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Hoskins S, Beale S, Nguyen VG, Byrne T, Yavlinsky A, Kovar J, Fong EWL, Geismar C, Navaratnam AMD, van Tongeren M, Johnson AM, Aldridge RW, Hayward A. The changing contributory role to infections of work, public transport, shopping, hospitality and leisure activities throughout the SARS-CoV-2 pandemic in England and Wales. NIHR OPEN RESEARCH 2023; 3:58. [PMID: 39286314 PMCID: PMC11403290 DOI: 10.3310/nihropenres.13443.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 10/26/2023] [Indexed: 09/19/2024]
Abstract
Background Understanding how non-household activities contributed to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections under different levels of national health restrictions is vital. Methods Among adult Virus Watch participants in England and Wales, we used multivariable logistic regressions and adjusted-weighted population attributable fractions (aPAF) assessing the contribution of work, public transport, shopping, and hospitality and leisure activities to infections. Results Under restrictions, among 17,256 participants (502 infections), work [adjusted odds ratio (aOR) 2.01 (1.65-2.44), (aPAF) 30% (22-38%)] and transport [(aOR 1.15 (0.94-1.40), aPAF 5% (-3-12%)], were risk factors for SARS-CoV-2 but shopping, hospitality and leisure were not. Following the lifting of restrictions, among 11,413 participants (493 infections), work [(aOR 1.35 (1.11-1.64), aPAF 17% (6-26%)] and transport [(aOR 1.27 (1.04-1.57), aPAF 12% (2-22%)] contributed most, with indoor hospitality [(aOR 1.21 (0.98-1.48), aPAF 7% (-1-15%)] and leisure [(aOR 1.24 (1.02-1.51), aPAF 10% (1-18%)] increasing. During the Omicron variant, with individuals more socially engaged, among 11,964 participants (2335 infections), work [(aOR 1.28 (1.16-1.41), aPAF (11% (7-15%)] and transport [(aOR 1.16 (1.04-1.28), aPAF 6% (2-9%)] remained important but indoor hospitality [(aOR 1.43 (1.26-1.62), aPAF 20% (13-26%)] and leisure [(aOR 1.35 (1.22-1.48), aPAF 10% (7-14%)] dominated. Conclusions Work and public transport were important to transmissions throughout the pandemic with hospitality and leisure's contribution increasing as restrictions were lifted, highlighting the importance of restricting leisure and hospitality alongside advising working from home, when facing a highly infectious and virulent respiratory infection.
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Affiliation(s)
- Susan Hoskins
- Institute of Health Informatics, University College London, London, England, NW1 2DA, UK
| | - Sarah Beale
- Institute of Health Informatics, University College London, London, England, NW1 2DA, UK
- Institute of Epidemiology and Health Care, University College London, London, WC1E 6BT, UK
| | - Vincent G Nguyen
- Institute of Health Informatics, University College London, London, England, NW1 2DA, UK
- Institute of Epidemiology and Health Care, University College London, London, WC1E 6BT, UK
| | - Thomas Byrne
- Institute of Health Informatics, University College London, London, England, NW1 2DA, UK
| | - Alexei Yavlinsky
- Institute of Health Informatics, University College London, London, England, NW1 2DA, UK
| | - Jana Kovar
- Institute of Epidemiology and Health Care, University College London, London, WC1E 6BT, UK
| | - Erica Wing Lam Fong
- Institute of Health Informatics, University College London, London, England, NW1 2DA, UK
| | - Cyril Geismar
- Institute of Health Informatics, University College London, London, England, NW1 2DA, UK
- Department of Infectious Disease Epidemiology, Imperial College London, London, W2 1NY, UK
| | - Annalan M D Navaratnam
- Institute of Health Informatics, University College London, London, England, NW1 2DA, UK
- Institute of Epidemiology and Health Care, University College London, London, WC1E 6BT, UK
| | - Martie van Tongeren
- Centre for Occupational and Environmental Health, The University of Manchester, Manchester, England, UK
| | - Anne M Johnson
- Institute for Global Health, University College London, London, England, WC1E 6BT, UK
| | - Robert W Aldridge
- Institute of Health Informatics, University College London, London, England, NW1 2DA, UK
| | - Andrew Hayward
- Institute of Epidemiology and Health Care, University College London, London, WC1E 6BT, UK
