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. Author manuscript; available in PMC: 2023 Apr 4.
Published in final edited form as: Environ Monit Assess. 2019 Jun 28;191(Suppl 2):280. doi: 10.1007/s10661-019-7421-4

Time-series analysis of satellite-derived fine particulate matter pollution and asthma morbidity in Jackson, MS

Howard H Chang 1, Anqi Pan 2, David J Lary 3, Lance A Waller 4, Lei Zhang 5, Bruce T Brackin 6, Richard W Finley 7, Fazlay S Faruque 8
PMCID: PMC10072932  NIHMSID: NIHMS1880467  PMID: 31254082

Abstract

In order to examine associations between asthma morbidity and local ambient air pollution in an area with relatively low levels of pollution, we conducted a time-series analysis of asthma hospital admissions and fine particulate matter pollution (PM2.5) in and around Jackson, MS, for the period 2003 to 2011. Daily patient-level records were obtained from the Mississippi State Department of Health (MSDH) Asthma Surveillance System. Patient geolocations were aggregated into a grid with 0.1° × 0.1° resolution within the Jackson Metropolitan Statistical Area. Daily PM2.5 concentrations were estimated via machine-learning algorithms with remotely sensed aerosol optical depth and other associated parameters as inputs. Controlling for long-term temporal trends and meteorology, we estimated a 7.2% (95% confidence interval 1.7–13.1%) increase in daily all-age asthma emergency room admissions per 10 μg/m3 increase in the 3-day average of PM2.5 levels (current day and two prior days). Stratified analyses reveal significant associations between asthma and 3-day average PM2.5 for males and blacks. Our results contribute to the current epidemiologic evidence on the association between acute ambient air pollution exposure and asthma morbidity, even in an area characterized by relatively good air quality.

Keywords: Asthma, Hospital admission, PM2.5, Remote sensing, Time-series

Introduction

In the United States (USA), national multi-city time-series studies have played a crucial role in establishing regulatory standards and protecting public health (Franklin et al. 2007; Peng et al. 2008; Dai et al. 2014; Krall et al. 2013). Multi-city studies using large national administrative databases have focused predominantly on estimating short-term effects of air pollution on mortality and hospital admissions among adults 65 years of age or older. Hence, there exists an important knowledge gap from population studies on the association between short duration exposure to air pollution and morbidity among the non-elderly. This has resulted in significant uncertainty in extending findings from past research to population-based risk assessments of air pollution-related morbidity as previous national studies have found considerable heterogeneity in the health effects of air pollution across regions (Dominici et al. 2003).

The impact of air pollution exposure on asthma morbidity is of particular interest because of the high prevalence of asthma among children, estimated to be 8.6% in the USA in 2014 (Blackwell et al. 2014). Previous city-specific time-series studies for asthma morbidity have utilized emergency department visits (Alhanti et al. 2016), hospital admissions (Sheppard et al. 1999), physician visits (Hajat et al. 1999), and medication use (Elliott et al. 2013). In this study, we focus on the association between temporal patterns of exposure to ambient (outdoor) fine particulate matter and all-age asthma morbidity as measured by hospital and emergency department visits in Jackson, MS. The Jackson, MS Metropolitan Statistical Area (JMSA) had a population of 539,057 people based on the 2010 Census, representing almost one-fifth of the total state population. JMSA is also characterized by relatively good air quality, and in air pollution epidemiology, there is increasing interest in understanding health effects of air pollution at low ambient concentrations (Makar et al. 2017). Environmental regulations for protecting public health are associated with economic costs, and a current knowledge gap is whether there a safe level of exposure to air pollutants.

Fine particulate matter of equal to or less than 2.5 μm in aerodynamic diameter (PM2.5) represents a complex mixture of solid and liquid particles that arise from various sources, including mobile traffic, power generation, and industrial processes (Karagulian et al. 2015). PM2.5 is a major air pollutant causing various adverse health outcomes. However, ground-monitoring stations for PM2.5 are limited with sparse spatial coverage due to labor-intensive operational costs. In addition, they are often temporally inconsistent with monitoring periods varying from daily to every 6 days (Faruque et al. 2014). Regulatory agencies typically determine monitoring locations in order to assess for non-compliance and not to provide exposure estimates for local adverse health risk assessment in the general population. Typically, monitors are preferentially located around industrialized areas, leaving large coverage gaps in many other populated areas. To address this limitation in spatial and temporal coverage of ground-monitored PM2.5 data, we utilize satellite-derived PM2.5 estimates to perform exposure assessment. These satellite-derived PM2.5 concentrations are increasingly useful for examining associations between PM2.5 and various adverse health outcomes, including asthma morbidity, premature birth, low birth weight, and heart diseases (Strickland et al. 2016; Kloog et al. 2012; Hu 2009).

