Risk effects of meteorological factors on human brucellosis in Jilin province, China, 2005-2019

Background

The impact of climate on zoonotic infectious diseases (or can be referred to as climate-sensitive zoonotic diseases) is confirmed. Yet, research on the association between brucellosis and climate is limited. We aim to understand the impact of meteorological factors on the risk of brucellosis, especially in northeastern China.

Methods

Monthly incidence data for brucellosis from 2005 to 2019 in Jilin province was obtained from the China Information System for Disease Control and Prevention (CDC). Monthly meteorological data (average temperature (°C), wind velocity (m/s), relative humidity (%), sunshine hours (h), air pressure (hPa), and rainfall (mm)) in Jilin province, China, from 2005 to 2019 were collected from the China Meteorological Information Center (http://data.cma.cn/). The Spearman's correlation was used to choose among the several meteorological variables. A distributed lag non-linear model (DLNM) was used to estimate the lag and non-linearity effect of meteorological factors on the risk of brucellosis.

Results

A total of 24,921 cases of human brucellosis were reported in Jilin province from 2005 to 2019, with the peak epidemic period from April to June. Low temperature and low sunshine hours were protective factors for the brucellosis, where the minimum RR values were 0.50 (95 % CI = 0.31-0.82) for -13.7 °C with 1 month lag and 0.61 (95 % CI = 0.41-0.91) for 110.5h with 2 months lag, respectively. High temperature, high sunshine hours, and low wind velocity were risk factors for brucellosis. The maximum RR values were 2.91 (95 % CI = 1.43-5.92, lag = 1, 25.7 °C), 1.85 (95 % CI = 1.23-2.80, lag = 2, 332.6h), and 1.68 (95 % CI = 1.25-2.26, lag = 2, 1.4 m/s). The trends in the impact of extreme temperature and extreme sunshine hours on the transmission of brucellosis were generally consistent.

Conclusion

High temperature, high sunshine hours, and low wind velocity are more conducive to the transmission of brucellosis with an obvious lag effect. The results will deepen the understanding of the relationship between climate and brucellosis and provide a reference for formulating relevant public health policies.

Spatial Bayesian distributed lag non-linear models (SB-DLNM) for small-area exposure-lag-response epidemiological modelling

BACKGROUND

Distributed lag non-linear models (DLNMs) are the reference framework for modelling lagged non-linear associations. They are usually used in large-scale multi-location studies. Attempts to study these associations in small areas either did not include the lagged non-linear effects, did not allow for geographically-varying risks or downscaled risks from larger spatial units through socioeconomic and physical meta-predictors when the estimation of the risks was not feasible due to low statistical power.

METHODS

Here we proposed spatial Bayesian DLNMs (SB-DLNMs) as a new framework for the estimation of reliable small-area lagged non-linear associations, and demonstrated the methodology for the case study of the temperature-mortality relationship in the 73 neighbourhoods of the city of Barcelona. We generalized location-independent DLNMs to the Bayesian framework (B-DLNMs), and extended them to SB-DLNMs by incorporating spatial models in a single-stage approach that accounts for the spatial dependence between risks.

RESULTS

The results of the case study highlighted the benefits of incorporating the spatial component for small-area analysis. Estimates obtained from independent B-DLNMs were unstable and unreliable, particularly in neighbourhoods with very low numbers of deaths. SB-DLNMs addressed these instabilities by incorporating spatial dependencies, resulting in more plausible and coherent estimates and revealing hidden spatial patterns. In addition, the Bayesian framework enriches the range of estimates and tests that can be used in both large- and small-area studies.

CONCLUSIONS

SB-DLNMs account for spatial structures in the risk associations across small areas. By modelling spatial differences, SB-DLNMs facilitate the direct estimation of non-linear exposure-response lagged associations at the small-area level, even in areas with as few as 19 deaths. The manuscript includes an illustrative code to reproduce the results, and to facilitate the implementation of other case studies by other researchers.

Unraveling varying spatiotemporal patterns of dengue and associated exposure-response relationships with environmental variables in Southeast Asian countries before and during COVID-19

The enforcement of COVID-19 interventions by diverse governmental bodies, coupled with the indirect impact of COVID-19 on short-term environmental changes (e.g. plant shutdowns lead to lower greenhouse gas emissions), influences the dengue vector. This provides a unique opportunity to investigate the impact of COVID-19 on dengue transmission and generate insights to guide more targeted prevention measures. We aim to compare dengue transmission patterns and the exposure-response relationship of environmental variables and dengue incidence in the pre- and during-COVID-19 to identify variations and assess the impact of COVID-19 on dengue transmission. We initially visualized the overall trend of dengue transmission from 2012-2022, then conducted two quantitative analyses to compare dengue transmission pre-COVID-19 (2017-2019) and during-COVID-19 (2020-2022). These analyses included time series analysis to assess dengue seasonality, and a Distributed Lag Non-linear Model (DLNM) to quantify the exposure-response relationship between environmental variables and dengue incidence. We observed that all subregions in Thailand exhibited remarkable synchrony with a similar annual trend except 2021. Cyclic and seasonal patterns of dengue remained consistent pre- and during-COVID-19. Monthly dengue incidence in three countries varied significantly. Singapore witnessed a notable surge during-COVID-19, particularly from May to August, with cases multiplying several times compared to pre-COVID-19, while seasonality of Malaysia weakened. Exposure-response relationships of dengue and environmental variables show varying degrees of change, notably in Northern Thailand, where the peak relative risk for the maximum temperature-dengue relationship rose from about 3 to 17, and the max RR of overall cumulative association 0-3 months of relative humidity increased from around 5 to 55. Our study is the first to compare dengue transmission patterns and their relationship with environmental variables before and during COVID-19, showing that COVID-19 has affected dengue transmission at both the national and regional level, and has altered the exposure-response relationship between dengue and the environment.

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.

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.

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.

The effect of temporal data aggregation to assess the impact of changing temperatures in Europe: an epidemiological modelling study

Background

Daily time-series regression models are commonly used to estimate the lagged nonlinear relation between temperature and mortality. A major impediment to this type of analysis is the restricted access to daily health records. The use of weekly and monthly data represents a possible solution unexplored to date.

Methods

We temporally aggregated daily temperatures and mortality records from 147 contiguous regions in 16 European countries, representing their entire population of over 400 million people. We estimated temperature-lag-mortality relationships by using standard time-series quasi-Poisson regression models applied to daily data, and compared the results with those obtained with different degrees of temporal aggregation.

Findings

We observed progressively larger differences in the epidemiological estimates with the degree of temporal data aggregation. The daily data model estimated an annual cold and heat-related mortality of 290,104 (213,745-359,636) and 39,434 (30,782-47,084) deaths, respectively, and the weekly model underestimated these numbers by 8.56% and 21.56%. Importantly, differences were systematically smaller during extreme cold and heat periods, such as the summer of 2003, with an underestimation of only 4.62% in the weekly data model. We applied this framework to infer that the heat-related mortality burden during the year 2022 in Europe may have exceeded the 70,000 deaths.

Interpretation

The present work represents a first reference study validating the use of weekly time series as an approximation to the short-term effects of cold and heat on human mortality. This approach can be adopted to complement access-restricted data networks, and facilitate data access for research, translation and policy-making.

Funding

The study was supported by the ERC Consolidator Grant EARLY-ADAPT (https://www.early-adapt.eu/), and the ERC Proof-of-Concept Grants HHS-EWS and FORECAST-AIR.

