The relationship between hot temperatures and hospital admissions for psychosis in adults diagnosed with schizophrenia: A case-crossover study in Quebec, Canada.

INTRODUCTION: Some studies have found hot temperatures to be associated with exacerbations of schizophrenia, namely psychoses. As climate changes faster in Northern countries, our understanding of the association between temperature and hospital admissions (HA) for psychosis needs to be deepened. OBJECTIVES: 1) Among adults diagnosed with schizophrenia, measure the relationship between mean temperatures and HAs for psychosis during summer. 2) Determine the influence of individual and ecological characteristics on this relationship. METHODS: A cohort of adults diagnosed with schizophrenia (n = 30,649) was assembled using Quebec's Integrated Chronic Disease Surveillance System (QICDSS). The follow-up spanned summers from 2001 to 2019, using hospital data from the QICDSS and meteorological data from the National Aeronautics and Space Administration's (NASA) Daymet database. In four geographic regions of the province of Quebec, a conditional logistic regression was used for the case-crossover analysis of the relationship between mean temperatures (at lags up to 6 days) and HAs for psychosis using a distributed lag non-linear model (DLNM). The analyses were adjusted for relative humidity, stratified according to individual (age, sex, and comorbidities) and ecological (material and social deprivation index and exposure to green space) factors, and then pooled through a meta-regression. RESULTS: The statistical analyses revealed a statistically significant increase in HAs three days (lag 3) after elevated mean temperatures corresponding to the 90th percentile relative to a minimum morbidity temperature (MMT) (OR 1.040; 95% CI 1.008-1.074), while the cumulative effect over six days was not statistically significant (OR 1.052; 95% IC 0.993-1.114). Stratified analyses revealed non statistically significant gradients of increasing HAs relative to increasing material deprivation and decreasing green space levels. CONCLUSIONS: The statistical analyses conducted in this project showed the pattern of admissions for psychosis after hot days. This finding could be useful to better plan health services in a rapidly changing climate.

The effects of meteorological factors and air pollutants on the incidence of tuberculosis in people living with HIV/AIDS in subtropical Guangxi, China.

BACKGROUND: Previous studies have shown the association between tuberculosis (TB) and meteorological factors/air pollutants. However, little information is available for people living with HIV/AIDS (PLWHA), who are highly susceptible to TB. METHOD: Data regarding TB cases in PLWHA from 2014 to2020 were collected from the HIV antiviral therapy cohort in Guangxi, China. Meteorological and air pollutants data for the same period were obtained from the China Meteorological Science Data Sharing Service Network and Department of Ecology and Environment of Guangxi. A distribution lag non-linear model (DLNM) was used to evaluate the effects of meteorological factors and air pollutant exposure on the risk of TB in PLWHA. RESULTS: A total of 2087 new or re-active TB cases were collected, which had a significant seasonal and periodic distribution. Compared with the median values, the maximum cumulative relative risk (RR) for TB in PLWHA was 0.663 (95% confidence interval [CI]: 0.507-0.866, lag 4 weeks) for a 5-unit increase in temperature, and 1.478 (95% CI: 1.116-1.957, lag 4 weeks) for a 2-unit increase in precipitation. However, neither wind speed nor PM10 had a significant cumulative lag effect. Extreme analysis demonstrated that the hot effect (RR = 0.638, 95%CI: 0.425-0.958, lag 4 weeks), the rainy effect (RR = 0.285, 95%CI: 0.135-0.599, lag 4 weeks), and the rainless effect (RR = 0.552, 95%CI: 0.322-0.947, lag 4 weeks) reduced the risk of TB. Furthermore, in the CD4(+) T cells < 200 cells/µL subgroup, temperature, precipitation, and PM10 had a significant hysteretic effect on TB incidence, while temperature and precipitation had a significant cumulative lag effect. However, these effects were not observed in the CD4(+) T cells ≥ 200 cells/µL subgroup. CONCLUSION: For PLWHA in subtropical Guangxi, temperature and precipitation had a significant cumulative effect on TB incidence among PLWHA, while air pollutants had little effect. Moreover, the influence of meteorological factors on the incidence of TB also depends on the immune status of PLWHA.

Modification effects of long-term air pollution levels on the relationship between short-term exposure to meteorological factors and hand, foot, and mouth disease: A distributed lag non-linear model-based study in Shandong Province, China.

