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Matias WR, Fulcher IR, Sauer SM, Nolan CP, Guillaume Y, Zhu J, Molano FJ, Uceta E, Collins S, Slater DM, Sánchez VM, Moheed S, Harris JB, Charles RC, Paxton RM, Gonsalves SF, Franke MF, Ivers LC. Disparities in SARS-CoV-2 Infection by Race, Ethnicity, Language, and Social Vulnerability: Evidence from a Citywide Seroprevalence Study in Massachusetts, USA. J Racial Ethn Health Disparities 2024; 11:110-120. [PMID: 36652163 PMCID: PMC9847437 DOI: 10.1007/s40615-022-01502-4] [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] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 12/17/2022] [Accepted: 12/20/2022] [Indexed: 01/19/2023]
Abstract
OBJECTIVES Uncovering and addressing disparities in infectious disease outbreaks require a rapid, methodical understanding of local epidemiology. We conducted a seroprevalence study of SARS-CoV-2 infection in Holyoke, Massachusetts, a majority Hispanic city with high levels of socio-economic disadvantage to estimate seroprevalence and identify disparities in SARS-CoV-2 infection. METHODS We invited 2000 randomly sampled households between 11/5/2020 and 12/31/2020 to complete questionnaires and provide dried blood spots for SARS-CoV-2 antibody testing. We calculated seroprevalence based on the presence of IgG antibodies using a weighted Bayesian procedure that incorporated uncertainty in antibody test sensitivity and specificity and accounted for household clustering. RESULTS Two hundred eighty households including 472 individuals were enrolled. Three hundred twenty-eight individuals underwent antibody testing. Citywide seroprevalence of SARS-CoV-2 IgG was 13.1% (95% CI 6.9-22.3) compared to 9.8% of the population infected based on publicly reported cases. Seroprevalence was 16.1% (95% CI 6.2-31.8) among Hispanic individuals compared to 9.4% (95% CI 4.6-16.4) among non-Hispanic white individuals. Seroprevalence was higher among Spanish-speaking households (21.9%; 95% CI 8.3-43.9) compared to English-speaking households (10.2%; 95% CI 5.2-18.0) and among individuals in high social vulnerability index (SVI) areas based on the CDC SVI (14.4%; 95% CI 7.1-25.5) compared to low SVI areas (8.2%; 95% CI 3.1-16.9). CONCLUSIONS The SARS-CoV-2 IgG seroprevalence in a city with high levels of social vulnerability was 13.1% during the pre-vaccination period of the COVID-19 pandemic. Hispanic individuals and individuals in communities characterized by high SVI were at the highest risk of infection. Public health interventions should be designed to ensure that individuals in high social vulnerability communities have access to the tools to combat COVID-19.
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Affiliation(s)
- Wilfredo R Matias
- Division of Infectious Diseases, Massachusetts General Hospital, 55 Fruit St, BUL-130, Boston, MA, 02114, USA.
- Division of Infectious Diseases, Brigham and Women's Hospital, Boston, MA, USA.
- Center for Global Health, Massachusetts General Hospital, Boston, MA, USA.
| | - Isabel R Fulcher
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA
- Harvard Data Science Initiative, Cambridge, MA, USA
| | - Sara M Sauer
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA
| | - Cody P Nolan
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Yodeline Guillaume
- Center for Global Health, Massachusetts General Hospital, Boston, MA, USA
| | - Jack Zhu
- Center for Global Health, Massachusetts General Hospital, Boston, MA, USA
| | - Francisco J Molano
- Center for Global Health, Massachusetts General Hospital, Boston, MA, USA
| | - Elizabeth Uceta
- Center for Global Health, Massachusetts General Hospital, Boston, MA, USA
| | - Shannon Collins
- Center for Global Health, Massachusetts General Hospital, Boston, MA, USA
| | - Damien M Slater
- Division of Infectious Diseases, Massachusetts General Hospital, 55 Fruit St, BUL-130, Boston, MA, 02114, USA
| | - Vanessa M Sánchez
- Division of Infectious Diseases, Massachusetts General Hospital, 55 Fruit St, BUL-130, Boston, MA, 02114, USA
| | - Serina Moheed
- Division of Infectious Diseases, Massachusetts General Hospital, 55 Fruit St, BUL-130, Boston, MA, 02114, USA
| | - Jason B Harris
- Division of Infectious Diseases, Massachusetts General Hospital, 55 Fruit St, BUL-130, Boston, MA, 02114, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Richelle C Charles
- Division of Infectious Diseases, Massachusetts General Hospital, 55 Fruit St, BUL-130, Boston, MA, 02114, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | | | | | - Molly F Franke
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA
| | - Louise C Ivers
- Division of Infectious Diseases, Massachusetts General Hospital, 55 Fruit St, BUL-130, Boston, MA, 02114, USA
- Center for Global Health, Massachusetts General Hospital, Boston, MA, USA
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA
- Harvard Global Health Institute, Cambridge, MA, USA
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Savi MK, Yadav A, Zhang W, Vembar N, Schroeder A, Balsari S, Buckee CO, Vadhan S, Kishore N. A standardised differential privacy framework for epidemiological modeling with mobile phone data. PLOS Digit Health 2023; 2:e0000233. [PMID: 37889905 PMCID: PMC10610440 DOI: 10.1371/journal.pdig.0000233] [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] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 09/03/2023] [Indexed: 10/29/2023]
Abstract
During the COVID-19 pandemic, the use of mobile phone data for monitoring human mobility patterns has become increasingly common, both to study the impact of travel restrictions on population movement and epidemiological modeling. Despite the importance of these data, the use of location information to guide public policy can raise issues of privacy and ethical use. Studies have shown that simple aggregation does not protect the privacy of an individual, and there are no universal standards for aggregation that guarantee anonymity. Newer methods, such as differential privacy, can provide statistically verifiable protection against identifiability but have been largely untested as inputs for compartment models used in infectious disease epidemiology. Our study examines the application of differential privacy as an anonymisation tool in epidemiological models, studying the impact of adding quantifiable statistical noise to mobile phone-based location data on the bias of ten common epidemiological metrics. We find that many epidemiological metrics are preserved and remain close to their non-private values when the true noise state is less than 20, in a count transition matrix, which corresponds to a privacy-less parameter ϵ = 0.05 per release. We show that differential privacy offers a robust approach to preserving individual privacy in mobility data while providing useful population-level insights for public health. Importantly, we have built a modular software pipeline to facilitate the replication and expansion of our framework.
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Affiliation(s)
- Merveille Koissi Savi
- Department of Medical Oncology, Dana Farber Cancer Institute, Harvard School of Medicine, Boston, Massachusetts, United States of America
| | - Akash Yadav
- Direct Relief, Santa Barbara, California, United States of America
| | - Wanrong Zhang
- Department of Computer Sciences, Harvard John A. Paulson School of Engineering & Applied Sciences, Boston, Massachusetts, United States of America
| | - Navin Vembar
- Camber Systems, Washington, District of Columbia, United States of America
| | - Andrew Schroeder
- Direct Relief, Santa Barbara, California, United States of America
| | - Satchit Balsari
- Department of Emergency Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Caroline O. Buckee
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Salil Vadhan
- Department of Computer Sciences, Harvard John A. Paulson School of Engineering & Applied Sciences, Boston, Massachusetts, United States of America
| | - Nishant Kishore
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, Massachusetts, United States of America
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