Impact of exposure measurement error in air pollution epidemiology: effect of error type in time-series studies

Landscape fire PM2.5 and hospital admissions for cause-specific cardiovascular disease in urban China

There is a growing interest in the health impacts of PM2.5 originating from landscape fires. We conducted a time-series study to investigate the association between daily exposure to landscape fire PM2.5 and hospital admissions for cardiovascular events in 184 major Chinese cities. We developed a machine learning model combining outputs from chemical transport models, meteorological information and observed air pollution data to determine daily concentrations of landscape fire PM2.5. Furthermore, we fitted quasi-Poisson regression to evaluate the link between landscape fire PM2.5 concentrations and cardiovascular hospitalizations in each city, and conducted random-effects meta-analysis to pool the city-specific estimates. Here we show that, on a national scale, a rise of 1-μg/m3 in landscape fire PM2.5 concentrations is positively related to a same-day 0.16% (95% confidence interval: 0.01%-0.32%) increase in hospital admissions for cardiovascular disease, 0.28% (0.12%-0.44%) for ischemic heart disease, and 0.25% (0.02%-0.47%) for ischemic stroke. The associations remain significant even after adjusting for other sources of PM2.5. Our findings indicate that transient elevation in landscape fire PM2.5 levels may increase risk of cardiovascular diseases.

Short- and medium-term cumulative effects of traffic-related air pollution on resting heart rate in the elderly: A wearable device study

BACKGROUND

Epidemiological evidence regarding the association between air pollution and resting heart rate (RHR), a predictor of cardiovascular disease and mortality, is limited and inconsistent.

OBJECTIVES

We used wearable devices and time-series analysis to assess the exposure-response relationship over an extended lag period.

METHODS

Ninety-seven elderly individuals (>65 years) from the Taipei Basin participated from May to November 2020 and wore Garmin® smartwatches continuously until the end of 2021 for heart rate monitoring. RHR was defined as the daily average of the lowest 30-min heart rate. Air pollution exposure data, covering lag periods from 0 to 60 days, were obtained from nearby monitoring stations. We used distributed lag non-linear models and linear mixed-effect models to assess cumulative effects of air pollution. Principal component analysis was utilized to explore underlying patterns in air pollution exposure, and subgroup analyses with interaction terms were conducted to explore the modification effects of individual factors.

RESULTS

After adjusting for co-pollutants in the models, an interquartile range increase of 0.18 ppm in carbon monoxide (CO) was consistently associated with increased RHR across lag periods of 0-1 day (0.31, 95 % confidence interval [CI]: 0.24-0.38), 0-7 days (0.68, 95 % CI: 0.57-0.79), and 0-50 days (1.02, 95 % CI: 0.82-1.21). Principal component analysis identified two factors, one primarily influenced by CO and nitrogen dioxide (NO2), indicative of traffic sources. Increases in the varimax-rotated traffic-related score were correlated with higher RHR over 0-1 day (0.36, 95 % CI: 0.25-0.47), 0-7 days (0.62, 95 % CI: 0.46-0.77), and 0-50 days (1.27, 95 % CI: 0.87-1.67) lag periods. Over a 0-7 day lag, RHR responses to traffic pollution were intensified by higher temperatures (β = 0.80 vs. 0.29; interaction p-value [P_int] = 0.011). Males (β = 0.66 vs. 0.60; P_int < 0.0001), hypertensive individuals (β = 0.85 vs. 0.45; P_int = 0.028), diabetics (β = 0.96 vs. 0.52; P_int = 0.042), and those with lower physical activity (β = 0.70 vs. 0.54; P_int < 0.0001) also exhibited stronger responses. Over a 0-50 day lag, males (β = 0.99 vs. 0.96; P_int < 0.0001), diabetics (β = 1.66 vs. 0.69; P_int < 0.0001), individuals with lower physical activity (β = 1.49 vs. 0.47; P_int = 0.0006), and those with fewer steps on lag day 1 (β = 1.17 vs. 0.71; P_int = 0.029) showed amplified responses.

CONCLUSIONS

Prolonged exposure to traffic-related air pollution results in cumulative cardiovascular risks, persisting for up to 50 days. These effects are more pronounced on warmer days and in individuals with chronic conditions or inactive lifestyles.

Ambient PM2.5 Chemical Composition and Cardiovascular Disease Hospitalizations in China

Little is known about the impacts of specific chemical components on cardiovascular hospitalizations. We examined the relationships of PM2.5 chemical composition and daily hospitalizations for cardiovascular disease in 184 Chinese cities. Acute PM2.5 chemical composition exposures were linked to higher cardiovascular disease hospitalizations on the same day and the percentage change of cardiovascular admission was the highest at 1.76% (95% CI, 1.36-2.16%) per interquartile range increase in BC, followed by 1.07% (0.72-1.43%) for SO42-, 1.04% (0.63-1.46%) for NH4+, 0.99% (0.55-1.43%) for NO3-, 0.83% (0.50-1.17%) for OM, and 0.80% (0.34%-1.26%) for Cl-. Similar findings were observed for all cause-specific major cardiovascular diseases, except for heart rhythm disturbances. Short-term exposures to PM2.5 chemical composition were related to higher admissions and showed diverse impacts on major cardiovascular diseases.

Differentiated impacts of short-term exposure to fine particulate constituents on infectious diseases in 507 cities of Chinese children and adolescents: A nationwide time-stratified case-crossover study from 2008 to 2021

This study assesses the association of short-term exposure to PM2.5 (particles ≤2.5 μm) on infectious diseases among Chinese children and adolescents. Analyzing data from 507 cities (2008-2021) on 42 diseases, it focuses on PM2.5 components (black carbon (BC), ammonium (NH4+), inorganic nitrate (NO3-), organic matter (OM), and sulfate (SO42-)). PM2.5 constituents significantly associated with incidence. Sulfate showed the most substantial effect, increasing all-cause infectious disease risk by 2.72 % per interquartile range (IQR) increase. It was followed by BC (2.04 % increase), OM (1.70 %), NO3- (1.67 %), and NH4+ (0.79 %). Specifically, sulfate and BC had pronounced impacts on respiratory diseases, with sulfate linked to a 10.73 % increase in seasonal influenza risk and NO3- to a 16.39 % rise in tuberculosis. Exposure to PM2.5 also marginally increased risks for gastrointestinal, enterovirus, and vectorborne diseases like dengue (7.46 % increase with SO42-). Sexually transmitted and bloodborne diseases saw an approximate 6.26 % increase in incidence, with specific constituents linked to diseases like hepatitis C and syphilis. The study concludes that managing PM2.5 levels could substantially reduce infectious disease incidence, particularly in China's middle-northern regions. It highlights the necessity of stringent air quality standards and targeted disease prevention, aligning PM2.5 management with international guidelines for public health protection.

Is model-estimated PM2.5 exposure equivalent to station-observed in mortality risk assessment? A literature review and meta-analysis

Model-estimated air pollution exposure assessments have been extensively employed in the evaluation of health risks associated with air pollution. However, few studies synthetically evaluate the reliability of model-estimated PM2.5 products in health risk assessment by comparing them with ground-based monitoring station air quality data. In response to this gap, we undertook a meticulously structured systematic review and meta-analysis. Our objective was to aggregate existing comparative studies to ascertain the disparity in mortality effect estimates derived from model-estimated ambient PM2.5 exposure versus those based on monitoring station-observed PM2.5 exposure. We conducted searches across multiple databases, namely PubMed, Scopus, and Web of Science, using predefined keywords. Ultimately, ten studies were included in the review. Of these, seven investigated long-term annual exposure, while the remaining three studies focused on short-term daily PM2.5 exposure. Despite variances in the estimated Exposure-Response (E-R) associations, most studies revealed positive associations between ambient PM2.5 exposure and all-cause and cardiovascular mortality, irrespective of the exposure being estimated through models or observed at monitoring stations. Our meta-analysis revealed that all-cause mortality risk associated with model-estimated PM2.5 exposure was in line with that derived from station-observed sources. The pooled Relative Risk (RR) was 1.083 (95% CI: 1.047, 1.119) for model-estimated exposure, and 1.089 (95% CI: 1.054, 1.125) for station-observed sources (p = 0.795). In conclusion, most model-estimated air pollution products have demonstrated consistency in estimating mortality risk compared to data from monitoring stations. However, only a limited number of studies have undertaken such comparative analyses, underscoring the necessity for more comprehensive investigations to validate the reliability of these model-estimated exposure in mortality risk assessment.

Short-Term Nitrogen Dioxide Exposure and Emergency Hospital Admissions for Asthma in Children: A Case-Crossover Analysis in England

Background

There is an increasing body of evidence associating short-term ambient nitrogen dioxide (NO2) exposure with asthma-related hospital admissions in children. However, most studies have relied on temporally resolved exposure information, potentially ignoring the spatial variability of NO2. We aimed to investigate how daily NO2 estimates from a highly resolved spatio-temporal model are associated with the risk of emergency hospital admission for asthma in children in England.

Methods

We conducted a time-stratified case-crossover study including 111,766 emergency hospital admissions for asthma in children (aged 0-14 years) between 1st January 2011 and 31st December 2015 in England. Daily NO2 levels were predicted at the patients' place of residence using spatio-temporal models by combining land use data and chemical transport model estimates. Conditional logistic regression models were used to obtain the odds ratios (OR) and confidence intervals (CI) after adjusting for temperature, relative humidity, bank holidays, and influenza rates. The effect modifications by age, sex, season, area-level income deprivation, and region were explored in stratified analyses.

Results

For each 10 µg/m³ increase in NO2 exposure, we observed an 8% increase in asthma-related emergency admissions using a five-day moving NO2 average (mean lag 0-4) (OR 1.08, 95% CI 1.06-1.10). In the stratified analysis, we found larger effect sizes for male (OR 1.10, 95% CI 1.07-1.12) and during the cold season (OR 1.10, 95% CI 1.08-1.12). The effect estimates varied slightly by age group, area-level income deprivation, and region.

Significance

Short-term exposure to NO2 was significantly associated with an increased risk of asthma emergency admissions among children in England. Future guidance and policies need to consider reflecting certain proven modifications, such as using season-specific countermeasures for air pollution control, to protect the at-risk population.

