Exploring the impact of three meteorological factors and their specific effect sizes on chronic obstructive pulmonary disease admission in Qingyang, China.

It's unclear exactly how variations in a number of meteorological factors relate to the chance of being hospitalized with COPD. The majority of earlier research was incomplete and non-systematic, describing only the association between a single climatic element and hospitalization for COPD. The results were often inconsistent. The purpose of this study is to investigate the relationship between three meteorological factors (temperature, DTR, and relative humidity) and the daily hospital admissions of COPD in Qingyang, China, as well as to examine the lag effects of each meteorological indicator on various subgroups. Based on daily COPD hospital admissions and meteorological data in Qingyang City, China, from 2015 to 2019, a generalized additive model (GAM) combined with a distributed lag non-linear model (DLNM) was used to examine the effects of three meteorological factors on COPD hospital admissions. At the same time, the 95th and 5th percentiles were utilized as the high and low effect nodes of each meteorological index to assess its relationship with COPD admission. Temperature, DTR, and relative humidity exhibited inverted J-, M-, and W-shaped exposure-response relationships with COPD admission. The negative effects of the cold effect and high DTR reached their peak at lag0-21. The RR values were 1.867 (95%CI:1.624,2.148) and 1.542 (95%CI:1.215,1.959), respectively. The highest negative effect of low humidity was in lag0-7, with RR values of 1.239 (95% CI:1.116,1.374). The three factors had a greater negative impact on those over 65 years old. Women were more susceptible to the cold, but men were more vulnerable to high DTR and low humidity. The study found significant correlations between meteorological factors and COPD hospitalizations. Lower temperatures, higher diurnal temperature variation, and lower humidity were associated with increased admission risks, with differential impacts observed across age groups and genders.