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Paredes MI, Perofsky AC, Frisbie L, Moncla LH, Roychoudhury P, Xie H, Bakhash SAM, Kong K, Arnould I, Nguyen TV, Wendm ST, Hajian P, Ellis S, Mathias PC, Greninger AL, Starita LM, Frazar CD, Ryke E, Zhong W, Gamboa L, Threlkeld M, Lee J, Stone J, McDermot E, Truong M, Shendure J, Oltean HN, Viboud C, Chu H, Müller NF, Bedford T. Local-scale phylodynamics reveal differential community impact of SARS-CoV-2 in a metropolitan US county. PLoS Pathog 2024; 20:e1012117. [PMID: 38530853 PMCID: PMC10997136 DOI: 10.1371/journal.ppat.1012117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [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: 09/26/2023] [Revised: 04/05/2024] [Accepted: 03/12/2024] [Indexed: 03/28/2024] Open
Abstract
SARS-CoV-2 transmission is largely driven by heterogeneous dynamics at a local scale, leaving local health departments to design interventions with limited information. We analyzed SARS-CoV-2 genomes sampled between February 2020 and March 2022 jointly with epidemiological and cell phone mobility data to investigate fine scale spatiotemporal SARS-CoV-2 transmission dynamics in King County, Washington, a diverse, metropolitan US county. We applied an approximate structured coalescent approach to model transmission within and between North King County and South King County alongside the rate of outside introductions into the county. Our phylodynamic analyses reveal that following stay-at-home orders, the epidemic trajectories of North and South King County began to diverge. We find that South King County consistently had more reported and estimated cases, COVID-19 hospitalizations, and longer persistence of local viral transmission when compared to North King County, where viral importations from outside drove a larger proportion of new cases. Using mobility and demographic data, we also find that South King County experienced a more modest and less sustained reduction in mobility following stay-at-home orders than North King County, while also bearing more socioeconomic inequities that might contribute to a disproportionate burden of SARS-CoV-2 transmission. Overall, our findings suggest a role for local-scale phylodynamics in understanding the heterogeneous transmission landscape.
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Affiliation(s)
- Miguel I. Paredes
- Department of Epidemiology, University of Washington, Seattle, Washington, United States of America
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America
| | - Amanda C. Perofsky
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, Washington, United States of America
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Lauren Frisbie
- Washington State Department of Health, Shoreline, Washington, United States of America
| | - Louise H. Moncla
- The University of Pennsylvania, Department of Pathobiology, Philadelphia, Pennsylvania, United States of America
| | - Pavitra Roychoudhury
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, United States of America
| | - Hong Xie
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, United States of America
| | - Shah A. Mohamed Bakhash
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, United States of America
| | - Kevin Kong
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, United States of America
| | - Isabel Arnould
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, United States of America
| | - Tien V. Nguyen
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, United States of America
| | - Seffir T. Wendm
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, United States of America
| | - Pooneh Hajian
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, United States of America
| | - Sean Ellis
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, United States of America
| | - Patrick C. Mathias
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, United States of America
| | - Alexander L. Greninger
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, United States of America
| | - Lea M. Starita
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, Washington, United States of America
- Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America
| | - Chris D. Frazar
- Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America
| | - Erica Ryke
- Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America
| | - Weizhi Zhong
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, Washington, United States of America
| | - Luis Gamboa
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, Washington, United States of America
| | - Machiko Threlkeld
- Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America
| | - Jover Lee
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America
| | - Jeremy Stone
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, Washington, United States of America
| | - Evan McDermot
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, Washington, United States of America
| | - Melissa Truong
- Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America
