Burden of Undiagnosed Depression Among Older Adults in India: a Population Based Study

doi:10.21203/rs.3.rs-2213853/v1

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Research Article

Burden of Undiagnosed Depression Among Older Adults in India: a Population Based Study

https://doi.org/10.21203/rs.3.rs-2213853/v1

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Journal Publication

published 30 Apr, 2024

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Background

The study explored the levels and associated factors of undiagnosed depression among community-dwelling older Indian adults. It also identified the predictors of undiagnosed depression among the study population at national and state levels.

Methods

The study employed data from the Longitudinal Ageing Study in India wave-I, 2017-18. Based on the data on depression from interviewee’s self-reporting and measurement on CIDI-SF, CES-D scale, we estimated undiagnosed depression among older adults (age 60+). We estimated multivariable binary logistic regressions to examine the socio-demographic and health-related predictors of undiagnosed depression among older adults.

Findings

Eight percent (95% confidence interval, CI: 7.8-8.4) of the total older adults had undiagnosed depression on CIDI-SF scale and 5% (95% confidence interval, CI: 4.8-5.3) on the combined CIDI-SF and CES-D. More than 97% of the older adults who had depression were not diagnosed for depression. Undiagnosed depression was higher among those who were widowed, worked in the past and currently not working, scheduled castes, higher educated and the richest. Lack of health insurance coverage, presence of any other physical or mental impairment, family history of Alzheimer’s/Parkinson’s disease/ psychotic disorder, lower self-rated health and poor life satisfaction were significant predictors of undiagnosed depression on both CIDI-SF and combined scales.

Conclusion

To improve the health of older adults in India, targeted policy efforts integrating mental health screening, awareness campaigns and decentralization of mental healthcare to primary level is needed. Further research could explore the causal factors behind different levels of undiagnosed depression.

Mental health

Depression

Geriatric mental health

Population ageing

older adults

The improvement in life expectancy has prepared all countries in the world to expect a considerable share of population to cross the age of 60, by 2030, every sixth person in the world will be an older adult¹. Mental and physical health difficulties such as dementia, Alzheimer’s, functional disability etc. comes as the part and package of the phenomenon of population ageing. However, the growing tendency to associate cognitive health issues such as depression with the process of biological aging is inherently flawed² especially when the single largest contribution to global disability and to suicides is attributable to it³. A World Health Organization (WHO) survey conducted in more than 180 countries shows that around 40% of them do not have a comprehensive mental health policy and about one-third of them lack specific budget allocation for the same⁴. In the context of growing older adult population and burden of mental health issues, institutional efforts for the promotion of healthy aging among older adults is the need of the hour, particularly after the global hit of the pandemic.

India is graying as the share of older adults (60+) population has increased from 7.7% of the total at the dawn of millennium to 9.4% in 2017, and is expected to reach 19% (324 million) by 2050⁵. Naturally, India is expected to witness rise in physical and mental health problems. A meta-analysis of the prevalence of depression among older adults in India during 1997-2016 found that one in every third older adult age 60+ was depressed, posing a humongous public health challenge for the country⁶. However, to address the huge burden of depression and the taboos associated with it, the country does not seem to have enough policies and programs focusing especially on the mental health of older adults. India had introduced National Policy for Older Persons (NPOP) in 1999 to ensure financial support, food security, shelter and other facilities by state governments. The policy envisaged to make health, social and economic provision for older adults and to provide them protection from all sorts of abuses and exploitation⁷. Both NPOP and National Policy for Senior Citizens (2011) however give scant attention to mental health of older adults⁸. However, the onus cannot be fully transferred to the policy makers as the country had been looking at a big lacuna created by the lack of data and literature, until the release of Longitudinal Ageing Study in India (LASI) Wave-I data. LASI provides in-depth data on the cognitive aspects of Indian older adults using international scales and standards. On an average 8.3% of Indian older adults are measured for major probable depression on the Composite International Diagnostic Interview – Short Form (CIDI-SF) scale and 0.8% have reported diagnosed for depression⁹. The social change in the country that followed the phenomena of globalization, urbanization and disintegration of a well-knit joint family system has pushed the older adults into more social and economic vulnerabilities that are expected to take a toll on their mental health. Hence, an exhaustive understanding of depression among older adults by focusing on the factors that aggravates the cognitive conditions and those which can improve the same is needed at the moment.