Association Between Air Pollution and Cardiovascular Disease Hospitalizations in Lanzhou City, 2013-2020: A Time Series Analysis

Extensive evidence has shown that air pollution increases the risk of cardiovascular disease (CVD) admissions. We aimed to explore the short-term effect of air pollution on CVD admissions in Lanzhou residents and their lag effects. Meteorological data, air pollution data, and a total of 309,561 daily hospitalizations for CVD among urban residents in Lanzhou were collected from 2013 to 2020. Distributed lag non-linear model was used to analyze the relationship between air pollutants and CVD admissions, stratified by gender, age, and season. PM2.5, NO2, and CO have the strongest harmful effects at lag03, while SO2 at lag3. The relative risks of CVD admissions were 1.0013(95% CI: 1.0003, 1.0023), 1.0032(95% CI: 1.0008, 1.0056), and 1.0040(95% CI: 1.0024, 1.0057) when PM2.5, SO2, and NO2 concentrations were increased by 10 μg/m³, respectively. Each 1 mg/m3 increase in CO concentration was associated with a relative risk of cardiovascular hospitalization of risk was 1.0909(95% CI: 1.0367, 1.1479). We observed a relative risk of 0.9981(95% CI: 0.9972, 0.9991) for each 10 μg/m³ increase in O3 for CVD admissions at lag06. We found a significant lag effects of air pollutants on CVD admissions. NO2 and CO pose a greater risk of hospitalization for women, while PM2.5 and SO2 have a greater impact on men. PM2.5, NO2, and CO have a greater impact on CVD admissions in individuals aged <65 years, whereas SO2 affects those aged ≥65 years. Our research indicates a possible short-term impact of air pollution on CVD. Local public health and environmental policies should take these preliminary findings into account.

The short-term effect of air pollution on the incidence of pulmonary tuberculosis in Chongqing, China, 2014-2020

INTRODUCTION

Tuberculosis (TB) is one of the top ten causes of death in the world. The purpose of this study was to explore the relationship between the short-term exposure to air pollutants and the risk of pulmonary TB in Chongqing.

METHODOLOGY

A distributed lag nonlinear model was used to explore the effect of short-term exposure to air pollutants on the risk of pulmonary TB. Stratified analysis was used to explore the impact of gender and age on the risk of pulmonary TB.

RESULTS

There were 170,934 confirmed cases of pulmonary TB in Chongqing from January 1st, 2014 to December 30th, 2020. There was a positive correlation between the exposure to particulate matter with aerodynamic diameter less than 2.5 µm (PM2.5), particulate matter with aerodynamic diameter less than 10 µm (PM10) ozone (O3) and the incidence risk of TB. The maximum lag-specific relative risk (RR) of pulmonary TB was 1.012 (95% CI: 1.001-1.023, 14 days delay) for each 10 μg/m3 increase in PM2.5; 1.010 (95% CI: 1.003-1.017, 14 days delay) for each 10μg/m3 increase in PM10; and 1.002 (95% CI:1.000-1.004, 2 days delay) for each 10 mg/m3 increase in O3. Stratified analysis showed that the exposure effects of PM2.5, PM10 and O3 were different between different genders and age.

CONCLUSIONS

This study suggested that exposure to PM2.5, PM10, and O3 was associated with the risk of pulmonary TB, and the risk was higher for males than females, while the exposure to PM2.5 and PM10 was riskier for people aged 15-60 years.

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.

Prior water availability modifies the effect of heavy rainfall on dengue transmission: a time series analysis of passive surveillance data from southern China

Introduction

Given the rapid geographic spread of dengue and the growing frequency and intensity of heavy rainfall events, it is imperative to understand the relationship between these phenomena in order to propose effective interventions. However, studies exploring the association between heavy rainfall and dengue infection risk have reached conflicting conclusions, potentially due to the neglect of prior water availability in mosquito breeding sites as an effect modifier.

Methods

In this study, we addressed this research gap by considering the impact of prior water availability for the first time. We measured prior water availability as the cumulative precipitation over the preceding 8 weeks and utilized a distributed lag non-linear model stratified by the level of prior water availability to examine the association between dengue infection risk and heavy rainfall in Guangzhou, a dengue transmission hotspot in southern China.

Results

Our findings suggest that the effects of heavy rainfall are likely to be modified by prior water availability. A 24-55 day lagged impact of heavy rainfall was associated with an increase in dengue risk when prior water availability was low, with the greatest incidence rate ratio (IRR) of 1.37 [95% credible interval (CI): 1.02-1.83] occurring at a lag of 27 days. In contrast, a heavy rainfall lag of 7-121 days decreased dengue risk when prior water availability was high, with the lowest IRR of 0.59 (95% CI: 0.43-0.79), occurring at a lag of 45 days.

Discussion

These findings may help to reconcile the inconsistent conclusions reached by previous studies and improve our understanding of the complex relationship between heavy rainfall and dengue infection risk.

Association between meteorological factors and varicella incidence: a multicity study in Yunnan Province, China

This multicenter study aimed to investigate the relationship between varicella incidence and meteorological factors including mean temperature, relative humidity, sunshine duration, diurnal temperature difference, wind speed, and rainfall, as previous studies have produced varying results. Our study also sought to identify potential sources of heterogeneity. Data on reported daily varicella numbers and meteorological factors were collected for 14 cities in Yunnan Province from 2017 to 2021. A distribution-lagged nonlinear model was constructed to explore the relationship between meteorological conditions and varicella incidence in each included city. We then used multiple meta-regression to explore sources of heterogeneity using demographic economics indicators, air pollutants, and geographic location as potential modifiers. The cumulative hazard effect plot showed an inverted S-shape for the relationship between temperature and varicella, with the smallest RR (relative risk) (0.533, 95% CI: 0.401-0.708) at temperatures up to 27.2 °C. The maximum RR (1.171, 95% CI: 1.001-1.371) was obtained when the relative humidity was equal to 98.5%. The RR (1.164, 95% CI: 1.002-1.352) was greatest at a diurnal temperature range of 2 °C (1.164, 95% CI: 1.002-1.352) and least (0.913, 95% CI: 0.834-0.999) at a diurnal temperature range of 16.1 °C. The maximum RR (1.214, 95% CI: 1.089-1.354) was obtained at 0 h of sunshine, and the minimum RR (0.808, 95% CI: 0.675-0.968) was obtained at 12.4 h of sunshine. The RR (0.792, 95% CI: 0.633-0.992) was minimum at a wind velocity of 4.8 m/s. Residual heterogeneity ranged from 1 to 42.7%, with PM10 (particles with an aerodynamic diameter less than 10 μm), GDP (gross domestic product), and population density explaining some of this heterogeneity. The temperature has a dual effect on varicella incidence. Varicella cases are negatively correlated with diurnal temperature range, sunshine duration, and wind speed, and positively correlated with relative humidity. GDP and PM10 may have a significant role in altering the association between temperature and varicella, while PM10 and population density may alter the association between wind velocity and varicella.

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.

Effects and interaction of temperature and relative humidity on the trend of influenza prevalence: A multi-central study based on 30 provinces in mainland China from 2013 to 2018

Background

Evidence is inefficient about how meteorological factors influence the trends of influenza transmission in different regions of China.

Methods

We estimated the time-varying reproduction number (R_t) of influenza and explored the impact of temperature and relative humidity on R_t using generalized additive quasi-Poisson regression models combined with the distribution lag non-linear model (DLNM). The effect of temperature and humidity interaction on R_t of influenza was explored. The multiple random-meta analysis was used to evaluate region-specific association. The excess risk (ER) index was defined to investigate the correlation between R_t and each meteorological factor with the modification of seasonal and regional characteristics.

