Measuring the financial impact of disabilities in India (an analysis of national sample survey data)

Abstract

Background

People with disabilities are vulnerable because of the many challenges they face attitudinal, physical, and financial. The National Policy for Persons with Disabilities (2006) recognizes that Persons with Disabilities are valuable human resources for the country and seeks to create an environment that provides equal opportunities, and protection of their rights, and full. There are limited studies on health care burden due to disabilities of various types.

Aim

The present study examines the socioeconomic and state-wise differences in the prevalence of disabilities and related household financial burden in India.

Methods

Data for this study was obtained from the National Sample Survey (NSS), 76th round Persons with Disabilities in India Survey 2018. The survey covered a sample of 1,18,152 households, 5,76,569 individuals, of which 1,06,894 of had any disability. This study performed descriptive statistics, and bivariate estimates.

Results

The finding of the analysis showed that prevalence of disability of any kind was 22 persons per 1000. Around, one-fifth (20.32%) of the household’s monthly consumption expenditure was spent on out-of-pocket expenditure for disability. More than half (57.1%) of the households were pushed to catastrophic health expenditure due to one of the members being disabled. Almost one-fifth (19.1%) of the households who were above the poverty line before one of members was treated for disability were pushed below the poverty line after the expenditure of the treatment and average percentage shortfall in income from the poverty line was 11.0 percent due to disability treatment care expenditure.

Conclusion

The study provides an insight on the socioeconomic differentials in out-of-pocket expenditure, catastrophic expenditure for treatment of any kind of disability. To attain SDG goal 3 that advocates healthy life and promote well-being for all at all ages, there is a need to recognize the disadvantaged and due to disability.

Background of the study

People with disabilities are vulnerable because of the many barriers we face attitudinal, physical, and financial. Addressing these barriers is within our reach and we have a moral duty to do so. But most important, addressing these barriers will unlock the potential of so many people with so much to contribute to the world. Governments everywhere can no longer overlook the hundreds of millions of people with disabilities who are denied access to health, rehabilitation, support, education, and employment and never get the chance to shine” -Stephen Hawking.

According to CDC disability is any condition of the body or mind (impairment) that makes it more difficult for the person with the condition to do certain activities (activity limitation) and interact with the world around them (participation restriction). The World Health Organization (WHO) recognizes three dimensions of disability viz. Impairment, activity limitation and participation restriction. According to the World Health Organisation, 15% of the world’s population experiences one or the other form of disability. According to government of India there were eight categories of disability were namely viz (i) seeing disability, (ii) hearing disability, (iii) speech disability, (iv) movement disability, (v) mental retardation, (vi) mental illness, (vii) any other disability, and (viii) multiple disabilities [1, 2]. The categories of mental retardation, mental illness, and any other disability were used for the first time. According to the Census 2011, there are 2.68 Crore Persons with Disabilities (PwDs) in India constituting 2.21% of the total population. The percentage of disabled in the total population increased from 2.13% in 2001 to 2.21% in 2011. In rural areas, the increase was from 2.21% in 2001 to 2.24% in 2011 whereas, in urban areas, it increased from 1.93% to 2.17% during this period [3–6]. The World Bank figures states that there are more than a billion people with disabilities among which 80 percent are in developing economies such as India. Although having a disability is often associated with severe socioeconomic disadvantages and poverty, only a small fraction of the people with disabilities in India receive government assistance [7, 8].

Disability imposes several kinds of economic burdens on the disabled individual, on the family of the disabled, and more broadly on the employer, organizations, and society. The disability of a family member affects the family’s standard of living. While the disabled person has to face the conversion handicap as described by Amartya Sen (2005) to maintain a standard of living equivalent to a normal individual, he also loses productivity due to loss in workdays and due to the disability, itself. The other members of the household who are caregivers are forced to reduce their working hours thus reducing the overall family income. Thus, a disability in the household results in dual challenges; increase in household consumption due to conversion handicaps and decrease in household consumption due to fall in the family resources. Not much is known about the magnitude of the medical expenses of disability in developing countries. In developed countries, a few studies estimate medical costs and loss in earnings due to some disabilities. Several previous studies [9–15] have demonstrated measurement issues in this area. No studies have been done in India on disabilities and their financial burden.

Rationale of the study

Persons living with disabilities excessively suffer from poor health and poverty. As per authors knowledge and literature review, there is paucity of literature that talk about financial impact of disability for this helpless subpopulation, mainly in countries like India. Therefore, there is a need for analysis of financial burden of disability, and it will help policy makers and programmers to design health systems for disabled persons. Based on the above literature, this study pursues to fill this gap and aims to examine the prevalence of different types of disabilities and related financial burden for treating the different types of disabilities in India.

