Catastrophic Health Expenditure and Poverty Impact Due to Mental Illness in India

Abstract

Majority of people in low- and middle-income countries with mental illness do not receive healthcare, leading to chronicity, suffering and increased costs of care. This study estimated the out-of-pocket expenditure (OOPE), catastrophic health expenditure (CHE), and poverty impact due to mental illness in India. Data was acquired from the 76th round data of the National Sample Survey (NSS) on the theme ‘Persons with Disabilities in India Survey’, July–December 2018. Data of 6,679 persons who reported mental illness during the survey was included for analysis. OOPE, CHE, poverty impact and state differentials of healthcare expenditure on mental illness were analysed using standard methods. In total, 18.1% of the household’s monthly consumption expenditure was spent on healthcare on mental illness. About 59.5% and 32.5% of the households were exposed to CHE based on 10% and 20% thresholds, respectively. About 20.7% of the households were forced to become poor from non-poor due to treatment care expenditure on mental illness. Our study suggests the critical need to accelerate on various measures for early diagnosis and management of mental health issues along with financial risk protection for reducing financial impact of healthcare expenditure on mental illness among households in India.

Keywords

Mental illness out-of-pocket expenditure catastrophic health expenditure (CHE)poverty

Key Messages

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In total, 18.1% of household’s monthly consumption expenditure among 5,935 persons surveyed as part of National Sample Survey 76th Round on ‘Persons with Disabilities in India Survey’, 2018 was spent on healthcare on mental illness in India.

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At 10% and 20% thresholds, 59.5% and 32.5% of the households were exposed to catastrophic health expenditure for treatment related to mental illness.

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About 20% of the households became poor from being non-poor initially due to treatment expenditure on mental illness.

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There is a critical need to provide financial risk protection to reduce financial impact of healthcare expenditure on mental illness among households in India.

Introduction

Mental illness has been overlooked, undermined and tabooed for a long in both developed and developing countries. Mental illness encompasses a spectrum of disorders that are typically characterised by some combination of abnormal thoughts, emotions, behaviour and relationships with others, and includes schizophrenia and other psychotic disorders, mood disorders including depressive disorders and bipolar mood disorders, substance use disorders, people with intellectual impairments with behavioural problems, neuropsychiatric conditions like seizure disorders, dementia, other organic mental disorders and developmental disorders (World Health Organization [WHO], 2015). Although mental illness is more commonly recognised in adults, studies indicate that the onset of the initial signs may occur in childhood or during adolescence (Jones et al., 1994; Kessler et al., 2007). Fortunately, most of the mental illnesses can be treated and managed to help the affected person lead a fruitful and productive life. Despite the availability of treatment, stigma and superstitions around mental illnesses prevent the sufferers from seeking timely care (Sangeeta & Mathew, 2017). In developing countries like India, the situation is even more worrying as people with mental illnesses also have problems in everyday life, especially with jobs and marriages where prejudice and contempt are very high (Baldwin & Marcus, 2011; Corrigan & Watson, 2002; Sharma et al., 2015; Trani et al., 2015). Studies have revealed that people with mental illness face a lack of acceptance and inclusiveness even among the educated strata of society (Bingham & O’Brien, 2018; Wang & Lai, 2008).

Globally, as per 2016 estimates, the disease burden for mental illness (or mental disorders) accounts for 32.4% of years lived with disability (YLD), and 13% of disability-adjusted life-years (DALYs) (Vigo et al., 2016). In low-and middle-income countries (LMICs), more than 11.1% of the total disease burden is due to mental health disorders, yet they receive less than 1% of many of these countries’ health budgets (Patel, 2007). In India, depressive disorders contribute to 7.1% of total YLDs, and 2.5% of total YLDs due to anxiety disorders (WHO, 2017).

The majority of people in LMICs with mental illness do not receive healthcare, leading to chronicity, suffering and increased costs of care (Patel, 2007). The stigma is so deep-rooted that despite treatment, many people with mental illnesses are not considered ‘suitable’ or responsible enough for a task, job or relationship. People with mental illness often suffer financial setbacks pushing them further away from seeking treatment for their problems. People with severe mental illness are found to be more likely to several physical disorders such as/or risk of comorbid conditions such as hypertension, heart diseases, diabetes, respiratory diseases and nutritional disorders (Coxon et al., 2020; De Hert et al., 2011). Additionally, mental illness is also a risk factor for other health problems, such as contribution of alcohol use or any other substance to road traffic accidents or liver disease (Patel, 2007). This poses additional financial burden on households with persons suffering from mental illness.

This study aimed to estimate the impact of out-of-pocket expenditure (OOPE) on catastrophic health expenditure (CHE), and poverty impact due to mental illness in India. To the best of our knowledge, there has been no study conducted towards analysing the financial burden of mental illnesses on Indian households. The present study, aims to highlight the burden of mental illness and the gaps in seeking care for mental health issues in India, thus highlighting the need in formulating and strengthening financial risk protection policies affected by mental illness in India.

Data and Methodology

Data Sources and Sample Size

The current study uses the 76th round data of the National Sample Survey (NSS) on the theme ‘Persons with Disabilities in India Survey’ which was conducted during July–December 2018 (National Statistical Office [NSO], 2018). The NSS is a nationally representative population-based survey organisation that collects data on several domains, like employment status, morbidity, healthcare utilisation, household consumer expenditure, housing conditions, disabilities, etc. The NSS is a national representative population-based survey organisation and findings of NSS surveys are generally used by policymakers and researchers. The NSS 76th round covered 1,18,152 households and 576,569 individuals. The present study covers a total of 6,679 individuals who reported mental illness during the survey.

