Unit Costing of Health Extension Worker Activities in Ethiopia: A Model for Managers at the District and Health Facility Level

Document Type : Original Article


Yale Global Health Leadership Institute, Yale University, New Haven, CT, USA


Over the last decade, Ethiopia has made impressive national improvements in health outcomes, including reductions in maternal, neonatal, infant, and child mortality attributed in large part to their Health Extension Program (HEP). As this program continues to evolve and improve, understanding the unit cost of health extension worker (HEW) services is fundamental to planning for future growth and ensuring adequate financial support to deliver effective primary care throughout the country.
We sought to examine and report the data needed to generate a HEW fee schedule that would allow for full cost recovery for HEW services. Using HEW activity data and estimates from national studies and local systems we were able to estimate salary costs and the average time spent by an HEW per patient/community encounter for each type of services associated with specific users. Using this information, we created separate fee schedules for activities in urban and rural settings with two estimates of non-salary multipliers to calculate the total cost for HEW services.
In the urban areas, the HEW fees for full cost recovery of the provision of services (including salary, supplies, and overhead costs) ranged from 55.1 birr to 209.1 birr per encounter. The rural HEW fees ranged from 19.6 birr to 219.4 birr.
Efforts to support health system strengthening in low-income settings have often neglected to generate adequate, actionable data on the costs of primary care services. In this study, we have combined time-motion and available financial data to generate a fee schedule that allows for full cost recovery of the provision of services through billable health education and service encounters provided by Ethiopian HEWs. This may be useful in other country settings where managers seek to make evidence-informed planning and resource allocation decisions to address high burden of disease within the context of weak administrative data systems and severe financial constraints.


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