What question did this study set out to answer?

To evaluate the causal effect of community health centres on health risk indicators in Kenya using a difference-in-differences model.

March 13, 2026Open Access

Evaluating the Impact of Community Health Centres on Risk Reduction in Kenya: A Difference-in-Differences Methodological Analysis

Key Points

To evaluate the causal effect of community health centres on health risk indicators in Kenya using a difference-in-differences model.
Longitudinal difference-in-differences design used to compare treatment and control groups.
Treatment group had access to a new community health centre; control group did not.
Statistical analysis employed cluster-robust standard errors to account for spatial autocorrelation.
Statistically significant 18% reduction in childhood diarrhoea prevalence in treatment areas after health centre establishment.
95% confidence interval for the reduction ranged from 12% to 24%.
Study highlights essential data requirements and design assumptions for effective causal inference.

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 = \0 + \1 (Treati \ Postt) + \ + \ +, where Y₈ₓ 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

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