India

Tertiary care is often too expensive for people with low incomes. As a result, those with conditions requiring tertiary care often go untreated or are left with devastating hospital bills, both of which exacerbate poverty. In addition, the burden of ischemic heart disease and cancer—diseases that can potentially be dealt with in tertiary care—is rising in many countries with a lot of poverty such as China, Bangladesh, and India.

This paper estimates the impact of social health insurance on financial risk by utilizing data from a natural experiment created by the phased roll-out of a social health insurance program for the poor in India. We estimate the distributional impact of insurance on of out-of-pocket costs and incorporate these results with a stylized expected utility model to compute associated welfare effects.

India's conditional cash transfer scheme to promote institutional deliveries, the Janani Suraksha Yojana (JSY), has stimulated extraordinary attention and curiosity by public health stakeholders worldwide because of its scale, coverage, and budget. In just 4 years, its beneficiaries multiplied 11-fold, from 0·74 million in 2005–06 to 8·43 million in 2008–09 (thus covering nearly a third of the 26 million women who deliver in the country annually). Budgetary allocation for the JSY increased from a mere US$8·5 million to $275 million in the same period.

Tertiary care is often too expensive for people with low incomes. As a result, those with conditions requiring tertiary care often go untreated or are left with devastating hospital bills, both of which exacerbate poverty.

In addition, the burden of ischemic heart disease and cancer—diseases that can potentially be dealt with in tertiary care—is rising in many countries with a lot of poverty such as China, Bangladesh, and India.

Objectives

To evaluate the effects of a government insurance program covering tertiary care for people below the poverty line in Karnataka, India, on out-of-pocket expenditures, hospital use, and mortality.

Design

Geographic regression discontinuity study.

Setting

572 villages in Karnataka, India.

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