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

Alternative adjustment for seasonality and long-term time-trend in time-series analysis for long-term environmental exposures and disease counts

Background

Time-series analysis with case-only data is a prominent method for the effect of environmental determinants on disease events in environmental epidemiology. In this analysis, adjustment for seasonality and long-term time-trend is crucial to obtain valid findings. When applying this analysis for long-term exposure (e.g., months, years) of which effects are usually studied via survival analysis with individual-level longitudinal data, unlike its application for short-term exposure (e.g., days, weeks), a standard adjustment method for seasonality and long-term time-trend can extremely inflate standard error of coefficient estimates of the effects. Given that individual-level longitudinal data are difficult to construct and often available to limited populations, if this inflation of standard error can be solved, rich case-only data over regions and countries would be very useful to test a variety of research hypotheses considering unique local contexts.

Methods

We discuss adjustment methods for seasonality and time-trend used in time-series analysis in environmental epidemiology and explain why standard errors can be inflated. We suggest alternative methods to solve this problem. We conduct simulation analyses based on real data for Seoul, South Korea, 2002–2013, and time-series analysis using real data for seven major South Korean cities, 2006–2013 to identify whether the association between long-term exposure and health outcomes can be estimated via time-series analysis with alternative adjustment methods.

Results

Simulation analyses and real-data analysis confirmed that frequently used adjustment methods such as a spline function of a variable representing time extremely inflate standard errors of estimates for associations between long-term exposure and health outcomes. Instead, alternative methods such as a combination of functions of variables representing time can make sufficient adjustment with efficiency.

Conclusions

Our findings suggest that time-series analysis with case-only data can be applied for estimating long-term exposure effects. Rich case-only data such as death certificates and hospitalization records combined with repeated measurements of environmental determinants across countries would have high potentials for investigating the effects of long-term exposure on health outcomes allowing for unique contexts of local populations.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-020-01199-1.

Short-term effects of ambient nitrogen dioxide on years of life lost in 48 major Chinese cities, 2013-2017

BACKGROUND

Evidence on the acute effect of short-term exposure to nitrogen dioxide (NO₂) on years of life lost (YLL) is rare, especially in multicity setting.

METHODS

We conducted a time series study among 48 major Chinese cities covering more than 403 million people from 2013 to 2017. The relative percentage changes of NO₂-YLL were estimated by generalized additive models in each city, then were pooled to generate average effects using random-effect models. In addition, stratified analyses by individual demographic factors and temperature as well as meta-regression analyses incorporating city-specific air pollutant concentrations, meteorological conditions, and socioeconomic indicators were performed to explore potential effect modification.

RESULTS

A 10 μg/m³ increase in two-day moving average (lag01) NO₂ concentration was associated with 0.64% (95% CI: 0.47%, 0.81%), 0.47% (95% CI: 0.27%, 0.68%), and 0.68% (95% CI: 0.34%, 1.02%) relative increments in YLL due to nonaccidental causes, cardiovascular diseases (CVD), and respiratory diseases (RD), respectively. These associations were generally robust to the adjustment of co-pollutants, except for NO₂-CVD that might be confounded by fine particulate matter. The increased YLL induced by NO₂ were more pronounced in elderly people, hotter days, and cities characterized by less severe air pollution or higher temperature.

CONCLUSIONS

Our results demonstrated robust evidence on the associations between NO₂ exposure and YLL due to nonaccidental causes, CVD, and RD, which provided novel evidence to better understand the disease burden related to NO₂ pollution and to facilitate allocation of health resources targeting high-risk subpopulation.

Association of hospital admission for bronchiectasis with air pollution: A province-wide time-series study in southern China

The relation of acute fluctuations of air pollution to hospital admission for bronchiectasis remained uncertain, and large-scale studies were needed. We collected daily concentrations of particulate matter (PM), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon monoxide (CO), ozone (O₃), and daily hospitalizations for bronchiectasis for 21 cities across Guangdong Province from 2013 through 2017. We examined their association using two-stage time-series analysis. Our analysis was stratified by specific sub-diagnosis, sex and age group to assess potential effect modifications. Relative risks of hospitalization for bronchiectasis were 1.060 (95%CI 1.014-1.108) for PM₁₀ at lag0-6, 1.067 (95%CI 1.020-1.116) for PM_2.5 at lag0-6, 1.038 (95%CI 1.005-1.073) for PM_coarse at lag0-6, 1.058 (95%CI 1.015-1.103) for SO₂ at lag0-4, 1.057 (95%CI 1.030-1.084) for NO₂ at lag0 and 1.055 (95%CI 1.025-1.085) for CO at lag0-6 per interquartile range increase of air pollution. Specifically, acute fluctuations of air pollution might be a risk factor for bronchiectasis patients with lower respiratory infection but not with hemoptysis. Patients aged ≥65 years, and female patients appeared to be particularly susceptible to air pollution. Acute fluctuations of air pollution, particularly PM may increase the risk of hospital admission for bronchiectasis exacerbations, especially for the patients complicated with lower respiratory infection. This study strengthens the importance of reducing adverse impact on respiratory health of air pollution to protect vulnerable populations.

Association of particulate matter pollution and case fatality rate of COVID-19 in 49 Chinese cities

The COVID-19 epidemic, caused by the SARS-CoV-2 virus, has resulted in 3352 deaths in China as of April 12, 2020. This study aimed to investigate the associations between particulate matter (PM) concentrations and the case fatality rate (CFR) of COVID-19 in 49 Chinese cities, including the epicenter of Wuhan. We used the Global Moran's I to analyze spatial distribution and autocorrelation of CFRs, and then we used multivariate linear regression to analyze the associations between PM_2.5 and PM₁₀ concentrations and COVID-19 CFR. We found positive associations between PM pollution and COVID-19 CFR in cities both inside and outside Hubei Province. For every 10 μg/m³ increase in PM_2.5 and PM₁₀ concentrations, the COVID-19 CFR increased by 0.24% (0.01%-0.48%) and 0.26% (0.00%-0.51%), respectively. PM pollution distribution and its association with COVID-19 CFR suggests that exposure to such may affect COVID-19 prognosis.

Association of particulate matter pollution and case fatality rate of COVID-19 in 49 Chinese cities

The COVID-19 epidemic, caused by the SARS-CoV-2 virus, has resulted in 3352 deaths in China as of April 12, 2020. This study aimed to investigate the associations between particulate matter (PM) concentrations and the case fatality rate (CFR) of COVID-19 in 49 Chinese cities, including the epicenter of Wuhan. We used the Global Moran's I to analyze spatial distribution and autocorrelation of CFRs, and then we used multivariate linear regression to analyze the associations between PM_2.5 and PM₁₀ concentrations and COVID-19 CFR. We found positive associations between PM pollution and COVID-19 CFR in cities both inside and outside Hubei Province. For every 10 μg/m³ increase in PM_2.5 and PM₁₀ concentrations, the COVID-19 CFR increased by 0.24% (0.01%-0.48%) and 0.26% (0.00%-0.51%), respectively. PM pollution distribution and its association with COVID-19 CFR suggests that exposure to such may affect COVID-19 prognosis.

Association of diurnal temperature range with daily hospitalization for exacerbation of chronic respiratory diseases in 21 cities, China

Background

The association between diurnal temperature range (DTR) and hospitalization for exacerbation of chronic respiratory diseases (CRD) was rarely reported.

Objectives

To examine the association between DTR and daily hospital admissions for exacerbation of CRD and find out the potential effect of modifications on this association.

Method

Data on daily hospitalization for exacerbation of chronic obstructive pulmonary disease (COPD), asthma and bronchiectasis and meteorology measures from 2013 through 2017 were obtained from 21 cities in South China. After controlling the effects of daily mean temperature, relative humidity (RH), particulate matter < 2.5 μm diameter (PM_2.5) and other confounding factors, a standard generalized additive model (GAM) with a quasi-Poisson distribution was performed to evaluate the relationships between DTR and daily hospital admissions of CRD in a two-stage strategy. Subgroup analysis was performed to find potential modifications, including seasonality and population characteristics.

Result

Elevated risk of hospitalization for exacerbation of CRD (RR = 1.09 [95%CI: 1.08 to 1.11]) was associated with the increase in DTR (the 75th percentile versus the 25th percentile of DTR at lag0–6). The effects of DTR on hospital admissions for CRD were strong at low DTR in the hot season and high DTR in the cold season. The RR (the 75th percentile versus the 25th percentile of DTR at lag0–6) of hospitalization was 1.11 (95%CI: 1.08 to 1.12) for exacerbations of COPD and 1.09 (95%CI: 1.05 to 1.13) for asthma. The adverse effect of DTR on hospitalization for bronchiectasis was only observed in female patients (RR = 1.06 [95%CI: 1.03 to 1.10]).

Conclusion

Our study provided additional evidence for the association between DTR and daily hospitalization for exacerbation of CRD, and these associations are especially stronger in COPD patients and in the cold season than the hot season. Preventive measures to reduce the adverse impacts of DTR were needed for CRD patients.

Association Between Ambient Air Pollution and Daily Hospital Admissions for Depression in 75 Chinese Cities

OBJECTIVE

Although the association between ambient air pollution and risk of depression has been investigated in several epidemiological studies, the evidence is still lacking for hospital admissions for depression, which indicates a more severe form of depressive episode. The authors used national morbidity data to investigate the association between short-term exposure to ambient air pollution and daily hospital admissions for depression.

METHODS

Using data from the Chinese national medical insurance databases for urban populations, the authors conducted a two-stage time-series analysis to investigate the associations of short-term exposure to major ambient air pollutants-fine particles (PM_2.5), inhalable particles (PM₁₀), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone (O₃), and carbon monoxide (CO)-and daily hospital admission risk for depression in 75 Chinese cities during the period 2013-2017.