The occurrence of hand, foot, and mouth disease (HFMD) is closely related to meteorological factors. However, location-specific characteristics, such as persistent air pollution, may increase the complexity of the impact of meteorological factors on HFMD, and studies across different areas and populations are largely lacking. In this study, a two-stage multisite time-series analysis was conducted using data from 16 cities in Shandong Province from 2015 to 2019. In the first stage, we obtained the cumulative exposure-response curves of meteorological factors and the number of HFMD cases for each city. In the second stage, we merged the estimations from the first stage and included city-specific air pollution variables to identify significant effect modifiers and how they modified the short-term relationship between HFMD and meteorological factors. High concentrations of air pollutants may reduce the risk effects of high average temperature on HFMD and lead to a distinct peak in the cumulative exposure-response curve, while lower concentrations may increase the risk effects of high relative humidity. Furthermore, the effects of average wind speed on HFMD were different at different levels of air pollution. The differences in modification effects between subgroups were mainly manifested in the diversity and quantity of significant modifiers. The modification effects of long-term air pollution levels on the relationship between sunshine hours and HFMD may vary significantly depending on geographical location. The people in age＜3 and male groups were more susceptible to long-term air pollution. These findings contribute to a deepening understanding of the relationship between meteorological factors and HFMD and provide evidence for relevant public health decision-making.

Study on the Influence of Label Image Accuracy on the Performance of Concrete Crack Segmentation Network Models.

A high-quality dataset is a basic requirement to ensure the training quality and prediction accuracy of a deep learning network model (DLNM). To explore the influence of label image accuracy on the performance of a concrete crack segmentation network model in a semantic segmentation dataset, this study uses three labelling strategies, namely pixel-level fine labelling, outer contour widening labelling and topological structure widening labelling, respectively, to generate crack label images and construct three sets of crack semantic segmentation datasets with different accuracy. Four semantic segmentation network models (SSNMs), U-Net, High-Resolution Net (HRNet)V2, Pyramid Scene Parsing Network (PSPNet) and DeepLabV3+, were used for learning and training. The results show that the datasets constructed from the crack label images with pix-el-level fine labelling are more conducive to improving the accuracy of the network model for crack image segmentation. The U-Net had the best performance among the four SSNMs. The Mean Intersection over Union (MIoU), Mean Pixel Accuracy (MPA) and Accuracy reached 85.47%, 90.86% and 98.66%, respectively. The average difference between the quantized width of the crack image segmentation obtained by U-Net and the real crack width was 0.734 pixels, the maximum difference was 1.997 pixels, and the minimum difference was 0.141 pixels. Therefore, to improve the segmentation accuracy of crack images, the pixel-level fine labelling strategy and U-Net are the best choices.

Short-term association between air pollution and daily genitourinary disorder admissions in Lanzhou, China.

The aim of this study was to determine the relationship between short-term exposure to ambient air pollution and the number of daily hospital admissions for genitourinary disorders in Lanzhou. Hospital admission data and air pollutants, including PM2.5, PM10, SO2, NO2, O38h and CO, were obtained from the period 2013 to 2020. A generalized additive model (GAM) combined with distribution lag nonlinear model (DLNM) based on quasi-Poisson distribution was used by the controlling for trends, weather, weekdays and holidays. Short-term exposure to PM2.5, NO2 and CO increased the risk of genitourinary disorder admissions with RR of 1.0096 (95% CI 1.0002-1.0190), 1.0255 (95% CI 1.0123-1.0389) and 1.0686 (95% CI 1.0083-1.1326), respectively. PM10, O38h and SO2 have no significant effect on genitourinary disorders. PM2.5 and NO2 are more strongly correlated in female and ≥ 65 years patients. CO is more strongly correlated in male and < 65 years patients. PM2.5, NO2 and CO are risk factors for genitourinary morbidity, and public health interventions should be strengthened to protect vulnerable populations.

Detecting the spatial clustering of exposure-response relationships with estimation error: a novel spatial scan statistic.