Many studies have investigated the utilization of aerosol optical depth (AOD) data obtained by satellites to estimate PM2.5 levels (e.g., Engel-Cox et al. 2004; Zhang et al. 2009; Hoff and Christopher 2009; Weber et al. 2010). AOD assesses aerosol concentrations in an entire air column in the atmosphere. The actual relationship between AOD and the concentration of ground level PM2.5 is influenced by a large number of parameters, such as humidity, temperature, land use, surface, and the proximity to particulate sources (Liu et al. 2009; Natunen et al. 2010; Paciorek and Liu 2012; Schaap et al. 2009; van Donkelaar et al. 2010; Weber et al. 2010; Zhang et al. 2009). Therefore, we utilized estimates of daily spatially resolved PM2.5 concentrations using machine learning algorithms (Lary et al. 2014a, b). One objective of this paper is to leverage this newly developed PM2.5 data product to conduct the first time-series analysis of PM2.5 and asthma morbidity in Mississippi.

Materials and methods

Health data

We obtained hospital discharge records from the Mississippi State Department of Health (MSDH) Asthma Surveillance System for the period 2003 to 2011. Patient-level records include information on patient’s race, gender, age, date of visit, diagnoses, and residence location. We identified asthma discharges via the primary International Classification of Diseases, 9th Revision (ICD-9) Code 493 (US Centers for Disease Control and Prevention 2009). Repeated visits from the same individuals were not excluded due to our focus on the association between short duration ambient exposures to PM2.5 and health outcomes (within 3 days). We assigned each residential location to a grid of 0.1° × 0.1° (spatial resolution of approximately 10 km × 10 km). Figure 1 shows the total number of emergency room admissions and inpatient/outpatient visits in Mississippi by grid cell. We aggregated daily counts of emergency room admissions and inpatient/outpatient visits over the five-county Jackson metropolitan statistical area (Copiah, Hinds, Madison, Rankin, and Simpson counties). Nineteen hospitals are located within this area including the University of Mississippi Medical Center, which serves about one-third of the total asthma hospital visits in the state. We obtained annual population data at the block group level from the ESRI Business Analyst dataset (ESRI 2017). We dissolved block group level population data into grid cells proportionally to the areas based on spatial overlay of the polygons. While apportioning block group data to grids, we excluded major waterbodies.

Fig. 1.

Fig. 1

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Log (base 10) of the total number of emergency room admissions and inpatient/outpatient in Mississippi by grid cell, 2003–2011. Grid cells with zero events are colored white. The five-county Jackson metropolitan area boundary is shown in black

Ambient PM2.5 concentration estimation

This analysis focused on quantifying the temporal association between daily hospital admissions and ambient PM2.5 levels, leveraging estimated PM2.5 concentrations from a previous project funded by the National Institutes of Health. The previous project developed a framework for estimating daily 24-h average PM2.5 concentrations using machine learning algorithms for the entire USA from 1997 to 2014 (Lary et al. 2014a, 2015). Details of the estimation of PM2.5 have been previously published and are beyond the scope of this paper so we will only present the major characteristics of the estimation method here. The method utilized a suite of comprehensive data on more than 100 parameters drawn from remote sensing, meteorology analyses, and demographics. Lary et al. (2015) utilize satellite data from multiple instruments with data sources for AOD and other variables that contribute to ground-level concentrations given in Table S1. We used an ensemble of multivariate, non-linear, non-parametric machine-learning approaches to establish empirical relationships between in-situ PM2.5 observations and remote sensing, meteorological, and other contextual environmental data. The independent validation experiment indicates a typical uncertainty of less than ± 1.5 μg/m3 for our PM2.5 estimates. Because the accuracy of estimated PM2.5 concentration is proportional to proximity to ground monitoring stations, the multiple monitoring stations within the JMSA provide PM2.5 estimates within this area that have a higher accuracy than rest of the Mississippi. In order to estimate the population’s average exposure over the five-county JMSA, we calculated daily population-weighted averages using population counts and estimated PM2.5 concentrations across grid cells, as required in a time-series design (Ivy et al. 2008).

Time-series analysis

We used a time-series approach to estimate the association between hospital admission and short duration (3-day) exposure to a given PM2.5 concentration (Bell et al. 2004; Sheppard 2003). The time-series design is a well-established population-based approach to estimate health effects of ambient air pollution. Specifically, total counts of adverse health outcomes aggregated over a geographical region are regressed on the corresponding population-averaged exposure to estimate the association between temporal fluctuations in health outcomes and exposures, accounting for time-varying confounders. The time-series designs offer three major advantages compared to other designs such as panel or cohort studies for estimating short-term exposure effects. First, effects of short-term exposure to ambient air pollution are typically very small. Time-series design is a case-only ap that utilizes large administrative databases to conduct epidemiologic studies without recruitment, monitoring, and identification of controls, which are infeasible for large population-based studies over a long period. Second, because the health outcomes are aggregated, the analysis does not require information on individual risk factors (e.g., environmental tobacco smoke, diet, physical activities). Specifically, by only examining daily variation, confounders that do not vary daily in the population can be controlled for by the inclusion of flexible time trends (Peng et al. 2006). Conducting analysis using only temporal contrasts also avoids the issue of sparse health outcomes at the grid level (Fig. 1).

