Long-Term Exposure to PM2.5, Facemask Mandates, Stay Home Orders and COVID-19 Incidence in the United States
Abstract
1. Introduction
2. Materials and Methods
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Study Area | Study Period | Statistical Model | Findings |
---|---|---|---|
Northern Italy [19] | 24 February 2020–13 March 2020 | Recursive binary partitioning tree approach | Daily PM10 exceeding 50 µg/m3 with a 15-day lag was a significant predictor for COVID-19 incidence |
Chinese cities (Wuhan, Xiaogan and Huanggang) [20] | 25 January 2020–29 February 2020 | Poison regression adjusting for other air pollutants and meteorological variables in each city | Daily PM2.5 was positively associated with COVID-19 incidence with RR from 1.036 to 1.144. The association with PM10 was negative with RR between 0.915 and 0.964. Results for other pollutants (SO2, CO, NO2, and 8-hour O3) were not consistent among the study sites. |
Chinese cities (Wuhan and Xiaogan) [21] | 26 January 2020–29 February 2020 | Univariate linear regression | PM2.5 and NO2 were positively associated with COVID-19 incidence 4 days later in both cities, while PM10 and CO were inconsistent between cities. |
120 Chinese cities [22] | 23 January 2020–29 February 2020 | Generalized additive model adjusting for meteorological variables with city fixed effects | PM2.5, PM10, NO2 and O3 with a 2-week lag were positively associated with COVID-19 incidence, while SO2 was negatively associated. A 10µg/m3 increase in PM2.5 with a 2-week lag was associated with a 2.24% increase in COVID-19 incidence. |
49 Chinese cities [23] | As of 22 March 2020 | Multivariate linear regression model adjusting for GDP per capita and hospital beds per capita | Both short-term (01/15/2020 – 02/29/2020) and long-term (2015–2019) exposure to elevated PM2.5 and PM10 were associated with increased COVID-19 fatality. A 0.24% and a 0.61% increase in COVID-19 fatality were associated with 10-µg/m3 increase in short-term and long-term PM2.5, respectively. |
7 metropolitan cities and 9 provinces in Korea [24] | 3 February 2020–5 May 2020 | Generalized additive model adjusting for meteorological variables, location and date | Significantly temporal associations were observed between COVID-19 incidence and daily NO2, CO and SO2, but not with PM2.5, PM10 or O3. |
3089 counties in the United States [25] | As of 18 June 2020 | Negative binomial fixed model adjusting for 20 covariates | Each 1-µg/m3 increase in long-term PM2.5 exposure (2000–2016 annual average) was associated with 11% increase in COVID-19 mortality. |
3223 counties in the United States [26] | As of 11 July 2020 | Negative binomial fixed model adjusting for other pollutants as well as county characteristics | HAPs was associated with increase COVID-19 mortality. Each 1-µg/m3 increase in long-term PM2.5 exposure (2000–2014 annual average) was associated with 7% increase in COVID-19 mortality |
355 municipalities in the Netherlands [27] | As of 5 June 2020 | Linear regression controlling for covariates | Long-term exposure to PM2.5 and NO2 were positively associated with COVID-19 outcomes, including incidence and mortality, but not with SO2. Each 1-µg/m3 increase in long-term PM2.5 exposure (2015–2019) was associated with 9.4 more COVID-19 cases, 3.0 more hospital admissions, and 2.3 more deaths. |
