Distributed Lag Non-linear Model Research Articles

Influenza Epidemic Trend Surveillance and Prediction Based on Search Engine Data: Deep Learning Model Study.

Influenza outbreaks pose a significant threat to global public health. Traditional surveillance systems and simple algorithms often struggle to predict influenza outbreaks in an accurate and timely manner. Big data and modern technology have offered new modalities for disease surveillance and prediction. Influenza-like illness can serve as a valuable surveillance tool for emerging respiratory infectious diseases like influenza and COVID-19, especially when reported case data may not fully reflect the actual epidemic curve. This study aimed to develop a predictive model for influenza outbreaks by combining Baidu search query data with traditional virological surveillance data. The goal was to improve early detection and preparedness for influenza outbreaks in both northern and southern China, providing evidence for supplementing modern intelligence epidemic surveillance methods. We collected virological data from the National Influenza Surveillance Network and Baidu search query data from January 2011 to July 2018, totaling 3,691,865 and 1,563,361 respective samples. Relevant search terms related to influenza were identified and analyzed for their correlation with influenza-positive rates using Pearson correlation analysis. A distributed lag nonlinear model was used to assess the lag correlation of the search terms with influenza activity. Subsequently, a predictive model based on the gated recurrent unit and multiple attention mechanisms was developed to forecast the influenza-positive trend. This study revealed a high correlation between specific Baidu search terms and influenza-positive rates in both northern and southern China, except for 1 term. The search terms were categorized into 4 groups: essential facts on influenza, influenza symptoms, influenza treatment and medicine, and influenza prevention, all of which showed correlation with the influenza-positive rate. The influenza prevention and influenza symptom groups had a lag correlation of 1.4-3.2 and 5.0-8.0 days, respectively. The Baidu search terms could help predict the influenza-positive rate 14-22 days in advance in southern China but interfered with influenza surveillance in northern China. Complementing traditional disease surveillance systems with information from web-based data sources can aid in detecting warning signs of influenza outbreaks earlier. However, supplementation of modern surveillance with search engine information should be approached cautiously. This approach provides valuable insights for digital epidemiology and has the potential for broader application in respiratory infectious disease surveillance. Further research should explore the optimization and customization of search terms for different regions and languages to improve the accuracy of influenza prediction models.

Association between air temperature and risk of hospitalization for genitourinary disorders: An environmental epidemiological study in Lanzhou, China.

The aim of this study was to investigate the relationship between air temperature and the risk of hospitalization for genitourinary disorders. Distributed lag non-linear models (DLNM) were used to estimate the association between air temperature and the risk of hospitalization for genitourinary disorders, with subgroup analysis by gender and age to identify the susceptible population of temperature-sensitive genitourinary system diseases. Low mean temperature (MT) (RR = 2.001, 95% CI: 1.856~2.159), high MT (RR = 2.884, 95% CI: 2.621~3.173) and low diurnal temperature range (DTR) (RR = 1.619, 95% CI: 1.508~1.737) were all associated with the increased risk of hospitalization for genitourinary disorders in the total population analysis, and the high MT effect was stronger than the low MT effect. Subgroup analysis found that high MT was more strongly correlated in male (RR = 2.998, 95% CI: 2.623~3.427) and those <65 years (RR = 3.003, 95% CI: 2.670~3.344), and low DTR was more strongly correlated in female (RR = 1.669, 95% CI: 1.510~1.846) and those <65 years (RR = 1.643, 95% CI: 1.518~1.780). The effect of high MT on the risk of hospitalization for genitourinary disorders is more significant than that of low MT. DTR was independently associated with the risk of hospitalization for genitourinary disorders.