RESULTS

The authors identified 111,620 hospital admissions for depression in 75 cities. In the single-pollutant models, the effect estimates of all included air pollutants, with the exception of O₃, were significant at several lags within 7 days. For example, 10 μg/m³ increases in PM_2.5, PM₁₀, and NO₂ at lag01 were associated with increases of 0.52% (95% CI=0.03, 1.01), 0.41% (95% CI=0.05, 0.78), and 1.78% (95% CI=0.73, 2.83), respectively, in daily hospital admissions for depression. Subgroup, sensitivity, and two-pollutant model analyses highlighted the robustness of the effect estimates for NO₂.

CONCLUSIONS

The study results suggest that short-term exposure to ambient air pollution is associated with an increased risk of daily hospital admission for depression in the general urban population in China, which may have important implications for improving mental wellness among the public.

STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part 1-Basic theory and simple methods of adjustment

Measurement error and misclassification of variables frequently occur in epidemiology and involve variables important to public health. Their presence can impact strongly on results of statistical analyses involving such variables. However, investigators commonly fail to pay attention to biases resulting from such mismeasurement. We provide, in two parts, an overview of the types of error that occur, their impacts on analytic results, and statistical methods to mitigate the biases that they cause. In this first part, we review different types of measurement error and misclassification, emphasizing the classical, linear, and Berkson models, and on the concepts of nondifferential and differential error. We describe the impacts of these types of error in covariates and in outcome variables on various analyses, including estimation and testing in regression models and estimating distributions. We outline types of ancillary studies required to provide information about such errors and discuss the implications of covariate measurement error for study design. Methods for ascertaining sample size requirements are outlined, both for ancillary studies designed to provide information about measurement error and for main studies where the exposure of interest is measured with error. We describe two of the simpler methods, regression calibration and simulation extrapolation (SIMEX), that adjust for bias in regression coefficients caused by measurement error in continuous covariates, and illustrate their use through examples drawn from the Observing Protein and Energy (OPEN) dietary validation study. Finally, we review software available for implementing these methods. The second part of the article deals with more advanced topics.

Effect of Ambient Air Pollution on Hospital Readmissions among the Pediatric Asthma Patient Population in South Texas: A Case-Crossover Study

Few studies have evaluated the association between ambient air pollution and hospital readmissions among children with asthma, especially in low-income communities. This study examined the short-term effects of ambient air pollutants on hospital readmissions for pediatric asthma in South Texas. A time-stratified case-crossover study was conducted using the hospitalization data from a children’s hospital and the air pollution data, including particulate matter 2.5 (PM_2.5) and ozone concentrations, from the Centers for Disease Control and Prevention between 2010 and 2014. A conditional logistic regression analysis was performed to investigate the association between ambient air pollution and hospital readmissions, controlling for outdoor temperature. We identified 111 pediatric asthma patients readmitted to the hospital between 2010 and 2014. The single-pollutant models showed that PM_2.5 concentration had a significant positive effect on risk for hospital readmissions (OR = 1.082, 95% CI = 1.008–1.162, p = 0.030). In the two-pollutant models, the increased risk of pediatric readmissions for asthma was significantly associated with both elevated ozone (OR = 1.023, 95% CI = 1.001–1.045, p = 0.042) and PM_2.5 concentrations (OR = 1.080, 95% CI = 1.005–1.161, p = 0.036). The effects of ambient air pollutants on hospital readmissions varied by age and season. Our findings suggest that short-term (4 days) exposure to air pollutants might increase the risk of preventable hospital readmissions for pediatric asthma patients.

Short-term exposure to air pollution and its interaction effects with two ABO SNPs on blood lipid levels in northern China: A family-based study

We examined the main effects of ambient particulate matters, as well as whether single-nucleotide polymorphisms (SNPs), located within ABO gene would modify the relationship. Data were collected from a family-based study conducted in Northern China. A generalized additive model with a Gaussian link and with each family as a stratum was applied to estimate the percentage change in blood lipid levels following a 10 μg/m³ increase in ambient particulate matter concentrations. Interaction analyses were conducted by including a cross-product term of PM_2.5 or PM₁₀ by SNP. Results showed that a 10 μg/m³ increase in Particulate matter with aerodynamic diameter <2.5 μm (PM_2.5) concentrations corresponded to the highest 0.010% (95% CI: 0.002%-0.018%), 0.018% (95% CI: 0.006%-0.029%), 0.019% (95% CI: 0.010%-0.029%) increase in total cholesterol (TC), triglyceride (TG), low density lipoprotein cholesterol (LDL-C), respectively and 0.005% (95% CI: 0.002%-0.008%) decrease in high density lipoprotein cholesterol (HDL-C)-to-LDL-C ratio. As for the PM₁₀, similar results were observed. Furthermore, our finding showed an interaction effect of PM₁₀ and rs505922/rs579459 C allele on TG. Specifically, individuals carrying the rs505922 and rs579459 T allele have higher TG concentrations following PM₁₀ exposure, with a 10 μg/m³ increase in PM₁₀ concentrations corresponding to the highest 0.028% and 0.034% increase in TG, respectively. In conclusion, short-term exposures to ambient particulate matters are associated with a higher blood lipid level, which can be modified by ABO polymorphism. The findings may be useful in identifying vulnerable population according to genetic background.

Comparing the performance of air pollution models for nitrogen dioxide and ozone in the context of a multilevel epidemiological analysis

Supplemental Digital Content is available in the text.

Using modeled air pollutant predictions as exposure variables in epidemiological analyses can produce bias in health effect estimation. We used statistical simulation to estimate these biases and compare different air pollution models for London.

Optimising the case-crossover design for use in shared exposure settings

With a case-crossover design, a case's exposure during a risk period is compared to the case's exposures at referent periods. The selection of referents for this self-controlled design is determined by the referent selection strategy (RSS). Previous research mainly focused on systematic bias associated with the RSS. We additionally focused on how RSS determines the number of referents per risk, sensitivity to overdispersion and time-varying confounding.

We illustrated the consequences of different RSS using a simulation study informed by data on meteorological variables and Legionnaires’ disease. By randomising the events and exposure time series, we explored statistical power associated with time-stratified and fixed bidirectional RSS and their susceptibility to systematic bias and confounding bias. In addition, we investigated how a high number of events on the same date (e.g. outbreaks) affected coefficient estimation. As illustrated by our work, referent selection alone can be insufficient to control for a time-varying confounding bias. In contrast to systematic bias, confounding bias can be hard to detect. We studied potential solutions: varying the model parameters and link-function, outlier-removal and aggregating the input-data over smaller areas. Our simulation study offers a framework for researchers looking to detect and to avoid bias in case-crossover studies.

Systematic review and meta-analysis of case-crossover and time-series studies of short term outdoor nitrogen dioxide exposure and ischemic heart disease morbidity

BACKGROUND

Nitrogen dioxide (NO₂) is a pervasive urban pollutant originating primarily from vehicle emissions. Ischemic heart disease (IHD) is associated with a considerable public health burden worldwide, but whether NO₂ exposure is causally related to IHD morbidity remains in question. Our objective was to determine whether short term exposure to outdoor NO₂ is causally associated with IHD-related morbidity based on a synthesis of findings from case-crossover and time-series studies.

METHODS

MEDLINE, Embase, CENTRAL, Global Health and Toxline databases were searched using terms developed by a librarian. Screening, data extraction and risk of bias assessment were completed independently by two reviewers. Conflicts between reviewers were resolved through consensus and/or involvement of a third reviewer. Pooling of results across studies was conducted using random effects models, heterogeneity among included studies was assessed using Cochran's Q and I² measures, and sources of heterogeneity were evaluated using meta-regression. Sensitivity of pooled estimates to individual studies was examined using Leave One Out analysis and publication bias was evaluated using Funnel plots, Begg's and Egger's tests, and trim and fill.

RESULTS

Thirty-eight case-crossover studies and 48 time-series studies were included in our analysis. NO₂ was significantly associated with IHD morbidity (pooled odds ratio from case-crossover studies: 1.074 95% CI 1.052-1.097; pooled relative risk from time-series studies: 1.022 95% CI 1.016-1.029 per 10 ppb). Pooled estimates for case-crossover studies from Europe and North America were significantly lower than for studies conducted elsewhere. The high degree of heterogeneity among studies was only partially accounted for in meta-regression. There was evidence of publication bias, particularly for case-crossover studies. For both case-crossover and time-series studies, pooled estimates based on multi-pollutant models were smaller than those from single pollutant models, and those based on older populations were larger than those based on younger populations, but these differences were not statistically significant.

CONCLUSIONS

We concluded that there is a likely causal relationship between short term NO₂ exposure and IHD-related morbidity, but important uncertainties remain, particularly related to the contribution of co-pollutants or other concomitant exposures, and the lack of supporting evidence from toxicological and controlled human studies.

Influence of exposure measurement errors on results from epidemiologic studies of different designs

In epidemiologic studies of health effects of air pollution, measurements or models are used to estimate exposure. Exposure estimates have errors that propagate to effect estimates in exposure-response models. We critically evaluate how types of exposure measurement error influenced bias and precision of effect estimates to understand conditions affecting interpretation of exposure-response models for epidemiologic studies of exposure to PM_2.5, NO₂, and SO₂. We reviewed available literature on exposure measurement error for time-series and long-term exposure epidemiology studies. For time-series studies, time-activity error (daily exposure concentration did not account for variation in exposure due to time-activity during a day) and nonambient (indoor) sources negatively biased the effect estimates and increased standard error, so uncertainty grew with increasing bias while underestimating the true health effect in these studies. Spatial error (deviation between true exposure concentration at an individual's location and concentration at a receptor) was ascribed to negatively biased effect estimates in most cases. Positive bias occurred for spatially variable pollutants when the variance of error correlated with the exposure estimate. For long-term exposure studies, most spatial errors did not bias the effect estimate. For both time-series and long-term exposure studies reviewed, large uncertainties were observed when exposure concentration was modeled with low spatial and temporal resolution for a spatially variable pollutant.

