Equity considerations for the implementation of health insurance benefit package in Ethiopia: result of expert Delphi exercise

• Main Authors: Solomon Tessema Memirie (Author), Muluken Argaw (Author), Mieraf Taddesse Tolla (Author), Frehiwot Abebe (Author), Wubaye Walelgne Dagnaw (Author), Ole F. Norheim (Author), Amanuel Yigezu (Author)
• Format: Book
• Published: BMC, 2024-09-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 10.1186/s12939-024-02226-z; 1475-9276
• Keywords: Equity; Health benefit package; Cost-effectiveness analysis; Universal health coverage; Ethiopia; Public aspects of medicine; RA1-1270
• Source: International Journal for Equity in Health, Vol 23, Iss 1, Pp 1-7 (2024)

Abstract Background Efficiency, equity and financial risk protection are key health systems objectives. Equitable distribution of health care is among the priority strategic initiative of the government of Ethiopia. However, data on the distribution of interventions benefits or on disease burden disaggregated by subpopulations to guide health care priority setting is not available in Ethiopia. Methods Aligned with policy documents, we identified the following groups to be the worse off in the Ethiopian context: under-five children, women of reproductive age, the poor, and rural residents. We used the Delphi technique by a panel of 28 experts to assign a score for 253 diseases/conditions over a period of two days, in phases. The expert panel represented different institutes and professional mix. Experts assigned a score 1 to 4; where 4 indicates disease/condition predominantly affecting the poor and rural residents and 1 indicates a condition more prevalent among the wealthy and urban residents. Subsequently, the average equity score was computed for each disease/condition. Results The average scores ranged from 1.11 (for vitiligo) to 3.79 (for obstetric fistula). We standardized the scores to be bounded between 1 and 2; 1 the lowest equity score and 2 the highest equity score. The scores for each disease/condition were then assigned to their corresponding interventions. We used these equity scores to adjust the CEA values for each of the interventions. To adjust the CEA values for equity, we multiplied the health benefits (the denominator of the cost-effectiveness value) of each intervention by the corresponding equity scores, resulting in equity adjusted CEA values. The equity adjusted CEA was then used to rank the interventions using a league table. Conclusions The Delphi method can be useful in generating equity scores for prioritizing health interventions where disaggregated data on the distribution of diseases or access to interventions by subpopulation groups are not available.