Detecting the spatial clustering of the exposure-response relationship (ERR) between environmental risk factors and health-related outcomes plays important roles in disease control and prevention, such as identifying highly sensitive regions, exploring the causes of heterogeneous ERRs, and designing region-specific health intervention measures. However, few studies have focused on this issue. A possible reason is that the commonly used cluster-detecting tool, spatial scan statistics, cannot be used for multivariate spatial datasets with estimation error, such as the ERR, which is often defined by a vector with its covariance estimated by a regression model. Such spatial datasets have been produced in abundance in the last decade, which suggests the importance of developing a novel cluster-detecting tool applicable for multivariate datasets with estimation error. In this work, by extending the classic scan statistic, we developed a novel spatial scan statistic called the estimation-error-based scan statistic (EESS), which is applicable for both univariate and multivariate datasets with estimation error. Then, a two-stage analytic process was proposed to detect the spatial clustering of ERRs in practical studies. A published motivating example and a simulation study were used to validate the performance of EESS. The results show that the clusters detected by EESS can efficiently reflect the clustering heterogeneity and yield more accurate ERR estimates by adjusting for such heterogeneity.

Study of the influence of meteorological factors on HFMD and prediction based on the LSTM algorithm in Fuzhou, China.

BACKGROUND: This study adopted complete meteorological indicators, including eight items, to explore their impact on hand, foot, and mouth disease (HFMD) in Fuzhou, and predict the incidence of HFMD through the long short-term memory (LSTM) neural network algorithm of artificial intelligence. METHOD: A distributed lag nonlinear model (DLNM) was used to analyse the influence of meteorological factors on HFMD in Fuzhou from 2010 to 2021. Then, the numbers of HFMD cases in 2019, 2020 and 2021 were predicted using the LSTM model through multifactor single-step and multistep rolling methods. The root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and symmetric mean absolute percentage error (SMAPE) were used to evaluate the accuracy of the model predictions. RESULTS: Overall, the effect of daily precipitation on HFMD was not significant. Low (4 hPa) and high (≥ 21 hPa) daily air pressure difference (PRSD) and low (< 7 °C) and high (> 12 °C) daily air temperature difference (TEMD) were risk factors for HFMD. The RMSE, MAE, MAPE and SMAPE of using the weekly multifactor data to predict the cases of HFMD on the following day, from 2019 to 2021, were lower than those of using the daily multifactor data to predict the cases of HFMD on the following day. In particular, the RMSE, MAE, MAPE and SMAPE of using weekly multifactor data to predict the following week's daily average cases of HFMD were much lower, and similar results were also found in urban and rural areas, which indicating that this approach was more accurate. CONCLUSION: This study's LSTM models combined with meteorological factors (excluding PRE) can be used to accurately predict HFMD in Fuzhou, especially the method of predicting the daily average cases of HFMD in the following week using weekly multifactor data.

Prenatal and childhood exposure to ambient air pollution and cognitive function in school-age children: Examining sensitive windows and sex-specific associations.

BACKGROUND: Combined effect of both prenatal and early postnatal exposure to ambient air pollution on child cognition has rarely been investigated and periods of sensitivity are unknown. This study explores the temporal relationship between pre- and postnatal exposure to PM10, PM2.5, NO2 and child cognitive function. METHODS: Using validated spatiotemporally resolved exposure models, pre- and postnatal daily PM2.5, PM10 (satellite based, 1 km resolution) and NO2 (chemistry-transport model, 4 km resolution) concentrations at the mother's residence were estimated for 1271 mother-child pairs from the French EDEN and PELAGIE cohorts. Scores representative of children's General, Verbal and Non-Verbal abilities at 5-6 years were constructed based on subscale scores from the WPPSI-III, WISC-IV or NEPSY-II batteries, using confirmatory factor analysis (CFA). Associations of both prenatal (first 35 gestational weeks) and postnatal (60 months after birth) exposure to air pollutants with child cognition were explored using Distributed Lag Non-linear Models adjusted for confounders. RESULTS: Increased maternal exposure to PM10, PM2.5 and NO2, during sensitive windows comprised between the 15th and the 33rd gestational weeks, was associated with lower males' General and Non-verbal abilities. Higher postnatal exposure to PM2.5 between the 35th and 52nd month of life was associated with lower males' General, Verbal and Non-verbal abilities. Some protective associations were punctually observed for the very first gestational weeks or months of life for both males and females and the different pollutants and cognitive scores. DISCUSSION: These results suggest poorer cognitive function at 5-6 years among males following increased maternal exposure to PM10, PM2.5 and NO2 during mid-pregnancy and child exposure to PM2.5 around 3-4 years. Apparent protective associations observed are unlikely to be causal and might be due to live birth selection bias, chance finding or residual confounding.