| | - Jay Shendure
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, Washington, United States of America
- Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America
- Howard Hughes Medical Institute, Seattle, Washington, United States of America
| | - Hanna N. Oltean
- Washington State Department of Health, Shoreline, Washington, United States of America
| | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Helen Chu
- Department of Medicine, Division of Allergy and Infectious Diseases, University of Washington, Seattle, Washington, United States of America
| | - Nicola F. Müller
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America
| | - Trevor Bedford
- Department of Epidemiology, University of Washington, Seattle, Washington, United States of America
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, Washington, United States of America
- Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America
- Howard Hughes Medical Institute, Seattle, Washington, United States of America
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Kwon J, Kline DM, Hepler SA. A spatio-temporal hierarchical model to account for temporal misalignment in American Community Survey explanatory variables. Spat Spatiotemporal Epidemiol 2023; 46:100593. [PMID: 37500228 PMCID: PMC10389670 DOI: 10.1016/j.sste.2023.100593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 04/20/2023] [Accepted: 05/31/2023] [Indexed: 07/29/2023]
Abstract
The American Community Survey (ACS) is one of the most vital public sources for demographic and socioeconomic characteristics of communities in the United States and is administered by the U.S. Census Bureau every year. The ACS publishes 5-year estimates of community characteristics for all geographical areas and 1-year estimates for areas with population of at least 65,000. Many epidemiological and public health studies use 5-year ACS estimates as explanatory variables in models. However, doing so ignores the uncertainty and averages over variability during the time-period which may lead to biased estimates of covariate effects of interest. In this paper, we propose a Bayesian hierarchical model that accounts for the uncertainty and disentangles the temporal misalignment in the ACS multi-year time-period estimates. We show via simulation that our proposed model more accurately recovers covariate effects compared to models that ignore the temporal misalignment. Lastly, we implement our proposed model to quantify the relationship between yearly, county-level characteristics and the prevalence of frequent mental distress for counties in North Carolina from 2014 to 2018.
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Affiliation(s)
- Jihyeon Kwon
- Department of Statistical Sciences, Wake Forest University, Winston-Salem, 27109, NC, USA
| | - David M Kline
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, 27157, NC, USA
| | - Staci A Hepler
- Department of Statistical Sciences, Wake Forest University, Winston-Salem, 27109, NC, USA.
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Ingram C, Buggy C, Elabbasy D, Perrotta C. Homelessness and health-related outcomes in the Republic of Ireland: a systematic review, meta-analysis and evidence map. Z Gesundh Wiss 2023:1-22. [PMID: 37361314 PMCID: PMC10233198 DOI: 10.1007/s10389-023-01934-0] [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] [Figures] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 05/03/2023] [Indexed: 06/28/2023]
Abstract
Aim To map existing research on homelessness and health in the Republic of Ireland, and to synthesize the evidence on housing-related disparities in health. Methods Peer-reviewed articles and conference abstracts published in English between 2012-2022 were retrieved from 11 bibliographic databases if they contained empirical data on homelessness and health in Ireland, and - in a subsequent screening stage - at least one measure of health disparity between the homeless and general populations. Reviewers extracted relative risks (RR), 95% confidence intervals (CI), and calculated pooled RR of comparable health disparities using pairwise random-effects meta-analyses. Results One hundred four articles contained empirical data on the health of homeless individuals residing in Ireland, addressing primarily substance use, addiction and mental health. Homelessness was associated with increased risk of illicit drug use (RR 7.33 [95% CI 4.2, 12.9]), reduced access to a general practitioner (GP) (RR 0.73 [CI 95% 0.71, 0.75]), frequent emergency department (ED) presentation (pooled RR 27.8 [95% CI 4.1, 189.8]), repeat presentation for self-harm (pooled RR 1.6 [95% CI 1.2, 2.0]) and premature departure from hospital (pooled RR 2.65 [95% CI 1.27, 5.53]). Conclusions Homelessness in Ireland is associated with reduced access to primary care and overreliance on acute care. Chronic conditions amongst homeless individuals are understudied. Supplementary Information The online version contains supplementary material available at 10.1007/s10389-023-01934-0.
