The role of race and scientific trust on support for COVID-19 social distancing measures in the United States.
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ABSTRACT:
SUBMITTER: Kazemian S
PROVIDER: S-EPMC8270185 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundWeeks after issuing social distancing orders to suppress severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission and reduce growth in cases of severe coronavirus disease 2019 (COVID-19), all US states and the District of Columbia partially or fully relaxed these measures.MethodsWe identified all statewide social distancing measures that were implemented and/or relaxed in the United States between 10 March and 15 July 2020, triangulating data from state government and third-party sources. Using segmented linear regression, we estimated the extent to which relaxation of social distancing affected epidemic control, as indicated by the time-varying, state-specific effective reproduction number (Rt).ResultsIn the 8 weeks prior to relaxation, mean Rt declined by 0.012 units per day (95% confidence interval [CI], -.013 to -.012), and 46/51 jurisdictions achieved Rt < 1.0 by the date of relaxation. After relaxation of social distancing, Rt reversed course and began increasing by 0.007 units per day (95% CI, .006-.007), reaching a mean Rt of 1.16. Eight weeks later, the mean Rt was 1.16 and only 9/51 jurisdictions were maintaining an Rt < 1.0. Parallel models showed similar reversals in the growth of COVID-19 cases and deaths. Indicators often used to motivate relaxation at the time of relaxation (eg, test positivity rate <5%) predicted greater postrelaxation epidemic growth.ConclusionsWe detected an immediate and significant reversal in SARS-CoV-2 epidemic suppression after relaxation of social distancing measures across the United States. Premature relaxation of social distancing measures undermined the country's ability to control the disease burden associated with COVID-19.
| S-EPMC7797755 | biostudies-literature
Project description:The "Aging Science Talks: Science for the Community" daily online seminar series was established in reaction to the cancellation of a myriad of regional, national, and international meetings focused on the biology of aging due to the COVID-19 pandemic. The inability to attend scientific meetings has far-reaching implications for our field, as we lose the ability to 1) disseminate both published and non-published data through talks and posters; 2) network and establish new collaborations to produce innovative science in the aging field; and 3) continue the career development of early career researchers (ECRs). Through these virtual seminars, we hope to offset the negative effects of these canceled meetings. We established the program rapidly using a "lean" approach, making use of existing technologies broadly available at academic institutions. Here, we provide an initial description of how this program was developed and implemented. We discuss advantages and limitations of this approach, including "real-time" participation and the creation of an on/off-line community of inquiry (CoI). In the future, we hope to formally evaluate the success of this program in building engagement, creating a community, and enhancing the development of ECRs, and to capture metrics associated with the continued progress of science. Our approach to building a CoI may be applied across multiple scientific disciplines during this time of uncertainty, and may offer a valuable example of how to continue to advance science during pandemics or similar events.
| S-EPMC7290191 | biostudies-literature
Project description:With the tourism and hospitality sector reopening post-lockdown of COVID-19, the recovery of customers' purchase intentions is essential to reboot the sector. This study aims to examine the relationship between social distancing measures and purchase intentions in the UK's restaurant and hotel sectors using a propensity score weighting experimental design method. The findings suggest that the impact of social distancing measures on purchase intentions is mediated by the trust in the targeted restaurant and hotel. Risk tolerance significantly moderates the influence of social distancing measures on trust; (non-) cash promotions have an insignificant impact on purchase intentions. The introduction of the propensity score weighting scheme addresses the endogeneity caused by the sampling bias in non-probability sampling experiment studies.
| S-EPMC8490006 | biostudies-literature
Project description:BACKGROUND:Across the United States, various social distancing measures were implemented to control the spread of coronavirus disease 2019 (COVID-19). However, the effectiveness of such measures for specific regions with varying population demographic characteristics and different levels of adherence to social distancing is uncertain. OBJECTIVE:To determine the effect of social distancing measures in unique regions. DESIGN:An agent-based simulation model. SETTING:Agent-based model applied to Dane County, Wisconsin; the Milwaukee metropolitan (metro) area; and New York City (NYC). PATIENTS:Synthetic population at different ages. INTERVENTION:Different times for implementing and easing social distancing measures at different levels of adherence. MEASUREMENTS:The model represented the social network and interactions among persons in a region, considering population demographic characteristics, limited testing availability, "imported" infections, asymptomatic disease transmission, and age-specific adherence to social distancing measures. The primary outcome was the total number of confirmed COVID-19 cases. RESULTS:The timing of and adherence to social distancing had a major effect on COVID-19 occurrence. In NYC, implementing social distancing measures 1 week earlier would have reduced the total number of confirmed cases from 203 261 to 41 366 as of 31 May 2020, whereas a 1-week delay could have increased the number of confirmed cases to 1 407 600. A delay in implementation had a differential effect on the number of cases in the Milwaukee metro area versus Dane County, indicating that the effect of social distancing measures varies even within the same state. LIMITATION:The effect of weather conditions on transmission dynamics was not considered. CONCLUSION:The timing of implementing and easing social distancing measures has major effects on the number of COVID-19 cases. PRIMARY FUNDING SOURCE:National Institute of Allergy and Infectious Diseases.
