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. Author manuscript; available in PMC: 2021 Feb 1.
Published in final edited form as: Environ Res. 2019 Nov 11;181:108912. doi: 10.1016/j.envres.2019.108912

Changes in the Hospitalization and ED Visit Rates for Respiratory Diseases to Source-Specific PM2.5 in New York State from 2005 to 2016

Philip K Hopke 1,2,*, Daniel Croft 3, Wangjian Zhang 4, Shao Lin 4, Mauro Masiol 1, Stefania Squizzato 1, Sally W Thurston 5, Edwin van Wijngaarden 1,6, Mark J Utell 3,6, David Q Rich 1,3,6
PMCID: PMC6982568  NIHMSID: NIHMS1545767  PMID: 31753467

Abstract

Prior work found increased rates for emergency department (ED) visits for asthma and hospitalizations for chronic obstructive pulmonary disease per unit mass of PM2.5 across New York State (NYS) during 2014 to 2016 after major reductions in ambient PM2.5 concentrations had occurred following implementation of various policy actions and major economic disruptions. The associations of source-specific PM2.5 concentrations with these respiratory diseases were assessed with a time-stratified case-control design and logistic regression models to identify the changes in the PM2.5 that have led to the apparently increased toxicity per unit mass. The rates of ED visits and hospitalizations for asthma and COPD associated with increases in source-specific PM2.5 concentrations in the prior 1, 4, and 7 days were estimated for 6 urban sites in New York State. Overall, there were similar numbers of significantly increased (n=9) and decreased rates (n=8) of respiratory events (asthma and COPD hospitalizations and ED visits) associated with increased source-specific PM2.5 concentrations in the previous 1, 4, and 7 days. Associations of source-specific PM2.5 concentrations with excess rates of hospitalizations for COPD for spark- and compression ignition vehicles increased in the 2014–2016 period, but the values were not statistically significant. Other source types showed inconsistent patterns of excess rates. For asthma ED visits, only biomass burning and road dust showed consistent positive associations with road dust having significant values for most lag times. Secondary nitrate also showed significant positive associations with asthma ED visits in the AFTER period compared to no associations in the prior periods. These results suggest that the relationships of asthma and COPD exacerbation with source-specific PM2.5 are not well defined and further work will be needed to determine the causes of the apparent increases in the per unit mass toxicity of PM2.5 in New York State in the 2014–16 period.

Keywords: asthma, COPD, source-specific PM2.5, hospitalizations, ED visits

Introduction

The exacerbation of asthma and chronic obstructive pulmonary disease (COPD consisting of chronic bronchitis and emphysema) by exposure to ambient airborne particulate matter has been extensively reported and reviewed (Guarnieri and Balmes. 2014; Orellano et al.. 2017; Anenberg et al., 2018; Li et al., 2016; Bloemsma et al., 2016; USEPA, 2018). Our recent study (Hopke et al., 2019) also found increased rates of hospitalizations and emergency department (ED) visits in New York State (NYS) associated with short term increases in ambient PM2.5 concentrations. However, there were changes in the estimated excess rates ([relative rate – 1]*100%) of COPD hospitalization and adult asthma ED visits per unit mass of PM2.5 concentration from 2005 to 2016, with the largest relative rates per μg/m3 increase in PM2.5 in the most recent (2014–2016) period compared to earlier periods (2005–2007; 2008–2013). Similar findings were observed in parallel studies of acute cardiovascular events (CVD) (Zhang et al., 2018) and respiratory infections (Croft et al., 2019a) across NYS. Overall rates of CVD hospitalizations (Zhang et al 2018), adult asthma ED visits and COPD hospitalizations (Hopke, 2019), and influenza ED visits and culture-negative pneumonia hospitalizations decreased with decreasing PM2.5 concentrations across NYS from 2005 to 2016 (Squizzato et al., 2018a). However, the rates did not decline proportionally to the declining PM2.5 concentrations suggesting that the observed changes in PM composition (Squizzato et al., 2018b) resulted in increased PM toxicity.

Subsequently, Rich et al. (2019) reported the rates of these CVD hospitalizations were associated with source-specific PM2.5 concentrations based on the source apportionment of speciated PM across NYS (Squizzato et al., 2018b). Analyses of U.S. Environmental Protection Agency Chemical Speciation Network data (Solomon et al., 2014) were performed for each site using positive matrix factorization (PMF) (Squizzato et al., 2018b) to provide estimates of the source-specific PM2.5 concentrations. Interquartile range (IQR) increases in spark-ignition vehicular emissions (GAS) concentrations were associated with increased excess rates of cardiac arrhythmia hospitalizations (2.3%; 95% CI = 0.4%, 4.2%; IQR=2.56 μg/m3) and ischemic stroke hospitalizations (3.7%; 95% CI= 1.1%, 6.4%; 2. 73 μg/m3) on lag day 0. IQR increases in diesel (DIE) concentrations were associated with increased rates of congestive heart failure hospitalizations (0.7%; 95% CI = 0.2% 1.3%; 0.51 μg/m3) and ischemic heart disease hospitalizations (0.8%; 95% CI = 0.3%, 1.3%; 0.60 μg/m3) on lag day 0. Increased acute cardiovascular hospitalization rates were also associated with IQR increases in concentrations of road dust (RD), residual oil (RO), and secondary nitrate (SN) over lag days 0, 0–3, and 0–6, but not other sources including biomass burning (BB), secondary sulfate (SS), pyrolytic organic carbon (OP), and fresh (FSS) and aged sea salt (AGS) (Rich et al., 2019). These findings suggested that several sources of PM2.5 in New York State (i.e. tailpipe emissions, non-tailpipe emissions such as brake and tire wear, residual oil, and nitrate that may also reflect traffic emissions) trigger acute cardiovascular events.

Croft et al. (2019b) has done similar analyses of the associations of source-specific PM2.5 with hospitalizations and emergency department visits for respiratory infections (culture negative pneumonia or influenza). Increased rates of ED visits for influenza were associated with interquartile range increases in concentrations of GAS (Excess Rate [ER] = 9.2%; 95% CI: 4.3%, 14.3%) and DIE (ER = 3.9%; 95% CI: 1.1%, 6.8%) from exposure during lag days 0–3. There were similar associations between BB, SS, OP, and RO, influenza ED visits and hospitalizations, but not culture negative pneumonia hospitalizations or ED visits. They concluded that increased rates of influenza hospitalizations and ED visits that were associated with short term increases in PM2.5 appear to be driven largely by PM from traffic and other combustion sources.

As part of the prior analyses (Zhang et al 2018; Croft et al 2019a), the measured organic carbon was separated into primary organic carbon (POC) and secondary organic carbon (SOC). It was hypothesized that SOC in the particles from these sources was likely to include reactive oxygen species (ROS) (Docherty et al., 2005; Hopke, 2015; Pagonis and Ziemann, 2019) or have substantial oxidative potential (Saffari et al., 2014) that could induce oxidative stress and systemic inflammation. Recent changes in light-duty vehicles from port fuel injection (PFI) to gasoline direct injection (GDI) technology coupled with changes in gasoline formulations appear to increase the formation of secondary organic aerosol (SOA) (Zhao et al., 2014; 2015; 2016, 2018) and thus, the potential for increased ROS associated with spark-ignition vehicular emissions (GAS). GAS PM had significant associations with several CVD outcomes as well as hospitalizations and ED visits for influenza. Diesel emissions (DIE) are reported to have substantial oxidative potential (Arimoto et al., 2005; Saffari et al., 2014) that would produce endogenous ROS (Hopke, 2015) and DIE PM was also associated with CVD and influenza rates of hospitalization and ED visits. It is not clear if the presence of oxidants or oxidative potential in PM2.5 affects the rates of hospitalizations or ED visits for either asthma or COPD.

Therefore, a further study of whether these source-specific PM2.5 components also exacerbate adult asthma and/or COPD leading to hospitalizations or ED visits was conducted. We hypothesized that the ROS-associated source components such as spark-ignition and diesel vehicle emissions, road dust, and residual oil combustion particles would be associated with increased rates of hospitalizations and ED visits for these respiratory diseases.

