Distributed Lag Non-linear Model Research Articles

Machine and deep learning for modelling heat-health relationships

Extreme heat events pose a significant threat to population health that is amplified by climate change. Traditionally, statistical models have been used to model heat-health relationships, but they do not consider potential interactions between temperature-related and air pollution predictors. Artificial intelligence (AI) methods, which have gained popularity for health applications in recent years, can account for these complex and non-linear interactions, but have been underutilized in modelling heat-related health impacts. In this paper, six machine and deep learning models were considered to model the heat-mortality relationship in Montreal (Canada) and compared to three statistical models commonly used in the field. Decision Tree (DT), Random Forest (RF), Gradient Boosting Machine (GBM), Single- and Multi-Layer Perceptrons (SLP and MLP), Long Short-Term Memory (LSTM), Generalized Linear and Additive Models (GLM and GAM), and Distributed Lag Non-Linear Model (DLNM) were employed. Heat exposure was characterized by air temperature, relative humidity and wind speed, while air pollution was also included in the models using five pollutants. The results confirmed that air temperature at lags of up to 3 days was the most important variable for the heat-mortality relationship in all models. NO2 concentration and relative humidity (at lags 1 to 3 days) were also particularly important. Ensemble tree-based methods (GBM and RF) outperformed other approaches to model daily mortality during summer months based on three performance criteria. However, a partial validation during two recent major heatwaves highlighted that non-linear statistical models (GAM and DLNM) and simpler decision tree may more closely reproduce the spike of mortality observed during such events. Hence, both machine learning and statistical models are relevant for modelling heat-health relationships depending on the end user goal. Such extensive comparative analysis should be extended to other health outcomes and regions.

Association between short-term exposure to ambient air pollution and outpatient visits for pulpitis in Hefei, China: a time series study.

Evidence suggests a possible association between ambient air pollutants and oral diseases. Nevertheless, information regarding the relationship between air pollutants and pulpitis is scarce and inconclusive. In view of this, the present study aimed to investigate the relationship between short-term exposure to air pollution and outpatient visits for pulpitis. Daily data on outpatient visits for pulpitis, air pollutants, and meteorological data in Hefei, China, was collected from January 1, 2015 to December 31, 2021. The association between exposure to air pollutants and pulpitis outpatient visits was evaluated using distributed lag non-linear model (DLNM) and a generalized linear model (GLM). Furthermore, stratified analyses were performed by gender, age and season. A total of 93,324 records of outpatient visits for pulpitis were included in this study. The results showed that exposure to NO2, PM2.5, and CO were positively correlated with an increased risk of pulpitis outpatient visits. Each 10 μg/m3 increase in NO2 and PM2.5 concentration, at lag 0-2 day, was associated with a 2.4% (relative risk (RR) = 1.024, 95% confidence interval (CI): 1.014-1.035) and 0.5% (RR = 1.005, 95% CI: 1.000-1.010) increase in pulpitis outpatient visits, respectively. With a 1 mg/m3 increase in CO concentration, the risk of pulpitis outpatient visits increased by 9.1% (RR = 1.091, 95% CI: 1.031-1.154, lag 0-1 day). Intriguingly, exposure to O3 was associated with a decreased risk of pulpitis outpatient visits (RR = 0.990, 95% CI: 0.984-0.995, lag 0-5 day). Subgroup analysis revealed that in the warm season, exposure to PM2.5, O3, and CO was related with a significantly higher outpatient risk of pulpitis than in the cold season. Additionally, the influence of PM2.5 and CO exposure at age < 65 years was significantly stronger than at age ≥ 65 years. In conclusion, exposure to ambient NO2, PM2.5, and CO is associated with an increase in pulpitis outpatient visits in Hefei, China. Conversely, exposure to O3 reduces the risk of outpatient visits for pulpitis. Age and season are effect modifiers of these associations.