One should not undermine the importance of depression as a public health concern owing to its commonness, as around 5% of adults and 5.7% of older adults around the world are estimated to be suffering from it¹⁰. Projections by Global Burden of Disease Study shows depression as the leading cause of Disability Adjusted Life Years (DALY) in developing countries by 2020¹¹. Apart from being the leading cause of disability, depression is also one of the major reasons for deaths by suicide, even in India¹². Studies have found that irrespective of the fact that the treatment for depression is available, 75% of people from lower-middle income countries did not receive any treatment due to reasons such as inaccessibility to the services, inadequate resources and personals and stigma associated with both mental health issues and their care-seeking¹³. Though the Indian academia has begun to look into the risk factors and other determinants of geriatric depression, the screening and diagnosis aspect has received only deficit attention.

The current study aims at understanding the level of undiagnosed depression among Indian older adults (60+) and their predictors using the LASI Wave-1 dataset. The study has utilised two international scales of measurement (i) Composite International Diagnostic Interview – Short Form (CIDI-SF) and (ii) Centre for Epidemiological Studies- Depression scale (CES-D) to measure depression among study population. CIDI-SF is a depression measurement scale available in a twelve-month format measuring major probable depression and other disorders such as generalized anxiety and Obsessive-Compulsive Disorder (OCDs)^14,15,16. CES-D scale, on the other hand, measures depressive symptomatology rather than the illness severity^17,18,19. Established literature has cautioned against the use of CES-D independently and suggests the application of it in combination with other well-established scales, owning to the concerns of reliability ^20,21,22,23. This study will hence utilise mainly the CIDI-SF scale of measurement, except for estimating the most severe depressive cases. In addition, the combined scale of measurement, that is CIDI-SF and CES-D together is also used in the current analysis to understand the severe cases of geriatric depression.

2.1 Data Source

This study has primarily utilized the Longitudinal Aging Study in India (LASI) Wave-1 data, which analyses the health, demographic and socio-economic nuances of the phenomenon of population ageing systematically. The survey was carried out in 30 states and six union territories of the country and has a sample size of 72,250 adults aged 45 years and above and their spouses irrespective of their age. A multi-stage stratified area-probability cluster sampling design with sub-districts/tehsils as primary sampling units (PSU) and village/urban wards as secondary sampling units (SSU) was used to collect the data. The detailed information about sampling design, fieldwork, data collection tools and processing etc. are available elsewhere^24,25. The current study analysed information from older adults age 60 and above.

The study did not require any ethical approval as publicly available secondary data source from a longitudinal study was utilised. LASI wave-1 has adhered to all the standard procedures and approved protocols, including informed consent to collect data from the participants.

2.2 Variable Description

2.2.1 Outcome Variable

Undiagnosed depression

Older adults (60+) are defined as having depressive symptoms if they secure a score of four or more on a 0-10 score based CES-D scale. A score of three or more on a 0-7 CIDI-SF scale is defined as probable major depression among the older adults. Those individuals who fulfil both these criteria simultaneously are defined as the older adults who are measured depressed on both the scales of measurement. An older adult is defined as undiagnosed for depression on CIDI-SF or combined scale (CIDI-SF & CES-D) if he/she/they are measured as having depression on the respective scale and answered “no” to the question on self-reporting of ever diagnosed depression, where they have ever been told by a health professional as depressed. The estimation based on combined scale shows severe cases of undiagnosed depression.