Research Design and Geographical Coverage

NSS 76th round survey used Census 2011 population with suitable growth rates for a sampling frame. At the national level, rural villages were divided into a number of sub-units (SU) of more or less equal population and the number of SUs formed in a village was determined a priori. SUs were formed in urban sector also, similar to that adopted in rural areas except that SUs were formed on the basis of households in the Urban Frame Survey (UFS) instead of population. Each UFS block with a number of households more than or equal to 200 was divided further into a number of SUs, and those with households less than 200, the entire UFS block was considered as one First Stage Unit (FSU). In the remaining UFS blocks, the SUs was considered as FSUs. A stratified two stage sampling design has been adopted for the NSS 76th round survey. In the first stage villages/urban blocks (FSU) were chosen by Probability Proportional to Size with Replacement (PPSWR). The ultimate stage units (USUs) were both households in both rural and urban areas. In the second stage of sampling, a total 20 households from each of the selected village/urban wards were selected by Simple Random Sampling Without Replacement scheme in both rural and urban areas. The survey covered all states and union territories of India (NSO, 2018).

Definition of Mental Illness

The NSS 76th round defined mental illness as

a substantial disorder of thinking, mood, perception, orientation or memory that grossly impairs judgment, behaviour, capacity to recognise reality or ability to meet the ordinary demands of life, but does not include retardation which is a condition of arrested or incomplete development of mind of a person, especially characterised by sub normality of intelligence. (NSO, 2018)

The NSS 76th round survey asked the following three questions to access the mental illness among individuals during the survey period:

Whether having unnecessary and excessive worry and anxiety, repetitive behaviour/thoughts, changes of mood or mood swings, talking/laughing to self, staring in space?

Whether having unusual experiences of hearing voices, seeing visions, strange smell or sensation or strange taste?

Whether having unusual behaviour or difficulty in social interactions and adaptability?

Measuring Out-of-Pocket Expenditure (OOPE) for Healthcare

The NSS 76th Round survey captured medical expenditure (surgery, equipment, hospitalisation, etc.) and non-medical expenditure (transport, lodging, food, etc.) for infrequent expenditure during the last 365 days from date of survey, and usual monthly expenditure (excluding those covered infrequent expenditure during the last 365 days) (NSO, 2018). We calculated yearly OOPE, monthly OOPE and average monthly total OOPE in monthly using the following formulas

Average yearly O O P E = \frac{YME + YNME in last 365 days}{Total persons sought treatment for mental illness}

where YME = yearly medical expenditure, YNME = yearly non-medical expenditure

Average monthly O O P E = \frac{MME + MNME in last 30 days}{Total persons sought treatment for mental illness}

where MME = monthly medical expenditure, MNME = monthly non-medical expenditure

Average monthly total O O P E = \frac{\frac{YME + YNME in last 365 days}{12} + MME + MNME}{Total persons sought treatment for mental illness}

Where YME = yearly medical expenditure, YNME = yearly non-medical expenditure, MME = monthly medical expenditure, MNME = monthly non-medical expenditure

Burden of Income Loss Due to Mental Illness

We have examined the OOPE burden as a percentage of OOPE on the total household consumption expenditure as used in several previous studies (Mitra et al., 2009; Sahoo & Madheswaran, 2014).

Burden of income loss due to mental illness = \frac{Healthcare expenditure on mental illness}{Households consumption Expenditure} \times 100

Households Monthly Consumption Expenditure (HMCE)

This study used monthly household consumption expenditure as a proxy variable for household income which has been used in numerous earlier studies (Joe & Rajpal, 2018; Kastor & Mohanty, 2018; Sangar et al., 2019). This is a preferred measure since it is less prone to disparity and bears fewer chances of being under reported or over reported when related to income (Kastor & Mohanty, 2018). Based on the information in the NSSO 76th round survey, this study considered the monthly consumer expenditure by households as below (Government of India, 2018):

Average monthly consumption expenditure = \frac{A + B + C \frac{D}{12}}{Total households}

where A = usual consumer expenditure in a month for household purposes out of purchase, B = imputed value of usual consumption in a month from homegrown stock, C = imputed value of usual consumption in a month from wages in kind, free collection, gifts, etc., and D = expenditure on purchase of household durables during the last 365 days.

Measuring Catastrophic Health Expenditure (CHE)

This study defines CHE as a situation where health expenditure exceeds α% threshold of households’ income (Kwesiga et al., 2015). In the published literature, there is no consensus on the threshold of CHE (Aregbeshola & Khan, 2018; O’Donnell et al., 2008). Furthermore, studies have used CHE if health expenditure is exceeding the 40% threshold of household’s on total non-food consumption expenditure or health expenditure exceeds 10% threshold of household’s on total consumption expenditure (Berki, 1986; Kumar et al., 2015; Russell, 2004; Somkotra & Lagrada, 2009; Van Doorslaer et al., 2007; Wagstaff & van Doorslaer, 2003; Xu et al., 2007). The present study measured CHE by two approaches as described below (Kumar et al., 2015; Wagstaff & van Doorslaer, 2003).

First approach

C H E = 1 if O O P E > {10% HCE}_{i} and C H E = 0 if O O P E \leq {10% HCE}_{i},

C H E = 1 if O O P E > {20% HCE}_{i} and C H E = 0 if O O P E \leq {20% HCE}_{i},

If OOPE exceeds 10% and 20% threshold of a household’s total consumption expenditure (HCE).