Fine particulate air pollution and adult hospital admissions in 200 Chinese cities: a time-series analysis

BACKGROUND

The association between short-term exposure to ambient fine particulate matter (PM2.5) and morbidity risk in developing countries is not fully understood. We conducted a nationwide time-series study to estimate the short-term effect of PM2.5 on hospital admissions in Chinese adults.

METHODS

Daily counts of hospital admissions for 2014-16 were obtained from the National Urban Employee Basic Medical Insurance database. We identified more than 58 million hospitalizations from 0.28 billion insured persons in 200 Chinese cities for subjects aged 18 years or older. Generalized additive models with quasi-Poisson regression were applied to examine city-specific associations of PM2.5 concentrations with hospital admissions. National-average estimates of the association were obtained from a random-effects meta-analysis. We also investigated potential effect modifiers, such as age, sex, temperature and relative humidity.

RESULTS

An increase of 10 μg/m3 in same-day PM2.5 concentrations was positively associated with a 0.19% (95% confidence interval: 0.07-0.30) increase in the daily number of hospital admissions at the national level. PM2.5 exposure remained positively associated with hospital admissions on days when the daily concentrations met the current Chinese Ambient Air Quality Standards (75 μg/m3). Estimates of admission varied across cities and increased in cities with lower PM2.5 concentrations (p = 0.044) or higher temperatures (p = 0.002) and relative humidity (p = 0.003). The elderly were more sensitive to PM2.5 exposure (p < 0.001).

CONCLUSIONS

Short-term exposure to PM2.5 was positively associated with adult hospital admissions in China, even at levels below current Chinese Ambient Air Quality Standards.

The acute effects of ultraviolet radiation exposure on solar dermatitis in Shanghai, China

Ultraviolet radiation (UVR) has long been considered associated with solar dermatitis, but the associations have not been well quantified. To depict the full-range exposure-response association between daily UVR exposures and daily outpatient visits of solar dermatitis. We collected the daily number of outpatient visits of solar dermatitis and monitored hourly ground data of UVR (the sum of A- and B-band) from 1 January 2013 to 31 December 2017 in Shanghai, China. The data were analyzed using the time-series approach, in which overdispersed generalized additive model was used and time trends and weather conditions were controlled for. During the study period, we recorded a total of 15,051 outpatient visits of solar dermatitis. There was a consistently increasing risk of solar dermatitis associated with stronger UVR without a discernible threshold. The effects occurred on the present day, increased to the largest at lag 1 or 2 days, and attenuated to the null at lag 5 days or more. A unit (w/m²) increase in daily maximum-hour UVR was associated with 1.70% (95%CI: 1.19%, 2.20%) increase of outpatient visits of solar dermatitis. Stronger effects occurred among the young people, females, and in the warm season. The risks of solar dermatitis due to UVR exposure would be overestimated if ambient temperature was not adjusted. This study provides quantitative epidemiological estimates for the positive associations between short-term exposure to UVR and increased risks of solar dermatitis. The associations were more prominent among young people, females, and in warm seasons.

Modification Effects of Temperature on the Ozone-Mortality Relationship: A Nationwide Multicounty Study in China

Both ozone exposure and extreme temperatures are found to be significantly associated with mortality; however, inconsistent results have been obtained on the modification effects of temperature on the ozone-mortality association. In the present study, we conducted a nationwide time-series analysis in 128 counties from 2013-2018 to examine whether temperature modifies the association between short-term ozone exposure with nonaccidental and cause-specific mortality in China. First, we analyzed the effects of ozone exposure on mortality at different temperature levels. Then, we calculated the pooled effects through a meta-analysis across China. We found that high-temperature conditions (>75th percentile in each county) significantly enhanced the effects of ozone on nonaccidental, cardiovascular, and respiratory mortality, with increases of 0.44% (95% confidence interval (CI): 0.36 and 0.51%), 0.42% (95% CI: 0.32 and 0.51%) and 0.50% (95% CI: 0.31 and 0.68%), respectively, for a 10 μg/m³ increase in ozone at high temperatures. Stronger effects on nonaccidental and cardiovascular mortality were observed at high temperatures among elderly individuals aged 65 years and older compared with the younger people. Our findings provide evidence that health damage because of ozone may be influenced by the impacts of increasing temperatures, which point to the importance of mitigating ozone exposure in China under the context of climate change to further reduce the public health burden.

The impact of ambient ozone pollution on pneumonia: A nationwide time-series analysis

Few large multicity studies have assessed acute effect of tropospheric ozone pollution on pneumonia risk. We aimed to examine the relation between day-to-day changes in ozone concentrations and hospital admissions for pneumonia in China. We conducted a national time-series study in 184 major Chinese cities from 2014 to 2017. City-specific relation between ozone concentrations and pneumonia admissions was evaluated using an over-dispersed generalized additive model. Random-effects meta-analysis was conducted to pool the city-specific estimates. Two-pollutant models were fitted to test the robustness of the relations. We also investigated potential effect modifiers. Overall, we observed increased admissions for pneumonia associated with ozone exposure. The national-average estimates per 10-μg/m³ increase in ozone were 0.14% (95% CI: 0.03%-0.25%) at lag 0 day in the whole year, 0.30% (95% CI: 0.17%-0.43%) at lag 0 day in the warm season, and 0.20% (95% CI: 0.05%-0.34%) at lag 1 day in the cool season. Two-pollutant models indicated that the ozone effects were not confounded by PM_2.5, SO₂, NO₂ or CO. The association between ozone and pneumonia was stronger in the elderly. Ozone levels and gross domestic product per capita reduced the effects of ozone, and smoking enhanced the effects of ozone. In conclusion, we estimated an increase in daily pneumonia admissions associated with ozone exposure in China. As the first national study in China to report acute effect of ozone on pneumonia hospitalizations, our findings are incredibly meaningful in terms of both ozone pollution related policy development and pneumonia prevention.

Effects of ambient particulate matter on fasting blood glucose: A systematic review and meta-analysis

Studies have found that ambient particulate matter (PM) affects fasting blood glucose. However, the results are not consistent. We conducted a systematic review and meta-analysis to determine the relationship between PM with an aerodynamic diameter of 10 μm or less (PM₁₀) and PM with an aerodynamic diameter of 2.5 μm or less (PM_2.5) and fasting blood glucose. We searched PubMed, Web of Science, the Wanfang Database and the China National Knowledge Infrastructure up to April 1, 2019. A total of 24 papers were included in the review, and 17 studies with complete or convertible quantitative information were included in the meta-analysis. The studies were divided into groups by PM size fractions (PM₁₀ and PM_2.5) and length of exposure. Long-term exposures were based on annual average concentrations, and short-term exposures were those lasting less than 28 days. In the long-term exposure group, fasting blood glucose increased 0.10 mmol/L (95% CI: 0.02, 0.17) per 10 μg/m³ of increased PM₁₀ and 0.23 mmol/L (95% CI: 0.01, 0.45) per 10 μg/m³ of increased PM_2.5. In the short-term exposure group, fasting blood glucose increased 0.02 mmol/L (95% CI: -0.01, 0.04) per 10 μg/m³ of increased PM₁₀ and 0.08 mmol/L (95% CI: 0.04, 0.11) per 10 μg/m³ of increased PM_2.5. Further prospective studies are needed to explore the relationship between ambient PM exposure and fasting blood glucose.

Reflection on modern methods: five myths about measurement error in epidemiological research

Epidemiologists are often confronted with datasets to analyse which contain measurement error due to, for instance, mistaken data entries, inaccurate recordings and measurement instrument or procedural errors. If the effect of measurement error is misjudged, the data analyses are hampered and the validity of the study's inferences may be affected. In this paper, we describe five myths that contribute to misjudgments about measurement error, regarding expected structure, impact and solutions to mitigate the problems resulting from mismeasurements. The aim is to clarify these measurement error misconceptions. We show that the influence of measurement error in an epidemiological data analysis can play out in ways that go beyond simple heuristics, such as heuristics about whether or not to expect attenuation of the effect estimates. Whereas we encourage epidemiologists to deliberate about the structure and potential impact of measurement error in their analyses, we also recommend exercising restraint when making claims about the magnitude or even direction of effect of measurement error if not accompanied by statistical measurement error corrections or quantitative bias analysis. Suggestions for alleviating the problems or investigating the structure and magnitude of measurement error are given.

The association between high particulate matter pollution and daily cause-specific hospital admissions: a time-series study in Yichang, China

Particulate matter (PM) air pollution is one of the major causes of morbidity and mortality in China. In this study, we estimated the short-term effects of PM on cause-specific hospitalization in Yichang, China. Daily data for PM level, meteorological factors, and hospital admissions (total hospitalization counts = 391,960) in Yichang between 2015 and 2017 were collected. We conducted a time-series study and applied a generalized additive model to evaluate the association between every 10 μg/m³ increment of PM and percent increase of hospitalization. We found positive and statistically significant associations between PM and hospital admissions for multiple outcomes, including all-cause, total respiratory, total cardiovascular diseases, and disease subcategories (hypertensive disease, coronary heart disease, stroke and the stroke subtype, chronic obstructive pulmonary disease, and lower respiratory infection). Each 10 μg/m³ increase in PM_2.5 at Lag01 (a moving average of Lag0 to Lag1), was significantly associated with an increase of 1.31% (95% CI: 0.79%, 1.83%), 1.12% (95% CI: 0.40%, 1.84%), and 1.14% (95% CI: 0.53%, 1.75%) in hospitalizations for all-cause, CVD, and respiratory, respectively. The association for PM₁₀ with all-cause, CVD, and respiratory admissions was similar but weaker than PM_2.5. The effect on admissions persisted for up to 7 days, and peaked at Lag01. The associations between PM and all-cause hospitalizations were stronger among older individuals and in cold seasons. It is therefore important to continue implementation of emission abatement and other effective measures in Yichang and other cities in China.

Statistical spatial-temporal modeling of ambient ozone exposure for environmental epidemiology studies: A review

BACKGROUND

Studies have discovered the adverse health impacts of ambient ozone. Most epidemiological studies explore the relationship between ambient ozone and health effects based on fixed site monitoring data. Fine modeling of ground-level ozone exposure conducted by statistical models has great advantages for improving exposure accuracy and reducing exposure bias. However, there is no review summarizing such studies.

