Aug 2026· International Journal of Environmental Health Research· pp.
1-21
· 0 citations· 39 references
Medicine
Abstract
This study investigated the impacts and interactive effects of apparent temperature and air pollutants on rural pneumonia hospitalizations in Jiuquan, China (2015-2020), utilizing NRCMS surveillance data, distributed lag non-linear models (DLNM), and stratified analyses. The findings revealed an inverted U-shaped relationship between apparent temperature and pneumonia hospitalizations, peaking at a relative risk (RR) of 1.50 (95% CI: 1.41-1.60). Per interquartile range (IQR) increases in CO, SO2, O3, NO2, PM2.5, and PM10 were associated with RR values of 1.11 (95% CI: 1.01-1.21), 1.04 (95% CI: 0.97-1.12), 1.08 (95% CI: 0.93-1.24), 1.02 (95% CI: 0.93-1.12), 1.04 (95% CI: 0.97-1.12), and 1.05 (95% CI: 1.00-1.12), respectively. Furthermore, synergistic interactive effects occurred between low apparent temperature and high air pollution, and among distinct air pollutants. Subgroup analyses showed higher risks for children under 7 years old, minimal gender disparities, and more pronounced risks during autumn and winter. The study underscores the critical need for targeted interventions in rural areas during concurrent periods of low apparent temperature and high air pollution to reduce public health risks.
Short-term exposure to PM2.5, PM10, SO2, NO2, and CO was associated with increased AECOPD hospitalization risk, with nonlinear, lagged, and cumulative effects, and older adults and females may be more susceptible to ambient air pollution.
Huixia Shi, Jiajun Li, Bo-Yan Li et al.· Medicine· 0 citations
The health hazards caused by air pollution vary significantly depending on factors such as the composition of pollutants in the region, climate, and the susceptibility of the population. Although numerous studies have explored the relationship between air quality and pneumonia, specific research evidence from a particular inland energy city like Qingyang in China is still scarce. This study aims to quantify the short-term impact of air pollution in this under-researched region on pneumonia hospitalizations. We collected the medical records of patients with pneumonia from January 1, 2015, to December 31, 2019, and also obtained data on 6 types of common air pollutants and meteorological conditions. We performed a time series analysis using a generalized additive model and a distributed nonlinear lag model. All measured data exhibited pronounced seasonality: particulate matter less than 2.5 μm in diameter, particulate matter less than 10 μm in diameter, sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO) concentrations and pneumonia admissions were higher in the cold season and lower in the warm season, a pattern conversely mirrored by ozone (O3) concentrations, average temperature, and relative humidity. In the single pollutant model, for every 10 μg/m3 increase in exposure levels of particulate matter less than 2.5 μm in diameter, particulate matter less than 10 μm in diameter, SO2, NO2, and O3 8 hours and for every 1 mg/m3 increase in CO exposure level, the risk of pneumonia admission was 1.004 (0.991, 1.017), 1.004 (0.999, 1.010), 1.035 (1.022, 1.048), 1.221 (1.174, 1.271), 0.999 (0.981, 1.017), and 1.382 (1.230, 1.552), respectively. The strongest effects occur in lag 1, lag 7, lag 07, lag 07, lag 3, and lag 05, respectively. Our study identifies significant gender- and age-specific susceptibilities to air pollution in Qingyang. Men were more vulnerable to SO2, whereas women were more affected by CO. Children aged ≤14 years constituted a high-risk group. Furthermore, we observed a distinct seasonal pattern: SO2, NO2, and CO were key drivers of pneumonia admissions with stronger effects in the cold season, in contrast to O3, which exhibited adverse effects only during the warm season.
Lisha Zhu, Jiyuan Dong, Jianhua You et al.· Medicine· 0 citations
Background: This study aims to thoroughly examine the relationship between weather conditions and reported pulmonary tuberculosis (PTB) cases in Haikou City. The goal is to provide a scientific basis for developing more targeted and timely prevention measures, thereby enhancing local efforts to control PTB. Methods: In this retrospective time-series study from 2010 to 2023, a total of 22,083 PTB cases (15,723 males and 6360 females) were analyzed. Using monthly PTB case counts (overall and stratified by sex and age) and meteorological variables, descriptive analyses along with distributed lag non-linear models (DLNMs) and generalized additive distributed lag models (GADLMs) combined with quasi-Poisson regression model regression were applied. Results: Taking the median of each meteorological factor as the reference, temperature exhibited significant delayed effects. For the overall population, temperatures between 14.30 °C and 24.24 °C posed significant lagged risks, with the effect decreasing as temperature rose. At the minimum monthly average temperature (14.30 °C), the relative risk (RR) peaked at lag 7 months (RR = 1.37, 95% CI: 1.23–1.53). Sex-stratified analysis showed similar patterns for males (14.30–23.94 °C) and females (14.30–23.84 °C), with peak RRs of 1.40 (1.25–1.58) and 1.37 (1.21–1.56), respectively, at 14.30 °C. In the adults group, temperatures between 14.30 °C and 25.45 °C produced significant lagged effects (maximum lag 14 months), with the highest RR of 1.42 (1.27–1.60) at lag 8 under 14.30 °C. The older age group showed a linear association. Average relative humidity exhibited a weak lag-4 correlation with PTB counts in some subgroups, but model testing indicated no statistically significant lagged risk effect in any group. Conclusions: A significant nonlinear lagged exposure–response relationship exists between temperature and PTB incidence in Haikou, with population-specific variations, while relative humidity shows no significant lagged effect. These findings underscore the necessity of incorporating temporal lag effects and population differences into regional PTB early warning and prevention strategies.
