Efficiency estimates of public healthcare facilities in southwestern Uganda an application of data envelopment analysis and tobit mode

The study estimates the efficiency of public healthcare facilities in Southwestern Uganda. Specifically, it determines the level of technical efficiency, scale efficiency and establishes the economic savings required to make inefficient health facilities efficient. It further analyses the determinants of technical efficiency that account for variations in performance. A sample of 57 health facilities was selected from a population of 111. The study employs a two-stage technique; using an output-oriented Data Envelopment Analysis in the first-stage to estimate technical and scale efficiency assuming variable returns to scale technology. The efficiency estimates from the first stage are regressed on the determinants of efficiency in the second-stage using a censored Tobit regression model. It was found out that technical efficiency averaged 77.7% for health center III and 72.3% for health center II facilities, inlying that the facilities could potentially increase their healthcare output endowment by 22.3% and 27.7% respectively using the current level of healthcare resource inputs. Variations in technical efficiency became wider with small sized health units than the large sized. Health facilities that scored scale efficiency of 100% imply that they obtained the most productive scale size for the specific input-output mix. Mean scale efficiency stood at 98.9% for health center III and 00.5% for health center II facilities implying that they could potentially augment their level of healthcare outputs by 1.1% and 0.5% respectively using the existing size. Whereas mean technical efficiency was found to reduce as the size and level of the health facility reduces, mean scale efficiency was found to increase as the size and level of the health facility reduces. The study finds a great potential for economic savings required to make inefficient health facilities efficient. The most significant institutional determinants of technical efficiency were; health unit management grade, essential medicines and health supplies, support supervision, leakages, health facility size, average length of stay and bed occupancy rate. The most influential socio-economic variables were found to be; competition, distance to the major town, distance travelled by patients, population size, patients aged < 5, patients aged 65+ and household size. The study has important policy implications. The health sector should embark on a rigorous, periodic research and development. There is urgent need for improved capacity for comprehensive budgeting, financing, costing and proper management of input resources. A comprehensive monitoring and evaluation plan with key verifiable indicators is essential. In conclusion therefore a study of this nature is important for guiding policy actions while maximizing returns from the present investment.