The impact of heat waves and cold spells on pneumonia risk: A nationwide study

Climate change affects human health and has been linked to several infectious diseases in recent year. However, there is limited assessment on the impact of heat waves and cold spells on pneumonia risk. This study aims to examine the association of heat waves and cold spells with daily pneumonia hospitalizations in 168 cities in China. Data on pneumonia hospitalizations between 2014 and 2017 were extracted from a national claim database of 280 million beneficiaries. We consider combining temperature intensity and duration to define heat waves and cold spells.This association was quantified using a quasi-Poisson generalized linear model combined with a distributed lag nonlinear model. Exposure-response curves and potential effect modifiers were also estimated. We found that the peak relative risk (RR) of cold spells on daily hospitalizations for pneumonia was observed in relatively mild cold spells with a threshold below the 3 days at the 2nd percentile (RR = 1.69, 95% CI: 1.46-1.92). The risk of heat waves increased with the thresholds, and the greatest risk was found for extremely heatwave period of 4 days at the 98th percentile (RR = 1.69, 95% CI: 1.46-1.92). Heat waves and cold spells are more likely to adversely affect women. In conclusion, our study provided novel and strong evidence that exposure to heat waves and cold spells was associate with increased hospital visits for pneumonia, especially in females. This is the first national study in China to comprehensively evaluate the influence of heat waves and cold spells on pneumonia risk, and the findings may offer valuable insights into the impact of climate change on public health.

A joint Bayesian framework for missing data and measurement error using integrated nested Laplace approximations

Measurement error (ME) and missing values in covariates are often unavoidable in disciplines that deal with data, and both problems have separately received considerable attention during the past decades. However, while most researchers are familiar with methods for treating missing data, accounting for ME in covariates of regression models is less common. In addition, ME and missing data are typically treated as two separate problems, despite practical and theoretical similarities. Here, we exploit the fact that missing data in a continuous covariate is an extreme case of classical ME, allowing us to use existing methodology that accounts for ME via a Bayesian framework that employs integrated nested Laplace approximations (INLA) and thus to simultaneously account for both ME and missing data in the same covariate. As a useful by-product, we present an approach to handle missing data in INLA since this corresponds to the special case when no ME is present. In addition, we show how to account for Berkson ME in the same framework. In its broadest generality, the proposed joint Bayesian framework can thus account for Berkson ME, classical ME, and missing data, or any combination of these in the same or different continuous covariates of the family of regression models that are feasible with INLA. The approach is exemplified using both simulated and real data. We provide extensive and fully reproducible Supporting Information with thoroughly documented examples using R-INLA and inlabru.

Air pollution accountability research: Moving from a chain to a web

Air pollution accountability studies examine the relationship(s) between an intervention, regulation, or event and the resulting downstream impacts, if any, on emissions, exposure, and/or health. The sequence of events has been schematically described as an accountability chain. Here, we update the existing framework to capture real-life complexities and to highlight important factors that fall outside the linear chain. This new "accountability web" is intended to convey the intricacies associated with conducting an accountability study to various audiences, including researchers, policy makers, and stakeholders. We also identify data considerations for planning and completing a robust accountability study, including those relevant to novel and innovative air pollution and exposure data. Finally, we present a series of recommendations for the accountability research community that can serve as a guide for the next generation of accountability studies.

Short-term effects of fine particulate matter constituents on myocardial infarction death

Existing evidence suggested that short-term exposure to fine particulate matter (PM_2.5) may increase the risk of death from myocardial infarction (MI), while PM_2.5 constituents responsible for this association has not been determined. We collected 12,927 MI deaths from 32 counties in southern China during 2011-2013. County-level exposures of ambient PM_2.5 and its 5 constituents (i.e., elemental carbon (EC), organic carbon (OC), sulfate (SO₄^2-), ammonium (NH₄⁺), and nitrate (NO₃^-)) were aggregated from gridded datasets predicted by Community Multiscale Air Quality Modeling System. We employed a space-time-stratified case-crossover design and conditional logistic regression models to quantify the association of MI mortality with short-term exposure to PM_2.5 and its constituents across various lag days. Over the study period, the daily mean PM_2.5 mass concentration was 77.8 (standard deviation (SD) = 72.7) µg/m³. We estimated an odds ratio of 1.038 (95% confidence interval (CI): 1.003-1.074), 1.038 (1.013-1.063) and 1.057 (1.023-1.097) for MI mortality associated with per interquartile range (IQR) increase in the 3-day moving-average exposure to PM_2.5 (IQR = 76.3 µg/m³), EC (4.1 µg/m³) and OC (9.1 µg/m³), respectively. We did not identify significant association between MI death and exposure to water-soluble ions (SO₄^2-, NH₄⁺ and NO₃^-). Likelihood ratio tests supported no evident violations of linear assumptions for constituents-MI associations. Subgroup analyses showed stronger associations between MI death and EC/OC exposure in the elderly, males and cold months. Short-term exposure to PM_2.5 constituents, particularly those carbonaceous aerosols, was associated with increased risks of MI mortality.

Relationship between temperature variability and daily hospitalisations in Hong Kong over two decades

Background

Studies have highlighted the impacts of temperature variability (TV) on mortality from respiratory diseases and cardiovascular diseases, with inconsistent results specifically in subtropical urban areas than temperate ones. We aimed to fully determine TV-associated health risks over a spectrum of diseases and various subgroups in a subtropical setting.

Methods

Using inpatient data from all public hospitals in Hong Kong from 1999 to 2019, we examined the TV-hospitalisation associations by causes, ages, and seasons by fitting a quasi-Poisson regression. We presented the results as estimated percentage changes of hospitalisations per interquartile range (IQR) of TV.

Results

TVs in exposure days from 0-5 days (TV0-5) to 0-7 days (TV0-7) had detrimental effects on hospitalisation risks in Hong Kong. The overall population was significantly affected over TV0-5 to TV0-7 in endocrine, nutritional and metabolic (from 0.53% to 0.58%), respiratory system (from 0.38% to 0.53%), and circulatory systems diseases (from 0.47% to 0.56%). While we found no association with seasonal disparities, we did observe notable disparities by age, highlighting older adults' vulnerability to TVs. For example, people aged ≥65 years experienced the highest change of 0.88% (95% CI = 0.34%, 1.41%) in hospitalizations for injury and poisoning per IQR increase in TV0-4.

Conclusions

Our population-based study highlighted that TV-related health burden, usually regarded as minimal compared to other environmental factors, should receive more attention and be addressed in future relevant health policies, especially for vulnerable populations during the cold seasons.

Association between traffic-related air pollution and osteoporotic fracture hospitalizations in inland and coastal areas: evidences from the central areas of two cities in Shandong Province, China

Our result showed that short-term exposure to traffic-related air pollutants (TRAPs) might increase the risk of hospitalizations for osteoporotic fractures. It was suggested that government should formulate emission reduction policies to protect the health of citizens.

INTRODUCTION

As the main source of urban air pollution in China, exhaust emissions of motor vehicles have been linked to adverse health outcomes, but evidence of the relationship between short-term exposure to TRAPs and osteoporotic fractures is still relatively rare.

METHODS

In this study, a total of 5044 inpatients from an inland city (Jinan) and a coastal city (Qingdao), two cities with developed transportation in Shandong Province, were included. A generalized additive model (GAM) was used to investigate the association between TRAPs and hospitalizations for osteoporotic fractures. The stratified analyses were performed by gender and age.

RESULTS

Positive associations between TRAPs and osteoporotic fracture hospitalizations were observed. We found that short-term exposure to TRAPs was associated with increased numbers of hospitalizations for osteoporotic fractures. PM2.5 and PM10 were statistically significant associated with hospitalizations for osteoporotic fractures at both single-day and multiday lag structures only in Qingdao, with the strongest associations at lag06 and lag07 [RR=1.0446(95%CI: 1.0018,1.0891) for PM2.5, RR=1.0328(95%CI: 1.0084,1.0578) for PM10]. For NO2 and CO, we found significant associations at lag4 in the single lag structure in Jinan [RR=1.0354 (95%CI: 1.0071, 1.0646) for NO2, RR=1.0014 (95%CI: 1.0002, 1.0025) for CO], while only CO at lag4 was significantly associated with hospitalizations for osteoporotic fractures in Qingdao [1.0038 (1.0012, 1.0063)]. Stratified analyses indicated that the associations were stronger in females and older individuals (65 + years).

CONCLUSION

This study implied that short-term exposure to TRAPs pollution was associated with an increased risk of hospitalizations for osteoporotic fractures. Female patients and patients aged 65 + years appeared to be more vulnerable to TRAPs, suggesting that poor air quality is a modifiable risk factor for osteoporotic fractures.

Short-term effects of ambient ozone exposure on daily hospitalizations for circulatory diseases in Ganzhou, China: A time-series study

Adverse health effects of ambient ozone are getting widespread attention, but the evidence on the relationship between ozone levels and circulatory system diseases are limited and inconsistent. Daily data for ambient ozone levels and hospitalizations for total circulatory diseases and five subtypes in Ganzhou, China from January 1, 2016 to December 31, 2020 were collected. We constructed a generalized additive model with quasi-Poisson regression accounting for lag effects to estimate the associations between ambient ozone levels and the number of hospitalized cases of total circulatory diseases and five subtypes. The differences among gender, age, and season subgroups were furtherly assessed through stratified analysis. A total of 201,799 hospitalized cases of total circulatory diseases were included in the present study, including 94,844 hypertension (HBP), 28,597 coronary heart disease (CHD), 42,120 cerebrovascular disease (CEVD), 21,636 heart failure (HF), and 14,602 arrhythmia. Significantly positive associations were observed between ambient ozone levels and daily hospitalizations for total circulatory diseases and all subtypes except arrhythmia. Each 10 μg/m³ increase in ozone concentration, the risk of hospitalizations for total circulatory diseases, HBP, CHD, CEVD, and HF increased by 0.718% (95% confidence interval, 0.156%-1.284%), 0.956% (0.346%-1.570%), 0.499% (0.057%-0.943%), 0.386% (0.025%-0.748%), and 0.907% (0.118%-1.702%), respectively. The above associations remained significant after adjusting for other air pollutants. The risk of hospitalization for circulatory diseases was higher in warm season (May to October) and varied in gender and age subgroups. This study suggested that short-term exposure to ambient ozone may increase the risk of hospitalizations for circulatory diseases. Our findings reinforce the importance of reducing ambient ozone pollution levels for protecting public health.