OBJECTIVES

A review is presented to summarize the basic process of model development and to provide some suggestions for researchers.

METHODS

A search of PubMed, Web of Science and the Wanfang Database was performed for dates through July 1, 2019 to obtain relevant studies worldwide. We also examined the references of the articles of interest to ensure that as many articles as possible were included.

RESULTS

The land use regression model (LUR model), random forest model and artificial neural network model have been used in this field. We summarized these studies in terms of model selection, data preparation, simulation scale selection, and model establishment and validation. Multiparameters are a major feature of models. Parameters that influence the formation of ground-level ozone concentrations and parameters that have been extremely important in previous articles should be considered first. The process of model establishment and validation is essentially a process of continuously optimizing the model performance, but there are certain differences in the specific models.

CONCLUSION

This review summarized the basic process of the statistical model for ambient ozone exposure. We gave the applicable conditions and application scope of different models and summarized the advantages and disadvantages of various models in ozone modeling research. In the future, research is still needed to explore this area based on its own research purposes and capabilities.

Association between short-term exposure to air pollution and ischemic stroke onset: a time-stratified case-crossover analysis using a distributed lag nonlinear model in Shenzhen, China

BACKGROUND

Stroke, especially ischemic stroke (IS), has been a severe public health problem around the world. However, the association between air pollution and ischemic stroke remains ambiguous.

METHODS

A total of 63, 997 IS cases aged 18 years or above in Shenzhen were collected from 2008 to 2014. We used the time-stratified case-crossover design combining with distributed lag nonlinear model (DLNM) to estimate the association between air pollution and IS onset. Furthermore, this study explored the variability across gender and age groups.

RESULTS

The cumulative exposure-response curves were J-shaped for SO₂, NO₂ and PM₁₀, and V-shaped for O₃, and crossed over the relative risk (RR) of one. The 99th, 50th (median) and 1st percentiles of concentration (μg/m³) respectively were 37.86, 10.06, 3.71 for SO₂, 116.26, 41.29, 18.51 for NO₂, 145.94, 48.29, 16.14 for PM₁₀, and 111.57, 49.82, 16.00 for O₃. Extreme high-SO₂, high-NO₂, high-PM₁₀, high-O₃, and low-O₃ concentration increased the risk of IS, with the maximum RR values and 95% CIs: 1.50(1.22, 1.84) (99th vs median) at 0-12 lag days, 1.37(1.13, 1.67) (99th vs median) at 0-10 lag days, 1.26(1.04, 1.53) (99th vs median) at 0-12 lag days, 1.25(1.04, 1.49) (99th vs median) at 0-14 lag days, and 1.29(1.03, 1.61) (1st vs median) at 0-14 lag days, respectively. The statistically significant minimal RR value and 95% CI was 0.79(0.66,0.94) at 0-10 lag days for extreme low-PM₁₀. The elderly aged over 65 years were susceptible to extreme pollution conditions. Difference from the vulnerability of males to extreme high-SO₂, high-NO₂ and low-O₃, females were vulnerable to extreme high-PM₁₀ and high-O₃. Comparing with the elderly, adults aged 18-64 year were immune to extreme low-NO₂ and low-PM₁₀. However, no association between CO and IS onset was found.

CONCLUSIONS

SO₂, NO₂, PM₁₀ and O₃ exerted non-linear and delayed influence on IS, and such influence varied with gender and age. These findings may have significant public health implications for the prevention of IS.

Accounting for measurement error to assess the effect of air pollution on omic signals

Studies on the effects of air pollution and more generally environmental exposures on health require measurements of pollutants, which are affected by measurement error. This is a cause of bias in the estimation of parameters relevant to the study and can lead to inaccurate conclusions when evaluating associations among pollutants, disease risk and biomarkers. Although the presence of measurement error in such studies has been recognized as a potential problem, it is rarely considered in applications and practical solutions are still lacking. In this work, we formulate Bayesian measurement error models and apply them to study the link between air pollution and omic signals. The data we use stem from the "Oxford Street II Study", a randomized crossover trial in which 60 volunteers walked for two hours in a traffic-free area (Hyde Park) and in a busy shopping street (Oxford Street) of London. Metabolomic measurements were made in each individual as well as air pollution measurements, in order to investigate the association between short-term exposure to traffic related air pollution and perturbation of metabolic pathways. We implemented error-corrected models in a classical framework and used the flexibility of Bayesian hierarchical models to account for dependencies among omic signals, as well as among different pollutants. Models were implemented using traditional Markov Chain Monte Carlo (MCMC) simulative methods as well as integrated Laplace approximation. The inclusion of a classical measurement error term resulted in variable estimates of the association between omic signals and traffic related air pollution measurements, where the direction of the bias was not predictable a priori. The models were successful in including and accounting for different correlation structures, both among omic signals and among different pollutant exposures. In general, more associations were identified when the correlation among omics and among pollutants were modeled, and their number increased when a measurement error term was additionally included in the multivariate models (particularly for the associations between metabolomics and NO2).

Association between ambient fine particulate pollution and hospital admissions for cause specific cardiovascular disease: time series study in 184 major Chinese cities

OBJECTIVE

To estimate the risks of daily hospital admissions for cause specific major cardiovascular diseases associated with short term exposure to ambient fine particulate matter (aerodynamic diameter ≤2.5 μm; PM_2.5) pollution in China.

DESIGN

National time series study.

SETTING

184 major cities in China.

POPULATION

8 834 533 hospital admissions for cardiovascular causes in 184 Chinese cities recorded by the national database of Urban Employee Basic Medical Insurance from 1 January 2014 to 31 December 2017.

MAIN OUTCOME MEASURES

Daily counts of city specific hospital admissions for primary diagnoses of ischaemic heart disease, heart failure, heart rhythm disturbances, ischaemic stroke, and haemorrhagic stroke among different demographic groups were used to estimate the associations between PM_2.5 and morbidity. An overdispersed generalised additive model was used to estimate city specific associations between PM_2.5 and cardiovascular admissions, and random effects meta-analysis used to combine the city specific estimates.

RESULTS

Over the study period, a mean of 47 hospital admissions per day (standard deviation 74) occurred for cardiovascular disease, 26 (53) for ischaemic heart disease, one (five) for heart failure, two (four) for heart rhythm disturbances, 14 (28) for ischaemic stroke, and two (four) for haemorrhagic stroke. At the national average level, an increase of 10 μg/m³ in PM_2.5 was associated with a 0.26% (95% confidence interval 0.17% to 0.35%) increase in hospital admissions on the same day for cardiovascular disease, 0.31% (0.22% to 0.40%) for ischaemic heart disease, 0.27% (0.04% to 0.51%) for heart failure, 0.29% (0.12% to 0.46%) for heart rhythm disturbances, and 0.29% (0.18% to 0.40%) for ischaemic stroke, but not with haemorrhagic stroke (-0.02% (-0.23% to 0.19%)). The national average association of PM_2.5 with cardiovascular disease was slightly non-linear, with a sharp slope at PM_2.5 levels below 50 μg/m³, a moderate slope at 50-250 μg/m³, and a plateau at concentrations higher than 250 μg/m³. Compared with days with PM_2.5 up to 15 μg/m³, days with PM_2.5 of 15-25, 25-35, 35-75, and 75 μg/m³ or more were significantly associated with increases in cardiovascular admissions of 1.1% (0 to 2.2%), 1.9% (0.6% to 3.2%), 2.6% (1.3% to 3.9%), and 3.8% (2.1% to 5.5%), respectively.According to projections, achieving the Chinese grade 2 (35 μg/m³), Chinese grade 1 (15 μg/m³), and World Health Organization (10 μg/m³) regulatory limits for annual mean PM_2.5 concentrations would reduce the annual number of admissions for cardiovascular disease in China. Assuming causality, which should be done with caution, this reduction would translate into an estimated 36 448 (95% confidence interval 24 441 to 48 471), 85 270 (57 129 to 113 494), and 97 516 (65 320 to 129 820), respectively.

CONCLUSIONS

These data suggest that in China, short term exposure to PM_2.5 is associated with increased hospital admissions for all major cardiovascular diseases except for haemorrhagic stroke, even for exposure levels not exceeding the current regulatory limits.

Associations between short-term exposure to fine particulate matter and acute exacerbation of asthma in Yancheng, China

Scarce evidence existed on the association between short-term exposure to fine particulate matter (PM_2.5) and asthma in China. In this study, we aimed to explore the relationship of PM_2.5 with acute asthma exacerbation in a coastal city of China. Cases of acute asthma exacerbation were identified from hospital outpatient visits in Yancheng, China, from 2015 to 2018. We utilized the generalized additive model linked by a quasi-Poisson distribution to assess the association between PM_2.5 and daily acute asthma exacerbation. Different lag structures were built, and we conducted stratification analyses by gender, age, and season. Two-pollutant models were fitted, and concentration-response (C-R) curves were pooled. A total of 3,520 cases of acute asthma exacerbation were recorded, with a daily average of 3. We observed positive and significant associations of PM_2.5 on lag 1, 2, lag 02, and lag 03 day. For each 10-μg/m³ increase in PM_2.5 (lag 02), the associated increment in asthma was 3.15% (95% CI: 0.99%, 5.31%). The association remained after adjusting for gaseous co-pollutants. We observed significant PM_2.5-asthma associations in males, patients ≤64 years, and during cold seasons. The C-R curves were positive and almost linear for total and strata-specific associations. In conclusion, this study provided robust evidence on the association of PM_2.5 with acute asthma exacerbation, which may benefit future prevention strategy and policy making.

Ambient particulate matter pollution and adult hospital admissions for pneumonia in urban China: A national time series analysis for 2014 through 2017

BACKGROUND

The effects of ambient particulate matter (PM) pollution on pneumonia in adults are inconclusive, and few scientific data on a national scale have been generated in low- or middle-income countries, despite their much higher PM concentrations. We aimed to examine the association between PM levels and hospital admissions for pneumonia in Chinese adults.