2.2.2 Predictor variables

The study extracted data on certain background characteristics, such as urban-rural residence, sex (male, female), marital status (currently married, widowed, divorced/separated/deserted/others), living arrangement (living alone, living with spouse and/or others, living with spouse and children, living with children and others, and living with others), religion (Hindu, Muslim, Others), Caste/Tribe (Scheduled Castes, Scheduled Tribes, Other Backward Class, Others), educational attainment (no schooling, less than 5-years completed, 5-9 years completed, more than 10 years completed), work status (currently working, worked in the past but currently not working, never worked), monthly per capita expenditure (MPCE) quintile (poorest, poorer, middle, richer, richest), and geographic region of residence (North, Central, East, North-East, West and South India).

Other predictor variables, such as health insurance coverage, any other diagnosed neurological/psychiatric issues except depression, any physical disability, family history of Alzheimer’s/Parkinson’s disease/psychotic disorder, self-rated health (good/moderate/poor) and life satisfaction (low, medium, high), were also included in the statistical analysis.

2.3 Data analysis

The study analysed the prevalence of undiagnosed depression among older adults with respect to multiple socio-economic, demographic, residence-related, and other predictors using cross-tabulation analysis. Two multivariable logistic regression models, one for the CIDI-SF and second for the combined scale, were estimated to delineate the factors associated with the outcome of interest. We carried out all analyses by utilising the survey weights given in LASI Wave 1 to ensure the nationally representative nature of the sample. All the analyses were done using STATA 15.0 (Stata Corp., College Station, USA) software.

3.1 Summary statistics

Figure 1 gives the summary estimates of the levels of undiagnosed depression among the total population for the age groups 45-59, 60+, and 45+ for CIDI-SF, CES-D, and combined scale. Eight percent, 30%, and 5% of older adults (60+) were not diagnosed for depression as per CIDI-SF, CES-D and combined scales respectively.

Undiagnosed depression by socio-economic, demographic, residence-related, and other predictors

Table 1 provides population-weighted numbers and percentages of undiagnosed depression among older adults measured on CIDI-SF and combined scales by different predictors. The rates of undiagnosed depression were slightly higher among respondents who were from rural areas, were female, were widowed, lived alone, belonged to Muslim, belonged to Scheduled castes, had no schooling, had worked in the past but currently not working, belonged to the richest wealth quintile, and belonged to the Central part of India. These results were consistent on both CIDI-SF and combined scale.

The rates of undiagnosed depression were also higher among those who did not have health insurance coverage, had any other diagnosed neurological/psychiatric issues except depression, suffered from physical/mental impairment, rated their health as poor on self-rated health, and had low life satisfaction.

Table 2 presents the rates of undiagnosed depression among different states and Union Territories (UTs) of the country. While Madhya Pradesh (14.4%) had the highest percentage of undiagnosed depression, Gujarat (3.3%) had the lowest. Undiagnosed depression was also high in Uttar Pradesh (13.1%) and Bihar (10.1%).

3.2 Multivariable Logistic Regression

Multivariable binary logistic regression results for undiagnosed depression on CIDI-SF scale by selected characteristics are shown in Table 3. The results show that older adults belonging to urban areas (Odds ratio, OR: 0.80; 95% CI: 0.67-0.96) were less likely than their rural counterparts to suffer from undiagnosed depression. Female older adults were more likely (OR: 1.49; 95% CI: 1.24-1.79) to be undiagnosed for depression. Older adults not covered under any health insurance scheme, having any other physical or mental impairment except depression, having family history of Alzheimer’s/Parkinson’s disease/Psychotic disorder, who rated poor on self-rated health and life satisfaction were more likely than their counterparts of going undiagnosed for probable major depression. Association of selected characteristics with undiagnosed depression measured on the combined scale are shown in Table 4. All the associations, except covered under any health insurance, remained unchanged when measured on combined scale.

