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Abstract With unintentional injuries being the leading mortality cause among children, the comprehensive evaluation of the unintentional injury burden concerning heat exposure remains unknown. Here we quantified the years lived with disability (YLD) due to unintentional child injuries in Guangzhou from 2016–2020 using the injury surveillance data. A Poisson regression model was employed to explore how various magnitudes of heat exposure could increase the injury burden in different children’s subgroups. Our findings suggest a positive link between heat exposure and childhood injuries. The successive heatwave led to a 16.8–23.8% higher risk of childhood injuries. Girls and preschoolers exhibited higher vulnerability to heat. Furthermore, heat exposure increased the road injury burden for preschoolers aged 3–6 years but not for adolescents. Our study links heat exposure to childhood unintentional injuries, providing insights into the demographic features and injury causes. This evidence can be used to inform healthy childhood development.

Abstract Background From January 2020 to June 2022, strict interventions against COVID-19 were implemented in Guangdong Province, China. However, the evolution of COVID-19 dynamics remained unclear in this period. Objectives This study aims to investigate the evolution of within- and between-city COVID-19 dynamics in Guangdong, specifically during the implementation of rigorous prevention and control measures. The intent is to glean valuable lessons that can be applied to refine and optimize targeted interventions for future crises. Methods Data of COVID-19 cases and synchronous interventions from January 2020 to June 2022 in Guangdong Province were collected. The epidemiological characteristics were described, and the effective reproduction number (R t ) was estimated using a sequential Bayesian method. Endemic-epidemic multivariate time-series model was employed to quantitatively analyze the spatiotemporal component values and variations, to identify the evolution of within- and between-city COVID-19 dynamics. Results The incidence of COVID-19 in Guangdong Province was 12.6/100,000 population (15,989 cases) from January 2020 to June 2022. The R t predominantly remained below 1 and increased to a peak of 1.39 in Stage 5. As for the evolution of variations during the study period, there were more spatiotemporal components in stage 1 and 5. All components were fewer from Stage 2 to Stage 4. Results from the endemic-epidemic multivariate time-series model revealed a strong follow-up impact from previous infections in Dongguan, Guangzhou and Zhanjiang, with autoregressive components of 0.48, 0.45 and 0.36, respectively. Local risk was relatively high in Yunfu, Shanwei and Shenzhen, with endemic components of 1.17, 1.04 and 0.71, respectively. The impact of the epidemic on the neighboring regions was significant in Zhanjiang, Shenzhen and Zhuhai, with epidemic components of 2.14, 1.92, and 1.89, respectively. Conclusion The findings indicate the presence of spatiotemporal variation of COVID-19 in Guangdong Province, even with the implementation of strict interventions. It’s significant to prevent transmissions within cities with dense population. Preventing spatial transmissions between cities is necessary when the epidemic is severe. To better cope with future crises, interventions including vaccination, medical resource allocation and coordinated non-pharmaceutical interventions were suggested.

Abstract Approximately 70% of treatment failures in nasopharyngeal carcinoma (NPC) patients are attributed to distant metastasis, yet the underlying mechanisms remain unclear. RNA 5-methylcytosine (m5C) is an emerging regulatory modification that controls gene expression and plays a critical role in tumor progression. However, there is little information on the potential roles of RNA m5C modification in NPC metastasis. In this study, we found that the m5C reader Aly/REF export factor (ALYREF) is significantly upregulated in NPC, whereby its high expression is associated with metastasis and poor prognosis. ALYREF overexpression was found to promote tumor metastasis of NPC cells in vitro and in vivo. Mechanistically, m5C-modified NOTCH1 mRNA was identified as a target of ALYREF. Moreover, ALYREF was found to upregulate NOTCH1 expression by enhancing its RNA stability in an m5C modification-dependent manner, thereby promoting the activation of the NOTCH signaling pathway and facilitating NPC metastasis. Overall, our data reveal the crucial role of ALYREF in NPC metastasis and provide a potential therapeutic target for NPC.

Abstract BackgroundParticulate matter (PM), which affects respiratory health, has been well documented; however, substantial evidence from large cohorts is still limited, particularly in highly polluted countries and for PM1 ObjectiveOur objective was to examine the potential causal links between long-term exposure to PMs (PM2.5101 MethodsA total of 580,757 participants from the Guangzhou area, China, were recruited from 2009 to 2015 and followed up through 2020. The annual average concentrations of PMs at a 1-km spatial resolution around the residential addresses were estimated using validated spatiotemporal models. The marginal structural Cox model was used to estimate the associations of PM exposure with respiratory mortality, accounting for time-varying PM exposure. Results were stratified by demographics and lifestyle behaviors factors. ResultsAmong the participants, the mean age was 48.33 (SD 17.55) years, and 275,676 (47.47%) of them were men. During the follow-up period, 7260 deaths occurred due to respiratory diseases. The annual average concentrations of PM12.510312.510110 ConclusionsThis cohort study provides causal clues of the respiratory impact of long-term ambient PM exposure, indicating that PM reduction efforts may continuously benefit the population’s respiratory health.

Background: Limited attention has been paid to the health effects of long-term PM1 exposure on stroke admission. Current investigations exploring the long-term PM exposure effect are largely based on observational studies, and PM generally is not allocated randomly to participants. Using traditional regression models might confuse messaging and hinder policy recommendations for pollution control and disease prevention policies. Methods: We conducted a cohort study among 36,271 adults from one of the largest cities in China in 2015 and followed up through 2020. Hazard ratios of stroke admissions following long-term PM1 exposure were estimated via a causal inference approach, marginal structural time-varying Cox proportional hazard model, accounting for multiple confounders. Additionally, several sensitivity analyses and impact modification analyses were carried out. Results and discussion: Associations with 1 μg/m3 increase in long-term PM1 were identified for total (HR, 1.079; 95 %CI, 1.012–1.151) and ischemic stroke admissions (HR, 1.092; 95 %CI, 1.018–1.171). The harmful associations varied with exposure duration, initially increasing and then decreasing. The 2–3 years cumulative exposure was associated with a 3.3–5.4 % raised risk for total stroke. For every 1 μg/m³ increase in long-term PM1 exposure, females exhibited a higher risk of both total and ischemic stroke (13 % and 16 %) than men (4 % and 5 %). Low-exposure individuals (whose annual PM1 concentrations were under the third quartile among the annual concentrations for all the participants) exhibited greater sensitivity to PM1 effects (total stroke: 1.079 vs. 1.107; ischemic stroke: 1.092 vs. 1.116). The results underline the importance of safeguarding low-exposed people in highly polluted areas and suggest that long-term PM1 exposure may increase stroke admission risk, warranting attention to vulnerable groups.

Abstract Objective This study aimed to verify if intraoperative radiotherapy (IORT) can achieve the same survival outcome as whole‐breast external beam radiotherapy (EBRT) in early breast cancer after breast‐conserving surgery (BCS), and to explore the suitable candidates that can safely receive IORT after BCS. Methods Eligible post‐BCS patients who received IORT or EBRT were included in the Surveillance, Epidemiology and End Results (SEER) database from 2010 to 2018. Risk factors that affected 5‐year overall survival (OS) or breast cancer specific survival (BCSS) were identified by Cox proportional hazards regression analysis. Clinical characteristics, OS, and BCSS were comparatively analyzed between the two treatment modalities. Results The survival analysis after propensity score matching confirmed that patients who received IORT (n = 2200) had a better 5‐year OS than those who received EBRT (n = 2200) (p = 0.015). However, the two groups did not differ significantly in 5‐year BCSS (p = 0.381). This feature persisted even after multivariate analyses that took into account numerous clinical characteristics. Although there was no significant difference in BCSS between different subgroups of patients treated with IORT or EBRT, patients over 55 years of age, with T1, N0, non‐triple negative breast cancers, hormone receptor‐positive, and histologic grade II showed a better OS after receiving IORT. Conclusion In low‐risk, early‐stage breast cancer, IORT was not inferior to EBRT considering 5‐year BCSS and OS. Considering the equivalent clinical outcome but less radiotoxicity, IORT might be a reasonable alternative to EBRT in highly selective patients undergoing BCS.