METHODS AND FINDINGS

A nationwide time series study was conducted in China between 2014 and 2017. Information on daily hospital admissions for pneumonia for 2014-2017 was collected from the database of Urban Employee Basic Medical Insurance (UEBMI), which covers 282.93 million adults. Associations of PM concentrations and hospital admissions for pneumonia were estimated for each city using a quasi-Poisson regression model controlling for time trend, temperature, relative humidity, day of the week, and public holiday and then pooled by random-effects meta-analysis. Meta-regression models were used to investigate potential effect modifiers, including cities' annual-average air pollutants concentrations, temperature, relative humidity, gross domestic product (GDP) per capita, and coverage rates by the UEBMI. More than 4.2 million pneumonia admissions were identified in 184 Chinese cities during the study period. Short-term elevations in PM concentrations were associated with increased pneumonia admissions. At the national level, a 10-μg/m3 increase in 3-day moving average (lag 0-2) concentrations of PM2.5 (PM ≤2.5 μm in aerodynamic diameter) and PM10 (PM ≤10 μm in aerodynamic diameter) was associated with 0.31% (95% confidence interval [CI] 0.15%-0.46%, P < 0.001) and 0.19% (0.11%-0.30%, P < 0.001) increases in hospital admissions for pneumonia, respectively. The effects of PM10 were stronger in cities with higher temperatures (percentage increase, 0.031%; 95% CI 0.003%-0.058%; P = 0.026) and relative humidity (percentage increase, 0.011%; 95% CI 0%-0.022%; P = 0.045), as well as in the elderly (percentage increase, 0.10% [95% CI 0.02%-0.19%] for people aged 18-64 years versus 0.32% [95% CI 0.22%-0.39%] for people aged ≥75 years; P < 0.001). The main limitation of the present study was the unavailability of data on individual exposure to PM pollution.

CONCLUSIONS

Our findings suggest that there are significant short-term associations between ambient PM levels and increased hospital admissions for pneumonia in Chinese adults. These findings support the rationale that further limiting PM concentrations in China may be an effective strategy to reduce pneumonia-related hospital admissions.

Short-term effects of ambient PM1 and PM2.5 air pollution on hospital admission for respiratory diseases: Case-crossover evidence from Shenzhen, China

BACKGROUND

Ambient PM₁ (particulate matter with aerodynamic diameter ≤1 μm) is an important contribution of PM_2.5 mass. However, little is known worldwide regarding the PM₁-associated health effects due to a wide lack of ground-based PM₁ measurements from air monitoring stations.

METHODS

We collected daily records of hospital admission for respiratory diseases and station-based measurements of air pollution and weather conditions in Shenzhen, China, 2015-2016. Time-stratified case-crossover design and conditional logistic regression models were adopted to estimate hospitalization risks associated with short-term exposures to PM₁ and PM_2.5.

RESULTS

PM₁ and PM_2.5 showed significant adverse effects on respiratory disease hospitalizations, while no evident associations with PM_1-2.5 were identified. Admission risks for total respiratory diseases were 1.09 (95% confidence interval: 1.04 to 1.14) and 1.06 (1.02 to 1.10), corresponding to per 10 μg/m³ rise in exposure to PM₁ and PM_2.5 at lag 0-2 days, respectively. Both PM₁ and PM_2.5 were strongly associated with increased admission for pneumonia and chronic obstructive pulmonary diseases, but exhibited no effects on asthma and upper respiratory tract infection. Largely comparable risk estimates were observed between male and female patients. Groups aged 0-14 years and 45-74 years were significantly affected by PM₁- and PM_2.5-associated risks. PM-hospitalization associations exhibited a clear seasonal pattern, with significantly larger risks in cold season than those in warm season among some subgroups.

CONCLUSIONS

Our study suggested that PM₁ rather than PM_1-2.5 contributed to PM_2.5-induced risks of hospitalization for respiratory diseases and effects of PM₁ and PM_2.5 mainly occurred in cold season.

The Effects of Short-Term and Very Short-Term Particulate Matter Exposure on Asthma-Related Hospital Visits: National Health Insurance Data

PURPOSE

The purpose of this study was to investigate the effects of short-term and very short-term exposure to particulate matter (PM) exceeding the daily average environmental standards for Korea (≤100 μg/m³ for PM10 and ≤50 μg/m³ for PM2.5) on on asthma-related hospital visits.

MATERIALS AND METHODS

This was a population-based, case-crossover study using National Health Insurance and air pollution data between January 1, 2014 and December 31, 2016. The event day was defined as a day when PM exceeded the daily average environmental standard (short-term exposure) or daily average environmental standard for 2 hours (very short-term exposure). The control day was defined as the same day of the week at 1 week prior to the event day.

RESULTS

Compared with control days, asthma-related hospital visits on the 24-hr event days and 2-hr event days increased by 4.10% and 3.45% for PM₁₀ and 5.66% and 3.74% for PM_2.5, respectively. Asthma-related hospital visits increased from the 24-hr event day for PM₁₀ to 4 days after the event day, peaking on the third day after the event day (1.26, 95% confidence interval, 1.22-1.30). Hospitalizations also increased on the third day after the event. While there was a difference in magnitude, PM_2.5 exposure showed similar trends to PM₁₀ exposure.

CONCLUSION

We found a significant association between short-term and very short-term PM exposure exceeding the current daily average environmental standards of Korea and asthma-related hospital visits. These results are expected to aid in establishing appropriate environmental standards and relevant policies for PM.

Personal Fine Particulate Matter Constituents, Increased Systemic Inflammation, and the Role of DNA Hypomethylation

Limited evidence is available on the effects of various fine particulate matter (PM_2.5) components on inflammatory cytokines and DNA methylation. We examined whether 16 PM_2.5 components are associated with changes in four blood biomarkers, that is, tumor necrosis factor-α (TNF-α), soluble cluster of differentiation 40 ligand (sCD40L), soluble intercellular adhesion molecule-1 (sICAM-1), and fibrinogen, as well as their corresponding DNA methylation levels in a panel of 36 healthy college students in Shanghai, China. We used linear mixed-effect models to evaluate the associations, with controls of potential confounders. We further conducted mediation analysis to evaluate the potential mediation effects of components on inflammatory markers through change in DNA methylation. We observed that several components were consistently associated with TNF-α and fibrinogen as well as their DNA hypomethylation. For example, an interquartile range increase in personal exposure to PM_2.5-lead (Pb) was associated with 65.20% (95% CI: 37.07, 99.10) increase in TNF-α and 2.66 (95% CI: 37.07, 99.10) decrease in TNF-α methylation, 30.51% (95% CI: 0.72, 69.11) increase in fibrinogen and 1.25 (95% CI: 0.67, 1.83) decrease in F3 methylation. PM_2.5 components were significantly associated with sICAM-1 methylation but not with sICAM-1 protein. DNA methylation mediated 19.89%-41.75% of the elevation in TNF-α expression by various PM_2.5 constituents. Our findings provide clues that personal PM_2.5 constituents exposure may contribute to increased systemic inflammation through DNA hypomethylation.

Short-term effects of ambient fine particulate air pollution on inpatient visits for myocardial infarction in Beijing, China

The effects of ambient fine particulate matter (PM_2.5) on the incidence of myocardial infarction have been reported, but little is known about this association in China. We conducted a time-series study of ambient PM_2.5 concentrations and inpatient visits for myocardial infarction in Beijing. A generalized additive model with a Poisson link was applied to estimate the percentage change in inpatient visits for myocardial infarction following a 10-μg/m³ increase in PM_2.5 concentrations. A total of 15,432 inpatient visits for myocardial infarction were identified between January 1, 2010, and June 30, 2012. A 10-μg/m³ increase in PM_2.5 concentrations was associated with a 0.46% (P ≤ 0.001) increase in daily inpatient visits for myocardial infarction. Males were more sensitive to the adverse effects, and the association was more significant during the warm season (May through October). Short-term exposure to PM_2.5 was associated with increased risk of inpatient visits for myocardial infarction in Beijing. The findings may be useful in developing more accurate targeted interventions.

Spatiotemporal Error in Rainfall Data: Consequences for Epidemiologic Analysis of Waterborne Diseases

The relationship between rainfall, especially extreme rainfall, and increases in waterborne infectious diseases is widely reported in the literature. Most of this research, however, has not formally considered the impact of exposure measurement error contributed by the limited spatiotemporal fidelity of precipitation data. Here, we evaluate bias in effect estimates associated with exposure misclassification due to precipitation data fidelity, using extreme rainfall as an example. We accomplished this via a simulation study, followed by analysis of extreme rainfall and incident diarrheal disease in an epidemiologic study in Ecuador. We found that the limited fidelity typical of spatiotemporal rainfall data sets biases effect estimates towards the null. Use of spatial interpolations of rain-gauge data or satellite data biased estimated health effects due to extreme rainfall (occurrence) and wet conditions (accumulated totals) downwards by 35%-45%. Similar biases were evident in the Ecuadorian case study analysis, where spatial incompatibility between exposed populations and rain gauges resulted in the association between extreme rainfall and diarrheal disease incidence being approximately halved. These findings suggest that investigators should pay greater attention to limitations in using spatially heterogeneous environmental data sets to assign exposures in epidemiologic research.

Carbon monoxide and risk of outpatient visits due to cause-specific diseases: a time-series study in Yichang, China

BACKGROUND

Previous studies showed inconsistent results on risk of increased outpatient visits for cause-specific diseases associated with ambient carbon monoxide (CO).

METHODS

Daily data for CO exposure and outpatient visits for all-causes and five specific diseases in Yichang, China from 1st January 2016 to 31st December 2017 were collected. Generalised additive models with different lag structures were used to examine the short-term effects of ambient CO on outpatient visits. Potential effect modifications by age, sex and season were examined.