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2
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Okada Y, Kayano T, Anzai A, Zhang T, Nishiura H. Protection against SARS-CoV-2 BA.4 and BA.5 subvariants via vaccination and natural infection: A modeling study. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:2530-2543. [PMID: 36899545 DOI: 10.3934/mbe.2023118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
With continuing emergence of new SARS-CoV-2 variants, understanding the proportion of the population protected against infection is crucial for public health risk assessment and decision-making and so that the general public can take preventive measures. We aimed to estimate the protection against symptomatic illness caused by SARS-CoV-2 Omicron variants BA.4 and BA.5 elicited by vaccination against and natural infection with other SARS-CoV-2 Omicron subvariants. We used a logistic model to define the protection rate against symptomatic infection caused by BA.1 and BA.2 as a function of neutralizing antibody titer values. Applying the quantified relationships to BA.4 and BA.5 using two different methods, the estimated protection rate against BA.4 and BA.5 was 11.3% (95% confidence interval [CI]: 0.01-25.4) (method 1) and 12.9% (95% CI: 8.8-18.0) (method 2) at 6 months after a second dose of BNT162b2 vaccine, 44.3% (95% CI: 20.0-59.3) (method 1) and 47.3% (95% CI: 34.1-60.6) (method 2) at 2 weeks after a third BNT162b2 dose, and 52.3% (95% CI: 25.1-69.2) (method 1) and 54.9% (95% CI: 37.6-71.4) (method 2) during the convalescent phase after infection with BA.1 and BA.2, respectively. Our study indicates that the protection rate against BA.4 and BA.5 are significantly lower compared with those against previous variants and may lead to substantial morbidity, and overall estimates were consistent with empirical reports. Our simple yet practical models enable prompt assessment of public health impacts posed by new SARS-CoV-2 variants using small sample-size neutralization titer data to support public health decisions in urgent situations.
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Affiliation(s)
- Yuta Okada
- Kyoto University School of Public Health, Yoshida-Konoe, Sakyo-ku, Kyoto 606-8601, Japan
| | - Taishi Kayano
- Kyoto University School of Public Health, Yoshida-Konoe, Sakyo-ku, Kyoto 606-8601, Japan
| | - Asami Anzai
- Kyoto University School of Public Health, Yoshida-Konoe, Sakyo-ku, Kyoto 606-8601, Japan
| | - Tong Zhang
- Kyoto University School of Public Health, Yoshida-Konoe, Sakyo-ku, Kyoto 606-8601, Japan
| | - Hiroshi Nishiura
- Kyoto University School of Public Health, Yoshida-Konoe, Sakyo-ku, Kyoto 606-8601, Japan
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3
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Robertson MM, Shamsunder MG, Brazier E, Mantravadi M, Zimba R, Rane MS, Westmoreland DA, Parcesepe AM, Maroko AR, Kulkarni SG, Grov C, Nash D. Racial/Ethnic Disparities in Exposure, Disease Susceptibility, and Clinical Outcomes during COVID-19 Pandemic in National Cohort of Adults, United States. Emerg Infect Dis 2022; 28:2171-2180. [PMID: 36191624 PMCID: PMC9622253 DOI: 10.3201/eid2811.220072] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
We examined racial/ethnic disparities for COVID-19 seroconversion and hospitalization within a prospective cohort (n = 6,740) in the United States enrolled in March 2020 and followed-up through October 2021. Potential SARS-CoV-2 exposure, susceptibility to COVID-19 complications, and access to healthcare varied by race/ethnicity. Hispanic and Black non-Hispanic participants had more exposure risk and difficulty with healthcare access than white participants. Participants with more exposure had greater odds of seroconversion. Participants with more susceptibility and more barriers to healthcare had greater odds of hospitalization. Race/ethnicity positively modified the association between susceptibility and hospitalization. Findings might help to explain the disproportionate burden of SARS-CoV-2 infections and complications among Hispanic/Latino/a and Black non-Hispanic persons. Primary and secondary prevention efforts should address disparities in exposure, vaccination, and treatment for COVID-19.