A longitudinal study on the effect of extreme temperature on non-accidental deaths in Hulunbuir City based on DLNM model

ObjectiveTo explore the frequency and effect of extreme temperature on the non-accidental death rate in Hulunbuir, a Chinese ice city.MethodsFrom 2014 to 2018, mortality data of residents residing in Hulunbuir City were collected. The lag and cumulative effects of extreme temperature conditions on non-accidental death and respiratory and circulatory diseases were analyzed by distributed lag non-linear models (DLNM).ResultsThe risk of death was the highest during high-temperature conditions, the RR value was 1.111 (95% CI 1.031 ~ 1.198). The effect was severe and acute. The risk of death during extreme low-temperature conditions peaked on the fifth day, (RR 1.057; 95% CI 1.012 ~ 1.112), then decreased and was maintained for 12 days. The cumulative RR value was 1.289 (95% CI 1.045 ~ 1.589). Heat significantly influenced the incidence of non-accidental death in both men (RR 1.187; 95% CI 1.059–1.331) and women (RR 1.252; 95% CI 1.085–1.445).ConclusionsRegardless of the temperature effect, the risk of death in the elderly group (≥ 65 years) was significantly higher than that of the young group (0–64 years). High-temperature and low-temperature conditions can contribute to the increased number of deaths in Hulunbei. While high-temperature has an acute effect, low-temperature has a lagging effect. Elderly and women, as well as people with circulatory diseases, are more sensitive to extreme temperatures.

Fluctuations in emergency department visits related to acute otitis media are associated with extreme meteorological conditions.

Climate change has been associated with a higher frequency of extreme weather events, resulting in an overall increase in morbidity and mortality. Acute otitis media (AOM) is one of the most common otolaryngological infections and accounts for 1.5% of emergency department visits. This study aimed to identify associations between extreme weather events and the immediate and delayed risks for AOM-related emergency department visits (EV). A total of 1,465 AOM-related EVs were identified in the Vienna General Hospital between 2015 and 2018. A distributed lag non-linear model was applied to evaluate the relationship between extreme weather conditions and the total number of AOM-related EVs per day. The relative risk (RR) and cumulative RR (cRR) of single-day events and extended weather events over three days were analyzed over a lag period of 14 days. AOM-related EVs showed a pronounced seasonality, with the highest occurrence during winter. Single-day weather events affected AOM-related EVs only at high relative humidity. Prolonged extreme weather conditions over three days, however, significantly increased the cRR for AOM-related EVs to 3.15 [1.26-7.88; p = 0.014] and 2.14 [1.14-4.04; p = 0.018] at mean temperatures of -4°C (1st-percentile - p1) and 0°C (p5) on the same day. Relative humidity of 37% (p1) decreased RR to 0.94 [0.88-0.99; p = 0.032] on day 7, while extremely high humidity of 89% (p99) led to an increased cRR of 1.43 [1.03-2.00; p = 0.034] on day 7. Heavy prolonged precipitation of 24mm (p95) reduced cRR beginning day 4 up until day 14 to 0.52 [0.31-0.86; p = 0.012]. Prolonged low atmospheric pressure events of 985hPa (p5) reduced the RR to 0.95 [0.91-1.00; p = 0.03], whereas extremely high atmospheric pressure events of 1013hPa (p99) increased the RR to 1.11 [1.03-1.20; p = 0.008]. Extremely low wind speeds significantly diminished the RR of AOM-related EVs. While single-day extreme weather events had little impact on the occurrence of AOM-related EVs, extended periods of extreme temperatures, relative humidity, precipitation, wind speeds and atmospheric pressure significantly impacted the RR for AOM-related EVs. These findings could help improve healthcare resource allocation in similar climates and aid in educating patients about the role of environmental factors in AOM.

Association between long-term exposure to ambient air pollutants and the risk of tuberculosis: A time-series study in Nantong, China

