Return on investments in the Health Extension Program in Ethiopia

BACKGROUND

Since 2003, the government of Ethiopia has trained and deployed more than 42,000 Health Extension Workers across the country to provide primary healthcare services. However, no research has assessed the return on investments into human resources for health in this setting. This study aims to fill this gap by analyzing the return on investment within the context of the Ethiopian Health Extension Program.

METHODS

We collected data on associated costs and benefits attributed to the Health Extension Program from primary and secondary sources. Primary sources included patient exit interviews, surveys with Health Extension Workers and other health professionals, key informant interviews, and focus groups conducted in the following regions: Amhara, Oromia, Tigray, and the Southern Nations Nationalities and Peoples' Region. Secondary sources consisted of financial and administrative reports gathered from the Ministry of Health and its subsidiaries, as well as data accessed through the Lives Saved Tool. A long-run return on investment analysis was conducted considering program costs (personnel, recurrent, and capital investments) in comparison to benefits gained through improved productivity, equity, empowerment, and employment.

FINDINGS

Between 2008-2017, Health Extension Workers saved 50,700 maternal and child lives. Much of the benefits were accrued by low income, less educated, and rural women who had limited access to services at higher level health centers and hospitals. Regional return ranged from $1.27 to $6.64, with an overall return on investment in the range of $1.59 to $3.71.

CONCLUSION

While evidence of return on investments are limited, results from the Health Extension Program in Ethiopia show promise for similar large, sustainable system redesigns. However, this evidence needs to be contextualized and adapted in different settings to inform policy and practice. The Ethiopian Health Extension Program can serve as a model for other nations of a large-scale human resources for health program containing strong economic benefits and long-term sustainability through successful government integration.

Realistic modeling of clinical laboratory operation by computer simulation

An important objective of laboratory management is to adjust the laboratory's capability to the needs of patients' care as well as economy. The consequences of management may be changes in laboratory organization, equipment, or personnel planning. At present only one's individual experience can be used for making such decisions. We have investigated whether the techniques of operations research could be transferred to a clinical laboratory and whether an adequate simulation model of the laboratory could be realized. First we listed and documented the system design and the process flow for each single laboratory request. These input data were linked by the simulation model (programming language SIMSCRIPT II.5). The output data (turnaround times, utilization rates, and analysis of queue length) were validated by comparison with the current performance data obtained by tracking specimen flow. Congruence of the data was excellent (within +/- 4%). In planning experiments we could study the consequences of changes in order entry, staffing, and equipment on turnaround times, utilization, and queue lengths. We conclude that simulation can be a valuable tool for better management decisions.

Realistic modeling of clinical laboratory operation by computer simulation

An important objective of laboratory management is to adjust the laboratory's capability to the needs of patients' care as well as economy. The consequences of management may be changes in laboratory organization, equipment, or personnel planning. At present only one's individual experience can be used for making such decisions. We have investigated whether the techniques of operations research could be transferred to a clinical laboratory and whether an adequate simulation model of the laboratory could be realized. First we listed and documented the system design and the process flow for each single laboratory request. These input data were linked by the simulation model (programming language SIMSCRIPT II.5). The output data (turnaround times, utilization rates, and analysis of queue length) were validated by comparison with the current performance data obtained by tracking specimen flow. Congruence of the data was excellent (within +/- 4%). In planning experiments we could study the consequences of changes in order entry, staffing, and equipment on turnaround times, utilization, and queue lengths. We conclude that simulation can be a valuable tool for better management decisions.