Data and methodology

Data sources

This study used the data from the countrywide NSS, 76th round which was based on the theme, Persons with Disabilities in India Survey conducted during July-December 2018. The NSS surveys were done by the Ministry of Statistics, Planning, and Implementation (MOSPI), India, and covered all the states of India [16]. The findings of NSS surveys are widely used for policy construction by experts, researchers, and governments.

Sampling design and sample size

The NSS 76^th round survey implemented a stratified two-stage sampling design using Census 2011 as a sampling frame. In the first stage of the sampling villages/urban blocks and in the second stage households in both rural and urban areas were selected. The survey covered all the states and union territories of India. NSS76^th round covered 8992 village/urban blocks (5378 rural village and 3614 urban blocks), covering 1,18,152 households (81004 in rural and 37148 in urban) enumerating 576,569 individuals (4,02,589 in rural and 1,73,980 in urban). The present study contains of a total 1, 06,894 persons (74,946 in rural, 31,948 in urban, 61,567 males, and 45,305 females) who reported at least one disability during the survey.

Outcome indicators

Disability

The major types of disability studied in this paper are discussed in Table 1.

[Figure omitted. See PDF.]

Average out-of-pocket expenditure

Out-of-pocket expenditure (OOPE) was calculated as the sum of direct medical costs (i.e., hospital stay, consultation, treatment medicines and procedures, laboratory, and other investigation charges), and direct non-medical costs (i.e., transportation, meals, lodging: for patients and care givers). The NSS 76th round survey captured both the medical expenditure (ME) (surgery, equipment, hospitalization, etc.) and nonmedical expenditure (NME) (transport, lodging, food, etc.) for infrequent expenditure during last 365 days and usual monthly medical expenditure (UMME) (excluding those infrequent medical expenditure covered during last 365 days) [17] (. For this study, the average out-of-pocket expenditure was calculated using the following procedure:

Where ME = Medical expenditure, NME = non-medical expenditure, UMME = usual monthly medical expenditure and UMNME = usual monthly non-medical expenditure, PWD = no. of persons who reported expenditure for their disability

Average household monthly consumption expenditure (HCE)

It is a preferred measure since it is lesser prone to disparity and has less chance of being underreported or overreported when compared to income [18]. We have used average monthly household consumption expenditure (HCE) as a proxy variable for household income which was used in several previous studies [18, 19]. Based on the NSSO 76th round we calculated the average monthly consumer expenditure by [17].

Where HCE = Household consumption expenditure

A = usual consumer expenditure in a month for household purposes out of purchase, B = imputed value of usual consumption in a month from home grown 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 last 365 days

Share of out-of-pocket expenditure on total household expenditure

Share of out-of-pocket expenditure on total household expenditure for the ith household is defined as the percentage yearly share of OOPE in the yearly household consumption expenditure (HCE) [20–23] i.e.,

Where HCE = Household consumption expenditure, OOPE = Out—of—pocket expenditure

Catastrophic health expenditure (CHE)

Catastrophic spending on health occurs when a household reduces its basic expenses over a certain period of time, sells assets, or accumulates debts in order to cope with the medical bills of one or more of its members who are being treated for any disability. A household experiences catastrophic health expenditure (CHE) if the health care payment exceeds a threshold (x%) of the total household consumption expenditure ie OOPE>x% HCE or Share of out-of-pocket expenditure on total household expenditure >x%. Various studies have stipulated this threshold based on various theories [20, 24–26]. This study has used two approaches to determine the CHE.

First approach.

In the first approach we have used two thresholds of 10% and 20% respectively i.e., when Share of out-of-pocket expenditure on total household expenditure >10% or Share of out-of-pocket expenditure on total household expenditure >20% based on the previous published studies [20, 27–31]. Thus the CHE definition for the ith household in the first approach

1. CHEi = 1 if Share of out-of-pocket expenditure on total household expenditure i>10%

2. CHEi = 0 if Share of out-of-pocket expenditure on total household expenditure i≤10%.

3. CHEi = 1 if Share of out-of-pocket expenditure on total household expenditure i>20%

4. CHEi = 0 if Share of out-of-pocket expenditure on total household expenditure i≤20%

where CHEi = 1 means the ith household experiences CHE and CHEi = 0 means the ith household does not exhibit CHE.

Second approach.

In the second approach a household exhibits catastrophic health expenditure if the annual OOPE exceeds the average consumption expenditure of a household member ie CHE = 1 if OOPE >HCE/n where n is the number of members in the household. i.e a household is pushed to CHE when the out-of-pocket expenditure exceeds the average consumption expenditure of a household member.

