How efficient are referral hospitals in Uganda? A data envelopment analysis and tobit regression approach

Abstract

BackgroundHospitals represent a significant proportion of health expenditures in Uganda, accounting for about 26 % of total health expenditure. Improving the technical efficiency of hospitals in Uganda can result in large savings which can be devoted to expand access to services and improve quality of care. This paper explores the technical efficiency of referral hospitals in Uganda during the 2012/2013 financial year.MethodsThis was a cross sectional study using secondary data. Input and output data were obtained from the Uganda Ministry of Health annual health sector performance report for the period July 1, 2012 to June 30, 2013 for the 14 public sector regional referral and 4 large private not for profit hospitals. We assumed an output-oriented model with Variable Returns to Scale to estimate the efficiency score for each hospital using Data Envelopment Analysis (DEA) with STATA13. Using a Tobit model DEA, efficiency scores were regressed against selected institutional and contextual/environmental factors to estimate their impacts on efficiency.ResultsThe average variable returns to scale (Pure) technical efficiency score was 91.4 % and the average scale efficiency score was 87.1 % while the average constant returns to scale technical efficiency score was 79.4 %. Technically inefficient hospitals could have become more efficient by increasing the outpatient department visits by 45,943; and inpatient days by 31,425 without changing the total number of inputs. Alternatively, they would achieve efficiency by for example transferring the excess 216 medical staff and 454 beds to other levels of the health system without changing the total number of outputs. Tobit regression indicates that significant factors in explaining hospital efficiency are: hospital size (p < 0.01); bed occupancy rate (p < 0.01) and outpatient visits as a proportion of inpatient days (p < 0.05).ConclusionsHospitals identified at the high and low extremes of efficiency should be investigated further to determine how and why production processes are operating differently at these hospitals. As policy makers gain insight into mechanisms promoting hospital services utilization in hospitals with high efficiency they can develop context-appropriate strategies for supporting hospitals with low efficiency to improve their service and thereby better address unmet needs for hospital services in Uganda.

Efficiency of health resource utilisation in primary-level maternal and child health hospitals in Shanxi Province, China: a bootstrapping data envelopment analysis and truncated regression approach

“…Given that the range of relative productive efficiency scores calculated by the DEA model falls between 0 and 1, a Tobit and truncated regression model has traditionally been used to evaluate the factors affecting the productive efficiency [10,16]. However, Simar and Wilson argued that the use of a Tobit or truncated regression in a two-stage analysis is inappropriate based on two reasons: firstly, the productive efficiency scores estimated by DEA may be corrected with each other; thus, the results in the error term in these models are serially correlated and a standard inference is not valid.…”

“…In the initial stage, we considered the number of open beds, the number of health workers, the total expenditure, the number of doctors, the number of nurses, the number of devices over 10,000 Chinese Yuan, the hospital area, the total fixed assets, the total service cost and the cost per visit as the input indicators based on previous studies [10,13,28]. The initial output variables selected included total revenue, income from medical services, the number of patient discharges, outpatient and emergency visits, the number of health examinations, average inpatient days and bed occupancy rate [10,16,29].…”

“…Xuan Wang et al suggested that the overall operational efficiency of county-level MCHHs in Guangxi is low and needs to be substantially improved [10]. Other relevant studies found that the inefficiency of MCHHs greatly affects the quality and equality of MCH services [10,[14][15][16].…”

“…Inputs, outputs and explanatory variables and their explanation , educational status, income, average length of stay and hospital scale are frequently selected as the internal factors[10,[14][15][16]31].…”

Technical efficiency decomposed – The case of Ugandan referral hospitals

“…The focus of the study is on methodology rather than the result. Finally, Mujasi et al (2016) investigate referral hospitals in Uganda. The authors found a long run efficiency (CRS) of almost 80% and a short run technical efficiency (VRS) of more than 91%.…”