Building the model: a review of input structures in extreme temperature-health.

backgroundThe health impacts of extreme temperatures have been extensively studied through epidemiological models. However, limited attention has been paid to the specification of these models, particularly regarding input structure and model selection. Although exposure metrics and statistical techniques have evolved over time, a comprehensive synthesis of the variables included in these models, and the rationale behind their inclusion, is still lacking. This gap limits the comparability of studies and may compromise the robustness of temperature-health evidence.

methodsWe conducted a systematic review of peer-reviewed studies published between 2014 and 2024 that employed quantitative epidemiological models to estimate the association between extreme temperatures and health outcomes. Following PRISMA guidelines, we selected 119 studies through searches conducted in Scopus, PubMed, and Web of Science. Each study was analysed in terms of spatial coverage, modelling framework, and model inputs. Inputs were classified into six functional groups: thermal exposures; environmental covariates (including both non-thermal meteorological variables and air pollutants); temporal controls; socio-demographic factors; health system indicators; and built environment characteristics.

resultsSubstantial heterogeneity was observed in both input selection and model specification. Daily mean temperature was the dominant exposure metric, though rarely justified over alternatives. Environmental covariates were inconsistently included: while relative humidity was frequent, other meteorological modifiers and air pollutants were often omitted without clear rationale. Temporal adjustments were common but heterogeneous. Distributed lag non-linear models were the prevailing framework, varying greatly in lag structure, spline specification, and covariate integration. Socio-economic, health, and infrastructural indicators appeared in less than one third of studies, typically as effect modifiers in meta-regression analyses, highlighting uneven integration of contextual determinants. No consensus currently exists on what constitutes a minimum model specification necessary to ensure reliable and interpretable effect estimates.

conclusionsCurrent temperature and health modelling remains fragmented, with notable variability in input specification and transparency. Strengthening methodological coherence through clearer guidance on input selection is essential. Greater integration of socio-economic and infrastructural variables would further enhance models' capacity to capture contextual vulnerability. To ensure reliability and policy relevance, future research should develop shared guidelines for input specification, define minimum modelling standards, and promote transparent reporting of analytical decisions.