We can also define CHE = 1 if OOPE>2HCE/n i.e a household is pushed to CHE when the out-of-pocket expenditure exceeds the consumption expenditure of two household members

We adopted this approach from a previous paper which argued that categorizing a household as experiencing CHE based on the first approach doesn’t reflect the true situation [17, 20, 32]. Instead, a household may be driven to CHE if its OOPE exceeds the average consumption of one or two household members.

Poverty impact due to OOPE

Poverty was measured using two indices namely Poverty Head Count Ratio (PHCR) [27, 31] which is the percentage of households that fall below the poverty line (PL) due to OOPE and the Poverty Gap Ratio (PGR) [20, 21, 30] measured as the average percentage deficit from the poverty line due to OOPE [20, 21, 29, 31, 33]. For the present study we used PL = 1,407 INR, in urban areas and 972 INR, in rural areas as per the recommendation of the Rangarajan committee in 2014, based on the 2012 data [34]. Further, the PL for 2012 was adjusted for inflation using the consumer price index for December 2018 to obtain PL for December 2018 [35, 36].

A household which was above the poverty line is defined as impoverished if it falls below poverty line after incurring the healthcare expenditure i.e.,

Otherwise, 0, where HCE is total yearly consumption expenditure of a household.

The poverty head count ratio (PHCR) is the percentage of households which fall below the poverty line due to OOPE i.e.,

The percentage deficit from the poverty line of an impoverished household (PHC = 1) is quantified using the poverty gap (PG) i.e.,

The poverty gap ratio (PGR) measures the poverty gap of all impoverished households as a percentage of total households in the population.

Socioeconomic variables

To study the financial burden of households due to disability, the present study included individual characteristics of the disabled person (age, sex, education, marital status), household characteristics (religion, caste, living arrangement, economic status, relation to head of the household) and community variable (place of residence and geographical region) based on the literature review and availability of data [37, 38].

Statistical methods

Firstly, we determined the prevalence of different types of disability. Secondly, OOPE, CHE and impoverishment effect due to disability were analysed. Analysis was done on STATA 13.1 software [39]. Sampling weights with clustering were already calculated in the dataset. The details of sampling weight have been described in the NSS 76^th round report [16]. We used SVY command while using sampling weights [40]. All expenditures are reported in Indian Rupees (INR).

Ethics approval

This study used the data from National Sample Survey (NSS), 76th round Persons with Disabilities in India Survey 2018. First, the NSS survey obtained the ethical consent from the Institutional review committee before the survey. Second, during the survey a usual taken written consent by the respondent. Therefore, no ethical approval is required separately for this study.

Results

Prevalence of disabilities

The prevalence of any disability was found to be 22 per 1000 persons in India with hearing impairment being most prevalent (2.2) followed by visual and speech disability. Among the disabled persons, 12.4 per 1000 person were found to be suffering from locomotor disability. Mental retardation and mental illness were found to be among 1.6 and 1.4 per 1000 persons. Irrespective of its type, disability was found to be higher among people who were aged 60 years and above, than those who were younger (Table 2). The prevalence of locomotor disability was highest in Punjab (18 persons per 1000 while visual disability was found to be highest in Sikkim (5.3 person per 1000), and mental retardation and mental illness was found to be highest in the State of Kerala (Table 3).

[Figure omitted. See PDF.]

[Figure omitted. See PDF.]

Out of pocket expenditure (OOPE)

OOPE for any disability was higher for males, those currently married, who belonged to other religion, and higher economic strata. OOPE among disabled was highest for those who suffered from other disability (INR, 3401), followed by speech and locomotor INR, 2942 and INR, 2675 respectively. Out of pocket expenditure due to health care for locomotor disability was higher among people who were working as a salaried employee/regular wage. Overall OOPE for disability-related health care amounted to INR, 2477 per person. Highest OOPE seen among those aged 15–35 years INR, 2809. OOPE increased as education level increased rising to INR, 3859 for those with secondary education and above (Table 4).

[Figure omitted. See PDF.]

Share of out-of-pocket expenditure on total household expenditure

Overall, the Share of out-of-pocket expenditure on total household expenditure for disability-related health care amounted to 20.32%. Share of out-of-pocket expenditure on total household expenditure was higher among those aged 15–35 years (24.31%), those with of secondary education and above (24.9%), males (23.4%), those who are currently married (22%), lower economic strata (30.4%), rural residents (22.1%). Share of out-of-pocket expenditure on total household expenditure among people with any disability was highest in Lakshadweep followed by Uttarakhand and Andaman and Nicobar Islands (INR, 4648, 3984 and 3929 respectively). Health care burden was higher in Lakshadweep (29.5%) and Uttarakhand (29.3%) (Tables 4 and 5).