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4
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Hoskins S, Beale S, Nguyen V, Fragaszy E, Navaratnam AM, Smith C, French C, Kovar J, Byrne T, Fong WLE, Geismar C, Patel P, Yavlinksy A, Johnson AM, Aldridge RW, Hayward A. Settings for non-household transmission of SARS-CoV-2 during the second lockdown in England and Wales - analysis of the Virus Watch household community cohort study. Wellcome Open Res 2022; 7:199. [PMID: 36874571 PMCID: PMC9975411 DOI: 10.12688/wellcomeopenres.17981.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/22/2022] [Indexed: 11/20/2022] Open
Abstract
Background: "Lockdowns" to control serious respiratory virus pandemics were widely used during the coronavirus disease 2019 (COVID-19) pandemic. However, there is limited information to understand the settings in which most transmission occurs during lockdowns, to support refinement of similar policies for future pandemics. Methods: Among Virus Watch household cohort participants we identified those infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outside the household. Using survey activity data, we undertook multivariable logistic regressions assessing the contribution of activities on non-household infection risk. We calculated adjusted population attributable fractions (APAF) to estimate which activity accounted for the greatest proportion of non-household infections during the pandemic's second wave. Results: Among 10,858 adults, 18% of cases were likely due to household transmission. Among 10,475 participants (household-acquired cases excluded), including 874 non-household-acquired infections, infection was associated with: leaving home for work or education (AOR 1.20 (1.02 - 1.42), APAF 6.9%); public transport (more than once per week AOR 1.82 (1.49 - 2.23), public transport APAF 12.42%); and shopping (more than once per week AOR 1.69 (1.29 - 2.21), shopping APAF 34.56%). Other non-household activities were rare and not significantly associated with infection. Conclusions: During lockdown, going to work and using public or shared transport independently increased infection risk, however only a minority did these activities. Most participants visited shops, accounting for one-third of non-household transmission. Transmission in restricted hospitality and leisure settings was minimal suggesting these restrictions were effective. If future respiratory infection pandemics emerge these findings highlight the value of working from home, using forms of transport that minimise exposure to others, minimising exposure to shops and restricting non-essential activities.
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Affiliation(s)
- Susan Hoskins
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, Greater London, WC1E 6BT, UK
| | - Sarah Beale
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, Greater London, WC1E 6BT, UK
- Institute of Epidemiology and Healthcare, University College London, London, Greater London, WC1E 7HB, UK
| | - Vincent Nguyen
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, Greater London, WC1E 6BT, UK
- Institute of Epidemiology and Healthcare, University College London, London, Greater London, WC1E 7HB, UK
| | - Ellen Fragaszy
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, Greater London, WC1E 6BT, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, Greater London, WC1E 7HT, UK
| | - Annalan M.D. Navaratnam
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, Greater London, WC1E 6BT, UK
- Institute of Epidemiology and Healthcare, University College London, London, Greater London, WC1E 7HB, UK
| | - Colette Smith
- Institute of Epidemiology and Healthcare, University College London, London, Greater London, WC1E 7HB, UK
| | - Clare French
- NIHR Health Protection Research Unit in Behavioural Science and Evaluation, Uinversity of Bristol, Bristol, BS8 2BN, UK
| | - Jana Kovar
- Institute of Epidemiology and Healthcare, University College London, London, Greater London, WC1E 7HB, UK
| | - Thomas Byrne
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, Greater London, WC1E 6BT, UK
| | - Wing Lam Erica Fong
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, Greater London, WC1E 6BT, UK
| | - Cyril Geismar
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, Greater London, WC1E 6BT, UK
- Institute of Epidemiology and Healthcare, University College London, London, Greater London, WC1E 7HB, UK
| | - Parth Patel
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, Greater London, WC1E 6BT, UK
| | - Alexei Yavlinksy
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, Greater London, WC1E 6BT, UK
| | - Anne M. Johnson
- Institute for Global Health, University College London, London, WC1N 1EH, UK
| | - Robert W. Aldridge
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, Greater London, WC1E 6BT, UK
| | - Andrew Hayward
- Institute of Epidemiology and Healthcare, University College London, London, Greater London, WC1E 7HB, UK
| | - Virus Watch Collaborative
- Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, Greater London, WC1E 6BT, UK
- Institute of Epidemiology and Healthcare, University College London, London, Greater London, WC1E 7HB, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, Greater London, WC1E 7HT, UK
- NIHR Health Protection Research Unit in Behavioural Science and Evaluation, Uinversity of Bristol, Bristol, BS8 2BN, UK
- Institute for Global Health, University College London, London, WC1N 1EH, UK
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Rodriguez J, Quintana Y. Understanding the social determinants of health and genetic factors contributing to the differences observed in COVID-19 incidence and mortality between underrepresented and other communities. J Natl Med Assoc 2022; 114:430-439. [PMID: 35513921 PMCID: PMC9060259 DOI: 10.1016/j.jnma.2022.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 02/28/2022] [Accepted: 04/05/2022] [Indexed: 11/16/2022]
Abstract
COVID-19 has been a devastating disease, especially in underserved communities. Data has shown that Indigenous peoples, Latinx communities, and Black Americans have a 3.3, 2.4, and 2 times higher mortality rate than White communities, respectively, due to COVID-19. Therefore, in this paper, we sought to understand how Social Determinants of Health and genetic factors influence COVID-19 incidence, mortality rates, and complications by assessing existing literature. Studies showed that identifying with a racial/ethnic minority, being homeless, housing insecurity, lower household median income, and living in an area with decreased air quality were associated with higher incidence and mortality from COVID-19. Analyses of these studies also showed a lack of resources to collect patients' social determinants of health, revealing an urgent need to create databases with information on local support programs and operationalize the referral and tracking outcomes to address the health inequities for Black, Indigenous, and Latinx communities.
