Decision-analytic models in the economic evaluation of community health worker programmes globally: a systematic review

Abstract

Introduction: Economic evidence on community health worker (CHW) programmes is crucial for scaling these initiatives. Although decision-analytic models (DAMs) are essential for projecting long-term value, it is unclear how rigorously they have been applied to CHW evaluations, potentially compromising the reliability and comparability of cost-effectiveness estimates used for policy decisions.

Methods: A systematic review was conducted to identify full economic evaluations of CHW-led or CHW-integrated interventions that employed a DAM. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, six databases (Medline, Embase, Global Health, CINAHL, Web of Science and Scopus) were searched from inception to June 2025. Eligible studies were full economic evaluations assessing CHW-led or CHW-integrated interventions using DAMs. Study selection and data extraction were conducted independently by two reviewers. Methodological quality was appraised using the Philips checklist, and data were extracted on model type, data sources and validation practices. Findings were synthesised narratively across model structures, income groups and quality domains.

Results: 37 studies met the inclusion criteria. Decision trees were used in 32% of studies and Markov models in 30% with the remainder applying microsimulation, dynamic transmission or hybrid approaches. Most evaluations were undertaken in low- and middle-income countries, with few from low-income or high-income settings. Data constraints in low-income settings limited model complexity, whereas models in high-income settings tended to adopt more sophisticated structures but narrower intervention scopes. The mean quality score was 67%, with substantial gaps in model validation and limited exploration of structural uncertainty. Overall, 84% of studies concluded that CHW-led interventions were cost-effective, with incremental cost-effectiveness ratios generally favourable across settings.

Conclusions: Although CHW interventions are generally cost-effective, the strength of this evidence is constrained by methodological limitations in existing models. Future modelling should prioritise rigorous validation, localisation of input data and explicit valuation of CHW and societal contributions to enhance the credibility of economic evidence for policy use.

PROSPERO registration number: CRD420251066586.