Analysis of the association between diurnal temperature difference and relative humidity and myocardial infarction in Shantou residents

Objective: To analyze the effect of meteorological conditions on mortality and population susceptibility of patients with acute myocardial infarction (AMI) in the Shantou area and to provide a scientific basis for the local public health system to prevent AMI. Methods: The AMI mortality data recorded in the resident cause of death surveillance database of Shantou from January 1, 2015, to December 31, 2020, were collected and the distribution lag nonlinear model was used to analyze the diurnal temperature range (DTR) and relative humidity (RH) on AMI mortality and the lag effect. Results: There were 13 932 deaths due to AMI in Shantou during the study period, with a male-to-female sex ratio of 1.3∶1. There was a significant association between high diurnal temperature difference exposure and low RH exposure and AMI deaths, with both single-day lag effects appearing and reaching a maximum at lag 2 day (RR=1.019, 95%CI: 1.000-1.039; RR=1.018, 95%CI: 1.003-1.034); the cumulative lag effect was all maximal at lag 0-14 day (RR=1.199, 95%CI: 1.025-1.401; RR=1.279, 95%CI: 1.117-1.465). The elderly (≥75 years) and female populations were susceptible to high DTR exposure and low RH exposure conditions. Conclusions: There was a significant association between DTR and RH and mortality of AMI in Shantou with a significant lag in their effects. Both female and elderly populations ≥75 years old were susceptible populations under high DTR and low RH exposure conditions.

Impact of meteorological factors on the incidence of hand-foot-mouth disease in Yangzhou from 2017 to 2022: a time series study.

Hand, foot, and mouth disease (HFMD) is a significant public health issue in China, and numerous studies have indicated a close association between HFMD incidence and meteorological factors. This study aims to investigate the relationship between meteorological factors and HFMD in Yangzhou City, Jiangsu Province, China. HFMD case reports and meteorological data from Yangzhou City between 2017 and 2022 were extracted from the National Notifiable Infectious Disease Surveillance System and the Meteorological Data Sharing Service System, respectively. A generalized additive model (GAM) was employed to assess the exposure-response relationship between meteorological factors and HFMD. Subsequently, a distributed lag nonlinear model (DLNM) was used to explore the exposure-lag-effect of meteorological factors on HFMD. HFMD in Yangzhou City exhibits obvious seasonality and periodicity. There is an inverted "U" shaped relationship between average temperature and the risk of HFMD, with the maximum lag effect observed at a temperature of 25°C with lag 0 day (RR = 2.07, 95% CI: 1.74-2.47). As the duration of sunshine and relative humidity increase, the risk of HFMD continuously rises, with the maximum lag effect observed at a sunshine duration of 12.4 h with a lag of 14 days (RR = 2.10, 95% CI: 1.17-3.77), and a relative humidity of 28% with a lag of 14 days (RR = 1.21, 95% CI: 1.01-1.64). There is a "U" shaped relationship between average atmospheric pressure and the risk of HFMD, with the maximum effect observed at an atmospheric pressure of 989 hPa with no lag (RR = 1.45, 95% CI: 1.25-1.69). As precipitation increases, the risk of HFMD decreases, with the maximum effect observed at a precipitation of 151 mm with a lag of 14 days (RR = 1.45, 95% CI: 1.19-2.53). Meteorological factors including average temperature, average atmospheric pressure, relative humidity, precipitation, and sunshine duration significantly influenced the risk of HFMD in Yangzhou City. Effective prevention measures for HFMD should be implemented, taking into account the local climate conditions.

Machine learning analysis and risk prediction of weather-sensitive mortality related to cardiovascular disease during summer in Tokyo, Japan