Background: The potential for residential greenness to improve cardiovascular health through both physical and psychological mechanisms is well recognized. However, evidence from rapidly urbanizing developing countries and cohort-based causal inference approaches, remains limited. We aim to examine the effect of residential greenness and time to cardiovascular mortality in South China. Methods: We utilized data from a community-based population survey involving 748,209 participants at baseline from 2009 to 2015, followed up until 2020. Residential greenness exposure was assessed by the annual Normalized Difference Vegetation Index (NDVI) in the 500 m radius of each participant’s residence. We used time-varying proportional hazard Cox models coupled with inverse probability weighting to fit marginal structural models and obtain hazard ratios (HRs) for cardiovascular disease (CVD) mortality after adjusting for confounders. Multiple effect modifiers on both additive and multiplicative scales were further explored. Results: A total of 15,139 CVD-related deaths were identified during a median of 7.9 years of follow-up. A protective effect was found between higher greenness exposure and reduced CVD mortality, with a 9.3 % lower rate of total CVD mortality (HR 0.907, 95 % CI 0.859–0.957) based on a 0.1 increase in annual average NDVI. Demographic (age, marital status) and lifestyle factors (smoking, drinking status) were found to modify the association between residential greenness and CVD mortality (all P interaction values

BackgroundHand, foot, and mouth disease (HFMD) is a major public health issue in China while temperature and humidity are well-documented predictors. However, evidence on the combined effect of temperature and humidity is still limited. It also remains unclear whether such an effect could be modified by the enterovirus 71 (EV71) vaccination.MethodsBased on 320,042 reported HFMD cases during the summer months between 2012 and 2019, we conducted a study utilizing Distributed Lag Non-Linear Models (DLNM) and time-varying DLNM to examine how China's HFMD EV71 vaccine strategy would affect the correlation between meteorological conditions and HFMD risk.ResultsThe incidence of HFMD changed with the Discomfort Index in an arm-shaped form. The 14-day cumulative risk of HFMD exhibited a statistically significant increase during the period of 2017-2019 (following the implementation of the EV71 vaccine policy) compared to 2012-2016 (prior to the vaccine implementation). For the total population, the range of relative risk (RR) values for HFMD at the 75th, 90th, and 99th percentiles increased from 1.082-1.303 in 2012-2016 to 1.836-2.022 in 2017-2019. In the stratified analyses, Han Chinese areas show stronger relative growth, with RR values at the 75th, 90th, and 99th percentiles increased by 14.3%, 39.1%, and 134.4% post-vaccination, compared to increases of 22.7%, 41.6%, and 38.8% in minority areas. Similarly, boys showed greater increases (24.4%, 47.7%, 121.5%) compared to girls (8.1%, 28.1%, 58.3%). Additionally, the central Guizhou urban agglomeration displayed a tendency for stronger relative growth compared to other counties.ConclusionsAlthough the EV71 vaccine policy has been implemented, it hasn't effectively controlled the overall risk of HFMD. There's been a shift in the main viral subtypes, potentially altering population susceptibility and influencing HFMD occurrences. The modulating effects of vaccine intervention may also be influenced by factors such as race, sex, and economic level.

Background: Evidence of a potential causal link between long-term exposure to particulate matter (PM) and all-site cancer mortality from large population cohorts remained limited and suffered from residual confounding issues with traditional statistical methods. Aims: We aimed to examine the potential causal relationship between long-term PM exposure and all-site cancer mortality in South China using causal inference methods. Methods: We used a cohort in southern China that recruited 580,757 participants from 2009 through 2015 and tracked until 2020. Annual averages of PM1, PM2.5, and PM10 concentrations were generated with validated spatiotemporal models. We employed a causal inference approach, the Marginal Structural Cox model, based on observational data to evaluate the association between long-term exposure to PM and all-site cancer mortality. Results: With an increase of 1 µg/m³ in PM1, PM2.5, and PM10, the hazard ratios (HRs) and 95% confidence interval (CI) for all-site cancer were 1.033 (95% CI: 1.025–1.041), 1.032 (95% CI: 1.027–1.038), and 1.020 (95% CI: 1.016–1.025), respectively. The HRs (95% CI) for digestive system and respiratory system cancer mortality associated with each 1 µg/m³ increase in PM1 were 1.022 (1.009–1.035) and 1.053 (1.038–1.068), respectively. In addition, inactive participants, who never smoked, or who lived in areas of low surrounding greenness were more susceptible to the effects of PM exposure, the HRs (95% CI) for all-site cancer mortality were 1.042 (1.031–1.053), 1.041 (1.032–1.050), and 1.0473 (1.025–1.070) for every 1 µg/m³ increase in PM1, respectively. The effect of PM1 tended to be more pronounced in the low-exposure group than in the general population, and multiple sensitivity analyses confirmed the robustness of the results. Conclusion: This study provided evidence that long-term exposure to PM may elevate the risk of all-site cancer mortality, emphasizing the potential health benefits of improving air quality for cancer prevention.

Abstract Background Infectious diarrhea remains a major public health problem worldwide. This study used stacking ensemble to developed a predictive model for the incidence of infectious diarrhea, aiming to achieve better prediction performance. Methods Based on the surveillance data of infectious diarrhea cases, relevant symptoms and meteorological factors of Guangzhou from 2016 to 2021, we developed four base prediction models using artificial neural networks (ANN), Long Short-Term Memory networks (LSTM), support vector regression (SVR) and extreme gradient boosting regression trees (XGBoost), which were then ensembled using stacking to obtain the final prediction model. All the models were evaluated with three metrics: mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE). Results Base models that incorporated symptom surveillance data and weekly number of infectious diarrhea cases were able to achieve lower RMSEs, MAEs, and MAPEs than models that added meteorological data and weekly number of infectious diarrhea cases. The LSTM had the best prediction performance among the four base models, and its RMSE, MAE, and MAPE were: 84.85, 57.50 and 15.92%, respectively. The stacking ensembled model outperformed the four base models, whose RMSE, MAE, and MAPE were 75.82, 55.93, and 15.70%, respectively. Conclusions The incorporation of symptom surveillance data could improve the predictive accuracy of infectious diarrhea prediction models, and symptom surveillance data was more effective than meteorological data in enhancing model performance. Using stacking to combine multiple prediction models were able to alleviate the difficulty in selecting the optimal model, and could obtain a model with better performance than base models.

Background: The new round of WHO/ILO Joint Estimates of the Work-related Burden of Disease assessment requires futher research to provide more evidence, especially on the health impact of ambient air pollution around the workplace. However, the evidence linking obstructive ventilatory dysfunction (OVD) to fine particulate matter (PM2.5) and its chemical components in workers is very limited. Evidence is even more scarce on the interactive effects between occupational factors and particle exposures. We aimed to fill these gaps based on a large ventilatory function examination of workers in southern China. Methods: We conducted a cross-sectional study among 363,788 workers in southern China in 2020. The annual average concentration of PM2.5 and its components were evaluated around the workplace through validated spatiotemporal models. We used mixed-effect models to evaluate the risk of OVD related to PM2.5 and its components. Results were further stratified by basic characteristics and occupational factors. Findings: Among the 305,022 workers, 119,936 were observed with OVD. We found for each interquartile range (IQR) increase in PM2.5 concentration, the risk of OVD increased by 27.8 (95 % confidence interval (CI): 26.5–29.2 %). The estimates were 10.9 % (95 %CI: 9.7–12.1 %), 15.8 % (95 %CI: 14.5–17.2 %), 2.6 % (95 %CI: 1.4–3.8 %), 17.1 % (95 %CI: 15.9–18.4 %), and 11 % (95 %CI: 9.9–12.2 %), respectively, for each IQR increment in sulfate, nitrate, ammonium salt, organic matter and black carbon. We observed greater effect estimates among females, younger workers, workers with a length of service of 24–45 months, and professional skill workers. Furthermore, it is particularly noteworthy that the noise-exposed workers, high-temperature-exposed workers, and less-dust-exposed workers were at a 5.7–68.2 % greater risk than others. Interpretation: PM2.5 and its components were significantly associated with an increased risk of OVD, with stronger links among certain vulnerable subgroups.