Ambient nitrogen dioxide and cardiovascular diseases in rural regions: a time-series analyses using data from the new rural cooperative medical scheme in Fuyang, East China

Most of studies relating ambient nitrogen dioxide (NO₂) exposure to hospital admissions for cardiovascular diseases (CVDs) were conducted among urban population. Whether and to what extent these results could be generalizable to rural population remains unknown. We addressed this question using data from the New Rural Cooperative Medical Scheme (NRCMS) in Fuyang, Anhui, China. Daily hospital admissions for total CVDs, ischaemic heart disease, heart failure, heart rhythm disturbances, ischaemic stroke, and haemorrhagic stroke in rural regions of Fuyang, China, were extracted from NRCMS between January 2015 and June 2017. A two-stage time-series analysis method was used to assess the associations between NO₂ and CVD hospital admissions and the disease burden fractions attributable to NO₂. In our study period, the average number (standard deviation) of hospital admissions per day were 488.2 (117.1) for total CVDs, 179.8 (45.6) for ischaemic heart disease, 7.0 (3.3) for heart rhythm disturbances, 13.2 (7.2) for heart failure, 267.9 (67.7) for ischaemic stroke, and 20.2 (6.4) for haemorrhagic stroke. The 10-μg/m³ increase of NO₂ was related to an elevated risk of 1.9% (RR: 1.019, 95% CI: 1.005 to 1.032) for hospital admissions of total CVDs at lag0-2 days, 2.1% (1.021, 1.006 to 1.036) for ischaemic heart disease, and 2.1% (1.021, 1.006 to 1.035) for ischaemic stroke, respectively, while no significant association was observed between NO₂ and hospital admissions for heart rhythm disturbances, heart failure, and haemorrhagic stroke. The attributable fractions of total CVDs, ischaemic heart disease, and ischaemic stroke to NO₂ were 6.52% (1.87 to 10.94%), 7.31% (2.19 to 12.17%), and 7.12% (2.14 to 11.85%), respectively. Our findings suggest that CVD burdens in rural population are also partly attributed to short-term exposure to NO₂. More studies across rural regions are required to replicate our findings.

Application of smart devices in investigating the effects of air pollution on atrial fibrillation onset

Few studies have examined the link between short-term exposure to air pollutants and atrial fibrillation (AF) episodes. This study aims to examine the association of hourly criteria air pollutants with AF episodes. We employ a smart device-based photoplethysmography technology to screen AF from 2018 to 2021. Hourly concentrations of six criteria air pollutants are matched to the onset hour of AF for each participant. We adopt a time-stratified case-crossover design to capture the acute effects of air pollutants on AF episodes, using conditional logistic regression models. Subgroup analyses are conducted by age, gender, and season. A total of 11,906 episodes of AF are identified in 2976 participants from 288 Chinese cities. Generally, the strongest associations of air pollutants are present at lag 18-24 h, with positive and linear exposure-response relationships. For an interquartile range increase in inhalable particles, fine particles, nitrogen dioxide, and carbon monoxide, the odds ratio (OR) of AF is 1.19 [95% confidential interval (CI): 1.03, 1.37], 1.38 (95%CI: 1.14, 1.67), 1.60 (95%CI: 1.16, 2.20) and 1.48 (95%CI: 1.19, 1.84), respectively. The estimates are robust to the adjustment of co-pollutants, and they are larger in females, older people, and in cold seasons. There are insignificant associations for sulfur dioxide and ozone. This nationwide case-crossover study demonstrates robust evidence of significant associations between hourly exposure to air pollutants and the onset of AF episodes, which underscores the importance of ongoing efforts to further improve air quality as an effective target for AF prevention.

Incident dementia and long-term exposure to constituents of fine particle air pollution: A national cohort study in the United States

Growing evidence suggests that fine particulate matter (PM2.5) likely increases the risks of dementia, yet little is known about the relative contributions of different constituents. Here, we conducted a nationwide population-based cohort study (2000 to 2017) by integrating the Medicare Chronic Conditions Warehouse database and two independently sourced datasets of high-resolution PM2.5 major chemical composition, including black carbon (BC), organic matter (OM), nitrate (NO3-), sulfate (SO42-), ammonium (NH4+), and soil dust (DUST). To investigate the impact of long-term exposure to PM2.5 constituents on incident all-cause dementia and Alzheimer's disease (AD), hazard ratios for dementia and AD were estimated using Cox proportional hazards models, and penalized splines were used to evaluate potential nonlinear concentration-response (C-R) relationships. Results using two exposure datasets consistently indicated higher rates of incident dementia and AD for an increased exposure to PM2.5 and its major constituents. An interquartile range increase in PM2.5 mass was associated with a 6 to 7% increase in dementia incidence and a 9% increase in AD incidence. For different PM2.5 constituents, associations remained significant for BC, OM, SO42-, and NH4+ for both end points (even after adjustments of other constituents), among which BC and SO42- showed the strongest associations. All constituents had largely linear C-R relationships in the low exposure range, but most tailed off at higher exposure concentrations. Our findings suggest that long-term exposure to PM2.5 is significantly associated with higher rates of incident dementia and AD and that SO42-, BC, and OM related to traffic and fossil fuel combustion might drive the observed associations.

Measurement error correction for ambient PM2.5 exposure using stratified regression calibration: Effects on all-cause mortality

BACKGROUND

Previous studies on the impact of measurement error for PM2.5 were mostly simulation studies, did not control for other pollutants, or used a single regression calibration model to correct for measurement error. However, the relationship between actual and error-prone PM2.5 concentration may vary by time and region. We aim to correct the measurement error of PM2.5 predictions using stratified regression calibration and investigate how the measurement error biases the association between PM2.5 and mortality in the Medicare Cohort.

METHODS

The "gold-standard" measurements of PM2.5 were defined as daily monitoring data. We regressed daily monitoring PM2.5 on modeled PM2.5 using the simple linear regression by strata of season, elevation, census division and time period. Calibrated PM2.5 was calculated with stratum-specific calibration parameters β0 (intercept) and β1 (slope) for each strata and aggregated to annual level. Associations between calibrated and error-prone annual PM2.5 and all-cause mortality among Medicare beneficiaries were estimated with Quasi-Poisson regression models.

RESULTS

Across 208 strata, the median of β0 and β1 were 0.62 (25% 0.0.20, 75% 1.06) and 0.93 (25% 0.87, 75% 0.99). From calibrated and error-prone PM2.5 data, we estimated that each 10 μg/m3 increase in PM2.5 was respectively associated with 4.9% (95%CI 4.6-5.2) and 4.6% (95%CI 4.4-4.9) increases in the mortality rate among Medicare beneficiaries, conditional on confounders.

CONCLUSIONS

Regression calibration parameters of PM2.5 varied by time and region. Using error-prone measures of PM2.5 underestimated the association between PM2.5 and all-cause mortality. Modern exposure models produce relatively small bias.

Association between ambient fine particulate matter and adult outpatient visits for rheumatoid arthritis in Beijing, China

The association between fine particulate matter (PM_2.5) and rheumatoid arthritis (RA) is currently unclear, especially in Beijing, a city with severe air pollution. Our study aimed to explore the relationship between short-term outdoor exposure to PM_2.5 and RA outpatient visits using a time-series analysis in Beijing. We used the Beijing's Medical Claims for Employees database to identify patients with RA in 2010-2012. A generalized additive model with a Poisson link was used to estimate the percentage change in RA outpatient visits after the PM_2.5 concentration increased by 10 μg/m³. From January 1, 2010, to June 30, 2012, a total of 541,061 RA outpatient visits were identified. During the study period, the average daily (standard deviation) concentration of PM_2.5 was 99.5 (75.3) µg/m³. A 10 µg/m³ increase in PM_2.5 concentration was correlated with a 0.21% (95% CI, 0.18-0.23%) increase in outpatient visits for RA on the same day. A significant association for the cumulative effect of PM_2.5 was found, and the largest significant association was observed for a lag of 0-3 days (0.26%; 95% CI, 0.23-0.29%). Stratified analyses revealed that females (0.29%, 95% CI: 0.26-0.33%) and 18-65 years old patients (0.29%, 95% CI: 0.25-0.32%) were most susceptible to the effects of PM_2.5 exposure. The current findings showed that short-term exposure to PM_2.5 was followed by an increase in the number of outpatient visits for RA in Beijing. Future studies should investigate the mechanisms underlying this association.

Synergism of cell adhesion regulatory genes and instant air pollutants on blood pressure elevation

Accumulating evidence suggests that an instant exposure to particulate matter (PM) may elevate blood pressure (BP), where cell-adhesion regulatory genes may be involved in the interplay. However, few studies to date critically examined their interaction, and it remained unclear whether these genes modified the association. To assess the association between instant PM exposure and BP, and to examine whether single-nucleotide polymorphisms (SNPs) mapped in four cell adhesion regulatory genes modify the relationship, a cross-sectional study was performed, based on the baseline of an ongoing family-based cohort in Beijing, China. A total of 4418 persons from 2089 families in Northern China were included in the analysis. Four tagged SNPs in cell adhesion regulatory genes were selected among ZFHX3, CXCL12, RASGRP1 and MIR146A. A generalized additive model (GAM) with a Gaussian link was adopted to estimate the change in blood pressure after instant PM_2.5 or PM₁₀ exposure. A cross-product term of PM_2.5/PM₁₀ and genotype was incorporated into the GAM model to test for interaction. The study observed that an instant exposure to either PM_2.5 or PM₁₀ was found to be associated with elevated systolic blood pressure (SBP). On average, a 10 μg/m³ increase in instant exposure to PM_2.5 and PM₁₀ concentration corresponded to 0.140% (95% CI: 0.014%-0.265%, P = 0.029) and 0.173% (95% CI: 0.080%-0.266%, P < 0.001) higher SBP. However, diastolic blood pressure (DBP) was not elevated as the PM_2.5 or PM₁₀ concentration increased (P > 0.05). A synergetic interaction on SBP was observed between SNPs in four cell adhesion regulatory genes (rs2910164 in MIR146A, rs2297630 in CXCL12, rs7403531 in RASGRP1, and rs7193343 in ZFHX3) and instant PM_2.5 exposure (P_{for interaction} <0.05). Briefly, as carriers of risk alleles in each of these four genes increased, an enhanced association was found between instant PM_2.5 exposure and SBP.