Methods and Materials

Exposure and Meteorological Data

We have used the same approach in this study as that described by Rich et al. (2019) and Croft et al. (2019b) to obtain the exposure and associated weather data. The source-specific PM2.5 concentrations were estimated from the chemical speciation data obtained from the analysis of samples collected at six urban sites in NYS (Albany, Bronx, Buffalo, Manhattan, Queens, and Rochester). The locations and details of sample collection for these sites are presented in Table S1 in the supplemental material file. Details of the sample collection and analyses were presented by Solomon et al. (2014). The PMF analyses and resulting source identifications were presented by Squizzato et al. (2018b) while the trends in the source contributions were reported by Masiol et al. (2019). Because of the limited sample collection schedules, source-specific PM2.5 concentrations were available only every 3rd or 6th day depending on the site (Table S1). Minimum values for the source specific mass concentrations often have small negative values as a result of the uncertainties in the PMF analysis (Paatero et al., 2014). However, we did not left censor the values to avoid the potential bias that such truncation would induces (e.g., Cain et al., 2011).

Hourly temperature and relative humidity data were obtained from the National Weather Service (National Climate Data Center, https://www.ncdc.noaa.gov/cdo-web/datatools/lcd) for the nearest major airport (BUF - Buffalo, ROC - Rochester, ALB - Albany, LGA - Bronx, and JFK - Queens) or the closest weather station (Central Park for Manhattan). For each study subject living within 15 miles of our six monitoring stations, we assigned PM2.5 source contributions, temperature, and relative humidity measurements from the nearest applicable monitoring site. If a person lived <15 miles from more than one monitor (e.g. Bronx vs. Manhattan), we assigned concentrations/values from the closer monitor to that person.

Study Population and Hospital Admissions Data

The hospitalization and ED visit data used have been previously described by Zhang et al. (2018), Croft et al. (2019a), and Hopke et al. (2019). Hospital admissions and ED visit records were obtained from the New York State Department of Health Statewide Planning and Research Cooperative System (SPARCS) database, which covers ~95% of hospitals in NYS, but not federal (e.g. Veterans Affairs Hospitals) and psychiatric facilities. The SPARCS data include a primary diagnoses and up to 24 other comorbidity diagnoses assigned at the time of hospital admission or ED visit as well as patient demographic information. Using each residential address for every subject hospitalization or ED visit, we retained those adult subjects (≥18 years old) with 1) a successfully geocoded address (using ArcGIS 10.3.1; ©ESRI, Inc. Redlands, CA) that was within 15 miles of each monitoring station (Buffalo, Rochester, Albany, Bronx, Manhattan, Queens); 2) a “principal” diagnoses of asthma (ICD9 =493; ICD10 = J45) or COPD (ICD9 = 491, 492; ICD10 = J41, J43); and 3) an admission date between January 1, 2005 and December 31, 2016 (N=43,315 asthma hospitalizations; N=128,055 asthma ED visits; N=32,240 COPD hospitalizations; N=10,854). This study was reviewed and approved by the Institutional Review Board at the State University of New York at Albany.

Study Design and Statistical Analyses

To estimate the relative rates of asthma or COPD associated with short term increases in PM2.5 source contributions (e.g. GAS or DIE), we used the same time-stratified, case-crossover design and conditional logistic regression analyses (Levy et al. 2001; Maclure 1991) used in our analysis of source-specific PM2.5 and acute cardiovascular hospitalizations (Rich et al., 2019). The case-crossover design compares pollutant concentrations immediately before the hospitalization or ED visit event (case periods) to other times matched to the case period by calendar month and weekday when the subject did not have an event (3–4 control periods per case). For example, if an event occurred on March 15, 2012, then the case-day would be March 15th, and the control days would be March 1st, 8th, 22nd, and 29th. Since the case and control are matched by subject, time-invariant factors such as age, gender, long term health history are controlled by design. We then contrast the air pollutant concentrations on the case and control days. To do so, we separately fit conditional logistic regression models for asthma hospitalizations, asthma ED visits, COPD hospitalizations, and COPD ED visit for each PM2.5 source described above. The case-control status (i.e., case = 1, control = 0) was regressed against the mean specific PM2.5 source concentration, mean residual PM2.5 (rPM2.5) concentration (i.e. rPM2.5 = PM2.5 concentration – specific PM2.5 source concentration). The use of the rPM2.5 term was to account for possible confounding by the correlation between the source-specific PM2.5 and total PM2.5 concentrations as discussed by Mostofsky et al. (2012). The use of the rPM2.5 substantially reduces the correlations between the source-specific PM2.5 and the marker of total PM2.5 as shown in Tables S2 and S3. This same approach was used in the prior studies of source-specific PM2.5 and cardiovascular disease (Rich et al., 2019). In each analysis, an indicator variable was included for holidays. Temperature and relative humidity (same lag days as PM2.5), time varying potential confounders, were modeled with a natural spline with 4 degrees of freedom. Akaike’s Information Criterion (Aho et al., 2014) was used to select the df for each natural spline. This case-crossover model provided estimates of the rate ratio and its 95% confidence interval. The excess rate is the percent (%) increase in the rate per interquartile range increase in source-specific PM2.5 concentration (i.e. [rate ratio – 1.0] * 100%). We then re-ran this set of models for each PM2.5 source for lag days 0, 0–3, and 0–6 for each outcome (asthma hospitalizations, asthma ED visits, COPD hospitalizations, and COPD ED visits). Since there were 3 lagged effects estimated for each outcome/PM2.5 source in separate models, a p<0.017 (0.05/3) was used to define statistical significance. In a further exploratory analyses, we then ran the model with an interaction term among periods [BEFORE (2005–2007), DURING (2008–2013), and AFTER (2014–2016)] and each individual source-specific PM2.5 concentration (e.g. GAS * DURING, GAS * AFTER). The site-specific OR estimates were pooled into an overall value across the study area with a fix-effect meta-analysis based on the generic inverse variance method (Viechtbauer, 2010). As a sensitivity analysis, we reran the meta-analysis with the mixed-effect model and found that the overall OR estimates were identical. All meta-analyses were done with the rma function in the metafor package ( (https://cran.r-project.org/web/packages/metafor/metafor.pdf)). All data management and statistical analyses were done using R version 3.0.1(https://www.r-project.org/). With the reduced number of subjects included in these analyses compared to the PM2.5 analyses (Hopke et al., 2019), particularly given the loss of Albany and Bronx PM speciation data for several years each (Table S1), we could not examine any trends in the number of adverse health events for those 2 sites.

Results

The demographical characterization of the subjects in each of the 4 outcome groups (asthma hospitalizations, asthma ED visits, COPD hospitalizations, and COPD ED visit) are presented in Table 1(a–d). The numbers of subjects in this study were smaller than in Hopke et al. (2019) because of the decreased frequency of the ambient PM2.5 species sampling (every 3rd or 6th day) compared to the PM2.5 concentrations (every day) used in Hopke et al. (2019).

Table 1a.