Results

Low temperature and low relative humidity contributed to influenza epidemics on the national level, while shapes of merged cumulative effect plots were different across regions. Compared to that of median temperature, the merged RR (95%CI) of low temperature in northern and southern regions were 1.40(1.24,1.45) and 1.20 (1.14,1.27), respectively, while those of high temperature were 1.10(1.03,1.17) and 1.00 (0.95,1.04), respectively. There were negative interactions between temperature and relative humidity on national (SI = 0.59, 95%CI: 0.57-0.61), southern (SI = 0.49, 95%CI: 0.17-0.80), and northern regions (SI = 0.59, 95%CI: 0.56,0.62). In general, with the increase of the change of the two meteorological factors, the ER of R_t also gradually increased.

Conclusions

Temperature and relative humidity have an effect on the influenza epidemics in China, and there is an interaction between the two meteorological factors, but the effect of each factor is heterogeneous among regions. Meteorological factors may be considered to predict the trend of influenza epidemic.

Increase in daily asthma medication sales in association with air pollution levels in Greater Stockholm

Daily air pollution levels are known to influence the number of patients with acute asthma. We investigated the short-term effects of air pollution exposure on the daily number of asthma medication purchases in the Greater Stockholm area, Sweden.

Methods

We conducted a time-series study with data on asthma medication purchases and daily mean values of particulate matter ≤10 µm (PM10), nitrogen oxides (NOx), and ozone during 2018-2019. We used nonlinear distributed lag quasi-Poisson regression models to estimate the associations between air pollution levels and medication purchases, adjusting for meteorological variables, pollen levels, day of the week, and long-term trends. The models established linear relationships between air pollutants and the outcome, and potential delayed effects were smoothed with a spline across a lag period of 2 weeks. We applied separate models for each municipality (n = 21) in Greater Stockholm, and calculated pooled estimates to achieve combined results for the whole region.

Results

We observed associations between daily levels of air pollution and purchases of asthma medications, most clearly for PM10. The pooled estimates of the relative risks for asthma medication purchases across all 21 municipalities associated with a 10 μg m-3 increase in PM10 the same day (lag 0) was 1.7% [95% confidence interval (CI): 1.2%, 2.1%], a cumulative increase of 4.6% (95% CI: 3.7%, 5.6%) over one week (lag 0-6), and a 6.5% (95% CI: 5%, 8%) increase over 2 weeks (lag 0-13). The corresponding pooled effect per 10 μg m-3 increase in NOx and ozone were 2.8% (95% CI: 1.6%, 4.1%) and 0.7% (95% CI: 0%, 1.4%) over 2 weeks (lag 0-13), respectively.

Conclusions

Our study revealed short-term associations between air pollution, especially PM10, and purchases of asthma medications.

Predicting the dengue cluster outbreak dynamics in Yogyakarta, Indonesia: a modelling study

Background

Human mobility and climate conditions are recognised key drivers of dengue transmission, but their combined and individual role in the local spatiotemporal clustering of dengue cases is not well understood. This study investigated the effects of human mobility and weather conditions on dengue risk in an urban area in Yogyakarta, Indonesia.

Methods

We established a Bayesian spatiotemporal model for neighbourhood outbreak prediction and evaluated the performances of two different approaches for constructing an adjacency matrix: one based on geographical proximity and the other based on human mobility patterns. We used population, weather conditions, and past dengue cases as predictors using a flexible distributed lag approach. The human mobility data were estimated based on proxies from social media. Unseen data from February 2017 to January 2020 were used to estimate the one-month ahead prediction accuracy of the model.

Findings

When human mobility proxies were included in the spatial covariance structure, the model fit improved in terms of the log score (from 1.748 to 1.561) and the mean absolute error (from 0.676 to 0.522) based on the validation data. Additionally, showed only few observations outside the credible interval of predictions (1.48%) and weather conditions were not found to contribute additionally to the clustering of cases at this scale.

Interpretation

The study shows that it is possible to make highly accurate predictions of the within-city cluster dynamics of dengue using mobility proxies from social media combined with disease surveillance data. These insights are important for proactive and timely outbreak management of dengue.

Funding

Swedish Research Council Formas, Umeå Centre for Global Health Research, Swedish Council for Working Life and Social Research, Swedish research council VINNOVA and Alexander von Humboldt Foundation (Germany).

The effect of ozone short-term exposure on flow-mediated dilation: Using data before and after COVID-19 lockdown in Shanghai

BACKGROUND

Short-term ambient ozone exposure has been shown to have an adverse impact on endothelial function, contributing to major cardiovascular diseases and premature death. However, only limited studies have focused on the impact of short-term ozone exposure on Flow-mediated Dilation (FMD), and their results have been inconsistent. The current study aims to explore the relationship between short-term ambient ozone exposure and FMD. In addition, the study aims to investigate how lockdown measures for COVID-19 may influence ozone concentration in the atmosphere.

METHODS

Participants were recruited from a hospital in Shanghai from December 2020 to August 2022. Individuals' ozone exposure was determined using residential addresses. A distributed lag nonlinear model was adopted to assess the exposure-response relationship between short-term ozone exposure and FMD. A comparison was made between ambient ozone concentration and FMD data collected before and after Shanghai's lockdown in 2022.

RESULTS

When ozone concentration was between 150 and 200 μg/m³, there was a significant reduction in FMD with a 2-day lag. Elderly individuals (age ≥ 65), females, non-drinkers, and non-smokers were found to be more susceptible to high concentrations of ozone exposure. The lockdown did elevate ambient ozone concentration compared to the same period previously.

INTERPRETATION

This study proposes that an ambient ozone concentration of 150-200 μg/m³ is harmful to endothelial function, and that a reduction in human activity during lockdown increased the concentration, which in turn reduced FMD. However, the underlying mechanism requires further research.

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.

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.

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.

Climate variability, socio-economic conditions and vulnerability to malaria infections in Mozambique 2016-2018: a spatial temporal analysis

Background

Temperature, precipitation, relative humidity (RH), and Normalized Different Vegetation Index (NDVI), influence malaria transmission dynamics. However, an understanding of interactions between socioeconomic indicators, environmental factors and malaria incidence can help design interventions to alleviate the high burden of malaria infections on vulnerable populations. Our study thus aimed to investigate the socioeconomic and climatological factors influencing spatial and temporal variability of malaria infections in Mozambique.

Methods

We used monthly malaria cases from 2016 to 2018 at the district level. We developed an hierarchical spatial-temporal model in a Bayesian framework. Monthly malaria cases were assumed to follow a negative binomial distribution. We used integrated nested Laplace approximation (INLA) in R for Bayesian inference and distributed lag nonlinear modeling (DLNM) framework to explore exposure-response relationships between climate variables and risk of malaria infection in Mozambique, while adjusting for socioeconomic factors.

Results

A total of 19,948,295 malaria cases were reported between 2016 and 2018 in Mozambique. Malaria risk increased with higher monthly mean temperatures between 20 and 29°C, at mean temperature of 25°C, the risk of malaria was 3.45 times higher (RR 3.45 [95%CI: 2.37-5.03]). Malaria risk was greatest for NDVI above 0.22. The risk of malaria was 1.34 times higher (1.34 [1.01-1.79]) at monthly RH of 55%. Malaria risk reduced by 26.1%, for total monthly precipitation of 480 mm (0.739 [95%CI: 0.61-0.90]) at lag 2 months, while for lower total monthly precipitation of 10 mm, the risk of malaria was 1.87 times higher (1.87 [1.30-2.69]). After adjusting for climate variables, having lower level of education significantly increased malaria risk (1.034 [1.014-1.054]) and having electricity (0.979 [0.967-0.992]) and sharing toilet facilities (0.957 [0.924-0.991]) significantly reduced malaria risk.