Evidence of the potential causal links between long-term exposure to particulate matters (PM, i.e., PM1, PM2.5, and PM1–2.5) and T2DM mortality based on large cohorts is limited. In contrast, the existing evidence usually suffers from inherent bias with the traditional association assessment.A prospective cohort of 580,757 participants in the southern region of China were recruited during 2009 and 2015 and followed up through December 2020. PM exposure at each residential address was estimated by linking to the well-established high-resolution simulation dataset. Hazard ratios (HRs) were calculated using time-varying marginal structural Cox models, an established causal inference approach, after adjusting for potential confounders.During follow-up, a total of 717 subjects died from T2DM. For every 1 μg/m3 increase in PM2.5, the adjusted HRs and 95% confidence interval (CI) for T2DM mortality was 1.036 (1.019–1.053). Similarly, for every 1 μg/m3 increase in PM1 and PM1–2.5, the adjusted HRs and 95% CIs were 1.032 (1.003–1.062) and 1.085 (1.054–1.116), respectively. Additionally, we observed a generally more pronounced impact among individuals with lower levels of education or lower residential greenness which as measured by the Normalized Difference Vegetation Index (NDVI). We identified substantial interactions between NDVI and PM1 (P-interaction = 0.003), NDVI and PM2.5 (P-interaction = 0.019), as well as education levels and PM1 (P-interaction = 0.049).The study emphasizes the need to consider environmental and socio-economic factors in strategies to reduce T2DM mortality. We found that PM1, PM2.5, and PM1–2.5 heighten the peril of T2DM mortality, with education and green space exposure roles in modifying it.

Introduction China initialised the expanded hepatitis A vaccination programme (EHAP) in 2008. However, the effectiveness of the programme remains unclear. We aimed to comprehensively evaluate the effectiveness of EHAP in the country.Methods Based on the provincial data on the incidence of hepatitis A (HepA), the population and meteorological variables in China, we developed interrupted time series (ITS) models to estimate the effectiveness of EHAP with the autocorrelation, seasonality and the meteorological confounders being controlled. Results were also stratified by economic zones, age groups and provinces.Results We found a 0.9% reduction (RR=0.991, 95% CI: 0.990 to 0.991) in monthly HepA incidence after EHAP, which was 0.3% greater than the reduction rate before EHAP in China. Across the three economic regions, we found a 1.1% reduction in HepA incidence in both central and western regions after EHAP, which were 0.3% and 1.2% greater than the reduction rates before EHAP, respectively. We found a decreased reduction rate for the eastern region. In addition, we found generally increased reduction rate after EHAP for age groups of 0–4, 5–14 and 15–24 years. However, we found decreased reduction rate among the 25–64 and ≥65 years groups. We found a slight increased rate after EHAP in Shanxi Province but not elsewhere.Conclusion Our finding provides comprehensive evidence on the effectiveness of EHAP in China, particularly in the central and western regions, and among the population aged 0–24 years old. This study has important implications for the adjustment of vaccination strategies for other regions and populations.

IntroductionAlthough the global COVID-19 emergency ended, the real-world effects of multiple non-pharmaceutical interventions (NPIs) and the relative contribution of individual NPIs over time were poorly understood, limiting the mitigation of future potential epidemics.MethodsBased on four large-scale datasets including epidemic parameters, virus variants, vaccines, and meteorological factors across 51 states in the United States from August 2020 to July 2022, we established a Bayesian hierarchical model with a spike-and-slab prior to assessing the time-varying effect of NPIs and vaccination on mitigating COVID-19 transmission and identifying important NPIs in the context of different variants pandemic.ResultsWe found that (i) the empirical reduction in reproduction number attributable to integrated NPIs was 52.0% (95%CI: 44.4, 58.5%) by August and September 2020, whereas the reduction continuously decreased due to the relaxation of NPIs in following months; (ii) international travel restrictions, stay-at-home requirements, and restrictions on gathering size were important NPIs with the relative contribution higher than 12.5%; (iii) vaccination alone could not mitigate transmission when the fully vaccination coverage was less than 60%, but it could effectively synergize with NPIs; (iv) even with fully vaccination coverage >60%, combined use of NPIs and vaccination failed to reduce the reproduction number below 1 in many states by February 2022 because of elimination of above NPIs, following with a resurgence of COVID-19 after March 2022.ConclusionOur results suggest that NPIs and vaccination had a high synergy effect and eliminating NPIs should consider their relative effectiveness, vaccination coverage, and emerging variants.

Background: Long-term ambient particulate matter (PM) exposure has been found associated with chronic obstructive pulmonary disease (COPD) mortality in an increasing body of research. However, limited evidence was available on the potential causal links between PM1 and COPD mortality, especially in highly exposed areas. Objectives: To examine the COPD mortality risk following long-term ambient PM1 exposure in south China. Methods: The cohort included 580,757 participants recruited during 2009–2015. Satellite-based annual concentrations of PM1 were estimated at a spatial resolution of 1 km × 1 km and assigned to each participant based on their residential addresses. We analyzed the potential causal links between time-varying PM1 exposure and COPD mortality using marginal structural cox models within causal frameworks. Stratified analyses were also performed to identify the potential susceptible groups. Results: The annual average PM1 concentration continuously decreased over time. After adjusting for confounders, each 1 μg/m3 increase in PM1 concentration corresponded to an 8.1 % (95% confidence interval: 6.4–9.9 %) increment in the risk of COPD mortality. The impact of PM1 was more pronounced among the elderly and those with low exercise frequency, with a 1.9–6.9 % higher risk than their counterparts. We further observed a 0.1–9.7 % greater risk among those who lived in lower greenness settings. Additionally, we observed higher effect estimates in participants with long-term low PM1 exposure compared to the general population. Conclusions: COPD mortality risk significantly increased following long term ambient PM1 exposure, particularly among groups with certain demographics or long-term low exposure.

Summary: Evidence concerning PM1 exposure, maternal blood pressure (BP), and hypertensive disorders of pregnancy (HDP) is sparse. We evaluated the associations using 105,063 participants from a nationwide cohort. PM1 concentrations were evaluated using generalized additive model. BP was measured according to the American Heart Association recommendations. Generalized linear mixed models were used to assess the PM1-BP/HDP associations. Each 10 μg/m3 higher first-trimester PM1 was significantly associated with 1.696 mmHg and 1.056 mmHg higher first-trimester SBP and DBP, and with 11.4% higher odds for HDP, respectively. The above associations were stronger among older participants (> 35 years) or those educated longer than 17 years or those with higher household annual income (> 400,000 CNY). To conclude, first-trimester PM1 were positively associated with BP/HDP, which may be modified by maternal age, education level, and household annual income. Further research is warranted to provide more information for both health management of HDP and environmental policies enactment.

