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Level and determinants of county health system technical efficiency in Kenya: two stage data envelopment analysis.
Barasa, Edwine; Musiega, Anita; Hanson, Kara; Nyawira, Lizah; Mulwa, Andrew; Molyneux, Sassy; Maina, Isabel; Tsofa, Benjamin; Normand, Charles; Jemutai, Julie.
Afiliação
  • Barasa E; Health Economics Research Unit, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya. ebarasa@kemri-wellcome.org.
  • Musiega A; Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK. ebarasa@kemri-wellcome.org.
  • Hanson K; Health Economics Research Unit, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya.
  • Nyawira L; Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, UK.
  • Mulwa A; Health Economics Research Unit, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya.
  • Molyneux S; County Department of Health, Makueni County Government, Makueni, Kenya.
  • Maina I; Health Systems and Research Ethics Department, KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya.
  • Tsofa B; Health Financing Department, Ministry of Health, Nairobi, Kenya.
  • Normand C; Health Systems and Research Ethics Department, KEMRI-Wellcome Trust Research Programme, Kilifi, Kenya.
  • Jemutai J; Centre for Health Policy and Management, Trinity College, The University of Dublin, Dublin, Ireland.
Cost Eff Resour Alloc ; 19(1): 78, 2021 Dec 06.
Article em En | MEDLINE | ID: mdl-34872560
ABSTRACT

BACKGROUND:

Improving 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.

METHODS:

We 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.

RESULTS:

The 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.

CONCLUSIONS:

The 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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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article