Climate-sensitive diseases developing from heat or cold stress threaten human health. Therefore, the future health risk induced by climate change and the aging of society need to be assessed. We developed a prediction model for mortality due to cardiovascular diseases such as myocardial infarction and cerebral infarction, which are weather or climate sensitive, using machine learning (ML) techniques. We evaluated the daily mortality of ischaemic heart disease (IHD) and cerebrovascular disease (CEV) in Tokyo and Osaka City, Japan, during summer. The significance of delayed effects of daily maximum temperature and other weather elements on mortality was previously demonstrated using a distributed lag nonlinear model. We conducted ML by a LightGBM algorithm that included specified lag days, with several temperature- and air pressure-related elements, to assess the respective mortality risks for IHD and CEV, based on training and test data for summer 2010–2019. These models were used to evaluate the effect of climate change on the risk for IHD mortality in Tokyo by applying transfer learning (TL). ML with TL predicted that the daily IHD mortality risk in Tokyo would averagely increase by 29% and 35% at the 95th and 99th percentiles, respectively, using a high-level warming-climate scenario in 2045–2055, compared to the risk simulated using ML in 2009–2019.

Association Between Prosuicide Website Searches Through Google and Suicide Death in the United States From 2010 to 2021: Lagged Time-Series Analysis.

The rate of suicide death has been increasing, making understanding risk factors of growing importance. While exposure to explicit suicide-related media, such as description of means in news reports or sensationalized fictional portrayal, is known to increase population suicide rates, it is not known whether prosuicide website forums, which often promote or facilitate information about fatal suicide means, are related to change in suicide deaths overall or by specific means. This study aimed to estimate the association of the frequency of Google searches of known prosuicide web forums and content with death by suicide over time in the United States, by age, sex, and means of death. National monthly Google search data for names of common prosuicide websites between January 2010 and December 2021 were extracted from Google Health Trends API (application programming interface). Suicide deaths were identified using the CDC (Centers for Disease Control and Prevention) National Vital Statistics System (NVSS), and 3 primary means of death were identified (poisoning, suffocation, and firearm). Distributed lag nonlinear models (DLNMs) were then used to estimate the lagged association between the number of Google searches on suicide mortality, stratified by age, sex, and means, and adjusted for month. Sensitivity analyses, including using autoregressive integrated moving average (ARIMA) modeling approaches, were also conducted. Months in the United States in which search rates for prosuicide websites increased had more documented deaths by intentional poisoning and suffocation among both adolescents and adults. For example, the risk of poisoning suicide among youth and young adults (age 10-24 years) was 1.79 (95% CI 1.06-3.03) times higher in months with 22 searches per 10 million as compared to 0 searches. The risk of poisoning suicide among adults aged 25-64 was 1.10 (95% CI 1.03-1.16) times higher 1 month after searches reached 9 per 10 million compared with 0 searches. We also observed that increased search rates were associated with fewer youth suicide deaths by firearms with a 3-month time lag for adolescents. These models were robust to sensitivity tests. Although more analysis is needed, the findings are suggestive of an association between increased prosuicide website access and increased suicide deaths, specifically deaths by poisoning and suffocation. These findings emphasize the need to further investigate sites containing potentially dangerous information and their associations with deaths by suicide, as they may affect vulnerable individuals.

Impacts of exposure to humidex on cardiovascular mortality: a multi-city study in Southwest China

BackgroundMany studies have reported the association between ambient temperature and mortality from cardiovascular disease (CVD). However, the health effects of humidity are still unclear, much less the combined effects of temperature and humidity. In this study, we used humidex to quantify the effect of temperature and humidity combined on CVD mortality.MethodsDaily meteorological, air pollution, and CVD mortality data were collected in four cities in southwest China. We used a distributed lag non-linear model (DLNM) in the first stage to assess the exposure–response association between humidex and city-specific CVD mortality. A multivariate meta-analysis was conducted in the second stage to pool these effects at the overall level. To evaluate the mortality burden of high and low humidex, we determined the attributable fraction (AF). According to the abovementioned processes, stratified analyses were conducted based on various demographic factors.ResultsHumidex and the CVD exposure–response curve showed an inverted “J” shape, the minimum mortality humidex (MMH) was 31.7 (77th percentile), and the cumulative relative risk (CRR) was 2.27 (95% confidence interval [CI], 1.76–2.91). At extremely high and low humidex, CRRs were 1.19 (95% CI, 0.98–1.44) and 2.52 (95% CI, 1.88–3.38), respectively. The burden of CVD mortality attributed to non-optimal humidex was 21.59% (95% empirical CI [eCI], 18.12–24.59%), most of which was due to low humidex, with an AF of 20.16% (95% eCI, 16.72–23.23%).ConclusionsLow humidex could significantly increase the risk of CVD mortality, and vulnerability to humidex differed across populations with different demographic characteristics. The elderly (> 64 years old), unmarried people, and those with a limited level of education (1–9 years) were especially susceptible to low humidex. Therefore, humidex is appropriate as a predictor in a CVD early-warning system.