Conclusion

Our current study identified lag patterns and association between climate variables and malaria incidence in Mozambique. Extremes in climate variables were associated with an increased risk of malaria transmission, peaks in transmission were varied. Our findings provide insights for designing early warning, prevention, and control strategies to minimize seasonal malaria surges and associated infections in Mozambique a region where Malaria causes substantial burden from illness and deaths.

Nonlinear and lagged effects of VOCs on SOA and O3 and multi-model validated control strategy for VOC sources

VOCs, as the common precursor of PM_2.5 and O₃ pollution, has not been paid enough attention in the previous phase. How to implement scientific and effective emission reduction on VOC sources is the focus of the next step in improving the atmospheric environmental quality in China. In this study, based on observations of VOC species, PM₁ components and O₃, the distributed lag nonlinear model (DLNM) was used to investigate the nonlinear and lagged effects of key VOC categories on secondary organic aerosol (SOA) and O₃. The control priorities of sources were determined by combining the VOC source profiles, which were afterwards verified using the source reactivity method and Weather Research and Forecasting Model-Community Multi-scale Air Quality Model (WRF-CMAQ). Finally, the optimized control strategy of VOC source was proposed. The results showed that SOA was more sensitive to benzene and toluene, and single-chain aromatics, while O₃ was more sensitive to dialkenes, C₂-C₄ alkenes, and trimethylbenzenes. The optimized control strategy based on the total response increments (TRI) of VOC sources suggests that passenger cars, industrial protective coatings, trucks, coking, and steel making should be considered as the key sources for continuous emission reduction throughout the year in the Beijing-Tianjin-Hebei region (BTH). Non-road, oil refining, glass manufacturing and catering sources should be strengthened in summer, while biomass burning, pharmaceutical manufacturing, oil storage and transportation, and synthetic resin need more emphasis in other seasons. The multi-model validated result can provide scientific guidance for more accurate and efficient VOCs reduction.

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.

Impact of diurnal temperature range on other infectious diarrhea in Tongcheng, China, 2010-2019: a distributed lag non-linear analysis

Our study aimed to quantify the exposure-lag-response effects of the diurnal temperature range (DTR) on other infectious diarrhea (OID) in Tongcheng city and examine the vulnerable populations. Distributed lag non-linear model (DLNM) and generalized additive model (GAM) were applied jointly to quantify the association between DTR and the daily number of OID cases compared with the median DTR. Stratified analysis was performed by gender, age, and seasons of onset. There are a total of 8231 cases during this decade. We observed a j-shaped relationship between DTR and OID, with a peak point at the maximum DTR (RR: 2.651, 95% CI: 1.320-5.323) compared to the median DTR. As DTR increased from 8.2 to 10.9 °C, we found the RRs started to decrease and then rise from day 0, and the minimum value occurred on day 7 (RR:1.003, 95% CI: 0.996-1.010). From stratified analysis, we observed that females and adults are more likely to be affected by high DTR significantly. In addition, the influence of DTR was different in cold and warm seasons. High DTR in warm seasons affects the number of OID daily cases, but no statistical significance was identified in cold seasons. This study suggests a significant relationship between high DTR and the incidence risk of OID.

Prediction and early warning model of mixed exposure to air pollution and meteorological factors on death of respiratory diseases based on machine learning

In recent years, with the repeated occurrence of extreme weather and the continuous increase of air pollution, the incidence of weather-related diseases has increased yearly. Air pollution and extreme temperature threaten sensitive groups' lives, among which air pollution is most closely related to respiratory diseases. Owing to the skewed attention, timely intervention is necessary to better predict and warn the occurrence of death from respiratory diseases. In this paper, according to the existing research, based on a number of environmental monitoring data, the regression model is established by integrating the machine learning methods XGBoost, support vector machine (SVM), and generalized additive model (GAM) model. The distributed lag nonlinear model (DLNM) is used to set the warning threshold to transform the data and establish the warning model. According to the DLNM model, the cumulative lag effect of meteorological factors is explored. There is a cumulative lag effect between air temperature and PM2.5, which reaches the maximum when the lag is 3 days and 5 days, respectively. If the low temperature and high environmental pollutants (PM2.5) continue to influence for a long time, the death risk of respiratory diseases will continue to rise, and the early warning model based on DLNM has better performance.

Explorative Assessment of the Temperature-Mortality Association to Support Health-Based Heat-Warning Thresholds: A National Case-Crossover Study in Switzerland

Defining health-based thresholds for effective heat warnings is crucial for climate change adaptation strategies. Translating the non-linear function between heat and health effects into an effective threshold for heat warnings to protect the population is a challenge. We present a systematic analysis of heat indicators in relation to mortality. We applied distributed lag non-linear models in an individual-level case-crossover design to assess the effects of heat on mortality in Switzerland during the warm season from 2003 to 2016 for three temperature metrics (daily mean, maximum, and minimum temperature), and various threshold temperatures and heatwave definitions. Individual death records with information on residential address from the Swiss National Cohort were linked to high-resolution temperature estimates from 100 m resolution maps. Moderate (90th percentile) to extreme thresholds (99.5th percentile) of the three temperature metrics implied a significant increase in mortality (5 to 38%) in respect of the median warm-season temperature. Effects of the threshold temperatures on mortality were similar across the seven major regions in Switzerland. Heatwave duration did not modify the effect when considering delayed effects up to 7 days. This nationally representative study, accounting for small-scale exposure variability, suggests that the national heat-warning system should focus on heatwave intensity rather than duration. While a different heat-warning indicator may be appropriate in other countries, our evaluation framework is transferable to any country.

Exposure-lag response of fine particulate matter on intrauterine fetal death: an analysis using a distributed lag non-linear model in Linxia Hui Autonomous Prefecture, China

The results of studies on intrauterine fetal death (IUFD) caused by exposure to fine particulate matter (PM_2.5) during pregnancy are inconsistent. Further exploration of the dose-response relationship and exposure window is required. We aimed to provide a reference for policy formulation by estimating the exposure-lag relationship of PM_2.5 on IUFD and looking for sensitive exposure windows. IUFD data was obtained from China Children Under 5 Death Surveillance Network in Linxia Hui Autonomous Prefecture from 2016 to 2020. Air pollution data and temperature data were obtained from ambient air monitoring stations and China Meteorological Data Network, respectively. The moving average is used to describe the trend and seasonality of PM_2.5 exposure; the distributed lag non-linear model (DLNM) is used to estimate the exposure-lag effect; the sandwich estimators are used to correct the variance-covariance matrix; and the model selected by Akaike's Information Criterion (AIC) finally adjusts gender, temperature, and district. About 180,622 infants were enrolled in the study, including 952 IUFDs (5.27‰). The median of PM_2.5 exposure is 34.08 μg/m³. There is an exposure-lag effect of PM_2.5 on IUFD approximate to a wavy shape; the concentration with effect is 40-90 μg/m³; and the sensitive lag time is 1, 2, 3, 8, 9, and 10 months. The maximum RR value of the exposure-lag effect of PM_2.5 on IUFD is 1.61 [95% CI 1.19, 2.19], in which the concentration of PM_2.5 is 62 μg/m³, and the lag month is 9 months. In the case of less than 6 months lag, the maximum RR value of the exposure-lag effect of PM_2.5 on IUFD is 1.43 [95% CI 1.24, 1.67], in which the concentration of PM_2.5 is 73 μg/m³, and the lag month is 3 months. Exposure to PM_2.5 concentrations above 40 μg/m³ may increase the risk of IUFD, especially in the first and third trimesters.

