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1.
Expert Rev Vaccines ; : 1-7, 2022 Jul 22.
Article in English | MEDLINE | ID: covidwho-1937580

ABSTRACT

BACKGROUND: During the rollout of COVID-19 vaccination, many states relaxed mask wearing guidance for those vaccinated. The aim of this study was to examine the association between vaccination status and mask wearing behaviors. METHODS: Seven waves of surveys (n = 6721) were conducted between August 2020 and June 2021. Participants were asked about initiation of COVID-19 vaccination and mask wearing behavior at work/school or a grocery store. Odds ratios (ORs) and 95% confidence intervals (CIs) from logistic regression were used to estimate associations between vaccination status and mask wearing at work/school and at the grocery store. RESULTS: Between April and June 2021, mask wearing at work or school declined among both those vaccinated (74% to 49%) and unvaccinated (46% to 35%). There was a similar decline for mask wearing at grocery stores. The odds of wearing a mask were 2.35 times higher at work/school (95% CI: 1.82, 3.04) and 1.65 times at a grocery store (95% CI: 1.29, 2.11) among the vaccinated compared to unvaccinated. CONCLUSION: Mask wearing decreased after mask guidelines were relaxed, with consistently lower mask wearing among the unvaccinated, indicating a reluctance among the unvaccinated to adopt COVID-19 risk reduction behaviors.

2.
Journal of Risk Research ; : 1-16, 2022.
Article in English | Taylor & Francis | ID: covidwho-1864859
3.
Epidemiology ; 2022 May 19.
Article in English | MEDLINE | ID: covidwho-1853260

ABSTRACT

BACKGROUND: U.S. long-term care facilities have experienced a disproportionate burden of COVID-19 morbidity and mortality. METHODS: We examined SARS-CoV-2 transmission among residents and staff in 60 long-term care facilities in Fulton County, Georgia, from March 2020 to September 2021. Using the Wallinga-Teunis method to estimate the time-varying reproduction number, R(t), and linear mixed regression models, we examined associations between case characteristics and R(t). RESULTS: Case counts, outbreak size and duration, and R(t) declined rapidly and remained low after vaccines were first distributed to long-term care facilities in December 2020, despite increases in community incidence in summer 2021. Staff cases were more infectious than resident cases (average individual reproduction number, Ri = 0.6 [95%CI: 0.4-0.7] and 0.1 [95%CI: 0.1-0.2], respectively). Unvaccinated resident cases were more infectious than vaccinated resident cases (Ri = 0.5 [95%CI: 0.4-0.6] and 0.2 [95%CI: 0.0-0.8], respectively), but estimates were imprecise. CONCLUSIONS: COVID-19 vaccines slowed transmission and contributed to reduced caseload in long-term care facilities. However, due to data limitations, we were unable to determine whether breakthrough vaccinated cases were less infectious than unvaccinated cases. Staff cases were six times more infectious than resident cases, consistent with the hypothesis that staff were the primary drivers of SARS-CoV-2 transmission in long-term care facilities.

4.
J Epidemiol Community Health ; 2022 May 13.
Article in English | MEDLINE | ID: covidwho-1846534
5.
Science ; 376(6593): 579-580, 2022 05 06.
Article in English | MEDLINE | ID: covidwho-1832324

ABSTRACT

What can modelers learn from recent history to help prepare for the next pandemic?


Subject(s)
COVID-19 , SARS-CoV-2 , Humans , Pandemics/prevention & control
6.
Patterns (N Y) ; 2(8): 100310, 2021 Aug 13.
Article in English | MEDLINE | ID: covidwho-1763926

ABSTRACT

We discuss several issues of statistical design, data collection, analysis, communication, and decision-making that have arisen in recent and ongoing coronavirus studies, focusing on tools for assessment and propagation of uncertainty. This paper does not purport to be a comprehensive survey of the research literature; rather, we use examples to illustrate statistical points that we think are important.