[Figure omitted. See PDF.]

Catastrophic health expenditure (CHE)

Table 6 displays the households who faced the CHE due to health care expenditure on disabilities. About 57.1% and 34.1% of the household’s experiences to CHE due to OOPE on disability based on 10% and 20% thresholds respectively while 29.8% of the household’s health care expenditure exceeded PHCE of one household member, and in 13.2% households of two household members due to OOPE on disability. More households face CHE when a male was hospitalized at both the 10% (59.6% compared to 54.2% in females) and 20% threshold (36.7% compared to 31% for females). Proportionately more households in the rural areas faced CHE than those in urban areas (Table 6). More households were pushed to CHE 10% in Northern Chhattisgarh and Madhya Pradesh Central, whereas more households were pushed to CHE 20% in Northern Chhattisgarh (Table 7).

[Figure omitted. See PDF.]

[Figure omitted. See PDF.]

Poverty impact

Table 8 describes the households falling into poverty line (poverty headcount ratio) and average deficit from the poverty line (poverty gap ratio) due to OOP health payments for the treatment of disability. About 19% of the households fall below the poverty line due to treatment care expenditure for any disability and further significant differentials in the poverty gap ratio were observed across different socioeconomic characteristics. Higher proportion of households with disabled people in the age group 15–35 years, fall below the poverty line due to treatment care expenditure for any disability than others. Males, currently married, Hindu, Other backward classes, Rural, better-off households, regular wage/salaried employee had higher chances of falling to below the poverty line due to health care expenditure for disability. Impoverishment due to health care was highest in Lakshadweep, followed by Madhya Pradesh and Uttarakhand (Table 9).

[Figure omitted. See PDF.]

[Figure omitted. See PDF.]

Discussion

The present study examined the prevalence of disability and associated cost to treating the different types of disability in India. The present study also attempts to analysis the burden on households like income loss, catastrophic health care expenditure, and poverty impact due health care expenditure on disability by selected socioeconomic characteristics across the states of India using the recent national representative cross-sectional NSS 76^th round survey data which is specially focus on disability on the theme ‘Persons with Disabilities in India Survey. There are several notable findings.

The prevalence of any disability was found to be 22 per 1000 persons in India. Among them, 12.4 per 1000 person were found to be suffering from locomotor disability. Prevalence of visual, hearing and speech disability was 2.1, 2.2 and 1.9 per 1000 person respectively. Mental retardation and mental illness were found to be among 1.6 and 1.4 per 1000 persons. Similar finding has been indicated by census 2011 [41].

In India, limited studies talk about the financial burden due to disability. Overall, OOPE for disability-related health care amounted to INR, 2477 per person with the highest OOPE seen among those aged 15–35 years INR, 2809. OOPE increased as education level increased rising to INR, 3859 for those with secondary education and above. OOPE among disabled was highest for those who suffered from other disability (INR, 3401), followed by speech and locomotor INR, 2942 and 2675 respectively. Though the OOPE was more among higher economic stat group (INR, 4168) compared to poorest economic group (INR, 1305) but the percentage of health care burden was higher among the poorest (30.4%) compared the Richest (17.0%). Our study results relating to financial burden follow similar forms to pat study done by Plamer et al., 2011 and highlighted that poor households are more faced the health care burden on disability treatment [42]. OOPE among people with any disability was highest in Lakshadweep followed by Uttarakhand and Andaman and Nicobar Islands (INR, 4648, 3984 and 3929 respectively). Health care burden was higher in Lakshadweep (29.5%) and Uttarakhand (29.3%). Region wise, OOPE was highest in Lakshadweep (INR, 4648) and Share of out-of-pocket expenditure on total household expenditure was highest in Rajasthan Southern (34.7%).