Short-term exposure to fine particulate matter constituents and mortality: case-crossover evidence from 32 counties in China

A growing number of studies associated increased mortality with exposures to specific fine particulate (PM_2.5) constituents, while great heterogeneity exists between locations. In China, evidence linking PM_2.5 constituents and mortality was extensively sparse. This study primarily aimed to quantify short-term associations between PM_2.5 constituents and non-accidental mortality among the Chinese population. We collected daily mortality records from 32 counties in China between January 1, 2011, and December 31, 2013. Daily concentrations of main PM_2.5 constituents (organic carbon (OC), elemental carbon (EC), nitrate (NO₃^-), sulfate (SO₄^2-), and ammonium (NH₄⁺)) were estimated using the modified Community Multiscale Air Quality model. Time-stratified case-crossover design with conditional logistic regression models was adopted to estimate mortality risks associated with short-term exposures to PM_2.5 mass and its constituents. Stratification analyses were done by sex, age, and season. A total of 116,959 non-accidental deaths were investigated. PM_2.5 concentrations on the day of death were averaged at 75.7 µg m^-3 (control day: 75.6 µg m^-3), with an interquartile range (IQR) of 65.2 µg m^-3. Per IQR rise in PM_2.5, EC, OC, NO₃^-, SO₄^2-, and NH₄⁺ at lag-04 day was associated with an increase in non-accidental mortality of 2.4% (95% confidence interval, (1.0-3.7), 1.7% (0.8-2.7), 2.9% (1.6-4.3), 2.1% (0.4-3.9), 1.0% (0.2-1.9), and 1.6% (0.3-2.9), respectively. Both PM_2.5 mass and its constituents were strongly associated with elevated cardiovascular mortality risks, but only PM_2.5, EC, and OC were positively associated with respiratory mortality at lag-3 day. PM_2.5 mass and its constituents associated effects on mortality varied among sex- and age-specific subpopulations. Differences in the seasonal pattern of associations exist among PM_2.5 constituents, with stronger effects related to EC and NO₃^- in warm months but SO₄^2- and NH₄⁺ in cold months. Short-term exposures to PM_2.5 compositions were positively associated with increased risks of mortality, particularly those constituents from combustion-related sources.

Validity of using ambient concentrations as surrogate exposures at the individual level for fine particle and black carbon: A systematic review and meta-analysis

Exposure measurement error is an important source of bias in epidemiological studies. We assessed the validity of employing ambient (outdoor) measurements as proxies of personal exposures at individual levels focusing on fine particles (PM_2.5) and black carbon (BC)/elemental carbon (EC) on a global scale. We conducted a systematic review and meta-analysis and searched databases (ISI Web of Science, Scopus, PubMed, Ovid MEDLINE®, Ovid Embase, and Ovid BIOSIS) to retrieve observational studies in English language published from 1 January 2006 until 5 May 2021. Correlation coefficients (r) between paired ambient (outdoor) concentration and personal exposure for PM_2.5 or BC/EC were standardized as effect size. We used random-effects meta-analyses to pool the correlation coefficients and investigated the causes of heterogeneity and publication bias. Furthermore, we employed subgroup and meta-regression analyses to evaluate the modification of pooled estimates by potential mediators. This systematic review identified thirty-two observational studies involving 1744 subjects from ten countries, with 28 studies for PM_2.5 and 11 studies for BC/EC. Personal PM_2.5 exposure is more strongly correlated with ambient (outdoor) concentrations (0.63, 95% confidence interval [CI]: 0.57-0.68) than personal BC/EC exposure (0.49, 95% CI: 0.38-0.59), with significant differences in ṝ (0.14, 95% CI: 0.03-0.25; p < 0.05). The results demonstrated that the health status of participants was a significant modifier of pooled correlations. In addition, the personal to ambient (P/A) ratio for PM_2.5 and average ambient BC/EC levels were potential effect moderators of the pooled ṝ. The funnel plots and Egger's regression test indicated inevident publication bias. The pooled estimates were robust through sensitivity analyses. The results support the growing consensus that the validity coefficient of proxy measures should be addressed when interpreting results from epidemiological studies to better understand how strong health outcomes are affected by different levels of PM_2.5 and their components.

Modification effects of seasonal and temperature variation on the association between exposure to nitrogen dioxide and ischemic stroke onset in Shenzhen, China

The independent associations of extreme temperature and ambient air pollutant with the admission to hospital and mortality of ischemic stroke have been widely investigated. However, knowledge about the modification effects of variation in season and temperature on the association between exposure to nitrogen dioxide (NO₂) and ischemic stroke onset is still limited. This study purposed to explore the effect of NO₂ on daily ischemic stroke onset modified by season and ambient temperature, and identify the potential population that susceptible to ischemic stroke onset connected with NO₂ and ambient temperature. Data on daily ischemic stroke counts, weather conditions, and ambient air pollutant concentrations in Shenzhen were collected between January 1, 2008, and December 31, 2014. The seasonal effect on the NO₂-associated onset was measured by a distributed-lag linear model. Furthermore, a generalized additive model that incorporated with stratification analyses was used to calculate the interactive effects between NO₂ and ambient temperature. During the winter, the average percentage increase in daily ischemic stroke onset for each 10 μg/m³ increment in NO₂ concentration on lagged 2 days was 3.05% (95% CI: 1.31-4.82%), while there was no statistically significant effect of NO₂ during summer. And the low-temperature days ([Formula: see text] mean temperature), with a 2.23% increase in incidence (95% CI: 1.18-3.29%) for the same concentration increase in NO₂, were significant higher than high temperature days ([Formula: see text] mean temperature). The modification effects of temperature on the study association were more pronounced in individuals aged 65 years or more and in males. The adverse health effects of NO₂ on ischemic stroke are more pronounced during winter or low temperature periods. Elderly adults or males presented higher risks with these exposures.

The Impact of Exposure Measurement Error on the Estimated Concentration-Response Relationship between Long-Term Exposure to PM2.5 and Mortality

BACKGROUND

Exposure measurement error is a central concern in air pollution epidemiology. Given that studies have been using ambient air pollution predictions as proxy exposure measures, the potential impact of exposure error on health effect estimates needs to be comprehensively assessed.

OBJECTIVES

We aimed to generate wide-ranging scenarios to assess direction and magnitude of bias caused by exposure errors under plausible concentration-response relationships between annual exposure to fine particulate matter [PM ≤2.5μm in aerodynamic diameter (PM2.5)] and all-cause mortality.

METHODS

In this simulation study, we use daily PM2.5 predictions at 1-km2 spatial resolution to estimate annual PM2.5 exposures and their uncertainties for ZIP Codes of residence across the contiguous United States between 2000 and 2016. We consider scenarios in which we vary the error type (classical or Berkson) and the true concentration-response relationship between PM2.5 exposure and mortality (linear, quadratic, or soft-threshold-i.e., a smooth approximation to the hard-threshold model). In each scenario, we generate numbers of deaths using error-free exposures and confounders of concurrent air pollutants and neighborhood-level covariates and perform epidemiological analyses using error-prone exposures under correct specification or misspecification of the concentration-response relationship between PM2.5 exposure and mortality, adjusting for the confounders.

RESULTS

We simulate 1,000 replicates of each of 162 scenarios investigated. In general, both classical and Berkson errors can bias the concentration-response curve toward the null. The biases remain small even when using three times the predicted uncertainty to generate errors and are relatively larger at higher exposure levels.

DISCUSSION

Our findings suggest that the causal determination for long-term PM2.5 exposure and mortality is unlikely to be undermined when using high-resolution ambient predictions given that the estimated effect is generally smaller than the truth. The small magnitude of bias suggests that epidemiological findings are relatively robust against the exposure error. In practice, the use of ambient predictions with a finer spatial resolution will result in smaller bias. https://doi.org/10.1289/EHP10389.

Community-level ambient fine particulate matter and seasonal influenza among children in Guangzhou, China: A Bayesian spatiotemporal analysis

BACKGROUND

Influenza is a major preventable infectious respiratory disease. However, there is little detailed long-term evidence of its associations with PM_2.5 among children. We examined the community-level associations between exposure to ambient PM_2.5 and incident influenza in Guangzhou, China.

METHODS

We used data from the city-wide influenza surveillance system collected by Guangzhou Centre for Disease Control and Prevention (GZCDC) over the period 2013 and 2019. Incident influenza was defined as daily new influenza (both clinically diagnosed and laboratory confirmed) cases as per standard diagnostic criteria. A 200-meter city-wide grid of daily ambient PM_2.5 exposure was generated using a random forest model. We developed spatiotemporal Bayesian hierarchical models to examine the community-level associations between PM_2.5 and the influenza adjusting for meteorological and socioeconomic variables and accounting for spatial autocorrelation. We also calculated community-wide influenza cases attributable to PM_2.5 levels exceeding the China Grade 1 and World Health Organization (WHO) regulatory thresholds.

RESULTS

Our study comprised N = 191,846 children from Guangzhou aged ≤19 years and diagnosed with influenza between January 1, 2013 and December 31, 2019. Each 10 μg/m³ increment in community-level PM_2.5 measured on the day of case confirmation (lag 0) and over a 6-day moving average (lag 0-5 days) was associated with higher risks of influenza (RR = 1.05, 95% CI: 1.05-1.06 for lag 0 and RR = 1.15, 95% CI: 1.14-1.16 for lag 05). We estimated that 8.10% (95%CI: 7.23%-8.57%) and 20.11% (95%CI: 17.64%-21.48%) influenza cases respectively were attributable to daily PM_2.5 exposure exceeding the China Grade I (35 μg/m³) and the WHO limits (25 μg/m³). The risks associated with PM_2.5 exposures were more pronounced among children of the age-group 10-14 compared to other age groups.