Characterization of the asthma patients requiring hospitalizations

Albany (N) Albany (%) Bronx (N) Bronx (%) Buffalo (N) Buffalo (%) Manhattan (N) Manhattan (%) Queens (N) Queens (%) Rochester (N) Rochester (%)
N 833 100 16,515 100 880 100 13,482 100 10,078 100 1,527 100
Male 237 28.45 4,932 29.86 223 25.34 4,112 30.5 3,087 30.63 401 26.26
AGE (years) 53.86 (18.16) 53.39 (17.59) 55.88 (17.92) 55.93 (17.71) 57.45 (18.32) 53.53 (17.26)
18–39 185 22.21 3,552 21.51 154 17.5 2,404 17.83 1,676 16.63 302 19.78
40–49 196 23.53 3,339 20.22 177 20.11 2,375 17.62 1,685 16.72 324 21.22
50–69 156 18.73 3,658 22.15 193 21.93 3,068 22.76 2,131 21.15 371 24.3
60–69 125 15.01 2,716 16.45 138 15.68 2,379 17.65 1,760 17.46 254 16.63
70–79 74 8.88 1,968 11.92 109 12.39 1,844 13.68 1,488 14.76 139 9.1
≥80 97 11.64 1,282 7.76 109 12.39 1,412 10.47 1,338 13.28 137 8.97
RACE/ETHNICITY
White 516 61.94 2,722 16.48 514 58.41 3,464 25.69 3,553 35.26 727 47.61
Black 248 29.77 5,517 33.41 273 31.02 6,097 45.22 3,733 37.04 601 39.36
American Indian 1 0.12 69 0.42 6 0.68 64 0.47 81 0.80 1 0.07
Asian 1 0.12 104 0.63 4 0.45 298 2.21 404 4.01 5 0.33
Native Hawaii 1 0.12 1 0.01 0 0.00 3 0.02 6 0.06 0 0
Hispanic 29 3.48 4,997 30.26 61 6.93 2,682 19.89 1,493 14.81 130 8.51
YEAR
 2005 N/A N/A 2,129 12.89 76 8.64 1,687 12.51 713 7.07 141 9.23
 2006 N/A N/A 2,238 13.55 69 7.84 1,436 10.65 904 8.97 130 8.51
 2007 44 5.28 2,283 13.82 65 7.39 1,534 11.38 946 9.39 134 8.78
 2008 112 13.45 2,240 13.56 98 11.14 1,308 9.70 866 8.59 163 10.67
 2009 130 15.61 2,148 13.01 112 12.73 1,389 10.3 852 8.45 164 10.74
 2010 100 12.00 701 4.24 75 8.52 920 6.82 1,033 10.25 147 9.63
 2011 105 12.61 N/A N/A 74 8.41 1,188 8.81 917 9.10 102 6.68
 2012 98 11.76 N/A N/A 98 11.14 279 2.07 922 9.15 97 6.35
 2013 40 4.80 N/A N/A 61 6.93 1,107 8.21 839 8.33 83 5.44
 2014 107 12.85 1,756 10.63 59 6.70 1,201 8.91 846 8.39 140 9.17
 2015 62 7.44 1,833 11.10 62 7.05 839 6.22 712 7.06 121 7.92
 2016 35 4.20 1,187 7.19 31 3.52 594 4.41 528 5.24 105 6.88
SEASON
Fall 197 23.65 3,815 23.1 214 24.32 3,461 25.67 2,442 24.23 378 24.75
Spring 216 25.93 4,782 28.96 253 28.75 3,670 27.22 2,804 27.82 407 26.65
Summer 171 20.53 3,210 19.44 155 17.61 2,420 17.95 2,040 20.24 309 20.24
Winter 249 29.89 4,708 28.51 258 29.32 3,931 29.16 2,792 27.70 433 28.36
length of stay (days, M (SD)) 4.04 (3.44) 3.70 (4.2) 4.21 (3.71) 4.03 (4.13) 4.32 (6.88) 3.63 (6.41)
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Table 1d.

Characterization of the COPD patients requiring ED visits

Albany (N) Albany (%) Bronx (N) Bronx (%) Buffalo (N) Buffalo (%) Manhattan (N) Manhattan (%) Queens (N) Queens (%) Rochester (N) Rochester (%)
N 1,124 100 2,648 100 1,162 100 2,716 100 1,845 100 1,359 100
Male 451 40.12 1,358 51.28 496 42.69 1,469 54.09 919 49.81 635 46.73
AGE (years) 62.28 (13) 62.45 (12.76) 63.89 (12.81) 62.08 (12.81) 64.04 (13.99) 63.92 (12.74)
18–39 29 2.58 81 3.06 26 2.24 99 3.65 65 3.52 26 1.91
40–49 144 12.81 275 10.39 142 12.22 318 11.71 186 10.08 144 10.6
50–69 339 30.16 811 30.63 269 23.15 769 28.31 486 26.34 353 25.97
60–69 267 23.75 697 26.32 310 26.68 761 28.02 442 23.96 396 29.14
70–79 213 18.95 517 19.52 278 23.92 519 19.11 379 20.54 248 18.25
≥80 132 11.74 267 10.08 137 11.79 250 9.2 287 15.56 192 14.13
RACE/ETHNICITY
White 920 81.85 701 26.47 863 74.27 1,153 42.45 1,024 55.5 1,042 76.67
Black 175 15.57 1,175 44.37 245 21.08 1,018 37.48 548 29.7 276 20.31
American Indian 0 0.00 8 0.30 4 0.34 3 0.11 11 0.60 0 0.00
Asian 1 0.09 16 0.6 1 0.09 45 1.66 38 2.06 4 0.29
Native Hawaii 0 0 0 0 0 0 0 0 2 0.11 0 0
Hispanic 15 1.33 281 10.61 17 1.46 226 8.32 159 8.62 36 2.65
YEAR
 2005 0 0 248 9.37 86 7.4 214 7.88 119 6.45 104 7.65
 2006 0 0 304 11.48 72 6.2 162 5.96 116 6.29 97 7.14
 2007 35 3.11 258 9.74 67 5.77 212 7.81 113 6.12 116 8.54
 2008 133 11.83 293 11.06 101 8.69 211 7.77 119 6.45 114 8.39
 2009 122 10.85 316 11.93 79 6.8 214 7.88 144 7.8 140 10.3
 2010 97 8.63 98 3.7 112 9.64 144 5.3 224 12.14 115 8.46
 2011 181 16.1 0 0 88 7.57 262 9.65 199 10.79 109 8.02
 2012 150 13.35 0 0 134 11.53 63 2.32 220 11.92 126 9.27
 2013 102 9.07 0 0 127 10.93 332 12.22 232 12.57 115 8.46
 2014 211 18.77 534 20.17 129 11.1 502 18.48 191 10.35 193 14.2
 2015 92 8.19 569 21.49 162 13.94 368 13.55 147 7.97 123 9.05
 2016 1 0.09 28 1.06 5 0.43 32 1.18 21 1.14 7 0.52
SEASON
Fall 280 24.91 569 21.49 282 24.27 693 25.52 474 25.69 327 24.06
Spring 287 25.53 774 29.23 305 26.25 719 26.47 465 25.2 369 27.15
Summer 250 22.24 694 26.21 266 22.89 626 23.05 413 22.38 333 24.5
Winter 307 27.31 611 23.07 309 26.59 678 24.96 493 26.72 330 24.28
length of stay (days, M (SD)) 0.19 (0.53) 0.08 (0.32) 0.2 (0.5) 0.1 (0.36) 0.13 (0.39) 0.39 (0.73)
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As discussed by Hopke et al. (2019), the majority of asthma patients only visiting the ED (discharged without being hospitalized) were predominantly female, while patients with asthma hospitalizations, COPD hospitalizations, and COPD ED visits were more often male. ED visit subjects were younger than those hospitalized patients, while asthma patients were younger than COPD patients. The majority of upstate (Albany, Buffalo, Rochester) and requiring hospitalization for asthma or COPD were white (Tables 1a, c). In NYC, the largest fractions of asthma and COPD hospitalized subjects were white (Tables 1a,c). For asthma ED visits (Table 1b), less than half of the subjects were white, but ~50% were black at each site. For COPD ED visits (Table 1d), the majority of patients were white at each site, except for the Bronx and Manhattan. For the Bronx, 44% of the subjects were black and almost 11% were Hispanic while in Manhattan, whites were the plurality with about 37.5% black and 8.3% Hispanic.

Table 1c.