To model the daily asthma morbidity measures, we used a Poisson log-linear model taking the general form

logEYt=α+βxt+ftempt+ghumidt+htimet

where E[Yt] is the expected value of total number of health outcomes Yt within the five-county Jackson area on day t and xt is the corresponding PM2.5 exposure of interest. Parameter β represents the log relative risk (RR) associated with short-term PM2.5 exposure. RR is a commonly used measure in epidemiology to describe how the risk of a binary disease outcome varies by observed levels of each risk factor. RR is defined as the ratio of diseases risks (estimated via observed and modeled rates) between the exposure and the unexposed group, where a RR greater (less) than 1 indicates the risk factor positively (negatively) impacts disease occurrence. We examined exposures at the current day (lag 0), one-day prior (lag 1), two-day prior (lag 2), and the 3-day moving average (lag 0–2) to capture the total/cumulative effect of acute exposures. To control for short-term effects of meteorology, we follow standard epidemiologic approaches and included smooth functions of 3-day moving averages of temperature f(tempt) and relative humidity g(humidt) using natural cubic splines with three degrees of freedom. We controlled for long-term trends and seasonality by including a smooth function of calendar date h(timet) using natural cubic splines with six degrees of freedom per year. The model also included indicator variables for day of the week and indicators for federal holidays, which are common confounders of daily admissions rates. The general model framework above has been employed routinely and successfully in many peer-reviewed publications to estimate short-term effects of ambient air pollution (Samet et al. 2000; Peng et al. 2008; Yin et al. 2017). We also addressed potential over-dispersion in Poisson counts via a quasi-likelihood approach assumingVarYt=ϕΕYt. We conducted sensitivity analyses by increasing the degrees of freedom for temperature, humidity, and calendar date (Peng et al. 2006). To examine potential effect modification, we ran separate models after stratifying the time series by sex (male versus female) and race (White versus Black).

Results and discussion

The study included a total of 24,108 emergency room admissions and 33,094 inpatient/outpatient visits. Table 1 presents summary statistics of daily admissions, revealing higher proportions of female and black patients. Figure S1 (Supplementary materials) shows time series plots of daily asthma morbidity outcomes. Emergency room admissions exhibit the strongest seasonal pattern, with peaks during the winter months possibly due to increased respiratory infections (e.g., influenza).

Table 1.

Summary statistics of daily asthma morbidity counts during 2003–2011 in Jackson, MS. Outcomes are also stratified by sex and race

Total Mean (SD)
Emergency room admission Overall 24,108 7.8 (3.6)
Male 11,080 3.6 (2.2)
Female 12,813 4.1 2.3)
White 4434 1.4 (1.3)
Black 19,183 6.2 (3.1)
Inpatient and outpatient Overall 33,094 10.7 (8.7)
Male 15,542 5.0 (4.4)
Female 17,813 4.1 (2.3)
White 7686 2.5 (2.4)
Black 24,582 7.9 (6.8)
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SD standard deviation

Figure 2 shows the daily population-weighted PM2.5 concentration estimates in the five-county Jackson area. Daily PM2.5 concentration had a mean of 12.6 μg/m3,a standard deviation of 5.6 μg/m3, and an interquartile range of 8.2 μg/m3. Pollution levels were highest during the summer months, likely due to increased electricity generation, wildfire, and favorable meteorological conditions for secondary formation. Unlike other regions of the country, our PM2.5 estimates do not indicate an overall reduction in PM2.5 concentrations across the years defining our study period. Figure 3 shows the long-term average PM2.5 concentrations at the 0.1° × 0.1° grid resolution in Mississippi with the five-county Jackson region. Long-term averages showed strong spatial heterogeneity within the study region, ranging from 11.0 to 15.5 μg/m3.

Fig. 2.

Fig. 2

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Daily population-weighted PM2.5 concentrations during 2003–2011 in Jackson, MS

Fig. 3.

