Research Article: Variations in catastrophic health expenditure across the states of India: 2004 to 2014

Date Published: October 22, 2018

Publisher: Public Library of Science

Author(s): Anamika Pandey, G. Anil Kumar, Rakhi Dandona, Lalit Dandona, Vijayaprasad Gopichandran.

http://doi.org/10.1371/journal.pone.0205510

Abstract

Financial protection is a key dimension of universal health coverage. Catastrophic health expenditure (CHE) has increased in India over time. The overall figures mask the subnational heterogeneity crucial for designing insurance coverage for 1.3 billion population across India. We estimated CHE in every state of India and the changes over a decade.

We used National Sample Survey data on health care utilisation in 2004 and 2014. The states were placed in four groups based on epidemiological transition level (ETL), defined on the basis of ratio of disability-adjusted life-years from communicable diseases to those from non-communicable diseases and injuries combined, with a low ratio denoting high ETL state group. CHE was defined as the proportion of households that had out-of-pocket payments for health care equalling or exceeding 10% of the household expenditure. We assessed variation in the magnitude and distribution of CHE between ETL state groups and between states of India.

In 2014, CHE was higher in the high (30.3%, 95% confidence interval: 28.5 to 32.1) and higher-middle (27.4%, 26.3 to 28.6) ETL state groups than the low (21.8%, 20.8 to 22.8) and lower-middle (19.0%, 17.1 to 21.0) groups. From 2004 to 2014, CHE increased only in the high and higher-middle ETL groups (1.19 and 1.34 times, respectively). However, the individual states with substantial increase in CHE were spread across all ETL groups. The gap between the highest CHE of an individual state and the lowest was 8-fold in 2014. CHE was disproportionately concentrated among the rich in 2004 for most of India, but in 2014 CHE was distributed equally among the rich and poor because of the substantial increase in CHE among the poor over time.

Better provision of quality health care should be accompanied by financial protection measures to safeguard the poor from increasing CHE in India. The state-specific CHE trends can provide useful input for the planning of the recently launched National Health Protection Mission such that it meets the requirement of each state.

Partial Text

The United Nations 2030 Agenda for Sustainable Development Goals (SDGs) has emphasized on universal health coverage (UHC) which aims to achieve equity in access to quality essential health services, ensuring that the cost of using services does not put households at risk of financial catastrophe [1, 2]. Financial protection is a key dimension of UHC which is central to the achievement of other health targets under the SDGs 2030 [3]. Incidence of catastrophe health expenditure (CHE) is a measure of the performance of the health system in a country and is a useful indicator to monitor the progress towards UHC [4].

We used data from two recent nationwide health care surveys done in India by the National Sample Survey Organisation in 2004 and 2014 [10, 11]. Data were collected from a nationally representative sample of 73,868 households with 383,338 persons in NSS 2004 and 65,932 households with 333,104 persons in NSS 2014. These surveys collected information on direct medical and non-medical expenditure on all individuals in the households pertaining to each episode of hospitalisation in the reference period of one year, and each spell of ailment treated as outpatient in 15 days reference period. OOP payments were obtained in both these surveys after the deduction of any amount reimbursed or expected to be reimbursed or paid directly by the employer, insurance companies, or other agencies. We included the expenditure on child birth in the hospital in both the surveys while calculating the hospitalisation expenditure. Any expenditure on the immunisation of children, pre and post-natal care, and child birth (not in the hospital) in the last one year were included in both the surveys while calculating the outpatient care expenditures. Details of the items used to access OOP payments on inpatient and outpatient care are presented in S1 Table.

The highest proportion of households belonged to the low ETL state groups (43.6%) followed by higher-middle (35.8%), high (14.3%) and lower-middle (6.3%) ETL groups in 2014. In 2004, this proportion was 41.5%, 37.0%, 14.9% and 6.6%. High ETL state group had a higher proportion of households with OOP payments than the low ETL group; this differential was 1.18 times in 2004 and 1.43 times in 2014 (Table 1). From 2004 to 2014, the households with OOP payments increased significantly by 1.17 times in the higher-middle and 1.20 times in the high ETL group, but remained similar in the low and lower-middle ETL group. Overall, the mean per capita OOP expenditure was 6.6 (6.50 to 6.77) US$ in 2014. This was 62% higher than that in 2004 (4.10, 4.00 to 4.20 US$). From 2004 to 2014, there was similar increase in mean per capita OOP payments in the three ETL state groups (range: 1.61 to 1.64 times) other than the lower-middle ETL group which had the least increase (1.38 times). The highest OOP payment of an individual state in 2014 was 5.1 times the lowest. From 2004 to 2014, OOP payment increased most in the higher-middle ETL state of Delhi (3.91 times) followed by high ETL state of Goa (2.17 times), low ETL states of Odisha (2.06 times), Chhattisgarh (2.03 times), and Bihar (1.90 times) (Table 1).

The OOP payments for health care increased by 62% and the CHE increased by 17% from 2004 to 2014, indicating that the financial protection offered to patients by the health care system has remained inadequate in India. Behind this, however, are huge variations in the magnitude of OOP payments and CHE across the states of India which are at various levels of epidemiological transitions. In this study, we present the trends in OOP payments and CHE for states grouped by the level of epidemiological transition as well as the key findings for the individual states. We also provide evidence on the economic inequality in CHE across state groups for more focused attention on addressing these inequalities.

 

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http://doi.org/10.1371/journal.pone.0205510

 

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