7.
PLoS Comput Biol ; 18(2): e1009795, 2022 02.
Article in English | MEDLINE | ID: covidwho-1753173

ABSTRACT

Mathematical models have come to play a key role in global pandemic preparedness and outbreak response: helping to plan for disease burden, hospital capacity, and inform nonpharmaceutical interventions. Such models have played a pivotal role in the COVID-19 pandemic, with transmission models-and, by consequence, modelers-guiding global, national, and local responses to SARS-CoV-2. However, these models have largely not accounted for the social and structural factors, which lead to socioeconomic, racial, and geographic health disparities. In this piece, we raise and attempt to clarify several questions relating to this important gap in the research and practice of infectious disease modeling: Why do epidemiologic models of emerging infections typically ignore known structural drivers of disparate health outcomes? What have been the consequences of a framework focused primarily on aggregate outcomes on infection equity? What should be done to develop a more holistic approach to modeling-based decision-making during pandemics? In this review, we evaluate potential historical and political explanations for the exclusion of drivers of disparity in infectious disease models for emerging infections, which have often been characterized as "equal opportunity infectors" despite ample evidence to the contrary. We look to examples from other disease systems (HIV, STIs) and successes in including social inequity in models of acute infection transmission as a blueprint for how social connections, environmental, and structural factors can be integrated into a coherent, rigorous, and interpretable modeling framework. We conclude by outlining principles to guide modeling of emerging infections in ways that represent the causes of inequity in infection as central rather than peripheral mechanisms.


Subject(s)
Health Equity , Infections , Models, Statistical , Socioeconomic Factors , COVID-19 , Computational Biology , Disease Outbreaks , Humans , Infections/epidemiology , Infections/transmission , SARS-CoV-2
8.
SSRN;
Preprint in English | SSRN | ID: ppcovidwho-325701

ABSTRACT

Background: U.S. long-term care facilities (LTCFs) have experienced a disproportionate burden of COVID-19 morbidity and mortality. Methods: We examined SARS-CoV-2 transmission in 60 LTCFs in Fulton County, Georgia, from March 2020 to September 2021. Using the Wallinga-Teunis method to estimate the time-varying reproduction number, R(t), and linear mixed regression models, we examined associations between case characteristics and R(t) . Findings: Case counts, outbreak size/duration, and R(t) declined rapidly and remained low after vaccines were first distributed to LTCFs in December 2020, despite increases in community incidence in summer 2021 . Staff cases were more infectious than resident cases (average individual reproduction number, R i = 0·6 [95%CI: 0·4-0·7] and 0·1 [95%CI:0·1-0·2], respectively). Unvaccinated resident cases were more infectious than vaccinated resident cases (R i = 0·5 [95%CI: 0·4-0·6] and 0·2 [95%CI:0-0·8], respectively), but estimates were imprecise. Interpretation: COVID-19 vaccines slowed transmission and contributed to reduced case load in LTCFs. However, due to data limitations, we were unable to determine whether breakthrough vaccinated cases were less infectious than unvaccinated cases. Staff cases were six times more infectious than resident cases, suggesting that staff were important drivers of SARS-CoV-2 transmission in LTCFs . Funding Information: This work was supported by the Agency for Healthcare Research and Quality (R01 HS025987), the National Science Foundation (2032084), and the Emory Covid-19 Response Collaborative, which is funded by a grant from the Robert W. Woodruff Foundation. NRG was supported by the US National Institutes of Health (K24AI114444). JZ was supported by award 1 U01 IP001138-01 from the Centers for Disease Control and Prevention. AC, SS and NRG are supported by a contract from the Fulton County Board of Health. We thank the Georgia Department of Public Health and the Fulton County Board of Health for collaborating on and supplying data for this project. The contents herein are those of the authors and do not necessarily represent the official views of, nor an endorsement by, the Georgia Department of Public Health or the Fulton County Board of Health. Declaration of Interests: AC is an epidemiology consultant with the Fulton County Board of Health. All other authors have nothing to declare.

9.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-324951

ABSTRACT

We discuss several issues of statistical design, data collection, analysis, communication, and decision making that have arisen in recent and ongoing coronavirus studies, focusing on tools for assessment and propagation of uncertainty. This paper does not purport to be a comprehensive survey of the research literature;rather, we use examples to illustrate statistical points that we think are important.

10.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-324950

ABSTRACT

Mathematical models have come to play a key role in global pandemic preparedness and outbreak response: helping to plan for disease burden, hospital capacity, and inform non-pharmaceutical interventions. Such models have played a pivotal role in the COVID-19 pandemic, with transmission models and, by consequence, modelers guiding global, national, and local responses to SARS-CoV-2. However, these models have systematically failed to account for the social and structural factors which lead to socioeconomic, racial, and geographic health disparities. Why do epidemiologic models of emerging infections ignore known structural drivers of disparate health outcomes? What have been the consequences of this limited framework? What should be done to develop a more holistic approach to modeling-based decision-making during pandemics? In this Perspective, we evaluate potential historical and political explanations for the exclusion of drivers of disparity in infectious disease models for emerging infections, which have often been characterized as "equal opportunity infectors" despite ample evidence to the contrary. We look to examples from other disease systems (HIV, STIs) as a potential blueprint for how social connections, environmental, and structural factors can be integrated into a coherent, rigorous, and interpretable modeling framework. We conclude by outlining principles to guide modeling of emerging infections in ways that represent the causes of inequity in infection as central rather than peripheral mechanisms.