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Affiliation(s)
- Jeslyn Rodriguez
- Albany Medical College, Albany, NY 12208, USA; Division of Clinical Informatics, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02115, USA.
| | - Yuri Quintana
- Division of Clinical Informatics, Beth Israel Deaconess Medical Center, Boston, MA 02115, USA; Harvard Medical School, Harvard University, Boston, MA, 02115, USA
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6
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Cardenas VM, Kennedy JL, Williams M, Nembhard WN, Zohoori N, Du R, Jin J, Boothe D, Fischbach LA, Kirkpatrick C, Modi Z, Caid K, Owens S, Forrest JC, James L, Boehme KW, Olgaard E, Gardner SF, Amick BC. State-wide random seroprevalence survey of SARS-CoV-2 past infection in a southern US State, 2020. PLoS One 2022; 17:e0267322. [PMID: 35476717 PMCID: PMC9045671 DOI: 10.1371/journal.pone.0267322] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 04/06/2022] [Indexed: 11/19/2022] Open
Abstract
The purpose of this cross-sectional study was to estimate the proportion of Arkansas residents who were infected with the SARS-CoV-2 virus between May and December 2020 and to assess the determinants of infection. To estimate seroprevalence, a state-wide population-based random-digit dial sample of non-institutionalized adults in Arkansas was surveyed. Exposures were age, sex, race/ethnicity, education, occupation, contact with infected persons, comorbidities, height, and weight. The outcome was past COVID-19 infection measured by serum antibody test. We found a prevalence of 15.1% (95% CI: 11.1%, 20.2%) by December 2020. Seropositivity was significantly elevated among participants who were non-Hispanic Black, Hispanic (prevalence ratio [PRs]:1.4 [95% CI: 0.8, 2.4] and 2.3 [95% CI: 1.3, 4.0], respectively), worked in high-demand essential services (PR: 2.5 [95% CI: 1.5, 4.1]), did not have a college degree (PR: 1.6 [95% CI: 1.0, 2.4]), had an infected household or extra-household contact (PRs: 4.7 [95% CI: 2.1, 10.1] and 2.6 [95% CI: 1.2, 5.7], respectively), and were contacted in November or December (PR: 3.6 [95% CI: 1.9, 6.9]). Our results indicate that by December 2020, one out six persons in Arkansas had a past SARS-CoV-2 infection.
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Affiliation(s)
- Victor M. Cardenas
- Department of Epidemiology, Fay W. Boozman College of Public Health, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Joshua L. Kennedy
- Department of Pediatrics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
- Department of Internal Medicine, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
- Arkansas Children’s Research Institute, Little Rock, Arkansas, United States of America
| | - Mark Williams
- Department of Health Behavior and Health Education, Fay W. Boozman College of Public Health, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Wendy N. Nembhard
- Department of Epidemiology, Fay W. Boozman College of Public Health, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Namvar Zohoori
- Department of Epidemiology, Fay W. Boozman College of Public Health, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
- Arkansas Department of Health, Little Rock, Arkansas, United States of America
| | - Ruofei Du
- Department of Biostatistics, College of Public Health, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Jing Jin
- Department of Epidemiology, Fay W. Boozman College of Public Health, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
- Department of Biostatistics, College of Public Health, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Danielle Boothe
- Department of Epidemiology, Fay W. Boozman College of Public Health, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Lori A. Fischbach
- Department of Epidemiology, Fay W. Boozman College of Public Health, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
- Los Angeles County Department of Public Health, Outbreak Management Branch, Los Angeles, California, United States of America
| | - Catherine Kirkpatrick
- Arkansas Children’s Research Institute, Little Rock, Arkansas, United States of America
| | - Zeel Modi
- Arkansas Children’s Research Institute, Little Rock, Arkansas, United States of America
| | - Katherine Caid
- Arkansas Children’s Research Institute, Little Rock, Arkansas, United States of America
| | - Shana Owens
- Arkansas Children’s Research Institute, Little Rock, Arkansas, United States of America
| | - J. Craig Forrest
- Department of Microbiology & Immunology, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Laura James
- Department of Pediatrics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Karl W. Boehme
- Department of Microbiology & Immunology, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
- Centre for Microbial Pathogenesis and Host Inflammatory Responses, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Ericka Olgaard