When we talk about CHE due to OOPE, analysis indicates that about 57.1%, and 34.1% of the households enforced to CHE due to OOPE on disability based on 10% and 20% thresholds respectively, similar finding observed by past study which shows that households with disabled persons are at faced higher risk to the CHE and distress financing [42]. Various past studies examine the occurrence of CHE based on the ratio method that is ratio of OOPE on household consumption expenditure, which exceeds a certain threshold [43–47]. Even though using the ratio methods, the present study also explores the new method for examine the CHE, best on the authors information it is very new and justify method for CHE calculation if disparity in households’ size is exits and this method claimed that categorizing a household as experiencing CHE based on the ratio method doesn’t reflect the actual condition [20, 32]. The results show that 29.8% of the household’s faced CHE based on the average per capita consumption expenditure and in 13.2% households of faced CHE based on average consumption of two household members due to OOPE on disability. The level of CHE was fluctuating across socioeconomic characteristics, States, and type of types of disability. More households faced CHE if the person was currently married at both thresholds (59.3% and 36.5% at the 10% threshold and 20% threshold respectively). More than 70 percent of the households in Lakshadweep, Chhattisgarh and Himachal Pradesh were pushed to CHE at the 10% and 20% threshold. More households were pushed to CHE 10% in Northern Chhattisgarh and Madhya Pradesh Central, whereas more households were pushed to CHE 20% in Northern Chhattisgarh. Punjab had highest prevalence of locomotor disability (18 persons per 1000 followed by visual disability was highest in Sikkim (5.3 person per 1000), and mental retardation and mental illness was highest in the State of Kerala. Previous studies have also discussed geographical variations of the disease and disability [48].

Our study indicates that about 19% of the households fall below the poverty line due to treatment care expenditure for any disability. Further significant differentials in the poverty gap ratio were observed across different socioeconomic characteristics and type of disability. Higher proportion of households with disabled people in the age group 15–35 years, fall below the poverty line due to treatment care expenditure for any disability than others age group. The impoverishment effect among Males, currently married, Hindu, Other backward classes, Rural, had higher chances of falling to below the poverty line due to incurred OOPE on disability. Impoverishment was highest in Lakshadweep, followed by Madhya Pradesh and Uttarakhand due OOPE on disability.

Strengths of the study

The strength of this study derived from its use of national representative cross-sectional from the NSS 76^th round based on the theme of Persons with Disabilities in India Survey among all the states of India which offers the generalizability to the study findings. This study analyzed indices, namely the health care burden, catastrophic expenditure and impoverishment using standard definitions.

Limitations of the study

While, this study has numerous strengths, there are some limitations as well. Firstly, this study reports a cross-sectional survey data where recall bias is a major limitation for health care expenditure. Secondly, the survey did not collate data on indirect cost like opportunity costs, such as income losses due to disability and wage loss of caregivers. Thirdly, the survey not collected data on the household hardship financing for manage the cost on disability.

Conclusion

Result from this study put forward significant insight that to avert catastrophic health care expenditure and poverty, proper interventions in terms of improving healthcare access and eliminating economic barriers to accomplish disability are essential and these interventions should target weaker section, such as households whose members are suffering from disabilities, households who belong the lowers economic Stata or households in the bottom socioeconomic status. Findings also confirm that socioeconomic inequalities in OOPE, catastrophic health care expenditure, hardship financing and poverty due to OOPE for disabilities exist in India, possibly due to poor utilization of healthcare service by poor households and socially weaker section, therefore, policymakers need to design policies that will make sure that resources for healthcare are equitably dispersed and benefit both the poor and rich. Out-of-pocket health expenditure due to disability in India has been established to be a progressive and unbalanced form of financing health system with catastrophic health expenditure and pushed household to poor from non-poor and effects the living standards of household too. The high Out-of-pocket health expenditure due to disability will affect to utilization and quality of healthcare facilities and household’s financial risk protection.

Program implications

Findings from this study provide evidence for policies and financial support to people with disabilities to avoid medical impoverishment due to their health care expenditure on disability. It is important to highlight the fact that even after the introduction of government social health insurance schemes, there is high prevalence of catastrophic expenditure due to disability. In response to the escalating burden of disability nationwide, there is a pressing and immediate need to establish comprehensive healthcare services aimed at aiding those affected. The implementation of programs and policies dedicated to supporting individuals with disabilities must operate with a mission-driven approach, ensuring that assistance reaches even the most underserved segments of our society.

Therefore, there is a need to tailor disability-specific insurance schemes designed for disabled people. Although the Ayushman Bharat Yojana India’s social protection scheme, is markable step towards addressing the health needs of disadvantaged groups but still there is need for public and media outlets to embark on the advocacy to reduce out of pocket health care expenditure and demand for health system reforms from governments at the national and states levels. This study also recommends that developing appropriate policies to expand the effectiveness of health insurance, such as developing specific disability service package to be included in the health insurance program like Ayushman Bharat.

Acknowledgments

This study used National Sample Survey (NSS), 76th round Persons with Disabilities in India Survey 2018. The authors gratefully acknowledge members of the study field team including who were involved in mapping/listing/segmentation and main survey during data collection. The authors also acknowledge all the respondent for their active participation in this study.