The association between air pollutants, meteorological factors and tuberculosis cases in Beijing, China: A seven-year time series study.

BACKGROUND: Tuberculosis (TB) is a severe public health problem globally. Previous studies have revealed insufficient and inconsistent associations between air pollutants, meteorological factors and TB cases. Yet few studies have examined the associations between air pollutants, meteorological factors and TB cases in Beijing. OBJECTIVE: The purpose of this study was to explore the impact of air pollutants and meteorological factors on TB in Beijing, and to provide novel insights into public health managers to formulate control strategies of TB. METHODS: Data on the daily case of TB in Beijing during 2014-2020 were obtained from Chinese tuberculosis information management system. Concurrent data on the daily PM10, PM2.5, SO2, NO2, CO and O3, were obtained from the online publication platform of the Chinese National Environmental Monitoring Center. Daily average temperature, average wind speed, relative humidity, sunshine duration and total precipitation were collected from the China Meteorological Science Data Sharing Service System. A distributed lag non-linear model was fitted to identify the non-linear exposure-response relationship and the lag effects between air pollutions, meteorological factors and TB cases in Beijing. RESULTS: In the single-factor model, the excess risk (ER) of TB was significantly positively associated with every 10 µg/m3 increase in NO2 in lag 1 week (ER: 1.3%; 95% confidence interval [CI]: 0.4%, 2.3%) and every 0.1 m/s increase in average wind speed in lag 5 weeks (ER: 0.3%; 95% CI: 0.1%, 0.5%), and was negatively associated with every 10 µg/m3 increase in O3 in lag 1 week (ER: -1.2%; 95% CI: -1.8%, -0.5%), every 5 °C increase in average temperature (ER: -1.7%; 95% CI: -2.9%, -0.4%) and every 10% increase in average relative humidity (ER: -0.4%; 95% CI: -0.8%, -0.1%) in lag 10 weeks, respectively. In the multi-factor model, the lag effects between TB cases and air pollutants, meteorological factors were similar. The subgroup analysis suggests that the effects of NO2, O3, average wind speed and relative humidity on TB were greater in male or labor age subgroup, while the effect of CO was greater in the elderly. In addition, no significant associations were found between PM2.5, SO2, sunshine duration and TB cases. CONCLUSION: Our findings provide a better understanding of air pollutants and meteorological factors driving tuberculosis occurrence in Beijing, which enhances the capacity of public health manager to target early warning and disease control policy-making.

The association of air pollutants with hospital outpatient visits for child and adolescence psychiatry in Shenzhen, China.

BACKGROUND: Although exposure to ambient air pollution has been associated with mental disorder, little is known about its potential effects on children and adolescents, especially in Chinese population. We aimed to reveal the relationship of air pollutants with hospital outpatient visits for child and adolescence psychiatry (HOVCAP) in Shenzhen. METHODS: A case-crossover study based on time-series data was applied, and a distributed lag non-linear model (DLNM) was used to evaluate the non-linear and delayed effects of 4 major air pollutants (NO2, PM2.5, SO2 and O3) on HOVCAP. Least absolute shrinkage and selection operator (LASSO) regression was used to control the multicollinearity between covariates and to filter variables. RESULT: A total of 94,660 cases aged 3-18 were collected from 2014 to 2019 in the Mental Health Center of Shenzhen. Results of pollutants at mode value (M0) showed that in the single lag effect result, when the average daily concentration of NO2 at 24 µg/m3, there was a significant effect on HOVCAP over lag 1, lag 4 and lag 5, respectively. The cumulative RR of NO2 M0 value to the outpatient visits were 1.438 (1.137-1.818) over lag 0-2, 1.454 (1.120-1.887) over lag 0-3, 1.466 (1.084-1.982) over lag 0-4, 1.680 (1.199-2.354) over lag 0-5, 1.993 (1.369-2.903) over lag 0-6, and 2.069 (1.372-3.119) over lag 0-7. However, PM2.5, SO2, O3 were not associated with HOVCAP over neither single lag effects nor cumulative effects. The RR values both shown an increase either when NO2 increases by 10 units or when the maximum concentration of NO2 is reached. CONCLUSION: Our study suggests that exposure to the normal air quality of NO2 in Shenzhen may associated with the risk of HOVCAP. However, PM2.5, SO2, O3 were not associated with HOVCAP.