Fig. 3

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Estimated average PM2.5 concentrations (μg/m3) during 2003–2011 in Mississippi. The five-county Jackson metropolitan area boundary is shown in black and PM2.5 monitoring locations are indicated by filled triangle

Table 2 gives the estimated association between asthma morbidity and short duration exposure to given PM2.5 concentrations. We found that overall emergency room admissions increased by 7.2% (95% confidence interval, CI 1.7%, 13.1%) per 10 μg/m3 increase in 3-day average PM2.5 exposure. The association was also strongest at the lagged 1-day exposure, being the only one that is statistically significant among the different individual lags examined. We observe similar magnitudes of associations when emergency room admissions are stratified by sex and race. The association is robust against increasing the degrees of freedom for the smooth effects of temperature and humidity (Table S2). However, increasing the degrees of freedom for temporal control sometimes results in an attenuation in magnitude and increased uncertainty of the estimated associations (Figure S2). No significant associations were found between PM2.5 exposures and inpatient and outpatient visits.

Table 2.

Estimated percent change with 95% confidence interval in asthma morbidity per 10 μg/m3 increase in single lag-day and 3-day moving average PM2.5 concentration in Jackson, MS, 2003–2011. Statistically significant associations at α = 0.05 are in italics

Lag 0 Lag 1 Lag 2 3-day average
Emergency room admission Overall 2.9 (− 1.0, 6.9) 4.7 (0.7, 8.8) 3.7 (− 0.2, 7.8) 7.2 (1.7, 13.1)
Male 4.1 (− 1.3, 9.9) 3.6 (− 1.9, 9.4) 4.3 (− 1.1, 10.1) 7.8 (0.0, 16.1)
Female 2.0 (− 3.1, 7.3) 5.8 (0.6, 11.3) 3.2 (− 1.9, 8.6) 7.0 (− 0.2, 14.7)
White − 1.3 (− 9.5, 7.6) 4.6 (− 4.1, 14.1) 7.1 (− 1.6, 16.7) 6.6 (− 5.3, 20.0)
Black 4.1 (− 0.3, 8.6) 4.9 (0.5, 9.5) 4.0 (− 0.3, 8.5) 8.4 (2.2, 14.9)
Inpatient and outpatient Overall − 2.1 (− 6.3, 2.4) − 1.5 (− 5.8, 3.0) 1.1 (− 3.3, 5.7) − 1.5 (− 7.4, 4.7)
Male − 0.2 (− 5.7, 5.6) 3.4 (− 2.2, 9.4) 3.8 (− 1.8, 9.7) 4.4 (− 3.3, 12.7)
Female − 3.6 (− 8.7, 1.7) − 5.7 (− 10.7, − 0.4) − 1.2 (− 6.4, 4.3) − 6.5 9–13.2, 0.7)
White − 2.2 (− 10.0, 6.2) 1.1 (− 6.9, 9.9) 4.8 (− 3.5, 13.7) 2.3 (− 8.7, 14.5)
Black − 1.2 (− 5.9, 3.6) − 2.0 (− 6.6, 2.9) 0.1 (− 4.6, 5.0) − 1.9 (− 8.2, 4.8)
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CI confidence interval

Our relative risk (RR) estimates are comparable to previous epidemiologic studies of the association between short-term exposure to PM2.5 and asthma-related emergency room visits. For example, in a case-crossover study, Cheng et al. (2014) found associations between asthma hospital admission and lag 0–2 day PM2.5 exposures with an odds ratio of 1.12 (95% CI 1.06–1.18) per interquartile range (IQR, 17.5 μg/m3) in Taipei, Taiwan. This association was also robust against controlling for other criteria pollutants. In a time-series analysis in Hong Kong, Qiu et al. (2014) found a RR of 1.04 (95% CI 1.02, 1.07) per IQR (26.3 μg/m3) for the same exposure and health outcome. A recent meta-analysis estimated an average RR of 1.023 (95% CI 1.015, 1.031) per 10 μg/m3 increase in PM2.5 exposure across 37 studies with various designs, exposure definitions, and populations (Zheng et al. 2015). We did not find an association between PM2.5 exposure and asthma inpatient/outpatient visits, which may include planned events not affected by acute exposure to ambient air pollution.

We analyzed asthma outcomes aggregated across the entire age distribution. However, there is increasing interest in age-specific associations to identify potential vulnerable subpopulations (Alhanti et al. 2016). For example, increase in pediatric emergency department visit for asthma has been associated with lag 0–2 PM2.5 exposure in Atlanta (Strickland et al. 2010) and lag 0 day exposures in New Jersey (Gleason et al. 2014) and Turkey (Tecer et al. 2008). Other studies have also identified the elderly as a vulnerable group (Halonen et al. 2008; Delfino et al. 2008). In our stratified analysis by race and sex, we find higher risk of emergency room visit among males and blacks (Table 2). Similar racial differences in risk have been observed in previous studies (Glad et al. 2012; Grineski et al. 2010; Nachman and Parker 2012). Racial minorities may be more susceptible to air pollution-mediated asthma exacerbation due to more poorly controlled asthma. Few studies have examined the relationship between asthma morbidity and ambient air pollution by sex, and results to date are mixed (Lin et al. 2002; Son et al. 2013; Alhanti et al. 2016).

