and health and wellbeing measures. Adults 45 years or older with co-existing chronic health conditions, functional limitations, and poor self rated health have higher odds for self–reported diagnosed depression.

Figure 3:

Figure 3:

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Odds ratios of self-reported depression, LASI wave 1, 2017–18.

Discussion

This is the first ever study that provides nationwide population-based age adjusted comparable prevalence estimates of depression for adult population 45 years and older in India and 35 states/union territories. The large nationally representative sample of adults assessed using validated methods for diagnosing depression is a major strength of this study. The results provide comprehensive new insights into the subnational variations, correlates, and associated health behaviour factors for these conditions among the adults in this age group in India. The findings have major implications for mental health interventions at the national and state level policy and future mental health research agenda for adults 45 years and older in India.

The GBD India study recently reported that the age-specific prevalence of depressive disorders increased with age in India, with the highest prevalence in older adults in 2017.9 Findings from this nationwide representative study of adults 45 years and older confirm the increase in weighted prevalence of depressive disorders among this age group population in India with one in sixteen Indians aged 45 years or more reporting depression. However, when adjusted for all other factors, the age gradient of depressive disorders turns negative suggesting the domineering influence of socioeconomic, health behaviour, and health risk factors counteracting the age effects. We further found an emerging subnational pattern for depression for adults in this age group with a higher prevalence in few of the northern states; a pattern that is consistent with higher prevalence of chronic health conditions such as cancer, urogenital problems, visual and hearing impairment etc. in those states.13 This is different than that was reported earlier in GBD study, which showed a higher prevalence in the more developed southern states than in the less developed northern states for population >10 years of age9. Our detailed state-wise assessment of SES patterning of depression with very higher prevalence of depression among the poor and those with no or lower education in the northern states support the pattern of sub-national variations documented in our study, in addition to age and sub-state regional differences. Nevertheless, the cross-sectional data of baseline survey limits our ability, and therefore the longitudinal phase of data will support to document a more robust subnational pattern. Importantly, though the prevalence of depression was higher in females and in rural areas for India overall as compared with that for males and urban areas, respectively, this difference in prevalence was driven only by certain states. These findings reiterate the need for state-specific planning to address depression in adults 45 years and older in India.

Many of the socio-demographic factors identified for depression in this study have been reported previously. Rural residence, sex, marital status, education, income have also been found to be strongly associated with depression, confirming findings of previous studies.7,9,28,29 This nationwide study also shows significant association of widowhood with depression among older adults.29 A higher share of females experience widowhood in India mainly because of marital age gap and higher life expectancy of females as compared with males. In addition, increasing years of education and per capita income were found to be negatively associated with depression in our study, as found in previous studies in India10,28 and other parts of the world.30–32 Overall, adults at the bottom of the socioeconomic spectrum bear a remarkably higher burden of depression particularly those in the poorer northern states of India. Specific community interventions models are crucial for addressing depression among widowed females, those in rural areas, the poor, and those with low education.

Depression is documented to have a dynamic relationship with non-communicable diseases33,34. The Lancet Psychiatry Commission recognized the close association of poor physical health conditions with mental illness and the need to integrate mental healthcare intervention with interventions for physical health, as a confluence of these services is essential to address health risk factors of body and mind and to achieve health related Sustainable Developmental Goals (SDG).35,36 Our findings also reveal strong associations between coexisting chronic health conditions and depression for adults 45 years and older. Building on these results, our study adds critical insights on the adverse association of depression with the overall health and wellbeing of these adults. Furthermore, the negative association of depression with subjective and functional health measures such as ADL and IADL limitations, poor self-rated health, and lower life satisfaction demonstrate the need for integrated healthcare approach. Major depression is known to be linked with poor concentration, sleep problems, faster cognitive decline, increase in cognitive impairments and decline in functional ability, which are all also risk factors for dementia in older ages.34,37,38 These associations confirm that depression poses a high burden in terms of disability, increased risk of suicides9 resulting in considerable social and economic impact, and leading families into poverty.28,34

With only 0·5% reporting previous diagnosis of depression, our results show more than 90% of depression remain undiagnosed in India. Poor implementation of mental health services, with a high treatment gap for mental disorders, poor evidence-based treatment, and gender-differentials in treatment has been documented in India.9,28,39,40 Knowledge about mental health disorders including risk factors for depression is lacking in India.28 Treatment seeking for depression and mental health problems is low in India, consistent with similar findings in LMICs due to stigma and social consequences associated with mental disorders and limited access to diagnosis and treatment.41 Estimates from the NMHS in India have shown that less than 20% of people with depressive symptoms sought treatment.28

Results show large subnational differences also in the prevalence of sub-component of depression such as anhedonia, thoughts of death which also have implications on planning of the mental health services at the sub-national level. The GBD India study reported a positive but modest relationship between depression and suicide death rate at the state level for population >10 years of age.9 Little is known about anhedonia, thoughts of death and other sub-components of depression in India; there is a need for more detailed understanding that can facilitate more specific policy actions.

Our results highlight the need for provision of health services across the country now that the distribution of depression across the states, the socioeconomic and physical health patterning is clearer. The large gap between diagnosis and CIDI-SF based prevalence call for anti-stigma programme through large-public health approaches to raise awareness and societal attitudes in reducing stigma associated with depression and mental illness among adults 45 years and older. Future research needs to focus on estimating more accurately treatment coverage across the different regions, assessment of the burden of depression including its disability and economic impact and understanding the longitudinal course and predictors of depression.