- Department of Pathology, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Stephanie F. Gardner
- College of Pharmacy, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Benjamin C. Amick
- Department of Epidemiology, Fay W. Boozman College of Public Health, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
- Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
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7
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Robertson MM, Kulkarni SG, Rane M, Kochhar S, Berry A, Chang M, Mirzayi C, You W, Maroko A, Zimba R, Westmoreland D, Grov C, Parcesepe AM, Waldron L, Nash D. Cohort profile: a national, community-based prospective cohort study of SARS-CoV-2 pandemic outcomes in the USA-the CHASING COVID Cohort study. BMJ Open 2021; 11:e048778. [PMID: 34548354 PMCID: PMC8458000 DOI: 10.1136/bmjopen-2021-048778] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Accepted: 08/05/2021] [Indexed: 12/13/2022] Open
Abstract
PURPOSE The Communities, Households and SARS-CoV-2 Epidemiology (CHASING) COVID Cohort Study is a community-based prospective cohort study launched during the upswing of the USA COVID-19 epidemic. The objectives of the cohort study are to: (1) estimate and evaluate determinants of the incidence of SARS-CoV-2 infection, disease and deaths; (2) assess the impact of the pandemic on psychosocial and economic outcomes and (3) assess the uptake of pandemic mitigation strategies. PARTICIPANTS We began enrolling participants from 28 March 2020 using internet-based strategies. Adults≥18 years residing anywhere in the USA or US territories were eligible. 6740 people are enrolled in the cohort, including participants from all 50 US states, the District of Columbia, Puerto Rico and Guam. Participants are contacted regularly to complete study assessments, including interviews and dried blood spot specimen collection for serologic testing. FINDINGS TO DATE Participants are geographically and sociodemographically diverse and include essential workers (19%). 84.2% remain engaged in cohort follow-up activities after enrolment. Data have been used to assess SARS-CoV-2 cumulative incidence, seroincidence and related risk factors at different phases of the US pandemic; the role of household crowding and the presence of children in the household as potential risk factors for severe COVID-19 early in the US pandemic; to describe the prevalence of anxiety symptoms and its relationship to COVID-19 outcomes and other potential stressors; to identify preferences for SARS-CoV-2 diagnostic testing when community transmission is on the rise via a discrete choice experiment and to assess vaccine hesitancy over time and its relationship to vaccine uptake. FUTURE PLANS The CHASING COVID Cohort Study has outlined a research agenda that involves ongoing monitoring of the incidence and determinants of SARS-CoV-2 outcomes, mental health outcomes and economic outcomes. Additional priorities include assessing the incidence, prevalence and correlates of long-haul COVID-19.
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Affiliation(s)
- McKaylee M Robertson
- City University of New York (CUNY) Institute for Implementation Science in Population Health, New York, New York, USA
| | - Sarah Gorrell Kulkarni
- City University of New York (CUNY) Institute for Implementation Science in Population Health, New York, New York, USA
| | - Madhura Rane
- City University of New York (CUNY) Institute for Implementation Science in Population Health, New York, New York, USA
| | - Shivani Kochhar
- City University of New York (CUNY) Institute for Implementation Science in Population Health, New York, New York, USA
| | - Amanda Berry
- City University of New York (CUNY) Institute for Implementation Science in Population Health, New York, New York, USA
| | - Mindy Chang
- City University of New York (CUNY) Institute for Implementation Science in Population Health, New York, New York, USA
| | - Chloe Mirzayi
- City University of New York (CUNY) Institute for Implementation Science in Population Health, New York, New York, USA
| | - William You
- City University of New York (CUNY) Institute for Implementation Science in Population Health, New York, New York, USA
| | - Andrew Maroko
- City University of New York (CUNY) Institute for Implementation Science in Population Health, New York, New York, USA
- Environmental Health Sciences, Graduate School of Public Health and Health Policy, City University of New York, New York, New York, USA
| | - Rebecca Zimba
- City University of New York (CUNY) Institute for Implementation Science in Population Health, New York, New York, USA
| | - Drew Westmoreland
- City University of New York (CUNY) Institute for Implementation Science in Population Health, New York, New York, USA
| | - Christian Grov
