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1. Examining disparities in the early adoption of Covid-19 personal mitigation across family structures

   https://doi.org/10.3934/publichealth.2022041  

   Harris, Casey T.; Fitzpatrick, Kevin; Niño, Michael; Thelapurath, Priya; Drawve, Grant (January 2022, AIMS Public Health)

   The United States' response to the COVID-19 pandemic has relied heavily on personal mitigation behaviors versus centralized governmental prevention strategies, especially early in the virus's outbreak. This study examines how family structure shapes mitigation, focusing on the intersectional effects of gender, marital status, and the presence of children while accounting for differences in worry about infection from the virus. Using data from a national survey of 10,368 United States adults early in the pandemic (March 2020), survey-weighted logistic regression models show important differences in the likelihood of personal mitigation adoption across family structures. Unmarried women with children were most likely to report personal mitigation behaviors, including washing hands more frequently and avoiding social gatherings. Our findings highlight the differential impacts of the pandemic on those living in specific family circumstances. 

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2. Social capital dimensions are differentially associated with COVID-19 vaccinations, masks, and physical distancing

   https://doi.org/10.1371/journal.pone.0260818  

   Ferwana, Ibtihal; Varshney, Lav R. (December 2021, PLOS ONE)

   Patel, Sanjay Kumar (Ed.)

   Background Social capital has been associated with health outcomes in communities and can explain variations in different geographic localities. Social capital has also been associated with behaviors that promote better health and reduce the impacts of diseases. During the COVID-19 pandemic, social distancing, face masking, and vaccination have all been essential in controlling contagion. These behaviors have not been uniformly adopted by communities in the United States. Using different facets of social capital to explain the differences in public behaviors among communities during pandemics is lacking. Objective This study examines the relationship among public health behavior—vaccination, face masking, and physical distancing—during COVID-19 pandemic and social capital indices in counties in the United States. Methods We used publicly available vaccination data as of June 2021, face masking data in July 2020, and mobility data from mobile phones movements from the end of March 2020. Then, correlation analysis was conducted with county-level social capital index and its subindices (family unity, community health, institutional health, and collective efficacy) that were obtained from the Social Capital Project by the United States Senate. Results We found the social capital index and its subindices differentially correlate with different public health behaviors. Vaccination is associated with institutional health: positively with fully vaccinated population and negatively with vaccination hesitancy. Also, wearing masks negatively associates with community health, whereases reduced mobility associates with better community health. Further, residential mobility positively associates with family unity. By comparing correlation coefficients, we find that social capital and its subindices have largest effect sizes on vaccination and residential mobility. Conclusion Our results show that different facets of social capital are significantly associated with adoption of protective behaviors, e.g., social distancing, face masking, and vaccination. As such, our results suggest that differential facets of social capital imply a Swiss cheese model of pandemic control planning where, e.g., institutional health and community health, provide partially overlapping behavioral benefits. 

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3. Cross-temporal relations of conditional risk perception measures with protective actions against COVID-19

   https://doi.org/10.1016/j.socscimed.2023.115867  

   Johnson, Branden B; Kim, Byungdoo (May 2023, Social Science & Medicine)

   Two decades ago a research team clarified that cross-sectional associations of risk perceptions and protective behavior can only test an “accuracy” hypothesis: e.g., people with higher risk perceptions at Ti should also exhibit low protective behavior and/or high risky behavior at Ti. They argued that these associations are too often interpreted wrongly as testing two other hypotheses, only testable longitudinally: the “behavioral motivation” hypothesis, that high risk perception at Ti increases protective behavior at Ti+1, and the “risk reappraisal” hypothesis, that protective behavior at Ti reduces risk perception at Ti+1. Further, this team argued that risk perception measures should be conditional (e.g., personal risk perception if one’s behavior does not change). Yet these theses have garnered relatively little empirical testing. An online longitudinal panel study of U.S. residents’ COVID-19 views across six survey waves over 14 months in 2020–2021 tested these hypotheses for six behaviors (hand washing, mask wearing, avoiding travel to infected areas, avoiding large public gatherings, vaccination, and [for five waves] social isolation at home). Accuracy and behavioral motivation hypotheses were supported for both behaviors and intentions, excluding a few waves (particularly in February–April 2020, when the pandemic was new in the U.S.) and behaviors. The risk reappraisal hypothesis was contradicted—protective behavior at one wave increased risk perception later—perhaps reflecting continuing uncertainty about efficacy of COVID-19 protective behaviors and/or that dynamic infectious diseases may yield different patterns than chronic diseases dominating such hypothesis-testing. These findings raise intriguing questions for both perception- behavior theory and behavior change practice. 