Assessing heat effects on respiratory mortality and location characteristics as modifiers of heat effects at a small area scale in Central-Northern Europe.

Heat effects on respiratory mortality are known, mostly from time-series studies of city-wide data. A limited number of studies have been conducted at the national level or covering non-urban areas. Effect modification by area-level factors has not been extensively investigated. Our study assessed the heat effects on respiratory mortality at a small administrative area level in Norway, Germany, and England and Wales, in the warm period (May-September) within 1996-2018. Also, we examined possible effect modification by several area-level characteristics in the framework of the EU-Horizon2020 EXHAUSTION project. Daily respiratory mortality counts and modeled air temperature data were collected for Norway, Germany, and England and Wales at a small administrative area level. The temperature-mortality association was assessed by small area-specific Poisson regression allowing for overdispersion, using distributed lag non-linear models. Estimates were pooled at the national level and overall using a random-effect meta-analysis. Age- and sex-specific models were also applied. A multilevel random-effects model was applied to investigate the modification of the heat effects by area-level factors. A rise in temperature from the 75th to 99th percentile was associated with a 27% (95% confidence interval [CI] = 19%, 34%) increase in respiratory mortality, with higher effects for females. Increased population density and PM2.5 concentrations were associated with stronger heat effects on mortality. Our study strengthens the evidence of adverse heat effects on respiratory mortality in Northern Europe by identifying vulnerable subgroups and subregions. This may contribute to the development of targeted policies for adaptation to climate change.

Hourly Associations between Heat Index and Heat-Related Emergency Medical Service (EMS) Calls in Austin-Travis County, Texas.

This paper aims to investigate the following research questions: (1) what are the hourly patterns of heat index and heat-related emergency medical service (EMS) incidents during summertime?; and (2) how do the lagged effects of heat intensity and hourly excess heat (HEH) vary by heat-related symptoms? Using the hourly weather and heat-related EMS call data in Austin-Travis County, Texas, this paper reveals the relationship between heat index patterns on an hourly basis and heat-related health issues and evaluates the immediate health effects of extreme heat events by utilizing a distributed lag non-linear model (DLNM). Delving into the heat index intensity and HEH, our findings suggest that higher heat intensity has immediate, short-term lagged effects on all causes of heat-related EMS incidents, including in cardiovascular, respiratory, neurological, and non-severe cases, while its relative risk (RR) varies by time. HEH also shows a short-term cumulative lagged effect within 5 h in all-cause, cardiovascular, and non-severe symptoms, while there are no statistically significant RRs found for respiratory and neurological cases in the short term. Our findings could be a reference for policymakers when devoting resources, developing extreme heat warning standards, and optimizing local EMS services, providing data-driven evidence for the effective deployment of ambulances.