Characterization of the COPD patients requiring hospitalizations

Albany (N) Albany (%) Bronx (N) Bronx (%) Buffalo (N) Buffalo (%) Manhattan (N) Manhattan (%) Queens (N) Queens (%) Rochester (N) Rochester (%)
N 2,085 100 7,174 100 1,863 100 9,074 100 9,685 100 2,359 100
Male 851 40.82 3,297 45.96 773 41.49 4,294 47.32 4,434 45.78 1,009 42.77
AGE (years) 69.26 (11.84) 70.55 (12.4) 70.54 (11.88) 71.19 (12.5) 72.91 (12.3) 69.83 (12.27)
18–39 11 0.53 41 0.57 6 0.32 61 0.67 56 0.58 12 0.51
40–49 73 3.50 332 4.63 74 3.97 386 4.25 294 3.04 115 4.87
50–69 376 18.03 1,055 14.71 279 14.98 1,246 13.73 1,131 11.68 374 15.85
60–69 594 28.49 1,759 24.52 480 25.76 2,121 23.37 2,106 21.74 604 25.6
70–79 571 27.39 2,099 29.26 544 29.2 2,703 29.79 2,830 29.22 690 29.25
≥80 460 22.06 1,888 26.32 480 25.76 2,557 28.18 3,268 33.74 564 23.91
RACE/ETHNICITY
White 1,858 89.11 3,017 42.05 1,517 81.43 5,212 57.44 6,395 66.03 1,924 81.56
Black 163 7.82 1,984 27.66 287 15.41 2,248 24.77 1,972 20.36 348 14.75
American Indian 1 0.05 28 0.39 2 0.11 57 0.63 42 0.43 1 0.04
Asian 7 0.34 65 0.91 4 0.21 297 3.27 313 3.23 7 0.3
Native Hawaii 1 0.05 0 0.00 0 0.00 1 0.01 5 0.05 1 0.04
Hispanic 31 1.49 1,006 14.02 21 1.13 863 9.51 634 6.55 58 2.46
YEAR
 2005 N/A N/A 956 13.33 148 7.94 1,216 13.4 668 6.9 247 10.47
 2006 N/A N/A 936 13.05 146 7.84 876 9.65 866 8.94 205 8.69
 2007 112 5.37 934 13.02 145 7.78 1,025 11.3 840 8.67 231 9.79
 2008 319 15.3 1,039 14.48 223 11.97 935 10.3 901 9.3 262 11.11
 2009 253 12.13 1,049 14.62 209 11.22 875 9.64 968 9.99 265 11.23
 2010 282 13.53 381 5.31 181 9.72 637 7.02 1,079 11.14 238 10.09
 2011 295 14.15 N/A N/A 180 9.66 825 9.09 978 10.1 168 7.12
 2012 245 11.75 N/A N/A 198 10.63 203 2.24 999 10.31 206 8.73
 2013 148 7.1 N/A N/A 151 8.11 839 9.25 931 9.61 192 8.14
 2014 296 14.2 1,027 14.32 129 6.92 935 10.3 870 8.98 178 7.55
 2015 134 6.43 834 11.63 153 8.21 688 7.58 562 5.8 162 6.87
 2016 1 0.05 18 0.25 0 0 20 0.22 23 0.24 5 0.21
SEASON
Fall 496 23.79 1,430 19.93 368 19.75 2,211 24.37 2,297 23.72 533 22.59
Spring 581 27.87 2,201 30.68 538 28.88 2,576 28.39 2,631 27.17 632 26.79
Summer 402 19.28 1,560 21.75 395 21.20 1,676 18.47 2,020 20.86 472 20.01
Winter 606 29.06 1,983 27.64 562 30.17 2,611 28.77 2,737 28.26 722 30.61
length of stay (days, M (SD)) 5.21 (8.84) 5.58 (7.88) 5.18 (6.43) 5.83 (6.62) 6.31 (6.49) 4.72 (5.5)
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Table 1b.

Characterization of the asthma patients requiring ED visits

Albany (N) Albany (%) Bronx (N) Bronx (%) Buffalo (N) Buffalo (%) Manhattan (N) Manhattan (%) Queens (N) Queens (%) Rochester (N) Rochester (%)
N 4,140 100 64,398 100 4,030 100 51,901 100 29,675 100 5,911 100
Male 1,557 37.61 25,576 39.72 1,477 36.65 21,042 40.54 11,754 39.61 2,167 36.66
AGE (years) 38.38 (14.5) 40.92 (14.95) 39.36 (15.04) 41.47 (15.29) 40.67 (15.52) 38.74 (15.24)
18–39 2,361 57.03 30,519 47.39 2,175 53.97 24,306 46.83 14,736 49.66 3,259 55.13
40–49 862 20.82 15,062 23.39 876 21.74 11,711 22.56 6,252 21.07 1,317 22.28
50–69 594 14.35 11,525 17.9 576 14.29 9,299 17.92 5,181 17.46 768 12.99
60–69 198 4.78 5,075 7.88 246 6.10 4,344 8.37 2,203 7.42 333 5.63
70–79 71 1.71 1,690 2.62 104 2.58 1,684 3.24 900 3.03 132 2.23
≥80 54 1.30 527 0.82 53 1.32 557 1.07 403 1.36 102 1.73
RACE/ETHNICITY
White 1,841 44.47 7,469 11.60 1,829 45.38 7,954 15.33 7,118 23.99 2,221 37.57
Black 1,739 42.00 28,356 44.03 1,679 41.66 29,803 57.42 14,298 48.18 2,525 42.72
American Indian 5 0.12 183 0.28 14 0.35 124 0.24 127 0.43 4 0.07
Asian 16 0.39 273 0.42 15 0.37 470 0.91 912 3.07 15 0.25
Native Hawaii 0 0.00 3 0.01 1 0.02 4 0.01 16 0.05 0 0.00
Hispanic 232 5.60 14,046 21.81 285 7.07 6,887 13.27 4,901 16.52 775 13.11
YEAR
 2005 N/A N/A 7,970 12.38 288 7.15 5,458 10.52 1,837 6.19 410 6.94
 2006 N/A N/A 8,185 12.71 316 7.84 4,284 8.25 2,418 8.15 413 6.99
 2007 204 4.93 7,902 12.27 284 7.05 4,953 9.54 2,309 7.78 456 7.71
 2008 543 13.12 7,258 11.27 358 8.88 4,199 8.09 1,948 6.56 495 8.37
 2009 425 10.27 6,556 10.18 409 10.15 3,863 7.44 2,544 8.57 554 9.37
 2010 461 11.14 2,265 3.52 361 8.96 3,047 5.87 2,911 9.81 556 9.41
 2011 531 12.83 N/A N/A 314 7.79 4,113 7.92 2,724 9.18 392 6.63
 2012 527 12.73 N/A N/A 425 10.55 1,094 2.11 3,010 10.14 438 7.41
 2013 312 7.54 N/A N/A 290 7.20 4,237 8.16 3,053 10.29 497 8.41
 2014 605 14.61 7,703 11.96 306 7.59 5,493 10.58 2,405 8.10 627 10.61
 2015 267 6.45 8,411 13.06 352 8.73 5,217 10.05 2,252 7.59 498 8.42
 2016 265 6.40 8,148 12.65 327 8.11 5,943 11.45 2,264 7.63 575 9.73
SEASON
Fall 1,221 29.49 15,792 24.52 1,122 27.84 14,540 28.01 8,110 27.33 1,670 28.25
Spring 1,031 24.90 19,078 29.63 1,043 25.88 14,426 27.80 8,359 28.17 1,430 24.19
Summer 831 20.07 13,131 20.39 890 22.08 9,665 18.62 5,733 19.32 1,334 22.57
Winter 1,057 25.53 16,397 25.46 975 24.19 13,270 25.57 7,473 25.18 1,477 24.99
length of stay (days, M (SD)) 0.09 (0.31) 0.05 (0.28) 0.13 (0.37) 0.06 (0.35) 0.09 (0.30) 0.26 (0.54)
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The distributional characteristics of the source-specific PM2.5 concentrations (μg/m3) by site, period, and source, separated for the case and control days for the 4 outcomes are presented in Tables S4 to S7. Since not all PM2.5 sources were resolved at all sites, the ‘N/A’ entries pertain to periods for which no speciation samples were collected. The distributional characteristics and the results of Kruskal-Wallis ANOVA on ranks among the three periods are shown in Figures S1 to S9 for all of the sources except road salt, fresh sea salt and aged sea salt that represent same and constant contributors to the ambient PM2.5. For most sources, concentrations of the resolved source contributions were least in the AFTER period. However, GAS increased in the AFTER period and DIE varied from site to site. Detailed trend analyses for each of the sources were presented in Masiol et al. (2019).

Table 2 presents the mean annual incidence rates per 1000 persons per year for asthma and COPD hospitalizations and ED visits for all subjects and each site individually. Incidence rates for asthma hospitalizations and ED visits in NYC were generally higher than those for the upstate cities. The Bronx had higher annual incidence rates for asthma ED visits and hospitalizations compared to Queens or Manhattan. COPD incidence rates among the NYC sites were comparable. However, rates of COPD hospitalizations and COPD ED visits did not have a clear upstate versus NYC pattern.

Table 2.

Mean annual incidence rate (#/1000 persons per year) of hospital admissions and ED visits by period for asthma and COPD.