| S-EPMC7598093 | biostudies-literature
Project description:In order to reduce the spread of SARS-CoV-2, much of the US was placed under social distancing guidelines during March 2020. We characterized risk perceptions and adherence to social distancing recommendations in March 2020 among US adults aged 18+ in an online survey with age and gender quotas to match the general US population (N = 713). We used multivariable logistic and linear regression to estimate associations between age (by generational cohort) and these outcomes. The median perceived risk of infection with COVID-19 within the next month was 32%, and 65% of individuals were practicing more social distancing than before the outbreak. Baby Boomers had lower perceived risk than Millennials (-10.6%, 95% CI: -16.2%, -5.0%), yet were more frequently social distancing (OR = 1.64; 95% CI: 1.05, 2.56). Public health outreach should focus on raising compliance with social distancing recommendations, especially among high risk groups. Efforts to address risk perceptions alone may be inadequate.
| S-EPMC7485770 | biostudies-literature
Project description:Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a viral pathogen that quickly became a global pandemic in the winter of 2020-2021. In response, governments issued social distancing orders to minimize transmission by reducing community contacts. We tested the efficacy of this social distancing at the state level during the first 2 months of the pandemic in the United States. We utilized data on daily SARS-CoV-2 case numbers and human community mobility (anonymized, aggregated cell phone location data stratified into six categories used as an index of social distancing), the date of government-issued social distancing orders, demographics, urbanization and public transportation. We implemented cross-correlation to identify lag times between declines in mobility and SARS-CoV-2 cases. Incorporating state-specific lag times, we tested for associations between case counts and mobility metrics using Bayesian multilevel models. Decreased mobility around grocery stores/pharmacies, retail/recreation locations, transit stations and workplaces was correlated with decreases in SARS-CoV-2 cases with significant lag times of ≥21 days. Social distancing orders were associated with fewer cumulative SARS-CoV-2 cases when they were put in place earlier. Community mobility had already started declining prior to most social distancing orders, especially the more restrictive orders implemented later in the pandemic. Social distancing is an important tool that has been implemented throughout the pandemic to decrease SARS-CoV-2 transmission, although with significant social and economic impacts. Our results suggest that declines in cases were observed several weeks subsequent to implementation of social distancing measures, and that implementing social distancing earlier could potentially minimize the duration of time these policies need to be in effect. Our findings can inform ongoing management of this pandemic and other emerging infectious disease outbreaks by identifying areas where reductions in mobility are associated with reduced disease transmission, and the expected time frame between behavioural changes and measurable population outcomes.
| S-EPMC9294068 | biostudies-literature
Project description:Community pharmacists are one of the most accessible healthcare professionals and are often served as the first point of contact when it comes to minor ailments and health advice. As such, community pharmacists can play a vital role in a country's response to various preventative and public health measures amid the COVID-19 pandemic. Given the essential nature of community pharmacy as a health service, community pharmacies are unlikely to shut down in any foreseeable lockdown scenario. It is therefore important to assess the preventative measure directives for community pharmacies that are in place to safeguard community pharmacy personnel from SARS-CoV-2 in the various parts of the world. Upon reviewing the recommendations of 15 selected countries across five continents (Asia, Europe, Oceania, North America, and Africa) on social distancing and the use of personal protective equipment (PPE) in community pharmacies, we found inconsistencies in the recommended social distance to be practiced within the community pharmacies. There were also varying recommendations on the use of PPE by the pharmacy personnel. Despite the differences in the recommendations, maintaining recommended social distance and the wearing of appropriate PPE is of utmost importance for healthcare workers, including community pharmacy personnel dealing with day-to-day patient care activities, though full PPE should be worn when dealing with suspected COVID-19 patients.