Citation: Yadav J, Tripathi N, Menon GR, Nair S, Singh J, Singh R, et al. (2023) Measuring the financial impact of disabilities in India (an analysis of national sample survey data). PLoS ONE 18(10): e0292592. https://doi.org/10.1371/journal.pone.0292592

About the Authors:

Jeetendra Yadav

Contributed equally to this work with: Jeetendra Yadav, Niharika Tripathi, Geetha R. Menon, Saritha Nair, Jitenkumar Singh, Ravinder Singh, M. Vishnu Vardhana Rao

Roles: Conceptualization, Methodology, Validation, Writing – review & editing

Affiliation: ICMR-National Institute of Medical Statistics (NIMS), New Delhi, India

ORICD: https://orcid.org/0000-0001-7666-4995

Niharika Tripathi

Contributed equally to this work with: Jeetendra Yadav, Niharika Tripathi, Geetha R. Menon, Saritha Nair, Jitenkumar Singh, Ravinder Singh, M. Vishnu Vardhana Rao

Roles: Formal analysis, Writing – original draft, Writing – review & editing

E-mail: [email protected]

Affiliation: Department of Sociology, Indraprastha College for Women, University of Delhi, New Delhi, India

ORICD: https://orcid.org/0000-0002-7502-132X

Geetha R. Menon

Contributed equally to this work with: Jeetendra Yadav, Niharika Tripathi, Geetha R. Menon, Saritha Nair, Jitenkumar Singh, Ravinder Singh, M. Vishnu Vardhana Rao

Roles: Conceptualization, Investigation, Resources, Supervision, Writing – review & editing

Affiliation: ICMR-National Institute of Medical Statistics (NIMS), New Delhi, India

Saritha Nair

Contributed equally to this work with: Jeetendra Yadav, Niharika Tripathi, Geetha R. Menon, Saritha Nair, Jitenkumar Singh, Ravinder Singh, M. Vishnu Vardhana Rao

Roles: Writing – review & editing

Affiliation: ICMR-National Institute of Medical Statistics (NIMS), New Delhi, India

Jitenkumar Singh

Contributed equally to this work with: Jeetendra Yadav, Niharika Tripathi, Geetha R. Menon, Saritha Nair, Jitenkumar Singh, Ravinder Singh, M. Vishnu Vardhana Rao

Roles: Methodology

Affiliation: ICMR-National Institute of Medical Statistics (NIMS), New Delhi, India

Ravinder Singh

Contributed equally to this work with: Jeetendra Yadav, Niharika Tripathi, Geetha R. Menon, Saritha Nair, Jitenkumar Singh, Ravinder Singh, M. Vishnu Vardhana Rao

Roles: Supervision, Writing – review & editing

Affiliation: Indian Council of Medical Research (ICMR), New Delhi, India

M. Vishnu Vardhana Rao

Contributed equally to this work with: Jeetendra Yadav, Niharika Tripathi, Geetha R. Menon, Saritha Nair, Jitenkumar Singh, Ravinder Singh, M. Vishnu Vardhana Rao

Roles: Conceptualization, Visualization, Writing – review & editing

Affiliation: ICMR-National Institute of Medical Statistics (NIMS), New Delhi, India

References

1. Saikia N, Moradhvaj Bora JK. Gender difference in health-care expenditure: Evidence from India human development survey. PLoS One. 2016;11(7):1–15. pmid:27391322

2. Krahn Gloria, and Campbell Vincent A. Evolving views of disability and public health: the roles of advocacy and public health. Disability and health journal. 2011; 4(1):12–18. pmid:21168802

3. Narayan Choudhary Laxmi, and John Thomas. The Rights of Persons with Disabilities Act, 2016: Does it address the needs of the persons with mental illness and their families. Indian journal of psychiatry. 2017; 59(1): 17. pmid:28529356

4. Banurekha Velayutham, Kangusamy Boopathi, and Mehendale Sanjay. Prevalence of disability in Tamil Nadu, India. The National medical journal of India. 2017; 30(3): 125–130. pmid:28936995

5. Deon Filmer. Disability, poverty, and schooling in developing countries: results from 14 household surveys. The World Bank Economic Review. 2008; 22(1): 141–163.

6. Kandamuthan Murugan, and Subodh Kandamuthan. The economic burden of disabled children on families in Kerala in South India. In iHEA 2007 6th World Congress: Explorations in Health Economics Paper, Centre for Development Studies Discussion Paper, no. 91. 2004.