RESULTS

A total of 5,408,021 outpatient visits were recorded. We found positive and statistically significant associations between CO and outpatient visits for multiple outcomes and all the estimated risks increased with longer moving average lags. An increase of 1 mg/m³ of CO at lag06 (a moving average of lag0 to lag6), was associated with 24.67% (95%CI: 14.48, 34.85%), 21.79% (95%CI: 12.24, 31.35%), 39.30% (95%CI: 25.67, 52.92%), 25.83% (95%CI: 13.91, 37.74%) and 19.04% (95%CI: 8.39, 29.68%) increase in daily outpatient visits for all-cause, respiratory, cardiovascular, genitourinary and gastrointestinal diseases respectively. The associations for all disease categories except for genitourinary diseases were statistically significant and stronger in warm seasons than cool seasons.

CONCLUSION

Our analyses provide evidences that the CO increased the total and cause-specific outpatient visits and strengthen the rationale for further reduction of CO pollution levels in Yichang. Ambient CO exerted adverse effect on respiratory, cardiovascular, genitourinary, gastrointestinal and neuropsychiatric diseases especially in the warm seasons.

Ambient Coarse Particulate Matter and Hospital Admissions for Ischemic Stroke

Background and Purpose—

Evidence on the effects of coarse particulate matter (PM 10–2.5 ) on ischemic stroke is limited and inconsistent. We evaluated the acute effects of PM 10–2.5 exposure on hospital admissions for ischemic stroke in China.

Methods—

We conducted a national time-series analysis of associations between daily PM 10–2.5 concentrations and daily hospital admissions for ischemic stroke in China between January 2014 and December 2016. Hospital admissions for ischemic stroke were identified from the database of Urban Employee Basic Medical Insurance, which contains data from 0.28 billion beneficiaries. We applied a city-specific Poisson regression to examine the associations of PM 10–2.5 and daily ischemic stroke admissions. We combined the city-specific effect estimates with a random effects meta-analysis, and further evaluated the exposure-response relationship curve and potential effect modifiers.

Results—

We identified >2 million hospital admissions for ischemic stroke in 172 Chinese cities. A 10 μg/m 3 increase in PM 10–2.5 concentrations (lag day 0) was associated with a 0.91% (95% CI, 0.73–1.10) increase in hospital admissions for ischemic stroke. The association remained significant after adjusting for PM 2.5 (percentage change, 0.96%; 95% CI, 0.75–1.18). The exposure-response relationship was approximately linear, with a moderate response at lower levels (<200 μg/m 3 ) and a steeper response at higher levels. The association was stronger in cities with lower PM 10–2.5 concentrations, higher temperatures, or higher relative humidity.

Conclusions—

This nationwide study provides robust evidence of the short-term association between exposure to PM 10–2.5 and increased hospital admissions for ischemic stroke and supports the hypothesis that the association differs by city characteristics.

Uncertainty associated with ambient ozone metrics in epidemiologic studies and risk assessments

Epidemiologic studies relating ambient ozone concentrations to adverse health outcomes have typically relied on spatial averages of concentrations from nearby monitoring stations, referred to as "composite monitors." This practice reflects the assumption that ambient ozone concentrations within an urban area are spatially homogenous. We tested the validity of this assumption by comparing ozone data measured at individual monitoring sites within selected US urban areas to their respective composite monitor time series. We first characterized the temporal correlation between the composite monitor and individual monitors in each area. Next, we analyzed the heteroskedasticity of each relationship. Finally, we compared the distribution of concentrations measured at individual monitors to the composite monitor distribution. Individual monitors showed high correlation with the composite monitor over much of the range of ambient ozone concentrations, though correlations were lower at higher concentrations. The variance between individual monitors and the composite monitor increased as a function of concentration in nearly all the urban areas. Finally, we observed statistical bias in the composite monitor concentrations at the high end of the distribution. The degree to which these results introduce uncertainty into studies that utilize composite monitors depends on the contributions of peak ozone concentrations to reported health effect associations.

A hierarchical modelling approach to assess multi pollutant effects in time-series studies

When assessing the short-term effect of air pollution on health outcomes, it is common practice to consider one pollutant at a time, due to their high correlation. Multi pollutant methods have been recently proposed, mainly consisting of collapsing the different pollutants into air quality indexes or clustering the pollutants and then evaluating the effect of each cluster on the health outcome. A major drawback of such approaches is that it is not possible to evaluate the health impact of each pollutant. In this paper we propose the use of the Bayesian hierarchical framework to deal with multi pollutant concentrations in a two-component model: a pollutant model is specified to estimate the ‘true’ concentration values for each pollutant and then such concentration is linked to the health outcomes in a time-series perspective. Through a simulation study we evaluate the model performance and we apply the modelling framework to investigate the effect of six pollutants on cardiovascular mortality in Greater London in 2011-2012.

Estimation of personal exposure to fine particles (PM2.5) of ambient origin for healthy adults in Hong Kong

Personal exposure and ambient fine particles (PM_2.5) measurements for 13 adult subjects (ages 19-57) were conducted in Hong Kong between April 2014 and June 2015. Six to 21 personal samples (mean = 19) per subject were obtained throughout the study period. Samples were analyzed for mass by gravimetric analysis, and 19 elements (from Na to Pb) were analyzed using X-Ray Fluorescence. Higher subject-specific correlations between personal and ambient sulfur (r_s = 0.92; p < 0.001) were found as compared to PM_2.5 mass (r_s = 0.79; p < 0.001) and other elements (0.06 < r_s < 0.86). Personal vs. ambient sulfur regression yielded an average exposure factor (F_pex) of 0.73 ± 0.02, supporting the use of sulfur as a surrogate to estimate personal exposure to PM_2.5 of ambient origin (E_a). E_a accounted for 41-82% and 57-73% of total personal PM_2.5 exposures (P) by season and by subject, respectively. The importance of both E_a and non-ambient exposures (E_na, 11.2 ± 5.6 μg/m³; 32.5 ± 10.9%) are noted. Mixed-effects models were applied to estimate the relationships between ambient PM_2.5 concentrations and their corresponding exposure variables (E_a, P). Higher correlations for E_a (0.90; p < 0.001) than for P (0.58; p < 0.01) were found. A calibration coefficient < 1 suggests an attenuation of 22% (ranging 16-28%) of the true effect estimates when using average ambient concentrations at central monitoring stations as surrogates for E_a. Stationary ambient data can be used to assess population exposure only if PM exposure is dominated by E_a.

Measurement error in a multi-level analysis of air pollution and health: a simulation study

BACKGROUND

Spatio-temporal models are increasingly being used to predict exposure to ambient outdoor air pollution at high spatial resolution for inclusion in epidemiological analyses of air pollution and health. Measurement error in these predictions can nevertheless have impacts on health effect estimation. Using statistical simulation we aim to investigate the effects of such error within a multi-level model analysis of long and short-term pollutant exposure and health.

METHODS

Our study was based on a theoretical sample of 1000 geographical sites within Greater London. Simulations of "true" site-specific daily mean and 5-year mean NO₂ and PM₁₀ concentrations, incorporating both temporal variation and spatial covariance, were informed by an analysis of daily measurements over the period 2009-2013 from fixed location urban background monitors in the London area. In the context of a multi-level single-pollutant Poisson regression analysis of mortality, we investigated scenarios in which we specified: the Pearson correlation between modelled and "true" data and the ratio of their variances (model versus "true") and assumed these parameters were the same spatially and temporally.

RESULTS

In general, health effect estimates associated with both long and short-term exposure were biased towards the null with the level of bias increasing to over 60% as the correlation coefficient decreased from 0.9 to 0.5 and the variance ratio increased from 0.5 to 2. However, for a combination of high correlation (0.9) and small variance ratio (0.5) non-trivial bias (> 25%) away from the null was observed. Standard errors of health effect estimates, though unaffected by changes in the correlation coefficient, appeared to be attenuated for variance ratios > 1 but inflated for variance ratios < 1.

CONCLUSION

While our findings suggest that in most cases modelling errors result in attenuation of the effect estimate towards the null, in some situations a non-trivial bias away from the null may occur. The magnitude and direction of bias appears to depend on the relationship between modelled and "true" data in terms of their correlation and the ratio of their variances. These factors should be taken into account when assessing the validity of modelled air pollution predictions for use in complex epidemiological models.

Fine Particulate Air Pollution and Hospital Utilization for Upper Respiratory Tract Infections in Beijing, China

Few studies have examined the association between fine particulate matter (PM_2.5) and upper respiratory tract infections (URTI) in urban cities. The principal aim of the present study was to evaluate the short-term impact of PM_2.5 on the incidence of URTI in Beijing, China. Data on hospital visits due to URTI from 1 October 2010 to 30 September 2012 were obtained from the Beijing Medical Claim Data for Employees, a health insurance database. Daily PM_2.5 concentration was acquired from the embassy of the United States of America (US) located in Beijing. A generalized additive Poisson model was used to analyze the effect of PM_2.5 on hospital visits for URTI. We found that a 10 μg/m³ increase in PM_2.5 concentration was associated with 0.84% (95% CI, 0.05⁻1.64%) increase in hospital admissions for URTI at lag 0⁻3 days, but there were no significant associations with emergency room or outpatient visits. Compared to females, males were more likely to be hospitalized for URTI when the PM_2.5 level increased, but other findings did not differ by age group or gender. The study suggests that short-term variations in PM_2.5 concentrations have small but detectable impacts on hospital utilization due to URTI in adults.