11.
J Infect Dis ; 224(4): 643-647, 2021 08 16.
Article in English | MEDLINE | ID: covidwho-1545949

ABSTRACT

Influenza is associated with primary viral and secondary bacterial pneumonias; however, the dynamics of this relationship in populations with varied levels of pneumococcal vaccination remain unclear. We conducted nested matched case-control studies in 2 prospective cohorts of Nicaraguan children aged 2-14 years: 1 before pneumococcal conjugate vaccine introduction (2008-2010) and 1 following introduction and near universal adoption (2011-2018). The association between influenza and pneumonia was similar in both cohorts. Participants with influenza (across types/subtypes) had higher odds of developing pneumonia in the month following influenza infection. These findings underscore the importance of considering influenza in interventions to reduce global pneumonia burden.


Subject(s)
Influenza, Human , Pneumococcal Infections , Pneumococcal Vaccines/administration & dosage , Case-Control Studies , Child , Child, Preschool , Humans , Infant , Influenza, Human/epidemiology , Nicaragua , Pneumococcal Infections/epidemiology , Pneumococcal Infections/prevention & control , Pneumonia, Pneumococcal/epidemiology , Pneumonia, Pneumococcal/prevention & control , Prospective Studies , Vaccines, Conjugate
12.
Emerg Infect Dis ; 27(5): 1266-1273, 2021.
Article in English | MEDLINE | ID: covidwho-1146234

ABSTRACT

We review the interaction between coronavirus disease (COVID-19) and coccidioidomycosis, a respiratory infection caused by inhalation of Coccidioides fungal spores in dust. We examine risk for co-infection among construction and agricultural workers, incarcerated persons, Black and Latino populations, and persons living in high dust areas. We further identify common risk factors for co-infection, including older age, diabetes, immunosuppression, racial or ethnic minority status, and smoking. Because these diseases cause similar symptoms, the COVID-19 pandemic might exacerbate delays in coccidioidomycosis diagnosis, potentially interfering with prompt administration of antifungal therapies. Finally, we examine the clinical implications of co-infection, including severe COVID-19 and reactivation of latent coccidioidomycosis. Physicians should consider coccidioidomycosis as a possible diagnosis when treating patients with respiratory symptoms. Preventive measures such as wearing face masks might mitigate exposure to dust and severe acute respiratory syndrome coronavirus 2, thereby protecting against both infections.


Subject(s)
COVID-19 , Coccidioidomycosis , Coinfection , Aged , Coccidioidomycosis/epidemiology , Humans , Minority Groups , Pandemics , SARS-CoV-2 , United States/epidemiology
13.
Clin Infect Dis ; 72(5): e88-e95, 2021 03 01.
Article in English | MEDLINE | ID: covidwho-1114839

ABSTRACT

BACKGROUND: As of 1 November 2020, there have been >230 000 deaths and 9 million confirmed and probable cases attributable to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the United States. However, this overwhelming toll has not been distributed equally, with geographic, race/ethnic, age, and socioeconomic disparities in exposure and mortality defining features of the US coronavirus disease 2019 (COVID-19) epidemic. METHODS: We used individual-level COVID-19 incidence and mortality data from the state of Michigan to estimate age-specific incidence and mortality rates by race/ethnic group. Data were analyzed using hierarchical Bayesian regression models, and model results were validated using posterior predictive checks. RESULTS: In crude and age-standardized analyses we found rates of incidence and mortality more than twice as high than for Whites for all groups except Native Americans. Blacks experienced the greatest burden of confirmed and probable COVID-19 (age-standardized incidence, 1626/100 000 population) and mortality (age-standardized mortality rate, 244/100 000). These rates reflect large disparities, as Blacks experienced age-standardized incidence and mortality rates 5.5 (95% posterior credible interval [CrI], 5.4-5.6) and 6.7 (95% CrI, 6.4-7.1) times higher than Whites, respectively. We found that the bulk of the disparity in mortality between Blacks and Whites is driven by dramatically higher rates of COVID-19 infection across all age groups, particularly among older adults, rather than age-specific variation in case-fatality rates. CONCLUSIONS: This work suggests that well-documented racial disparities in COVID-19 mortality in hard-hit settings, such as Michigan, are driven primarily by variation in household, community, and workplace exposure rather than case-fatality rates.