Hysteresis effects of different levels of storm flooding on susceptible enteric infectious diseases in a central city of China

BackgroundRecently, attention has focused on the impact of global climate change on infectious diseases. Storm flooding is an extreme weather phenomenon that not only impacts the health of the environment but also worsens the spread of pathogens. This poses a significant challenge to public health security. However, there is still a lack of research on how different levels of storm flooding affect susceptible enteric infectious diseases over time.MethodsData on enteric infectious diseases, storm flooding events, and meteorology were collected for Changsha, Hunan Province, between 2016 and 2020. The Wilcoxon Rank Sum Test was used to identify the enteric infectious diseases that are susceptible to storm flooding. Then, the lagged effects of different levels of storm flooding on susceptible enteric infectious diseases were analyzed using a distributed lag nonlinear model.ResultsThere were eleven storm flooding events in Changsha from 2016 to 2020, concentrated in June and July. 37,882 cases of enteric infectious diseases were reported. During non-flooding days, the daily incidence rates of typhoid/paratyphoid and bacillary dysentery were 0.3/100,000 and 0.1/100,000, respectively. During flooding days, the corresponding rates increased to 2.0/100,000 and 0.8/100,000, respectively. The incidence rates of both diseases showed statistically significant differences between non-flooding and flooding days. Correlation analysis shows that the best lags for typhoid/paratyphoid and bacillary dysentery relative to storm flooding events may be 1 and 3 days. The results of the distributed lag nonlinear model showed that typhoid/paratyphoid had the highest cumulative RR values of 2.86 (95% CI: 1.71–4.76) and 8.16 (95% CI: 2.93–22.67) after 4 days of general flooding and heavy flooding, respectively; and bacillary dysentery had the highest cumulative RR values of 1.82 (95% CI: 1.40–2.35) and 3.31 (95% CI: 1.97–5.55) after 5 days of general flooding and heavy flooding, respectively.ConclusionsTyphoid/paratyphoid and bacillary dysentery are sensitive enteric infectious diseases related to storm flooding in Changsha. There is a lagging effect of storm flooding on the onset of typhoid/paratyphoid and bacillary dysentery, with the best lagging periods being days 1 and 3, respectively. The cumulative risk of typhoid/paratyphoid and bacillary dysentery was highest at 4/5 days lag, respectively. The higher of storm flooding, the higher the risk of disease, which suggests that the authorities should take appropriate preventive and control measures before and after storm flooding.

The added effects of cold spells on stroke admissions: Differential effects on ischemic and hemorrhagic stroke.

Epidemiological evidence suggests an association between low ambient temperature and stroke risk, but available data are limited particularly on associations with different stroke subtypes. The aim of this study is to estimate the relationship between cold spells and stroke admissions, including the effect of cold spells on different stroke subtypes (ischemic stroke and intracerebral hemorrhage (ICH)). A total of 144,405 stroke admissions from the Tianjin Centre for Health and Meteorology Multidisciplinary Innovation in China, covering the period from January 2016 to December 2020, were studied, as well as meteorological and air pollutant data. A generalized additive model with a distributed lag nonlinear model was employed to assess the relationship, considering 12 different definitions of a cold spell based on various temperature thresholds and durations. The analysis controlled for lagged and nonlinear effects of temperature. Analyses were performed on all strokes as well as ischemic stroke and ICH. There was a significant increase in stroke admissions during cold spells. Generally, the increased risk during cold spells increased as the temperature threshold decreased, but was not significantly affected by the duration. The optimal model was obtained using the cold-spell definition based on an average daily temperature below the 10th percentile (0.11°C) for 2 or more consecutive days. According to this model, the effect of cold spells on ischemic stroke admissions had a significant lag effect and was long-lasting, with a single-day effect occurring on lag 7d, peaking on lag 13d (relative risk (RR) = 1.05; 95% confidence interval (CI) = 1.02 to 1.09), and lasting until lag 20d. In contrast, the effect on ICH was immediate and short-lived, with the most significant single-day effect occurring on the current day (RR = 1.17; 95% CI = 1.06 to 1.29) and limited within 3 days. 14.15% of stroke cases could be attributed to cold spells, with ICH exhibiting a higher burden than ischemic stroke except for strict temperature threshold definitions. Cold spells are associated with an increased stroke risk. Different patterns of association were seen for different stroke subtypes. The effect on ischemic stroke had a lag effect and a longer duration, whereas the effect on ICH had an immediate effect and a shorter duration. These findings support the development and improvement of stroke cold-spell early warning systems and highlight the importance of public health interventions to mitigate the adverse health impacts of cold spells.