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4. A versatile web app for identifying the drivers of COVID-19 epidemics

   https://doi.org/10.1186/s12967-021-02736-2  

   Getz, Wayne M.; Salter, Richard; Luisa Vissat, Ludovica; Horvitz, Nir (March 2021, Journal of Translational Medicine)

   Abstract BackgroundNo versatile web app exists that allows epidemiologists and managers around the world to comprehensively analyze the impacts of COVID-19 mitigation. Thehttp://covid-webapp.numerusinc.com/web app presented here fills this gap. MethodsOur web app uses a model that explicitly identifies susceptible, contact, latent, asymptomatic, symptomatic and recovered classes of individuals, and a parallel set of response classes, subject to lower pathogen-contact rates. The user inputs a CSV file of incidence and, if of interest, mortality rate data. A default set of parameters is available that can be overwritten through input or online entry, and a user-selected subset of these can be fitted to the model using maximum-likelihood estimation (MLE). Model fitting and forecasting intervals are specifiable and changes to parameters allow counterfactual and forecasting scenarios. Confidence or credible intervals can be generated using stochastic simulations, based on MLE values, or on an inputted CSV file containing Markov chain Monte Carlo (MCMC) estimates of one or more parameters. ResultsWe illustrate the use of our web app in extracting social distancing, social relaxation, surveillance or virulence switching functions (i.e., time varying drivers) from the incidence and mortality rates of COVID-19 epidemics in Israel, South Africa, and England. The Israeli outbreak exhibits four distinct phases: initial outbreak, social distancing, social relaxation, and a second wave mitigation phase. An MCMC projection of this latter phase suggests the Israeli epidemic will continue to produce into late November an average of around 1500 new case per day, unless the population practices social-relaxation measures at least 5-fold below the level in August, which itself is 4-fold below the level at the start of July. Our analysis of the relatively late South African outbreak that became the world’s fifth largest COVID-19 epidemic in July revealed that the decline through late July and early August was characterised by a social distancing driver operating at more than twice the per-capita applicable-disease-class (pc-adc) rate of the social relaxation driver. Our analysis of the relatively early English outbreak, identified a more than 2-fold improvement in surveillance over the course of the epidemic. It also identified a pc-adc social distancing rate in early August that, though nearly four times the pc-adc social relaxation rate, appeared to barely contain a second wave that would break out if social distancing was further relaxed. ConclusionOur web app provides policy makers and health officers who have no epidemiological modelling or computer coding expertise with an invaluable tool for assessing the impacts of different outbreak mitigation policies and measures. This includes an ability to generate an epidemic-suppression or curve-flattening index that measures the intensity with which behavioural responses suppress or flatten the epidemic curve in the region under consideration. 

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5. The Relationship Between Medical Diagnoses, Risk Perceptions, and Social Distancing Compliance: An Analysis of Data from the Toledo Adolescent Relationships Study

   https://doi.org/10.18061/ojph.v4i2.8352  

   King, Ian Y.; Manning, Wendy D.; Longmore, Monica A.; Giordano, Peggy C. (January 2022, Ohio Journal of Public Health)

   Background: The health belief model suggests that individuals' beliefs affect behaviors associated with health. This study examined whether Ohioans' pre-existing medical health diagnoses affected their belief about personal health risk and their compliance with social distancing during the coronavirus disease 2019 (COVID-19) pandemic. Prior research examining physical and mental diagnoses and social distancing compliance is nearly nonexistent. We examined whether physical and mental health diagnoses influenced individuals' beliefs that their health is at risk and their adherence with social distancing guidelines. Methods: The study used longitudinal cohort data from the Toledo Adolescent Relationships Study (TARS) (n = 790), which surveyed Ohioans prior to and during the COVID-19 pandemic. Dependent variables included belief that an individual's own health was at risk and social distancing compliance. Independent variables included physical and mental health diagnoses, pandemic-related factors (fear of COVID-19, political beliefs about the pandemic, friends social distance, family social distance, COVID-19 exposure), and sociodemographic variables (age, gender, race/ethnicity, educational level). Results: Individuals who had a pre-existing physical health diagnosis were more likely to believe that their personal health was at risk during the pandemic but were not more likely to comply with social distancing guidelines. In contrast, individuals who had a pre-existing mental health diagnosis were more compliant with social distancing guidelines but were not more likely to believe their personal health was at risk. Individuals who expressed greater fear of COVID-19 believed their health is more at risk than those who expressed lower levels of fear. Conclusion: Health considerations are important to account for in assessments of responses to the pandemic, beliefs about personal health risk, and social distancing behavior. Additional research is needed to understand the divergence in the findings regarding physical health, beliefs about personal health risk, and social distancing compliance. Further, research is needed to understand how mental health issues impact decision-making related to social distancing compliance. 

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