Ambient air pollution and daily hospital admissions: A nationwide study in 218 Chinese cities

There have been few large multicity studies to evaluate the acute health effects of ambient air pollution on morbidity risk in developing counties. In this study, we examined the short-term associations of air pollution with daily hospital admissions in China. We conducted a nationwide time-series study in 218 Chinese cities between 2014 and 2016. Data on daily hospital admissions counts were obtained from the National Health Insurance Database for Urban Employees covering 0.28 billion enrollees. We used generalized additive model with Poisson regression to estimate the associations in each city, and we performed random-effects meta-analysis to pool the city-specific estimates. More than 60 million hospital admissions were analyzed in this study. At the national-average level, each 10 μg/m³ increase in PM₁₀, SO₂, and NO₂, and 1 mg/m³ increase in CO at lag 0 day was associated with a 0.29% (95% CI, 0.23%-0.36%), 1.16% (95% CI, 0.92%-1.40%), 1.68% (95% CI, 1.40%-1.95%), and 2.59% (95% CI, 1.69%-3.50%) higher daily hospital admissions, respectively. The associations of air pollution with hospital admissions remained statistically significant at levels below the current Chinese Ambient Air Quality Standards. The effect estimates were larger in cities with lower air pollutants levels or higher air temperatures and relative humidity, as well as in the elderly. In conclusion, our findings provide robust evidence of increased hospital admissions in association with short-term exposure to ambient air pollution in China.

Association Between PM2.5 and Daily Hospital Admissions for Heart Failure: A Time-Series Analysis in Beijing

There is little evidence that acute exposure to fine particulate matter (PM_2.5) impacts the rate of hospitalization for congestive heart failure (CHF) in developing countries. The primary purpose of the present retrospective study was to evaluate the short-term association between ambient PM_2.5 and hospitalization for CHF in Beijing, China. A total of 15,256 hospital admissions for CHF from January 2010 to June 2012 were identified from Beijing Medical Claim Data for Employees and a time-series design with generalized additive Poisson model was used to assess the obtained data. We found a clear significant exposure response association between PM_2.5 and the number of hospitalizations for CHF. Increasing PM_2.5 daily concentrations by 10 μg/m³ caused a 0.35% (95% CI, 0.06⁻0.64%) increase in the number of CHF admissions on the same day. We also found that female and older patients were more susceptible to PM_2.5. These associations remained significant in sensitivity analyses involving changing the degrees of freedom of calendar time, temperature, and relative humidity. PM_2.5 was associated with significantly increased risk of hospitalization for CHF in this citywide study. These findings may contribute to the limited scientific evidence about the acute impacts of PM_2.5 on CHF in China.

Association between ambient air pollution and daily hospital admissions for ischemic stroke: A nationwide time-series analysis

BACKGROUND

Evidence of the short-term effects of ambient air pollution on the risk of ischemic stroke in low- and middle-income countries is limited and inconsistent. We aimed to examine the associations between air pollution and daily hospital admissions for ischemic stroke in China.

METHODS AND FINDINGS

We identified hospital admissions for ischemic stroke in 2014-2016 from the national database covering up to 0.28 billion people who received Urban Employee Basic Medical Insurance (UEBMI) in China. We examined the associations between air pollution and daily ischemic stroke admission using a two-stage method. Poisson time-series regression models were firstly fitted to estimate the effects of air pollution in each city. Random-effects meta-analyses were then conducted to combine the estimates. Meta-regression models were applied to explore potential effect modifiers. More than 2 million hospital admissions for ischemic stroke were identified in 172 cities in China. In single-pollutant models, increases of 10 μg/m3 in particulate matter with aerodynamic diameter <2.5 μm (PM2.5), sulfur dioxide (SO2), nitrogen dioxide (NO2), and ozone (O3) and 1 mg/m3 in carbon monoxide (CO) concentrations were associated with 0.34% (95% confidence interval [CI], 0.20%-0.48%), 1.37% (1.05%-1.70%), 1.82% (1.45%-2.19%), 0.01% (-0.14%-0.16%), and 3.24% (2.05%-4.43%) increases in hospital admissions for ischemic stroke on the same day, respectively. SO2 and NO2 associations remained significant in two-pollutant models, but not PM2.5 and CO associations. The effect estimates were greater in cities with lower air pollutant levels and higher air temperatures, as well as in elderly subgroups. The main limitation of the present study was the unavailability of data on individual exposure to ambient air pollution.

CONCLUSIONS

As the first national study in China to systematically examine the associations between short-term exposure to ambient air pollution and ischemic stroke, our findings indicate that transient increase in air pollution levels may increase the risk of ischemic stroke, which may have significant public health implications for the reduction of ischemic stroke burden in China.

Association of short-term exposure to ambient carbon monoxide with hospital admissions in China

Evidence on the acute effects of ambient carbon monoxide (CO) pollution on morbidity risk in developing countries is scarce and inconsistent. We conducted a multicity case-crossover study in 26 largest cities in China from January, 2014 to December, 2015 to examine the association between short-term exposure to CO and daily hospital admission. We fitted conditional logistic regression to obtain effect estimates of the associations. We also performed subset analyses to explore the health effects of CO at low levels. During the study period, a total of 14,569,622, 2,008,786 and 916,388 all-cause, cardiovascular and respiratory admissions were identified, respectively. A 1 mg/m³ increase in the CO concentrations corresponded to a 3.75% (95% CI, 3.63–3.87%), 4.39% (95% CI, 4.07–4.70%), and 4.44% (95% CI, 3.97–4.92%) increase in all-cause, cardiovascular, and respiratory admissions on the same day, respectively. The associations were robust to controlling for criteria co-pollutants. In subset analyses, negative effects of short-term CO exposure on hospital admission were observed at lower concentrations (<1 mg/m³), while positive effects were observed at higher concentrations (>2 mg/m³). In conclusion, current CO levels in China were significantly associated with increased daily hospital admissions.

Acute effects of ambient air pollution on outpatient children with respiratory diseases in Shijiazhuang, China

Background

Associations between ambient air pollution and child health outcomes have been well documented in developed countries such as the United States; however, only a limited number of studies have been conducted in developing countries. This study aimed to explore the acute effects of five ambient air pollutants (inhalable particles [PM₁₀], fine particles [PM_2.5], sulfur dioxide [SO₂], nitrogen dioxide [NO₂] and 0zone [O₃]) on children hospital outpatients with respiratory diseases in Shijiazhuang, China.

Methods

Three years (2013–2015) of daily data, including cause-specific respiratory outpatient records and the concentrations of five air pollutants, were collected to examine the short-term association between air pollution and children’s respiratory diseases; using a quasi-Poisson regression generalized additive model. Stratified analyses by season and age were also performed.

Results

From 2013 to 2015, a total of 551,678 hospital outpatient records for children with respiratory diseases were collected in Shijiazhuang, China. A 10 μg/m³ increase in a two-day average concentration (lag01) of NO₂, PM_2.5, and SO₂ corresponded to an increase of 0.66% (95% confidence interval [CI]: 0.30–1.03%), 0.13% (95% CI: 0.02–0.24%), and 0.33% (95% CI: 0.10–0.56%) in daily hospital outpatient visits for children with respiratory diseases, respectively. The effects were stronger in the transition season (April, May, September and October) than in other seasons (the hot season [June to August] and the cool season [November to March]). Furthermore, results indicated a generally stronger association in older (7–14 years of age) than younger children (< 7 years of age).

Conclusions

This research found a significant association between ambient NO₂, PM_2.5, and SO₂ levels and hospital outpatient visits in child with respiratory diseases in Shijiazhuang, China.

Association of Air Pollution and Mortality of Acute Lower Respiratory Tract Infections in Shenyang, China: A Time Series Analysis Study

BACKGROUND

We aimed to evaluate the risk factors of the daily mortality associated with air pollution causing acute lower respiratory tract infections.

METHODS

We applied a short time series analysis to the air pollution record, meteorological data and 133 non-accidental death data in Shengyang, China, in 2013-2015. After controlling the seasonality, day of week and weather conditions, the group employed an over-dispersed Possion generalized addictive model to discuss the associations among different variables, then performed the stratified analysis according to age, gender, and season.

RESULTS

Mean concentrations of particulate matter with aerodynamic diameters of < 10 μm (PM₁₀) and < 2.5 μm (PM_2.5), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), and ozone (O₃) were 122.4, 74.8, 79.4, 47.7, and 86.2 μg/m³, respectively. An increase of 10 μg/m³ in the 8-day moving average concentrations of PM₁₀, PM_2.5, SO₂, NO₂, and O₃ corresponded to 0.18% (95% confidence interval [CI]: 0.10%, 0.26%), 0.21% (95% CI: 0.11%, 0.31%), 0.16% (95% CI: 0.04%, 0.30%), 0.43% (95% CI: 0.07%, 0.90%), and 0.10% (95% CI: -0.08%, 0.31%) increase in the daily mortality. The effects of air pollution lasted 9 days (lag 0-8), and they were more statistically significant in the elderly than in other age groups.

CONCLUSION

These findings clarified the burden of air pollution on the morbidity of acute lower respiratory tract infections and emphasized the urgency of the control and prevention of air pollution and respiratory diseases in China.

Associations between short-term exposure to ambient sulfur dioxide and increased cause-specific mortality in 272 Chinese cities

BACKGROUND

Ambient sulfur dioxide (SO₂) remains a major air pollutant in developing countries, but epidemiological evidence about its health effects was not abundant and inconsistent.

OBJECTIVES

To evaluate the associations between short-term exposure to SO₂ and cause-specific mortality in China.

METHODS

We conducted a nationwide time-series analysis in 272 major Chinese cities (2013-2015). We used the over-dispersed generalized linear model together with the Bayesian hierarchical model to analyze the data. Two-pollutant models were fitted to test the robustness of the associations. We conducted stratification analyses to examine potential effect modifications by age, sex and educational level.

RESULTS

On average, the annual-mean SO₂ concentrations was 29.8 μg/m³ in 272 cities. We observed positive and associations of SO₂ with total and cardiorespiratory mortality. A 10 μg/m³ increase in two-day average concentrations of SO₂ was associated with increments of 0.59% in mortality from total non-accidental causes, 0.70% from total cardiovascular diseases, 0.55% from total respiratory diseases, 0.64% from hypertension disease, 0.65% from coronary heart disease, 0.58% from stroke, and 0.69% from chronic obstructive pulmonary disease. In two-pollutant models, there were no significant differences between single-pollutant model and two-pollutant model estimates with fine particulate matter, carbon monoxide and ozone, but the estimates decreased substantially after adjusting for nitrogen dioxide, especially in South China. The associations were stronger in warmer cities, in older people and in less-educated subgroups.