Our analyses focused on health effects of ambient air pollution for asthma, which is of interest in support of the development, definition, and assessment of regulatory standards, both in the USA and globally. Such estimated relative risks represent the predominant risk estimate used in health impact and burden analyses. While our results draw from detailed health outcome and exposure data, we note that our heath effect estimates may be overly conservative. We do not take into account additional factors potentially influencing the level of personal exposure received by an individual as a result of local ambient PM2.5 level, i.e., activity patterns, building infiltration rate, and mobility (Chang et al. 2012; Dionisio et al. 2016). Moreover, panel studies have shown that the PM2.5 attributed to ambient and indoor sources are only weakly correlated and that total indoor/personal exposures are not correlated with outdoor concentrations (Janssen et al. 2005).

The health dataset compiled in this analysis, the estimated ambient PM2.5 levels, and the general modeling framework allow many further studies to better elucidate the health effect of ambient air quality on asthma morbidity. First, investigation of other environmental exposures such as traffic-related air pollutants (carbon monoxide, nitrogen dioxides) and chemical constituents of PM2.5 may help identify the most toxic component or sources of the air pollution mixture. Second, effect modifications by age, seasonality (e.g., warm versus cold), and proxies for socio-economic status (e.g., neighborhood percent poverty or insurance status) may help identify vulnerable sub-populations most at-risk of air pollution exposures (Strickland et al. 2014; O’Lenick et al. 2017).

While our results provide novel insight and comparison to previous studies in other locations, there are also some limitations and areas for additional analyses. First, we utilized point estimates of local PM2.5 concentrations without considering associated prediction errors, which can vary spatially due to the representativeness of monitoring locations. Our time-series design only examines short-term temporal between-day variation in health outcomes and systematic bias in PM2.5 estimates that exhibit long-term trends are not likely to impact our results. However, spatio-temporal health analyses utilizing estimated pollutant concentrations, especially for pollutants that exhibit high spatial heterogeneity and prediction error, yield more complex relationships and require more careful consideration and are the subject of ongoing research (Peng and Bell 2010; Keller et al. 2016). Second, compared to other single-city time-series analysis, the Jackson Metropolitan Area has a much smaller population size. This generally results in lower statistical precision (as reflected in the wide confidence intervals associated with our RR estimates) and lower statistical power to detect effects, creating challenges for additional stratified analysis (e.g., by age or co-morbid conditions). Finally, our focus on asthma-related emergency room admissions may not reflect the full burden of asthma morbidity because emergency room visits may disproportionally capture severe asthma exacerbation and events from populations with higher utilization of emergency care.

Conclusion

We conducted, to our knowledge, the first population-based time-series study of ambient PM2.5 exposure and asthma morbidity in Mississippi and examined associations over a 9-year period. We utilized estimates of PM2.5 derived from machine learning algorithms and remotely sensed measures to improve exposure assessment. We found evidence for positive associations between PM2.5 and daily asthma-related emergency room admission, especially among the female and black subpopulations. Our results provide support for a relationship between air quality and respiratory morbidity even in an area of relatively low-exposure levels.

Supplementary Material

NIHMS825232-Supplementary Materials

Acknowledgements

Collection of asthma hospital discharge data used in this research was supported by Cooperative Agreement Number 5U59EH000208 from the Centers for Disease Control and Prevention (CDC), National Center for Environmental Health, Air Pollution and Respiratory Health Branch.

The contents of the article are solely responsibility of the authors and do not necessarily represent the official views of the NIH, the CDC or the Mississippi State Department of Health.

Funding information

Support for this research was provided by a grant from the National Institute of Environmental Health Sciences of the National Institutes of Health (NIH) under award number R21ES019713.

Footnotes

This article is part of the Topical Collection on Geospatial Technology in Environmental Health Applications

Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10661-019-7421-4) contains supplementary material, which is available to authorized users.

Contributor Information

Howard H. Chang, Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 1518 Clifton Rd NE, Atlanta, GA 30322, USA

Anqi Pan, Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 1518 Clifton Rd NE, Atlanta, GA 30322, USA.

David J. Lary, Hanson Center for Space Sciences, University of Texas at Dallas, 800 West Campbell Road Richardson, Dallas, TX 75080, USA

Lance A. Waller, Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 1518 Clifton Rd NE, Atlanta, GA 30322, USA

Lei Zhang, Office of Health Data and Research, Mississippi State Department of Health, 570 East Woodrow Wilson, Jackson, MS 39216, USA.