This is perhaps the first study from India that has examined the burden of undiagnosed depression among older adults using a large-scale population representative household survey. We used CIDI-SF and a combined scale (CIDI-SF and CES-D) to identify undiagnosed depression among older adults. Five percent of older adults were undiagnosed for depression on the combined scale. When converted into numbers, 6.8 million older adults in India were undiagnosed for depression on the combined scale for the year 2019. These statistics indicate that undiagnosed depression among the older adults is an important public health challenge that India is facing. The burden of undiagnosed depression is likely to grow in the country due to the ongoing population ageing in the country. Even after being common than dementia, geriatric depression is not given ample attention as it goes heavily undiagnosed and untreated²⁶. Multiple studies have established the significant impact of late life depression on the quality of life^27,28.

Female older adults were more likely than the male older adults to bear the burden of undiagnosed depression. This finding is in line with the previous research that indicates that female older adults are less likely to seek treatment for ailments compared to their male counterparts^29,30,31. In addition, evidence also suggests discrimination against female older adults in health care expenditure and distressed health care financing^32,33. Our findings call for greater focus on screening for depression among the female older adults in India.

There is a clear rural urban differential in undiagnosed geriatric depression on both the CIDI-SF and the combined scale. Our study results show a lesser chance of being undiagnosed for depression among the urban older adults in comparison to their rural counterparts^34,35,36. Multiple studies from India and abroad show similar results on rural-urban differentials of geriatric depression. A major reason for the same could be the low treatment seeking behaviour among rural population owing to the lack of facilities and institutional support.

On both CIDI-SF and the combined scale of measurement, older adults not covered under any insurance scheme had higher vulnerability towards being undiagnosed for depression. Literature establishes that the lack of insurance often acts as a hindrance for undergoing screening and receiving treatment for depression^37,38,39. In a country like India with evident income inequality, the out-of-pocket expenditure for the identification and treatment for a prolonged condition like depression might be huge and the catastrophic health spending would push the already poor ones into the deep poverty. The initial health insurance schemes of the country were giving scant attention to the mental health issues and some schemes even disqualified those suffering from mental health issues from availing the facilities under it. The Ayushman Bharat Scheme has however focused on mental health issues as well, where the scheme offers 17 packages under it for the same, including psychoactive substance abuse. The issue, however is that, the mental health packages are only available in public hospitals of the country⁴⁰. As per LASI Wave-1 report, 26% of the households in the country are covered under any insurance scheme. When narrowed down, only 18.1% of the older adults were covered under the Publicly funded health insurance schemes (PFHI)⁴¹. Ensuring the coverage of schemes among the target population is as important, if not more, as introducing the schemes.

The presence of co-morbid disorders, such as other physical or mental impairment, having family history of Alzheimer’s/Parkinson’s disease/Psychotic disorder, etc., also put older adults at higher risk of undiagnosed depression. Studies have found that the presence of such disorders makes the detection of conditions like depression difficult, as the symptoms might be confused with the ones that are associated with the co-morbidities³². It could also be the case that when multiple conditions are present, the undivided attention is given to the physical ailments and the mental health issues are side-lined. These findings call for holistic screening of older adults who visit health facilities with associated co-morbidities or physical impairments. Focus may also be given on those older adults whose self-rated health and life satisfaction are poor.

Our study is not devoid of limitations. The question on self-reporting of diagnosed depression is subject to the risk of recall bias among the respondents. Secondly, though the CIDI-SF and CES-D scales are widely used for screening of depression, the use of the same by field interviewers may not strictly qualify as clinical diagnosis of depression. Third, the cross-sectional nature of the LASI wave 1 does not allow to establish causal linkages. Fourth, our analyses could not explore the reasons for different levels of depression diagnosis among the study population as the relevant information is not available in LASI wave 1. Nevertheless, it is one of the first ever attempts to measure the burden of undiagnosed depression among older adults residing in a large and diverse country like India. Our findings are likely to provide key inputs for formulating appropriate policies and programmes for the older adults in India in particular and other low- and middle- income countries in general.