Period Overall Albany Bronx Buffalo Manhattan Queens Rochester
Asthma hospitalizations BEFORE 0.42 0.03 0.76 0.08 0.44 0.29 0.19
DURING 0.26 0.18 0.29 0.10 0.29 0.30 0.17
AFTER 0.28 0.12 0.52 0.06 0.24 0.23 0.17
COPD hospitalizations BEFORE 0.28 0.07 0.32 0.16 0.29 0.27 0.31
DURING 0.24 0.46 0.14 0.22 0.20 0.33 0.30
AFTER 0.17 0.26 0.21 0.11 0.15 0.16 0.16
Asthma ED visits BEFORE 1.37 0.12 2.75 0.33 1.39 0.74 0.59
DURING 0.86 0.84 0.90 0.41 0.95 0.90 0.67
AFTER 1.44 0.68 2.66 0.37 1.51 0.75 0.77
COPD ED visits BEFORE 0.07 0.02 0.09 0.08 0.06 0.04 0.15
DURING 0.07 0.24 0.04 0.12 0.06 0.06 0.16
AFTER 0.09 0.18 0.12 0.11 0.08 0.04 0.15
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Table 3 presents the estimated excess rates (%) of hospitalization or ED visits for asthma or COPD associated with each interquartile range increase in source-specific PM2.5 concentration on the day of the event (lag day 0), the average of the values on the day of the event and the sample from the prior 3rd day (lag days 0–3), and the 2 or 3 samples from every 6th or 3rd day prior, respectively, and the event day (lag days 0–6). Overall, there were similar numbers of significantly increased (n=9) and decreased rates (n=8) of respiratory events (asthma and COPD hospitalizations and ED visits) associated with increased source-specific PM2.5 concentrations on lag days 0, 0–3, and 0–6 (Table 3). For example, an interquartile range (IQR) increase in GAS PM2.5 concentration was associated with both decreased rates of asthma hospitalizations (−3.7%, 95% CI = −6.1%, −1.3%) and increased rates of COPD hospitalizations (4.2%, 95% CI = 1.1%, 7.5%). Similarly, IQR increases in concentrations of road dust PM2.5 were associated with increased rates of asthma ED visits (e.g. previous 0–6 days: 2.2%, 95% CI = 1.4%, 2.9%), and increased rates of COPD ED visits (e.g. previous 0–3 days: 3.1%; 95% CI = 0.5%, 5.7%), but also decreased rates of COPD hospitalizations (e.g. previous 0–3 days: −2.2%, 95% CI = −3.8%, − 0.6%). Similarly for secondary sulfate, there were both positive and negative relative rates. Decreased rates of asthma ED visits were associated with IQR increases in secondary nitrate PM2.5 concentration in the previous 0–3 and 0–6 days (e.g. −1.2%, 95% CI = −2.1%, −0.4%), while increased rates of asthma ED visits were associated with IQR increases in pyrolytic organic carbon PM2.5 concentrations in the previous 0–6 days (1.1%, 95% CI = 0.2%, 1.9%) and aged sea salt concentration in the previous 0–3 days (1.5%, 95% CI = 0.6%, 2.5%). Last, IQR increases in residual oil PM2.5 concentration were associated with significantly decreased rates of COPD hospitalizations (e.g. previous 0–6 days: −4.6%, 95% CI = −8.3%, −0.9%)(Table 3).

Table 3.

Excess risk (%) of hospitalization or ED visits for asthma and COPD across NYS for the 2005–2014 period