CONCLUSIONS

This nationwide study demonstrated associations of daily SO₂ concentrations with increased total and cardiorespiratory mortality, but the associations might not be independent from NO₂.

Ambient concentrations of particulate matter and hospitalization for depression in 26 Chinese cities: A case-crossover study

OBJECTIVE

Air pollution with high ambient concentrations of particulate matter (PM) has been frequently reported in China. However, no Chinese study has looked into the short-term effect of PM on hospitalization for depression. We used a time-stratified case-crossover design to identify possible links between ambient PM levels and hospital admissions for depression in 26 Chinese cities.

METHODS

Electronic hospitalization summary reports (January 1, 2014-December 31, 2015) were used to identify hospital admissions related to depression. Conditional logistic regression was applied to determine the association between PM levels and hospitalizations for depression, with stratification by sex, age, and comorbidities.

RESULTS

Both PM_2.5 and PM₁₀ levels were positively associated with the number of hospital admissions for depression. The strongest effect was observed on the day of exposure (lag day 0) for PM₁₀, with an interquartile range increase in PM₁₀ associated with a 3.55% (95% confidence interval: 1.69-5.45) increase in admissions for depression. For PM_2.5, the risks of hospitalization peaked on lag day 0 (2.92; 1.37-4.50) and lag day 5 (3.65; 2.09-5.24). The elderly (>65) were more sensitive to PM_2.5 exposure (9.23; 5.09-13.53) and PM₁₀ exposure (6.35; 3.31-9.49) on lag day 0, and patients with cardiovascular disease were likely to be hospitalized for depression following exposure to high levels of PM₁₀ (4.47; 2.13-6.85).

CONCLUSIONS

Short-term elevations in PM may increase the risk of hospitalization for depression, particularly in the elderly and in patients with cardiovascular disease.

Impacts of Regulations on Air Quality and Emergency Department Visits in the Atlanta Metropolitan Area, 1999-2013

INTRODUCTION

The United States and Western Europe have seen great improvements in air quality, presumably in response to various regulations curtailing emissions from the broad range of sources that have contributed to local, regional, and global pollution. Such regulations, and the ensuing controls, however, have not come without costs, which are estimated at tens of billions of dollars per year. These costs motivate accountability-related questions such as, to what extent do regulations lead to emissions changes? More important, to what degree have the regulations provided the expected human health benefits?

Here, the impacts of specific regulations on both electricity generating unit (EGU) and on-road mobile sources are examined through the classical accountability process laid out in the 2003 Health Effects Institute report linking regulations to emissions to air quality to health effects, with a focus on the 1999-2013 period. This analysis centers on regulatory actions in the southeastern United States and their effects on health outcomes in the 5-county Atlanta metropolitan area. The regulations examined are largely driven by the 1990 Clean Air Act Amendments (C). This work investigates regulatory actions and controls promulgated on EGUs: the Acid Rain Program (ARP), the NOx Budget Trading Program (NBP), and the Clean Air Interstate Rule (CAIR) - and mobile sources: Tier 2 Gasoline Vehicle Standards and the 2007 Heavy Duty Diesel Rule.

METHODS

Each step in the classic accountability process was addressed using one or more methods. Linking regulations to emissions was accomplished by identifying major federal regulations and the associated state regulations, along with analysis of individual facility emissions and control technologies and emissions modeling (e.g., using the U.S. Environmental Protection Agency's [U.S. EPA's] MOtor Vehicle Emissions Simulator [MOVES] mobile-source model). Regulators, including those from state environmental and transportation agencies, along with the public service commissions, play an important role in implementing federal rules and were involved along with other regional stakeholders in the study. We used trend analysis, air quality modeling, satellite data, and a ratio-of-ratios technique to investigate a critical current issue, a potential large bias in mobile-source oxides of nitrogen (NOx) emissions estimates.

The second link, emissions-air quality relationships, was addressed using both empirical analyses as well as chemical transport modeling employing the Community Multiscale Air Quality (CMAQ) model. Kolmogorov-Zurbenko filtering accounting for day of the year was used to separate the air quality signal into long-term, seasonal, weekday-holiday, and short-term meteorological signals. Regression modeling was then used to link emissions and meteorology to ambient concentrations for each of the species examined (ozone [O3], particulate matter ≤ 2.5 μm in aerodynamic diameter [PM2.5], nitrogen dioxide [NO2], sulfur dioxide [SO2], carbon monoxide [CO], sulfate [SO4-2], nitrate [NO3-], ammonium [NH4+], organic carbon [OC], and elemental carbon [EC]). CMAQ modeling was likewise used to link emissions changes to air quality changes, as well as to further establish the relative roles of meteorology versus emissions change impacts on air quality trends. CMAQ and empirical modeling were used to investigate aerosol acidity trends, employing the ISORROPIA II thermodynamic equilibrium model to calculate pH based on aerosol composition. The relationships between emissions and meteorology were then used to construct estimated counterfactual air quality time series of daily pollutant concentrations that would have occurred in the absence of the regulations. Uncertainties in counterfactual air quality were captured by the construction of 5,000 pollutant time series using a Monte Carlo sampling technique, accounting for uncertainties in emissions and model parameters.

Health impacts of the regulatory actions were assessed using data on cardiorespiratory emergency department (ED) visits, using patient-level data in the Atlanta area for the 1999-2013 period. Four outcome groups were chosen based on previous studies identifying associations with ambient air pollution: a combined respiratory disease (RD) category; the subgroup of RD presenting with asthma; a combined cardiovascular disease (CVD) category; and the subgroup of CVD presenting with congestive heart failure (CHF).

Models were fit to estimate the joint effects of multiple pollutants on ED visits in a time-series framework, using Poisson generalized linear models accounting for overdispersion, with a priori model formulations for temporal and meteorological covariates and lag structures. Several parameterizations were considered for the joint-effects models, including different sets of pollutants and models with nonlinear pollutant terms and first-order interactions among pollutants. Use of different periods for parameter estimates was assessed, as associations between pollutant levels and ED visits varied over the study period. A 7-pollutant, nonlinear model with pollutant interaction terms was chosen as the baseline model and fitted using pollutant and outcome data from 1999-2005 before regulations might have substantially changed the toxicity of pollutant mixtures. In separate analyses, these models were fitted using pollutant and outcome data from the entire 1999-2013 study period. Daily counterfactual time series of pollutant concentrations were then input into the health models, and the differences between the observed and counterfactual concentrations were used to estimate the impacts of the regulations on daily counts of ED visits. To account for the uncertainty in both the estimation of the counterfactual time series of ambient pollutant levels and the estimation of the health model parameters, we simulated 5,000 sets of parameter estimates using a multivariate normal distribution based on the observed variance-covariance matrix, allowing for uncertainty at each step of the chain of accountability. Sensitivity tests were conducted to assess the robustness of the results.

RESULTS

EGU NOx and SO2 emissions in the Southeast decreased by 82% and 83%, respectively, between 1999 and 2013, while mobile-source emissions controls led to estimated decreases in Atlanta-area pollutant emissions of between 61% and 93%, depending on pollutant. While EGU emissions were measured, mobile-source emissions were modeled. Our results are supportive of a potential high bias in mobile-source NOx and CO emissions estimates. Air quality benefits from regulatory actions have increased as programs have been fully implemented and have had varying impacts over different seasons. In a scenario that accounted for all emissions reductions across the period, observed Atlanta central monitoring site maximum daily 8-hour (MDA8h) O3 was estimated to have been reduced by controls in the summertime and increased in the wintertime, with a change in mean annual MDA8h O3 from 39.7 ppb (counterfactual) to 38.4 ppb (observed). PM2.5 reductions were observed year-round, with average 2013 values at 8.9 μg/m3 (observed) versus 19.1 μg/m3 (counterfactual). Empirical and CMAQ analyses found that long-term meteorological trends across the Southeast over the period examined played little role in the distribution of species concentrations, while emissions changes explained the decreases observed. Aerosol pH, which plays a key role in aerosol formation and dynamics and may have health implications, was typically very low (on the order of 1-2, but sometimes much lower), with little trend over time despite the stringent SO2 controls and SO42- reductions.

Using health models fit from 1999-2005, emissions reductions from all selected pollution-control policies led to an estimated 55,794 cardiorespiratory disease ED visits prevented (i.e., fewer observed ED visits than would have been expected under counterfactual scenarios) - 52,717 RD visits, of which 38,038 were for asthma, and 3,057 CVD visits, of which 2,104 were for CHF - among the residents of the 5-county area over the 1999-2013 period, an area with approximately 3.5 million people in 2013. During the final two years of the study (2012-2013), when pollution-control policies were most fully implemented and the associated benefits realized, these policies were estimated to prevent 5.9% of the RD ED visits that would have occurred in the absence of the policies (95% interval estimate: -0.4% to 12.3%); 16.5% of the asthma ED visits (95% interval estimate: 7.5% to 25.1%); 2.3% of the CVD ED visits (95% interval estimate: -1.8% to 6.2%); and -.6% of the CHF ED visits (95% interval estimate: 26.3% to 10.4%). Estimates of ED visits prevented were generally lower when using health models fit for the entire 1999-2013 study period.

Sensitivity analyses were conducted to show the impact of the choice of parameterization of the health models and to assess alternative definitions of the study area. When impacts were assessed for separate policy interventions, policies affecting emissions from EGUs, especially the ARP and the NBP, appeared to have had the greatest effect on prevention of RD and asthma ED visits.

CONCLUSIONS

This study demonstrates the effectiveness of regulations on improving air quality and health in the southeastern United States. It also demonstrates the complexities of accountability assessments as uncertainties are introduced in each step of the classic accountability process. While accounting for uncertainties in emissions, air quality-emissions relationships, and health models does lead to relatively large uncertainties in the estimated outcomes due to specific regulations, overall the benefits of regulations have been substantial.