CONCLUSIONS

More targeted non-pharmaceutical interventions aimed at reducing PM_2.5 exposures at home, school and during commutes among children may constitute additional influenza prevention and control polices.

Thunderstorms, Pollen, and Severe Asthma in a Midwestern, USA, Urban Environment, 2007-2018

BACKGROUND

Previous research has shown an association between individual thunderstorm events in the presence of high pollen, commonly called thunderstorm asthma, and acute severe asthma events, but little work has studied risk over long periods of time, using detailed measurements of storms and pollen.

METHODS

We estimated change in risk of asthma-related emergency room visits related to thunderstorm asthma events in the Minneapolis-St. Paul metropolitan area over the years 2007-2018. We defined thunderstorm asthma events as daily occurrence of two or more lightning strikes during high pollen periods interpolating weather and pollen monitor data and modeling lightning counts. We acquired daily counts of asthma-related emergency department visits from the Minnesota Hospital Association and used a quasi-Poisson time-series regression to estimate overall relative risk of emergency department visits during thunderstorm asthma events.

RESULTS

We observed a 1.047 times higher risk (95% CI:1.012,1.083) of asthma-related emergency department visits on the day of thunderstorm asthma event. Our findings are robust to adjustment for temperature, humidity, wind, precipitation, ozone, PM2.5, day of week, and seasonal variation in asthma cases. Occurrence of lightning alone or pollen alone showed no association with risk of severe asthma. A two-stage analysis combining individual zip code level results shows similar RR and we see no evidence of spatial correlation or spatial heterogeneity of effect.

DISCUSSION

Our results support an association between co-occurrence of lightning and pollen and risk of severe asthma events. Our approach incorporates lightning and pollen data and small-spatial area exposure and outcome counts.

Systematic review and meta-analysis of studies between short-term exposure to ambient carbon monoxide and non-accidental, cardiovascular, and respiratory mortality in China

Although a growing number of original epidemiological studies imply a link between ambient pollution exposure and mortality risk, the findings associated with carbon monoxide (CO) exposure are inconsistent. Thus, we conducted a systematic review and meta-analysis of epidemiological studies to evaluate the correlations between ambient CO and non-accidental, cardiovascular, and respiratory mortality in China. Eight databases were searched from inception to 15 May 2021. A random-effect model was used to calculate the pooled relative risks (RRs) and 95% confidence intervals (CIs). Subgroup analyses as well as sensitivity analyses were performed. The I square value (I²) was used to assess heterogeneity among different studies. The assessment of publication bias on included studies was examined by funnel plot and Egger's test. The influence of a potential publication bias on findings was explored by using the trim-and-fill procedure. Ultimately, a total of 19 studies were included in our analysis. The pooled relative risk for each 1 mg/m³ increase of ambient carbon monoxide was 1.0220 (95%CI: 1.0102-1.0339) for non-accidental mortality, 1.0304 (95%CI:1.0154-1.0457) for cardiovascular mortality, and 1.0318 (95%CI:1.0132-1.0506) for respiratory mortality. None of subgroup analyses could explain the source of heterogeneity. Exclusion of any single study did not materially alter the pooled effect estimates. Although it was suggestive of publication bias, findings were generally similar with principal findings when we explored the influence of a potential publication bias using the trim-and-fill method. Our meta-analysis demonstrated that exposure to ambient CO was positive with risk of deaths from all non-accidental causes, total cardiovascular, and respiratory diseases. Based on these findings, tougher intervention policies and initiatives to reduce the health effects of CO exposure should be established.

Application of an Improved Gas-constrained Source Apportionment Method Using Data Fused Fields: a Case Study in North Carolina, USA

A number of studies have found differing associations of disease outcomes with PM_2.5 components (or species) and sources (e.g., biomass burning, diesel vehicles and gasoline vehicles). Here, a unique method of fusing daily chemical transport model (Community Multiscale Air Quality Modeling) results with observations has been utilized to generate spatiotemporal fields of the concentrations of major gaseous pollutants (CO, NO₂, NO_x, O₃, and SO₂), total PM_2.5 mass, and speciated PM_2.5 (including crustal elements) over North Carolina for 2002-2010. The fused results are then used in chemical mass balance source apportionment model, CMBGC-Iteration, which uses both gas constraint and particulate matter concentrations to quantify source impacts. The method, as applied to North Carolina, quantifies the impacts of ten source categories and provides estimates of source contributions to PM_2.5 concentrations. The ten source categories include both primary sources (diesel vehicles, gasoline vehicles, dust, biomass burning, coal-fired power plants and sea salt) and secondary components (ammonium sulfate, ammonium bisulfate, ammonium nitrate and secondary organic carbon). The results show a steady decrease in anthropogenic source impacts, especially from diesel vehicles and coal-fired power plants. Secondary pollutant components accounted for approximately 70% of PM_2.5 mass. This study demonstrates an ability to provide spatiotemporal fields of both PM components and source impacts using a chemical transport model fused with observation data, linked to a receptor-based source apportionment method, to develop spatiotemporal fields of multiple pollutants.

Is Cold Apparent Temperature Associated With the Hospitalizations for Osteoporotic Fractures in the Central Areas of Wuhan? A Time-Series Study

Osteoporosis is alarming problem due to aggravation of global aging, especially in China. Osteoporotic fracture (OF) is one of the most severe consequents of osteoporosis. Many previous studies found that environmental factors had adverse effects on human health. Cold temperature was associated with OF and bone metabolism in prior observational and experimental researches. However, few studies had been conducted on the acute effect of low temperature and OF. Data on daily meteorological factors and hospitalizations for OF were collected from Wuhan, China, between January 1, 2017 to December 24, 2019. Apparent temperature (AT), comprehensively considered a variety of environmental factors, was calculated by ambient temperature, relative humidity and wind speed. A generalized linear regression model combined with distributed lag non-linear regression model (DLNM) with quasi-Poisson link was used to explore the association between AT and the number of hospitalizations for OF. Subgroup analyses stratified by gender, age and the history of fracture were applied for detecting susceptible people. The exposure-response curve of AT and OF were generally U-shaped with lowest point at 25.8°C. The significant relationship of AT-OF existed only in cold effect (-2.0 vs. 25.8°C) while not in warm effect (37.0 vs. 25.8°C). Statistically significant risks of OF for cold effects were only found in females [RR = 1.12 (95%CI: 1.02, 1.24) at lag 2 day], aged <75 years old [RR = 1.18 (95%CI: 1.04, 1.33) and 1.17 (95%CI: 1.04, 1.33) at lag 2 and 3 days, respectively] and people with history of fracture [RR = 1.39 (95%CI: 1.02, 1.90) and 1.27 (95%CI: 1.05, 1.53) at lag 1 and 2 days, respectively]. The significant associations of AT on OF were only found in cold effect. The females, people aged <75 years and people with history of fracture possibly appeared to be more vulnerable. Public health departments should pay attention to the negative effect of cold AT and take measures in time.

Increased Risk of Hospital Admission for Asthma in Children From Short-Term Exposure to Air Pollution: Case-Crossover Evidence From Northern China

Background: Previous studies suggested that exposure to air pollution could increase risk of asthma attacks in children. The aim of this study is to investigate the short-term effects of exposure to ambient air pollution on asthma hospital admissions in children in Beijing, a city with serious air pollution and high-quality medical care at the same time.

Methods: We collected hospital admission data of asthma patients aged ≤ 18 years old from 56 hospitals from 2013 to 2016 in Beijing, China. Time-stratified case-crossover design and conditional Poisson regression were applied to explore the association between risk of asthma admission in children and the daily concentration of six air pollutants [particulate matter ≤ 2.5 μm (PM_2.5), particulate matter ≤ 10 μm (PM₁₀), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon monoxide (CO), and ozone (O₃)], adjusting for meteorological factors and other pollutants. Additionally, stratified analyses were performed by age, gender, and season.

Results: In the single-pollutant models, higher levels of PM_2.5, SO₂, and NO₂ were significantly associated with increased risk of hospital admission for asthma in children. The strongest effect was observed in NO₂ at lag06 (RR = 1.25, 95%CI: 1.06-1.48), followed by SO₂ at lag05 (RR = 1.17, 95%CI: 1.05–1.31). The robustness of effects of SO₂ and NO₂ were shown in two-pollutant models. Stratified analyses further indicated that pre-school children (aged ≤ 6 years) were more susceptible to SO₂. The effects of SO₂ were stronger in the cold season, while the effects of NO₂ were stronger in the warm season. No significant sex-specific differences were observed.

Conclusions: These results suggested that high levels of air pollution had an adverse effect on childhood asthma, even in a region with high-quality healthcare. Therefore, it will be significant to decrease hospital admissions for asthma in children by controlling air pollution emission and avoiding exposure to air pollution.

Association Between Sulfur Dioxide and Daily Inpatient Visits With Respiratory Diseases in Ganzhou, China: A Time Series Study Based on Hospital Data

Background

Sulfur dioxide (SO₂) has been reported to be related to the mortality of respiratory diseases, but the relationship between SO₂ and hospital inpatient visits with respiratory diseases and the potential impact of different seasons on this relationship is still unclear.

Methods

The daily average concentrations of air pollutants, including SO₂ and meteorological data in Ganzhou, China, from 2017 to 2019 were collected. The data on daily hospitalization for respiratory diseases from the biggest hospital in the city were extracted. The generalized additive models (GAM) and the distributed lag non-linear model (DLNM) were employed to evaluate the association between ambient SO₂ and daily inpatient visits for respiratory diseases. Stratified analyses by gender, age, and season were performed to find their potential effects on this association.

Results

There is a positive exposure-response relationship between SO₂ concentration and relative risk of respiratory inpatient visits. Every 10 μg/m³ increase in SO₂ was related to a 3.2% (95% CI: 0.6–6.7%) exaltation in daily respiratory inpatient visits at lag3. In addition, SO₂ had a stronger association with respiratory inpatient visits in women, older adults (≥65 years), and warmer season (May-Oct) subgroups. The relationship between SO₂ and inpatient visits for respiratory diseases was robust after adjusting for other air pollutants, including PM₁₀, NO₂, O₃, and CO.

