Using Microsimulation to Estimate the Future Health and Economic Costs of Salmonellosis under Climate Change in Central Queensland, Australia

BACKGROUND: The incidence of salmonellosis, a costly foodborne disease, is rising in Australia. Salmonellosis increases during high temperatures and rainfall, and future incidence is likely to rise under climate change. Allocating funding to preventative strategies would be best informed by accurate estimates of salmonellosis costs under climate change and by knowing which population subgroups will be most affected. OBJECTIVE: We used microsimulation models to estimate the health and economic costs of salmonellosis in Central Queensland under climate change between 2016 and 2036 to inform preventative strategies. METHODS: We projected the entire population of Central Queensland to 2036 by simulating births, deaths, and migration, and salmonellosis and two resultant conditions, reactive arthritis and postinfectious irritable bowel syndrome. We estimated salmonellosis risks and costs under baseline conditions and under projected climate conditions for Queensland under the A1FI emissions scenario using composite projections from 6 global climate models (warm with reduced rainfall). We estimated the resulting costs based on direct medical expenditures combined with the value of lost quality-adjusted life years (QALYs) based on willingness-to-pay. RESULTS: Estimated costs of salmonellosis between 2016 and 2036 increased from 456.0 QALYs (95% CI: 440.3, 473.1) and [Formula: see text] (95% CI: [Formula: see text] , [Formula: see text]), assuming no climate change, to 485.9 QALYs (95% CI: 469.6, 503.5) and [Formula: see text] (95% CI: [Formula: see text] , [Formula: see text]) under the climate change scenario. CONCLUSION: We applied a microsimulation approach to estimate the costs of salmonellosis and its sequelae in Queensland during 2016–2036 under baseline conditions and according to climate change projections. This novel application of microsimulation models demonstrates the models’ potential utility to researchers for examining complex interactions between weather and disease to estimate future costs. https://doi.org/10.1289/EHP1370