We evaluated whether demographics and COVID-19 symptoms predicted COVID-19 deaths among healthcare workers (HCWs) in the United States by comparing COVID-19 deaths in HCWs with 3 control groups (HCW nondeaths, non-HCW deaths, and non-HCW nondeaths) using a case–control design. We obtained patient-level data of 33 variables reported during January 1, 2020–October 12, 2021, in all US states. We used logistic regression analysis while controlling for confounders. We found that persons who were >50 years of age, male, Black, or Asian experienced significantly more deaths than matched controls. In addition, HCWs who died had higher risks for the most severe clinical indicators. We also found that the most indicative symptoms were preexisting medical conditions, shortness of breath, fever, cough, and gastrointestinal symptoms. In summary, minority, male, and older HCWs had greater risk for COVID-19 death. Severe clinical indicators and specific symptoms may predict COVID-19–related deaths among HCWs.

Background: Cardiovascular disease (CVD) mortality is associated with long-term particulate matter (PM) exposure. However, evidence from large, highly-exposed population cohort and observational-data-based causal inference approaches remains limited. Aims: We examined the potential causal links between PM exposure and the CVD mortality in South China. Methods: 580,757 participants were recruited during 2009–2015 and followed up through 2020. Satellite-based annual concentrations of PM2.5, PM10, and PMcoarse (i.e., PM10 - PM2.5) at 1 km2 spatial resolution were estimated and assigned to each participant. Marginal structural Cox models with time-varying covariates, adjusted using inverse probability weighting, were developed to evaluate the association between prolonged PM exposure and CVD mortality. Results: For overall CVD mortality, the hazard ratios and 95% confidence interval for each 1 μg/m3 increase in the annual average concentration of PM2.5, PM10, and PMcoarse were 1.033 (1.028–1.037), 1.028 (1.024–1.032), and 1.022 (1.012–1.033), respectively. All three PMs were linked to a higher mortality risk for myocardial infarction and ischemic heart disease (IHD). The mortality risk of chronic IHD and hypertension was linked to PM2.5 and PM10. Significant association between PMcoarse and other heart disease mortality was also observed. The older, women, less-educated participants, or inactive participants exhibited particularly higher susceptibility. Participants who were generally exposed to PM10 concentrations below 70 μg/m3 were more vulnerable to PM2.5-, PM10- and PMcoarse-CVD mortality risks. Conclusion: This large cohort study provides evidence for the potential causal links between increased CVD mortality and ambient PM exposure, as well as socio-demographics linked to the highest vulnerability.

BackgroundAlthough the association between short-term air pollution exposure and certain hospitalizations has been well documented, evidence on the effect of longer-term (e. g., monthly) air pollution on a comprehensive set of outcomes is still limited.MethodA total of 68,416 people in South China were enrolled and followed up during 2019–2020. Monthly air pollution level was estimated using a validated ordinary Kriging method and assigned to individuals. Time-dependent Cox models were developed to estimate the relationship between monthly PM10 and O3 exposures and the all-cause and cause-specific hospitalizations after adjusting for confounders. The interaction between air pollution and individual factors was also investigated.ResultsOverall, each 10 μg/m3 increase in PM10 concentration was associated with a 3.1% (95%CI: 1.3%−4.9%) increment in the risk of all-cause hospitalization. The estimate was even greater following O3 exposure (6.8%, 5.5%−8.2%). Furthermore, each 10 μg/m3 increase in PM10 was associated with a 2.3%-9.1% elevation in all the cause-specific hospitalizations except for those related to respiratory and digestive diseases. The same increment in O3 was relevant to a 4.7%−22.8% elevation in the risk except for respiratory diseases. Additionally, the older individuals tended to be more vulnerable to PM10 exposure (Pinteraction: 0.002), while the alcohol abused and those with an abnormal BMI were more vulnerable to the impact of O3 (Pinteraction: 0.052 and 0.011). However, the heavy smokers were less vulnerable to O3 exposure (Pinteraction: 0.032).ConclusionWe provide comprehensive evidence on the hospitalization hazard of monthly PM10 and O3 exposure and their interaction with individual factors.

BackgroundInjuries during work are often exogenous and can be easily influenced by environmental factors, especially weather conditions. Precipitation, a crucial weather factor, has been linked to unintentional injuries, yet evidence of its effect on work-related injuries is limited. Therefore, we aimed to clarify the impact of precipitation on injuries during work as well as its variation across numerous vulnerability features.MethodsRecords on the work-related injury during 2016–2020 were obtained from four sentinel hospitals in Guangzhou, China, and were matched with the daily weather data during the same period. We applied a time-stratified case-crossover design followed by a conditional logistic regression to evaluate the association between precipitation and work-related injuries. Covariates included wind speed, sunlight, temperature, SO2, NO2, and PM2.5. Results were also stratified by multiple factors to identify the most vulnerable subgroups.ResultsDaily precipitation was a positive predictor of work-related injuries, with each 10 mm increase in precipitation being associated with an increase of 1.57% in the rate of injuries on the same day and 1.47–1.14% increase of injuries on subsequent 3 days. The results revealed that precipitation had a higher effect on work-related injuries in winter (4.92%; 95%CI: 1.77–8.17%). The elderly (2.07%; 95%CI: 0.64–3.51%), male (1.81%; 95%CI: 0.96–2.66%) workers or those with lower educational levels (2.58%; 95%CI: 1.59–3.54%) were more likely to suffer from injuries on rainy days. There was a higher risk for work-related injuries caused by falls (2.63%; 95%CI: 0.78–4.52%) or the use of glass products (1.75%; 95%CI: 0.49–3.02%) on rainy days.ConclusionsPrecipitation was a prominent risk factor for work-related injury, and its adverse effect might endure for 3 days. Certain sub-groups of workers were more vulnerable to injuries in the rain.

Background: The association between long-term particulate matter (PM) exposure and all-cause mortality has been well-documented. However, evidence is still limited from high-exposed cohorts, especially for PM1 which is smaller while more toxic than other commonly investigated particles. We aimed to examine the potential casual links of long-term PMs exposure with all-cause mortality in high-exposed areas. Methods: A total of 580,757 participants in southern China were enrolled during 2009–2015 and followed up to 2020. The annual average concentration of PM1, PM2.5, and PM10 at 1 km2 spatial resolution was assessed for each residential address through validated spatiotemporal models. We used marginal structural Cox models to estimate the PM-mortality associations which were further stratified by sociodemographic, lifestyle factors and general exposure levels. Results: 37,578 deaths were totally identified during averagely 8.0 years of follow-up. Increased exposure to all 3 PM size fractions were significantly associated with increased risk of all-cause mortality, with hazard ratios (HRs) of 1.042 (95 % confidence interval (CI): 1.037–1.046), 1.031 (95 % CI: 1.028–1.033), and 1.029 (95 % CI: 1.027–1.031) per 1 μg/m3 increase in PM1, PM2.5, and PM10 concentrations, respectively. We observed greater effect estimates among the elderly (age ≥ 65 years), unmarried participants, and those with low education attainment. Additionally, the effect of PM1, PM2.5, and PM10 tend to be higher in the low-exposure group than in the general population. Conclusions: We provided comprehensive evidence for the potential causal links between long-term PM exposure and all-cause mortality, and suggested stronger links for PM1 compared to large particles and among certain vulnerable subgroups.