71 Italian provinces [28] | As of 27 April 2020 | Spatial correlation | Positive correlations were observed between COVID-19 incidence and long-term exposure (2016–2019) to NO2, PM2.5, PM10 and O3. |
20 Italian regions and up to 110 provinces [29] | As of 31 March 2020 | Multiple linear regression | Both long-term exposure (2017 annual mean) to PM2.5 and PM10 were associated with COVID-19 incidence. Each 1-µg/m3 increase in PM2.5 was associated with 0.26 increase in base-10 transformed COVID-19 incidence. |
3108 counties in the United States [30] | As of 31 May 2020 | Linear regression with adjusting for county-level covariates | PM2.5 (2016 annual mean) and diesel PM were associated with both COVID-19 incidence and mortality. Additional 23.5 cases and 1.08 deaths were associated with each 1-µg/m3 increase in PM2.5. |
Sources | Description |
---|---|
Johns Hopkins University Center for Systems Science and Engineering Coronavirus Resource Center (CSSE) [3] | Cumulative county-level confirmed cases up to 12 September 2020 |
GitHub repository by Wu et al. [25] | Annual average PM2.5 concentration between 2000 and 2016 |
The US Census/American Community Survey | County-level socioeconomic and demographic variables in 2016 |
The County Health Rankings & Roadmaps program [31] | Country-level behavioral variables in 2020 |
Boston University of Public Health [5] | State-level policy of face masks mandates and stay home orders |
The New York Times [32] | State-level reopening policies |
The COVID tracking project [33] | State-level total tests performed |
County Characteristics | Total (n = 3096) | COVID risk ≤ 1.29% (n = 1548) | COVID risk > 1.29% (n = 1548) |
---|---|---|---|
Mean (SD) | Mean (SD) | Mean (SD) | |
Risk of COVID-19 as of 9/12 (%) | 1.65 (1.60) | 0.69 (0.34) | 2.62 (1.78) |
Average ambient PM2.5 (µg/m3) 1 | 8.40 (2.52) | 7.49 (2.49) | 9.32 (2.20) |
Days since first case reported | 163 (28) | 156 (35) | 170 (17) |
Total test results reported by state (1000 tests) | 2333 (2394) | 2114 (2415) | 2553 (2353) |
Duration of stay at home issued by state | 48 (40) | 54 (44) | 41 (35) |
State stay-home order 2, n (%) | |||
Ever issued | 2659 (85.89) | 1312 (84.75) | 1347 (87.02) |
Never issued | 437 (14.11) | 236 (15.25) | 201 (12.98) |
State facemask policy 2, n (%) | |||
Ever issued | 1853 (59.85) | 964 (62.27) | 889 (57.43) |
Never issued | 1243 (40.15) | 584 (37.73) | 659 (42.57) |
State reopening status, n (%) | |||
Reopened | 1225 (39.57) | 815 (52.65) | 410 (26.49) |
Reopening | 580 (18.73) | 248 (16.02) | 332 (21.45) |
Pausing or reversing reopening plan | 1291 (41.70) | 485 (31.33) | 806 (52.07) |
Population density per square mile | 427.39 (2184.38) | 201.44 (720.43) | 653.34 (2987.47) |
African Americans population (%) | 8.02 (14.07) | 2.14 (5.07) | 13.89 (17.35) |
Hispanic Americans population (%) | 7.54 (12.28) | 5.14 (8.61) | 9.94 (14.69) |
Population living in poverty (%) | 10.46 (5.90) | 9.39 (5.36) | 11.54 (6.20) |
Population over 65 years old (%) | 18.43 (4.50) | 19.85 (4.28) | 17.01 (4.27) |
Male (%) | 50.07 (2.20) | 50.25 (1.93) | 49.90 (2.43) |