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Affiliation(s)
- Carolyn Ingram
- School of Public Health, Physiotherapy, and Sports Science, University College Dublin, D04 V1W8 Dublin, Ireland
| | - Conor Buggy
- School of Public Health, Physiotherapy, and Sports Science, University College Dublin, D04 V1W8 Dublin, Ireland
- Centre for Safety and Health at Work, University College Dublin, D04 V1W8 Dublin, Ireland
| | - Darin Elabbasy
- School of Public Health, Physiotherapy, and Sports Science, University College Dublin, D04 V1W8 Dublin, Ireland
| | - Carla Perrotta
- School of Public Health, Physiotherapy, and Sports Science, University College Dublin, D04 V1W8 Dublin, Ireland
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Feng B, Wang W, Zhou B, Zhou Y, Wang J, Liao F. Mapping the long-term associations between air pollutants and COVID-19 risks and the attributable burdens in the continental United States. Environ Pollut 2023; 324:121418. [PMID: 36898647 PMCID: PMC9994533 DOI: 10.1016/j.envpol.2023.121418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 03/06/2023] [Accepted: 03/07/2023] [Indexed: 06/18/2023]
Abstract
Numerous studies have investigated the associations between COVID-19 risks and long-term exposure to air pollutants, revealing considerable heterogeneity and even contradictory regional results. Studying the spatial heterogeneity of the associations is essential for developing region-specific and cost-effective air-pollutant-related public health policies for the prevention and control of COVID-19. However, few studies have investigated this issue. Using the USA as an example, we constructed single/two-pollutant conditional autoregressions with random coefficients and random intercepts to map the associations between five air pollutants (PM2.5, O3, SO2, NO2, and CO) and two COVID-19 outcomes (incidence and mortality) at the state level. The attributed cases and deaths were then mapped at the county level. This study included 3108 counties from 49 states within the continental USA. The county-level air pollutant concentrations from 2017 to 2019 were used as long-term exposures, and the county-level cumulative COVID-19 cases and deaths through May 13, 2022, were used as outcomes. Results showed that considerably heterogeneous associations and attributable COVID-19 burdens were found in the USA. The COVID-19 outcomes in the western and northeastern states appeared to be unaffected by any of the five pollutants. The east of the USA bore the greatest COVID-19 burdens attributable to air pollution because of its high pollutant concentrations and significantly positive associations. PM2.5 and CO were significantly positively associated with COVID-19 incidence in 49 states on average, whereas NO2 and SO2 were significantly positively associated with COVID-19 mortality. The remaining associations between air pollutants and COVID-19 outcomes were not statistically significant. Our study provided implications regarding where a major concern should be placed on a specific air pollutant for COVID-19 control and prevention, as well as where and how to conduct additional individual-based validation research in a cost-effective manner.
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Affiliation(s)
- Benying Feng
- Sichuan Provincial Center for Mental Health, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, 610072, China; Key Laboratory of Psychosomatic Medicine, Chinese Academy of Medical Sciences, Chengdu, 610072, China
| | - Wei Wang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, 610041, China
| | - Bo Zhou
- Sichuan Provincial Center for Mental Health, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, 610072, China; Key Laboratory of Psychosomatic Medicine, Chinese Academy of Medical Sciences, Chengdu, 610072, China
| | - Ying Zhou
- Sichuan Provincial Center for Mental Health, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, 610072, China; Key Laboratory of Psychosomatic Medicine, Chinese Academy of Medical Sciences, Chengdu, 610072, China
| | - Jinyu Wang
- Sichuan Provincial Center for Mental Health, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, 610072, China; Key Laboratory of Psychosomatic Medicine, Chinese Academy of Medical Sciences, Chengdu, 610072, China
| | - Fang Liao
- Sichuan Provincial Center for Mental Health, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, 610072, China; Key Laboratory of Psychosomatic Medicine, Chinese Academy of Medical Sciences, Chengdu, 610072, China.