In the context of the results, it is of paramount importance that the policies and programs of the country take into consideration these findings to improve the diagnosis of depression among older adults. Efforts should be made to address both the demand and supply sides of diagnosis and treatment for geriatric depression. Structural interventions, such as improving the knowledge and attitudes of the service providers and older adults, integration of depression screening with physical health check-ups, collaborative care models of coordination of primary health care and mental health services at local levels, etc. would be beneficial for addressing the burden of undiagnosed depression among the older adults. Effective efforts are also required to improve the discussions on mental health and related issues to eliminate the taboo surrounding the same. Given that India’s older adults’ population is likely to reach 324 million (19% of the total population) by 2040⁴¹, it is important for the country to adopt a targeted approach focusing on early diagnosis and treatment of depression among older adults.

CIDI-SF: Composite International Diagnostic Interview – Short Form

CES-D: Centre for Epidemiological Studies- Depression scale

OR: odds ratio

CI: Confidence interval

DALY: Disability Adjusted Life years

LASI: Longitudinal Ageing Study in India

MPCE: Monthly per capita consumption expenditure

Ethics approval and consent to participate

The survey agencies that conducted the field survey for the data collection have collected prior informed consent (written and verbal) from all the participants. The Indian Council of Medical Research (ICMR) extended the necessary guidance and ethical approval for conducting the LASI survey. All methods related to the current analysis were carried out in accordance with relevant guidelines and regulations by the Indian Council of Medical Research (ICMR). Ethics committee of The Indian Council of Medical Research (ICMR), has waived ethical approval and informed consent for this study

Consent for publication

Not applicable

Availability of data and materials

The study uses secondary data which is available in the public repository of International Institute for Population Sciences, Mumbai, accessible through

https://www.iipsindia.ac.in/content/lasi-wave-i

Competing interest

The authors declare that there is no competing interest

Funding

No funding was received for the study

Contributor statement

Conceived and designed the research paper: DNB; analyzed the data: DNB, NM; Contributed agents/materials/analysis tools: DNB, NM; Wrote the manuscript: DNB; Supervision of formal analysis and critical revision of the article: AS; All authors read, reviewed and approved the manuscript.

Acknowledgements

Not applicable

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Table 1: Undiagnosed depression among older adults on CIDI-SF and combined scales by selected characteristics, India, 2017-18

