Inequity in out-of-pocket payments for hospitalisation in India: Evidence from the National Sample Surveys, 1995–2014

Pandey, Anamika; Clarke, Lynda; Dandona, Lalit; Ploubidis, George B.

doi:10.1016/j.socscimed.2018.01.031

Cited by 39 publications

(41 citation statements)

References 31 publications

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“…A large body of literature suggests that the high OOP payment are positively associated with CHE and distress financing, and increases the probability of poverty and impoverishment [ 15 – 21 ]. The risk of CHE is said to be higher among the rich [ 22 ], possibly due to choice of methods. Impoverishment, defined an increase in poverty level owing to OOP payment for health care is an important measure of financial protection.…”

Section: Introductionmentioning

confidence: 99%

Addressing data and methodological limitations in estimating catastrophic health spending and impoverishment in India, 2004–18

Mohanty

Dwivedi

2021

Int J Equity Health

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Background Estimates of catastrophic health expenditure (CHE) are counterintuitive to researchers, policy makers, and developmental partners due to data and methodological limitation. While inferences drawn from use of capacity-to-pay (CTP) and budget share (BS) approaches are inconsistent, the non-availability of data on food expenditure in the health survey in India is an added limitation. Methods Using data from the health and consumption surveys of National Sample Surveys over 14 years, we have overcome these limitations and estimated the incidence and intensity of CHE and impoverishment using the CTP approach. Results The incidence of CHE for health services in India was 12.5% in 2004, 13.4% in 2014 and 9.1% by 2018. Among those households incurring CHE, they spent 1.25 times of their capacity to pay in 2004 (intensity of CHE), 1.71 times in 2014 and 1.31 times by 2018. The impoverishment due to health spending was 4.8% in 2004, 5.1% in 2014 and 3.3% in 2018. The state variations in incidence and intensity of CHE and incidence of impoverishment is large. The concentration index (CI) of CHE was − 0.16 in 2004, − 0.18 in 2014 and − 0.22 in 2018 suggesting increasing inequality over time. The concentration curves based on CTP approach suggests that the CHE was concentrated among poor. The odds of incurring CHE were lowest among the richest households [OR 0.22; 95% CI: 0.21, 0.24], households with elderly members [OR 1.20; 95% CI:1.12, 1.18] and households using both inpatient and outpatient services [OR 2.80, 95% CI 2.66, 2.95]. Access to health insurance reduced the chance of CHE and impoverishment among the richest households. The pattern of impoverishment was similar to that of CHE. Conclusion In the last 14 years, the CHE and impoverishment in India has declined while inequality in CHE has increased.

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Section: Introductionmentioning

confidence: 99%

Addressing data and methodological limitations in estimating catastrophic health spending and impoverishment in India, 2004–18

Mohanty

Dwivedi

2021

Int J Equity Health

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“…For example, the OOP healthcare payment for sick postpartum women in Bangladesh was $261 [10]. OOP healthcare payments accounted for 40% in Chile [11], more than 50% for Indian sick neonates [12], and they were three times higher during hospitalisation among the poorest Indian households [13]. Approximately 4 to 6% of households in Vietnam faced CHE [14], 2 to 3% in Iran [15], and 2 to 28% in Kenya [5].…”

Section: Introductionmentioning

confidence: 99%

Financial risk of seeking maternal and neonatal healthcare in southern Ethiopia: a cohort study of rural households

Borde

Loha

Johansson

et al. 2020

Int J Equity Health

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Introduction: Ethiopian households' out-of-pocket healthcare payments constitute one-third of the national healthcare budget and are higher than the global and low-income countries average, and even the global target. Such out-of-pocket payments pose severe financial risks, can be catastrophic, impoverishing, and one of the causal barriers for low utilisation of healthcare services in Ethiopia. This study aimed to assess the financial risk of seeking maternal and neonatal healthcare in southern Ethiopia. Methods: A population-based cohort study was conducted among 794 pregnant women, 784 postpartum women, and their 772 neonates from 794 households in rural kebeles of the Wonago district, southern Ethiopia. The financial risk was estimated using the incidence of catastrophic healthcare expenditure, impoverishment, and depth of poverty. Annual catastrophic healthcare expenditure was determined if out-of-pocket payments exceeding 10% of total household or 40% of non-food expenditure. Impoverishment was analysed based on total household expenditure and the international poverty line of ≈ $1.9 per capita per day. Results: Approximately 93% (735) of pregnant women, 31% (244) of postpartum women, and 48% (369) of their neonates experienced illness. However, only 56 households utilised healthcare services. The median total household expenditure was $527 per year (IQR = 390: 370,760). The median out-of-pocket healthcare payment was $46 per year (IQR = 46: 46, 92) with two episodes per household, and shared 19% of the household's budget. The poorer households paid more than did the richer for healthcare, during pregnancy-related and neonatal illness. However, the richer paid more than did the poorer during postpartum illness. Forty-six percent of households faced catastrophic healthcare expenditure at the threshold of 10% of total household expenditure, or 74% at a 40% nonfood expenditure, and associated with neonatal illness (aRR: 2.56, 95%CI: 1.02, 6.44). Moreover, 92% of households were pushed further into extreme poverty and the poverty gap among households was 45 Ethiopian Birr per day. The average household size among study households was 4.7 persons per household.

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“…There are a wealth of studies describing socioeconomic inequality in health in India in terms of various access and process indicators as well as in terms of various disease specific outcome measures 12–17. What is missing, however, is a quantification of socioeconomic inequality in overall lifetime health, providing a holistic measure synthesising inequalities emerging across the life course 18.…”

Section: Introductionmentioning

confidence: 99%

Socioeconomic inequality in life expectancy in India

et al. 2019

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IntroductionConcern for health inequalities is an important driver of health policy in India; however, much of the empirical evidence regarding health inequalities in the country is piecemeal focusing only on specific diseases or on access to particular treatments. This study estimates inequalities in health across the whole life course for the entire Indian population. These estimates are used to calculate the socioeconomic disparities in life expectancy at birth in the population.MethodsPopulation mortality data from the Indian Sample Registration System were combined with data on mortality rates by wealth quintile from the National Family Health Survey to calculate wealth quintile specific mortality rates. Results were calculated separately for males and females as well as for urban and rural populations. Life tables were constructed for each subpopulation and used to calculate distributions of life expectancy at birth by wealth quintile. Absolute gap and relative gap indices of inequality were used to quantify the health disparity in terms of life expectancy at birth between the richest and poorest fifths of households.ResultsLife expectancy at birth was 65.1 years for the poorest fifth of households in India as compared with 72.7 years for the richest fifth of households. This constituted an absolute gap of 7.6 years and a relative gap of 11.7 %. Women had both higher life expectancy at birth and narrower wealth-related disparities in life expectancy than men. Life expectancy at birth was higher across the wealth distribution in urban households as compared with rural households with inequalities in life expectancy widest for men living in urban areas and narrowest for women living in urban areas.ConclusionAs India progresses towards Universal Health Coverage, the baseline social distributions of health estimated in this study will allow policy makers to target and monitor the health equity impacts of health policies introduced.

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Inequity in out-of-pocket payments for hospitalisation in India: Evidence from the National Sample Surveys, 1995–2014

Cited by 39 publications

References 31 publications

Addressing data and methodological limitations in estimating catastrophic health spending and impoverishment in India, 2004–18

Addressing data and methodological limitations in estimating catastrophic health spending and impoverishment in India, 2004–18

Financial risk of seeking maternal and neonatal healthcare in southern Ethiopia: a cohort study of rural households

Socioeconomic inequality in life expectancy in India

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