Background and objectives: Diabetes is an increasing public health concern worldwide. The impact of extreme heat exposure on diabetes healthcare utilization such as diabetes-related hospital admissions and emergency department (ED) visits was understudied although extreme temperature exposure was linked with diabetes mortality. In addition, very few systematic reviews have been conducted in this field. This review aims to systematically evaluate the currently available evidence on the association between extreme ambient heat exposure and hospital admissions/ED visits for diabetes and the vulnerable population to heat extremes. Methods: A systematic literature review was conducted by using the keywords/terms “ambient temperature or heatwave or heat wave or extreme temperature or high temperature effect” and “diabetes morbidity or diabetes hospital admissions or diabetes emergency room visits” for available publications until August 2022. The heat exposure was categorized into four groups using difference definitions. The outcomes were diabetes-related hospital admissions/ED visits. A meta-analysis was performed to estimate the pooled effects of relative risk (RR)/odds ratio (OR) and 95% confidence intervals (CI) for each of the associations of interest. Results: Eighteen articles were selected from forty full-text, English written papers based on the inclusion and exclusion criteria. The overall pooled effect of excessive heat on diabetes, across all groups, was 1.045 (95% CI: 1.024-1.066). The pooled effects for each exposure group were significant/borderline significant. Additionally, the pooled effect of the RR/OR was 1.100 (95% CI: 1.067-1.135) among adults aged 65 years or older. The most controlled confounders were air pollutants. The commonly listed limitation in those studies was misclassification of exposure. Conclusions: The body of evidence supports that ambient extreme heat exposure is associated with diabetes-related hospital admissions/ED visits. Additionally, adults 65 years of age or older with diabetes are vulnerable to heat extremes. Future studies should consider controlling for various biases and confounders.

Background: Although power outage (PO) is one of the most important consequences of increasing weather extremes and the health impact of POs has been reported previously, studies on the neighborhood environment underlying the population vulnerability in such situations are limited. This study aimed to identify dominant neighborhood environmental predictors which modified the impact of POs on multiple health outcomes in New York State. Methods: We applied a two-stage approach. In the first stage, we used time series analysis to determine the impact of POs (versus non-PO periods) on multiple health outcomes in each power operating division in New York State, 2001-2013. In the second stage, we classified divisions as risk-elevated and non-elevated, then developed predictive models for the elevation status based on 36 neighborhood environmental factors using random forest and gradient boosted trees. Results: Consistent across different outcomes, we found predictors representing greater urbanization, particularly, the proportion of residents having access to public transportation (importance ranging from 4.9–15.6%), population density (3.3–16.1%), per capita income (2.3–10.7%), and the density of public infrastructure (0.8–8.5%), were associated with a higher possibility of risk elevation following power outages. Additionally, the percent of minority (-6.3–27.9%) and those with limited English (2.2–8.1%), the percent of sandy soil (6.5–11.8%), and average soil temperature (3.0–15.7%) were also dominant predictors for multiple outcomes. Spatial hotspots of vulnerability generally were located surrounding New York City and in the northwest, the pattern of which was consistent with socioeconomic status. Conclusion: Population vulnerability during power outages was dominated by neighborhood environmental factors representing greater urbanization.

Background: Thermal comfort is essential for human well-being. Consistent exposure to uncomfortable thermal conditions indoors leads to reduced academic performance and adverse health outcomes in schoolchildren. In addition, children are more sensitive to thermal conditions due to physiological differences, yet their exposure to thermal conditions has not been adequately measured.Methods: This cross-sectional study evaluated students' exposure to temperature and relative humidity (RH) using personal air monitors. In this study, we recruited 90 students from 13 classrooms – 60 from 11 NYS primary school classrooms and 30 from two State University of New York at Albany classrooms. Each participant wore an AirBeam air monitor for 48 hours, and their data was transmitted to a cell phone provided by the researchers.Results: Primary school students were routinely exposed to temperatures exceeding ASHRAE standards (winter: 75⁰F, summer: 80.5⁰F), particularly in school in the spring (max=102⁰F, median=82⁰F). At home, temperatures exceeded standards in the evening around dinnertime. However, primary students were routinely exposed to RH below recommended standards in classrooms during all seasons (median=29%). However, the RH was significantly lower in the winter than in any other season (median=17%). Finally, university students were exposed to slightly higher temperatures and significantly lower RH than primary schoolchildren in the spring.Conclusion: These results suggest that students are exposed to uncomfortable thermal conditions, particularly in classrooms when attending class. Teachers should therefore be given more control over classroom ventilation and thermal conditions, while indoor temperature standards should be adjusted for school children.

Background: Research is lacking in examining how multiple climate factors affect the incidence of seasonal influenza. We investigated the associations between El Niño Southern Oscillation (ENSO), meteorological factors, and influenza incidence in New York State, United States. Method: We collected emergency department visit data for influenza from the New York State Department of Health. ENSO index was obtained from the National Oceanic and Atmospheric Administration. Meteorological factors, Google Flu Search Index (GFI), and Influenza-like illness (ILI) data in New York State were also collected. Wavelet analysis was used to quantitatively estimate the coherence and phase difference of ENSO, temperature, precipitation, relative humidity, and absolute humidity with emergency department visits of influenza in New York State. Generalized additive models (GAM) were employed to examine the exposure-response relationships between ENSO, weather, and influenza. GFI and ILI data were used to simulate synchronous influenza visits. Results: The influenza epidemic in New York State had multiple periodic and was primarily on the 1-year scale. The incidence of influenza closely followed the low ENSO index by an average of two months, and the lag period of ENSO on influenza was shorter during 2015–2018. Low temperature in the previous 2 weeks and low absolute humidity in the prior week were positively associated with influenza incidence in New York State. We found an l-shaped association between ENSO index and influenza, a parabolic relationship between temperature in the previous two weeks and influenza, and a linear negative association between absolute humidity in the previous week and influenza. The simulation models including GFI and ILI had higher accuracy for influenza visit estimation. Conclusions: Low ENSO index, low temperature, and low absolute humidity may drive the influenza epidemics in New York State. The findings can help us deepen the understanding of the climate-influenza association, and help to develop an influenza forecasting model.

As the global COVID-19 pandemic continues and new SARS-CoV-2 variants of concern emerge, vaccines remain an important tool for preventing the pandemic. The inactivated or subunit vaccines themselves generally exhibit low immunogenicity, which needs adjuvants to improve the immune response. We previously developed a receptor binding domain (RBD)-targeted and self-assembled nanoparticle to elicit a potent immune response in both mice and rhesus macaques. Herein, we further improved the RBD production in the eukaryote system by in situ Crispr/Cas9-engineered CHO cells. By comparing the immune effects of various Toll-like receptor-targeted adjuvants to enhance nanoparticle vaccine immunization, we found that Pam2CSK4, a TLR2/6 agonist, could mostly increase the titers of antigen-specific neutralizing antibodies and durability in humoral immunity. Remarkably, together with Pam2CSK4, the RBD-based nanoparticle vaccine induced a significant Th1-biased immune response and enhanced the differentiation of both memory T cells and follicular helper T cells. We further found that Pam2CSK4 upregulated migration genes and many genes involved in the activation and proliferation of leukocytes. Our data indicate that Pam2CSK4 targeting TLR2, which has been shown to be effective in tuberculosis vaccines, is the optimal adjuvant for the SARS-CoV-2 nanoparticle vaccine, paving the way for an immediate clinical trial.

Abstract Background Chronic obstructive pulmonary disease (COPD) is the third-leading cause of death worldwide with continuous rise. Limited studies indicate that COPD was associated with major storms and related power outages (PO). However, significant gaps remain in understanding what PO’s role is on the pathway of major storms-COPD. This study aimed to examine how PO mediates the major storms-COPD associations. Methods In this time-series study, we extracted all hospital admissions with COPD as the principal diagnosis in New York, 2001–2013. Using distributed lag nonlinear models, the hospitalization rate during major storms and PO was compared to non-major storms and non-PO periods to determine the risk ratios (RRs) for COPD at each of 0–6 lag days respectively after controlling for time-varying confounders and concentration of fine particulate matter (PM2.5). We then used Granger mediation analysis for time series to assess the mediation effect of PO on the major storms-COPD associations. Results The RRs of COPD hospitalization following major storms, which mainly included flooding, thunder, hurricane, snow, ice, and wind, were 1.23 to 1.49 across lag 0–6 days. The risk was strongest at lag3 and lasted significantly for 4 days. Compared with non-outage periods, the PO period was associated with 1.23 to 1.61 higher risk of COPD admissions across lag 0–6 days. The risk lasted significantly for 2 days and was strongest at lag2. Snow, hurricane and wind were the top three contributors of PO among the major storms. PO mediated as much as 49.6 to 65.0% of the major storms-COPD associations. Conclusions Both major storms and PO were associated with increased hospital admission of COPD. PO mediated almost half of the major storms-COPD hospitalization associations. Preparation of surrogate electric system before major storms is essential to reduce major storms-COPD hospitalization.