This study aims to quantify the associations between extreme temperatures, relative humidity, and mortality risk in patients with hematological diseases within a subtropical urban environment, and to identify vulnerable demographic subgroups. We conducted a time-series analysis using data from Chuzhou, China, spanning 2019 to 2024. Daily mortality counts for hematological diseases (ICD-10 codes C82-C95), meteorological variables (daily mean temperature and relative humidity), and air pollution data were analyzed. A Poisson generalized linear model combined with a distributed lag nonlinear model (DLNM) was applied to assess nonlinear and delayed effects, with adjustments for long-term trends, day of the week, and air pollutants. Stratified analyses were performed by age and gender. A total of 1,651 hematological disease deaths were included. A U-shaped relationship was observed between daily mean temperature and mortality, with minimum risk at approximately 22 °C. Extreme heat exhibited a delayed but prolonged effect, with risk emerging after 7 days and persisting. In contrast, extreme cold triggered a more immediate response, with significant risk appearing at lag 5 days. An optimal relative humidity threshold was identified at 76%. Both high and low humidity increased mortality risk, with low humidity showing an earlier effect (lag 2 days) than high humidity (lag 5 days). Stratified analyses revealed males and younger individuals (< 65 years) were particularly vulnerable to these environmental stressors. Extreme temperatures and abnormal humidity levels are significant environmental risk factors for mortality in hematological disease patients, with distinct temporal patterns and demographic variations. These findings underscore the need to incorporate this vulnerable population into climate‑adaptive public health strategies.
Xunhua Li, Mingjun Shao, Ming Han et al.· Scientific Reports· 0 citations
Background: Extreme temperatures affect health, but nationwide evidence on injury-related hospitalization and economic burden in East Asia is scarce. We aimed to quantify the nonlinear and lagged associations between ambient temperature and injury-related hospitalization and the attributable economic burden. Methods: This nationwide time-stratified case-crossover study used the National Health Insurance Service–National Sample Cohort (2010–2019) of adults (≥20 years). The exposure was the district-specific percentile of daily mean temperature, and the outcome was inpatient hospital admission for injury. Conditional logistic regression with distributed lag nonlinear models (7-day lag, 3 degrees of freedom) was used to estimate relative risks (RRs) and 95% confidence intervals (CIs) at the 1st and 99th percentiles, each relative to the minimum injury temperature, adjusting for holidays, particulate matter concentrations (lag 0–1), and humidity. Analyses were stratified by age, sex, economic activity, residential area, and injury type. Societal economic burden was estimated using a human capital-based cost-of-illness approach. Results: On analyzing 198,937 hospitalizations, a distinct U-shaped relationship emerged. At the 1st percentile (cold), the RR was 1.151 (95% CI = 1.091, 1.214) with delayed effects (lags 1–6) primarily affecting limb injuries. Conversely, at the 99th percentile (heat), the RR was 1.157 (95% CI = 1.081, 1.239) with acute risks (lags 0–3) associated with severe injuries, such as multiple trauma (RR = 2.434, 95% CI = 0.886, 6.687) and burns (RR = 1.995, 95% CI = 1.272, 3.129). The total heat-related economic burden (24,649 million South Korean Won) exceeded that of cold (7,853 million South Korean Won). Individuals with low income, economically active groups, and provincial residents exhibited increased vulnerability to extreme heat, whereas females and the economically inactive population exhibited susceptibility to extreme cold. Conclusions: Nonoptimal temperatures increase injury risk and economic burdens while exacerbating inequalities. Integrating economic evaluations and targeted interventions into climate adaptation strategies is thus essential.
Climate change may influence the transmission dynamics of enteric pathogens through changes in temperature, humidity, precipitation, and environmental exposure. Humid heat poses a significant threat, yet its synergistic impact on Other Infectious Diarrhea (OID) remains under-characterized. This study quantifies humid-heat-related OID risks and investigates the socioeconomic determinants driving spatial vulnerability in Anhui, China.
Daily data on OID cases (
n
= 822,220) and meteorological variables were collected from 16 cities in Anhui Province (2015–2023). We employed a two-stage strategy: city-specific distributed lag non-linear models (DLNM) to estimate exposure-response associations, followed by multivariate meta-regression to evaluate geographical and socioeconomic effect modifiers. Spatial clustering was quantified using Global Moran’s
I
statistics.
The association between Humidex and OID was non-linear, with significant spatial clustering that intensified over time (Moran’s
I
: 0.36–0.47,
P
< 0.001). Substantial heterogeneity was observed (
I
² = 70.03%), masking marked disparities: less-developed northern cities faced significantly higher risks (e.g., Bengbu, RR = 2.08) and larger disease burdens (e.g., Suzhou, AN = 7,671) than southern counterparts. Multivariate meta-regression identified urbanization rate, GDP per capita, and latitude as the primary drivers of this regional heterogeneity.
Humid-heat-related OID risk is structurally modulated by socioeconomic development. Urbanization and economic capacity act as critical buffers, highlighting an “adaptation deficit” in less-developed regions. Public health interventions should prioritize high-risk northern hotspots to mitigate climate-driven health inequities.
Mengyuan Gao, Jinling Song, Jing Wu et al.· BMC Public Health· 0 citations