Subject(s)
COVID-19 , African Americans , Aged , Bayes Theorem , Health Status Disparities , Humans , Michigan , Mortality , SARS-CoV-2 , United States/epidemiology
14.
Int J Health Geogr ; 20(1): 3, 2021 01 07.
Article in English | MEDLINE | ID: covidwho-1035104

ABSTRACT

BACKGROUND: Like many scientific fields, epidemiology is addressing issues of research reproducibility. Spatial epidemiology, which often uses the inherently identifiable variable of participant address, must balance reproducibility with participant privacy. In this study, we assess the impact of several different data perturbation methods on key spatial statistics and patient privacy. METHODS: We analyzed the impact of perturbation on spatial patterns in the full set of address-level mortality data from Lawrence, MA during the period from 1911 to 1913. The original death locations were perturbed using seven different published approaches to stochastic and deterministic spatial data anonymization. Key spatial descriptive statistics were calculated for each perturbation, including changes in spatial pattern center, Global Moran's I, Local Moran's I, distance to the k-th nearest neighbors, and the L-function (a normalized form of Ripley's K). A spatially adapted form of k-anonymity was used to measure the privacy protection conferred by each method, and its compliance with HIPAA and GDPR privacy standards. RESULTS: Random perturbation at 50 m, donut masking between 5 and 50 m, and Voronoi masking maintain the validity of descriptive spatial statistics better than other perturbations. Grid center masking with both 100 × 100 and 250 × 250 m cells led to large changes in descriptive spatial statistics. None of the perturbation methods adhered to the HIPAA standard that all points have a k-anonymity > 10. All other perturbation methods employed had at least 265 points, or over 6%, not adhering to the HIPAA standard. CONCLUSIONS: Using the set of published perturbation methods applied in this analysis, HIPAA and GDPR compliant de-identification was not compatible with maintaining key spatial patterns as measured by our chosen summary statistics. Further research should investigate alternate methods to balancing tradeoffs between spatial data privacy and preservation of key patterns in public health data that are of scientific and medical importance.


Subject(s)
Data Anonymization , Privacy , Cluster Analysis , Confidentiality , Humans , Reproducibility of Results
15.
Proc Natl Acad Sci U S A ; 117(45): 28506-28514, 2020 11 10.
Article in English | MEDLINE | ID: covidwho-892049

ABSTRACT

The United States experienced historically high numbers of measles cases in 2019, despite achieving national measles vaccination rates above the World Health Organization recommendation of 95% coverage with two doses. Since the COVID-19 pandemic began, resulting in suspension of many clinical preventive services, pediatric vaccination rates in the United States have fallen precipitously, dramatically increasing risk of measles resurgence. Previous research has shown that measles outbreaks in high-coverage contexts are driven by spatial clustering of nonvaccination, which decreases local immunity below the herd immunity threshold. However, little is known about how to best conduct surveillance and target interventions to detect and address these high-risk areas, and most vaccination data are reported at the state-level-a resolution too coarse to detect community-level clustering of nonvaccination characteristic of recent outbreaks. In this paper, we perform a series of computational experiments to assess the impact of clustered nonvaccination on outbreak potential and magnitude of bias in predicting disease risk posed by measuring vaccination rates at coarse spatial scales. We find that, when nonvaccination is locally clustered, reporting aggregate data at the state- or county-level can result in substantial underestimates of outbreak risk. The COVID-19 pandemic has shone a bright light on the weaknesses in US infectious disease surveillance and a broader gap in our understanding of how to best use detailed spatial data to interrupt and control infectious disease transmission. Our research clearly outlines that finer-scale vaccination data should be collected to prevent a return to endemic measles transmission in the United States.


Subject(s)
Epidemics/statistics & numerical data , Measles Vaccine/administration & dosage , Measles/epidemiology , Models, Statistical , Space-Time Clustering , Vaccination/statistics & numerical data , Bias , Data Accuracy , Epidemics/prevention & control , Epidemiological Monitoring , Humans , Measles/prevention & control , Measles Vaccine/therapeutic use , United States
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