Second approach

C H E = 1 if O O P E > α and C H E = 0 if O O P E \leq α

where α is the average household consumption expenditure per person and β is the average household consumption expenditure for two members of the household (Joe & Rajpal, 2018).

Measuring Poverty Impact Due to Healthcare OOPE on Mental Illness

For measuring the poverty impact of OOPE on households, this study used the poverty head count ratio (PHCR) and poverty gap indices. For measuring the poverty line, this study used per capita household monthly expenditure at INR 972 in rural areas and INR 1,407 in urban areas as per recommendation by the Rangarajan committee based on 2012 data (Raveendran, 2016) which described below

Poverty head count (P H C_{i}) = 1 if H C E_{i} \geq P L_{i} & (H C E_{i} - O O P E_{i}) < P L_{i}

$Otherwise 0$ , where $H C E_{i}$ is monthly consumption expenditure of $i^{t h}$ household

The PHCR is a percentage of households who falls below the poverty line due to OOPE (Kumar et al., 2015; Somkotra & Lagrada, 2009; Van Doorslaer et al., 2007).

Poverty head count ratio (P H C R) = \frac{\sum_{i = 1}^{N} {HH Impoershument}_{i}}{Total households (N)} \times 100

The percentage shortfall from the poverty line of those households that have become poor due to OOPE is counted using poverty gap, and the poverty gap ratio defines the percentage shortfall from the poverty line of households that have become poor due to OOPE as a proportion of all the households in the population (Kumar et al., 2015; Van Doorslaer et al., 2007; Wagstaff & van Doorslaer, 2003).

Povery gap (P G_{i}) = {HH Impoershument}_{i} \times \{P L_{i} - (H C E_{i} - O O P E_{i})\} / P L_{i}

Poverty gap ratio (P G R) = \frac{1}{Total H H (N)} \sum_{i = 1}^{N} {Poverty gap}_{i} \times 100

Data Analysis

Descriptive statistics, bivariate analyses and multivariate models were used in the analyses. In the first part of the analysis, descriptive analysis was carried out to describe the background characteristics of persons who were reported mental illness. In the second step, bivariate analyses were carried out to access the burden of income loss due to healthcare expenditure on mental illness, CHE and poverty impact on households by selected socioeconomics characteristics using predictor variables such as age, sex, education, economic status, relation to the head of the household, place of residence and type of mental illness. These variables were chosen based on previous studies and data availability in the dataset (Government of India, 2018; Ringel & Sturm, 2001). In the third step of the analysis, binary logistic regression analysis was carried out to observed the effect of selected socioeconomics characteristics on CHE and poverty impact. In order to take into account, the survey design (i.e., sampling weights with clustering and strata), while estimating bivariate and multivariable statistics, the Survey Data Reference (SVY) command was used in STATA 13.1 (StataCorp. 2013). All expenditures are reported in Indian Rupees (₹).

Results

Socioeconomic and Demographic Characteristics of Respondents

Table 1 shows the selected socioeconomic and demographic characteristics of those persons who reported any types of mental illness during the survey period. Higher percentage (71.2%) of persons identified in the survey belonged to working age group 19–59 years, among them 56.8% were male and almost half 49.7% were illiterate. More than half, 52.2%, persons reported that they felt unnecessary and excessive worry, while 29.6% reported unusual behaviour. About 36.6% required personal assistance for all their daily activities (Table 1).

Table 1.

Percentage Distribution of Persons with Mental Illness by Selected Socioeconomic Characteristics and Type of Mental Illness.

Background Characteristics	Mental Illness		Mental Illness from Birth		Personal Assistance Required Always in Doing Daily Activities		Treatment Taken/Undergoing
Background Characteristics	n	%	n	%	n	%	n	%
Age (in years)
0–14	645	9.6	453	71.0	376	56.8	579	87.4
15–35	2,339	33.8	813	32.0	860	35.3	2,101	90.6
36–59	2,492	37.4	311	12.1	698	30.1	2,221	89.2
60 and above	1,203	19.0	45	3.0	525	41.7	1,034	85.8
Gender
Male	3,813	56.8	966	24.1	1,354	35.1	3,397	89.5
Female	2,864	43.2	655	21.0	1,104	38.7	2,537	88.0
Education
Illiterate	3,254	49.7	1,189	32.9	1,439	44.2	2,790	86.5
Up to primary	1,616	23.3	315	20.5	527	31.3	1,467	89.1
Middle	821	12.7	76	8.2	225	27.6	746	90.9
Secondary and above	988	14.3	42	3.9	268	27.2	932	94.6
MPCE quintile
Poorest	1,196	18.6	240	23.9	395	31.7	967	80.2
Poorer	1,132	18.8	363	26.8	375	37.4	1,006	89.8
Middle	1,373	20.3	329	22.5	506	36.4	1,242	91.3
Richer	1,481	20.9	383	24.6	578	38.0	1,334	90.2
Richest	1,497	21.5	307	16.8	605	39.1	1,386	91.9
Relation to head of the household
Self/spouse of head	2,230	35.0	97	4.4	625	27.4	1,984	88.4
Married child and spouse of child	565	8.4	63	10.8	153	28.5	520	91.1
Unmarried child	2,435	35.2	1,134	44.7	1,081	42.5	2,180	89.7
Grandchildren	163	2.3	106	67.5	97	57.3	152	93.4
Others family members and relatives	1,286	19.1	222	15.8	503	43.8	1,099	86.5
Place of residence
Rural	4,730	73.2	1,212	24.1	1,759	37.3	4,153	87.7
Urban	1,949	26.8	410	19.1	700	34.7	1,782	92.0
Type of mental illness
Unnecessary and excessive worry	3,485	52.2	822	21.8	1,188	32.3	3,106	89.3
Unusual experiences of hearing voices, and seeing	435	6.4	93	18.9	144	30.5	399	91.4
Unusual behaviour	2,072	29.6	575	27.4	767	37.2	1,800	85.8
All above symptoms	687	11.8	132	17.1	360	58.1	630	92.9
Total	6,679	100	1,622	22.8	2,459	36.6	5,935	88.8