BackgroundIncreasing evidence has shown that the risk of tuberculosis (TB) might be related to the exposure to air pollutants; however, the findings are inconsistent and studies on long-term air pollutant exposure and TB risk are scarce. This study aime to assess the relationship between monthly exposure to air pollution and TB risk in Nantong, China. MethodsWe collected the time series data on the number of TB cases, as well as environmental and socioeconomic covariates from January 2005 to December 2020. The impact of air pollutant exposure on TB risk was evaluated using the distributed lag nonlinear model (DLNM). Stratified analyses were conducted to examine the effect modifications of sex and age on the association between air pollutants and TB risk. Sensitivity analyses were applied to test the stability of the model. ResultsThere were a total of 54,096 cases of TB in Nantong during the study period. In the single-pollutant model, for each 10 μg/m3 increase in concentration, the pooled relative risks (RRs) of TB reached the maximum to 1.10 (95% confidence interval (CI): 1.04–1.16, lag 10 months) for particulate matter with aerodynamic diameter less than 2.5 μm (PM2.5), 1.05 (95% CI: 1.01–1.10, lag 9 months) for particulate matter with aerodynamic diameter less than 10 μm (PM10), and 1.11 (95%CI: 1.04–1.19, lag 10 months) for nitrogen dioxide (NO2). Ozone (O3) did not show significant effect on TB risk. Effect modifications of sex and age on the association between air pollutants and TB risk were not observed. The multi-pollutant model results showed no significant variation compared with the single-pollutant model. ConclusionsOur study suggests that air pollutants pose a substantial threat to the TB risk. Reducing air pollution might be crucial for TB prevention and control.

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

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. 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. 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. 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.

Effect of different heat wave timing on cardiovascular and respiratory mortality in France

BackgroundPrevious epidemiological evidence suggests that the impact of heat waves on mortality may change over time within the summer season. The consideration of heat wave timing could help to optimize the implementation of heat alert systems. We explored the effect of the timing of extreme heat events on mortality risk during the summer season in France. MethodsSummertime daily mortality data for 21 French cities from 2000 to 2015 were obtained from the French National Institute of Health and Medical Research. Heat waves were defined according to the official definition of Météo France. The order of heat wave over time, from June to August, was assessed. We also used ambient temperature and considered different summer periods. To quantify mortality risk (for cardiovascular and respiratory causes) for the first and second or later heat waves, quasi-Poisson models were performed. We used distributed lag non-linear models to estimate whether the non-linear exposure–response associations between temperature and mortality differ across different summer periods. ResultsCompared with non-heat wave days, the second and later heat waves of the summer season were associated with a higher relative risk (RR) for cardiovascular and respiratory mortality (RR, 95%CI: 1.38, 1.23–1.53; RR, 95%CI: 1.74, 1.45–2.08, respectively) as compared to first heat wave (RR, 95%CI: 1.30, 1.17–1.45, RR, 95%CI: 1.56, 1.33–1.83, respectively). Small increase from the median temperature was associated to an increased risk in mortality in the first stage of the summer (from June to mid-July), while only more extreme temperatures were harmful later in the summer. After the exclusion from the analysis of the August 2003 heat-wave, only results for earlier heat waves episodes and first-period exposures were confirmed. ConclusionsThe timing of extreme temperatures modulates heat-related risks in France. Such information could be used to update local heat action plans to optimize health benefits.

Association between air pollution exposure and outpatient visits for dermatomyositis in a humid subtropical region of China: a time-series study.

In recent years, a growing number of studies have found that air pollution plays critical roles in the onset and development of autoimmune diseases, but few studies have shown an association between air pollutants and dermatomyositis (DM). We sought to investigate the relationship between short-term exposure to air pollution and outpatient visits for DM and to quantify the burden of DM due to exposure to air pollutants in Hefei, China. Daily records of hospital outpatient visits for DM, air pollutants and meteorological factors data in Hefei from January 1, 2018 to December 31, 2021 were obtained. We used a distributed lag non-linear model (DLNM) in conjunction with a generalized linear model (GLM) to explore the association between air pollution and outpatient visits for DM, and conducted stratified analyses by gender, age and season. Moreover, we used attributable fraction (AF) and attributable number (AN) to reflect the burden of disease. A total of 4028 DM clinic visits were recorded during this period. High concentration nitrogen dioxide (NO2) exposure was associated with increased risk of DM outpatient visits (relative risk (RR) 1.063, 95% confidence interval (CI) 1.015-1.114, lag 0-5). Intriguingly, exposure to high concentration ozone (O3) was associated with reduced risk of outpatient visits for DM (RR 0.974, 95% CI 0. 0.954-0.993, lag 0-6). The results of stratified analyses showed that the cold season (vs. warm season) were more susceptible to outpatient visits for DM associated with NO2 and O3 exposure. In addition, we observed that an increased risk of DM outpatient visits was attributable to high concentration NO2 exposure, while high concentration O3 exposure was associated with a decreased risk of DM outpatient visits. This study provided a scientific basis for the etiology research and health protection of DM.