Intra-urban risk assessment of occupational injuries and illnesses associated with current and projected climate: Evidence from three largest Australian cities.

BACKGROUND: Increased risk of occupational injuries and illnesses (OI) is associated with ambient temperature. However, most studies have reported the average impacts within cities, states, or provinces at broader scales. METHODS: We assessed the intra-urban risk of OI associated with ambient temperature in three Australian cities at statistical area level 3 (SA3). We collected daily workers' compensation claims data and gridded meteorological data from July 1, 2005, to June 30, 2018. Heat index was used as the primary temperature metric. We performed a two-stage time series analysis: we generated location-specific estimates using Distributed Lag Non-Linear Models (DLNM) and estimated the cumulative effects with multivariate meta-analysis. The risk was estimated at moderate heat (90th percentile) and extreme heat (99th percentile). Subgroup analyses were conducted to identify vulnerable groups of workers. Further, the OI risk in the future was estimated for two projected periods: 2016-2045 and 2036-2065. RESULTS: The cumulative risk of OI was 3.4% in Greater Brisbane, 9.5% in Greater Melbourne, and 8.9% in Greater Sydney at extreme heat. The western inland regions in Greater Brisbane (17.4%) and Greater Sydney (32.3%) had higher risk of OI for younger workers, workers in outdoor and indoor industries, and workers reporting injury claims. The urbanized SA3 regions posed a higher risk (19.3%) for workers in Greater Melbourne. The regions were generally at high risk for young workers and illness-related claims. The projected risk of OI increased with time in climate change scenarios. CONCLUSIONS: This study provides a comprehensive spatial profile of OI risk associated with hot weather conditions across three cities in Australia. Risk assessment at the intra-urban level revealed strong spatial patterns in OI risk distribution due to heat exposure. These findings provide much-needed scientific evidence for work, health, and safety regulators, industries, unions, and workers to design and implement location-specific preventative measures.

Gender inequalities in heat-related mortality in the Czech Republic.

It is acknowledged that climate change exacerbates social inequalities, and women have been reported as more vulnerable to heat than men in many studies in Europe, including the Czech Republic. This study aimed at investigating the associations between daily temperature and mortality in the Czech Republic in the light of a sex and gender perspective, taking into account other factors such as age and marital status. Daily mean temperature and individual mortality data recorded during the five warmest months of the year (from May to September) over the period 1995-2019 were used to fit a quasi-Poisson regression model, which included a distributed lag non-linear model (DLNM) to account for the delayed and non-linear effects of temperature on mortality. The heat-related mortality risks obtained in each population group were expressed in terms of risk at the 99th percentile of summer temperature relative to the minimum mortality temperature. Women were found generally more at risk to die because of heat than men, and the difference was larger among people over 85 years old. Risks among married people were lower than risks among single, divorced, and widowed people, while risks in divorced women were significantly higher than in divorced men. This is a novel finding which highlights the potential role of gender inequalities in heat-related mortality. Our study underlines the relevance of including a sex and gender dimension in the analysis of the impacts of heat on the population and advocates the development of gender-based adaptation policies to extreme heat.

Projections of temperature-associated mortality risks under the changing climate in an ageing society.

OBJECTIVES: This study aimed to project future temperature-associated mortality risk and additional deaths among Taiwan's elderly (aged >65 years) population. STUDY DESIGN: This study investigated retrospective temperature-mortality risk associations and future mortality projections. METHODS: A distributed lag non-linear model and random effect meta-analyses were employed to assess the risk of daily temperature-associated deaths in all-cause, circulatory, and respiratory diseases. Using the statistical downscaling temperature projections of the Representative Concentration Pathways (RCPs; i.e. RCP2.6, RCP6.0 and RCP8.5), future risk of mortalities were projected among the elderly for 2030-2039, 2060-2069 and 2090-2099, with a 30%, 40% and 50% expected increase in elderly population proportions, respectively. RESULTS: The baseline analysis from 2005 to 2018 identified that Taiwan's population is more vulnerable to cold effects than heat, with the highest cold-related mortality risk being attributed to circulatory diseases, followed by all-cause and respiratory diseases. However, future projections suggest a declining trend in cold-related mortalities and a significant rise in heat-related mortalities under different RCP scenarios. Heat-attributable mortalities under the RCP8.5 scenario by 2090-2099 would account for almost 170,360, 36,557 and 29,386 additional annual deaths among the elderly due to all-cause, circulatory and respiratory diseases, respectively. Heat-attributable all-cause mortalities among the elderly would increase by 3%, 11% and 30% under RCP2.6, RCP6.0 and RCP8.5, respectively, by 2090-2099. CONCLUSIONS: The findings of this study provide predictions on future temperature-related mortality among the elderly in a developed, ageing society with a hot and humid climate. The results from this study can guide public health interventions and policies for climate change and ageing society-associated health risks.

