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Examining the level and inequality in health insurance coverage in 36 sub-Saharan African countries
  1. Edwine Barasa1,2,
  2. Jacob Kazungu1,
  3. Peter Nguhiu1,
  4. Nirmala Ravishankar3
  1. 1Health Economics Research Unit, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya
  2. 2Center for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
  3. 3Stratetic purchasing for PHC, Thinkwell, Washington, District of Columbia, USA
  1. Correspondence to Prof Edwine Barasa; EBarasa{at}


Introduction Low/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.

Methods Using 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.

Results Only 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%).

Conclusion Coverage 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.

  • health insurance
  • health systems
  • health economics

Data availability statement

Data are available in a public, open access repository.

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See:

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Data availability statement

Data are available in a public, open access repository.

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  • Handling editor Lei Si

  • Twitter @edwinebarasa

  • Contributors EB designed the study, analysed the data and wrote the first draft of the manuscript. JK compiled the data, did a literature search, analysed the data and contributed to the writing of subsequent versions of the manuscript. PN analysed the data and contributed to the writing of subsequent versions of the manuscript. NR designed the study, did a literature search and contributed to the writing of subsequent versions of the manuscript.

  • Funding EB is funded by a Wellcome Trust Research Training Fellowship (#107527). Additional funds from a Wellcome Trust core grant awarded to the KEMRI-Wellcome Trust Research Program (#092654) supported this work.

  • Competing interests None declared.

  • Patient and public involvement statement The public and patients were not involved since this is an analysis of publicly available secondary data (Demographic and Health Surveys).

  • Provenance and peer review Not commissioned; externally peer reviewed.