Background
Governments of developing countries lack information about the process of providing health services (WHO, 2000). When services are provided inefficiently, scarce resources that could be used to treat additional patients are wasted. Even when the political will for efficiency assessment exists, the lack of adequate data represents a barrier to conducting accurate studies on the production and costs of health care services.
Structure/method/design
We use detailed facility-level data from Colombia, Ghana, India, Kenya, Lebanon, Zambia, and Uganda. In each country, we collected data in approximately 200 facilities over a five-year period. In addition, 12,000 patient interviews were conducted with the aim of gathering information on consumer perception of health facility quality.
We specify a production model with five inputs and seven outputs. Inputs include the number of beds as proxy for capital, and four categories for labor (doctors, nurses, other medical staff, and administrative staff). With respect to output, outpatient visits include basic outpatient services, ART (antiretroviral treatment), malaria, antenatal care, and emergency. For inpatient services we use inpatient days, births, and surgery.
To avoid biased efficiency estimates due to heterogeneous technology, we propose an innovative approach that adjust outputs across facilities. We first identify all pharmaceuticals and equipment related to the production of each output and build a score that reflects the extent to which technology is available in the facility.
We then use consistent bootstrap DEA models using the adjusted outputs to compute technical efficiency scores by controlling for measurement error and noisy data. We include minimal weight restrictions to reflect the relative importance of inputs and outputs in the production process of health facilities. Weight restrictions are chosen to maintain the radial nature of efficiency valid.
We finally use output weights provided by DEA to calculate the marginal rate of transformation between outputs. This information is critical to the estimation of average costs for each output.
Results
We find evidence of important inefficiency (40% on average) with massive variation across facilities. Inefficiency substantially increases average costs to produce health services (35% on average). Also, we find evidence of efficiency increases over time by about 10%, likely due to the scale-up of ART treatment and related services. Additional evidence is necessary to assess the causal relationship.
Summary/conclusion
We find evidence of potential efficiency increases. Efficiency increase of health services production in developing countries is paramount to exploit the potential of service coverage extension and fair allocation of resources. For this purpose, higher-quality data and systematic efficiency assessment analyses are needed.
