A Transparent Universal Health Coverage Index with Decomposition by Socioeconomic Groups: Application in Asian and African Settings

Jahangir A. M. Khan (), Sayem Ahmed, Tao Chen, Ewan M. Tomeny and Louis W. Niessen
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Jahangir A. M. Khan: Liverpool School of Tropical Medicine
Sayem Ahmed: Liverpool School of Tropical Medicine
Tao Chen: Liverpool School of Tropical Medicine
Ewan M. Tomeny: Liverpool School of Tropical Medicine
Louis W. Niessen: Liverpool School of Tropical Medicine

Applied Health Economics and Health Policy, 2019, vol. 17, issue 3, No 11, 399-410

Abstract: Abstract Background Health and wellbeing as one of the Sustainable Development Goals requires all countries to achieve Universal Health Coverage (UHC). That is, all people must have access to healthcare when needed at an affordable price. While several indices were developed recently to assess UHC status, these indices appeared to be difficult for practitioners to apply without statistical knowledge. Objective This paper presents a transparent and step-by-step practical calculation method of such an index using Excel spreadsheets, applied to some Asian and African countries. We also decompose the contribution of socioeconomic groups to UHC index values. Methods We utilized the well known UHC illustration (three-dimensional box, showing population coverage, service coverage and financial protection) to calculate the UHC index. We also broke down the index into socioeconomic groups. For validation, correlation coefficients between our index and other UHC indices were calculated and the relationship of our index with out-of-pocket (OOP) payments was estimated. Results World Bank data from six Asian and 15 African countries on health-service coverage of people in five socioeconomic quintiles with financial protection were used to calculate our UHC index. Among the Asian countries, indices ranged between 26.0% (Nepal) and 58.7% (Kazakhstan), while in African countries indices ranged between 8.9% (Chad) and 55.3% (Namibia). Decomposition of the UHC index showed a higher contribution to the index by richer socioeconomic groups. The correlation coefficients between our estimated UHC index values and those of others ranged between 0.774 and 0.900. Our index reduced by 1.4% in response to a 1% increase in OOP payments. Conclusions This spreadsheet approach for calculating the UHC index appeared to be useful, where the interrelation of UHC dimensions was easily observed. Decomposition of the index could be useful for policy-makers to identify the subpopulations and health services with need for further interventions towards UHC achievement.

Date: 2019
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DOI: 10.1007/s40258-019-00464-9

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