Source: Based on the authors’ computation from Persons with Disabilities in India Survey, NSS, 2018.

Note: All ‘n’ are unweighted. Total may not be equal due to some missing cases.

Burden of Income Loss Due to Mental Illness

The average yearly and monthly OOPE and proportion of OOPE on healthcare due to mental illness on the total consumption expenditure of households is reported in Table 2. Age group 60 years and above age group reported the highest OOPE on mental illness in the last 365 days across all age groups. The richest households reported around nine times the OOPE expenditure compared to the poorest household. However, the burden of income loss due to mental illness remained almost consistent across all income quintiles. Overall, 18.1% of the household’s monthly consumption expenditure was spent on healthcare due to mental illness of member in the household. Finding showed that states like Daman and Diu (19.4%), Himachal Pradesh (18.0%), Sikkim (17.4%), Lakshadweep (14.6%), Maharashtra (13.7%), Telangana (13.3%), Punjab (12.7%), Uttarakhand (12.5%) reported higher healthcare burden than the national average for India (10.4%). On the other hand, Mizoram (0.5%), Andaman and Nicobar Islands (1.2%), Puducherry (1.7%), Dadra and Nagar Haveli (2.0%), Arunachal Pradesh (3.0%) and Nagaland (3.5%) reported lower healthcare burden (Table 3).

Table 2.

Average OOPE Expenditure (in ₹) and Burden of Income Loss Due to Mental Illness by Selected Socioeconomic and Demographic Characteristics.

Selected Background Characteristics	Infrequent Expenditure Last 365 Days			Usual Monthly Expenditure			Total OOPE Monthly			Consumption Expenditure		Burden of Income Loss due to Mental Illness
Selected Background Characteristics	Medical Expenditure	Non-Medical Expenditure	OOPE	Medical Expenditure	Non-Medical Expenditure	OOPE	Medical Expenditure	Non-Medical Expenditure	OOPE	YPCE	MPCE	Yearly Loss	Monthly Loss	Total Loss
Age (in years)
0–14	6,179	941	7,120	1,312	388	1,700	1,827	466	2,293	139,778	11,648	5.1	14.6	19.7
15–35	5,058	1,216	6,274	1,089	276	1,365	1,510	377	1,887	132,132	11,011	4.7	12.4	17.1
36–59	6,532	980	7,512	1,118	265	1,383	1,662	346	2,008	131,864	10,989	5.7	12.6	18.3
60 and above	9,381	1,159	10,540	1,413	281	1,694	2,195	378	2,573	167,619	13,968	6.3	12.1	18.4
Gender
Male	6,990	1,165	8,155	1,273	306	1,579	1,855	403	2,258	137,470	11,456	5.9	13.8	19.7
Female	6,273	991	7,264	1,075	246	1,321	1,597	328	1,925	144,283	12,024	5.0	11.0	16.0
Education
Illiterate	7,336	1,028	8,364	1,049	266	1,315	1,660	352	2,012	129,735	10,811	6.4	12.2	18.6
Up to Primary	4,930	867	5,797	1,070	275	1,345	1,480	347	1,827	134,007	11,167	4.3	12.0	16.4
Middle	5,904	1,065	6,969	1,199	257	1,456	1,619	346	1,965	138,233	11,519	5.0	12.6	17.1
Secondary and above	8,162	1,492	9,654	1,577	330	1,907	2,257	454	2,711	169,518	14,126	5.7	13.5	19.2
MPCE quintile
Poorest	3,120	701	3,821	642	159	801	902	217	1,119	54,586	4,549	7.0	17.6	24.6
Poorer	4,619	809	5,428	1,013	227	1,240	1,398	295	1,693	90,402	7,534	6.0	16.5	22.5
Middle	5,636	1,014	6,650	1,150	270	1,420	1,620	354	1,974	121,726	10,144	5.5	14.0	19.5
Richer	7,782	1,096	8,878	1,315	348	1,663	1,964	439	2,403	171,199	14,267	5.2	11.7	16.8
Richest	13,836	2,060	15,896	1,997	432	2,429	3,150	604	3,754	300,347	25,029	5.3	9.7	15.0
Relation to head of the household
Self/ spouse of head	7,872	1,175	9,047	1,219	273	1,492	1,875	371	2,246	130,139	10,845	7.0	13.8	20.7
Married child and spouse of child	4,946	1,596	6,542	1,153	270	1,423	1,566	403	1,969	152,928	12,744	4.3	11.2	15.5
Unmarried child	5,344	1,014	6,358	1,138	291	1,429	1,583	375	1,958	134,692	11,224	4.7	12.7	17.4
Grandchildren	4,825	713	5,538	1,118	403	1,521	1,520	463	1,983	166,821	13,902	3.3	10.9	14.3
Others family members and relatives	7,371	788	8,159	1,223	270	1,493	1,833	335	2,168	162,764	13,564	5.0	11.0	16.0
Place of residence
Rural	5,300	941	6,241	1,041	254	1,295	1,483	333	1,816	112,446	9,371	5.6	13.8	19.4
Urban	9,263	1,370	10,633	1,461	329	1,790	2,233	443	2,676	192,668	16,056	5.5	11.1	16.7
Total	6,680	1,090	7,770	1,187	280	1,467	1,744	371	2,115	140,390	11,699	5.5	12.5	18.1

