Mitigation behavior prior to COVID-19 vaccination availability is associated with COVID-19 infection and time to vaccination.
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2023-01
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Abstract
Background
Mitigation behaviors reduce the incidence of COVID-19 infection. Determining characteristics of groups defined by mitigation behaviors compliance may be useful to inform targeted public health policies and interventions. This study aimed to identify groups of individuals according to self-reported compliance with COVID-19 mitigation behaviors, define compliance class characteristics, and explore associations between compliance classes and important study and public health outcomes.Methods and findings
We studied 1,410 participants in the Cabarrus County COVID-19 Prevalence and Immunity longitudinal cohort study (June 2020 to December 2021) who were asked 10 questions regarding compliance with recommended COVID-19 mitigation behaviors. By Latent Class Analysis, 1,381 participants were categorized into 3 classes (most [49.4%], moderately [45.0%], and least [5.6%] compliant). Compared with the most compliant class, the least and moderately compliant classes were younger (mean = 61.9 v. 59.0 v. 53.8 years), had fewer medical conditions per individual (1.37 v. 1.08 v. 0.77), and differed in Hispanic ethnicity (6.2% v. 2.8% v. 9.1%) and COVID-19 vaccine intention (65.8% v. 59.8% v. 35.1%). Compared to the most compliant class, the least compliant class had fewer women (54.6% v. 76.3%), fewer insured individuals (92.2% v. 97.4%), and more withdrew from study participation early (28.6% v. 16.0%). Relative to the most compliant class, the least compliant class had a higher likelihood of COVID-19 infection (OR = 2.08 [95% CI 1.13, 3.85]), lower rate of COVID-19 vaccination (72.6% v. 95.1%), and longer time to 50% COVID-19 vaccination following eligibility (8-9 vs 16 days).Conclusions
Classes defined by mitigation behaviors compliance had distinct characteristics, including age, sex, medical history, and ethnicity, and were associated with important study and public health outcomes. Targeted public health policies and interventions according to the compliance group characteristics may be of value in current and future pandemic responses to increase compliance.Type
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Neighbors, Coralei E, Richard Sloane, Carl F Pieper, Douglas Wixted, Christopher W Woods and L Kristin Newby (2023). Mitigation behavior prior to COVID-19 vaccination availability is associated with COVID-19 infection and time to vaccination. PloS one, 18(3). p. e0283381. 10.1371/journal.pone.0283381 Retrieved from https://hdl.handle.net/10161/29284.
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Coralei Neighbors
Coralei Neighbors, MS, is a third-year Ph.D. candidate in Population Health Sciences at the Duke University School of Medicine. Her research integrates infectious disease surveillance, economic evaluation, and policy analysis to inform evidence-based and equitable vaccine strategies. Her work sits at the intersection of infectious disease epidemiology, health economics, and global health policy, applying decision-analytic modeling and surveillance data to support population-level decision-making and resource allocation.
Coralei holds a Bachelor of Science in Health Science Studies from Baylor University and a Master of Science in Global Health from Duke University. She is currently pursuing graduate certificates in East Asian Studies, International Development Policy, and College Teaching, enhancing the global relevance and instructional impact of her work.
Her research contributes to advancing approaches that translate economic and epidemiologic evidence into actionable policy insights. She aims to support policymakers in developing effective, sustainable, and equity-driven immunization strategies. Long term, she aspires to contribute to global health systems strengthening through economic evaluation, decision-analytic modeling, and policy engagement.
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
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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