Evidence map›Paper›PMID 33903176›Full record

ArticleBMJ global health2021

Examining the level and inequality in health insurance coverage in 36 sub-Saharan African countries.

Edwine Barasa, Jacob Kazungu, Peter Nguhiu, Nirmala Ravishankar

Abstract read
In one paragraph

Article in BMJ global health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 97 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
97citing papers in PubMed, 6 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

97 citing papers in PubMed, 6 syntheses or guidelines pooled it.

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37 more citing papers are in PubMed but not listed here.

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

4 authors.

Edwine BarasaHealth Economics Research Unit, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya EBarasa@kemri-wellcome.org.ORCID 0000-0001-5793-7177
Jacob KazunguHealth Economics Research Unit, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya.
Peter NguhiuHealth Economics Research Unit, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya.
Nirmala RavishankarStratetic purchasing for PHC, Thinkwell, Washington, District of Columbia, USA.

Funding

Wellcome Trust 092654Wellcome Trust 107527
6 · The paper itself

Abstract

introductionLow/middle-income countries (LMICs) in sub-Saharan Africa (SSA) are increasingly turning to public contributory health insurance as a mechanism for removing financial barriers to access and extending financial risk protection to the population. Against this backdrop, we assessed the level and inequality of population coverage of existing health insurance schemes in 36 SSA countries.

methodsUsing secondary data from the most recent Demographic and Health Surveys, we computed mean population coverage for any type of health insurance, and for specific forms of health insurance schemes, by country. We developed concentration curves, computed concentration indices, and rich-poor differences and ratios to examine inequality in health insurance coverage. We decomposed the concentration index using a generalised linear model to examine the contribution of household and individual-level factors to the inequality in health insurance coverage.

resultsOnly four countries had coverage levels with any type of health insurance of above 20% (Rwanda-78.7% (95% CI 77.5% to 79.9%), Ghana-58.2% (95% CI 56.2% to 60.1%), Gabon-40.8% (95% CI 38.2% to 43.5%), and Burundi 22.0% (95% CI 20.7% to 23.2%)). Overall, health insurance coverage was low (7.9% (95% CI 7.8% to 7.9%)) and pro-rich; concentration index=0.4 (95% CI 0.3 to 0.4, p<0.001). Exposure to media made the greatest contribution to the pro-rich distribution of health insurance coverage (50.3%), followed by socioeconomic status (44.3%) and the level of education (41.6%).

conclusionCoverage of health insurance in SSA is low and pro-rich. The four countries that had health insurance coverage levels greater than 20% were all characterised by substantial funding from tax revenues. The other study countries featured predominantly voluntary mechanisms. In a context of high informality of labour markets, SSA and other LMICs should rethink the role of voluntary contributory health insurance and instead embrace tax funding as a sustainable and feasible mechanism for mobilising resources for the health sector.

Indexed as

Insurance, HealthPovertyAfrica South of the SaharaHumansInsurance CoverageSocioeconomic Factorshealth economicshealth insurancehealth systems

Identifiers

PMID33903176
PMCPMC8076950

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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.