| S-EPMC7252013 | biostudies-literature
Project description:Social distancing measures have been implemented in the United States (US) since March 2020, to mitigate the spread of SARS-CoV-2, the causative agent of COVID-19. However, by mid-May most states began relaxing these measures to support the resumption of economic activity, even as disease incidence continued to increase in many states. To evaluate the impact of relaxing social distancing restrictions on COVID-19 dynamics and control in the US, we developed a transmission dynamic model and calibrated it to US state-level COVID-19 cases and deaths from March to June 20th, 2020, using Bayesian methods. We used this model to evaluate the impact of reopening, social distancing, testing, contact tracing, and case isolation on the COVID-19 epidemic in each state. We found that using stay-at-home orders, most states were able to curtail their COVID-19 epidemic curve by reducing and achieving an effective reproductive number below 1. But by June 20th, 2020, only 19 states and the District of Columbia were on track to curtail their epidemic curve with a 75% confidence, at current levels of reopening. Of the remaining 31 states, 24 may have to double their current testing and/or contact tracing rate to curtail their epidemic curve, and seven need to further restrict social contact by 25% in addition to doubling their testing and contact tracing rates. When social distancing restrictions are being eased, greater state-level testing and contact tracing capacity remains paramount for mitigating the risk of large-scale increases in cases and deaths.
| S-EPMC7362894 | biostudies-literature
Project description:BackgroundSocial distancing and public policy have been crucial for minimizing the spread of SARS-CoV-2 in the United States. Publicly available, county-level time series data on mobility are derived from individual devices with global positioning systems, providing a variety of indices of social distancing behavior per day. Such indices allow a fine-grained approach to modeling public behavior during the pandemic. Previous studies of social distancing and policy have not accounted for the occurrence of pre-policy social distancing and other dynamics reflected in the long-term trajectories of public mobility data.ObjectiveWe propose a differential equation state-space model of county-level social distancing that accounts for distancing behavior leading up to the first official policies, equilibrium dynamics reflected in the long-term trajectories of mobility, and the specific impacts of four kinds of policy. The model is fit to each US county individually, producing a nationwide data set of novel estimated mobility indices.MethodsA differential equation model was fit to three indicators of mobility for each of 3054 counties, with T=100 occasions per county of the following: distance traveled, visitations to key sites, and the log number of interpersonal encounters. The indicators were highly correlated and assumed to share common underlying latent trajectory, dynamics, and responses to policy. Maximum likelihood estimation with the Kalman-Bucy filter was used to estimate the model parameters. Bivariate distributional plots and descriptive statistics were used to examine the resulting county-level parameter estimates. The association of chronology with policy impact was also considered.ResultsMobility dynamics show moderate correlations with two census covariates: population density (Spearman r ranging from 0.11 to 0.31) and median household income (Spearman r ranging from -0.03 to 0.39). Stay-at-home order effects were negatively correlated with both (r=-0.37 and r=-0.38, respectively), while the effects of the ban on all gatherings were positively correlated with both (r=0.51, r=0.39). Chronological ordering of policies was a moderate to strong determinant of their effect per county (Spearman r ranging from -0.12 to -0.56), with earlier policies accounting for most of the change in mobility, and later policies having little or no additional effect.ConclusionsChronological ordering, population density, and median household income were all associated with policy impact. The stay-at-home order and the ban on gatherings had the largest impacts on mobility on average. The model is implemented in a graphical online app for exploring county-level statistics and running counterfactual simulations. Future studies can incorporate the model-derived indices of social distancing and policy impacts as important social determinants of COVID-19 health outcomes.
| S-EPMC7759510 | biostudies-literature
Project description:Eliminating disparities in the burden of COVID-19 requires equitable access to control measures across socio-economic groups. Limited research on socio-economic differences in mobility hampers our ability to understand whether inequalities in social distancing are occurring during the SARS-CoV-2 pandemic. To assess how mobility patterns have varied across the United States during the COVID-19 pandemic, and identify associations with socio-economic factors of populations. We used anonymized mobility data from tens of millions of devices to measure the speed and depth of social distancing at the county level between February and May 2020. Using linear mixed models, we assessed the associations between social distancing and socio-economic variables, including the proportion of people below the poverty level, the proportion of Black people, the proportion of essential workers, and the population density. We find that the speed, depth, and duration of social distancing in the United States is heterogeneous. We particularly show that social distancing is slower and less intense in counties with higher proportions of people below the poverty level and essential workers; and in contrast, that social distancing is intense in counties with higher population densities and larger Black populations. Socio-economic inequalities appear to be associated with the levels of adoption of social distancing, potentially resulting in wide-ranging differences in the impact of COVID-19 in communities across the United States. This is likely to amplify existing health disparities, and needs to be addressed to ensure the success of ongoing pandemic mitigation efforts.
| S-EPMC7668753 | biostudies-literature