India launched the National Programme for Health Care of the Elderly (NPHCE) to provide healthcare services for the elderly in 2011 and the first National Mental Health Policy in 2014 with a revised Mental Healthcare Act in 2017 to provide access to mental health care services.42,43 However, these programs lack a well-framed health system strategy for screening, diagnosis, and access to mental healthcare services but also universal screening and healthcare access for NCD prevention and control 9,40,41,44,45. Our findings show the strong link between depression and physical health, and the need for exploration of population-scale strategies for primary prevention of all co-occurring physical and mental health conditions.

In conclusion, depression affect a large proportion of adult population 45 years and older in India. It is imperative that India takes a comprehensive view of mental health and addresses the gaps to improve the quality of life of for this age group. Furthermore, the Covid-19 pandemic is resulted in increase in long-lasting mental health impact, which calls for urgent redressal measures to invest in building robust mental health care system in India.46

Strengths and Limitations

The LASI data used in this has many strengths; that it is India’s first, world’s largest and most comprehensive with globally comparable survey tools, novel large-scale survey methods, and standardized study and quality control protocols that existing studies lack. The representativeness of LASI data at the national, state and across socioeconomic spectrum are invaluable for this study in documenting depression as a significant mental health problem with considerable state and socioeconomic patterning and state specific policy consequences.

However, we recognize the following limitations of this study. First, the study lacked additional data on clinical evaluation of depression of CIDI-SF scale-based assessment and treatment effectiveness in case of diagnosed conditions which could have been useful in presenting additional insights on subnational patterns of depression in India. Second, while the data was valuable to generate hypotheses of associations, the cross-sectional nature of first wave of LASI data constraints our ability to establish clear causal link of the associations.47 The strong association reported between chronic health conditions, self-assessed health, and wellbeing measures and depression are more likely to reflect bi-directional association. Although chronic health conditions and life satisfactions are normally measured using self-report assessments in epidemiological studies, we cannot exclude that this could have introduced some information bias. Self-reported health and wellbeing measures are important part of health data on large-scale health surveys. Third, cross-sectional data presents additional constraints of participant’s self-selection survival bias influencing prevalence of health outcomes of those with worse health outcomes less likely to be survivors. The longitudinal phase of LASI will provide data on additional dimensions for assessing the robust pattern of subnational differences and the temporality of the associations of the correlates reported in this study.

Supplementary Material

1

Research in context.

Evidence before this study

We searched PubMed, google scholar with the terms “mental disorders” “depression” “major depressive disorders” “states” “older adults” “India”, “socioeconomic status”, and “chronic diseases” functional health, quality of life, and wellbeing on 22 December 2020 without language or publication date restrictions. We found the Global Burden of Disease (GBD) India Study that reported prevalence of depressive disorders for all states of India for population >10 years of age, with no individual level correlates for depression. Other studies on either prevalence or correlates were available from parts of India, such as the National Mental Health Survey (2015–16) that focused adults aged 18 years focused on 12 states and the World Health Organization Study on global AGEing and adult health (SAGE India 2007), in addition to other regionally focused studies. A study led by the 10/66 Dementia Research Group assessed the prevalence of depression and its impact on disability and care dependence in developing countries. We found no nationally representative study focused on adults 45 years and older in India that provided the prevalence, correlates, risk factors of depression and its impact on health and wellbeing of this age group.

Added value of this study

This is the first largest nationwide population-based study on depressive disorders carried out among adults aged 45 years or more in all states and union territories of India except Sikkim. The study offers comprehensive new insights not only on sociodemographic correlates, associated behavioral factors and chronic health conditions but also documents the adverse impact of depression on functional abilities, and life satisfaction. These data at the state-level are valuable for developing state-specific health system intervention strategies for screening, prevention and treatment of depressive disorders and their impact on health and wellbeing of Indians 45 years and older.

Implications of all the available evidence

This study in addition to confirming that most depressive disorders remain undiagnosed in India’s adult population 45 years and older, documented that depression is strongly linked with poor health and wellbeing outcomes such as activities of daily living limitations, poor self-rated health, and reduced life satisfaction. The advancing ageing demography of India’s population and the subnational variations further amplify the implications of this evidence highlighting the need for state specificity in mental intervention models with an integrated healthcare strategy for prevention and control of non-communicable diseases.

Acknowledgements

The research reported in this publication was funded by Ministry of Health and Family Welfare, Government of India, the National Institute of Ageing, USA and the United Nations Population Fund, India. The content of this publication is solely the responsibility of the authors and does not necessarily represent the official views of the funders.

Role of the funding source

The Ministry of Health and Family Welfare, Government of India, the National Institute of Ageing, USA and the United Nations Population Fund, India funded this study; the funders had no role in decision to submit this paper for publication. The first author had full access to all of the data in the study and had final responsibility for the decision to submit for publication.

Footnotes

Declaration of interests

The authors declare no competing interests.

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Data sharing

The data used in these analyses are available at public domain at from the IIPS website www.iipsindia.ac.in/lasi; the data webpage of the Government of India, http://www.data.gov.in; the HSPH website, lasi.hsph.harvard.edu; the USC website, lasi-india.org; and from the website of the Gateway to Global Aging Data, g2aging.org.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1
NIHMS1825530-supplement-1.docx (357.2KB, docx)

Data Availability Statement

The data used in these analyses are available at public domain at from the IIPS website www.iipsindia.ac.in/lasi; the data webpage of the Government of India, http://www.data.gov.in; the HSPH website, lasi.hsph.harvard.edu; the USC website, lasi-india.org; and from the website of the Gateway to Global Aging Data, g2aging.org.

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