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Considine EM, Braun D, Kamareddine L, Nethery RC, deSouza P. Investigating Use of Low-Cost Sensors to Increase Accuracy and Equity of Real-Time Air Quality Information. Environ Sci Technol 2023; 57:10.1021/acs.est.2c06626. [PMID: 36623253 PMCID: PMC10329730 DOI: 10.1021/acs.est.2c06626] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
U.S. Environmental Protection Agency (EPA) air quality (AQ) monitors, the "gold standard" for measuring air pollutants, are sparsely positioned across the U.S. Low-cost sensors (LCS) are increasingly being used by the public to fill in the gaps in AQ monitoring; however, LCS are not as accurate as EPA monitors. In this work, we investigate factors impacting the differences between an individual's true (unobserved) exposure to air pollution and the exposure reported by their nearest AQ instrument (which could be either an LCS or an EPA monitor). We use simulations based on California data to explore different combinations of hypothetical LCS placement strategies (e.g., at schools or near major roads), for different numbers of LCS, with varying plausible amounts of LCS device measurement errors. We illustrate how real-time AQ reporting could be improved (or, in some cases, worsened) by using LCS, both for the population overall and for marginalized communities specifically. This work has implications for the integration of LCS into real-time AQ reporting platforms.
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Affiliation(s)
- Ellen M. Considine
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, 02115, USA
| | - Danielle Braun
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, 02115, USA
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts, 02215, USA
| | - Leila Kamareddine
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, 02115, USA
| | - Rachel C. Nethery
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, 02115, USA
| | - Priyanka deSouza
- Department of Urban and Regional Planning, University of Colorado Denver, Denver, Colorado, 80202, USA
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Jbaily A, Zhou X, Liu J, Lee TH, Kamareddine L, Verguet S, Dominici F. Air pollution exposure disparities across US population and income groups. Nature 2022; 601:228-233. [PMID: 35022594 PMCID: PMC10516300 DOI: 10.1038/s41586-021-04190-y] [Citation(s) in RCA: 139] [Impact Index Per Article: 69.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 10/26/2021] [Indexed: 11/09/2022]
Abstract
Air pollution contributes to the global burden of disease, with ambient exposure to fine particulate matter of diameters smaller than 2.5 μm (PM2.5) being identified as the fifth-ranking risk factor for mortality globally1. Racial/ethnic minorities and lower-income groups in the USA are at a higher risk of death from exposure to PM2.5 than are other population/income groups2-5. Moreover, disparities in exposure to air pollution among population and income groups are known to exist6-17. Here we develop a data platform that links demographic data (from the US Census Bureau and American Community Survey) and PM2.5 data18 across the USA. We analyse the data at the tabulation area level of US zip codes (N is approximately 32,000) between 2000 and 2016. We show that areas with higher-than-average white and Native American populations have been consistently exposed to average PM2.5 levels that are lower than areas with higher-than-average Black, Asian and Hispanic or Latino populations. Moreover, areas with low-income populations have been consistently exposed to higher average PM2.5 levels than areas with high-income groups for the years 2004-2016. Furthermore, disparities in exposure relative to safety standards set by the US Environmental Protection Agency19 and the World Health Organization20 have been increasing over time. Our findings suggest that more-targeted PM2.5 reductions are necessary to provide all people with a similar degree of protection from environmental hazards. Our study is observational and cannot provide insight into the drivers of the identified disparities.
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Affiliation(s)
- Abdulrahman Jbaily
- Department of Global Health and Population, Harvard T. H. Chan School of Public Health, Boston, MA, USA.
| | - Xiaodan Zhou
- Environmental Systems Research Institute, Redlands, CA, USA
| | - Jie Liu
- Environmental Systems Research Institute, Redlands, CA, USA
| | - Ting-Hwan Lee
- Environmental Systems Research Institute, Redlands, CA, USA
| | - Leila Kamareddine
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Stéphane Verguet
- Department of Global Health and Population, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Francesca Dominici
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA.
- Harvard Data Science Initiative, Cambridge, MA, USA.
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Dey T, Tyagi P, Sabath MB, Kamareddine L, Henneman L, Braun D, Dominici F. Counterfactual time series analysis of short-term change in air pollution following the COVID-19 state of emergency in the United States. Sci Rep 2021; 11:23517. [PMID: 34876601 PMCID: PMC8651777 DOI: 10.1038/s41598-021-02776-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 11/19/2021] [Indexed: 12/23/2022] Open
Abstract
Lockdown measures implemented in response to the COVID-19 pandemic produced sudden behavioral changes. We implement counterfactual time series analysis based on seasonal autoregressive integrated moving average models (SARIMA), to examine the extent of air pollution reduction attained following state-level emergency declarations. We also investigate whether these reductions occurred everywhere in the US, and the local factors (geography, population density, and sources of emission) that drove them. Following state-level emergency declarations, we found evidence of a statistically significant decrease in nitrogen dioxide (NO2) levels in 34 of the 36 states and in fine particulate matter (PM2.5) levels in 16 of the 48 states that were investigated. The lockdown produced a decrease of up to 3.4 µg/m3 in PM2.5 (observed in California) with range (- 2.3, 3.4) and up to 11.6 ppb in NO2 (observed in Nevada) with range (- 0.6, 11.6). The state of emergency was declared at different dates for different states, therefore the period "before" the state of emergency in our analysis ranged from 8 to 10 weeks and the corresponding "after" period ranged from 8 to 6 weeks. These changes in PM2.5 and NO2 represent a substantial fraction of the annual mean National Ambient Air Quality Standards (NAAQS) of 12 µg/m3 and 53 ppb, respectively. As expected, we also found evidence that states with a higher percentage of mobile source emissions (obtained from 2014) experienced a greater decline in NO2 levels after the lockdown. Although the socioeconomic restrictions are not sustainable, our results provide a benchmark to estimate the extent of achievable air pollution reductions. Identification of factors contributing to pollutant reduction can help guide state-level policies to sustainably reduce air pollution.