- City University of New York (CUNY) Institute for Implementation Science in Population Health, New York, New York, USA
- Community Health and Social Sciences, Graduate School of Public Health and Health Policy, City University of New York, New York, New York, USA
| | - Angela Marie Parcesepe
- City University of New York (CUNY) Institute for Implementation Science in Population Health, New York, New York, USA
- Maternal and Child Health, University of North Carolina at Chapel Hill Gillings School of Global Public Health, Chapel Hill, North Carolina, USA
| | - Levi Waldron
- City University of New York (CUNY) Institute for Implementation Science in Population Health, New York, New York, USA
| | - Denis Nash
- City University of New York (CUNY) Institute for Implementation Science in Population Health, New York, New York, USA
- Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York, New York, New York, USA
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8
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Mutantu PN, Ngwe Tun MM, Nabeshima T, Yu F, Mukadi PK, Tanaka T, Tashiro M, Fujita A, Kanie N, Oshiro R, Takazono T, Imamura Y, Hirayama T, Moi ML, Inoue S, Izumikawa K, Yasuda J, Morita K. Development and Evaluation of Quantitative Immunoglobulin G Enzyme-Linked Immunosorbent Assay for the Diagnosis of Coronavirus Disease 2019 Using Truncated Recombinant Nucleocapsid Protein as Assay Antigen. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:9630. [PMID: 34574555 PMCID: PMC8469721 DOI: 10.3390/ijerph18189630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 09/04/2021] [Accepted: 09/09/2021] [Indexed: 11/24/2022]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of coronavirus disease 2019 (COVID-19). Real-time RT-PCR is the most commonly used method for COVID-19 diagnosis. However, serological assays are urgently needed as complementary tools to RT-PCR. Hachim et al. 2020 and Burbelo et al. 2020 demonstrated that anti-nucleocapsid(N) SARS-CoV-2 antibodies are higher and appear earlier than the spike antibodies. Additionally, cross-reactive antibodies against N protein are more prevalent than those against spike protein. We developed a less cross-reactive immunoglobulin G (IgG) indirect ELISA by using a truncated recombinant SARS-CoV-2 N protein as assay antigen. A highly conserved region of coronaviruses N protein was deleted and the protein was prepared using an E. coli protein expression system. A total of 177 samples collected from COVID-19 suspected cases and 155 negative control sera collected during the pre-COVID-19 period were applied to evaluate the assay's performance, with the plaque reduction neutralization test and the commercial SARS-CoV-2 spike protein IgG ELISA as gold standards. The SARS-CoV-2 N truncated protein-based ELISA showed similar sensitivity (91.1% vs. 91.9%) and specificity (93.8% vs. 93.8%) between the PRNT and spike IgG ELISA, as well as also higher specificity compared to the full-length N protein (93.8% vs. 89.9%). Our ELISA can be used for the diagnosis and surveillance of COVID-19.
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Affiliation(s)
- Pierre Nsele Mutantu
- Graduate School of Biomedical Sciences, Nagasaki University, 1-12-4 Sakamoto, Nagasaki 852-8523, Japan; (P.N.M.); (P.K.M.)
- Department of Virology, Institute of Tropical Medicine, Nagasaki University, 1-12-4 Sakamoto, Nagasaki 852-8523, Japan; (M.M.N.T.); (T.N.); (M.L.M.); (K.M.)
- Program for Nurturing Global Leaders in Tropical and Emerging Communicable Diseases, Nagasaki University, 1-12-4 Sakamoto, Nagasaki 852-8523, Japan
| | - Mya Myat Ngwe Tun
- Department of Virology, Institute of Tropical Medicine, Nagasaki University, 1-12-4 Sakamoto, Nagasaki 852-8523, Japan; (M.M.N.T.); (T.N.); (M.L.M.); (K.M.)
| | - Takeshi Nabeshima
- Department of Virology, Institute of Tropical Medicine, Nagasaki University, 1-12-4 Sakamoto, Nagasaki 852-8523, Japan; (M.M.N.T.); (T.N.); (M.L.M.); (K.M.)
| | - Fuxun Yu
- Guizhou Provincial People’s Hospital, Guiyang 550002, China;
| | - Patrick Kakoni Mukadi
- Graduate School of Biomedical Sciences, Nagasaki University, 1-12-4 Sakamoto, Nagasaki 852-8523, Japan; (P.N.M.); (P.K.M.)
- Program for Nurturing Global Leaders in Tropical and Emerging Communicable Diseases, Nagasaki University, 1-12-4 Sakamoto, Nagasaki 852-8523, Japan
- Department of Clinical Medicine, Institute of Tropical Medicine, Nagasaki University, 1-12-4 Sakamoto, Nagasaki 852-8523, Japan
| | - Takeshi Tanaka
- Infection Control and Education Center, Nagasaki University Hospital, 1-7-1 Sakamoto, Nagasaki 852-8501, Japan; (T.T.); (M.T.); (A.F.); (K.I.)
| | - Masato Tashiro
- Infection Control and Education Center, Nagasaki University Hospital, 1-7-1 Sakamoto, Nagasaki 852-8501, Japan; (T.T.); (M.T.); (A.F.); (K.I.)
| | - Ayumi Fujita
- Infection Control and Education Center, Nagasaki University Hospital, 1-7-1 Sakamoto, Nagasaki 852-8501, Japan; (T.T.); (M.T.); (A.F.); (K.I.)