Bruce T. Brackin, Office of Epidemiology, Mississippi State Department of Health, 570 East Woodrow Wilson, Jackson, MS 39216, USA

Richard W. Finley, Department of Medicine, the University of Mississippi Medical Center, 2500 N. State St., Jackson, MS 39216, USA

Fazlay S. Faruque, Department of Preventive Medicine, University of Mississippi Medical Center, 2500 N. State St., Jackson, MS 39216, USA

References

  1. Alhanti BA, Chang HH, Winquist A, Mulholland J, Darrow L, & Sarnat SE (2016). Ambient air pollution and emergency department visits for asthma: a multi-city assessment of effect modification by age. Journal of Exposure Science & Environmental Epidemiology, 26, 180–188. [DOI] [PubMed] [Google Scholar]
  2. Bell ML, Samet JM, & Dominici F (2004). Time-series studies of particulate matter. Annual Review of Public Health, 25, 247–280. [DOI] [PubMed] [Google Scholar]
  3. Blackwell DL, Lucas JW, & Clarke TC (2014). Summary health statistics for U.S. adults: national health interview survey, 2012. Vital and Health Statistics, 206, 1–161. [PubMed] [Google Scholar]
  4. Chang HH, Fuentes M, & Frey HC (2012). Time series analysis of personal exposure to ambient PM2.5 and mortality using an exposure simulator. Journal of Exposure Science & Environmental Epidemiology, 22, 483–488. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Cheng MH, Chen CC, Chiu HF, & Yang CY (2014). Fine particulate air pollution and hospital admissions for asthma: a case-crossover study in Taipei. Journal of Toxicology and Environmental Health. Part A, 77, 1075–1083. [DOI] [PubMed] [Google Scholar]
  6. Dai L, Zanobetti A, Koutrakis P, & Schwartz JD (2014). Associations of fine particulate matter species with mortality in the United States: a multicity time-series analysis. Environmental Health Perspectives, 122, 837–842. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Delfino RJ, Brummel S, Wu J, Stern H, Ostro B, Lipsett M, Winer A, Street DH, Zhang L, Tjoa T, & Gillen DL (2008). The relationship of respiratory and cardiovascular hospital admissions to the southern California wildfires of 2003. Occupational and Environmental Medicine, 66, 189–197. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Dionisio KL, Chang HH, & Baxter LK (2016). A simulation study to quantify attenuation and bias of health risk estimates due to exposure measurement in bipollutant epidemiologic model. Environmental Health, 15, 114. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Dominici F, McDermott A, Zeger SL, & Samet JM (2003). National maps of the effects of particulate matter on mortality: exploring geographical variation. Environmental Health Perspectives, 111, 39–44. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Elliott CT, Henderson SB, & Wan V (2013). Time series analysis of fine particulate matter and asthma reliever dispensations in populations affected by forest fires. Environmental Health, 12, 11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Engel-Cox JA, Hoff RM, & Haymet A (2004). Recommendations on the use of satellite remote-sensing data for urban air quality. Journal of the Air & Waste Management Association (1995), 54, 1360–1371. [DOI] [PubMed] [Google Scholar]
  12. ESRI. (2017). Business analyst data & reports http://www.esri.com/software/businessanalyst/data-and-reports. Accessed 20 July 2017.
  13. Faruque FS, Li H, Williams WB, Waller LA, Brackin BT, Zhang L, & Finley RW (2014). GeoMedStat: an integrated spatial surveillance system to track air pollution and associated healthcare events. Geospatial Health, 8, 631–646. [DOI] [PubMed] [Google Scholar]
  14. Franklin M, Zeka A, & Schwartz J (2007). Association between PM2.5 and all-cause and specific-cause mortality in 27 US communities. Journal of Exposure Science & Environmental Epidemiology, 17, 279–287. [DOI] [PubMed] [Google Scholar]
  15. Glad JA, Brink LL, Talbott EO, Lee PC, Xu X, Saul M, & Rager J (2012). The relationship of ambient ozone and PM2.5 levels and asthma emergency department visits: possible influence of gender and ethnicity. Archives of Environmental & Occupational Health, 67, 103–108. [DOI] [PubMed] [Google Scholar]
  16. Gleason JA, Bielory L, & Fagliano JA (2014). Associations between ozone, PM2.5, and four pollen types on emergency department pediatric asthma events during the warm season in New Jersey: a case-crossover study. Environmental Research, 132, 421–429. [DOI] [PubMed] [Google Scholar]
  17. Grineski SE, Staniswalis JG, Peng Y, & Atkinson-Palombo C (2010). Children’s asthma hospitalizations and relative risk due to nitrogen dioxide (NO2): effect modification by race, ethnicity, and insurance status. Environmental Research, 110, 178–188. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Hajat S, Haines A, Goubet SA, Atkinson RW, & Anderson HR (1999). Association of air pollution with daily GP consultations for asthma and other lower respiratory conditions in London. Thorax, 54, 587–605. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Halonen JI, Lanki T, Yli-Tuomi T, Kulmala M, Tiittanen P, & Pekkanen J (2008). Urban air pollution and asthma and COPD hospital emergency room visits. Thorax, 63, 635–641. [DOI] [PubMed] [Google Scholar]