High Summer Temperatures and Heat Stroke Mortality in Spain.

Heat stroke is a significant cause of mortality in response to high summer temperatures. There is limited evidence on the pattern and magnitude of the association between temperature and heat stroke mortality. We examined this association in Spain, using data from a 27-year follow-up period. We used a space-time-stratified case-crossover design. We analyzed data using conditional quasi-Poisson regression with distributed lag nonlinear models. Spain recorded a total of 285 heat stroke deaths between 1990 and 2016. Heat stroke deaths occurred in 6% of the days in the summer months. The mean temperature was, on average, 5 °C higher on days when a heat stroke was recorded than on days without heat stroke deaths. The overall relative risk was 1.74 (95% confidence interval = 1.54, 1.96) for a 1 °C rise in mean temperature above the threshold of 16 °C, at which a heat stroke death was first recorded. We observed lagged effects as long as 10 days. Although heat stroke represents a small fraction of total heat-attributable mortality during the summer, it is strongly associated with high temperatures, providing an immediately visible warning of heat-related risk.

Ambient carbon monoxide and relative risk of daily hospital outpatient visits for respiratory diseases in Lanzhou, China.

At present, evidence of the associations between carbon monoxide (CO) and respiratory diseases (RD) in Northwest China is limited and controversial. The aim of this study is to evaluate the impact of ambient CO on outpatient visits for RD in Lanzhou, China. The daily amount of outpatient visits for total and cause-specific RD, air pollutant, and weather variables were collected in Lanzhou, China from 1st January 2013 to 31st December 2019. A generalized additive model and distributed lag nonlinear model were used to assess associations between CO and outpatient visits for RD. During the study period, a total of 1,623,361 RD outpatient visits were recorded. For each interquartile range (IQR) (0.77mg/m3) increase in CO, the relative risk (RR) was 1.163 (95% CI: 1.138, 1.188) for total RD at lag07, 1.153 (95% CI: 1.128,1.179) for upper respiratory tract infection (URTI) at lag07, 1.379 (95% CI: 1.338,1.422) for pneumonia at lag07, 1.029 (95% CI: 0.997,1.062) for chronic obstructive pulmonary disease (COPD) lag04, 1.068 (95% CI: 1.028,1.110) for asthma lag03, and 1.212 (95% CI: 1.178,1.247) for bronchitis lag07, respectively. In the subgroup analyses, the impacts of CO were more pronounced on total RD, pneumonia, COPD, and bronchitis in males than females, while the opposite was true in URTI and asthma. The impact of CO on RD was the strongest for children under 15years-of-age. We also found significantly stronger effects during cold seasons compared to warm seasons. In addition, we observed a roughly linear exposure-response curve between CO and RD with no threshold effect. This study in Lanzhou revealed a remarkable association between CO level and an elevated risk of total and cause-specific RD outpatient visits, especially for pneumonia.

Differential effects of size-specific particulate matter on lower respiratory infections in children: A time-series analysis in Lanzhou, China

This study aimed to assess the differential effects of size-specific PM (PM2.5, PM10–2.5, and PM10) on daily outpatients for Lower Respiratory Infection (LRI), Pneumonia, and Bronchitis among children in Lanzhou, China. A distributed lag nonlinear model (DLNM) was employed to evaluate the non-linear and delayed effects. The models were fitted with different lag structures, including single lag days from the current to the previous seven days (lag0 to lag7) and moving average concentrations over seven lag days (lag0–1 to lag0–7). We found that different PM fractions were significantly associated with hospital visits for LRI, pneumonia, and bronchitis, and cumulative effects increased with the increasing number of lag days investigated. Relative risk (RR) of LRI outpatient visits among children per 10 µg/m3 increase in PM2.5, PM10–2.5, and PM10 were 1.070 (95% CI: 1.059, 1.081), 1.010 (95% CI: 1.006, 1.015), and 1.013 (95% CI: 1.010, 1.016), respectively. For every 10 μg/m3 increase in PM2.5, PM10–2.5, and PM10, the RR values for pneumonia were 1.081, 1.013, and 1.019, and those for bronchitis were 1.069, 1.009, and 1.012, respectively. Boys and children aged 0–3 years were more sensitive to PM exposure. We also found significantly higher effects of PM in cold seasons than that in warm seasons. Our results suggested that short-term exposure to size-specific PM, PM2.5 in particular, might be an important trigger of outpatient visits for overall and cause-specific LRI.

