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Level and determinants of county health system technical efficiency in Kenya: two stage data envelopment analysis

Authors
  • Barasa, Edwine1, 2
  • Musiega, Anita1
  • Hanson, Kara3
  • Nyawira, Lizah1
  • Mulwa, Andrew4
  • Molyneux, Sassy5
  • Maina, Isabel6
  • Tsofa, Benjamin5
  • Normand, Charles7, 8
  • Jemutai, Julie1
  • 1 KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya , Nairobi (Kenya)
  • 2 University of Oxford, Oxford, UK , Oxford (United Kingdom)
  • 3 London School of Hygiene and Tropical Medicine, London, UK , London (United Kingdom)
  • 4 Makueni County Government, Makueni, Kenya , Makueni (Kenya)
  • 5 KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya , Kilifi (Kenya)
  • 6 Ministry of Health, Nairobi, Kenya , Nairobi (Kenya)
  • 7 The University of Dublin, Dublin, Ireland , Dublin (Ireland)
  • 8 Kings College London, London, England , London (United Kingdom)
Type
Published Article
Journal
Cost Effectiveness and Resource Allocation
Publisher
BioMed Central
Publication Date
Dec 06, 2021
Volume
19
Issue
1
Identifiers
DOI: 10.1186/s12962-021-00332-1
Source
Springer Nature
Keywords
Disciplines
  • Research
License
Green

Abstract

BackgroundImproving health system efficiency is a key strategy to increase health system performance and accelerate progress towards Universal Health Coverage. In 2013, Kenya transitioned into a devolved system of government granting county governments autonomy over budgets and priorities. We assessed the level and determinants of technical efficiency of the 47 county health systems in Kenya.MethodsWe carried out a two-stage data envelopment analysis (DEA) using Simar and Wilson’s double bootstrap method using data from all the 47 counties in Kenya. In the first stage, we derived the bootstrapped DEA scores using an output orientation. We used three input variables (Public county health expenditure, Private county health expenditure, number of healthcare facilities), and one outcome variable (Disability Adjusted Life Years) using 2018 data. In the second stage, the bias corrected technical inefficiency scores were regressed against 14 exogenous factors using a bootstrapped truncated regression.ResultsThe mean bias-corrected technical efficiency score of the 47 counties was 69.72% (95% CI 66.41–73.01%), indicating that on average, county health systems could increase their outputs by 30.28% at the same level of inputs. County technical efficiency scores ranged from 42.69% (95% CI 38.11–45.26%) to 91.99% (95% CI 83.78–98.95%). Higher HIV prevalence was associated with greater technical inefficiency of county health systems, while higher population density, county absorption of development budgets, and quality of care provided by healthcare facilities were associated with lower county health system inefficiency.ConclusionsThe findings from this analysis highlight the need for county health departments to consider ways to improve the efficiency of county health systems. Approaches could include prioritizing resources to interventions that will reduce high chronic disease burden, filling structural quality gaps, implementing interventions to improve process quality, identifying the challenges to absorption rates and reforming public finance management systems to enhance their efficiency.

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