  20. Hoff RM, & Christopher SA (2009). Remote sensing of particulate pollution from space: have we reached the promised land? Journal of the Air & Waste Management Association (1995), 59(6), 645–675. [PubMed] [Google Scholar]
  21. Hu Z (2009). Spatial analysis of MODIS aerosol optical depth, PM2.5, and chronic coronary heart disease. International Journal of Health Geographics, 8, 27. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Ivy D, Mulholland JA, & Russell AG (2008). Development of ambient air quality population-weighted metrics for use in time-series health studies. Journal of the Air & Waste Management Association (1995), 58, 711–720. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Janssen NAH, Lanki T, Hoek G, Vallius M, de Hartog JJ, Grieken RV, Pekkanen J, & Brunekreef B (2005). Associations between ambient, personal, and indoor exposure to fine particulate matter constituents in Dutch and Finnish panels of cardiovascular patients. Occupational and Environmental Medicine, 62, 868–877. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Karagulian F, Belis CA, Dora CFC, Pruss-Ustun AM, Bonjour S, Adair-Rohani H, & Amann M (2015). Contributions to cities’ ambient particulate matter (PM): a systemic review of local sources contributions at global level. Atmospheric Environment, 120, 475–483. [Google Scholar]
  25. Keller JP, Chang HH, Strickland MJ, & Szpiro AA (2016). Measurement error correction for predicted spatio-temporal air pollution exposures. Epidemiology, 28, 338–345. 10.1097/EDE.0000000000000623. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Kloog I, Melly SJ, Ridgway WL, Coull BA, & Schwartz J (2012). Using new satellite based exposure methods to study the association between pregnancy PM 2.5 exposure, premature birth and birth weight in Massachusetts. Environmental Health, 11(1), 40. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Krall JR, Anderson GB, Dominici F, Bell ML, & Peng RD (2013). Short-term exposure to particulate matter constituents and mortality in a national study of U.S. urban communities. Environmental Health Perspectives, 121, 1148–1153. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Lary DJ, Faruque FS, Malakar N, Moore A, Roscoe B, Adams ZL, & Eggelston Y (2014a). Estimating the global abundance of ground level presence of particulate matter (PM2.5). Geospatial Health, 8, 611–630. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Lary DJ, Woolf S, Faruque F, & Le Page JP (2014b). Holistics 3.0 for health. ISPRS International Journal of Geo-Information, 3, 1023–1038. [Google Scholar]
  30. Lary D, Lary T, & Sattler B (2015). Using machine learning to estimate global PM2.5 for environmental health studies. Environmental Health Insights, 9, 41. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Lin M, Chen Y, Burnett RT, Villeneuve PJ, & Krewski D (2002). The influence of ambient coarse particulate matter on asthma hospitalization in children: case-crossover and time-series analyses. Environmental Health Perspectives, 110, 575–581. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Liu Y, Paciorek CJ, & Koutrakis P (2009). Estimating regional spatial and temporal variability of PM2.5 concentrations using satellite data, meteorology, and land use information. Environmental Health Perspectives, 117(6), 886–892. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Makar M, Antonelli J, Di Q, Cutler D, Schwartz J, & Dominici F (2017). Estimating the causal effect of low levels of fine particulate matter on hospitalization. Epidemiology, 28(5), 627–634. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Nachman KE, & Parker JD (2012). Exposures to fine particulate air pollution and respiratory outcomes in adults using two national datasets: a cross-sectional study. Environmental Health, 11(25), 2012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Natunen A, Arola A, Mielonen T, Huttunen J, Komppula M, & Lehtinen KE (2010). A multi-year comparison of PM2.5 and AOD for the Helsinki region. Boreal Environment Research, 15, 544–552. [Google Scholar]
  36. O’Lenick CR, Winquist A, Chang HH, Kramer MR, Mulholland JA, Grundstein A, & Sarnat SE (2017). Ozone and childhood respiratory disease in three US cities: evaluation of effect measure modification by neighborhood socioeconomic status using a Bayesian hierarchical approach. Environmental Health, 16, 36. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Paciorek CJ, & Liu Y (2012). Assessment and statistical modeling of the relationship between remotely sensed aerosol optical depth and PM2.5 in the eastern United States. Research Report. Health Effects Institute, 167, 5–83 discussion 85–91. [PubMed] [Google Scholar]