Gender and seasonal different effects of temperature in Hand, foot, and mouth disease: A distributed lag non-linear model

Gender and seasonal different effects of temperature in Hand, foot, and mouth disease: A distributed lag non-linear model

Impact of diurnal temperature range on rhinitis in Lanzhou, China: Accounting for COVID-19 effects

Rhinitis is a global public health issue. A distributed lag non-linear model (DLNM) was used to examine the impact of diurnal temperature range (DTR) on nonallergic rhinitis (NAR) and allergic rhinitis (AR) in Lanzhou, China. Stratified analyses by age, gender, seasons, and the COVID-19 period were conducted, alongside extreme-DTR and attributable risk analyses. Using 37,225 cases, we found:1.The nonlinear DTR-rhinitis relationship: high- and low-DTR acutely impacted AR, while primarily high DTR moderately affected NAR during full and pre-COVID-19 periods.2.COVID-19 transformed the adverse impact of low-DTR into protective effects.3.In full study period, AR showed age heterogeneity, and NAR displayed age and gender differences. In pre-COVID-19 study period, AR had age and gender variations. The age < 18 group, vulnerable to NAR and AR, requires concerns.4.Low-DTR exhibits protective effects during cold seasons, while it poses adverse effects and increased risks in warm seasons.5.Moderate high DTR contributed most to rhinitis outpatients.In conclusion, NAR and AR demonstrate distinct responses to short-term DTR exposure, which may be influenced by COVID-19 and seasonal factors. Future research is essential to assess the long-term impacts of DTR and COVID-19 effects on NAR and AR.

The potential impact of PM2.5 on the covid-19 crisis in the Brazilian Amazon region.

This study aims to assess covid-19 morbidity, mortality, and severity from 2020 to 2021 in five Brazilian Amazon states with the highest records of wildfires. A distributed lag non-linear model was applied to estimate the potential exposure risk association with particulate matter smaller than 2.5-µm in diameter (PM2.5). Daily mean temperature, relative humidity, percentual of community mobility, number of hospital beds, days of the week, and holidays were considered in the final models for controlling the confounding factors. The states of Para, Mato Grosso, and Amazonas have reported the highest values of overall cases, deaths, and severe cases of covid-19. The worrying growth in the percentual rates in 2020/2021 for the incidence, severity, and mortality were highlighted in Rondônia and Mato Grosso. The growth in 2020/2021 in the estimations of PM2.5 concentrations was higher in Mato Grosso, with an increase of 24.4%, followed by Rondônia (14.9%). This study establishes an association between wildfire-generated PM2.5 and increasing covid-19 incidence, mortality, and severity within the studied area. The findings showed that the risk of covid-19 morbidity and mortality is nearly two times higher among individuals exposed to high concentrations of PM2.5. The attributable fraction to PM2.5 in the studied area represents an important role in the risk associated with covid-19 in the Brazilian Amazon region.

Impact of short-term exposure to ambient air pollution on osteoarthritis: a multi-city time-series analysis in Central-Eastern China.

