COVID-19 Infection Risk Among Previously Uninfected Adults: Development of a Prognostic Model.
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2023-01
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Abstract
Background
Few models exist that incorporate measures from an array of individual characteristics to predict the risk of COVID-19 infection in the general population. The aim was to develop a prognostic model for COVID-19 using readily obtainable clinical variables.Methods
Over 74 weeks surveys were periodically administered to a cohort of 1381 participants previously uninfected with COVID-19 (June 2020 to December 2021). Candidate predictors of incident infection during follow-up included demographics, living situation, financial status, physical activity, health conditions, flu vaccination history, COVID-19 vaccine intention, work/employment status, and use of COVID-19 mitigation behaviors. The final logistic regression model was created using a penalized regression method known as the least absolute shrinkage and selection operator. Model performance was assessed by discrimination and calibration. Internal validation was performed via bootstrapping, and results were adjusted for overoptimism.Results
Of the 1381 participants, 154 (11.2%) had an incident COVID-19 infection during the follow-up period. The final model included six variables: health insurance, race, household size, and the frequency of practicing three mitigation behavior (working at home, avoiding high-risk situations, and using facemasks). The c-statistic of the final model was 0.631 (0.617 after bootstrapped optimism-correction). A calibration plot suggested that with this sample the model shows modest concordance with incident infection at the lowest risk.Conclusion
This prognostic model can help identify which community-dwelling older adults are at the highest risk for incident COVID-19 infection and may inform medical provider counseling of their patients about the risk of incident COVID-19 infection.Type
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Sloane, Richard, Carl F Pieper, Richard Faldowski, Douglas Wixted, Coralei E Neighbors, Christopher W Woods and L Kristin Newby (2023). COVID-19 Infection Risk Among Previously Uninfected Adults: Development of a Prognostic Model. Health services research and managerial epidemiology, 10. p. 23333928231154336. 10.1177/23333928231154336 Retrieved from https://hdl.handle.net/10161/27258.
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Scholars@Duke
Carl F. Pieper
Analytic Interests.
1) Issues in the Design of Medical Experiments: I explore the use of reliability/generalizability models in experimental design. In addition to incorporation of reliability, I study powering longitudinal trials with multiple outcomes and substantial missing data using Mixed models.
2) Issues in the Analysis of Repeated Measures Designs & Longitudinal Data: Use of Hierarchical Linear Models (HLM) or Mixed Models in modeling trajectories of multiple variables over time (e.g., physical and cognitive functioning and Blood Pressure). My current work involves methodologies in simultaneous estimation of trajectories for multiple variables within and between domains, modeling co-occuring change.
Areas of Substantive interest: (1) Experimental design and analysis in gerontology and geriatrics, and psychiatry,
(2) Multivariate repeated measures designs,
Douglas Wixted
Coralei Neighbors
Coralei Neighbors is a second-year Ph.D. candidate in Population Health Sciences at Duke University School of Medicine. Her research focuses on the intersection of infectious diseases, health economics, and global health policy. With a strong foundation in epidemiology and disease surveillance, gained through a Bachelor of Science from Baylor University and a Master of Science in Global Health from Duke University, Coralei has experience in tackling global health challenges through a dual lens of scientific inquiry and policy analysis.
Her research encompasses infectious disease surveillance, economic modeling, and policy evaluation. With experience in both national and international settings, she is currently contributing to infectious disease surveillance initiatives and developing models to assess the economic impact and sustainability of vaccines and other health interventions in diverse populations. Coralei's work aims to inform the development of evidence-based policies to improve global health outcomes.
Christopher Wildrick Woods
1. Emerging Infections
2. Global Health
3. Epidemiology of infectious diseases
4. Clinical microbiology and diagnostics
5. Bioterrorism Preparedness
6. Surveillance for communicable diseases
7. Antimicrobial resistance
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