Source: Based on the authors’ computation from Persons with Disabilities in India Survey, NSS, 2018.

Notes: Healthcare Burden = [Average monthly out of pocket expenditure (in ₹) /Monthly Consumption Expenditure (MCE in (in ₹)] × 100.

Table 3.

Average OOPE Expenditure and Burden of Income Loss Due to Mental Illness by Selected Socioeconomic and Demographic Characteristics.

States	Infrequent Expenditure Last 365 Days			Usual Monthly Expenditure			Total OOPE Monthly			Consumption Expenditure		Burden of Income Loss Due to Mental Illness
States	Medical Expenditure	Non-Medical Expenditure	Yearly OOPE	Medical Expenditure	Non-Medical Expenditure	Monthly OOPE	Medical Expenditure	Non-Medical Expenditure	Total OOPE	YPCE	MPCE	Yearly Loss	Monthly Loss	Total Loss
Jammu & Kashmir	1,822	601	2,422	1,155	338	1,492	1,306	388	1,694	175,987	14,666	1.4	10.2	11.6
Himachal Pradesh	11,238	2,596	13,833	1,528	583	2,111	2,464	799	3,264	163,663	13,639	8.5	15.5	23.9
Punjab	7,140	1,044	8,184	1,936	321	2,257	2,531	408	2,939	203,625	16,969	4.0	13.3	17.3
Chandigarh	10,000	1,200	11,200	4,000	500	4,500	4,833	600	5,433	300,000	25,000	3.7	18.0	21.7
Uttarakhand	28,331	2,907	31,238	977	372	1,348	3,337	614	3,951	122,698	10,225	25.5	13.2	38.6
Haryana	9,817	1,166	10,983	1,464	375	1,840	2,282	473	2,755	213,489	17,791	5.1	10.3	15.5
Delhi	3,718	2,102	5,820	1,484	644	2,128	1,793	819	2,613	245,983	20,499	2.4	10.4	12.7
Rajasthan	11,345	1,683	13,028	1,759	482	2,241	2,704	622	3,327	176,698	14,725	7.4	15.2	22.6
Uttar Pradesh	8,694	1,227	9,921	1,444	285	1,730	2,169	388	2,556	124,172	10,348	8.0	16.7	24.7
Bihar	4,260	811	5,072	643	146	789	998	214	1,212	98,199	8,183	5.2	9.6	14.8
Sikkim	18,466	1,836	20,302	1,033	233	1,267	2,572	386	2,958	111,249	9,271	18.2	13.7	31.9
Arunachal Pradesh	542	173	715	93	923	1,016	139	937	1,076	101,336	8,445	0.7	12.0	12.7
Nagaland	11,753	1,500	13,253	2,108	111	2,218	3,087	236	3,323	152,276	12,690	8.7	17.5	26.2
Manipur	5,525	9,635	15,160	926	1,137	2,063	1,386	1,940	3,326	164,090	13,674	9.2	15.1	24.3
Mizoram	2,101	208	2,309	384	0	384	559	17	576	280,050	23,337	0.8	1.6	2.5
Tripura	2,032	470	2,502	774	172	946	943	211	1,155	116,927	9,744	2.1	9.7	11.9
Meghalaya	2,844	6,400	9,244	600	35	635	837	568	1,405	120,163	10,014	7.7	6.3	14.0
Assam	4,130	701	4,831	807	192	999	1,152	250	1,402	107,853	8,988	4.5	11.1	15.6
West Bengal	2,328	584	2,912	906	251	1,158	1,100	300	1,400	109,639	9,137	2.7	12.7	15.3
Jharkhand	2,370	436	2,805	1,056	373	1,430	1,254	410	1,663	120,877	10,073	2.3	14.2	16.5
Odisha	4,196	941	5,137	957	164	1,121	1,306	242	1,549	76,655	6,388	6.7	17.5	24.2
Chhattisgarh	7,191	2,337	9,529	837	176	1,013	1,436	371	1,807	118,210	9,851	8.1	10.3	18.3
Madhya Pradesh	6,687	1,368	8,055	1,325	331	1,656	1,882	445	2,327	119,543	9,962	6.7	16.6	23.4
Gujarat	4,332	534	4,866	1,161	272	1,433	1,522	317	1,838	182,726	15,227	2.7	9.4	12.1
Daman & Diu	1,782	80	1,862	2,367	610	2,977	2,516	617	3,132	160,469	13,372	1.2	22.3	23.4
Dadra & Nagar Haveli	0.00	0.00	0.00	706	184	891	706	184	891	153,134	12,761	0.0	7.0	7.0
Maharashtra	13,440	1,619	15,059	1,163	236	1,399	2,283	371	2,654	149,202	12,433	10.1	11.3	21.3
Andhra Pradesh	2,323	480	2,804	1,551	203	1,755	1,745	243	1,988	114,381	9,532	2.5	18.4	20.9
Karnataka	10,359	838	11,197	1,347	272	1,619	2,210	342	2,552	141,473	11,789	7.9	13.7	21.6
Goa	510	618	1,128	990	412	1,402	1,033	463	1,496	193,309	16,109	0.6	8.7	9.3
Lakshadweep	7,343	4,494	11,837	637	510	1,146	1,249	884	2,133	175,027	14,586	6.8	7.9	14.6
Kerala	3,489	712	4,201	950	238	1,188	1,241	297	1,539	164,655	13,721	2.6	8.7	11.2
Tamil Nadu	10,959	1,676	12,634	1,184	313	1,496	2,097	452	2,549	140,017	11,668	9.0	12.8	21.8
Puducherry	314	598	912	479	250	729	506	299	805	194,530	16,211	0.5	4.5	5.0
Andaman & Nicobar Islands	0.00	3,303	3,303	0.00	170	170	0.00	445	445	231,598	19,300	1.4	0.9	2.3
Telangana	15,065	2,773	17,838	1,097	228	1,325	2,352	459	2,811	152,271	12,689	11.7	10.4	22.2
Total	6,680	1,090	7,770	1,187	280	1,467	1,744	371	2,115	140,390	11,699	5.5	12.5	18.1