Dual seasonal pattern for hemorrhagic fever with renal syndrome and its potential determinants in China

Hemorrhagic fever with renal syndrome (HFRS) continued to affect human health across Eurasia, which complicated by climate change has posed a challenge for the disease prevention measures. Nation-wide surveillance data of HFRS cases were collected during 2008-2020.The seasonality and epidemiological features were presented by combining the HFRS incidence and the endemic types data. Factors potentially involved in affecting incidence and shaping disease seasonality were investigated by generalized additive mixed model, distributed lag nonlinear model and multivariate meta-analysis. A total of 76 cities that reported totally 111,054 cases were analyzed. Three endemic types were determined, among them the Type I cities (Hantaan virus-dominant) were related to higher incidence level, showing one spike every year in Autumn-Winter season; Type II (Seoul virus-dominant) cities were related to lower incidence, showing one spike in Spring, while Type III (Hantaan/Seoul-mixed type) showed dual peaks with incidence lying between. Persistently heavy rainfall had significantly negative influence on HFRS incidence in Hantaan virus-dominant endemic area, while a significantly opposite effect was identified when continuously heavy rainfall induced floods, where temperature and relative humidity affected HFRS incidence via an approximately parabolic or linear manner, however few or no such effects was shown in Seoul virus-dominant endemic areas, which was more vulnerable to temperature variation. Dual seasonal pattern of HFRS was depended on the dominant genotypes of hantavirus, and impact of climate on HFRS was greater in Hantaan virus-dominant endemic areas, than in Seoul virus-dominant areas.

The Interactive Effects between Drought and Air Pollutants on Children's Upper Respiratory Tract Infection: A Time-Series Analysis in Gansu, China

As a destructive and economic disaster in the world, drought shows an increasing trend under the continuous global climate change and adverse health effects have been reported. The interactive effects between drought and air pollutants, which may also be harmful to respiratory systems, remain to be discussed. We built the generalized additive model (GAM) and distributed lag nonlinear model (DLNM) to estimate the effects of drought and air pollutants on daily upper respiratory infections (URTI) outpatient visits among children under 6 in three cities of Gansu province. The Standardized Precipitation Index (SPI) based on monthly precipitation (SPI-1) was used as an indicator of drought. A non-stratified model was established to explore the interaction effect of SPI-1 and air pollutants. We illustrated the number of daily pediatric URTI outpatient visits increased with the decrease in SPI-1. The interactive effects between air pollutants and the number of daily pediatric URTIs were significant. According to the non-stratified model, we revealed highly polluted and drought environments had the most significant impact on URTI in children. The occurrence of drought and air pollutants increased URTI in children and exhibited a significant interactive effect.

Analysis of the effect of temperature on tuberculosis incidence by distributed lag non-linear model in Kashgar city, China

The aim of this study was to explore the effect of temperature on tuberculosis (TB) incidence using the distributed lag non-linear model (DLNM) from 2017 to 2021 in Kashgar city, the region with higher TB incidence than national levels, and assist public health prevention and control measures. From January 2017 to December 2021, a total of 8730 cases of TB were reported, with the higher incidence of male than that of female. When temperature was below 1 °C, it was significantly correlated with TB incidence compared to the median observed temperature (15 °C) at lag 7, 14, and 21, and lower temperatures showed larger RR (relative risk) values. High temperature produced a protective effect on TB transmission, and higher temperature from 16 to 31 °C has lower RR. In discussion stratified by gender, the maximum RRs were achieved for both male group and female group at - 15 °C with lag 21, reporting 4.28 and 2.02, respectively. At high temperature (higher than 20 °C), the RR value of developing TB for female group was significantly larger than 1. In discussion stratified by age, the maximum RRs were achieved for all age groups (≤ 35, 36-64, ≥ 65) at - 15 °C with lag 21, reporting 3.20, 2.07, and 3.45, respectively. When the temperature was higher than 20 °C, the RR of the 36-64-year-old group and the ≥ 65-year-old group was significantly larger than 1 at lag 21, while significantly smaller than 1 for cumulative RR at lag 21, reporting 0.11, 95% confidence interval (CI) (0.01, 0.83) and 0.06, 95% CI (0.01, 0.44), respectively. In conclusion, low temperature, especially in extreme level, acts as a high-risk factor inducing TB transmission in Kashgar city. Males exhibit a significantly higher RR of developing TB at low temperature than female, as well as the elderly group in contrast to the young or middle-aged groups. High temperature has a protective effect on TB transmission in the total population, but female and middle-aged and elderly groups are also required to be alert to the delayed RR induced by it.

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, PM_2.5, 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, PM_2.5, 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.

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, PM_2.5, 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 PM_2.5, SO_2, 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.

Influence and prediction of meteorological factors on brucellosis in a northwest region of China

This paper aims to study the cumulative lag effect of meteorological factors on brucellosis incidence and the prediction performance based on Random Forest model. The monthly number of brucellosis cases and meteorological data from 2015 to 2019 in Yongchang of Gansu Province, northwest China, were used to build distributed lag nonlinear model (DLNM). The number of brucellosis cases of lag 1 month and meteorological data from 2015 to 2018 were used to build RF model to predict the brucellosis incidence in 2019. Meanwhile, SARIMA model was established to compare the prediction performance with RF model according to R² and RMSE. The results indicated that the population had a high incidence risk at temperature between 5 and 13 °C and lag between 0 and 18 days, sunshine duration between 225 and 260 h and lag between 0 and 1 month, and atmosphere pressure between 789 and 793.5 hPa and lag between 0 and 18 days. The R² and RMSE of train set and test set in RF model were 0.903, 1.609, 0.824, and 2.657, respectively, and the R² and RMSE in SARIMA model were 0.530 and 7.008. This study found significant nonlinear and lag associations between meteorological factors and brucellosis incidence. The prediction performance of RF model was more accurate and practical compared with SARIMA model.

A Time Series Study for Effects of PM10 on Coronary Heart Disease in Ganzhou, China

Objective: To investigate the effect of PM10 exposure in low concentration areas on the daily hospitalized patients with coronary heart disease. Methods: Daily air quality monitoring data, meteorological monitoring data and daily hospitalization data of coronary heart disease during 2019−2021 in Ganzhou, China were collected. Generalized additive model and distributed lag nonlinear model were used to evaluate the association between environmental PM10 and daily hospital visits for coronary heart disease. Stratified by sex and age to see their potential impact on this association. Results: PM10 exposure was correlated with an increased risk of hospitalization in coronary heart disease patients. Single-pollutant model analysis shows that at the day of lag1, for every 10 µg/m3 increase in PM10, the risk of coronary heart disease hospitalization increased by 1.69% (95%CI 0.39~3.00%); Subgroup analysis showed that females and older adults (>65 years) were more sensitive to PM10 exposure. In addition, in the dual-pollutant model, by adjusting other pollutants (including SO2, CO and O3), it was found that the relationship between PM10 exposure and coronary heart disease hospitalization was robust. And with changing the model’s degree of freedom was still robust. Conclusion: Short-term exposure to low concentrations of PM10 is associated with hospitalization for coronary heart disease. These results are important for local environmental public health policy development, so as to protect vulnerable populations.