| | - Nobuhiro Kanie
- Department of Infectious Diseases, Nagasaki University Hospital, 1-7-1 Sakamoto, Nagasaki 852-8501, Japan; (N.K.); (R.O.)
| | - Ryosaku Oshiro
- Department of Infectious Diseases, Nagasaki University Hospital, 1-7-1 Sakamoto, Nagasaki 852-8501, Japan; (N.K.); (R.O.)
| | - Takahiro Takazono
- Department of Respiratory Medicine, Nagasaki University Hospital, 1-7-1 Sakamoto, Nagasaki 852-8501, Japan; (T.T.); (Y.I.); (T.H.)
| | - Yoshifumi Imamura
- Department of Respiratory Medicine, Nagasaki University Hospital, 1-7-1 Sakamoto, Nagasaki 852-8501, Japan; (T.T.); (Y.I.); (T.H.)
- Medical Education Development Center, Nagasaki University Hospital, 1-7-1 Sakamoto, Nagasaki 852-8501, Japan
| | - Tatsuro Hirayama
- Department of Respiratory Medicine, Nagasaki University Hospital, 1-7-1 Sakamoto, Nagasaki 852-8501, Japan; (T.T.); (Y.I.); (T.H.)
| | - Meng Ling Moi
- Department of Virology, Institute of Tropical Medicine, Nagasaki University, 1-12-4 Sakamoto, Nagasaki 852-8523, Japan; (M.M.N.T.); (T.N.); (M.L.M.); (K.M.)
| | - Shingo Inoue
- Department of Virology, Institute of Tropical Medicine, Nagasaki University, 1-12-4 Sakamoto, Nagasaki 852-8523, Japan; (M.M.N.T.); (T.N.); (M.L.M.); (K.M.)
| | - Koichi Izumikawa
- Infection Control and Education Center, Nagasaki University Hospital, 1-7-1 Sakamoto, Nagasaki 852-8501, Japan; (T.T.); (M.T.); (A.F.); (K.I.)
| | - Jiro Yasuda
- Department of Emerging Infectious Diseases, Institute of Tropical Medicine, Nagasaki University, 1-12-4 Sakamoto, Nagasaki 852-8523, Japan;
| | - Kouichi Morita
- Department of Virology, Institute of Tropical Medicine, Nagasaki University, 1-12-4 Sakamoto, Nagasaki 852-8523, Japan; (M.M.N.T.); (T.N.); (M.L.M.); (K.M.)
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9
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Galmiche S, Charmet T, Schaeffer L, Paireau J, Grant R, Chény O, Von Platen C, Maurizot A, Blanc C, Dinis A, Martin S, Omar F, David C, Septfons A, Cauchemez S, Carrat F, Mailles A, Levy-Bruhl D, Fontanet A. Exposures associated with SARS-CoV-2 infection in France: A nationwide online case-control study. THE LANCET REGIONAL HEALTH. EUROPE 2021; 7:100148. [PMID: 34124709 PMCID: PMC8183123 DOI: 10.1016/j.lanepe.2021.100148] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
BACKGROUND We aimed to assess the role of different setting and activities in acquiring SARS-CoV-2 infection. METHODS In this nationwide case-control study, cases were SARS-CoV-2 infected adults recruited between 27 October and 30 November 2020. Controls were individuals from the Ipsos market research database matched to cases by age, sex, region, population density and time period. Participants completed an online questionnaire on recent activity-related exposures. FINDINGS Among 3426 cases and 1713 controls, in multivariable analysis, we found an increased risk of infection associated with any additional person living in the household (adjusted-OR: 1•16; 95%CI: 1•11-1•21); having children attending day-care (aOR: 1•31; 95%CI: 1•02-1•62), kindergarten (aOR: 1•27; 95%CI: 1•09-1•45), middle school (aOR: 1•30; 95%CI: 1•15-1•47), or high school (aOR: 1•18; 95%CI: 1•05-1•34); with attending professional (aOR: 1•15; 95%CI: 1•04-1•26) or private gatherings (aOR: 1•57; 95%CI: 1•45-1•71); and with having frequented bars and restaurants (aOR: 1•95; 95%CI: 1•76-2•15), or having practiced indoor sports activities (aOR: 1•36; 95%CI: 1•15-1•62). We found no increase in risk associated with frequenting shops, cultural or religious gatherings, or with transportation, except for carpooling (aOR: 1•47; 95%CI: 1•28-1•69). Teleworking was associated with decreased risk of infection (aOR: 0•65; 95%CI: 0•56-0•75). INTERPRETATION Places and activities during which infection prevention and control measures may be difficult to fully enforce were those with increased risk of infection. Children attending day-care, kindergarten, middle and high schools, but not primary schools, were potential sources of infection for the household. FUNDING Institut Pasteur, Research & Action Emerging Infectious Diseases (REACTing), Fondation de France (Alliance" Tous unis contre le virus").