Association between High Ambient Temperatures and Road Crashes in an Australian City with Temperate Climate: A Time-Series Study, 2012-2021.

(1) Background: High ambient temperatures are associated with increased morbidity and mortality rates, and some evidence suggests that high temperatures increase the risk of road crashes. However, little is known regarding the burden of road crashes attributable to no-optimal high temperatures in Australia. Therefore, this study examined the effects of high temperatures on road crashes using Adelaide in South Australia as a case study. (2) Methods: Ten-year daily time-series data on road crashes (n = 64,597) and weather during the warm season (October-March) were obtained between 2012 and 2021. A quasi-Poisson distributed lag nonlinear model (DLNM) was used to quantify the cumulative effect of high temperatures over the previous five days. The associations and attributable burden at moderate and extreme temperature ranges were computed as relative risk (RR) and attributable fraction. (3) Results: There was a J-shaped association between high ambient temperature and the risk of road crashes during the warm season in Adelaide, and pronounced effects were observed for minimum temperatures. The highest risk was observed at a 1 day lag and lasting for 5 days. High temperatures were responsible for 0.79% (95% CI: 0.15-1.33%) of road crashes, with moderately high temperatures accounting for most of the burden compared with extreme temperatures (0.55% vs. 0.32%). (4) Conclusions: In the face of a warming climate, the finding draws the attention of road transport, policy, and public health planners to design preventive plans to reduce the risk of road crashes attributable to high temperatures.

Extremely Hot Ambient Temperature and Injury-related Mortality

This pilot study aimed to evaluate the effects of extremely hot ambient temperatures on the total number of fatal injuries. Data were collected from a population-based mortality registry of Thanh Hoa, a province in the North Central region of Vietnam. This study qualified the distributed lag non-linear model and calculated the RR and 95% CI adjusted for long-term trend and absolute humidity. For the entire study population with 3,949 registered deaths due to injuries collected during 2005-2007, after the onset of extremely hot ambient temperatures, an increased risk of death was observed on the 9th day RR (95% CI) = 1.44 (1.06–1.97) and reached the peak on the 12th day RR (95% CI) = 1.58 (1.14–2.17), and at the 15th day RR (95% CI) = 1.49 (1.08–2.06). Men and old adults were identified as the most vulnerable groups. This study confirmed a positive association between hot temperatures and injury-related deaths in the province of 3.6 million people. The findings motivated further investigation into the effect of warm climate changes and the risk of deaths related to other specific causes such as road traffic, work-related injury, and etc.

Short-term effects of fine particulate matter constituents on mortality considering the mortality displacement in Zhejiang province, China

BackgroundEvidence linking mortality and short-term exposure to particulate matter (PM2.5) constituents was sparse. The mortality displacement was often unconsidered and may induce incorrect risk estimation. ObjectivesTo assess the short-term effects of PM2.5 constituents on all-cause mortality considering the mortality displacement. MethodsDaily data on all-cause mortality and PM2.5 constituents, including sulfate (SO42−), nitrate (NO3−), ammonium (NH4+), organic matters (OM), and black carbon (BC), were collected from 2009 to 2020. The mortality effect of PM2.5 and its constituents was estimated using a distributed lag non-linear model. Stratified analyses were performed by age, sex, and season. ResultsPer interquartile range increases in SO42−, NO3−, NH4+, OM, and BC were associated with the 1.42% (95%CI: 0.98, 1.87), 3.76% (3.34, 4.16), 2.26% (1.70, 2.83), 2.36% (2.02, 2.70), and 1.26% (0.91, 1.61) increases in all-cause mortality, respectively. Mortality displacements were observed for PM2.5, SO42−, NH4+, OM, and BC, with their overall effects lasting for 7–15 days. Stratified analyses revealed a higher risk for old adults (>65 years) and females, with stronger effects in the cold season. ConclusionsShort-term exposures to PM2.5 constituents were positively associated with increased risks of mortality. The mortality displacement should be considered in future epidemiological studies on PM constituents. Data availabilityData will be made available on request.