Background characteristics	Undiagnosed CIDI-SF			Undiagnosed on both scales CIDI-SF and CES-D
Background characteristics	%	95% CI	N	%	95% CI	N
Place of residence
Rural	9.0	8.6-9.4	1,961	5.7	5.4-6.0	1,232
Urban	5.7	5.3-6.1	508	3.5	3.2-3.9	311
Sex
Male	7.0	6.6-7.4	1,016	4.1	3.8-4.4	591
Female	9.0	8.5-9.4	1,453	5.9	5.6-6.3	952
Marital status
Currently married	7.2	6.9-7.6	1,371	4.3	4.0-4.6	808
Widowed	9.6	9.0-10.1	1,057	6.4	5.9-6.9	705
Divorced/Separated/Deserted/Others	6.2	4.5-7.9	41	4.7	3.2-6.1	30
Living arrangement
Living alone	12.8	11.2-14.5	226	8.8	7.3-10.1	152
Living with spouse and/or others	7.9	7.2-8.6	480	4.7	4.2-5.3	284
Living with spouse and children	6.9	6.5-7.4	880	4.1	3.8-4.5	518
Living with children and others	8.3	7.7-8.9	704	5.5	5.0-6.0	462
Living with others	10.8	9.3-12.3	180	7.8	6.5-9.1	126
Religion
Hindu	8.0	7.7-8.4	2,031	5.2	4.9-5.4	1,296
Muslim	8.9	8.0-9.8	293	5.3	4.5-6.0	172
Others	7.2	6.4-7.9	144	3.7	3.2-4.3	74
Caste/tribe
Scheduled caste	9.6	8.8-10.4	557	6.5	5.8-7.2	378
Scheduled tribe	4.5	3.9-5.1	112	3.0	2.6-3.5	75
Other backward classes	8.4	7.9-8.9	168	5.4	5.0-5.8	736
None of the above	7.5	6.9-8.0	633	4.2	3.8-4.6	354
Education
No schooling	8.9	8.5-9.4	1,549	5.9	5.5-6.2	1,005
Less than 5 years completed	9.3	8.4-10.2	329	5.8	5.1-6.6	205
5-9 years completed	6.9	6.2-7.5	376	3.8	3.3-4.3	208
10 or more years completed	5.0	4.4-5.6	215	2.9	2.4-3.4	124
Work status
Currently working	7.4	6.9-7.9	711	4.3	3.9-4.7	411
Worked in past but currently not working	9.2	8.7-9.7	1,194	6.3	5.9-6.7	804
Never worked	7.0	6.4-7.5	564	4.1	3.6-4.5	327
MPCE quintile
Poorest	8.5	7.8-9.2	565	6.1	5.5-6.7	405
Poorer	7.2	6.6-7.9	481	4.0	3.5-4.5	264
Middle	7.6	6.9-8.2	480	4.7	4.2-5.3	299
Richer	8.0	7.3-8.7	476	4.9	4.4-5.5	287
Richest	9.3	8.5-10.0	467	5.8	5.2-6.4	288
Region
North	6.6	5.9-7.2	257	3.7	3.2-4.2	144
Central	13.8	12.7-14.8	883	9.3	8.4-10.2	592
East	7.8	7.1-8.5	569	4.1	3.6-4.6	298
North-East	5.1	4.4-5.8	47	2.4	1.9-2.9	22
West	7.1	6.3-8.0	373	4.9	4.2-5.7	258
South	5.0	4.5-5.4	340	3.4	3.0-3.8	228
Health insurance coverage
No	8.6	8.2-8.9	2,147	5.4	5.1-5.7	1,346
Yes	5.7	5.2-6.3	322	3.5	3.1-4.0	197
Any other diagnosed neurological/psychiatric issues except depression
No	8.0	7.7-8.3	2,391	5.0	4.7-5.2	1,480
Yes	10.8	8.4-13.1	79	8.7	6.5-10.8	63
Any physical/mental impairment
No	7.4	7.1-7.7	2,068	4.6	4.3-4.8	1,274
Yes	15.6	14.1-17.0	401	10.6	9.3-11.8	269
Family history of Alzheimer's/Parkinson's disease/Psychotic disorder
No	7.8	7.5-8.1	2,259	4.8	4.6-5.1	1,388
Yes	13.6	11.9-15.4	210	10.2	8.6-11.7	154
Self-rated health
Good	4.1	3.7-4.5	386	2.2	1.9-2.5	202
Moderate	6.9	6.5-7.3	956	4.2	3.8-4.5	571
Poor	15.2	14.4-16.0	1,126	10.4	9.7-11.1	769
Life satisfaction
Low	12.3	11.6-12.9	1,194	8.6	8.0-9.2	841
Medium	7.3	6.7-7.9	517	4.5	4.0-5.0	308
High	5.5	5.1-5.9	758	2.9	2.6-3.1	393
Total	8.1	7.8-8.4	2,469	5.1	4.8-5.3	1,543

Table 2: Undiagnosed depression among older adults on CIDI-SF and combined scales by states and UTs, India, 2017-18

