title: Efficiency of Health Care Production in Low-Resource Settings: A Monte-Carlo Simulation to Compare the Performance of Data Envelopment Analysis, Stochastic Distance Functions, and an Ensemble Model.
abstract: Low-resource countries can greatly benefit from even small increases in efficiency of health service provision, supporting a strong case to measure and pursue efficiency improvement in low- and middle-income countries (LMICs). However, the knowledge base concerning efficiency measurement remains scarce for these contexts. This study shows that current estimation approaches may not be well suited to measure technical efficiency in LMICs and offers an alternative approach for efficiency measurement in these settings. We developed a simulation environment which reproduces the characteristics of health service production in LMICs, and evaluated the performance of Data Envelopment Analysis (DEA) and Stochastic Distance Function (SDF) for assessing efficiency. We found that an ensemble approach (ENS) combining efficiency estimates from a restricted version of DEA (rDEA) and restricted SDF (rSDF) is the preferable method across a range of scenarios. This is the first study to analyze efficiency measurement in a simulation setting for LMICs. Our findings aim to heighten the validity and reliability of efficiency analyses in LMICs, and thus inform policy dialogues about improving the efficiency of health service production in these settings.
authors.forename: Laura
authors.initials: L
authors.lastname: Di Giorgio
authors.affiliation: Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, United States of America.
authors.forename: Laura Di
authors.initials: LD
authors.lastname: Giorgio
authors.affiliation: Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, United States of America.
authors.forename: Abraham D
authors.initials: AD
authors.lastname: Flaxman
authors.affiliation: Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, United States of America.
authors.forename: Mark W
authors.initials: MW
authors.lastname: Moses
authors.affiliation: Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, United States of America.
authors.forename: Nancy
authors.initials: N
authors.lastname: Fullman
authors.affiliation: Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, United States of America.
authors.forename: Michael
authors.initials: M
authors.lastname: Hanlon
authors.affiliation: Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, United States of America.
authors.forename: Ruben O
authors.initials: RO
authors.lastname: Conner
authors.affiliation: Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, United States of America.
authors.forename: Alexandra
authors.initials: A
authors.lastname: Wollum
authors.affiliation: Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, United States of America.
authors.forename: Christopher J L
authors.initials: CJ
authors.lastname: Murray
mesh_terms: D003695:Delivery of Health Care / Q000458:organization & administration*; D017598:Efficiency, Organizational; D006801:Humans; D007182:Income; D008962:Models, Theoretical; D009010:Monte Carlo Method; D013269:Stochastic Processes
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