Association between ambient temperature and varicella among adults in Qingdao, China during 2008-2019.

Little concern has been paid to the relationship between temperature and varicella among adults. Daily meteorological data and varicella cases in Qingdao among adults from 1 January 2008 to 31 December 2019 were collected. A combination of quasi-Poisson generalized additive model (GAM) and distributed lag non-linear model (DLNM) was conducted to assess the temperature-lag-varicella relationship. We also estimated the lag-response curves for different temperatures and the exposure-response relationships for different lag days. The number of varicella cases was 10,296. Compared with the minimum-varicella temperature (25°C), we found the largest effect of temperature on varicella within 21 lag days was at 1°C (RR, 6.72; 95% CI, 2.90-15.57), and then the effect declined as the temperature increased. A similar trend of rising first and then falling was found in temperature-response curves for different lag days. A reverse U-shape lag pattern was found for different levels of temperatures. Temperature may affect varicella.

Association between ambient temperatures and injuries: a time series analysis using emergency ambulance dispatches in Chongqing, China.

BACKGROUND: Global warming and increasing extreme weather have become a severe problem in recent years, posing a significant threat to human health worldwide. Research exploring the link between injury as one of the leading causes of death globally and ambient temperature was lacking. Based on the hourly injury emergency ambulance dispatch (IEAD) records from 2019-2021 in the main urban area of Chongqing, this study explored the role of temperature extremes on the pathogenesis of injury by different mechanisms and identified sensitive populations for different mechanisms of injury. METHODS: In this study, we collected hourly injury emergency ambulance dispatch (IEAD) records from Chongqing Emergency Dispatch Center in the main urban area of Chongqing from 2019 to 2021, and used a distributed lagged nonlinear model (DLNM) with quasi-Poisson distribution to evaluate the association between ambient temperature and IEADs. And the stratified analysis was performed by gender, age and different injury mechanisms to identify susceptible groups. Finally, the attributable burden of ambient extreme temperatures was also investigated. RESULTS: The risk for total IEADs increased significantly at high temperature (32 °C) compared with optimal temperature (9 °C) (CRR: 1.210; 95%CI[1.127,1.300]). The risks of traffic accident injury (CRR: 1.346; 95%CI[1.167,1.552]), beating injury (CRR: 1.508; 95%CI[1.165,1.952]), fall-height injury (CRR: 1.871; 95%CI[1.196-2.926]) and injury of sharp penetration (CRR: 2.112; 95%CI[1.388-3.213]) were significantly increased. At low temperature (7 °C), the risk of fall injury (CRR: 1.220; 95% CI [1.063,1.400]) increased significantly. Lag for 24 hours at extreme low temperature (5 °C), the risk of 18-45 years (RR: 1.016; 95%CI[1.009,1.024]) and over 60 years of age (RR: 1.019; 95%CI[1.011,1.025]) increased significantly. The effect of 0 h delay in extreme high temperature (36 °C) on males aged 18-45 years (RR: 1.115; 95%CI[1.071,1.162]) and 46-59 years (RR: 1.069; 95%CI[1.023,1.115]) had significant impact on injury risk. CONCLUSIONS: This study showed that ambient temperature was significantly related to the risk of injury, and different mechanisms of injury were affected differently by extreme temperature. The increasing risk of traffic accident injury, beating injury, fall-height injury and sharp penetrating injury was associated with extreme heat, while fall injury was associated with extreme cold. The risk of injury in high temperature environment was mainly concentrated in males and young adults. The results of this study can help to identify the sensitive population with different injury mechanisms in extreme temperature environment, and provide reference for public health emergency departments to respond to relevant strategies in extreme temperature environment to minimize the potential risk to the public.

Nonlinear effect of air pollution on adult pneumonia hospital visits in the coastal city of Qingdao, China: A time-series analysis.