Low ambient temperature and hospitalization for cardiorespiratory diseases in Brazil

Studies have shown that larger temperature-related health impacts may be associated with cold rather than with hot temperatures. Although it remains unclear the cold-related health burden in warmer regions, in particular at the national level in Brazil. We address this gap by examining the association between low ambient temperature and daily hospital admissions for cardiovascular and respiratory diseases in Brazil between 2008 and 2018. We first applied a case time series design in combination with distributed lag non-linear modeling (DLNM) framework to assess the association of low ambient temperature with daily hospital admissions by Brazilian region. Here, we also stratified the analyses by sex, age group (15–45, 46–65, and >65 years), and cause (respiratory and cardiovascular hospital admissions). In the second stage, we performed a meta-analysis to estimate pooled effects across the Brazilian regions. Our sample included more than 23 million hospitalizations for cardiovascular and respiratory diseases nationwide between 2008 and 2018, of which 53% were admissions for respiratory diseases and 47% for cardiovascular diseases. Our findings suggest that low temperatures are associated with a relative risk of 1.17 (95% CI: 1.07; 1.27) and 1.07 (95% CI: 1.01; 1.14) for cardiovascular and respiratory admissions in Brazil, respectively. The pooled national results indicate robust positive associations for cardiovascular and respiratory hospital admissions in most of the subgroup analyses. In particular, for cardiovascular hospital admissions, men and older adults (>65 years old) were slightly more impacted by cold exposure. For respiratory admissions, the results did not indicate differences among the population groups by sex and age. This study can help decision-makers to create adaptive measures to protect public health from the effects of cold temperature.

Long-term exposure to ambient fine particulate matter and carotid intima media thickness at bilateral, left and right in adults from Mexico City: Results from GEA study

BackgroundPM2.5 exposure has been associated with intima-media thickness (cIMT) increase. However, very few studies distinguished between left and right cIMT in relation to PM2.5 exposure. AimTo evaluate associations between chronic exposure to PM2.5 and cIMT at bilateral, left, and right in adults from Mexico City. MethodsThis study comprised 913 participants from the control group, participants without personal or family history of cardiovascular disease, of the Genetics of Atherosclerosis Disease Mexican study (GEA acronym in Spanish), recruited at the Instituto Nacional de Cardiología Ignacio Chávez from June 2008 to January 2013. To assess the associations between chronic exposure to PM2.5 (per 5 μg/m3 increase) at different lag years (1–4 years) and cIMT (bilateral, left, and right) we applied distributed lag non-linear models (DLNMs). ResultsThe median and interquartile range for cIMT at bilateral, left, and right, were 630 (555, 735), 640 (550, 750), and 620 (530, 720) μm, respectively. Annual average PM2.5 exposure was 26.64 μg/m3, with median and IQR, of 24.46 (23.5–25.46) μg/m3. Results from DLNMs adjusted for age, sex, body mass index, low-density lipoproteins, and glucose, showed that PM2.5 exposure for year 1 and 2, were positively and significantly associated with right-cIMT [6.99% (95% CI: 3.67; 10.42) and 2.98% (0.03; 6.01), respectively]. Negative associations were observed for PM2.5 at year 3 and 4 and right-cIMT; however only year 3 was statistically significant [-2.83% (95% CI: 5.12; −0.50)]. Left-cIMT was not associated with PM2.5 exposure at any lag year. The increase in bilateral cIMT followed a similar pattern as that observed for right-cIMT, but with lower estimates. ConclusionsOur results suggest different susceptibility between left and right cIMT associated with PM2.5 exposure highlighting the need of measuring both, left and right cIMT, regarding ambient air pollution in epidemiological studies.

Short-term effects of cold spells on hematocrit among adults in Nanjing, China: A distributed-lagged effect analysis