Outcome Lag (days) IQR N Cases Excess Risk (%) P value
Secondary Nitrate
Asthma hospitalizations 0 1.36 175,852 0.1 (−0.6, 0.8) 0.795
0–3 1.59 114,047 0.3 (−1.0, 1.6) 0.705
0–6 1.60 123,240 0.3 (−1.3, 2.0) 0.699
COPD hospitalizations 0 1.38 127,282 −0.3 (−1.1, 0.6) 0.569
0–3 1.59 76,137 −0.1 (−1.7, 1.5) 0.858
0–6 1.55 85,869 0.1 (−1.8, 2.1) 0.894
Asthma ED visits 0 1.12 657,749 −0.2 (−0.5, 0.2) 0.319
0–3 1.53 437,102 −1.1 (−1.8, −0.5) 0.001
0–6 1.51 470,947 −1.2 (−2.1, −0.4) 0.005
COPD ED visits 0 1.20 43,219 0.8 (−0.6, 2.3) 0.249
0–3 1.57 24,592 3.2 (0.0, 6.4) 0.049
0–6 1.60 29,831 0.2 (−3.5, 4.1) 0.898
Secondary Sulfate
Asthma hospitalizations 0 2.07 175,852 1.2 (0.4, 2.0) 0.003
0–3 2.06 114,047 0.9 (−0.4, 2.2) 0.176
0–6 1.74 123,240 2.1 (0.7, 3.5) 0.004
COPD hospitalizations 0 2.05 127,282 1.0 (0.0, 1.9) 0.041
0–3 2.03 76,137 0.6 (−0.9, 2.2) 0.428
0–6 1.66 85,869 1.7 (0.1, 3.3) 0.039
Asthma ED visits 0 2.08 657,749 0.1 (−0.3, 0.5) 0.659
0–3 2.03 437,102 −1.0 (−1.7, −0.3) 0.005
0–6 1.62 470,947 −1.5 (−2.2, −0.9) <0.001
COPD ED visits 0 2.06 43,219 −0.9 (−2.6, 0.7) 0.271
0–3 1.89 24,592 −1.0 (−3.8, 1.7) 0.458
0–6 1.46 29,831 −0.4 (−2.9, 2.2) 0.749
Spark-ignition
Asthma hospitalizations 0 2.44 175,852 −1.0 (−3.2, 1.2) 0.360
0–3 1.63 114,047 −3.7 (−6.1, −1.3) 0.003
0–6 1.42 123,240 −2.4 (−4.9, 0.3) 0.079
COPD hospitalizations 0 2.35 127,282 1.5 (−1.1, 4.1) 0.250
0–3 1.69 76,137 2.5 (−0.8, 5.9) 0.146
0–6 1.34 85,869 4.2 (1.1, 7.5) 0.008
Asthma ED visits 0 2.55 657,749 −0.4 (−1.5, 0.8) 0.513
0–3 1.67 437,102 −0.5 (−1.7, 0.8) 0.472
0–6 1.34 470,947 −0.4 (−1.6, 0.8) 0.516
COPD ED visits 0 2.55 43,219 3.1 (−1.4, 7.7) 0.175
0–3 1.67 24,592 −0.2 (−5.4, 5.2) 0.929
0–6 1.33 29,831 0.8 (−3.9, 5.7) 0.745
Diesel
Asthma hospitalizations 0 0.54 175,852 0.5 (−0.3, 1.4) 0.214
0–3 0.81 114,047 −0.2 (−2.4, 2.1) 0.865
0–6 0.72 123,240 −0.2 (−2.6, 2.3) 0.895
COPD hospitalizations 0 0.59 127,282 0.7 (−0.4, 1.7) 0.220
0–3 0.65 76,137 −0.5 (−2.7, 1.6) 0.625
0–6 0.73 85,869 0.4 (−2.4, 3.3) 0.784
Asthma ED visits 0 0.49 657,749 −0.4 (−0.8, 0.0) 0.058
0–3 0.56 437,102 −0.9 (−1.7, −0.1) 0.027
0–6 0.63 470,947 −1.4 (−2.5, −0.2) 0.022
COPD ED visits 0 0.50 43,219 −0.8 (−2.5, 1.0) 0.397
0–3 0.51 24,592 1.1 (−2.3, 4.6) 0.526
0–6 0.58 29,831 −3.0 (−7.0, 1.2) 0.156
Biomass Burning
Asthma hospitalizations 0 0.58 175,852 0.2 (−1.3, 1.8) 0.786
0–3 0.43 114,047 −0.2 (−2.2, 1.8) 0.809
0–6 0.36 123,240 0.7 (−1.4, 2.8) 0.531
COPD hospitalizations 0 0.58 127,282 −0.5 (−2.1, 1.1) 0.508
0–3 0.44 76,137 0.1 (−2.2, 2.3) 0.964
0–6 0.38 85,869 2.1 (−0.2, 4.4) 0.077
Asthma ED visits 0 0.56 657,749 0.7 (−0.1, 1.6) 0.082
0–3 0.42 437,102 0.5 (−0.6, 1.6) 0.419
0–6 0.36 470,947 1.3 (0.1, 2.5) 0.031
COPD ED visits 0 0.56 43,219 2.5 (−0.2, 5.2) 0.067
0–3 0.43 24,592 2.3 (−2.0, 6.8) 0.305
0–6 0.41 29,831 0.8 (−3.3, 5.2) 0.704
Road Dust
Asthma hospitalizations 0 0.31 175,852 0.7 (−0.2, 1.7) 0.118
0–3 0.26 114,047 0.4 (−0.9, 1.7) 0.539
0–6 0.25 123,240 1.4 (−0.2, 3.0) 0.087
COPD hospitalizations 0 0.31 127,282 −0.4 (−1.5, 0.8) 0.525
0–3 0.25 76,137 −2.2 (−3.8, −0.6) 0.006
0–6 0.26 85,869 −2.5 (−4.5, −0.5) 0.015
Asthma ED visits 0 0.30 657,749 1.1 (0.6, 1.5) <0.001
0–3 0.22 437,102 1.1 (0.6, 1.7) <0.001
0–6 0.24 470,947 2.2 (1.4, 2.9) <0.001
COPD ED visits 0 0.30 43,219 −0.6 (−2.6, 1.5) 0.600
0–3 0.21 24,592 3.1 (0.5, 5.7) 0.017
0–6 0.21 29,831 1.2 (−1.9, 4.4) 0.453
Pyrolytic Organic Carbon
Asthma hospitalizations 0 1.38 114,149 −0.3 (−1.6, 1.0) 0.639
0–3 1.01 71,074 −0.7 (−2.2, 0.9) 0.376
0–6 0.86 77,885 −0.4 (−2.2, 1.3) 0.625
COPD hospitalizations 0 1.48 82,890 1.1 (−0.6, 2.8) 0.210
0–3 0.98 48,191 0.7 (−1.1, 2.6) 0.446
0–6 0.93 55,291 1.5 (−0.8, 3.9) 0.199
Asthma ED visits 0 1.41 458,685 −0.0 (−0.7, 0.6) 0.900
0–3 0.87 296,284 0.4 (−0.3, 1.1) 0.269
0–6 0.76 322,121 1.1 (0.2, 1.9) 0.011
COPD ED visits 0 1.46 32,319 1.3 (−1.6, 4.3) 0.377
0–3 0.88 18,104 3.2 (0.2, 6.2) 0.034
0–6 0.83 22,204 0.1 (−3.4, 3.7) 0.963
Residual Oil
Asthma hospitalizations 0 0.92 163,484 −0.9 (−2.6, 0.8) 0.282
0–3 0.82 109,438 −3.1 (−5.5, −0.5) 0.020
0–6 0.81 115,477 0.3 (−2.8, 3.4) 0.875
COPD hospitalizations 0 0.86 103,762 −3.5 (−5.6, −1.4) 0.001
0–3 0.78 67,954 −2.9 (−6.1, 0.3) 0.074
0–6 0.76 71,260 −4.6 (−8.3, −0.9) 0.016
Asthma ED visits 0 0.84 604,317 0.1 (−0.8, 0.9) 0.905
0–3 0.73 418,065 −0.7 (−1.9, 0.6) 0.282
0–6 0.68 437,278 0.5 (−1.0, 1.9) 0.536
COPD ED visits 0 0.83 29,465 −2.9 (−6.7, 1.0) 0.140
0–3 0.73 20,039 0.2 (−5.5, 6.1) 0.958
0–6 0.72 21,070 0.2 (−6.4, 7.4) 0.943
Aged Sea Salt
Asthma hospitalizations 0 0.69 163484.00 −0.4 (−1.7, 0.9) 0.540
0–3 0.64 109438.00 0.2 (−1.7, 2.1) 0.846
0–6 0.61 115477.00 −2.3 (−4.6, 0.1) 0.059
COPD hospitalizations 0 0.70 103762.00 −0.2 (−1.9, 1.5) 0.822
0–3 0.64 67954.00 −0.5 (−2.9, 2.0) 0.708
0–6 0.63 71260.00 −2.8 (−5.9, 0.3) 0.079
Asthma ED visits 0 0.68 604317.00 0.3 (−0.3, 1.0) 0.351
0–3 0.61 418065.00 1.5 (0.6, 2.5) 0.001
0–6 0.59 437278.00 1.4 (0.2, 2.6) 0.019
COPD ED visits 0 0.68 29465.00 −0.3 (−3.4, 2.9) 0.854
0–3 0.61 20039.00 0.8 (−3.5, 5.3) 0.712
0–6 0.55 21070.00 0.1 (−5.0, 5.5) 0.964
Fresh Sea Salt
Asthma hospitalizations 0 0.11 163,484 −0.1 (−0.4, 0.1) 0.174
0–3 0.17 109,438 −0.0 (−0.6, 0.5) 0.862
0–6 0.22 115,477 −0.6 (−1.4, 0.3) 0.196
COPD hospitalizations 0 0.12 103,762 −0.2 (−0.5, 0.1) 0.251
0–3 0.17 67,954 −0.7 (−1.5, −0.0) 0.047
0–6 0.21 71,260 −0.6 (−1.6, 0.6) 0.325
Asthma ED visits 0 0.09 604,317 0.1 (−0.0, 0.2) 0.199
0–3 0.15 418,065 0.2 (−0.1, 0.5) 0.116
0–6 0.18 437,278 0.5 (0.2, 0.9) 0.005
COPD ED visits 0 0.1 29,465 0.0 (−0.4, 0.5) 0.857
0–3 0.16 20,039 −0.2 (−1.5, 1.1) 0.762
0–6 0.18 21,070 −1.0 (−2.8, 0.9) 0.318
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Hopke et al. (2019) saw only consistent patterns of increased rates of asthma ED visits and COPD hospitalizations associated with increased PM2.5 concentrations in the AFTER period. To assess the results with the smaller data set resulting from only having PM2.5 data on every 3rd or 6th day, we have repeated the total PM2.5 analyses in the same manner as Hopke et al. (2019) but with only the PM2.5 concentration measured on the same days as the speciation samples were collected. These results are presented in Table S8. The results are very consistent with those reported by Hopke et al. (2019) suggesting that the smaller data set did not affect the previously observed patterns.

Thus, each period’s (BEFORE, DURING, and AFTER) source-specific PM analyses were used to help understand which PM source(s) may have triggered these respiratory events. The excess rates of asthma and COPD hospitalizations and ED visits associated with source-specific PM2.5 concentrations in the BEFORE, DURING, and AFTER periods are shown in Figures 1 and 2 and Table S9. In the BEFORE period, there were no clear patterns of increased or decreased rates of COPD and asthma hospitalizations and ED visits across PM2.5 sources with similar numbers of statistically significant increased (n=9) and decreased excess rates (n=11). In the DURING period, there were again no clear increased or decreased excess rate patterns with only n=8 statistically significant results (n=7 increased excess rates and n=1 decreased excess rate).

Figure 1.

Figure 1.

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Excess rates (%) of hospitalizations and ED visits for asthma by source and period.

Figure 2.

Figure 2.

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Excess rates (%) of hospitalizations and ED visits for COPD by source and period.

However, in the AFTER period, increased rates of asthma hospitalizations and ED visits were associated with multiple PM2.5 sources at multiple lag times. For example, increased rates of asthma hospitalizations were significantly associated with IQR increases in concentrations of secondary nitrate PM2.5 (e.g. during lag days 0–3: 8.2%, 95% CI = 3.3%, 13.4%), secondary sulfate PM2.5 (e.g. during lag days 0–6: 10.8%, 95% CI = 4.8%, 17.1%), road dust PM2.5 (e.g. during lag days 0–3: 2.5%, 95% CI = 0.0% 5.1%), pyrolytic organic carbon PM2.5 (e.g. lag day 0: 3.0%, 95% CI = 0.3%, 5.7%), and aged sea salt PM2.5 (e.g. during lag days 0–6: 4.4%, 95% CI = 0.2%, 8.9%). Similarly, increased rates of asthma ED visits were associated with IQR increases in concentrations of secondary nitrate PM2.5 (e.g. during lag days 0–6 3.0%, 95% CI = 0.2%, 5.8%), secondary sulfate PM2.5 (e.g. lag day 0: 2.6%, 95% CI = 1.1%, 4.2%), road dust PM2.5 (e.g. lag day 0: 1.1%, 95% CI = 0.4%, 1.9%), and aged sea salt PM2.5 (e.g. during lag days 0–3: 2.8%, 95% CI = 1.4%, 4.3%).