Study on the influence of meteorological factors on influenza in different regions and predictions based on an LSTM algorithm

BACKGROUND

Influenza epidemics pose a threat to human health. It has been reported that meteorological factors (MFs) are associated with influenza. This study aimed to explore the similarities and differences between the influences of more comprehensive MFs on influenza in cities with different economic, geographical and climatic characteristics in Fujian Province. Then, the information was used to predict the daily number of cases of influenza in various cities based on MFs to provide bases for early warning systems and outbreak prevention.

METHOD

Distributed lag nonlinear models (DLNMs) were used to analyse the influence of MFs on influenza in different regions of Fujian Province from 2010 to 2021. Long short-term memory (LSTM) was used to train and model daily cases of influenza in 2010-2018, 2010-2019, and 2010-2020 based on meteorological daily values. Daily cases of influenza in 2019, 2020 and 2021 were predicted. The root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and symmetric mean absolute percentage error (SMAPE) were used to quantify the accuracy of model predictions.

RESULTS

The cumulative effect of low and high values of air pressure (PRS), air temperature (TEM), air temperature difference (TEMD) and sunshine duration (SSD) on the risk of influenza was obvious. Low (< 979 hPa), medium (983 to 987 hPa) and high (> 112 hPa) PRS were associated with a higher risk of influenza in women, children aged 0 to 12 years, and rural populations. Low (< 9 °C) and high (> 23 °C) TEM were risk factors for influenza in four cities. Wind speed (WIN) had a more significant effect on the risk of influenza in the ≥ 60-year-old group. Low (< 40%) and high (> 80%) relative humidity (RHU) in Fuzhou and Xiamen had a significant effect on influenza. When PRS was between 1005-1015 hPa, RHU > 60%, PRE was low, TEM was between 10-20 °C, and WIN was low, the interaction between different MFs and influenza was most obvious. The RMSE, MAE, MAPE, and SMAPE evaluation indices of the predictions in 2019, 2020 and 2021 were low, and the prediction accuracy was high.

CONCLUSION

All eight MFs studied had an impact on influenza in four cities, but there were similarities and differences. The LSTM model, combined with these eight MFs, was highly accurate in predicting the daily cases of influenza. These MFs and prediction models could be incorporated into the influenza early warning and prediction system of each city and used as a reference to formulate prevention strategies for relevant departments.

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 (PM_2.5) exposure and gestational diabetes mellitus (GDM). However, limited studies explored the relationships between PM_2.5 exposure and blood glucose levels during pregnancy, especially in highly polluted areas.

OBJECTIVES

To examine the associations of prenatal ambient PM_2.5 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 PM_2.5 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 PM_2.5 exposure.

RESULTS

Of 165 pregnant women, 23 (13.94%) developed GDM. After adjusting for potential confounders, PM_2.5 exposure during the 1^st 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 PM_2.5 during the 2^nd 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. PM_2.5 exposure at 24^th-27^th weeks after LMP was associated with increased GDM risk. We identified sensitive exposure windows of 21^st-24^th weeks for higher 1-h and 2-h blood glucose levels and of 20^th-22^nd weeks for increased Δ1h-fasting and Δ2h-fasting.

CONCLUSIONS

Ambient PM_2.5 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.

Effect modifications of green space and blue space on heat-mortality association in Hong Kong, 2008-2017

BACKGROUND

Despite emerging recognition of the benefits of green and blue spaces on human health, evidence for their effect modifications on heat-mortality associations is limited. We aimed to investigate the effect modifications of green and blue spaces on heat-mortality associations among different age and sex groups and at different heat levels.

METHODS

Daily mortality and meteorological data from 2008 to 2017 in Hong Kong, China were collected. The Normalized Difference Vegetation Index and distance to coast were used as proxies for green and blue space exposure, respectively. Time-series analyses was performed using fitting generalized linear mixed models with an interaction term between heat and levels of exposure to either green or blue space. Age-, sex-, and heat level-stratified analyses were also conducted.

RESULTS

With a 1 °C increase in temperature above the 90th percentile (29.61 °C), mortality increased by 5.7% (95% confidence interval [CI]: 1.6, 10.1%), 5.4% (1.4, 9.5%), and 4.6% (0.8, 8.9%) for low, medium and high levels of green space exposure, respectively, and by 7.5% (3.9, 11.2%) and 3.5% (0.3, 6.8%) for low and high levels of blue space exposure, respectively. Significant effect modifications of green and blue spaces were not observed for the whole population or any specific age and sex group, either at a moderate heat level or a heat level (Ps > 0.05).

CONCLUSIONS

No significant effect modifications of green and blue spaces on heat-related mortality risk were observed in Hong Kong. These findings challenge the existing evidence on the prominent protective role of green and blue spaces in mitigating heat-related mortality risks.

An assessment of meteorological parameters effects on COVID-19 pandemic in Bangladesh using machine learning models

Coronavirus (COVID-19) is a highly contagious virus (SARS-CoV-2) that has caused a global pandemic since January 2020. Scientists around the world are doing extensive research to control this disease. They are working tirelessly to find out the origin and causes of the disease. Several studies and experiments mentioned that there are some meteorological parameters which are highly correlated with COVID-19 transmission. In this work, we studied the effects of 11 meteorological parameters on the transmission of COVID-19 in Bangladesh. We first applied statistical analysis and observed that there is no significant effect of these parameters. Therefore, we proposed a novel technique to analyze the insight effects of these parameters by using a combination of Random Forest, CART, and Lasso feature selection techniques. We observed that 4 parameters are highly influential for COVID-19 where [Formula: see text] and Cloud have positive association whereas WS and AQ have negative impact. Among them, Cloud has the highest positive impact which is 0.063 and WS has the highest negative association which is [Formula: see text]. Moreover, we have validated our performance using DLNM technique. The result of this investigation can be used to develop an alert system that will assist the policymakers to know the characteristics of COVID-19 against meteorological parameters and can impose different policies based on the weather conditions.

Global trends in total fertility rate and its relation to national wealth, life expectancy and female education

Objectives

Along with the development of the times and progress of the society, the total fertility rate (TFR) markedly changed in each country. Therefore, it is critical to describe the trend of TFR and explore its influencing factors. However, previous studies did not consider the time lag and cumulative effect in the associations between the influencing factors and TFR. Thus, our study aimed to analyze the associations from a new dimension.

Methods

The study was employed using national-level data from the World Bank and United Nations Development Programme. Distributed lag non-linear models with 5-year lag were used to examine the independent associations between the relevant factors and TFR.

Results

The cumulative exposure-TFR curves were inverted U-shaped for log gross domestic product (GDP) per capita and life expectancy at birth, while the cumulative exposure-response curves were approximately linear for female expected years of schooling and human development index (HDI). However, it is worth noting that in the developed regions, TFR increased slightly with the high level of GDP per capita, female expected years of schooling and HDI.

Conclusions

Nowadays, with the growth of GDP per capita, life expectancy at birth, female expected years of schooling and HDI, TFR are on a drastic downward trend in most regions. Besides, with the development of society, when levels of the factors continued to increase, TFR also showed a slight rebound. Therefore, governments, especially those in developing countries, should take measures to stimulate fertility and deal with a series of problems caused by declining TFR.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-13656-1.