BackgroundPrevious studies have linked exposure to cold spells with cardiovascular diseases, however, underlying mechanisms remained to be understood. We aimed to explore the short-term effects of cold spells on hematocrit, a blood indicator associated with cardiovascular disease. MethodsOur study was performed among 50,538 participants (68,361 health examination records) who visited the health examination centers of Zhongda Hospital in Nanjing City, China, during the cold seasons from 2019 to 2021. Data on meteorology and air pollution were obtained from the China Meteorological Data Network and the Nanjing Ecological Environment Bureau, respectively. Cold spells in this study were defined as daily mean temperatures (Tmean) <3rd or 5th percentile with two or more consecutive days. Linear mixed-effect models combined with distributed lag nonlinear models were applied to estimate associations of cold spells with hematocrit. ResultsCold spells were found to be significantly correlated with increased hematocrit on lag 0 to 26 days. Moreover, the cumulative effects of cold spells on hematocrit remained significant at varying lag days. These single and cumulative effects were robust across different definitions of cold spells and conversions of hematocrit. For instance, cold spells (Tmean <3rd percentile) at lags 0, 0–1, and 0–27 days were significantly associated with 0.09 [95 % confidence interval (CI): 0.03, 0.15], 0.17 (95 % CI: 0.07, 0.28), and 3.71 (95 % CI: 3.06, 4.35) − unit (%) increases in original hematocrit, respectively. In subgroup analyses, stronger effects of cold spells on hematocrit were observed in females and participants aged 50 years or over. ConclusionCold spells have significant immediate and longer-lagged effects (up to 26 days) on hematocrit. Females and individuals aged 50 years or over are more sensitive to cold spells. These findings might provide a new perspective for exploring the effects of cold spells on adverse cardiac events.

Association of high ambient temperature with daily hospitalization for cardiorespiratory diseases in Brazil: A national time-series study between 2008 and 2018

Further research is needed to examine the nationwide impact of temperature on health in Brazil, a region with particular challenges related to climate conditions, environmental characteristics, and health equity. To address this gap, in this study, we looked at the relationship between high ambient temperature and hospital admissions for circulatory and respiratory diseases in 5572 Brazilian municipalities between 2008 and 2018. We used an extension of the two-stage design with a case time series to assess this relationship. In the first stage, we applied a distributed lag non-linear modeling framework to create a cross-basis function. We next applied quasi-Poisson regression models adjusted by PM2.5, O3, relative humidity, and time-varying confounders. We estimated relative risks (RRs) of the association of heat (percentile 99th) with hospitalization for circulatory and respiratory diseases by sex, age group, and Brazilian regions. In the second stage, we applied meta-analysis with random effects to estimate the national RR. Our study population includes 23,791,093 hospital admissions for cardiorespiratory diseases in Brazil between 2008 and 2018. Among those, 53.1% are respiratory diseases, and 46.9% are circulatory diseases. The robustness of the RR and the effect size varied significantly by region, sex, age group, and health outcome. Overall, our findings suggest that i) respiratory admissions had the highest RR, while circulatory admissions had inconsistent or null RR in several subgroup analyses; ii) there was a large difference in the cumulative risk ratio across regions; and iii) overall, women and the elderly population experienced the greatest impact from heat exposure. The pooled national results for the whole population (all ages and sex) suggest a relative risk of 1.29 (95% CI: 1.26; 1.32) associated with respiratory admissions. In contrast, national meta-analysis for circulatory admissions suggested robust positive associations only for people aged 15–45, 46–65, >65 years old; for men aged 15–45 years old; and women aged 15–45 and 46–65 years old. Our findings are essential for the body of scientific evidence that has assisted policymakers to promote health equity and to create adaptive measures and mitigations.

Acute effects of air pollution on type II diabetes mellitus hospitalization in Lanzhou, China.

Studies on the effects of short-term air pollution exposure on hospitalization for type 2 diabetes mellitus (T2DM) are relatively scarce in developing regions. The time-series study was used to explore the acute effects of air pollutants on hospitalization for T2DM in Lanzhou, China. A distribution lag nonlinear model based on the generalized additive model was used to analyze the hospitalization impact of air pollution on T2DM. Stratified analysis by gender, age and season was obtained. The results were indicated as the relative risk (RR) with 95% confidence interval (CI) for single-day lags (from lag0 to lag7) and cumulative lag days (from lag0-1 to lag0-7). The strongest correlations (RR, 95% CI) of hospitalization for T2DM and PM10 (RR = 1.003, 95% CI 1.000, 1.001) at lag7 and NO2 (RR = 1.022, 95% CI 1.000, 1.045) at lag0-4 were observed for an increase of 10µg/m3 in the concentrations and CO (RR = 1.091, 95% CI 1.017, 1.170) at lag0-4 for an increase of 1mg/m3 in the concentration. The hazardous impacts of PM10, NO2 and CO were greater for females, people aged ≥ 65years and in the cold season. However, there was no significant association between PM2.5, SO2 and O38h and the number of hospitalizations for T2DM.

