Association of meteorological exposures with major cancer mortality in Minhang District, Shanghai: a time series study based on distributed lag non-linear models.

Objective: This study examined the associations between meteorological exposures and major cancer mortality, with a focus on exposure-lag relationships. Methods: We collected daily cancer death records and weather data from Minhang District, Shanghai, 2014 to 2023. Joinpoint regression estimated annual percent changes in age-standardized cancer mortality rates. Spearman's rank correlation was employed to explore how meteorological factors related to cancer death. Finally, a distributed lag non-linear model (DLNM) evaluated exposure-lag effects of meteorological factors on daily cancer death risk. Results: This study included 26,684 cancer deaths. Spearman correlation analysis showed no significant linear associations between meteorological factors and daily mortality. However, the DLNM revealed significant non-linear and delayed effects, with low temperature and low humidity showing increased risks at specific lag days. Over a 0-21-day lag period, extreme weather affected lung cancer death risk more strongly than colorectal or gastric cancer risk. When daily mean temperature dropped to its 2.5th percentile, the estimated relative risk (RR) was 2.807, with a 95% confidence interval (95% CI) of 1.412-5.579. At the same percentile of daily minimum temperature, the cumulative RR for lung cancer death was 3.386 (95% CI: 1.697-6.755). When relative humidity was at its 2.5th percentile, the RR was 1.690 (95% CI: 1.269-2.250). Low temperature and low humidity showed increasing cumulative death risk with longer lag times. Conclusions: Lower temperatures and humidity may raise major cancer mortality, especially for lung cancer. Focusing on extreme weather and early warnings could thus improve health management for high-risk groups.