High‐Throughput Geocoding to Assess Short‐Term Air Pollution in Correlation With Mortality in a French Cancer Patient Cohort
Abstract
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 (PM 2.5 , PM 10 , NO 2 , O 3 , 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 (PM 2.5 , PM 10 , NO 2 ) 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 PM 2.5 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.
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BibTeX
@article{pokam2026highthroughput,
title = {{High‐Throughput Geocoding to Assess Short‐Term Air Pollution in Correlation With Mortality in a French Cancer Patient Cohort}},
author = {Loice Pokam and Joséphine Bocquet and Nadia Hégarat and Julie Perlbarg-Samson and Thomas Balezeau and Maud Milder and Bastien Rance and Hector Countouris and Julien Guerin and Flavien Gilles and Jean Gaudart and Nicolas Girard and Clémence Basse},
journal = {International Journal of Cancer},
year = {2026},
month = jun,
doi = {10.1002/ijc.70589},
url = {https://inserm.hal.science/inserm-05848851v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - High‐Throughput Geocoding to Assess Short‐Term Air Pollution in Correlation With Mortality in a French Cancer Patient Cohort
AU - Loice Pokam
AU - Joséphine Bocquet
AU - Nadia Hégarat
AU - Julie Perlbarg-Samson
AU - Thomas Balezeau
AU - Maud Milder
AU - Bastien Rance
AU - Hector Countouris
AU - Julien Guerin
AU - Flavien Gilles
AU - Jean Gaudart
AU - Nicolas Girard
AU - Clémence Basse
PY - 2026
DA - 2026/06/07
JO - International Journal of Cancer
DO - 10.1002/ijc.70589
UR - https://inserm.hal.science/inserm-05848851v1
AB - 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 (PM 2.5 , PM 10 , NO 2 , O 3 , 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 (PM 2.5 , PM 10 , NO 2 ) 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 PM 2.5 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.
SN - 0020-7136
SN - 1097-0215
ER -