Maternal exposure to fine particulate matter and congenital heart defects during preconception and pregnancy period: A cohort-based case-control study in the Taiwan maternal and child health database

BackgroundFew studies have explored the association between maternal exposure to particulate matter with an aerodynamic diameter of ≤2.5 μm (PM2.5) and congenital heart defects occurring before and during pregnancy. We aimed to investigate the association and the critical time windows between the maternal exposure to PM2.5 and congenital heart defects. MethodWe conducted a cohort-based case-control study of 507,960 participants obtained from the Taiwan Maternal and Child Health Database between 2004 and 2015. We applied satellite-based spatiotemporal models with 1-km resolution to calculate the average PM2.5 concentration during preconception and the specific periods of pregnancy. We also performed conditional logistic regression with distributed lag non-linear models (DLNMs) to assess the effects of weekly average PM2.5 on both congenital heart defects and their isolated subtypes, as well as the concentration-response relationships. ResultsIn DLNMs, exposure to PM2.5 (per 10 μg/m3) during weeks 7–12 before conception and weeks 3–9 after conception was associated with congenital heart defects. The strongest association at 12 weeks before conception (odds ratio [OR] = 1.026, 95% confidence intervals [CI]: 1.012–1.040) and 7 weeks after conception (OR = 1.024, 95% CI: 1.012–1.036) for every 10 μg/m3 increase in PM2.5 concentration. In modification analysis, strongest associations were observed for low SES. ConclusionsOur study revealed that exposure to ambient PM2.5 raises the risk of congenital heart defects, particularly among individuals with lower socioeconomic status. Moreover, our findings suggest that preconception exposure to PM2.5 may be a crucial period for the development of congenital heart defects.

Time Series Analysis of the Acute Effect of Atmospheric Fine Particulate Matter on Hospitalization for Heart Failure in Lanzhou, China.

The purpose of this study was to investigate the correlation between environmental PM 2.5 and heart failure (HF) hospitalization in Lanzhou, China. The correlation between atmospheric PM 2.5 and HF hospitalization was evaluated using a distributed lag nonlinear model. During the study period, 16,122 HF patients were hospitalized. The average concentration of PM 2.5 is 45.24 μg/m 3 . PM 2.5 was positively correlated with HF hospitalization with single lag (lag0) and cumulative lag (lag01, lag02, lag03). The maximum lag effect is estimated to occur in lag03, and with the PM 2.5 concentration increasing by 10 μg/m 3 , HF hospitalization risk increased by 1.023 (95% confidence interval, 1.006-1.039). The risk of hospitalization for HF in the male group, the age group 65 years or older, and the cold season was statistically significant and more positively correlated. PM 2.5 exposure increases the risk of HF admission in Lanzhou.

Association between apparent temperature and hypertension hospital admissions: a case study in rural areas in western China.

Although it has previously been shown that temperature is associated with cardiovascular disease, no investigations exploring the association between apparent temperature (AT) and hypertension in farmers in Zhangye and Longnan, Gansu Province, China, have been undertaken. As hypertension is a commonly known risk factor for cardiovascular disease, the relationship between apparent temperature (AT) and hypertension is examined in Zhangye and Longnan to provide advice to local governments on preventive measures. Daily data and weather conditions were collected in Zhangye and Longnan from 2014-2015. The Poisson generalized linear model and the distributed lag nonlinear model (DLNM) were combined to investigate the relationship between AT and hypertension in hospital admissions in the study areas. A non-linear relationship between AT and hypertension in hospital admissions in both Zhangye and Longnan were recorded. The cold effects were stronger in Zhangye than that in Longnan for both study group and subgroups. The heat effects were more deleterious for the entire study group, female subgroup and adult subgroup in Longnan, but stronger for the male subgroup and elderly subgroup in Zhangye. This investigation indicates that AT has adverse impacts on hypertension hospital admissions in Zhangye and Longnan, especially under low AT exposure levels. The results from this study may promote the formulation of further prevention measures for hypertension disease.

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 PM2.5 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.