Many studies have illustrated adverse effects of short-term exposure to air pollution on human health, which usually assumes a linear exposure-response (E-R) function in the delineation of health effects due to air pollution. However, nonlinearity may exist in the association between air pollutant concentrations and health outcomes such as adult pneumonia hospital visits, and there is a research gap in understanding the nonlinearity. Here, we utilized both the distributed lag model (DLM) and nonlinear model (DLNM) to compare the linear and nonlinear impacts of air pollution on adult pneumonia hospital visits in the coastal city of Qingdao, China. While both models show adverse effects of air pollutants on adult pneumonia hospital visits, the DLNM shows an attenuation of E-R curves at high concentrations. Moreover, the DLNM may reveal delayed health effects that may be missed in the DLM, e.g., ozone exposure and pneumonia hospital visits. With the stratified analysis of air pollutants on adult pneumonia hospital visits, both models consistently reveal that the influence of air pollutants is higher during the cold season than during the warm season. Nevertheless, they may behave differently in terms of other subgroups, such as age, gender and visit types. For instance, while no significant impact due to PM2.5 in any of the subgroups abovementioned emerges based on DLM, the results from DLNM indicate statistically significant impacts for the subgroups of elderly, female and emergency department (ED) visits. With respect to adjustment by two-pollutants, PM10 effect estimates for pneumonia hospital visits were the most robust in both DLM and DLNM, followed by NO2 and SO2 based on the DLNM. Considering the estimated health effects of air pollution relying on the assumed E-R functions, our results demonstrate that the traditional linear association assumptions may overlook some potential health risks.

Mortality risk attributable to high and low ambient temperature in Pune city, India: A time series analysis from 2004 to 2012.

BACKGROUND: Exposure to high and low ambient temperatures is associated with morbidity and mortality across the globe. Most of these studies assessing the effects of non-optimum temperatures on health and have been conducted in the developed world, whereas in India, the limited evidence on ambient temperature and health risks and has focused mostly on the effects of heat waves. Here we quantify short term association between all temperatures and mortality in urban Pune, India. METHODS: We applied a time series regression model to derive temperature-mortality associations based on daily mean temperature and all-cause mortality records of Pune city from year January 2004 to December 2012. We estimated high and low temperature-mortality relationships by using standard time series quasi-Poisson regression in conjunction with a distributed lag non-linear model (DLNM). We calculated temperature attributable mortality fractions for total heat and total cold. FINDINGS: The analysis provides estimates of the total mortality burden attributable to ambient temperature. Overall, 6∙5% [95%CI 1.76-11∙43] of deaths registered in the observational period were attributed to non-optimal temperatures, cold effect was greater 5.72% [95%CI 0∙70-10∙06] than heat 0∙84% [0∙35-1∙34]. The gender stratified analysis revealed that the highest burden among men both for heat and cold. CONCLUSION: Non-optimal temperatures are associated with a substantial mortality burden. Our findings could benefit national, and local communities in developing preparedness and prevention strategies to reduce weather-related impacts immediately due to climate change.

Association of ambient fine particulate matter exposure with gestational diabetes mellitus and blood glucose levels during pregnancy.

BACKGROUND: Previous studies have examined the associations between ambient fine particulate matter (PM2.5) exposure and gestational diabetes mellitus (GDM). However, limited studies explored the relationships between PM2.5 exposure and blood glucose levels during pregnancy, especially in highly polluted areas. OBJECTIVES: To examine the associations of prenatal ambient PM2.5 exposure with GDM and blood glucose levels, and to identify the sensitive exposure windows in a highly air-polluted area. METHODS: From July 2016 to October 2017, a birth cohort study was conducted in Beijing, China. Participants were interviewed in each trimester regarding demographics, lifestyle, living and working environment, and medical conditions. Participant's daily ambient PM2.5 levels from 3 m before last menstrual period (LMP) to the third trimester was estimated by a hybrid spatiotemporal model. Indoor air quality index was calculated based on environmental tobacco smoke, ventilation, cooking, painting, pesticide, and herbicide use. Distributed lag non-linear model was applied to explore the sensitive weeks of PM2.5 exposure. RESULTS: Of 165 pregnant women, 23 (13.94%) developed GDM. After adjusting for potential confounders, PM2.5 exposure during the 1st trimester was associated with higher odds of GDM (10 µg/m3 increase: OR = 1.89, 95% CI: 1.04-3.49). Each 10 µg/m3 increase in PM2.5 during the 2nd trimester was associated with 17.70% (2.21-33.20), 15.99% (2.96-29.01), 18.82% (4.11-33.52), and 17.10% (3.28-30.92) increase in 1-h, 2-h, Δ1h-fasting (1-h minus fasting), and Δ2h-fasting (2-h minus fasting) blood glucose levels, respectively. PM2.5 exposure at 24th-27th weeks after LMP was associated with increased GDM risk. We identified sensitive exposure windows of 21st-24th weeks for higher 1-h and 2-h blood glucose levels and of 20th-22nd weeks for increased Δ1h-fasting and Δ2h-fasting. CONCLUSIONS: Ambient PM2.5 exposure during the second trimester was associated with higher odds of GDM and higher blood glucose levels. Avoiding exposure to high air pollution levels during the sensitive windows might prevent women from developing GDM.