BackgroundHepatocellular carcinoma (HCC) is the most prevalent pathological type of liver cancer worldwide with high mortality and poor prognosis. N6-methyladenosine (m6A) can modify RNAs such as mRNA, lncRNA, miRNA, and tRNA, thereby playing a critical role in the pathogenesis of HCC. However, the role of m6A-associated small nuclear RNA (snRNA) in the prognostic value and immunotherapeutic response in HCC remains unclear.Materials and methodsIn this study, snRNA expression data, gene mutation data, and clinical data of HCC patients were acquired from The Cancer Genome Atlas (TCGA) database. We used the least absolute shrinkage and selection operator (LASSO) Cox regression analysis to identify significant prognostic m6A-associated snRNAs, and then developed a multivariate Cox model based on the selected snRNAs. HCC patients were split into low- and high-risk groups based on the median risk score. We subsequently performed Kaplan-Meier curve analysis to estimate overall survival (OS) by clinicopathological characteristics and tumor mutational burden (TMB) status in low- and high-risk HCC patients. Finally, we compared the immunotherapeutic response as represented by tumor immune dysfunction and exclusion (TIDE) scores between the two risk groups.ResultsEight m6A-associated snRNAs were selected as independent predictors to develop the risk model. Our results revealed that the OS of HCC patients in the high-risk group was significantly worse than that in the low-risk group on clinicopathologic characteristics, including age (≤65 years and >65 years), gender (male), grade (G I-II and G III-IV) and TNM staging (Stage I-II and Stage III-IV). In addition, the OS of low-TMB and low-risk group was longer than that of high-TMB and high-risk group. The TIDE score indicated that HCC patients in the high-risk group were more susceptible to immunotherapy.ConclusionOur study suggests that m6A-associated snRNAs may be useful biomarkers for the prognosis of HCC and that m6A-associated snRNA models can predict the effect of immunotherapy in HCC patients.

Background: Mental disorders (MDs) are behavioral or mental patterns that cause significant distress or impairment of personal functioning. Previously, temperature has been linked to MDs, but most studies suffered from exposure misclassification due to limited monitoring sites. We aimed to assess whether multiple meteorological factors could jointly trigger MD-related emergency department (ED) visits in warm season, using a highly dense weather monitoring system. Methods: We conducted a time-stratified, case-crossover study. MDs-related ED visits (primary diagnosis) from May-October 2017–2018 were obtained from New York State (NYS) discharge database. We obtained solar radiation (SR), relative humidity (RH), temperature, heat index (HI), and rainfall from Mesonet, a real-time monitoring system spaced about 17 miles (126 stations) across NYS. We used conditional logistic regression to assess the weather-MD associations. Results: For each interquartile range (IQR) increase, both SR (excess risk (ER): 4.9%, 95% CI: 3.2–6.7%) and RH (ER: 4.0%, 95% CI: 2.6–5.4%) showed the largest risk for MD-related ED visits at lag 0–9 days. While temperature presented a short-term risk (highest ER at lag 0–2 days: 3.7%, 95% CI: 2.5–4.9%), HI increased risk over a two-week period (ER range: 3.7–4.5%), and rainfall hours showed an inverse association with MDs (ER: −0.5%, 95% CI: 0.9-(-0.1)%). Additionally, we observed stronger association of SR, RH, temperature, and HI in September and October. Combination of high SR, RH, and temperature displayed the largest increase in MDs (ER: 7.49%, 95% CI: 3.95–11.15%). The weather-MD association was stronger for psychoactive substance usage, mood disorders, adult behavior disorders, males, Hispanics, African Americans, individuals aged 46–65, or Medicare patients. Conclusions: Hot and humid weather, especially the joint effect of high sun radiation, temperature and relative humidity showed the highest risk of MD diseases. We found stronger weather-MD associations in summer transitional months, males, and minority groups. These findings also need further confirmation.

ObjectiveCongenital heart disease (CHD) is complex in its etiology. Its genetic causes have been investigated, whereas the non-genetic factor related studies are still limited. We aimed to identify dominant parental predictors and develop a predictive model and nomogram for the risk of offspring CHD.MethodsThis was a retrospective study from November 2017 to December 2021 covering 44,578 participants, of which those from 4 hospitals in eastern China were assigned to the development cohort and those from 5 hospitals in central and western China were used as the external validation cohort. Univariable and multivariable analyses were used to select the dominant predictors of CHD among demographic characteristics, lifestyle behaviors, environmental pollution, maternal disease history, and the current pregnancy information. Multivariable logistic regression analysis was used to construct the model and nomogram using the selected predictors. The predictive model and the nomogram were both validated internally and externally. A web-based nomogram was developed to predict patient-specific probability for CHD.ResultsDominant risk factors for offspring CHD included increased maternal age [odds ratio (OR): 1.14, 95% CI: 1.10–1.19], increased paternal age (1.05, 95% CI: 1.02–1.09), maternal secondhand smoke exposure (2.89, 95% CI: 2.22–3.76), paternal drinking (1.41, 95% CI: 1.08–1.84), maternal pre-pregnancy diabetes (3.39, 95% CI: 1.95–5.87), maternal fever (3.35, 95% CI: 2.49–4.50), assisted reproductive technology (2.89, 95% CI: 2.13–3.94), and environmental pollution (1.61, 95% CI: 1.18–2.20). A higher household annual income (100,000–400,000 CNY: 0.47, 95% CI: 0.34–0.63; > 400,000 CNY: 0.23, 95% CI: 0.15–0.36), higher maternal education level (13–16 years: 0.68, 95% CI: 0.50–0.93; ≥ 17 years: 0.87, 95% CI: 0.55–1.37), maternal folic acid (0.21, 95% CI: 0.16–0.27), and multivitamin supplementation (0.33, 95% CI: 0.26–0.42) were protective factors. The nomogram showed good discrimination in both internal [area under the receiver-operating-characteristic curve (AUC): 0.843] and external validations (development cohort AUC: 0.849, external validation cohort AUC: 0.837). The calibration curves showed good agreement between the nomogram-predicted probability and actual presence of CHD.ConclusionWe revealed dominant parental predictors and presented a web-based nomogram for the risk of offspring CHD, which could be utilized as an effective tool for quantifying the individual risk of CHD and promptly identifying high-risk population.