States & UTs	Undiagnosed CIDI-SF			Undiagnosed CIDI-SF & CES-D
States & UTs	%	95% CI	N	%	95% CI	N
Jammu & Kashmir	4.4	2.9-5.9	14	2.3	1.2-3.4	7
Himachal Pradesh	6.1	4.2-8.0	12	2.3	1.1-3.5	4
Punjab	9.0	7.2-10.8	71	3.6	2.4-4.8	28
Chandigarh	*	*	*	*	*	*
Uttarakhand	7.4	5.4-9.4	20	3.5	2.1-4.9	9
Haryana	5.3	3.8-6.8	30	3.6	2.3-4.8	20
Delhi	10.2	7.5-12.9	39	6.9	4.6-9.1	26
Rajasthan	5.6	4.2-7.0	97	3.6	2.5-4.8	62
Uttar Pradesh	13.7	12.3-15.2	642	8.9	7.7-10.2	415
Bihar	10.1	8.7-11.5	265	4.6	3.6-5.5	118
Arunachal Pradesh	*	*	*	*	*	*
Nagaland	*	*	*	*	*	*
Manipur	*	*	*	*	*	*
Mizoram	*	*	*	*	*	*
Tripura	*	*	*	*	*	*
Meghalaya	*	*	*	*	*	*
Assam	6.8	5.0-8.5	35	3.1	1.9-4.4	16
West Bengal	7.2	5.9-8.5	159	4.8	3.7-5.9	107
Jharkhand	6.5	5.1-7.9	51	3.5	2.4-4.6	27
Odisha	4.5	3.3-5.7	53	2.0	1.2-2.7	23
Chhattisgarh	3.4	2.1-4.6	17	2.3	1.2-3.4	12
Madhya Pradesh	16.1	14.1-18.2	282	11.6	9.8-13.4	198
Gujarat	3.1	2.0-4.2	41	1.6	0.8-2.4	21
Daman & Diu	*	*	*	*	*	*
Dadra & Nagar Haveli	*	*	*	*	*	*
Maharashtra	8.8	7.4-10.1	287	6.3	5.2-7.5	205
Andhra Pradesh	3.7	2.5-4.8	51	2.1	1.2-2.9	29
Karnataka	6.5	4.9-8.0	82	4.9	3.5-6.2	60
Goa	*	*	*	*	*	*
Lakshadweep	*	*	*	*	*	*
Kerala	5.5	4.2-6.8	66	3.5	2.4-4.5	41
Tamil Nadu	5.0	3.9-6.1	117	3.9	2.9-4.9	91
Puducherry	*	*	*	*	*	*
Andaman & Nicobar Islands	*	*	*	*	*	*
Telangana	4.6	3.3-5.8	43	1.9	1.0-2.7	17
India	8.1	7.8-8.4	2,469	5.1	4.8-5.3	1543
*Insufficient sample size

Table 3: Results of logistic regression of undiagnosed depression measured on the CIDI-SF by selected variables, India, 2017-18

Background characteristics	Odds ratio [95% CI]
Place of residence
Rural	Ref.
Urban	0.80* [0.67-0.96]
Sex
Male	Ref.
Female	1.49* [1.24-1.79]
Marital status
Currently married	Ref.
Widowed	1.63 [0.76-3.48]
Divorced/Separated/Deserted/Others	0.95 [0.40-2.26]
Living arrangement
Living alone	Ref.
Living with spouse and/or others	1.13 [0.51-2.47]
Living with spouse and children	1.06 [0.48-2.32]
Living with children and others	0.73* [0.55-0.97]
Living with others	0.92 [0.64-1.33]
Religion
Hindu	Ref.
Muslim	1.05 [0.84-1.32]
Others	1.18 [0.90-1.57]
Caste/tribe
Scheduled caste	Ref.
Scheduled tribe	0.47* [0.35-0.63]
Other backward classes	1.11 [0.92-1.34]
None of the above	0.97 [0.79-1.2]
Education
No schooling	Ref.
Less than 5 years completed	1.33* [1.03-1.73]
5-9 years completed	1.07 [0.88-1.29]
10 or more years completed	0.91 [0.71-1.16]
Work status
Currently working	Ref.
Worked in past but currently not working	1.01 [0.85-1.20]
Never worked	0.62* [0.49-0.77]
MPCE quintile
Poorest	Ref.
Poorer	0.89 [0.73-1.08]
Middle	1.02 [0.81-1.30]
Richer	1.12 [0.90-1.38]
Richest	1.40* [1.13-1.72]
Region
North	Ref.
Central	2.21* [1.77-2.76]
East	1.07 [0.81-1.30]
North-East	0.89 [0.65-1.23]
West	1.25 [0.98-1.60]
South	0.50* [0.39-0.64]
Health insurance coverage
No	Ref.
Yes	0.79* [0.65-0.95]
Any other diagnosed neurological/psychiatric issues except depression
No	Ref.
Yes	0.99 [0.70-1.39]
Any physical/mental impairment
No	Ref.
Yes	1.96* [1.61-2.39]
Family history of Alzheimer's/Parkinson's disease/Psychotic disorder
No	Ref.
Yes	1.67* [1.33-2.10]
Self-rated health
Good	Ref.
Moderate	1.64* [1.34-2.01]
Poor	3.60* [2.90-4.47]
Life satisfaction
Low	Ref.
Medium	0.58* [0.48-0.69]
High	0.47* [0.40-0.56]
*p<0.05

