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.
Abstract
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.
The identification of climate-related risk factors for pathogen-specific diarrhea is critical for building resilience in high-risk and low-resource settings. Here we performed secondary analysis of published data from the Global Enteric Multicenter Study (GEMS) and Vaccine Impact on Diarrhea in Africa (VIDA) follow-up. We used daily counts of rotavirus, Shigella, Cryptosporidium, and enterotoxigenic E. coli producing heat stable toxin from two case-control studies of moderate-to-severe diarrhea (MSD) in The Gambia, Kenya and Mali (n = 9378). Using Random forests we identify influential climate and vulnerability factors for predicting pathogen presence. Accounting for time trends, our Zero-inflated Poisson models found that associations between weather and MSD were modified by socio-economic factors including transportation, television ownership, electricity access, and introduction of the rotavirus vaccine, but these were site- and pathogen-specific. Our study provides evidence that socio-demographic-associated vulnerability factors are correlated with variable pathogen-specific disease risks. Across the three African countries of The Gambia, Kenya and Mali, Kowalcyk et al identify socio-demographic factors that may mitigate the risks of climate drivers on pathogen-specific diarrheal diseases in young children.
M. Kowalcyk, K. Karnauskas, Indira Bose et al.· Nature Communications· 0 citations
Background Meteorological factors are key triggers for infectious diseases in high-density cities, yet their influence on the acute disease patterns of influenza, mumps, scarlet fever, and hand-foot-and-mouth disease (HFMD) remains unclear. This study aims to reveal the variations in relative risk and secondary infection associated with meteorological factors, thereby providing clues for further confirmatory research. Methods We analyzed city-wide incidence data from 2012 to 2020 alongside daily meteorological observations. Distributed lag non-linear models (DLNM) were used to quantify exposure-lag-response relationships. Andersen-Gill models assessed effects on disease recurrence, and Cox models evaluated risks of sequential infections between disease pairs, stratified by season. Results The peak-risk temperatures were 14.5 °C for influenza, 18.5 °C for mumps, 17 °C for scarlet fever, and 28.5 °C for hand-foot-and-mouth disease (HFMD). Average temperature (AT) increase raised influenza recurrence risk (e.g., 1.3% higher risk per 1 °C in children aged 0–14 years). Greater temperature variability reduced sequential infection risk for influenza after HFMD in the cold season (Hazard Ratio [HR] = 0.16) and warm season (HR = 0.14). Increased diurnal temperature range also lowered this risk in the cold season (HR = 0.09). Higher AT in warm season reduced sequential infection risks from influenza to HFMD (HR = 0.61). Increased relative humidity was protective in the cold season but became a risk factor in the warm season in specific disease. Conclusion Meteorological factors are pivotal in modulating the dynamics of common infections, and influencing both recurrence and sequential transmission. The protective effect of increased temperature variability against secondary infections highlights a complex climate-susceptibility interaction at the population level.
Hongxin Lyu, Huawei Xiong, Jue Liao et al.· Frontiers in Public Health· 0 citations
Meteorological factors may influence the risk of foodborne diseases (FBDs), but evidence comparing different case definitions and pathogen-specific associations within the same surveillance system remains limited. This study aimed to investigate the relationship between short-term exposure to meteorological factors and FBDs in Ningbo, China. We used data from the Ningbo Foodborne Disease Surveillance System and the daily meteorological data of the Ningbo Meteorological Bureau for time-series analysis. Generalized additive models and distributed lag nonlinear models were used to evaluate the associations adjusted for long-term trends, seasonal changes, day of week, and holidays. A stratified analysis was carried out by sex, age, and pathogens. We found that short-term temperature rise was significantly associated with risk of clinically diagnosed FBD cases (mean: relative risks [RR] = 1.028, 95% confidence interval [CI]: 1.006-1.049; minimum: RR = 1.025, 95% CI: 1.004-1.047). Sex- and age-stratified analyses indicated positive temperature associations in both sexes and in several age groups between 15 and 64 years. Pathogen-specific analyses show that there is a positive relationship for Vibrio parahaemolyticus (mean temperature: RR = 1.087, 95% CI: 1.021-1.157), whereas an inverse relationship for Norovirus (mean temperature: RR = 0.965, 95% CI: 0.938-0.993). The findings remained robust in sensitivity analyses. These findings may inform targeted seasonal and pathogen-specific surveillance.
Yi-Chao Dai, Dan-Jie Jiang, B. Lu et al.· Foodborne pathogens and dise...· 0 citations
The findings suggest that climatic conditions influence transmission through their effects on environmental suitability, rodent ecology, and opportunities for human exposure through their effects on environmental suitability, rodent ecology, and opportunities for human exposure during the dry season.
A. Henry, K. Lasisi, Hamisu Idi et al.· American Journal of Bioscien...· 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
Background Severe fever with thrombocytopenia syndrome (SFTS) is an emerging tick-borne zoonotic disease with a high case fatality rate and expanding geographic distribution in Japan. Comprehensive evaluations that integrate meteorological, environmental, and demographic factors remain limited. This study aimed to quantify these associations. Methods We conducted a nationwide prefecture-level time-series analysis using weekly SFTS surveillance data in Japan from March 2013 to December 2025. A generalized additive mixed model adjusting for nonlinear seasonality and long-term trends was used to assess (1) the associations between SFTS risk and meteorological, environmental and demographic factors, and (2) the value of the factor at which risk become highest. Results A total of 1237 SFTS cases were reported. Mean temperature and precipitation showed inverted U-shaped associations with SFTS risk, with peak risks observed at 21.8 °C (relative risk [RR]: 1.78, 95% confidence interval [CI]: 1.26–2.52) and 156.6 mm (RR: 1.30, 95% CI: 1.04–1.62), respectively. Atmospheric pressure and forest coverage were positively associated with SFTS risk, with maximum RRs of 2.06 (95% CI: 1.44–2.93) at 1027.1 hPa and 6.58 (95% CI: 4.95–8.74) at 0.84, respectively. Mean altitude showed an inverse association, with the highest RR observed at 45.0 m (RR: 3.99, 95% CI: 1.81–8.76). Conclusions The increasing number and geographic expansion of SFTS cases highlight the growing public health importance of this disease in Japan. Our findings improve understanding of factors associated with SFTS transmission and may inform future risk assessment and public health preparedness.
Fangyu Yan, Sophearen Ith, N. Kitamura et al.· One Health· 0 citations
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