7. Singh Nirbhay N, Giulio E Lancioni, Alan SW Winton, Ashvind N Singh, Angela D Adkins, and Judy Singh. Clinical and benefit—cost outcomes of teaching a mindfulness-based procedure to adult offenders with intellectual disabilities. Behavior Modification. 2008; 32(5): 622–637. pmid:18362201

8. Harriss-White, Barbara. On to a loser: Disability in India. B. Harriss-White and S. Subramaniam (eds) 1999; 135–59.

9. Leibson Cynthia L., Katusic Slavica K., Barbaresi William J., Ransom Jeanine, and Peter C. O’Brien. Use and costs of medical care for children and adolescents with and without attention-deficit/hyperactivity disorder. Jama. 2001; 285(1): 60–66. pmid:11150110

10. Swensen Andrine R., Howard G Birnbaum, Kristina Secnik, Maryna Marynchenko, Paul Greenberg, and Claxton A M I. Attention-deficit/hyperactivity disorder: increased costs for patients and their families. Journal of the American Academy of Child & Adolescent Psychiatry. 2003; 42(12): 1415–1423. pmid:14627876

11. Goetzel Ron Z, Stacey R Long, Ronald J Ozminkowski, Kevin Hawkins, Shaohung Wang, and Wendy Lynch. Health, absence, disability, and presenteeism cost estimates of certain physical and mental health conditions affecting US employers. Journal of occupational and environmental medicine. 2004; 398–412.

12. Montgomery Kyle, Ryan Timothy J Andrew Krause, and Starkey Chad. Assessment of athletic health care facility surfaces for MRSA in the secondary school setting. Journal of Environmental Health. 2010; 72(6): 8–11. pmid:20104827

13. Mitchell Rebecca J, and Paul Bates. Measuring health-related productivity loss. Population health management 14, no. 2 (2011): 93–98. pmid:21091370

14. Sen Amartya. Human rights and capabilities. Journal of human development. 2005; 6(2): 151–166.

15. Raut Lakshmi K, Manoranjan Pal, and Premananda Bharati The economic burden of disability in India: Estimates from the NSS data. Available at SSRN 2432546. 2014.

16. National Sample Survey Organisation (NSSO) Persons with Disabilities in India. Government of India, Ministry of Statistics and Programme Implementation, National Statistical Office. 2018 Report No. 583.

17. Yadav J, Allarakha S, John D, Menon G, Venkateswaran C, Singh R. Catastrophic Health Expenditure and Poverty Impact Due to Mental Illness in India. Journal of Health Management. 2023; 25(1):8–21

18. Davoudi-Kiakalayeh Ali, Dalal Koustuv, Shahrokh Yousefzade-Chabok Bjarne Jansson, and Mohammadi Reza. Costs related to drowning and near drowning in northern Iran (Guilan province). Ocean & coastal management. 2011; 54(3): 250–255.

19. Barnsley Paul D, Amy E Peden, and Justin Scarr. Calculating the economic burden of fatal drowning in Australia. Journal of safety research. 2018; 67: 57–63. pmid:30553430

20. Yadav J, Menon GR, & John D. Disease-specific out-of-pocket payments, catastrophic health expenditure and impoverishment effects in India: an analysis of National Health Survey data. Applied Health Economics and Health Policy. 2021; 19, 769–782. pmid:33615417

21. Yadav J, John D, Menon G. Out of pocket expenditure on tuberculosis in India: Do households face hardship financing? Indian J Tuberc. 2019;66(4):448–460. pmid:31813431

22. Mitra S, Findley PA, Sambamoorthi U. Health care expenditures of living with a disability: Total expenditures, out-of-pocket expenses, and burden, 1996 to 2004. Arch. Phys. Med. Rehabil. 2009; 90:1532–1540. pmid:19735781

23. Sahoo AK, Madheswaran S. Socio-economic Disparities in Health Care Seeking Behaviour, Health Expenditure and Its Source of Financing in Orissa. J Health Manag. 2014;16(3):397–414.

24. Shetty BS, Shetty M. Epidemiology of drowning in Mangalore, a coastal Taluk of South India. Journal of Forensic and Legal Medicine. 2007 Oct 1;14(7):410–5. pmid:17720592

25. Jeetendra Yadav, John Denny, Geetha R Menon, Richard C Franklin, and Amy E Peden. Nonfatal drowning-related hospitalizations and associated healthcare expenditure in India: An analysis of nationally representative survey data. Journal of safety research. 2022; 82: 283–292. pmid:36031256

26. Caroline Lukaszyk, Mittal Srabani, Gupta Medhavi, Das Rumeli, Ivers Rebecca, and Jagnoor Jagnoor. The impact and understanding of childhood drowning by a community in West Bengal, India, and the suggested preventive measures. Acta paediatrica. 2019; 108(4):731–739. pmid:30252948

27. Mohanty Sanjay K, and Kastor Anshul. Out-of-pocket expenditure and catastrophic health spending on maternal care in public and private health centres in India: a comparative study of pre and post national health mission period. Health Economics Review. 2017;7: 1–15.