The relative rate patterns for COPD hospitalization and COPD ED visits across PM2.5 sources were less clear. In the AFTER period, increased rates of COPD hospitalizations were associated with increased concentrations of pyrolytic organic carbon PM2.5 (e.g. during lag days 0–6 days: 8.1%, 95% CI = 3.6%, 12.7%), while increased rates of COPD ED visits were associated with increased concentrations of diesel PM2.5 (e.g. during lag days 0–3 days: 9.7%, 95% CI = 0.1%, 20.2%). There were no clear patterns of associations between COPD hospitalizations and ED visits and other PM2.5 sources (Table S9).

Discussion

There was a lack of clear patterns of asthma or COPD hospitalization and ED visits being associated with increased source-specific PM2.5 concentrations across the whole study period (2005–2016) (Table 3). The only strong association was for asthma ED visits with road dust (RD) for all 3 lag days. Other sources that had some strong positive associations for varying lag days included secondary sulfate (SS) with both COPD and asthma hospitalizations, gasoline vehicles (GAS) with COPD hospitalizations, biomass burning (BB), pyrolized organic carbon (OP), aged sea salt (AGS) and fresh sea salt (FSS) with asthma ED visits and secondary nitrate (SN), RD, and OP with COPD ED visits. Thus, our results do not support our hypothesis that increased PM concentrations from oxidant-related sources (e.g., GAS, DIE, and RO) would be the major contributors to increased rates of hospitalizations or ED visits for COPD or asthma.

The sources that showed patterns of positive associations with asthma or COPD events (ED visits or hospitalizations on lag days 0, 0–3, and 0–6 although with many imprecise values are seen in Figures 1 and 2, respectively. For asthma (Figure 1 and Table S9), consistent patterns of association were observed for SN and SS and hospitalizations, and for RD with ED visits over the 2005–2016 period. There were increased asthma hospitalization rates in the AFTER period for SN, SS, RD, RO, and AGS. There were increased rates of asthma ED visits for BB and AGS. For COPD (Figure 2 and Table S9), there are associations of hospitalization for OP at all lag days and SN, GAS and BB for the lag days 0–6 and for DIE for lag days 0 and 0–3 during the AFTER period.

Previously, Bell et al. (2014) examined hospital admission rates for COPD (ICD codes 490–492) in Connecticut and Massachusetts for adults older than 65 years associated with source-specific PM2.5 and some of its major constituent species over lags of 0, 1, or 2 days. Increased rates of respiratory diseases/events were associated with increased concentrations of PM2.5 road dust, and sea salt, as well as crustal species aluminum, calcium, silicon, and titanium, sea salt elements (chlorine), residual oil combustion markers (nickel and vanadium), and black carbon (typically used as a marker of traffic). They found that individual chemical species were better indicators than the resolved source-specific PM. Krall et al. (2017) assesses the association between short term increases in source-specific PM2.5 concentrations (lags of 0 to 2 days) and respiratory disease emergency department (ED) visits in 4 U.S. cities: Atlanta, Georgia; Birmingham, Alabama; St. Louis, Missouri; and Dallas, Texas. Although increased rates of respiratory disease ED visits were associated with increased concentrations of biomass burning PM2.5, rates of respiratory ED visits associated with diesel and gasoline PM2.5 were frequently imprecise and null. They found little evidence of associations with crustal PM2.5 materials.

Previously, we reported increased rates of cardiovascular, respiratory infectious disease, and respiratory hospitalizations and ED visits associated with increased PM2.5 concentrations in the previous 0 to 6 days (Zhang et al., 2018; Croft et al., 2019a; Hopke et al., 2019). Increases in source-specific PM2.5 concentrations in the previous 0, 0–3, and 0–6 days, including traffic (GAS, DIE, RD) and non-traffic emissions (secondary nitrate) were associated with increased excess rates of cardiovascular hospitalizations or ED visits (Rich et al., 2019). These results focus largely on motor vehicle emissions as the major influencing factor in the increased particulate toxicity. Spark-ignition vehicle (GAS) contributions to PM2.5 had increased during the 2005 to 2016 period while diesel (DIE) and road dust (RD) contributions had remained largely constant (Masiol et al., 2019). Thus, as secondary nitrate and sulfate concentrations declined, these traffic-related sources represented a larger fraction of the residual PM2.5 mass concentrations. Secondary organic carbon (SOC) also increased (Zhang et al., 2018; Croft et al., 2019a) and was moderately well correlated with the GAS contributions (Rich et al., 2019). The r2 values between SOC and GAS were 0.423, 0.548, 0.504, 0.303 0.533, 0.533 for Albany, the Bronx, Buffalo, Manhattan, Queens, and Rochester, respectively. Thus, for all sites except Albany and Manhattan, the majority of the SOC variance was related to GAS mass contributions. More detailed discussion of the correlations of the source specific component and SOC are discussed below. For the cardiovascular outcomes, residual oil (RO) and secondary nitrate (SN) also were associated with increased rates of hospitalizations. There were no strong patterns of temporal changes in the toxicity of the source-specific PM for the cardiovascular outcomes across the study period (Rich et al., 2019), suggesting that it was primarily the changes in relative concentrations that drove the increase per unit mass toxicity.

Croft et al. (2019b) reported the associations between respiratory infections (influenza and culture negative pneumonia) and source-specific PM2.5 concentrations. For influenza, increased hospitalization rates were associated with interquartile range (IQR) increases in the mean PM2.5 concentrations on lag days 0 and 0–3. Increases in SN concentrations on lag day 0 were associated with an increase in the influenza hospitalization rates. Increased but uncertain rates of influenza hospitalization were associated with increased concentrations of SS, GAS, DIE, RO, and BB on lag day 0. No consistent associations were observed between influenza hospitalizations and the OP, RD, FSS or AGS concentrations. The patterns of influenza ED visit rates were similar. Increased rates of influenza ED visits were also associated with increases in GAS concentration during lag days 0–3 and DIE concentration during several lag periods. Imprecise, increased rates of influenza ED visits were also associated with increases in SS, OP, RO, and BB during lag days 0–3. However, increased RD, RSS and AGS concentrations were not associated with increased influenza ED visit rates. No consistent patterns of effects were observed for increases in source specific PM2.5 contributions and the rates of culture negative pneumonia hospitalizations or ED visits.

In the prior study, sharp increases in the rate of asthma ED visits and COPD hospitalizations associated with increased PM2.5 concentrations in the previous 0–6 days (i.e. greater PM toxicity) were found in the AFTER period (Hopke et al, 2019). As noted above, increased asthma hospitalization rates were found in the AFTER period for SN, SS, RD, RO, and AGS and increased rates of asthma ED visits for BB and AGS. For COPD, there were increased hospitalizations associated with increased OP for all lag periods and with SN, GAS and BB for only lag days 0–6 and for DIE for lag days 0 and 0–3. Thus, these source types appear to drive the previously observed increased rates during the AFTER period.

To investigate the interrelationships among the sources and other PM species including primary organic carbon (POC), secondary organic carbon (SOC) as well as with temperature, relative humidity, and measured gaseous pollutants where available, pairwise correlation coefficients were calculated. Tables S10 to S15 and S16 to S17 present the correlation coefficient matrices for the Queens and Rochester sites, respectively. These sites represent the relationships seen in NYC and in the upstate cities and are the two sites with the most complete data records. The correlation coefficients have been calculated for the entire study period (Tables S10 and S14) and for the BEFORE (Tables S11 and S15), DURING (Tables S12 and S16), and AFTER periods (Tables S13 and S17). These period-specific results could provide indications of changes in the interrelationships among these variables over the study subperiods. In these tables, correlations coefficients greater than 0.50 are shown in bold. Large negative correlations <−0.50 are shown in red and bold.