Table 4: Results of logistic regression of undiagnosed combined scale by selected explanatory variables

Background characteristics	Odds ratio [95% CI]
Place of residence
Rural	Ref.
Urban	0.80* [0.64-1.00]
Sex
Male	Ref.
Female	1.66* [1.30-2.12]
Marital status
Currently married	Ref.
Widowed	2.02 [0.73-5.58]
Divorced/Separated/Deserted/Others	1.30 [0.42-4.04]
Living arrangement
Living alone	Ref.
Living with spouse and/or others	1.39 [0.49-4.0]
Living with spouse and children	1.36 [0.48-3.90]
Living with children and others	0.79 [0.57-1.09]
Living with others	1.05 [0.68-1.62]
Religion
Hindu	Ref.
Muslim	1.02 [0.77-1.37]
Others	0.90 [0.61-1.32]
Caste/tribe
Scheduled caste	Ref.
Scheduled tribe	0.50* [0.35-0.71]
Other backward classes	1.02 [0.80-1.32]
None of the above	0.85 [0.66-1.12]
Education
No schooling	Ref.
Less than 5 years completed	1.36 [0.93-1.97]
5-9 years completed	1.03 [0.79-1.33]
10 or more years completed	0.96 [0.70-1.33]
Work status
Currently working	Ref.
Worked in past but currently not working	1.13 [0.90-1.42]
Never worked	0.60* [0.46-0.80]
MPCE quintile
Poorest	Ref.
Poorer	0.69* [0.54-0.88]
Middle	0.93 [0.67-1.28]
Richer	0.99 [0.75-1.29]
Richest	1.26[0.96-1.65]
Region
North	Ref.
Central	2.47* [1.84-3.32]
East	0.90 [0.68-1.18]
North-East	0.72 [0.45-1.13]
West	1.62* [1.17-2.23]
South	0.57* [0.41-0.79]
Health insurance coverage
No	Ref.
Yes	0.79 [0.61-1.04]
Any other diagnosed neurological/psychiatric issues except depression
No	Ref.
Yes	1.28 [0.88-1.86]
Any physical/mental impairment
No	Ref.
Yes	1.92* [1.49-2.48]
Family history of Alzheimer's/Parkinson's disease/Psychotic disorder
No	Ref.
Yes	2.00* [1.52-2.63]
Self-rated health
Good	Ref.
Moderate	1.79* [1.32-2.43]
Poor	4.22* [3.01-5.84]
Life satisfaction
Low	Ref.
Medium	0.52* [0.40-0.66]
High	0.35* [0.27-0.44]
*p<0.05

No competing interests reported.

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Burden of Undiagnosed Depression Among Older Adults in India: a Population Based Study

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