High-Throughput Geocoding to Assess Short-Term Air Pollution in Correlation With Mortality in a French Cancer Patient Cohort

Air pollution significantly affects human health and mortality, including cancer-related deaths. However, methodological approaches and spatial precision often vary across studies. Implementing geomatic tools to investigate mortality in vulnerable populations such as cancer patients may enhance understanding of environmental impacts. This study aimed to assess the relationship between short-term air pollution exposure and mortality among cancer patients in France. A retrospective cohort of 44,268 consecutive cancer patients (2017–2020) was analyzed using a geomatic framework at fine spatial granularity. Air quality data (PM2.5, PM10, NO2, O3, temperature) were linked to each patient. Principal component analysis (PCA) identified exposure groups, and a random forest algorithm was applied to predict mortality. Overall, 9% of patients died during follow-up. Four clusters of patients with distinct air quality profiles were identified. Clusters with the highest particulate matter levels (PM2.5, PM10, NO2) showed increased mortality (12%–13%), whereas the cluster with the lowest pollution showed reduced mortality (8%) (chi-square test, p < 0.001). The prediction algorithm achieved a recall of 80%. The main predictors of death included higher temperature and elevated PM2.5 within the previous 31 days, as well as older age, male sex, and thoracic cancer. Short-term exposure to degraded air quality, captured at an infra-communal scale, is associated with excess mortality among cancer patients. The identification of a 31-day lag window for predictive algorithm highlights opportunities for targeted prevention and timely public health interventions.

International Journal of Cancer , article en libre accès, 2026

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