The formation of secondary organic carbon (SOC) results in the concurrent formation of oxidant species (Chen et al., 2011). Thus, SOC may provide a surrogate for exogenous oxidants (Hopke, 2015). Thus, the correlations between source specific PM2.5 concentrations and SOC may provide an indication of sources that are associated with oxidants. The SOC concentrations estimated at the Queens site in the BEFORE period were strongly correlated with secondary sulfate (r= 0.608) and GAS (r=0.749), while POC was strongly correlated with secondary nitrate (r = 0.608) and DIE (r = 0.785). POC was also strongly correlated with the gaseous species that would be associated with diesel exhaust including NO, NO2, NOx, SO2, and CO (Table S9). In the DURING period (Table S10), the SOC correlations with the secondary aerosol species (AN and AS) were lower, but SOC remained well correlated with GAS (r = 0.766). SOC was now also moderately correlated with biomass burning (r = 0.535) and pyrolyzed organic carbon (r = 0.624). The POC correlations with nitrate (r = 0.529) and DIE (r = 0.483) weakened. However, GAS was now about equally correlated with POC (r = 0.512), and remained correlated with the pollutant gases. In the AFTER period (Table S11), POC and SOC correlations with GAS and OP were even larger, but the POC and SOC correlations with biomass burning decreased. There was also a strong correlation between POC and SOC (r = 0.597) in the AFTER period. Similar correlation patterns across and within periods were also seen in Rochester (Tables S13–S15). However, the vehicular factors (i.e. GAS and DIE) were only associated with increased COPD hospitalizations and ED visits (Figures 1 and 2). However, increased asthma rates were not observed suggesting that exposure to the hypothesized exogenous ROS was not a major driver of asthma exacerbation. However, RD and RO were associated with asthma hospitalizations in the AFTER period and RD was associated with ED visits across all 3 periods suggesting that redox active metals that can form endogen oxidants may be important for inducing asthmatic effects. Road dust in PM2.5 provided only a relatively small contribution to the PM mass because it represents only the lower end of the coarse particle size distribution that extends into the PM2.5. Thus, the actual road dust exposures could be much larger since the bulk of the RD mass would be in particle sizes from 2.5 to 10 μm.

The types of source-specific PM2.5 that were associated with increased rates of asthma hospitalizations and ED visits in the AFTER period were SN, SS, OP, RD, and AGS. The only source-specific PM2.5 associated with increased COPD hospitalizations and ED visits in the AFTER period in addition to DIE, GAS, was OP that was significant for all 3 lag days. There are no obvious relationships among the source types identified in our study that affect asthma except that they are likely to represent larger sized particles within the PM2.5 compared to the smaller traffic related species (Kodros et al., 2018). Asthma affects the bronchial/bronchiolar region of the respiratory tract and thus, could be affected by those particle sizes that can more effectively deposit in this region. It has been reported that coarse mode particles may play a larger role in the exacerbation of respiratory diseases (USEPA, 2018) and thus, sources producing larger particles might be more effective in inducing the observed asthma associations. Additionally, Feng et al. (2017) reported a strong correlation between oxidative potential (the ability to induce the formation of endogenous oxidants) and the surface area of the PM. Several of these identified source types (SN and SS) represent the bulk of the ambient PM2.5 surface area (Zhang et al., 2005) and may represent sources of endogenous oxidants and the associations of these source-specific PM with asthma events. However, these results do not provide as clear a picture of the source-specific PM concentrations and the changes over time in the associations for asthma and COPD hospitalization and ED visit reported by Hopke et al. (2019).

A major limitation of using source-specific PM2.5 concentrations is the substantial reduction in the number of subjects for which exposure data are available, and thus reduced statistical power, since samples were only collected every 3rd or 6th day for chemical speciation. However, inference was made primarily by examining the pattern of excess rates across outcomes, PM2.5 sources, and lag times, and secondly by whether any individual results were statistically significant. Second, the PMF results for each site (Squizzato et al., 2018b) were obtained from a single analysis using all 12 years of data. The individual sources were identified and named based on common chemical compositions across these 12 years (Squizzato et al. 2018b).

However, it is possible that if the source apportionment was done separately for each individual time period (e.g. 2005–2007, 2008–2013, and 2014–2016), there might be differences in the daily concentrations of individual source-specific mass concentrations (e.g. secondary sulfate) from those used in this analysis. Assuming that this exposure misclassification is non-differential with regard to time (i.e. not different for case and control periods), then it would likely result in a bias toward the null and an underestimate of effects. Third, additional exposure misclassification may have arisen because all study subjects (those with a COPD or asthma hospitalization or ED visit) lived within 15 miles of a given PM2.5 monitoring site, and were assigned the same source-specific PM2.5 contribution for that specific day irrespective of the distance between their residence and the site. For the same reasons, such misclassification would result in biases toward the null and additional underestimation of effects. Fourth, we also had the problem of a change in the hospital admission diagnosis codes used in SPARCS starting October 1, 2015 when they shifted from ICD-9 to ICD-10 codes. Certain ICD-9 codes could be divided into multiple specific ICD-10 codes, resulting in possible misclassification or undercounting of cases. However, since the case-crossover design contrasts pollutant concentrations between case and control time periods within a short period (one month), the change of case classification should have less impact on exposure ratio as the both periods used similar case definition. Fifth, as most COPD cases are limited to older adults, our study population included adults only, and children who have the highest rate of asthma were not included in this study. Finally, case-crossover designs analyzed with conditional logistic regression cannot fully adjust for possible overdispersion (Armstrong et al. 2014) and that could result in larger confidence intervals than we reported.

Conclusions

Although Hopke et al. (2019) had showed large increases in the rates of ED visits for asthma and hospitalizations for COPD per unit mass of PM2.5 across NYS in 2014–2016 compared to 2005–2007 and 2008–2013, there were not as clear patterns in associations of these outcomes with source-specific PM2.5. In Rich et al. (2019), the decreases in secondary sulfate and secondary nitrate meant that traffic-related sources (spark-ignition vehicles, diesel, and road dust) and sources with redox metals like residual oil combustion became larger fractions of the decreased PM2.5 concentrations. Spark-ignition vehicles had the strongest correlation with secondary organic carbon that was hypothesized to represent a source of short-lived reactive oxygen species that drove the association of these source-specific PM2.5 concentrations and the observed rates of ED visits and hospitalizations for cardiovascular disease. The analyses of the associations of source-specific PM2.5 concentrations with hospitalizations for COPD found that excess rates for GAS and DIE did increase in the AFTER period as hypothesized, but none of the values were statistically significant. The other source types showed inconsistent patterns of excess rates. For asthma ED visits, only biomass burning and road dust showed consistent positive associations with road dust having significant values for most lag times. Secondary nitrate also showed significant positive associations with asthma ED visits in the AFTER period compared to no associations in the prior periods. The asthma ED results did not support the hypothesized effects of the major traffic sources (GAS and DIE) as the major drivers of the observed increase in per unit mass toxicity during the AFTER period. These results suggest that the relationships of asthma and COPD exacerbations by source-specific PM2.5 are not as well defined as those for some of the cardiovascular outcomes previously reported and further work will be needed to determine what has caused the apparent increase in the per unit mass toxicity of PM2.5 in New York State in the AFTER (2014–16) period.

Supplementary Material

1

Highlights.

Asthma and COPD hospitalizations and ED visits rates related to source specific PM2.5

Traffic sources associated with increased COPD hospitalization rates per unit mass

No clear pattern of associations of asthma ED visit rates with specific sources

Cause of recently increased excess rates of asthma per unit mass PM need more study

Acknowledgements

This work has been supported by the New York State Energy Research and Development Authority (NYSERDA) under contract # #59800, 59802, and 100412 and the National Institutes of Environmental Health Sciences Grant # P30 ES01247. Daniel Croft was supported by a National Institutes of Health training grant (T32-HL066988-1).

Footnotes

Conflicts of Interest

The authors all declare that we have no conflicts of interest associated with this work. It is a pure epidemiological study of source-specific PM and hospitalizations and ED visits for asthma and COPD across NYS funded by the New York State Energy Research and Development Authority.

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NIHMS1545767-supplement-1.pdf (3.3MB, pdf)

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