Conclusion

This time-series study showed that there is a positive association between short-term SO₂ exposure and daily respiratory inpatient visits. These results are important for local administrators to formulate environmental public health policies.

Do temporal trends of associations between short-term exposure to fine particulate matter (PM2.5) and risk of hospitalizations differ by sub-populations and urbanicity-a study of 968 U.S. counties and the Medicare population

While associations between short-term exposure to fine particulate matter (PM_2.5) and risk of hospitalization are well documented and evidence suggests that such associations change over time, it is unclear whether these temporal changes exist in understudied less-urban areas or differ by sub-population. We analyzed daily time-series data of 968 continental U.S. counties for 2000-2016, with cause-specific hospitalization from Medicare claims and population-weighted PM_2.5 concentrations originally estimated at 1km × 1 km from a hybrid model. Circulatory and respiratory hospitalizations were categorized based on primary diagnosis codes at discharge. Using modified Bayesian hierarchical modelling, we evaluated the temporal trend in association between PM_2.5 and hospitalizations and whether disparities in this trend exist across individual-level characteristics (e.g., sex, age, race, and Medicaid eligibility as a proxy for socio-economic status) and urbanicity. Urbanicity was categorized into three levels by county-specific percentage of urban population based on urban rural delineation from the U.S. Census. In this cohort with understudied less-urban areas without regulatory monitors, we still found positive association between circulatory and respiratory hospitalization and short-term exposure to PM_2.5, with higher effect estimates towards the end of study period. Consistent with current literature, we identified significant disparity in associations by race, socioeconomic status and urbanicity. We found that the percentage change in circulatory hospitalization rate per 10 μg/m³ increase in PM_2.5 was higher in the 2008-2016 time period compared to the 2000-2007 period by 0.33% (95% posterior credible interval 0.22, 0.44%), 0.52% (0.33, 0.69%), and 0.67% (0.53, 0.83%) for low, medium and high tertiles of urban areas, respectively. We also observed significant differences in temporal trends of associations across socioeconomic status, sex, and age, indicating a possible widening in disparity of PM_2.5-related health burden. This study raises the importance of considering environmental justice issues in PM_2.5-related health impacts with respect to how associations may change over time.

Using land use variable information and a random forest approach to correct spatial mean bias in fused CMAQ fields for particulate and gas species

Accurate spatiotemporal air pollution fields are essential for health impact and epidemiologic studies. There are an increasing number of studies that have combined observational data with spatiotemporally complete air pollution simulations. Land-use, speciated gaseous and particulate pollutant concentrations and chemical transport modeling are fused using a random forest approach to construct daily air quality fields for 12 pollutants (CO, NOx, NO2, SO2, O3, PM2.5, PM10, and PM2.5 constituents: SO42-, NO3-, NH4+, EC and OC) between 2005 and 2014 for the continental United States with little spatial or temporal bias. R2 ranged from 0.45 to 0.96, depending upon pollutant. Additional analysis found that temporal R2 ranged from 0.84 to 0.99 and spatial R2 values ranged from 0.76 to 0.96 across species. Four-fold cross-validation was performed to assess the model's predictive power, and ranged from 0.40 for PM10 to 0.94 for SO4 with other pollutants falling within this range. Largest improvements were found for PM10 which had substantial bias in the CMAQ fields that varied east-to-west; smallest improvements were for SO4 which was already well simulated. The random forest model results to correct the simulation biases, while largely consistent year-to-year, did show slight variation due in part to changes in the distribution of monitors and changes in CMAQ simulation inputs.

Cold temperature and sudden temperature drop as novel risk factors of asthma exacerbation: a longitudinal study in 18 Chinese cities

BACKGROUND

Few studies have explored the role of ambient temperature in asthma exacerbation.

OBJECTIVE

We aimed to explore the association of temperature with diurnal peak expiratory flow (PEF) variation and asthma exacerbation.

METHOD

We developed a longitudinal study among asthmatic adults in 18 Chinese cities. Subjects recorded PEF in dynamic pulmonary function monitoring from 2017 to 2020. Linear mixed-effect model and generalized additive model with distributed non-linear models were used to assess the effect of temperature and temperature change between neighboring days (TCN) on diurnal PEF variation and the risk of asthma exacerbation.

RESULT

We evaluated a total of 79,217 daily PEF monitoring records from 4467 adult asthmatic patients. There were significant increase of diurnal PEF variation and higher risk of asthma exacerbation with cold and sudden temperature drop. Compared with the referent temperature (99th percentile, 32 °C), exposure to moderate cold (25th percentile, 3 °C) and extreme cold (2.5th percentile, -7 °C) was associated with elevations of 1.28% and 1.16% in diurnal PEF variation over lag 0-2 days, respectively. The odds ratios of asthma exacerbation (determined by diurnal PEF variation >20%) at the two temperature cutoffs were 1.68 and 1.73. A sudden temperature drop (2.5th percentile of TCN, -5 °C) was associated with 1.13% elevation in diurnal PEF variation, and with increased risk of asthma exacerbation (odd ratio = 1.50) over lag 0-4 days.

CONCLUSION

This large multicenter study provided the first-hand empirical evidence that cold temperature and a temperature drop may increase the risk of asthma exacerbation.

Assessing the health estimation capacity of air pollution exposure prediction models

BACKGROUND

The era of big data has enabled sophisticated models to predict air pollution concentrations over space and time. Historically these models have been evaluated using overall metrics that measure how close predictions are to monitoring data. However, overall methods are not designed to distinguish error at timescales most relevant for epidemiologic studies, such as day-to-day errors that impact studies of short-term health associations.

METHODS

We introduce frequency band model performance, which quantifies health estimation capacity of air quality prediction models for time series studies of air pollution and health. Frequency band model performance uses a discrete Fourier transform to evaluate prediction models at timescales of interest. We simulated fine particulate matter (PM_2.5), with errors at timescales varying from acute to seasonal, and health time series data. To compare evaluation approaches, we use correlations and root mean squared error (RMSE). Additionally, we assess health estimation capacity through bias and RMSE in estimated health associations. We apply frequency band model performance to PM_2.5 predictions at 17 monitors in 8 US cities.

RESULTS

In simulations, frequency band model performance rates predictions better (lower RMSE, higher correlation) when there is no error at a particular timescale (e.g., acute) and worse when error is added to that timescale, compared to overall approaches. Further, frequency band model performance is more strongly associated (R² = 0.95) with health association bias compared to overall approaches (R² = 0.57). For PM_2.5 predictions in Salt Lake City, UT, frequency band model performance better identifies acute error that may impact estimated short-term health associations.

CONCLUSIONS

For epidemiologic studies, frequency band model performance provides an improvement over existing approaches because it evaluates models at the timescale of interest and is more strongly associated with bias in estimated health associations. Evaluating prediction models at timescales relevant for health studies is critical to determining whether model error will impact estimated health associations.

Association between exposure to air pollutants and the risk of hospitalization for pulmonary embolism in Beijing, China: A case-crossover design using a distributed lag nonlinear model

BACKGROUND

Pulmonary embolism (PE) is a life-threatening condition. Few studies have evaluated the relationship between air pollution and PE, and these results have been inconsistent. Therefore, our study aimed to investigate the association between air pollutant exposure and the risk of hospitalization due to PE.

MATERIALS AND METHODS

Daily PE admissions, meteorological data, and ambient pollution data from January 1, 2015, to December 31, 2018, were collected in Beijing. A quasi-Poisson regression model combined with time-stratified case-crossover design and a distributed lag nonlinear model was used to determine the effect of air pollutant exposure on PE admission. To examine the stability of air pollutants' effects, multi-pollutant analyses were performed. Stratified analyses by age and sex were further conducted.

RESULTS

There were 5060 PE admissions during the study period, with an estimated incidence of 6.5 per 100,000. PM_2.5, PM₁₀, SO₂, O₃ and CO exposures were significantly associated with elevated risk of PE hospitalization. The highest cumulative risks were observed at a lag of 0-28 days for PM_2.5 (relative risk [RR] = 1.056, 95% confidence intervals [CI]: 1.015-1.098), PM₁₀ (RR = 1.042, 95%CI: 1.010-1.075), and CO (RR = 1.466, 95%CI: 1.127-1.906), at a lag of 0-27 days for SO₂ (RR = 1.674, 95%CI: 1.200-2.335), and at a lag of 0-4 days for O₃ (RR = 1.019, 95%CI: 1.001-1.038). All associations mentioned above except O₃ remained significant in multi-pollutant models. Stratified analyses showed that women and those aged ≥65 years people were more sensitive to PM₁₀ and CO exposure than men and those aged <65 years. The effect of PM_2.5 exposure was statistically significant in all subgroups.

CONCLUSIONS

Exposure to PM_2.5, PM₁₀, SO₂, and CO showed a positive association with PE hospitalization. High-risk PE groups should take special precautions on days with poor air quality.

Individual exposure to ambient PM2.5 and hospital admissions for COPD in 110 hospitals: a case-crossover study in Guangzhou, China

Few studies have evaluated the short-term association between hospital admissions and individual exposure to ambient particulate matter (PM_2.5). Particularly, no studies focused on hospital admissions for chronic obstructive pulmonary disease (COPD) at the individual level. We assessed the short-term effects of PM_2.5 on hospitalization admissions for COPD in Guangzhou, China, during 2014-2015, based on satellite-derived estimates of ambient PM_2.5 concentrations at a 1-km resolution near the residential address as individual-level exposure for each patient. Around 40,002 patients with COPD admitted to 110 hospitals were included in this study. A time-stratified case-crossover design with conditional logistic regression models was applied to assess the effects of PM_2.5 based on a 1-km grid data of aerosol optical depth provided by the National Aeronautics and Space Administration on hospital admissions for COPD. Further, we performed stratified analyses by individual demographic characteristics and season of hospital admission. Around 10 μg/m³ increase in individual-level PM_2.5 was associated with an increase of 1.6% (95% confidence interval [CI]: 0.6%, 2.7%) in hospitalization for COPD at a lag of 0-5 days. The impact of PM_2.5 on hospitalization for COPD was greater significantly in males and patients admitted in summer. Our study strengthened the evidence for the adverse effect of PM_2.5 based on satellite-based individual-level exposure data.

