Journal Design Clinical Emerald
African Food Systems Research (Interdisciplinary - incl Agri/Env) | 21 February 2000

Evaluating the Impact of Community Health Centres on Risk Reduction in Kenya

A Difference-in-Differences Methodological Analysis
W, a, n, j, i, k, u, M, w, a, n, g, i
Difference-in-differencesImpact EvaluationCommunity HealthKenya
Applied a quasi-experimental DiD model to assess causal impact of community health centres.
Found 18% relative reduction in childhood diarrhoea in treatment areas (95% CI: 12% to 24%).
Highlights critical data requirements for valid causal inference in real-world African contexts.
Demonstrates DiD as viable framework for evaluating health system interventions.

Abstract

{ "background": "Community health centres are a cornerstone of primary healthcare delivery in many African nations, yet robust quantitative evidence of their impact on population health risks remains limited. This creates a significant evidence gap for policymakers seeking to optimise health system investments.", "purpose and objectives": "This case study aimed to develop and apply a quasi-experimental difference-in-differences (DiD) model to rigorously evaluate the causal effect of a community health centre system on key health risk indicators in a Kenyan context.", "methodology": "We employed a longitudinal DiD design, comparing changes in outcomes for populations gaining access to a new health centre (treatment group) against matched populations without access (control group). The core model is specified as: $Y{it} = \\beta0 + \\beta1 (Treati \\times Postt) + \\gammai + \\deltat + \\epsilon{it}$, where $Y_{it}$ is the outcome for area $i$ at time $t$. Inference was based on cluster-robust standard errors to account for spatial autocorrelation.", "findings": "The analysis revealed a statistically significant reduction in the prevalence of childhood diarrhoea, a primary outcome, in treatment areas following centre establishment. The DiD estimator indicated an 18% relative reduction (95% CI: 12% to 24%) compared to control trends. The methodological application highlighted critical data requirements and design assumptions for valid causal inference in this setting.", "conclusion": "The DiD framework provides a viable and powerful methodological approach for evaluating the impact of health system interventions in observational, real-world contexts common in African health systems research.", "recommendations": "Future programme evaluations should incorporate quasi-experimental designs like DiD during the planning phase to ensure necessary data collection. Health ministries should invest in routine, geocoded health surveillance data to facilitate such analyses.", "key words": "difference-in-differences, impact evaluation, community health, primary healthcare, quasi-experimental design, Kenya", "cont