Evidence map›Paper›PMID 29527346›Full record

ReviewBMJ global health2018

Social health insurance for the poor: lessons from a health insurance programme in Karnataka, India.

Neeraj Sood, Zachary Wagner

Abstract readReview
In one paragraph

Review in BMJ global health, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Neeraj SoodSol Price School of Public Policy, University of Southern California, Los Angeles, California, USA.
Zachary WagnerCenter for Population Health Sciences, Stanford University, Palo Alto, California, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Life-saving technology used to treat catastrophic illnesses such as heart disease and cancer is often out of reach for the poor. As life expectancy increases in poor countries and the burden from chronic illnesses continues to rise, so will the unmet need for expensive tertiary care. Understanding how best to increase access to and reduce the financial burden of expensive tertiary care is a crucial task for the global health community in the coming decades. In 2010, Karnataka, a state in India, rolled out the Vajpayee Arogyashree scheme (VAS), a social health insurance scheme focused on increasing access to tertiary care for households below the poverty line. VAS was rolled out in a way that allowed for robust evaluation of its causal effects and several studies have examined various impacts of the scheme on poor households. In this analysis article, we summarise the key findings and assess how these findings can be used to inform other social health insurance schemes. First, the evidence suggests that VAS led to a substantial reduction in mortality driven by increased tertiary care utilisation as well as use of better quality facilities and earlier diagnosis. Second, VAS significantly reduced the financial burden of receiving tertiary care. Third, these benefits of social health insurance were achieved at a reasonable cost to society and taxpayers. Several unique features of VAS led to its success at improving health and financial well-being including effective outreach via health camps, targeting expensive conditions with high disease burden, easy enrolment process, cashless treatment, bundled payment for hospital services, participation of both public and private hospitals and prior authorisation to improve appropriateness of care.

Indexed as

cancercardiovascular diseasehealth insurancescreeningtreatment

Identifiers

PMID29527346
PMCPMC5841491

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.