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Affiliation(s)
- Simon Galmiche
- Institut Pasteur, Emerging Diseases Epidemiology Unit, Paris, France
| | - Tiffany Charmet
- Institut Pasteur, Emerging Diseases Epidemiology Unit, Paris, France
| | - Laura Schaeffer
- Institut Pasteur, Emerging Diseases Epidemiology Unit, Paris, France
| | - Juliette Paireau
- Institut Pasteur, Mathematical Modelling of Infectious Diseases Unit; UMR2000; CNRS, Paris, France
- Santé Publique France, Saint-Maurice, France
| | - Rebecca Grant
- Institut Pasteur, Emerging Diseases Epidemiology Unit, Paris, France
- Sorbonne University, Paris, France
| | - Olivia Chény
- Institut Pasteur, Centre for Translational Research, Paris, France
| | | | | | - Carole Blanc
- Caisse Nationale d'Assurance Maladie, Paris, France
| | - Annika Dinis
- Caisse Nationale d'Assurance Maladie, Paris, France
| | | | | | | | | | - Simon Cauchemez
- Institut Pasteur, Mathematical Modelling of Infectious Diseases Unit; UMR2000; CNRS, Paris, France
| | - Fabrice Carrat
- Sorbonne Université, Inserm, IPLESP, hôpital Saint-Antoine, APHP, 27 rue Chaligny, Paris, France F75571
| | | | | | - Arnaud Fontanet
- Institut Pasteur, Emerging Diseases Epidemiology Unit, Paris, France
- Conservatoire national des arts et métiers, Unité PACRI, Paris, France
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10
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Rane MS, Profeta A, Poehlein E, Kulkarni S, Robertson M, Gainus C, Parikh A, LeBenger K, Frogel D, Nash D. The emergence, surge and subsequent wave of the SARS-CoV-2 pandemic in New York metropolitan area: The view from a major region-wide urgent care provider. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2021:2021.04.06.21255009. [PMID: 33880480 PMCID: PMC8057248 DOI: 10.1101/2021.04.06.21255009] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
BACKGROUND Describing SARS-CoV-2 testing and positivity trends among urgent care users is crucial for understanding the trajectory of the pandemic. OBJECTIVE To describe demographic and clinical characteristics, positivity rates, and repeat testing patterns among patients tested for SARS-CoV-2 at CityMD, an urgent care provider in the New York City metropolitan area. DESIGN Retrospective study of all persons testing for SARS-CoV-2 between March 1, 2020 and January 8, 2021 at 115 CityMD locations in the New York metropolitan area. PATIENTS Individuals receiving a SARS-CoV-2 diagnostic or serologic test. MEASUREMENTS Test and individual level SARS-CoV-2 positivity by PCR, rapid antigen, or serologic tests. RESULTS During the study period, 3.4 million COVID tests were performed on 1.8 million individuals. In New York City, CityMD diagnosed 268,298 individuals, including 17% of all reported cases. Testing levels were higher among 20-29 year olds, non-Hispanic Whites, and females compared with other groups. About 24.8% (n=464,902) were repeat testers. Test positivity was higher in non-Hispanic Black (6.4%), Hispanic (8.0%), and Native American (8.0%) patients compared to non-Hispanic White (5.4%) patients. Overall seropositivity was estimated to be 21.7% (95% Confidence Interval [CI]: 21.6-21.8) and was highest among 10-14 year olds (27.3%). Seropositivity was also high among non-Hispanic Black (24.5%) and Hispanic (30.6%) testers, and residents of the Bronx (31.3%) and Queens (30.5%). Using PCR as the gold standard, SARS-CoV-2 rapid tests had a false positive rate of 5.4% (95%CI 5.3-5.5). CONCLUSION Urgent care centers can provide broad access to critical evaluation, diagnostic testing and treatment of a substantial number of ambulatory patients during pandemics, especially in population-dense, urban epicenters.
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Affiliation(s)
- Madhura S Rane
- Institute for Implementation Science in Population Health, City University of New York. New York, NY USA
| | | | - Emily Poehlein
- Institute for Implementation Science in Population Health, City University of New York. New York, NY USA
| | - Sarah Kulkarni
- Institute for Implementation Science in Population Health, City University of New York. New York, NY USA
| | - McKaylee Robertson
- Institute for Implementation Science in Population Health, City University of New York. New York, NY USA
| | | | | | | | | | - Denis Nash
- Institute for Implementation Science in Population Health, City University of New York. New York, NY USA
- Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York. New York, NY USA
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