BackgroundThe association between the incidence of hand, foot, and mouth disease (HFMD) and ambient temperature has been well documented. Although the severity of symptoms is an important indicator of disease burden and varies significantly across cases, it usually was ignored in previous studies, potentially leading to biased estimates of the health impact of temperature.MethodsWe estimated the disability-adjusted life year (DALY) by considering the severity of symptoms for each HFMD case reported during 2010-2012 in Guangdong and used distributed lag-nonlinear models to estimate the association between the daily average temperature and daily DALY of HFMD cases at the city-level. We investigated the potential effect modifiers on the pathway between temperature and DALY and pooled city-specific estimates to a provincial association using a meta-regression. The overall impact of temperature was further evaluated by estimates of DALYs that could be attributed to HFMD.ResultsThe overall cumulative effect of daily mean temperature on the DALY of HFMD showed an inverse-U shape, with the maximum effect estimated to be β = 0.0331 (95%CI: 0.0199-0.0463) DALY at 23.8°C. Overall, a total of 6.432 (95%CI: 3.942-8.885) DALYs (attributable fraction = 2.721%, 95%CI: 1.660-3.759%) could be attributed to temperature exposure. All the demographic subgroups had a similar trend as the main analysis, while the magnitude of the peak of the temperature impact tended to be higher among the males, those aged ≥3yrs or from the Pear-River Delta region. Additionally, the impact of temperature on DALY elevated significantly with the increasing population density, per capita GDP, and per capita green space in parks.ConclusionsTemperature exposure was associated with increased burden of HFMD nonlinearly, with certain groups such as boys and those from areas with greater population density being more vulnerable.

Abstract Background Colonoscopy is an important procedure for early colorectal cancer (CRC) detection, however, patients with positive preliminary screening results in China may not seek for colonoscopy to confirm the diagnosis. We evaluated the spatial accessibility of colonoscopy among the residents with positive preliminary screening results in Guangzhou, China, and investigated how colonoscopy accessibility was associated with the population adherence and adverse intestinal outcomes. Methods This study was based on the Guangzhou community‐based CRC screening program. Spatial accessibility was measured using three metrics including travel time from home to nearest colonoscopy hospital, physician‐to‐population ratio (PPR) and accessibility indicator estimated with enhanced two‐step floating catchment area method (E2SFCA). We used Cox regression and logistic regression to assess the association of colonoscopy accessibility with population adherence and adverse intestinal outcomes, respectively. Results A total of 34 606 people were identified with positive preliminary screening findings. Central areas were reported with higher E2SFCA scores, higher PPR and less travel time. The model adjusting for potential individual level confounders found that PPR > 50 (Hazard Ratio (HR) = 1.88, 95% Confidence Interval (CI): 1.79‐1.97) and higher scores of E2SFCA (HR = 3.78, 95% CI: 2.07‐6.92) were associated with increased adherence, although estimates were not significant in the model adjusting for both individual and district‐level confounders. For adverse intestinal outcomes, the final multilevel logistic model suggested a lower risk of intestinal lesions among the residents in areas with PPR > 50 (Odds Ratio (OR) = 0.49, 95% CI: 0.24‐0.99) and higher scores of E2SFCA (OR = 0.20, 95% CI: 0.05‐0.82). Conclusion Significant inequality of colonoscopy accessibility was observed across Guangzhou. The increased incidence of intestinal lesions was associated with spatial inequalities of medical resources. Policies against the spatial inequality in medical resources should be developed.

Evidence on the spatial-temporal distribution of tuberculosis (TB) mortality across China and its relationship with long-term particulate matter (PM _2.5 ) exposure is limited. We aimed to address significant gaps in our understanding of the spatial-temporal clustering patterns of TB mortality in China and provide evidence for its causal links with long-term PM _2.5 exposure. Annual pulmonary TB mortality, PM _2.5 concentrations, and socioeconomic factors for provinces in mainland China between 2004 and 2017 were obtained. Turning points in the temporal trend and spatial clustering patterns of pulmonary TB mortality were identified. A difference-in-differences causal inference approach was applied to estimate the long-term effect of PM _2.5 exposure on the mortality. The average annual percent change of pulmonary TB mortality in China was −2.5% (95% CI : −5.6%, 0.7%), with an 11.1% annual increase in the Northwest since 2012 ( P = 0.029). The hot and cold spots, determined by the local Moran’s I index, were all located in northern China, where Xinjiang in the Northwest had the highest mortality across the study period. We found a significant association between long-term PM _2.5 exposure and pulmonary TB mortality, with percent increase risk of mortality ( IR %) being 0.74% (95 CI %, 0.04%, 1.45%) for 1 µ g/m ^3 increase in PM _2.5 concentration. This association varied across multiple socioeconomic groups, with the highest IR % in provinces with lower level of latitude ( IR % = 0.83%, 95% CI : 0.01%, 1.65%), lower quartile of gross domestic product ( IR % = 1.01%, 95% CI: 0.23%, 1.80%) or higher proportion (⩾14%) of people >65 years of age ( IR % = 1.24%, 95% CI: 0.44%, 2.04%). Comprehensive sensitivity analyses showed a robust adverse effect of long-term PM _2.5 exposure on pulmonary TB mortality. Attention needs to be paid to the rising trend of pulmonary TB mortality in Northwest China. Our study provides the stable evidence to date of the causal association between long-term PM _2.5 exposure and the risk of death from pulmonary TB, especially in low-altitude, underdeveloped, and aged provinces.

Abstract Background Hand, foot, and mouth disease (HFMD) raises an urgent public health issue in the Asia-Pacific region, especially in China. The associations between weather factors and HFMD have been widely studied but with inconsistent results. Moreover, previous studies utilizing ecological design could not rule out the bias of exposure misclassification and unobserved confounders. Methods We used case-crossover analysis to assess the associations of weather factors on HFMD. Individual HFMD cases from 2009 to 2012 in Guangdong were collected and cases located within 10 km of the meteorological monitoring sites were included. Lag effects were examined through the previous 7 days. In addition, we explored the variability by changing the distance within 20 km and 30 km. Results We observed associations between HFMD and weather factors, including temperature and relative humidity. An approximately U-shaped relationship was observed for the associations of temperature on HFMD across the same day and the previous 7 days, while an approximately exponential-shaped was seen for relative humidity. Statistically significant increases in rates of HFMD were associated with each 10-unit increases in temperature [Excess rate (ER): 7.7%; 95% Confidence Interval (CI): 3.9, 11.7%] and relative humidity (ER: 1.9%; 95% CI: 0.7, 3.0%) on lag days 0–6, when assessing within 10 km of the monitoring sites. Potential thresholds for temperature (30.0 °C) and relative humidity (70.3%) detected showed associations with HFMD. The associations remained robust for 20 km and 30 km. Conclusions Our study found that temperature and relative humidity are significantly associated with the increased rates of HFMD. Thresholds and lag effects were observed between weather factors and HFMD. Our findings are useful for planning on targeted prevention and control of HFMD.

Background: Previous work reported increased rates of acute cardiovascular hospitalizations associated with increased PM2.5 concentrations in the previous few days across urban centers in New York State from 2005 to 2016. These relative rates were higher after air quality policies and economic changes resulted in decreased PM2.5 concentrations and changes in PM composition (e.g. increased secondary organic carbon), compared to before and during these changes. Changes in PM composition and sources may explain this difference. Objectives: To estimate the rate of acute cardiovascular hospitalizations associated with increases in source specific PM2.5 concentrations. Methods: Using source apportioned PM2.5 concentrations at the same NYS urban sites, a time-stratified case-crossover design, and conditional logistic regression models adjusting for ambient temperature and relative humidity, we estimated the rate of these acute cardiovascular hospitalizations associated with increases in mean source specific PM2.5 concentrations in the previous 1, 4, and 7 days. Results: Interquartile range (IQR) increases in spark-ignition emissions (GAS) concentrations were associated with increased excess rates of cardiac arrhythmia hospitalizations (2.3%; 95% CI = 0.4%, 4.2%; IQR = 2.56 μg/m3) and ischemic stroke hospitalizations (3.7%; 95% CI = 1.1%, 6.4%; 2. 73 μg/m3) over the next day. IQR increases in diesel (DIE) concentrations were associated with increased rates of congestive heart failure hospitalizations (0.7%; 95% CI = 0.2% 1.3%; 0.51 μg/m3) and ischemic heart disease hospitalizations (0.8%; 95% CI = 0.3%, 1.3%; 0.60 μg/m3) over the next day, as hypothesized. However, secondary sulfate PM2.5 (SS) was not. Increased acute cardiovascular hospitalization rates were also associated with IQR increases in concentrations of road dust (RD), residual oil (RO), and secondary nitrate (SN) over the previous 1, 4, and 7 days, but not other sources. Conclusions: These findings suggest a role of several sources of PM2.5 in New York State (i.e. traffic emissions, non-traffic emissions such as brake and tire wear, residual oil, and nitrate that may also reflect traffic emissions) in the triggering of acute cardiovascular events. Keywords: Cardiovascular hospitalizations, Air pollution, Source apportionment, Traffic emissions