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Affiliation(s)
- Tanujit Dey
- Center for Surgery and Public Health, Department of Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Pooja Tyagi
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA, 02115, USA
| | - M Benjamin Sabath
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA, 02115, USA
- Faculty of Arts and Sciences, Research Computing, Harvard University, 38 Oxford Street, Cambridge, MA, 02138, USA
| | - Leila Kamareddine
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA, 02115, USA
| | - Lucas Henneman
- Department of Civil, Environmental, and Infrastructure Engineering, George Mason University, 4400 University Drive, Fairfax, VA, 22030, USA
| | - Danielle Braun
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA, 02115, USA
- Department of Data Science, Dana-Farber Cancer Institute, 450 Brookline Ave, Boston, MA, 02215, USA
| | - Francesca Dominici
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA, 02115, USA.
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Zhou X, Josey K, Kamareddine L, Caine MC, Liu T, Mickley LJ, Cooper M, Dominici F. Excess of COVID-19 cases and deaths due to fine particulate matter exposure during the 2020 wildfires in the United States. Sci Adv 2021; 7:7/33/eabi8789. [PMID: 34389545 PMCID: PMC8363139 DOI: 10.1126/sciadv.abi8789] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 06/24/2021] [Indexed: 05/03/2023]
Abstract
The year 2020 brought unimaginable challenges in public health, with the confluence of the COVID-19 pandemic and wildfires across the western United States. Wildfires produce high levels of fine particulate matter (PM2.5). Recent studies reported that short-term exposure to PM2.5 is associated with increased risk of COVID-19 cases and deaths. We acquired and linked publicly available daily data on PM2.5, the number of COVID-19 cases and deaths, and other confounders for 92 western U.S. counties that were affected by the 2020 wildfires. We estimated the association between short-term exposure to PM2.5 during the wildfires and the epidemiological dynamics of COVID-19 cases and deaths. We adjusted for several time-varying confounding factors (e.g., weather, seasonality, long-term trends, mobility, and population size). We found strong evidence that wildfires amplified the effect of short-term exposure to PM2.5 on COVID-19 cases and deaths, although with substantial heterogeneity across counties.
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Affiliation(s)
- Xiaodan Zhou
- Environmental Systems Research Institute, Redlands, CA, USA
| | - Kevin Josey
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Leila Kamareddine
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Miah C Caine
- Harvard John A. Paulson School of Engineering and Applied Sciences, Cambridge, MA, USA
| | - Tianjia Liu
- Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA, USA
| | - Loretta J Mickley
- Harvard John A. Paulson School of Engineering and Applied Sciences, Cambridge, MA, USA
| | - Matthew Cooper
- Department of Global Health and Population, Harvard University, Boston, MA, USA
| | - Francesca Dominici
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Harvard Data Science Initiative, Cambridge, MA, USA
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Jbaily A, Feldhaus I, Bigelow B, Kamareddine L, Tolla MT, Bouvier M, Kiros M, Verguet S. Toward health system strengthening in low- and middle-income countries: insights from mathematical modeling of drug supply chains. BMC Health Serv Res 2020; 20:776. [PMID: 32838778 PMCID: PMC7445921 DOI: 10.1186/s12913-020-05549-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Accepted: 07/15/2020] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Global health priority setting increasingly focuses on understanding the functioning of health systems and on how they can be strengthened. Beyond vertical programs, health systems research should examine system-wide delivery platforms (e.g. health facilities) and operational elements (e.g. supply chains) as primary units of study and evaluation. METHODS We use dynamical system methods to develop a simple analytical model for the supply chain of a low-income country's health system. In doing so, we emphasize the dynamic links that integrate the supply chain within other elements of the health system; and we examine how the evolution over time of such connections would affect drug delivery, following the implementation of selected interventions (e.g. enhancing road networks, expanding workforce). We also test feedback loops and forecasts to study the potential impact of setting up a digital system for tracking drug delivery to prevent drug stockout and expiration. RESULTS Numerical simulations that capture a range of supply chain scenarios demonstrate the impact of different health system strengthening interventions on drug stock levels within health facilities. Our mathematical modeling also points to how implementing a digital drug tracking system could help anticipate and prevent drug stockout and expiration. CONCLUSION Our mathematical model of drug supply chain delivery represents an important component toward the development of comprehensive quantitative frameworks that aim at describing health systems as complex dynamical systems. Such models can help predict how investments in system-wide interventions, like strengthening drug supply chains in low-income settings, may improve population health outcomes.
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Affiliation(s)
- Abdulrahman Jbaily
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, 655 Huntington Ave, Boston, 02115, MA, USA
| | - Isabelle Feldhaus
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, 655 Huntington Ave, Boston, 02115, MA, USA
| | | | - Leila Kamareddine
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, 655 Huntington Ave, Boston, 02115, MA, USA
| | - Mieraf Taddesse Tolla
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, 655 Huntington Ave, Boston, 02115, MA, USA
| | | | - Mizan Kiros
- Ethiopian Health Insurance Agency, 1 bis Rue Georges Mandel, Addis Ababa, 59000, Ethiopia
| | - Stéphane Verguet
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, 655 Huntington Ave, Boston, 02115, MA, USA.
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