  38. Peng RD, & Bell ML (2010). Spatial misalignment in time series studies of air pollution and health data. Biostatistics, 11(4), 720–740. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Peng RD, Dominici F, & Louis TA (2006). Model choice in time series studies of air pollution and mortality (with discussion). Journal of the Royal Statistical Society. Series C, Applied Statistics, 169, 179–203. [Google Scholar]
  40. Peng RD, Chang HH, Bell ML, McDermott A, Zeger SL, Samet JM, & Dominici F (2008). Coarse particulate matter air pollution and hospital admissions for cardiovascular and respiratory diseases among Medicare patients. JAMA, 299, 2172–2179. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Qiu H, Yu IT, Tian L, Wang X, Tse LA, Tam W, & Wong TW (2014). Effects of coarse particulate matter on emergency hospital admissions for respiratory diseases: a time-series analysis in Hong Kong. Environmental Health Perspectives, 120, 572–576. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Samet JM, Dominici F, Curriero FC, Coursac I, & Zeger SC (2000). Fine particulate air pollution and mortality in 20 U.S. cities, 1987–1994. The New England Journal of Medicine, 343, 1742–1749. [DOI] [PubMed] [Google Scholar]
  43. Schaap M, Apituley A, Timmermans R, Koelemeijer R, & Leeuw GD (2009). Exploring the relation between aerosol optical depth and PM2.5 at Cabauw, the Netherlands. Atmospheric Chemistry and Physics, 9, 909–925. [Google Scholar]
  44. Sheppard L (2003). Insights on bias and information in group-level studies. Biostatistics, 4, 265–278. [DOI] [PubMed] [Google Scholar]
  45. Sheppard L, Levy D, Norris G, Larson TV, & Koenig JQ (1999). Effects of ambient air pollution on nonelderly asthma hospital admissions in Seattle, Washington, 1987–1994. Epidemiology, 10, 23–30. [PubMed] [Google Scholar]
  46. Son JY, Lee JT, Park YH, & Bell ML (2013). Short-term effects of air pollution on hospital admissions in Korea. Epidemiology, 24, 545–554. [DOI] [PubMed] [Google Scholar]
  47. Strickland MJ, Darrow LA, Klein M, Flanders WD, Sarnat JA, Waller LA, Sarnat SE, Mulholland JA, & Tolbert PE (2010). Short-term associations between ambient air pollutants and pediatric asthma emergency department visits. American Journal of Respiratory and Critical Care Medicine, 182(3), 307–316. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Strickland MJ, Klein M, Flanders WD, Chang HH, Mulholland JA, Tolbert PE, & Darrow LA (2014). Modification of the effect of ambient air pollution on pediatric asthma emergency visits: susceptible subpopulations. Epidemiology, 25, 843–850. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Strickland MJ, Hao H, Hu X, Chang HH, Darrow LA, & Liu Y (2016). Pediatric emergency visits and short-term changes in PM2.5 concentrations in the US State of Georgia. Environmental Health Perspectives, 124(5), 690–696. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Tecer LH, Alagha O, Karaca F, Tuncel G, & Eldes N (2008). Particulate matter (PM2.5, PM10-2.5, and PM10) and children’s hospital admissions for asthma and respiratory diseases: a bidirectional case-crossover study. Journal of Toxicology and Environmental Health. Part A, 71, 512–520. [DOI] [PubMed] [Google Scholar]
  51. US Centers for Disease Control and Prevention. (2009). The International Classification of Diseases, Ninth Revision, National Center for Health Statistics https://www.cdc.gov/nchs/icd/icd9cm.htm.
  52. van Donkelaar A, Martin RV, Brauer M, Kahn R, Levy R, Verduzco C, & Villeneuve PJ (2010). Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: development and application. Environmental Health Perspectives, 118, 847–855. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Weber SA, Engel-Cox JA, Hoff RM, Prados AI, & Zhang H (2010). An improved method for estimating surface fine particle concentrations using seasonally adjusted satellite aerosol optical depth. Journal of the Air & Waste Management Association (1995), 60, 574–585. [DOI] [PubMed] [Google Scholar]
  54. Yin P, He G, Fan M, Chiu KY, Fan M, Liu C, Xue A, Liu T, Pan Y, Mu Q, & Zhou M (2017). Particulate air pollution and mortality in 38 of China’s largest cities: time series analysis. BMJ, 14, 356. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Zhang H, Hoff RM, & Engel-Cox JA (2009). The relation between Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth and PM2.5 over the United States: a geographical comparison by US Environmental Protection Agency regions. Journal of the Air & Waste Management Association (1995), 59, 1358–1369. [DOI] [PubMed] [Google Scholar]
  56. Zheng X, Ding H, Jiang L, Chen S, Zheng J, Qiu M, Zhou Y, Chen Q, & Guan W (2015). Association between air pollutants and asthma emergency room visits and hospital admissions in time series studies: a systematic review and meta-analysis. PLoS One, 10, e0138146. [DOI] [PMC free article] [PubMed] [Google Scholar]

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NIHMS825232-Supplementary Materials
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