Osteoarthritis (OA) is a threat to public health issue with high morbidity and disability worldwide. However, unequivocal evidence on the link between air pollution and OA remains little, especially in multi-study sites. This study aimed to explore the relationship between short-term exposure to main air pollutants and the risk of OA outpatient visits in multi-study sites. A multi-city time-series analysis was performed in Anhui Province, Central-Eastern China from January 1, 2015, to December 31, 2020. We used a two-stage analysis to assess the association between air pollution and daily OA outpatient visits. City-specific associations were estimated with a distributed lag nonlinear model and then pooled by random-effects or fixed-effects meta-analysis. Stratified analysis was conducted by gender, age, and season. Additionally, the disease burden of OA attributable to air pollutant exposure was calculated. A total of 35,700 OA outpatients were included during the study period. The pooled exposure-response curves showed that PM2.5 and PM10 concentrations below the reference values could increase the risk of OA outpatient visits. Concretely, per 10 ug/m3 increase in PM2.5 concentration was linked to an elevated risk of OA outpatient visits at lag 2 and lag 3days, where the effect reached its highest value on lag 2day (RR: 1.023, 95%CI: 1.005-1.041). We observed that a 10μg/m3 increase in PM10 was positively correlated with OA outpatient visits (lag2day, RR: 1.011, 95%CI: 1.001-1.025). Nevertheless, no statistical significance was discovered in gaseous pollutants (including SO2, O3, and CO). Additionally, a significant difference was found between cold and warm seasons, but not between different genders or age groups. This study reveals that particulate matter is an important factor for the onset of OA in Anhui Province, China. However, there is no evidence of a relationship of gaseous pollutants with OA in this area.

Effect of Ambient Temperature on Hospital Admissions for Respiratory Disease in Suburban Rural Villages of a Semi-Arid Region in Northwest China.

This study evaluated the effects of extreme temperatures on hospital admissions for respiratory diseases (RDs) in a semi-arid region in the Northwest of China. Distributed lag nonlinear model was constructed, and stratified analysis by gender and age was performed. The exposure-response curve between temperature and RD hospital admissions was almost W-shaped. Either extremely cold temperatures or moderately cold temperatures presented a short-term acute harmful effect, and the relative risks were higher among males (1.976, 95% confidence interval [CI]: 1.773-2.203; 1.242, 95% CI: 1.203-1.282) and the elderly (2.363, 95% CI: 1.724-3.240; 1.267, 95% CI: 1.154-1.390). Both extreme and moderately hot temperatures had higher risks among females (2.040, 95% CI: 1.815-2.292; 1.328, 95% CI: 1.276-1.381). The relationship between air temperature and RD hospital admissions was nonlinear. Vulnerable populations varied according to extreme temperature conditions.

Nonlinear and Multidelayed Effects of Meteorological Drivers on Human Respiratory Syncytial Virus Infection in Japan.

In this study, we aimed to characterize the nonlinear and multidelayed effects of multiple meteorological drivers on human respiratory syncytial virus (HRSV) infection epidemics in Japan. The prefecture-specific weekly time-series of the number of newly confirmed HRSV infection cases and multiple meteorological variables were collected for 47 Japanese prefectures from 1 January 2014 to 31 December 2019. We combined standard time-series generalized linear models with distributed lag nonlinear models to determine the exposure-lag-response association between the incidence relative risks (IRRs) of HRSV infection and its meteorological drivers. Pooling the 2-week cumulative estimates showed that overall high ambient temperatures (22.7 °C at the 75th percentile compared to 16.3 °C) and high relative humidity (76.4% at the 75th percentile compared to 70.4%) were associated with higher HRSV infection incidence (IRR for ambient temperature 1.068, 95% confidence interval [CI], 1.056-1.079; IRR for relative humidity 1.045, 95% CI, 1.032-1.059). Precipitation revealed a positive association trend, and for wind speed, clear evidence of a negative association was found. Our findings provide a basic picture of the seasonality of HRSV transmission and its nonlinear association with multiple meteorological drivers in the pre-HRSV-vaccination and pre-coronavirus disease 2019 (COVID-19) era in Japan.