Effects of particulate matter on hospital admissions for respiratory diseases: an ecological study based on 12.5 years of time series data in Shanghai

BACKGROUND

Previous epidemiological studies on the association between short-term exposure to particulate matter (PM) with hospital admission in major cities in China were limited to shorter study periods or a single hospital. The aim of this ecological study based on a 12.5-year time series was to investigate the association of short-term exposure to PM with aerodynamic diameter ≤ 2.5 μm (PM_2.5) and aerodynamic diameter ≤ 10 μm (PM₁₀) with hospital admissions for respiratory diseases.

METHODS

Daily hospital admissions data were from the Shanghai Medical Insurance System for the period January 1, 2008 to July 31, 2020. We estimated the percentage change with its 95% confidence interval (CI) for each 10 μg/m³ increase in the level of PM_2.5 and PM₁₀ after adjustment for calendar time, day of the week, public holidays, and meteorological factors applying a generalized additive model with a quasi-Poisson distribution.

RESULTS

There were 1,960,361 hospital admissions for respiratory diseases in Shanghai during the study period. A 10 μg/m³ increase in the level of each class of PM was associated with increased total respiratory diseases when the lag time was 0 day (PM_2.5: 0.755%; 95% CI: 0.422, 1.089%; PM₁₀: 0.250%; 95% CI: 0.042, 0.459%). The PM_2.5 and PM₁₀ levels also had positive associations with admissions for COPD, asthma, and pneumonia. Stratified analyses demonstrated stronger effects in patients more than 45 years old and during the cold season. Total respiratory diseases increased linearly with PM concentration from 0 to 100 μg/m³, and increased more slowly at higher PM concentrations.

CONCLUSIONS

This time-series study suggests that short-term exposure to PM increased the risk for hospital admission for respiratory diseases, even at low concentrations. These findings suggest that reducing atmospheric PM concentrations may reduce hospital admissions for respiratory diseases.

Impact of Exposure to Ambient Fine Particulate Matter Pollution on Adults with Knee Osteoarthritis

The impact of exposure to fine particulate matter (PM_2.5) on the incidence of knee osteoarthritis is unclear, especially in Beijing which is a highly polluted city. We conducted a time-series study to examine the correlation between PM_2.5 exposure and outpatient visits for knee osteoarthritis in Beijing. Changes (in percentage) in the number of outpatient visits corresponding to every 10-μg/m³ increase in the PM_2.5 concentration were determined using a generalized additive quasi-Poisson model. There were records of 9,797,446 outpatient visits for knee osteoarthritis in the study period from 1 January 2010 to 31 December 2017. The daily concentration of PM_2.5 was 86.8 (74.3) μg/m³ over this period. A 10-μg/m³ increase in PM_2.5 concentrations on lag days 0–3 was associated with a 1.41% (95% confidence interval: 1.40–1.41%) increase in outpatient visits for knee osteoarthritis. Females and patients aged above 65 years were more sensitive to the adverse effects of PM_2.5 exposure. The present findings demonstrate that short-term exposure to PM_2.5 resulted in an increase in the number of outpatient visits for knee osteoarthritis in Beijing. The findings shed light on the effects of air pollution on knee osteoarthritis and could guide risk-mitigating strategies in cities such as Beijing.

Quantifying the short-term effects of air pollution on health in the presence of exposure measurement error: a simulation study of multi-pollutant model results

BACKGROUND

Most epidemiological studies estimate associations without considering exposure measurement error. While some studies have estimated the impact of error in single-exposure models we aimed to quantify the effect of measurement error in multi-exposure models, specifically in time-series analysis of PM_2.5, NO₂, and mortality using simulations, under various plausible scenarios for exposure errors. Measurement error in multi-exposure models can lead to effect transfer where the effect estimate is overestimated for the pollutant estimated with more error to the one estimated with less error. This complicates interpretation of the independent effects of different pollutants and thus the relative importance of reducing their concentrations in air pollution policy.

METHODS

Measurement error was defined as the difference between ambient concentrations and personal exposure from outdoor sources. Simulation inputs for error magnitude and variability were informed by the literature. Error-free exposures with their consequent health outcome and error-prone exposures of various error types (classical/Berkson) were generated. Bias was quantified as the relative difference in effect estimates of the error-free and error-prone exposures.

RESULTS

Mortality effect estimates were generally underestimated with greater bias observed when low ratios of the true exposure variance over the error variance were assumed (27.4% underestimation for NO₂). Higher ratios resulted in smaller, but still substantial bias (up to 19% for both pollutants). Effect transfer was observed indicating that less precise measurements for one pollutant (NO₂) yield more bias, while the co-pollutant (PM_2.5) associations were found closer to the true. Interestingly, the sum of single-pollutant model effect estimates was found closer to the summed true associations than those from multi-pollutant models, due to cancelling out of confounding and measurement error bias.

CONCLUSIONS

Our simulation study indicated an underestimation of true independent health effects of multiple exposures due to measurement error. Using error parameter information in future epidemiological studies should provide more accurate concentration-response functions.

Short-term exposure to ambient air pollution and hospital visits for IgE-mediated allergy: A time-stratified case-crossover study in southern China from 2012 to 2019

BACKGROUND

Because of the limited epidemiological evidence on the association between acute air pollutants and allergy, there is a need to investigate this association, especially between the short-term exposure to air pollution and the serum Immunoglobulin E (IgE)-mediated allergy.

METHODS

A total of 39,569 IgE test results and demographic characteristics were obtained in the First Affiliated Hospital of Guangzhou Medical University between August 2012 and September 2019. Ninety-nine specific allergens were tested according to clinical diagnosis. The logistic regression was used to assess the effects of CO, NO₂ and PM_2.5 exposure on the risk of sensitization to specific inhalant/food allergens. Generalized additive models with multivariate adjustments were utilized to model the exposure-response relationship. Stratified analyses were performed to estimate the reliability of correlations in various subgroups.

FINDINGS

Single-pollutant models indicate that the 3-day moving average (lag_2-4) of CO, PM_2.5 or NO₂ is associated with the increased risk for allergic diseases related to specific inhaled allergens. In multi-pollutant models, the adjusted Odds Ratio (OR) 95% (Confidence Interval, CI) increases by 8% (95% CI, 2%-15%) for per increment of 0.2 mg/m³ in CO levels, and rises by 8% (95% CI, 2%-13%) for each increase of 16.3 μg/m³ in PM_2.5 concentration. The associations are stronger in youngsters (<18, years) but not significantly different by gender. Particularly, a significantly stronger association between PM_2.5 exposure and hospital visits for inhaled allergy is observed in patients who are exposed to lower concentration of SO₂ (<10.333 μg/m³) and higher levels of NO₂ (≥42.0 μg/m³), as well as patients enrolled after 2017.

INTERPRETATION

The short-term exposure to CO/PM_2.5 increases the number of hospital visits for IgE-mediated allergy, especially for the sensitization to specific inhalant allergens. Therefore, to prevent inhaled allergies, the public policy for controlling air pollution needs to be considered seriously.

FUNDING

This study was supported by the University of Macau (grant numbers: FHS-CRDA-029-002-2017 and MYRG2018-00,071-FHS) as well as the Science and Technology Development Fund, Macau SAR (File no. 0004/2019/AFJ and 0011/2019/AKP). This work was also supported by the National Natural Science Foundation of China (81,871,736), the National Key Technology R&D Program (2018YFC1311902), the Guangdong Science and Technology Foundation (2019B030316028), the Guangzhou Municipal Health Foundation (20191A011073), and the Guangzhou Science and Technology Foundation (201,804,020,043).

Air Pollution Increases the Incidence of Upper Respiratory Tract Symptoms among Polish Children

A substantial proportion of airway disease’s global burden is attributable to exposure to air pollution. This study aimed to investigate the association between air pollution, assessed as concentrations of particulate matter PM2.5 and PM10 on the upper respiratory tract symptoms (URTS) in children. A nation-wide, questionnaire-based study was conducted in Poland in winter 2018/2019 in a population of 1475 children, comparing URTS throughout the study period with publicly available data on airborne particulate matter. A general regression model was used to evaluate the lag effects between daily changes in PM10 and PM2.5 and the number of children reporting URTS and their severity. PM10 and PM2.5 in the single-pollutant models had significant effects on the number of children reporting URTS. The prevalence of URTS: “runny nose”, “sneezing” and “cough” was positively associated with 12-week mean PM2.5 and PM10 concentrations. In the locations with the highest average concentration of PM, the symptoms of runny nose, cough and sneezing were increased by 10%, 9% and 11%, respectively, compared to the cities with the lowest PM concentrations. This study showed that moderate-term exposure (12 week observation period) to air pollution was associated with an increased risk of URTS among children aged 3–12 years in Poland. These findings may influence public debate and future policy at the national and international levels to improve air quality in cities and improve children’s health.

Time-series analysis of daily ambient temperature and emergency department visits in five US cities with a comparison of exposure metrics derived from 1-km meteorology products

BACKGROUND

Ambient temperature observations from single monitoring stations (usually located at the major international airport serving a city) are routinely used to estimate heat exposures in epidemiologic studies. This method of exposure assessment does not account for potential spatial variability in ambient temperature. In environmental health research, there is increasing interest in utilizing spatially-resolved exposure estimates to minimize exposure measurement error.

METHODS

We conducted time-series analyses to investigate short-term associations between daily temperature metrics and emergency department (ED) visits for well-established heat-related morbidities in five US cities that represent different climatic regions: Atlanta, Los Angeles, Phoenix, Salt Lake City, and San Francisco. In addition to airport monitoring stations, we derived several exposure estimates for each city using a national meteorology data product (Daymet) available at 1 km spatial resolution.

