{ "background": "Public health surveillance systems are critical for monitoring disease burden and guiding interventions in sub-Saharan Africa. In Rwanda, the evolution of these systems has necessitated rigorous methodological evaluation to assess their effectiveness in measuring risk reduction for priority health conditions.", "purpose and objectives": "This systematic review aims to methodologically evaluate the application of multilevel regression analyses within Rwanda's public health surveillance frameworks. It seeks to appraise model specifications, data integration techniques, and the inferential robustness of risk reduction estimates derived from these systems.", "methodology": "A systematic search of multiple electronic databases was conducted for peer-reviewed studies. Eligible publications were those employing multilevel models (e.g., $y{ij} = \\beta{0} + \\beta{1}X{ij} + u{j} + e{ij}$) to analyse surveillance data for risk assessment. Studies were critically appraised using a predefined tool focusing on methodological rigour, handling of clustering, and reporting of uncertainty.", "findings": "The review identified a predominant focus on infectious disease surveillance, with 60% of analysed studies investigating HIV or malaria. A key methodological finding was the frequent omission of robust standard error estimation in models analysing spatially clustered outbreak data, potentially affecting the precision of risk ratios. The reporting of confidence intervals was inconsistent.", "conclusion": "Multilevel regression is an established but inconsistently applied methodological tool within the country's surveillance ecosystem. While it provides a framework for analysing hierarchically structured health data, gaps in advanced statistical practice limit the strength of causal inference for policy.", "recommendations": "Future surveillance research should prioritise the use of longitudinal multilevel models to capture temporal trends, explicitly report cluster-robust variance estimates, and better integrate demographic and environmental covariate data to strengthen causal pathways for risk reduction.", "key words": "surveillance systems, multilevel modelling, risk assessment, methodological review, sub-Saharan Africa", "contribution statement": "This review provides the first dedicated methodological critique of multilevel regression applications