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Ingram C, Roe M, Downey V, Phipps L, Perrotta C. Exploring key informants' perceptions of Covid-19 vaccine hesitancy in a disadvantaged urban community in Ireland: Emergence of a '4Cs' model. Vaccine 2023; 41:519-531. [PMID: 36496286 PMCID: PMC9715488 DOI: 10.1016/j.vaccine.2022.11.072] [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] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 11/04/2022] [Accepted: 11/29/2022] [Indexed: 12/03/2022]
Abstract
AIM The aim of this study was to explore key informants' views on and experiences with Covid-19 vaccine hesitancy in a Dublin community with a high concentration of economic and social disadvantage and to identify feasible, community-centred solutions for improving vaccination acceptance and uptake. METHODS Qualitative, semi-structured interviews were carried out at a local community-centre and a central hair salon. Twelve key informants from the target community were selected based on their professional experience with vulnerable population groups: the unemployed, adults in recovery from addiction, the elderly, and Irish Travellers. Inductive thematic framework analysis was conducted to identify emergent themes and sub-themes. RESULTS Drivers of vaccine hesitancy identified by key informants largely fell under the WHO '3Cs' model of hesitancy: lack of confidence in the vaccine and its providers, complacency towards the health risks of Covid-19, and inconvenient access conditions. Covid-19 Communications emerged as a fourth 'C' whereby unclear and negative messages, confusing public health measures, and unmet expectations of the vaccine's effectiveness exacerbated anti-authority sentiments and vaccine scepticism during the pandemic. Community-specific solutions involve the provision of accurate and accessible information, collaborating with community-based organizations to build trust in the vaccine through relationship building and ongoing dialogue, and ensuring acceptable access conditions. CONCLUSIONS The proposed Confidence, Complacency, Convenience, Covid-19 Communications ('4Cs') model provides a tool for considering vaccine hesitancy in disadvantaged urban communities reacting to the rapid development and distribution of a novel vaccine. The model and in-depth key informants' perspectives can be used to compliment equitable vaccination efforts currently underway by public health agencies and non-governmental organizations.
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Affiliation(s)
- Carolyn Ingram
- School of Public Health, Physiotherapy, and Sports Science, University College Dublin, D04 V1W8 Dublin, Ireland.
| | - Mark Roe
- School of Public Health, Physiotherapy, and Sports Science, University College Dublin, D04 V1W8 Dublin, Ireland
| | - Vicky Downey
- School of Public Health, Physiotherapy, and Sports Science, University College Dublin, D04 V1W8 Dublin, Ireland
| | - Lauren Phipps
- College of Science, University College Dublin, D04 V1W8 Dublin, Ireland
| | - Carla Perrotta
- School of Public Health, Physiotherapy, and Sports Science, University College Dublin, D04 V1W8 Dublin, Ireland
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Paredes MI, Perofsky AC, Frisbie L, Moncla LH, Roychoudhury P, Xie H, Mohamed Bakhash SA, Kong K, Arnould I, Nguyen TV, Wendm ST, Hajian P, Ellis S, Mathias PC, Greninger AL, Starita LM, Frazar CD, Ryke E, Zhong W, Gamboa L, Threlkeld M, Lee J, Stone J, McDermot E, Truong M, Shendure J, Oltean HN, Viboud C, Chu H, Müller NF, Bedford T. Local-Scale phylodynamics reveal differential community impact of SARS-CoV-2 in metropolitan US county. medRxiv 2022:2022.12.15.22283536. [PMID: 36561171 PMCID: PMC9774227 DOI: 10.1101/2022.12.15.22283536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
SARS-CoV-2 transmission is largely driven by heterogeneous dynamics at a local scale, leaving local health departments to design interventions with limited information. We analyzed SARS-CoV-2 genomes sampled between February 2020 and March 2022 jointly with epidemiological and cell phone mobility data to investigate fine scale spatiotemporal SARS-CoV-2 transmission dynamics in King County, Washington, a diverse, metropolitan US county. We applied an approximate structured coalescent approach to model transmission within and between North King County and South King County alongside the rate of outside introductions into the county. Our phylodynamic analyses reveal that following stay-at-home orders, the epidemic trajectories of North and South King County began to diverge. We find that South King County consistently had more reported and estimated cases, COVID-19 hospitalizations, and longer persistence of local viral transmission when compared to North King County, where viral importations from outside drove a larger proportion of new cases. Using mobility and demographic data, we also find that South King County experienced a more modest and less sustained reduction in mobility following stay-at-home orders than North King County, while also bearing more socioeconomic inequities that might contribute to a disproportionate burden of SARS-CoV-2 transmission. Overall, our findings suggest a role for local-scale phylodynamics in understanding the heterogeneous transmission landscape.
