{ "background": "Predictive modelling is increasingly used to forecast the impact of community health interventions, yet a synthesis of frameworks specific to the Ugandan context is lacking. This review systematically maps and evaluates the methodological approaches employed in this domain.", "purpose and objectives": "This systematic review aims to identify, categorise, and critically appraise predictive modelling frameworks used for clinical outcome forecasting in Ugandan community health interventions, assessing their methodological rigour and contextual applicability.", "methodology": "A systematic search was conducted across multiple electronic databases. Pre-defined inclusion criteria targeted studies employing quantitative models to predict clinical outcomes from community-based interventions. Study selection, data extraction, and quality assessment were performed independently by two reviewers, with discrepancies resolved through consensus.", "findings": "Of the 42 studies meeting the inclusion criteria, a dominant theme was the reliance on generalised linear models, with logistic regression for binary outcomes being the most prevalent. A common model structure was $\\logit(pi) = \\beta0 + \\beta1 x{1i} + ... + \\betak x{ki}$, where $p_i$ is the probability of a clinical outcome for individual $i$. Model performance was frequently inadequately reported, with fewer than 30% of studies providing confidence intervals for key predictive parameters.", "conclusion": "While predictive modelling is established in this field, significant gaps exist in methodological transparency and the incorporation of spatial or hierarchical structures that reflect community-level data dependencies. The evidence base is heterogeneous in quality.", "recommendations": "Future research should prioritise the development and use of models that account for clustered data, improve reporting standards for model validation, and explicitly integrate contextual socio-economic variables to enhance predictive accuracy and utility for policymakers.", "key words": "predictive modelling, clinical outcomes, community health, systematic review, Uganda, public health interventions", "contribution statement": "This review provides the first comprehensive methodological taxonomy and quality assessment of predictive modelling frameworks for community health outcomes in Uganda, identifying a critical need