Short-term effect of sulfur dioxide (SO2) change on the risk of tuberculosis outpatient visits in 16 cities of Anhui Province, China: the first multi-city study to explore differences in occupational patients

A growing number of biological studies suggest that exogenous sulfur dioxide (SO₂) at a certain concentration may promote human resistance to Mycobacterium tuberculosis. However, the results of most relevant studies are inconsistent, and few studies have explored the relationship between SO₂ exposure and tuberculosis risk at provincial level. In addition, occupational exposure has long been considered to have a certain impact on the human body, so for the first time, we discussed the differences between different occupations in the study on the relationship between air pollutant exposure and tuberculosis risk, and evaluated the impact of occupational exposure. This study aimed to explore the association between short-term SO₂ exposure and the risk of outpatient visits to tuberculosis in Anhui province and 16 prefecture-level cities from 2015 to 2020. We used several models for multi-stage analysis, including distributed lag nonlinear model (DLNM), Poisson generalized linear regression model, and random-effects model. The association was assessed using the 28-day cumulative lag effect RR and 95%CI for each 10-unit increase in SO₂ concentration. We divided all patients into the following six occupations: Worker, Farmer, Retired people, Children and Students, Cadre and Office clerk, and Service staff (catering, business, etc.). Sex, age, and season were analyzed by subgroup. Finally, the robustness of the multi-pollutant model was tested. At provincial level, the overall effect value of SO₂ was RR=0.8191 (95%CI: 07702~0.8712); after grouping all patients by occupation, the association found only among Farmers (RR = 0.7150, 95%CI: 0.6699-0.7632, lag 0-28 days) and Workers (RR = 0.8566, 95%CI: 0.7930-0.9930, lag 0-4 days) was still statistically significant. Estimates for individual cities and using random-effects models to estimate average associations showed that SO₂ exposure was associated with a reduced risk of outpatient TB visits in 14 municipalities, which remained significant when aggregated (RR = 0.9030, 95%CI: 0.8730-0.9340). Analysis of patients grouped by occupation in each municipality showed that statistical significance was again observed only in the Farmer (RR = 0.8880, 95%CI: 0.8610-0.9160) and Worker (RR = 0.8250, 95%CI: 0.7290-0.9340) groups. Stratified analysis of age, sex, and season showed that the effect of SO₂ exposure was greater for middle-aged people (18-64 years old) and males, and less for seasonal changes. In summary, we found that exposure to SO₂ reduces the risk of outpatient visits to tuberculosis, with farmers and workers more susceptible to SO₂. Gender and age had a greater impact on the risk of TB outpatient visits than seasonal variations.

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 PM_2.5 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.

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 PM_2.5 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 PM_2.5 concentration, but led to an increase in the O₃ concentration. Multi-scenario simulation results showed that anthropogenic emission reduction could reduce the average PM_2.5 concentration after the Chinese Spring Festival, but improvement during days with heavy pollution was limited. Considering that O₃ enhances the PM_2.5 levels, to achieve the collaborative improvement of PM_2.5 and O₃ concentrations, further research should explore the collaborative emission reduction scheme with VOCs and NO_x to achieve the collaborative improvement of PM_2.5 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.

Cause-specific cardiovascular disease mortality attributable to ambient temperature: A time-stratified case-crossover study in Jiangsu province, China

BACKGROUND

Exposure to non-optimum ambient temperature has been linked to increased risk of total cardiovascular disease (CVD) mortality; however, the adverse effects on mortality from specific types of CVD remain less understood.

OBJECTIVES

To comprehensively investigate the association of ambient temperature with cause-specific CVD mortality, and to estimate and compare the corresponding mortality burden.

METHODS

We conducted a time-stratified case-crossover study of 1000,014 CVD deaths in Jiangsu province, China during 2015-2019 using data from the China National Mortality Surveillance System. Residential daily 24-hour average temperature for each subject was extracted from a validated grid data at a spatial resolution of 0.0625° × 0.0625°. We fitted distributed lag non-linear models (DLNM) based on conditional logistic regression to quantitatively investigate the association of ambient temperature with total and cause-specific CVD mortality, which was used to further estimate mortality burden attributable to non-optimum ambient temperatures.

RESULTS

With adjustment for relative humidity, we observed reverse J-shaped exposure-response associations of ambient temperature with total and cause-specific CVD mortality, with minimum mortality temperatures ranging from 19.5 °C to 23.0 °C. An estimated 20.3% of the total CVD deaths were attributable to non-optimum temperatures, while the attributable fraction (AF) of mortality from chronic rheumatic heart diseases, hypertensive diseases, ischemic heart diseases (IHD), pulmonary heart disease, stroke, and sequelae of stroke was 22.4%, 23.2%, 23.3%, 20.9%, 17.6% and 21.3%, respectively. For total and cause-specific CVDs, most deaths were attributable to moderate cold temperature. We observed significantly higher mortality burden from total and certain cause-specific CVDs in adults 80 years or older and those who were widowed.

CONCLUSION

Exposure to ambient temperature was significantly associated with increased risk of cause-specific CVD mortality. The burden of CVD mortality attributable to non-optimum temperature was substantial especially in older and widowed adults, and significantly varied across specific types of CVD.

Short-term effect of ambient air pollutant change on the risk of tuberculosis outpatient visits: a time-series study in Fuyang, China

There is growing evidence that air pollution plays a role in TB, and most studies have been conducted in the core countries with inconsistent results. Few studies have comprehensively included the six common air pollutants, so they cannot consider whether various pollutants interact with each other. Our objectives were to investigate the association between short-term exposure to six common air pollutants and the risk of tuberculosis outpatient visits in Fuyang, China, 2015-2020. We combined the two models to explore the effects of exposure to six air pollutants on the risk of tuberculosis outpatient visits, including the Poisson generalized linear regression model and distributed lag non-linear model (DLNM). We performed stratified analyses for the season, type of cases, gender, and age. We used the lag-specific relative risks and cumulative relative risk obtained by increasing pollutant concentration by per 10 units to evaluate the connection between six air pollutants and TB; PM_2.5 (RR = 1.0018, 95% CI: 1.0004-1.0032, delay of 12 days) and SO₂ (RR = 1.0169, 95% CI: 1.0007-1.0333, lag 0-16 days) were 0.9549 (95% CI: 0.9389-0.9712, lag 0 day) and 0.8212 (95% CI: 0.7351-0.9173, 0-20-day lag). Stratified analyses showed that seasonal differences had a greater impact on TB, males were more likely to develop TB than females, older people were more likely to develop TB than younger people, and air pollution had a great impact on new cases. Exposure to O₃, CO, PM₁₀, PM_2.5, and NO₂ increases the risk of TB outpatient visits, except SO₂ which reduces the risk. The incidence of TB has seasonal fluctuations. It is necessary for the government to establish a sound environmental monitoring and early warning system to strengthen the monitoring and emission management of pollutants in the atmosphere. Management, prevention, and treatment measures should be developed for high-risk groups (males and older people), reducing the risk of TB by reducing their specific behaviors and changing their lifestyle. We need to pay more attention to the impact of seasonal effects on TB to protect TB patients and avoid a shortage of medical resources, and it is necessary for the government to develop some seasonal preventive measures in the future.

Ambient temperature, humidity, and urinary system diseases: a population-based study in Western China

Climate has received an increasing attention due to its adverse effects on human health, but the effects on the urinary system are still short of enough evidence. Therefore, we carry out this study to analyze the relationship between meteorological factors and urinary system health in arid areas of western China. In this study, the daily numbers of outpatients with the urinary system diseases from multiple hospitals in three cities in Gansu province (Lanzhou, Zhangye, and Tianshui city) were collected and used for analysis. The distributed lag non-linear models (DLNM) with a quasi-Poisson distribution were used to estimate the associations between meteorological factors and daily outpatients for urinary system diseases in these three cities, and then a multivariate meta-analysis was applied to pool the estimates of city-specific effects. We found that the ambient temperature (AT) and relative humidity (RH) were significantly associated with the outpatient visits of urinary system diseases. The effects of meteorological factors on outpatients with urinary system diseases for both males and females were statistically significant at different lag days. The higher AT and lower RH were associated with the higher risk of urinary system diseases. We also observed substantial lag effects of meteorological factors on outpatients for both males and females. Among all disease types, renal tubule-interstitial diseases had the strongest relationships with meteorological factors. Our results indicate that the higher AT and lower RH may increase the outpatient visits for urinary system diseases, with significant lag effects in semi-arid areas.