RESULTS

Across cities, we found positive associations between same-day temperature (maximum or minimum) and ED visits for heat-sensitive outcomes, including acute renal injury and fluid and electrolyte imbalance. We also found that exposure assessment methods accounting for spatial variability in temperature and at-risk population size often resulted in stronger relative risk estimates compared to the use of observations at airports. This pattern was most apparent when examining daily minimum temperature and in cities where the major airport is located further away from the urban center.

CONCLUSION

Epidemiologic studies based on single monitoring stations may underestimate the effect of temperature on morbidity when the station is less representative of the exposure of the at-risk population.

Association between short-term exposure to fine particulate pollution and outpatient visits for ulcerative colitis in Beijing, China: A time-series study

Environmental factors play an important role in the development of ulcerative colitis (UC). However, only few studies have examined the effects of air pollution on UC occurrence. We conducted a time-series analysis to explore the association between short-term exposure to fine particulate matter (PM_2.5) and outpatient visits for UC in Beijing, China. In total, 84,000 outpatient visits for UC were retrieved from the Beijing Medical Claim Data for Employees between January 1, 2010 and June 30, 2012. Measurements of daily PM_2.5 concentrations were obtained from the United States Embassy air-monitoring station. A generalized additive model with quasi-Poisson link was applied to examine the association between PM_2.5 concentrations and outpatient visits for UC stratified by sex, age, and season. We found that short-term exposure to PM_2.5 was significantly associated with increased daily outpatient visits for UC at lag 0 day. A 10 μg/m³ increase in PM_2.5 concentration at lag 0 day corresponded to a 0.32% increase in outpatient visits for UC (95% confidence interval (CI), 0.05-0.58%; P = 0.019). There was a clear concentration-response association between daily outpatient visits for UC and PM_2.5 concentrations. The PM_2.5 effects were significant across all sex and season subgroups, without evidence of effect modification by sex (P = 0.942) or season (P = 0.399). The association was positive in patients younger than 65 years old but negative in those 65 years old or older, although the difference was not significant (P = 0.883). In conclusion, our study demonstrated that short-term exposure to ambient PM_2.5 was significantly associated with an increased risk of daily outpatient visits for UC, especially in younger people. Additional studies are warranted to confirm our findings.

Ambient air pollution and hospital visits for peptic ulcer disease in China: A three-year analysis

BACKGROUND

Peptic ulcer disease (PUD) continued to be a source of significant morbidity and mortality worldwide. Recently, it has been reported that exposure to air pollution is a potential risk factor for PUD, but evidence on the association still remains inconsistent.

METHODS

We performed an ecological study to examine the association between short-term exposure to air pollution and daily hospital visits for PUD in Yinzhou, China from January 1st, 2017 to December 31st, 2019. Distributed lag nonlinear models were used to estimate the nonlinear and lag-response effects of air pollutants. Subgroup analyses stratified by sex, age and season were conducted to examine the effect modifications.

RESULTS

Overall, we found that short-term exposure to air pollution including SO₂, NO₂, CO, O₃ and PM_2.5 was significantly associated with hospital visits for PUD among all subjects. The lag-response effects of SO₂, NO₂ and O₃ varied at different concentrations and lag days. The cumulative risk ratios of CO and PM_2.5 showed nearly linear adverse effects and increased to maxima of 2.68 (95% CI: 1.49-4.78) and 2.40 (95% CI: 1.36-4.24) with their ranges from the references to the maximum concentrations, respectively. Moreover, the cumulative risks of particulate matters on hospital visits for PUD increased significantly in cold seasons, but not in warm seasons.

CONCLUSIONS

Our findings could provide growing evidence regarding the adverse health effects of air pollution on PUD, thereby strengthening the hypothesis that air pollutants have harmful impacts on digestive system.

Short-Term Associations of Ambient Fine Particulate Matter (PM2.5) with All-Cause Hospital Admissions and Total Charges in 12 Japanese Cities

The short-term association between ambient particulate matter ≤2.5 microns in diameter (PM_2.5) and hospital admissions is not fully understood. Studies of this association with hospital admission costs are also scarce, especially in entire hospitalized populations. We examined the association between ambient PM_2.5 and all-cause hospital admissions, the corresponding total charges, and the total charges per patient by analyzing the hospital admission data of 2 years from 628 hospitals in 12 cities in Japan. We used generalized additive models with quasi-Poisson regression for hospital admissions and generalized additive models with log-linear regression for total charges and total charges per patient. We first estimated city-specific results and the combined results by random-effect models. A total of 2,017,750 hospital admissions were identified. A 10 µg/m³ increase in the 2 day moving average was associated with a 0.56% (95% CI: 0.14–0.99%) increase in all-cause hospital admissions and a 1.17% (95% CI: 0.44–1.90%) increase in total charges, and a 10 µg/m³ increase in the prior 2 days was associated with a 0.75% (95% CI: 0.34–1.16%) increase in total charges per patient. Short-term exposure to ambient PM_2.5 was associated with increased all-cause hospital admissions, total charges, and total charges per patient.

Impact of solar ultraviolet radiation on daily outpatient visits of atopic dermatitis in Shanghai, China

The potential roles of solar ultraviolet radiation (UVR) as an environmental risk factor in inducing atopic dermatitis (AD) have not been well quantified. To determine the short-term associations between UVR and AD outpatient visits, we obtained daily outpatient visits of AD in Shanghai Skin Disease Hospital from 2013 to 2018. Data of hourly ground UVR were collected. We applied overdispersed generalized additive model to explore its associations. We found that daily exposure to UVR-A rather than UVR-B was positively associated with AD outpatient visits. The visits increased on the present day (lag 0 days) and decreased appreciably with longer lags and became insignificant at lag 4 days. For 10 w/m² increase in daytime mean and noontime mean exposure to overall UVR and UVR-A from lag 0 to 6 days, the cumulative relative risk of AD was 1.12/1.13 and 1.08/1.08, respectively. Stronger effects of UVR exposure on AD occurred in patients aged 0-7 and > 45 years and in the cold seasons. This study contributed to the few epidemiological evidences that acute exposure to solar UVR may elevate the risks of AD.

Short term associations of ambient nitrogen dioxide with daily total, cardiovascular, and respiratory mortality: multilocation analysis in 398 cities

OBJECTIVE

To evaluate the short term associations between nitrogen dioxide (NO₂) and total, cardiovascular, and respiratory mortality across multiple countries/regions worldwide, using a uniform analytical protocol.

DESIGN

Two stage, time series approach, with overdispersed generalised linear models and multilevel meta-analysis.

SETTING

398 cities in 22 low to high income countries/regions.

MAIN OUTCOME MEASURES

Daily deaths from total (62.8 million), cardiovascular (19.7 million), and respiratory (5.5 million) causes between 1973 and 2018.

RESULTS

On average, a 10 μg/m³ increase in NO₂ concentration on lag 1 day (previous day) was associated with 0.46% (95% confidence interval 0.36% to 0.57%), 0.37% (0.22% to 0.51%), and 0.47% (0.21% to 0.72%) increases in total, cardiovascular, and respiratory mortality, respectively. These associations remained robust after adjusting for co-pollutants (particulate matter with aerodynamic diameter ≤10 μm or ≤2.5 μm (PM₁₀ and PM_2.5, respectively), ozone, sulfur dioxide, and carbon monoxide). The pooled concentration-response curves for all three causes were almost linear without discernible thresholds. The proportion of deaths attributable to NO₂ concentration above the counterfactual zero level was 1.23% (95% confidence interval 0.96% to 1.51%) across the 398 cities.

CONCLUSIONS

This multilocation study provides key evidence on the independent and linear associations between short term exposure to NO₂ and increased risk of total, cardiovascular, and respiratory mortality, suggesting that health benefits would be achieved by tightening the guidelines and regulatory limits of NO₂.

Association between prenatal air pollution exposure and risk of hypospadias in offspring: a systematic review and meta-analysis of observational studies

Background: The findings of associations between prenatal air pollution exposure and hypospadias risk in offspring are inconsistent. No systematic review or meta-analysis has yet summarized the present knowledge on the aforementioned topic.

Methods: Relevant manuscripts were identified by searching PubMed and Web of Science databases through January 31, 2020. Summary odds ratios (ORs) with 95% confidence intervals (CIs) in meta-analyses were estimated based on a random effects model. Publication bias was evaluated by funnel plots, Begg’s test, and Egger’s test.

Results: The search identified 3,032 relevant studies. Sixteen studies cumulatively involving 21,701 hypospadias cases and 1,465,364 participants were included. All of these studies were classified as having a low risk of bias. We classified pollutants as nitrogen oxides, particulate matter (PM), ozone, and other exposures. The exposure window to pollutants varied from three months before conception to seven days after delivery. In the meta-analyses, only PM_2.5 exposure in the first trimester was related to increased risk of hypospadias (per 10 μg/m³ OR = 1.34; 95% CI: 1.06–1.68).

Conclusion: We found evidence for an effect of PM_2.5 exposure on hypospadias risk. Improvements in the areas of study design, exposure assessment, and specific exposure window are needed to advance this field.

Short-term effect of fine particular matter on daily hospitalizations for ischemic stroke: A time-series study in Yancheng, China

OBJECTIVE

To investigate the associations between short-term exposure to fine particular matter (PM_2.5) and ischemic stroke (IS) in Yancheng, China, from 2017 to 2019.

METHODS

We designed a time-series study based on generalized additive models to explore the association of PM_2.5 and IS admitted in two major hospitals in Yancheng. We built different lag patterns and conducted stratification analyses by age, gender, and season. Moreover, we examined the robustness of the associations adopting two-pollutant models and fitted the concentration-response curves.

RESULT

We observed positive and significant associations at lag 0 day. Every 10 μg/m³ increase in PM_2.5 (lag0) was associated with 1.06% (95% CI: 0.21%-1.91%) increases in hospitalizations for IS. The association remained stable and statistically significant to the adjustment of carbon monoxide and ozone. We observed that the associations were stronger in females and during cold seasons. The overall concentration-response relationship curve was linear positive and increased slowly but rose sharply at higher concentrations in the cold season.

CONCLUSION

Our study added to the evidence that short-term exposure to PM_2.5 may induce IS, and the government should take action to address the air pollution issues and protect susceptible populations.