Abstract
Background: Everyone has a right to quality life with good health of the household and, thus, health sector financing should be a top priority because when the population is healthy, it is very productive and wealthy. In Uganda, Health Centre IVs (HCIVs) created under Uganda National Minimum Health Care Package provide curative, prevention and promotion services. The efficiency of these HCIVs is as critical as people’s health and this paper measures efficiency in utilization of resources allocated to them.Methods: The study used Hospital and HCIV Census data for 2014 and health sector data for FY2015/16 reported by MOH in the Annual Health Sector Performance Report. STATA software was used to perform Data Envelopment Analysis for a preferred model was out-put oriented that optimizes variable returns to scale. In this way, efficiency scores for every HCIV were calculated. Also, a Tobit regression model was run to estimate the factors contributing to the adjusted inefficiency scores for HCIVs.Results: Overall, 7 HCIVs (23.3%) were operating under constant returns to scale, implying that they were efficient (both pure technical and scale efficiency) while the 19 (63.3%) were operating under increasing returns to scale, implying that their health service outputs would increase by a greater proportion compared to any proportionate increase in health services if more inputs were added in the facility. Four HCIVs (13.3%) were operating at decreasing returns to scale meaning an additional input to the HCIVs would produce a less proportional change of outputs. The study identified catchment population, average length of stay, bed occupancy rate, and outpatient department visits as a proportion of inpatient days as the main factors of efficiency among HCIVs.Conclusions: This study has shown how Data Envelope Analysis methods can be applied at the HCIV level of the health system to gain an insight into variation in efficiency across health centers using routinely available data. And, with the majority of HCIVs operating at increasing returns to scale, it showed that there is a need to increase inputs like staff, medicines and beds to achieve the desired optimal scale in case of constant returns to scale.