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.
Abstract
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.
High temperature and high relative humidity were associated with increased daily outpatient visits for HA in Guangzhou, whereas low temperature and lower relative humidity were associated with fewer visits in the main model.
Fei-Fei Yan, Rong Xu, Yi Dong et al.· Frontiers in Pediatrics· 0 citations
Objective Meteorological factors influence dengue transmission through nonlinear and lagged effects. This study assessed these associations in Hainan Province and developed an early risk prediction index. Methods Weekly dengue case and meteorological data from Hainan Province (2018–2019 and 2023–2024) were analyzed using distributed lag non-linear models (DLNMs). An early risk prediction index(ERPI) was constructed from DLNM-derived cumulative meteorological risk signals and imported cases. Predictive performance was evaluated using leave-one-year-out cross-validation and assessed by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results Among 927 dengue cases reported during the four modelled years, 727 (78.43%) were locally acquired. Weekly mean daily maximum temperature (Tmax), precipitation (Pmean), wind speed (WSmean), and diurnal temperature range (DTRmean) showed nonlinear, lag-dependent associations with dengue risk, with distinct temporal patterns. Higher Tmax showed longer-lag effects, peaking at 33.73 °C at lag 8 weeks (RR = 2.45, 95% CI: 1.70–3.63). Its cumulative effect was strongest over lags 5–8 weeks, peaking at approximately 32 °C. Pmean showed a nonlinear association, peaking at 4 mm/day at a lag of 4 weeks (RR = 1.92, 95% CI: 1.59–2.31), with significant cumulative effect across both the 1–4- and 5–8-week windows. Low WSmean and moderate DTRmean showed their strongest cumulative associations over lags 1–4 weeks, peaking at 2.0 m/s and 6.1 °C, respectively. These harmful associations attenuated over lags 5–8 weeks, whereas higher WSmean and DTRmean were generally protective. The ERPI predicted local dengue occurrence within the subsequent 1–4-week windows, with AUCs of 0.762–0.843. Across these windows, sensitivities were 0.768–0.797, specificities were 0.644–0.708, PPVs were 0.468–0.624, and NPVs were 0.822–0.911. Conclusion In Hainan, high temperature was primarily associated with medium-to-long-term dengue risk, whereas low-to-moderate diurnal temperature range increased short-term risk. Precipitation was associated with risk across both short and medium-to-long lags, while cumulative wind-speed associations were strongest over shorter lags and attenuated thereafter. The DLNM-based early risk prediction index may support meteorology-informed early warning and dengue control in tropical settings, although requiring external validation is needed before operational implementation.
Yi-Yao Lian, Yan Jin, Yu-Fei Wang et al.· Frontiers in Public Health· 0 citations
This study quantifies humid-heat-related OID risks and investigates the socioeconomic determinants driving spatial vulnerability in Anhui, China, highlighting an “adaptation deficit” in less-developed regions.
Mengyuan Gao, Jinling Song, Jing Wu et al.· BMC Public Health· 0 citations
This study aims to quantify the short-term association between ambient air pollutants and outpatient visits for conjunctivitis using empirical data. Methods: This study collected daily outpatient visits for conjunctivitis in Jiayuguan City from January 1, 2023 to December 31, 2024, as well as meteorological and air pollutant data during the same period. We used a quasi-Poisson generalized linear regression model that incorporates a distributed lag nonlinear model to analyze the nonlinear relationship and lag effect between pollutant exposure and the risk of outpatient visits for conjunctivitis, and conducted stratified analysis by gender, age and season to identify susceptible populations. Results: A total of 14,598 cases of conjunctivitis were included during the study. The results showed that particulate matter (PM
2.5
and PM
10
) was not statistically significantly associated with outpatients for conjunctivitis, and the exposure-response curve showed a downward trend. Conversely, gaseous pollutants (NO
2
and CO) showed a significant positive linear correlation with outpatients for conjunctivitis, with the effect being stronger in the cold season. NO
2
was significant at lag1-3, lag5 days and lag02–07 days, with each 10 µg/m
3
increase corresponding to an RR of 1.126 (95% CI 1.050, 1.208), corresponding to a 12.60% increase in patient visits. CO had the strongest effect at lag07 days (RR = 1.836, 95% CI 1.126, 2.991). Furthermore, NO
2
primarily increases conjunctivitis visits in women and children (0–14 years), while CO exposure is significantly associated with conjunctivitis visits in men and older adults (≥ 65 years). Conclusions: In this arid, industrial city in Northwest China, gaseous pollutants (rather than particulate matter) are the key environmental factor driving the increase in conjunctivitis outpatient visits. This study reveals the differentiated effects of specific pollutants on populations with different demographic characteristics, highlighting the public health significance of strengthening ocular surface health protection for specific vulnerable subgroups during the cold season.