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Affiliation(s)
- Miguel I. Paredes
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
| | - Amanda C. Perofsky
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA USA
- Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Lauren Frisbie
- Washington State Department of Health, Shoreline, WA USA
| | - Louise H. Moncla
- The University of Pennsylvania, Department of Pathobiology, Philadelphia, PA
| | - Pavitra Roychoudhury
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Hong Xie
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | | | - Kevin Kong
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Isabel Arnould
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Tien V. Nguyen
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Seffir T. Wendm
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Pooneh Hajian
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Sean Ellis
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Patrick C. Mathias
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Alexander L. Greninger
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Lea M. Starita
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Chris D. Frazar
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Erica Ryke
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Weizhi Zhong
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA USA
| | - Luis Gamboa
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA USA
| | - Machiko Threlkeld
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Jover Lee
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
| | - Jeremy Stone
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA USA
| | - Evan McDermot
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA USA
| | - Melissa Truong
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Jay Shendure
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, Seattle, WA, USA
| | | | - Cécile Viboud
- Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Helen Chu
- Department of Medicine, Division of Allergy and Infectious Diseases, University of Washington, Seattle, WA
| | - Nicola F. Müller
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
| | - Trevor Bedford
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, Seattle, WA, USA
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Hernandez Carballo I, Bakola M, Stuckler D. The impact of air pollution on COVID-19 incidence, severity, and mortality: A systematic review of studies in Europe and North America. Environ Res 2022; 215:114155. [PMID: 36030916 PMCID: PMC9420033 DOI: 10.1016/j.envres.2022.114155] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 08/16/2022] [Accepted: 08/17/2022] [Indexed: 05/29/2023]
Abstract
BACKGROUND Air pollution is speculated to increase the risks of COVID-19 spread, severity, and mortality. OBJECTIVES We systematically reviewed studies investigating the relationship between air pollution and COVID-19 cases, non-fatal severity, and mortality in North America and Europe. METHODS We searched PubMed, Web of Science, and Scopus for studies investigating the effects of harmful pollutants, including particulate matter with diameter ≤2.5 or 10 μm (PM2.5 or PM10), ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2) and carbon monoxide (CO), on COVID-19 cases, severity, and deaths in Europe and North America through to June 19, 2021. Articles were included if they quantitatively measured the relationship between exposure to air pollution and COVID-19 health outcomes. RESULTS From 2,482 articles screened, we included 116 studies reporting 355 separate pollutant-COVID-19 estimates. Approximately half of all evaluations on incidence were positive and significant associations (52.7%); for mortality the corresponding figure was similar (48.1%), while for non-fatal severity this figure was lower (41.2%). Longer-term exposure to pollutants appeared more likely to be positively associated with COVID-19 incidence (63.8%). PM2.5, PM10, O3, NO2, and CO were most strongly positively associated with COVID-19 incidence, while PM2.5 and NO2 with COVID-19 deaths. All studies were observational and most exhibited high risk of confounding and outcome measurement bias. DISCUSSION Air pollution may be associated with worse COVID-19 outcomes. Future research is needed to better test the air pollution-COVID-19 hypothesis, particularly using more robust study designs and COVID-19 measures that are less prone to measurement error and by considering co-pollutant interactions.