Source: Based on the authors’ computation from Persons with Disabilities in India Survey, NSSO, 2018.

Notes: Healthcare Burden = [Average monthly out of pocket expenditure (in ₹) /Monthly Consumption Expenditure (MCE in (in ₹)] × 100.

Exposure to Catastrophic Health Expenditure

Table 4 provides the analysis of households who faced the CHE due to healthcare expenditure on mental illness using two approaches, first if OOPE exceeded 10% and 20% of the total household’s consumption expenditure and second when the average OOPE on healthcare exceeded per capita household consumption expenditure (PHCE) of one and two household members (Table 4). About 59.5%, and 32.4% of the households forced to CHE due to treatment care of mental illness based on 10% and 20% thresholds respectively while 30.3% of the household’s healthcare expenditure exceeded PHCE of one household member, and in 12.3% households of two household members due to treatment care expenditure on mental illness. Odds of pushed households to CHE were significantly associated with the education of persons and being located in an urban area for households with person(s) with mental illness (Table 4).

Table 4.

Percentage of Households Exposed to CHE and Determinates of CHE by Selected Background Characteristics.

Background Characteristics	Percentage of Households Exposed to CHE				Determinants of CHE at 10 % Threshold (At α = 0.1)
	First Approach		Second Approach		Odds Ratio	P-Value	95 % CI
	At α = 0.1	At α = 0.2	At α = 1	At α = 2	Odds Ratio	P-Value	95 % CI
Age (in years)
0–14	56.5	33.1	35.0	17.5	1.00
15–35	64.5	30.2	29.6	12.0	0.95	.63	0.77–1.17
36–59	58.6	34.4	30.4	11.4	1.21	.15	0.94–1.56
60 and above	55.8	32.1	29.7	12.8	1.31	.12	0.83–1.34
Gender
Male	66.0	35.1	33.4	13.8	1.00
Female	51.6	29.2	26.5	10.5	0.91	.11	0.8–1.02
Education
Illiterate	51.3	29.9	28.1	11.5	1.00
Up to primary	59.2	32.7	29.6	12.5	1.49	.00	1.28–1.73
Middle	70.7	29.6	30.9	11.2	2.18	.00	1.82–2.62
Secondary and above	66.4	39.1	34.7	14.5	2.71	.00	2.29–3.21
MPCE quintile
Poorest	74.6	35.9	38.8	18.5	1.00
Poorer	67.1	38.2	41.5	17.1	1.05	.67	0.85–1.28
Middle	54.3	31.7	31.3	13.8	1.02	.86	0.84–1.24
Richer	60.6	33.9	31.0	10.6	0.90	.28	0.74–1.09
Richest	53.3	28.1	20.9	8.1	0.89	.28	0.73–1.1
Relation to the head of the household
Self/spouse of head	61.4	37.6	30.7	11.7	1.00
Married child and spouse of child	59.8	25.8	34.9	13.7	0.79	.04	0.63–0.98
Unmarried child	65.1	32.1	28.5	12.5	0.57	.00	0.48–0.67
Grandchild	59.3	33.7	49.7	17.2	0.71	.11	0.46–1.08
Others family members and relatives	46.7	25.3	27.6	12.2	0.50	.00	0.42–0.6
Place of residence
Rural	63.6	34.3	32.6	13.0	1.00
Urban	51.8	29.1	25.8	11.0	1.21	.01	1.06–1.39
Total	59.5	32.5	30.3	12.3

Source: Based on the authors’ computation from Persons with Disabilities in India Survey, NSS, 2018.

Poverty Impact Due to Healthcare Expenditure on Mental Illness

Table 5 describes the households falling below the poverty line (PHCR) and average deficit from the poverty line (poverty gap ratio) due to OOPE health payments for the treatment of mental illness. About 20.7% of the households fall below the poverty line due to treatment care expenditure for mental illness. The percentage of households falling below the poverty line was higher among rural residents than urban residents. About 22.5% of rural households, were pushed from being non-poor initially to below poor, due to treatment cost of mental illness compared to 17% in patients belonging to urban households. Average percentage shortfall in income from the poverty line due to mental illness treatment care expenditure was 7.6%. Results indicate that age, education and relation to the head of the household were found to be statistically significant predictors of household falling below poverty line due to healthcare expenditure towards mental illness.