28. Sunil Rajpal, Kumar Abhishek, and Joe William. Economic burden of cancer in India: Evidence from cross-sectional nationally representative household survey, 2014. PloS ONE. 2018; 13(2): e0193320. pmid:29481563

29. Soumitra Ghosh. Catastrophic payments and impoverishment due to out-of-pocket health spending. Economic and Political Weekly. 2011; 63–70.

30. Sakthivel Selvaraj, Farooqui Habib Hasan, and Karan Anup. Quantifying the financial burden of households’ out-of-pocket payments on medicines in India: a repeated cross-sectional analysis of National Sample Survey data, 1994–2014. BMJ open. 2018; 8(5):e018020. pmid:29858403

31. Kumar K, Singh A, Kumar S, et al. Socio-Economic Differentials in Impoverishment Effects of Out-of-Pocket Health Expenditure in China and India: Evidence from WHO SAGE. Nugent RA, ed. PLoS One. 2015;10(8):e0135051. pmid:26270049

32. Joe W, Rajpal S. Unravelling the socioeconomic gradient in the incidence of catastrophic health care expenditure: A comment. Health Policy Plan. 2018;33(5):699–701. pmid:29617995

33. Wagstaff A, Van Doorslaer E. Catastrophe and impoverishment in paying for health care: With applications to Vietnam 1993–1998. Health Econ. 2003;12(11):921–933. pmid:14601155

34. Raveendran G. A Review of Rangarajan Committee Report on Poverty Estimation: http://dx.doi.org/101177/0973703016648033. Published online July 14, 2016. https://doi.org/10.1177/0973703016648033

35. All India Consumer Price Index (AICPI), 2020 cyberjournalist.org.in, http://cyberjournalist.org.in/manisana/aicpi.html. Accessed 27 Apr. 2022.

36. Bajwala VR, John D, Rajasekar TD, Murhekar MV. Severity and costs associated with hospitalization for dengue in public and private hospitals of Surat city, Gujarat, India, 2017–2018. Transactions of the Royal Society of Tropical Medicine and Hygiene. 2019; 113(11):661–669 pmid:31294808

37. McDonald KE, Balcazar FE, Keys CB. Youth with Disabilities. Handb Youth Mentor. 2012;29(2011):493–507.

38. CDC. Disability and Health—People with Disabilities | CDC. Centers for Disease Control and Prevention. 2020, www.cdc.gov, https://www.cdc.gov/ncbddd/disabilityandhealth/people.html

39. StataCorp. Stata Statistical Software: Release 13. 2013; College Station, TX: StataCorp LP.

40. Korn Edward L, and Barry I Graubard. Simultaneous testing of regression coefficients with complex survey data: Use of Bonferroni t statistics. The American Statistician. 1990; 44(4): 270–276.

41. Census of India Office of the Registrar General & Census Commissioner, India 2011.

42. Plamer M, Nguyen T, Neeman T, Berry H, Hull T, Harley D. Health care utilization, cost burden and coping strategies by disability status: an analysis of the Vietnam National Health Survey. The International journal of health planning and management. 2011;26(3):e151–e168. pmid:20583316

43. Van Doorslaer E, O’Donnell O, Rannan‐Eliya RP, Somanathan A, Adhikari SR, Garg CC, et al. Catastrophic payments for health care in Asia. Health economics. 2007; 16(11), 1159–1184. pmid:17311356

44. Mataria A, Raad F, Abu-Zaineh M, & Donaldson C. Catastrophic healthcare payments and impoverishment in the occupied Palestinian territory. Applied Health Economics and Health Policy. 2010; 8: 393–405. pmid:21043541

45. Yadav J, John D, Menon GR, Franklin RC, & Peden AE. Nonfatal drowning-related hospitalizations and associated healthcare expenditure in India: An analysis of nationally representative survey data. Journal of safety research. 2022; 82, 283–292. pmid:36031256

46. Somkotra T, Lagrada LP. Which households are at risk of catastrophic health spending: Experience in Thailand after universal coverage. Health Aff. 2009;28(3). pmid:19336470

47. Yadav J, Devi S, Singh MN, & Manchanda N. Health care utilization and expenditure inequities in India: Benefit incidence analysis. Clinical Epidemiology and Global Health. 2022; 15, 101053.

48. Nilima N, Puranik A, Kaushik S, et al. Geographic variation and factors associated with breast cancer screening in India using a spatial durbin approach. Spatial Demography. 2023;11(3).