Short-term effects of ambient temperature on the risk of preeclampsia in Nanjing, China: a time-series analysis.

OBJECTIVES: Previous studies on the association between temperature and preeclampsia mainly considered temperature on a monthly or seasonal time scale. The objective of this study was to assess the preeclampsia risk associated with short-term temperature exposure using daily data. STUDY DESIGN: Daily preeclampsia hospitalization data, daily meteorological data and daily air pollutant data from Nanjing were collected from 2016 to 2017. Both the T test and distributed lag nonlinear model (DLNM) were applied to assess the short-term effect of temperature on preeclampsia risk. Three kinds of daily temperature, including the daily mean temperature, daily minimum temperature and daily maximum temperature, were analysed. RESULTS: When the daily number of preeclampsia hospital admissions was divided into two subgroups based on temperature, it was significantly larger on cold days than on hot days. Regarding the mean temperature, a very low level of mean temperature (4.5 °C, lag = 0-20) and a low level of mean temperature (9.1 °C, lag = 0-20) increased the cumulative relative risk of preeclampsia by more than 60%. At the same time, a very high level of mean temperature (28.7 °C, lags = 0-10, 0-15, 0-20) and a high level of mean temperature (24.1 °C, lags = 0-10, 0-15) decreased the cumulative relative risk of preeclampsia by more than 35%. At a minimum temperature, a very low level of minimum temperature (0.9 °C, lag 0-5) and a low level of minimum temperature (5.6 °C, lag 0-5) increased the cumulative relative risk of preeclampsia by more than 55%. At the same time, a high level of mean temperature (20.9 °C, lags = 0, 0-5) decreased the cumulative relative risk of preeclampsia by more than 20%. The maximum temperature result was similar to the mean temperature result. CONCLUSIONS: Both direct and lag effects of low temperature on preeclampsia were demonstrated to be significant risk factors. These results could be used to help pregnant women and the government reduce preeclampsia risk.

Nonlinear influence of winter meteorology and precursor on PM2.5 based on mathematical and numerical models: A COVID-19 and Winter Olympics case study.

Air pollution during the COVID-19 epidemic in Beijing and its surrounding regions has received substantial attention. We collected observational data, including air pollutant concentrations and meteorological parameters, during January and February from 2018 to 2021. A statistical and a numerical model were applied to identify the formation of air pollution and the impact of emission reduction on air quality. Relative humidity, wind speed, SO2, NO2, and O3 had nonlinear effects on the PM2.5 concentration in Beijing, among which the effects of relative humidity, NO2, and O3 were prominent. During the 2020 epidemic period, high pollution concentrations were closely related to adverse meteorological conditions, with different parameters having different effects on the three pollution processes. In general, the unexpected reduction of anthropogenic emissions reduced the PM2.5 concentration, but led to an increase in the O3 concentration. Multi-scenario simulation results showed that anthropogenic emission reduction could reduce the average PM2.5 concentration after the Chinese Spring Festival, but improvement during days with heavy pollution was limited. Considering that O3 enhances the PM2.5 levels, to achieve the collaborative improvement of PM2.5 and O3 concentrations, further research should explore the collaborative emission reduction scheme with VOCs and NOx to achieve the collaborative improvement of PM2.5 and O3 concentrations. The conclusions of this study provide a basis for designing a plan that guarantees improved air quality for the 2022 Winter Olympics and other international major events in Beijing.