Table 5.

Households Falling Below Poverty Line (Poverty Headcount Ratio) and Average Deficit from the Poverty Line (Poverty Gap Ratio) Due to Out-of-Pocket Health Payments by Selected Background Characteristics.

Background Characteristics	Poverty Impact Due to Health Care Expenditure		Determinants of Becoming Poor from Non-Poor Due to Healthcare Expenditure
Background Characteristics	Poverty Headcount Ratio (%)	Poverty Gap Ratio (Mean)	Odds Ratio	P-Value	95 % CI
Age (in years)
0–14	18.21	6.70	1.00
15–35	20.61	5.10	0.73	.05	0.53–1.0
36–59	21.45	8.01	0.78	.22	0.53–1.15
60 and above	20.27	10.59	0.81	.31	0.60–1.12
Gender
Male	21.12	7.97	1.00
Female	20.19	7.29	1.02	.84	0.85–1.22
Education
Illiterate	19.24	8.02	1.00
Up to primary	20.59	6.28	1.32	.02	1.04–1.66
Middle	21.43	6.41	2.05	.00	1.57–2.66
Secondary and above	22.93	9.60	2.03	.00	1.57–2.61
MPCE quintile
Poorest	0.00	0.00	1.00
Poorer	30.25	6.58	1.24	.17	0.91–1.68
Middle	33.38	10.04	1.27	.11	0.95–1.7
Richer	31.06	10.88	1.08	.61	0.8–1.46
Richest	8.84	7.34	0.85	.33	0.61–1.18
Relation to the head of the household
Self/spouse of head	23.93	10.63	1.00
Married child and spouse of child	19.90	7.35	0.63	.01	0.45–0.89
Unmarried child	18.44	5.32	0.51	.00	0.39–0.66
Grandchildren	18.26	1.66	0.46	.04	0.23–0.95
Others family members and relatives	17.97	5.83	0.67	.00	0.52–0.87
Place of residence
Rural	22.59	7.63	1.00
Urban	17.08	7.74	0.93	.51	0.76–1.15
Total	20.7	7.67

Source: Based on the authors’ computation from Persons with Disabilities in India Survey, NSS, 2018.

State-Wise Variations in Prevalence, OOPE, CHE and Poverty Impact Due to Mental Illness

This study also compares the prevalence of mental illness, OOPE, CHE and PHCR by the states of India (Table 6). The prevalence of mental illness per 1,000 was higher in Tripura, Manipur, followed by Manipur, Haryana, Maharashtra and Madhya Pradesh while the OOPE was higher in Chhattisgarh, followed by Himachal Pradesh, Daman and Diu, Lakshadweep and Punjab. The percentage of households pushed to CHE due to high healthcare expenditure was much higher in Daman and Diu, Himachal Pradesh, Chandigarh, Lakshadweep, Sikkim, Punjab and Andhra Pradesh while the percentage of households who become the poor from non-poor was higher in Lakshadweep, Himachal Pradesh, Manipur, Uttarakhand and Haryana.

Table 6.

State-Wise Variations in Prevalence, OOPE, CHE and Poverty Impact Due to Mental Illness.

State	Prevalence	OOPE	10% Threshold	Impoverished by Health Payments (%)
Jammu & Kashmir	1.2	1,694	47.3	3.4
Himachal Pradesh	1.6	3,264	84.7	24.4
Punjab	1.4	2,939	57.8	17.6
Chandigarh	0.0	5,433	100.0	0.0
Uttarakhand	1.4	3,951	80.4	49.8
Haryana	1.3	2,755	54.7	19.7
Delhi	0.9	2,613	41.6	13.3
Rajasthan	1.4	3,327	69.1	24.2
Uttar Pradesh	1.4	2,556	72.5	27.1
Bihar	0.8	1,212	56.5	26.8
Sikkim	0.6	2,958	79.6	10.6
Arunachal Pradesh	2.1	1,076	92.3	0.9
Nagaland	0.9	3,323	100.0	0.0
Manipur	0.6	3,326	63.8	24.9
Mizoram	0.7	576	0.0	0.0
Tripura	0.9	1,155	55.1	8.5
Meghalaya	0.4	1,405	55.1	16.4
Assam	1.0	1,402	54.8	16.6
West Bengal	1.8	1,400	43.6	19.6
Jharkhand	2.0	1,663	61.4	19.8
Odisha	2.2	1,549	72.1	26.4
Chhattisgarh	1.0	1,807	71.8	14.4
Madhya Pradesh	1.1	2,327	74.6	36.0
Gujarat	0.9	1,838	54.8	13.0
Daman & Diu	0.5	3,132	95.5	0.0
Dadra & Nagar Haveli	0.8	891	54.2	0.0
Maharashtra	1.0	2,654	65.5	17.7
Andhra Pradesh	1.0	1,988	77.8	21.7
Karnataka	0.8	2,552	67.3	19.5
Goa	1.1	1,496	37.4	0.0
Lakshadweep	0.6	2,133	47.6	47.6
Kerala	3.9	1,539	43.9	13.4
Tamil Nadu	0.9	2,549	59.1	20.9
Puducherry	1.7	805	26.4	8.8
Andaman & Nicobar Islands	1.3	445	0.0	0.0
Telangana	1.0	2,811	52.6	20.2
Total	1.3	2,115	59.5	20.7

Source: Based on the authors’ computation from Persons with Disabilities in India Survey, NSS, 2018.