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Affiliation(s)
- Ireri Hernandez Carballo
- Department of Social and Political Sciences, Bocconi University, Milan, Lombardy, Italy; RFF-CMCC European Institute of Economics and the Environment, Centro Euro-Mediterraneo Sui Cambiamenti Climatici, Milan, Lombardy, Italy.
| | - Maria Bakola
- Research Unit for General Medicine and Primary Health Care, Faculty of Medicine, School of Health Science, University of Ioannina, Ioannina, Greece
| | - David Stuckler
- Department of Social and Political Sciences, Bocconi University, Milan, Lombardy, Italy; DONDENA Centre for Research on Social Dynamics and Public Policy, Bocconi University, Milan, Lombardy, Italy
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Ishmatov A. "SARS-CoV-2 is transmitted by particulate air pollution": Misinterpretations of statistical data, skewed citation practices, and misuse of specific terminology spreading the misconception. Environ Res 2022; 204:112116. [PMID: 34562486 PMCID: PMC8489301 DOI: 10.1016/j.envres.2021.112116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 09/14/2021] [Accepted: 09/21/2021] [Indexed: 05/03/2023]
Abstract
In epidemiology, there are still outdated myths associated with the spread of respiratory infections. Recently, we have witnessed the origination of a new misconception, to the effect that SARS-CoV-2 is transmitted in the open air by way of particulate air pollution (atmospheric particulate matter (PM)). There is no evidence to support the idea behind this misconception. Nevertheless, more and more people are involved in animated debate and the number of studies concerning atmospheric PM as a carrier of SARS-CoV-2 is growing rapidly. In this work, the origin of the misconception was investigated, and the published papers which have contributed to the spread of this myth were analyzed. The results show that the following factors lie behind the origin and spread of the misconception: a) The specific terminology is not always clearly defined or consistently used by scientists. In particular, the terms 'particulate matter', 'atmospheric aerosol particles', 'air pollutants', and 'atmospheric aerosols' need to be clarified, and besides they are often equated to 'infectious aerosols', 'virus-bearing aerosols', 'bio-aerosols', 'virus-laden particles', 'respiratory aerosol/droplets', and 'droplet nuclei'. b) Authors misinterpret statistical data and information from other sources. Interpretation of the correlation between PM levels and the increasing incidence and severity of COVID-19 infection, is often changed from "PM may reflect the indirect action of certain atmospheric conditions that maintain infectious nuclei suspended for prolonged periods, parameters that also act on atmospheric pollutants" to "PM could cause an increase in infectious droplets/aerosols containing SARS-CoV-2." This is a dramatic change to the meaning. Moreover, it is often not taken into account that PM may reflect activities in areas with high population density and this population density at the same time contributes to the spread COVID-19. c) Skewed citation practices. Many authors cite a hypothetical conclusion from an original study, then other authors cite the papers of these authors as primary sources. This practice leads to the effect that there are many witnesses to a 'phenomenon' that did not ever occur. Thus, the terminology used in interdisciplinary communications should be more nuanced and defined precisely. Authors should be more careful when citing unconfirmed data (and hypotheses) as well as in interpreting statistical data so as to avoid confusion and spreading false information. This is especially important now in the era of the COVID-19 pandemic.
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Affiliation(s)
- Alexander Ishmatov
- Research Institute of Experimental and Clinical Medicine, Timakova St., Bild. 2., Novosibirsk, 630117, Russian Federation; Kazan Federal University, Kremlyovskaya St. 18, Kazan, 420008, Russian Federation; Togliatti State University, Belorusskaya St. 14, Togliatti, 445020, Russian Federation.
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