"background": "Community health centres are a cornerstone of primary healthcare delivery in Ghana, yet systematic methodological appraisals of their cost-effectiveness remain scarce. Existing evaluations often lack the analytical rigour to account for the hierarchical structure of health system data, potentially leading to biased estimates. ", "purpose and objectives": "This review aims to critically evaluate methodological approaches used in assessing the cost-effectiveness of Ghanaian community health centres and to demonstrate the application of a multilevel regression framework for generating more accurate, policy-relevant estimates. ", "methodology": "We conducted a systematic methodological review of published and grey literature. The proposed analytical framework employs a three-level random intercepts model to analyse cost-effectiveness, specified as: Outcome{ijk = \0 + \1ijk + uk + vjk + e₈₉₊, where i, j, and k index individuals, health centres, and districts, respectively. Inference is based on robust standard errors clustered at the district level. ", "findings": "The review identified a predominant reliance on simple cost-per-output ratios, which fail to control for confounding variables or hierarchical data structures. Application of the multilevel model to illustrative data revealed that a 10% increase in operational expenditure was associated with a 3. 2% increase in facility-level service coverage (95% CI: 1. 8% to 4. 6%), after accounting for district-level heterogeneity. ", "conclusion": "Standard cost-effectiveness methodologies applied to Ghana's community health systems are often methodologically flawed. Multilevel regression provides a superior analytical tool by explicitly modelling the clustered nature of health system data, yielding more reliable estimates for resource allocation decisions. ", "recommendations": "Future economic evaluations of community health systems should adopt multilevel modelling techniques. Policymakers and researchers must prioritise the collection of standardised, hierarchical cost and outcome data to facilitate such analyses. ", "key words": "health economics, primary healthcare, mult
Asare et al. (Tue,) studied this question.