Impact of Short-Term Exposure to Extreme Temperatures on Mortality: A Multi-City Study in Belgium

In light of climate change, health risks are expected to be exacerbated by more frequent high temperatures and reduced by less frequent cold extremes. To assess the impact of different climate change scenarios, it is necessary to describe the current effects of temperature on health. A time-stratified case-crossover design fitted with conditional quasi-Poisson regressions and distributed lag non-linear models was applied to estimate specific temperature-mortality associations in nine urban agglomerations in Belgium, and a random-effect meta-analysis was conducted to pool the estimates. Based on 307,859 all-cause natural deaths, the mortality risk associated to low temperature was 1.32 (95% CI: 1.21-1.44) and 1.21 (95% CI: 1.08-1.36) for high temperature relative to the minimum mortality temperature (23.1 °C). Both cold and heat were associated with an increased risk of cardiovascular and respiratory mortality. We observed differences in risk by age category, and women were more vulnerable to heat than men. People living in the most built-up municipalities were at higher risk for heat. Air pollutants did not have a confounding effect. Evidence from this study helps to identify specific populations at risk and is important for current and future public health interventions and prevention strategies.

Effects of temperature and PM2.5 on the incidence of hand, foot, and mouth in a heavily polluted area, Shijiazhuang, China

The influence of weather and air pollution factors on hand, foot, and mouth disease (HFMD) has received widespread attention. However, most of the existing studies came from lightly polluted areas and the results were inconsistent. There was a lack of relevant evidence of heavily polluted areas. This study aims to quantify the relationship between weather factors and air pollution with HFMD in heavily polluted areas. We collected the daily number of hand, foot, and mouth disease in Shijiazhuang, China from 2014 to 2018, as well as meteorological and air pollutant data over the same period. The generalized linear model combined with the distributed lag model was used to study the effect of meteorological factors and air pollutants on the daily cases of HFMD and its hysteresis effect. We found that the dose-response relationship between temperature, PM_2.5, and the risk of hand-foot-mouth disease was non-linear. Both low temperature and high temperature increased the risk of hand-foot-mouth disease. The cumulative effect of high temperature reached the maximum at 0-10 lag days, and the cumulative effect of low temperature reached the maximum at 0-3 lag days. The concentration of PM_2.5 between 76 and 200 μg/m³ has a certain risk of the onset of hand, foot, and mouth disease, but the extreme PM_2.5 concentration has a certain protective effect. In addition, low humidity, low wind speed, and low-O₃ can increase the risk of HFMD. Risks of humidity and low concentration of O₃ increased as lag days extended. In conclusion, our study found that climate factors and air pollutants exert varying degrees of impact on HFMD. Our research provided the scientific basis for establishing an early warning system so that medical staff and parents can take corresponding measures to prevent HFMD.

Short-Term Joint Effects of PM10, NO2 and SO2 on Cardio-Respiratory Disease Hospital Admissions in Cape Town, South Africa

Background/Aim: In sub-Sahara Africa, few studies have investigated the short-term association between hospital admissions and ambient air pollution. Therefore, this study explored the association between multiple air pollutants and hospital admissions in Cape Town, South Africa. Methods: Generalized additive quasi-Poisson models were used within a distributed lag linear modelling framework to estimate the cumulative effects of PM₁₀, NO₂, and SO₂ up to a lag of 21 days. We further conducted multi-pollutant models and stratified our analysis by age group, sex, and season. Results: The overall relative risk (95% confidence interval (CI)) for PM₁₀, NO₂, and SO₂ at lag 0–1 for hospital admissions due to respiratory disease (RD) were 1.9% (0.5–3.2%), 2.3% (0.6–4%), and 1.1% (−0.2–2.4%), respectively. For cardiovascular disease (CVD), these values were 2.1% (0.6–3.5%), 1% (−0.8–2.8%), and −0.3% (−1.6–1.1%), respectively, per inter-quartile range increase of 12 µg/m³ for PM₁₀, 7.3 µg/m³ for NO₂, and 3.6 µg/m³ for SO₂. The overall cumulative risks for RD per IQR increase in PM₁₀ and NO₂ for children were 2% (0.2–3.9%) and 3.1% (0.7–5.6%), respectively. Conclusion: We found robust associations of daily respiratory disease hospital admissions with daily PM₁₀ and NO₂ concentrations. Associations were strongest among children and warm season for RD.

Temperature might increase the hospital admission risk for rheumatoid arthritis patients in Anqing, China: a time-series study

Temperature has been studied in relation to many health outcomes. However, few studies have explored its effect on the risk of hospital admission for rheumatoid arthritis (RA). A distributed lag non-linear model (DLNM) was used to analyze associations between mean temperature, diurnal temperature range (DTR), temperature change between neighboring days (TCN), and daily admissions for RA from 2015 to 2019 in Anqing, China. Subgroup analyses based on age, gender, rheumatoid factors, and admission route were performed. In total, 1456 patients with RA were hospitalized. Regarding the cumulative-lag effects of extreme cold temperature (5th percentile = 3℃), the risks of admissions for RA were increased and highest at lag 0-11 (RR = 2.68, 95% CI: 1.23-5.86). Exposing to low (5th percentile = 1.9℃) and high (95th percentile = 14.2℃) DTRs both had increased risks of RA admission, with highest RRs of 1.40 (95% CI: 1.03-1.91) and 1.24 (95% CI: 1.0-1.53) at lag 0 day, respectively. As for TCN, the marginal risk of admission in RA patients was found when exposed to high TCN (95th percentile = 2.9℃) with the largest single-day effect at lag 10 (RR = 1.11, 95% CI: 1.01-1.23). In subgroup analyses, females were more susceptible to extreme cold temperature, low and high DTRs, and high TCN. In regard to extreme cold temperature, significant risk of hospital admission in females only appeared at lag 2 (RR = 1.48, 95% CI: 1.02-2.15) and lag 0-2 (RR = 2.35, 95% CI: 1.11-4.95). It is clear that RA patients exposed to changing temperature may increase risks of admission.

Pricing air pollution: evidence from short-term exposure to air pollution on hospitalization of acute bronchitis and chronic obstructive pulmonary disease in southwestern China

Existing evidence suggests that ambient air pollution has serious adverse effects on respiratory diseases, yet there is little direct evidence from China regarding corresponding economic losses. Here we quantified air pollution-related acute health effects and related economic losses of the most common two respiratory diseases in southwestern China, acute bronchitis and chronic obstructive pulmonary disease (COPD). We applied a distributed lag non-linear model to analyse the relationship between ambient air pollutants and hospital admissions of acute bronchitis and COPD, then applied the cost of illness method to explore the attributing economic burden. During the study period, 528 334 and 99 419 hospital admissions of acute bronchitis and COPD, respectively, were recorded. As a result, during the study period the total hospitalization economic losses attributable to air pollution were 486.40 and 254.74 million yuan for acute bronchitis and COPD, respectively, accounting for 0.015% of local gross domestic product. Our research provides intuitive evidence on the health and economic impacts of short-term exposure to air pollution, which is a key basis for the formulation of environmental policies.