Discussion

In the present article, we examined the OOPE, CHE and OOPE, and poverty impact due to healthcare expenditure on mental illness in India. Based on the authors’ knowledge, this is the first study that discusses about the household’s economic burden due to healthcare OOPE on mental illness in India using a national representative survey. In this article, we have defined monthly OOPE as a total of medical expenditure and non-medical expenditure and healthcare burden is defined as a share of OOPE on the total households consumption expenditure which has been used in many previous studies study (Mitra et al., 2009; Ringel & Sturm, 2001; Sahoo & Madheswaran, 2014). The following are the salient findings of our study.

Our study reveals that a higher proportion of the people with mental illness is seen in the economically productive age group (19–55 years). Reports from India have suggested that the mental illness rate is high among productive age groups and is the largest contributor to economic loss in India (Isalkar, 2015). Among non-communicable diseases, mental health is expected to account for 20% of economic loss (estimated at $6.2 trillion) from 2012–2030. In LMICs 40%–50% of mental healthcare costs are borne out of pocket, and the higher prevalence among the productive age group (19–55 years) in our study imply the disability effects of mental illness limiting the ability of persons to search for and sustain productive employment (Patel, 2007).

Overall, 18.1% of the household’s monthly consumption expenditure was spent on treatment care on mental illness and varies across socioeconomic groups. Burden of OOPE was higher in urban and more literate patients compared to rural and illiterate patients, respectively. The results reveal that 59.5%, and 32.4% of the households experienced CHE due OOPE for mental illness based on 10% and 20% thresholds respectively while 30.3% and 12.3% of the households had catastrophic impact based on the modified approach of average per capita and average two capita monthly consumption expenditure respectively. This approach is new and has been highlighted in a previous study (Joe & Rajpal, 2018). Our findings are consistent with the incidence of CHE among households in other developing countries (Hailemichael et al., 2019).

Our results revealed that mental illnesses push around 20% of the households below the poverty line and the extent of financial trauma may vary with different socio-economic parameters. Previous studies have also shown a vicious cycle in which mental health pushes towards poverty while poverty itself may increase the vulnerability towards mental illnesses (Lund et al., 2010; Sriram, 2018). Although people who had the education of or above secondary level were more likely to get poorer due to mental health issues than less educated people, mental illnesses may also affect a person from seeking education as supported by previous studies (Sriram, 2018; Trani et al., 2015).

Strengths and Limitations of the Study

The strength of the present study is that results of this study can be generalised subsequently as they were derived from the current countrywide representative cross-sectional NSS data which was well designed, covering all the states of India and more particularly the survey was based on the theme of persons living with disability in India. Second, this study used standard methods from previous studies to capture the households’ financial burden due to healthcare expenditure on mental illness and strictly followed all the standardised steps calculating the dependent and independent variables for analysis. While this study has several strengths, there are some limitations as well. First, recall bias is a major limitation of expenditure data reported by households on healthcare. Second, we cannot make any causal inferences as our study is based on the cross-sectional survey study design. Third, due to the study design limitations, we could not calculate indirect costs due to unemployment, reduced educational attainment, expenses for social support, costs associated with chronic disability, homelessness, crime, suicide, homicide, caregiver burden, family caregiver’s time, other medical complications of mental illness, early mortality, substance use and other unquantifiable costs such as emotional burden on family (Isalkar, 2015). Future research on mental illness in India should aim to focus on estimation on mental illness’s indirect costs, as these are higher than direct costs (Isalkar, 2015).

Conclusion

India is currently facing a serious mental health crisis, with mental illness constituting one-sixth of all health-related disorders (WHO, 2017). There have been rising cases of mental issues in India, particularly depression, anxiety, bipolar disorders and schizophrenia, and suicide being a leading cause of deaths among young people—reference. The National Mental Health Policy 2014 and the Mental Healthcare Act, 2017 has proposed several steps to combat mental illnesses through promoting awareness, ending the stigma associated and provision of good quality and affordable care for mental illnesses in the country (Government of India, 2017; Gururaj et al., 2016; MOHFW, 2014). Our article suggests that there is a critical need to accelerate on ground various measures for the early diagnosis and management of mental health issues in order to reduce OOPE, CHE and poverty impact due to healthcare expenditure on mental illness among households in India.

Footnotes

Acknowledgement

An earlier version of this article was presented at 41st annual conference of the Indian Association for the Study of Population from 28 to 30 November 2020, and this article got the best poster award. The authors are grateful to session chairpersons and participants for comments and constructive suggestions facilitating improvement in the article.

Author contribution

JY and DJ conceived the idea for the study and developed the analysis plan. JY led the analysis and organised the first draft of the manuscript. JY and DJ conducted data interpretation and manuscript writing. JY, GRM, DJ, CV and RS read and approved the preliminary manuscript. All authors approved the final version of the manuscript.

Data Availability Statement

The present article is based on NSS 76th round survey data, which is freely available to individuals at http://www.mospi.gov.in/.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Ethics Approval

The present study used the data from the recent NSS 76th round survey. First, the NSS obtained the ethical consent from the review committee of the NSS before the survey. Second, during the survey, a usual written consent had been taken by the respondent once they are agreed to take part in the study. Therefore, no ethical approval is required separately for the present study.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

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