Background: While previous studies uncovered individual vulnerabilities to health risks during catastrophic storms, few evaluated the population vulnerability which is more important for identifying areas in greatest need of intervention. Objectives: We assessed the association between community factors and multiple health outcomes, and developed a community vulnerability index. Methods: We retained emergency department visits for several health conditions from the 2005–2014 New York Statewide Planning and Research Cooperative System. We developed distributed lag nonlinear models at each spatial cluster across eight counties in downstate New York to evaluate the health risk associated with Superstorm Sandy (10/28/2012–11/9/2012) compared to the same period in other years, then defined census tracts in clusters with an elevated risk as “risk-elevated communities”, and all others as “unelevated”. We used machine-learning techniques to regress the risk elevation status against community factors to determine the contribution of each factor on population vulnerability, and developed a community vulnerability index (CVI). Results: Overall, community factors had positive contributions to increased community vulnerabilities to Sandy-related substance abuse (91.35%), injuries (70.51%), cardiovascular diseases (8.01%), and mental disorders (2.71%) but reversely contributed to respiratory diseases (−34.73%). The contribution of low per capita income (max: 22.08%), the percentage of residents living in group quarters (max: 31.39%), the percentage of areas prone to flooding (max: 38.45%), and the percentage of green coverage (max: 29.73%) tended to be larger than other factors. The CVI based on these factors achieved an accuracy of 0.73–0.90 across outcomes. Conclusions: Our findings suggested that substance abuse was the most sensitive disease susceptible to less optimal community indicators, whereas respiratory diseases were higher in communities with better social environment. The percentage of residents in group quarters and areas prone to flooding were among dominant predictors for community vulnerabilities. The CVI based on these factors has an appropriate predictive performance. Keywords: Catastrophic storms, Multiple health outcomes, Community vulnerability index, ROC

The relationship between meteorological factors and the risk of hand, foot, and mouth disease (HFMD) has been well documented. However, researchers have failed to consider the complex interactive relationships among meteorological factors. The weekly number of HFMD cases along with meteorological factors were collected between 2009 to 2017 in four cities in Guangdong Province. We used Bayesian kernel machine regression to investigate the nonlinear and interactive relationship between meteorological factors, such as temperature and humidity, on the risk of HFMD. Multivariate meta-analysis was used to pool the city-specific effect estimates and identify factors underlying the inter-city heterogeneity. The risk ratios (RRs) for each percentile increase in temperature from the 50th percentile value, while humidity was at its 10th, 50th, and 90th percentile values, were 1.621(95%CI: 1.226, 2.141), 2.638(2.169, 3.208), and 3.734(2.908, 4.792), respectively ( Q = 19.132, P _interaction < 0.001). In contrast, the RRs for each percentile increase in humidity from its 50th percentile, while holding temperature at its 10th, 50th, and 90th percentile values, were 0.901(95%CI: 0.592, 1.369), 2.026(1.679, 2.448), and 0.884(0.632, 1.238), respectively ( Q = 24.876, P _interaction < 0.001). Increased wind speed and sunshine duration were also observed to strengthen the impact of other meteorological factors. Furthermore, we found increased gross domestic product per capita and per capital area of parks and green land in city tended to significantly strengthen the interactive effects of humidity on other meteorological factors including sunshine duration ( P = 0.013 and 0.042), rainfall ( P = 0.017 and 0.035), temperature ( P = 0.021 and 0.031), win speed ( P = 0.011 and 0.045), and pressure ( P = 0.013 and 0.042). Our study contributed further understanding of complex interactions between meteorological factors on the risk of HFMD. Our findings provide epidemiological evidence for meteorological interactions on HFMD, which may provide knowledge for future research on the health effects of meteorological factors.

Meteorological exposures are well-documented factors underlying the dengue pandemics, and air pollution was reported to have the potential to change the behaviors and health conditions of mosquitos. However, it remains unclear whether air pollution could modify the association of meteorological exposures and the incidence of dengue fever. We matched the dengue surveillance data with the meteorological and air pollution data collected from monitoring sites from 2015 through 2019 in Guangzhou area. We developed generalized additive models with Poisson distribution to regress the daily counts of dengue against four meteorological exposures, while controlling for pollution and normalized difference vegetation index to evaluate the risk ratio (RR) of dengue for each unit increase in different exposures. The interaction terms of meteorological exposures and air pollution were then included to assess the modification effect of different pollution on the associations. Daily dengue cases were nonlinearly associated with one-week cumulative temperature and precipitation, while not associated with humidity and wind speed. RRs were 1.07 (1.04, 1.11) and 0.95 (0.88, 1.03) for temperature below and above 27.1 °C, 0.97 (0.96, 0.98) and 1.05 (1.01, 1.08) for precipitation below and above 20.3 mm, respectively. For the modification effect, the RRs of low-temperature, wind speed on higher SO _2 days and low-precipitation on both higher PM _2.5 and SO _2 days were greater compared to the low-pollution days with P _interaction being 0.037, 0.030, 0.022 and 0.018. But the RRs of both high-temperature on higher SO _2 days and high-precipitation on higher PM _2.5 d were smaller with P _interaction being 0.001 and 0.043. Air pollution could alter the meteorology-dengue associations. The impact of low-temperature, low-precipitation and wind speed on dengue occurrence tended to increase on days with high SO _2 levels while the impact of high-temperature decreased. The impact of low-precipitation increased on high-PM _2.5 d while the impact of high-precipitation decreased.

Background More intense and longer‐lasting heat events are expected in the United States as a consequence of climate change. This study aimed to project the potential changes in maternal heat exposure during early pregnancy (3–8 weeks post conception) and the associated burden of congenital heart defects (CHDs) in the future. Methods and Results This study expanded on a prior nationwide case‐control study that evaluated the association between CHDs and maternal heat exposure during early pregnancy in summer and spring. We defined multiple indicators of heat exposure, and applied published odds ratios obtained for the matching season of the baseline (1995–2005) into the projection period (2025–2035) to estimate potential changes in CHD burden throughout the United States. Increases in maternal heat exposure were projected across the United States and to be larger in the summer. The Midwest will potentially have the highest increase in summer maternal exposure to excessively hot days (3.42; 95% CI, 2.99–3.88 per pregnancy), heat event frequency (0.52; 95% CI, 0.44–0.60) and heat event duration (1.73; 95% CI, 1.49–1.97). We also found large increases in specific CHD subtypes during spring, including a 34.0% (95% CI, 4.9%–70.8%) increase in conotruncal CHD in the South and a 38.6% (95% CI, 9.9%–75.1%) increase in atrial septal defect in the Northeast. Conclusions Projected increases in maternal heat exposure could result in an increased CHD burden in certain seasons and regions of the United States.

Hand, foot, and mouth disease (HFMD) has caused a substantial burden of disease in China, especially in Guangdong Province. Based on notifiable cases, we use the time series Susceptible-Infected-Recovered model to estimate the basic reproduction rate (R0) and the herd immunity threshold, understanding the transmission and persistence of HFMD more completely for efficient intervention in this province. The standardized difference between the reported and fitted time series of HFMD was 0.009 (