Objective To explore the association of long-term temperature variability with self-rated health status in a cohort in China.

Methods From the Chinese Family Panel Studies (CFPS) using multi-stage stratified cluster sampling, four waves (2014, 2016, 2018, and 2020) of datasets were extracted, including a total of 21 730 individuals aged ≥16 years, with an average follow-up frequency of 3.49 times. Long-term temperature variability was defined as the standard deviation of the minimum and maximum daily temperatures during the year prior to the survey, which was classified into low (< 8.6 ℃), medium (8.6-12.6 ℃), and high (>12.6 ℃) levels. The association of long-term temperature variability with self-rated health status was assessed using a generalized estimating equation model, and the robustness of the results was validated through subgroup analysis and sensitivity analysis.

Results After adjusting for covariates, long-term temperature variability showed a non-linear negative association with self-rated health status. The exposure-response curve showed that when the temperature variability increased from 4.8 ℃ to 17 ℃, the probability of self-rated health decreased from 67% to 21%. Compared with low temperature variability, medium and high temperature variability levels were associated with reduced probabilities of self-rated health, with the odds ratios (95% confidence intervals) being 0.75 (0.67, 0.84) and 0.64 (0.56, 0.74), respectively.

Conclusion Temperature variability is negatively associated with self-rated health status. With the changing global climate in the future, health guidance for residents living in areas with high temperature variability can be provided based on the predicted meteorological data.