Estimating the health impacts of climate change for policy decision-support: a systematic review of spatial microsimulation methods

Spatial microsimulation models have recently emerged as a new method to quantify health impacts associated with climate change for policy decision-support. These individual-based methods, previously used in tax and health policy planning, have been adapted by combining climate data with exposure-response associations to estimate the distributional health impacts attributable to climate hazards using synthetic populations. To evaluate their methodological characteristics, we conducted a systematic review of the literature. We searched five electronic databases, Google Scholar and the International Journal of Microsimulation, and screened 762 articles to reach a final study set of seven articles. Most models simulated populations based in high income countries (n=5) and applied dynamic methods to forecast future health outcomes (n=5). Multiple diverse climate-health pathways of impact were investigated, ranging from heatwave mortality to air pollution-induced cardiovascular outcomes, to climate-sensitive infectious disease occurrence. Baseline and projected spatial climate data was mapped to individuals in city, state, or regional-level synthetic populations to allocate personal hazard exposure. Most models included socio-economic and demographic attributes (n=6) to integrate vulnerabilities for burden assessments in marginalised groups such as children, women, and the elderly. Climate policies mainly focused on mitigation and simulated future emissions scenarios (n=5), or policy mixes (n=1); one study tested an incremental adaptation intervention. Methods to enhance decision-support among alternative policy options such as economic evaluation (n=2) or stakeholder engagement (n=3) were under-represented. All models acknowledged uncertainty of parameters, and most reported uncertainty analyses (n=5). High data needs may limit accessibility of these methods in some contexts, however options to build on existing models and improve data and computing power access could overcome these challenges. This systematic review documents this evolving, state of the art application of microsimulation and finds a promising and versatile quantitative tool for health impact assessments and climate policy decision-support.