Yanan Zhang, Guorui Song, Bo Zheng et al.· Aerosol and Air Quality Rese...· 0 citations
Background: Dengue is a major and expanding public health problem in India, with increasing case numbers and geographic spread. Climatic factors, particularly temperature and rainfall, play an important role in its transmission dynamics. Objectives: To analyze epidemiological trends and the association of climatic variables with dengue across different geopolitical zones of Odisha during 2012–2016. Methods: This multi-center retrospective observational study was based on passive surveillance, supplemented by active case finding, and conducted across selected healthcare facilities in Odisha between January 2012 and December 2016. A total of 4,325 patients with dengue-like illness were enrolled. Laboratory confirmation was performed using serological and molecular methods. Monthly climatic data were obtained from meteorological sources, and associations with dengue cases were assessed using Spearman correlation with a 1-month lag analysis. Results: Of 4,325 cases, 2,212 (51.14%) were laboratory confirmed. Adults aged ≥ 20 years and males were more commonly affected. Dengue cases demonstrated a clear seasonal pattern, peaking during August to October. Rainfall showed a strong positive correlation with dengue cases (ρ =0.66, P < 0.01), which increased with a one-month lag (ρ =0.72, P < 0.01). Temperature exhibited a moderate positive association (ρ =0.44, P < 0.05), with higher transmission observed between 27 and 34°C. The Western and Mid-Central Tableland reported the highest number of cases, followed by the Coastal Plains. Conclusion: Dengue transmission in Odisha shows marked spatial and temporal variation influenced by climatic factors. Rainfall, particularly with a lag effect, is strongly associated with dengue cases. Integration of climatic data into surveillance systems may support early warning and targeted interventions.
S. Panda, Gayatri Patra, D. S. Mishra et al.· Indian Journal of Community...· 0 citations
BACKGROUND
Subarachnoid hemorrhage (SAH) carries high mortality worldwide. Seasonal patterns have been documented in temperate regions, but evidence from tropical and subtropical populations remains limited. We investigated associations between meteorological variables and SAH hospital admissions across diverse climate zones in Brazil.
METHODS
We conducted an ecological time-series analysis linking nationwide SAH hospitalizations [International Classification of Diseases Version 10 (ICD-10; I60.0-I60.9)] from Brazil's Unified Health System with meteorological data from the National Institute of Meteorology (January 2020-November 2024). Quasi-Poisson generalized linear models were used to estimate relative risks (RR) per 1 °C decrease in mean minimum temperature, overall and stratified by season. Sensitivity analyses excluded the first coronavirus disease 2019 (COVID-19) pandemic year.
RESULTS
Among 9903 SAH hospital admissions over 59 months, seasonal variation was observed, with the highest proportion occurring in autumn (27.2%, n = 2697) and the lowest in spring (23.4%, n = 2316; Chi-squared p < 0.001). The mean summer-winter difference in minimum temperature was 3.6 °C. Overall, the temperature-SAH association did not reach statistical significance [RR per 1 °C decrease: 1.019; 95% confidence interval (CI): 0.994-1.045; p = 0.14]. In season-stratified analyses, winter was the only season demonstrating a significant inverse association (RR = 1.133; 95% CI 1.008-1.275; p = 0.037). This finding was robust to the exclusion of the pandemic year 2020 (RR = 1.140; 95% CI 1.013-1.282; p = 0.030). In-hospital case fatality did not differ significantly across seasons (range 5.2-5.7%; p = 0.38).
CONCLUSIONS
SAH hospital admissions in Brazil exhibit modest seasonal variation, with a significant association with temperature observed only during the winter months. These findings extend evidence on environmental determinants of SAH to a tropical and subtropical setting, though the small effect size and ecological design warrant cautious interpretation.
Daniela Laranja Gomes Rodrigues, João Brainer Clares de Andrade, G. Silva· Neurocritical Care· 0 citations
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