Equitable Distribution of Poor Quality of Care? Equity in Quality of Reproductive Health Services in Ethiopia.

The Ethiopian health system faces persistent inequities in health-care utilization and outcomes, despite continued efforts to expand health service coverage. There is little evidence in the literature describing the status of equity in the quality of healthcare. This paper aims to understand the disparities in quality of antenatal care (ANC) and family planning (FP) among the poor and non-poor communities. We used the 2016 Ethiopia Demographic and Health Survey (DHS) data to compute a Multidimensional Poverty Index (MPI), and the 2014 Service Provision Assessment (SPA) data to assess quality of ANC and FP services-defined as the level of adherence to World Health Organization (WHO) clinical and service guidelines. We merged the two datasets using geographical coordinates, and aggregated service users into facility catchment area clusters using a 2-km radius for urban and 10-km radius for rural facilities. We computed ANC and FP quality and MPI indices for each facility and assigned these to catchment areas. Using the international cutoff point for deprivation (MPI = 33.3%), we evaluated whether the quality of ANC and FP services varies by poor and non-poor catchment areas. We found that most of catchment areas (75.7%) were deprived. While the overall quality of ANC and FP services are low (33% and 34% respectively), we found little variation in the distribution of the quality of these services between poor and non-poor areas, urban and rural settings, or regionally. The short-term focus needs to be on improving the overall quality of services rather than on its distribution.

A practical measure of health facility efficiency: an innovation in the application of routine health information to determine health worker productivity in Ethiopia.

BACKGROUND: A simple indicator of technical efficiency, such as productivity of health workers, measured using routine health facility data, can be a practical approach that can inform initiatives to improve efficiency in low- and middle-income countries. This paper presents a proof of concept of using routine information from primary healthcare (PHC) facilities to measure health workers' productivity and its application in three regions of Ethiopia. METHODS: In four steps, we constructed a productivity measure of the health workforce of Health Centers (HCs) and demonstrated its practical application: (1) developing an analytical dataset using secondary data from health management information systems (HMIS) and human resource information system (HRIS); (2) principal component analysis and factor analysis to estimate a summary measure of output from five indicators (annual service volume of outpatient visits, family planning, first antenatal care visits, facility-based deliveries by skilled birth attendants, and children [< 1 year] with three pentavalent vaccines); (3) calculating a productivity score by combining the summary measure of outputs and the total number of health workers (input), and (4) implementing regression models to identify the determinant of productivity and ranking HCs based on their adjusted productivity score. RESULTS: We developed an analytical dataset of 1128 HCs; however, significant missing values and outliers were reported in the data. The principal component and factor scores developed from the five output measures were highly consistent (correlation coefficient = 0.98). We considered the factor score as the summary measure of outputs for estimating productivity. A very weak association was observed between the summary measure of output and the total number of staff. The result also highlighted a large variability in productivity across similar health facilities in Ethiopia, represented by the significant dispersion in summary measure of output occurring at similar levels of the health workers. CONCLUSIONS: We successfully demonstrated the analytical steps to estimate health worker productivity and its practical application using HMIS and HRIS. The methodology presented in this study can be readily applied in low- and middle-income countries using widely available data-such as DHIS2-that will allow further explorations to understand the causes of technical inefficiencies in the health system.