This study aimed to investigate the univariate and bivariate effects of ambient temperature and air pollutants on 57,251 inpatients with AECOPD (Acute Exacerbation of Chronic Obstructive Pulmonary Disease) in Ganzhou from January 1, 2016, to December 31, 2019. We categorized the daily mean temperature and air pollutant variables based on the exposure–response curve of the Distributed Lag Non-Linear Model. Poisson regression model was used for interaction and stratification analysis. The Relative Excess Risk due to Interaction (RERI) with 95% confidence intervals (95% CI) between daily mean temperature (Tmean) and air pollutants including NO₂, PM_2.5, and PM₁₀ were − 0.428 (95% CI − 0.637, − 0.218), -− 0.227 (95% CI − 0.293, − 0.161), and − 0.119 (95% CI − 0.159, − 0.079). Further stratification analysis showed the relative risk (RR) (95% CI) of high NO₂ (> 33 μg/m³) at low Tmean (≤ 28 °C) was 1.119 (95% CI 1.096, 1.142). Low temperatures with high PM₁₀ in men and high PM_2.5 in women were associated with a higher risk of AECOPD hospitalization. The results indicate a higher risk of hospitalization for AECOPD when there is with high concentrations of air pollution at low temperatures.

Infectious diarrhea cases have increased during the past years in the Anhui Province of China, but little is known about its spatial cluster pattern and associated socioeconomic factors. We obtained county-level total cases of infectious diarrhea in 105 counties of Anhui in 2016 and computed age-adjusted rates. Socioeconomic factors were collected from the Statistical Yearbook. Hot spot analysis was used to identify hot and cold spot counties for infectious diarrhea incidence. We then applied binary logistic regression models to determine the association between socioeconomic factors and hot spot or cold spot clustering risk. Hot spot analysis indicated there were both significant hot spot (29 counties) and cold spot (18 counties) clustering areas for infectious diarrhea in Anhui (P < 0.10). Multivariate binary logistic regression results showed that infectious diarrhea hot spots were positively associated with per capita gross domestic product (GDP), with an adjusted odds ratio (AOR): 3.51, 95% CI: 2.09–5.91, whereas cold spots clustering were positively associated with the number of medical staffs (AOR: 1.18, 95% CI: 1.08–1.29) and negatively associated with the number of public health physicians (AOR: 0.27, 95% CI: 0.09–0.86). We identified locations for hot and cold spot clusters of infectious diarrhea incidence in Anhui, and the clustering risks were significantly associated with health workforce resources and the regional economic development. Targeted interventions should be carried out with considerations of regional socioeconomic conditions.

To analyze the relationship between hot or cold, and wet or dry interacting weather, and urolithiasis-related hospitalizations. Distributed lag nonlinear model for time series design was used to build exposure-response curves for the association of daily mean temperature and relative humidity with urolithiasis-related hospitalizations. Cut-off values were determined for temperature, humidity, and dichotomous categories to define heat-cold and wet-dry meteorological conditions. Based on a total of 38,184 urolithiasis-related hospitalizations from 2018 to 2019 in Ganzhou as a large prefecture-level city in southern China, dry-heat weather during the hot season (May to September) increased the risk of urolithiasis-related hospitalizations (relative risk [RR]: 1.17, 95% confidence interval [CI]: 1.02, 1.35), with the most pronounced effects in those aged 21–40 years and male. Wet-cold weather in the cold season (December to February) increased the risk of urolithiasis-related hospitalizations in the total population (RR: 1.58, 95% CI: 1.20, 2.07). The effect was significant in the 21–60 years old age and both genders groups. Both dry-heat weather in the hot season and wet-cold weather in the cold season could trigger urolithiasis.

We collected meteorological and urolithiasis-related hospitalization data from four counties in Ganzhou City for 2018-2019 and used the DLNM method to assess the lagged and cumulative effects of temperature on urolithiasis hospitalizations and obtain the total effect after meta-combination. Based on the nonlinear association between temperature and urolithiasis hospitalizations, the relative risk of overall high temperature (30celcius) was 2.10 (95% CI: 1.07-4.10). No statistically significant difference (p = 0.07) was observed between males (RR = 2.04, 95% CI: 1.42-2.94) and females (RR = 1.45, 95% CI: 1.09-1.92) for the heat effect, which was higher in the >=; 60 years age group (RR = 3.18, 95% CI: 1.76-5.76) than in the < 60 years age group (p = 0.007). High temperatures increased the risk of hospitalization for urolithiasis in Ganzhou, China, and the risk was greatest for individuals aged 60 and above, with similar risks observed across counties and genders.