Population with less than high school education (%) | 21.28 (10.68) | 18.23 (9.53) | 24.32 (10.90) |
Owner occupied properties (%) | 74.92 (8.41) | 77.05 (6.94) | 72.80 (9.18) |
Median house value ($1000) | 136.31 (91.08) | 137.13 (88.39) | 135.49 (93.71) |
Median household income ($1000) | 49.30 (13.41) | 50.04 (11.87) | 48.57 (14.75) |
Ever smokers (%) | 17.43 (3.54) | 16.95 (3.44) | 17.92 (3.57) |
Obesity (%) | 32.86 (5.41) | 32.11 (5.09) | 33.61 (5.62) |
RR (95% CI) (95% CI with Robust SE) | |||
---|---|---|---|
Surge 1 (as of 28 May 2020) 3 | Surge 2 (between 28 May 2020 and 12 September 2020) 4 | Cumulative (as of 12 September 2020) 4 | |
PM2.5 1 | 1.0506 (1.0269, 1.0747) (0.9857, 1.1197) | 1.0852 (1.0696, 1.1011) (1.0361, 1.1366) | 1.0756 (1.0612, 1.0901) (1.0376, 1.1149) |
Facemask policy 2 | |||
Never issued | Reference | ||
Ever issued | 0.9889 (0.9180, 1.0652) (0.8667, 1.1283) | 0.8360 (0.8030, 0.8704) (0.7298, 0.9577) | 0.8466 (0.8166, 0.8776) (0.7598, 0.9432) |
Stay home policy 2 | |||
Never issued | Reference | ||
Ever issued | 0.7615 (0.6928, 0.8370) (0.5619, 1.0321) | 0.9168 (0.8664, 0.9701) (0.7833, 1.0730) | 0.9193 (0.8734, 0.9677) (0.8021, 1.0537) |
RR (95% CI) (95% CI with Robust SE) | |||
---|---|---|---|
Surge 1 (as of 28 May 2020) 4 | Surge 2 (between 28 May 2020 and 12 September 2020) 5 | Cumulative (as of 12 September 2020) 5 | |
Face mask policy 1 | |||
Never issued | 1.0426 (1.0144, 1.0717) (0.9645, 1.1270) | 1.0417 (1.0165, 1.0675) (0.9905, 1.0955) | 1.0547 (1.0293, 1.0807) (1.0109, 1.1004) |
Ever issued | 1.0854 (1.0327, 1.1407) (0.9817, 1.2000) | 1.1161 (1.0958, 1.1368) (1.0640, 1.1708) | 1.0852 (1.0673, 1.1034) (1.0420, 1.1301) |
Stay home policy | |||
Never issued 2 | 1.4050 (1.2885, 1.5319) (1.2961, 1.5230) | 1.1056 (1.0406, 1.1746) (1.0478, 1.1665) | 1.1543 (1.0870, 1.2257) (1.1016, 1.2095) |
Ever issued 3 | 1.0186 (0.9947, 1.0431) (0.9565, 1.0848) | 1.0970 (1.0803, 1.1140) (1.0441, 1.1526) | 1.0798 (1.0648, 1.0949) (1.0386, 1.1226) |
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Fang, F.; Mu, L.; Zhu, Y.; Rao, J.; Heymann, J.; Zhang, Z.-F. Long-Term Exposure to PM2.5, Facemask Mandates, Stay Home Orders and COVID-19 Incidence in the United States. Int. J. Environ. Res. Public Health 2021, 18, 6274. https://doi.org/10.3390/ijerph18126274
Fang F, Mu L, Zhu Y, Rao J, Heymann J, Zhang Z-F. Long-Term Exposure to PM2.5, Facemask Mandates, Stay Home Orders and COVID-19 Incidence in the United States. International Journal of Environmental Research and Public Health. 2021; 18(12):6274. https://doi.org/10.3390/ijerph18126274
Chicago/Turabian StyleFang, Fang, Lina Mu, Yifang Zhu, Jianyu Rao, Jody Heymann, and Zuo-Feng Zhang. 2021. "Long-Term Exposure to PM2.5, Facemask Mandates, Stay Home Orders and COVID-19 Incidence in the United States" International Journal of Environmental Research and Public Health 18, no. 12: 6274. https://doi.org/10.3390/ijerph18126274
APA StyleFang, F., Mu, L., Zhu, Y., Rao, J., Heymann, J., & Zhang, Z.-F. (2021). Long-Term Exposure to PM2.5, Facemask Mandates, Stay Home Orders and COVID-19 Incidence in the United States. International Journal of Environmental Research and Public Health, 18(12), 6274. https://doi.org/10.3390/ijerph18126274