Dataset Information

Social distancing, population density, and spread of COVID-19 in England: a longitudinal study.

ABSTRACT: BACKGROUND:The UK government introduced social distancing measures between 16-22 March 2020, aiming to slow down transmission of COVID-19. AIM:To explore the spreading of COVID-19 in relation to population density after the introduction of social distancing measures. DESIGN & SETTING:Longitudinal design with 5-weekly COVID-19 incidence rates per 100 000 people for 149 English Upper Tier Local Authorities (UTLAs), between 16 March and 19 April 2020. METHOD:Multivariable multilevel model to analyse weekly incidence rates per 100 000 people; time was level-1 unit and UTLA level-2 unit. Population density was divided into quartiles. The model included an interaction between week and population density. Potential confounders were percentage aged ?65, percentage non-white British, and percentage in two highest classes of the National Statistics Socioeconomic Classification. Co-variates were male life expectancy at birth, and COVID-19 prevalence rate per 100 000 people on March 15. Confounders and co-variates were standardised around the mean. RESULTS:Incidence rates per 100 000 people peaked in the week of March 30-April 5, showing higher adjusted incidence rate per 100 000 people (46.2; 95% confidence interval [CI] = 40.6 to 51.8) in most densely populated ULTAs (quartile 4) than in less densely populated ULTAs (quartile 1: 33.3, 95% CI = 27.4 to 37.2; quartile 2: 35.9, 95% CI = 31.6 to 40.1). Thereafter, incidence rate dropped in the most densely populated ULTAs resulting in rate of 22.4 (95% CI = 16.9 to 28.0) in the week of April 13-19; this was lower than in quartiles 1, 2, and 3, respectively 31.4 (95% CI = 26.5 to 36.3), 34.2 (95% CI = 29.9 to 38.5), and 43.2 (95% CI = 39.0 to 47.4). CONCLUSION:After the introduction of social distancing measures, the incidence rates per 100 000 people dropped stronger in most densely populated ULTAs.

SUBMITTER: Tammes P

PROVIDER: S-EPMC7465584 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Social distancing, population density, and spread of COVID-19 in England: a longitudinal study.

Tammes Peter P

BJGP open 20200825 3

<h4>Background</h4>The UK government introduced social distancing measures between 16-22 March 2020, aiming to slow down transmission of COVID-19.<h4>Aim</h4>To explore the spreading of COVID-19 in relation to population density after the introduction of social distancing measures.<h4>Design & setting</h4>Longitudinal design with 5-weekly COVID-19 incidence rates per 100 000 people for 149 English Upper Tier Local Authorities (UTLAs), between 16 March and 19 April 2020.<h4>Method</h4>Multivariab ...[more]

PMID: 32636204

Similar Datasets

Project description:BackgroundSocial distancing measures to address the US coronavirus disease 2019 (COVID-19) epidemic may have notable health and social impacts.Methods and findingsWe conducted a longitudinal pretest-posttest comparison group study to estimate the change in COVID-19 case growth before versus after implementation of statewide social distancing measures in the US. The primary exposure was time before (14 days prior to, and through 3 days after) versus after (beginning 4 days after, to up to 21 days after) implementation of the first statewide social distancing measures. Statewide restrictions on internal movement were examined as a secondary exposure. The primary outcome was the COVID-19 case growth rate. The secondary outcome was the COVID-19-attributed mortality growth rate. All states initiated social distancing measures between March 10 and March 25, 2020. The mean daily COVID-19 case growth rate decreased beginning 4 days after implementation of the first statewide social distancing measures, by 0.9% per day (95% CI -1.4% to -0.4%; P < 0.001). We did not observe a statistically significant difference in the mean daily case growth rate before versus after implementation of statewide restrictions on internal movement (0.1% per day; 95% CI -0.04% to 0.3%; P = 0.14), but there is substantial difficulty in disentangling the unique associations with statewide restrictions on internal movement from the unique associations with the first social distancing measures. Beginning 7 days after social distancing, the COVID-19-attributed mortality growth rate decreased by 2.0% per day (95% CI -3.0% to -0.9%; P < 0.001). Our analysis is susceptible to potential bias resulting from the aggregate nature of the ecological data, potential confounding by contemporaneous changes (e.g., increases in testing), and potential underestimation of social distancing due to spillover effects from neighboring states.ConclusionsStatewide social distancing measures were associated with a decrease in the COVID-19 case growth rate that was statistically significant. Statewide social distancing measures were also associated with a decrease in the COVID-19-attributed mortality growth rate beginning 7 days after implementation, although this decrease was no longer statistically significant by 10 days.

Project description:ImportancePolicies to promote social distancing can minimize COVID-19 transmission but come with substantial social and economic costs. Quantifying relative preferences among the public for such practices can inform locally relevant policy prioritization and optimize uptake.ObjectiveTo evaluate relative utilities (ie, preferences) for COVID-19 pandemic social distancing strategies against the hypothetical risk of acquiring COVID-19 and anticipated income loss.Design, setting, and participantsThis survey study recruited individuals living in the Missouri area from May to June 2020 via randomly distributed unincentivized social media advertisements and local recruitment platforms for members of minority racial and ethnic groups. Participants answered 6 questions that asked them to choose between 2 hypothetical counties where business closures, social distancing policy duration, COVID-19 infection risk, and income loss varied.Main outcomes and measuresReweighted population-level relative preferences (utilities) for social distancing policies, subgroups, and latent classes.ResultsThe survey had a 3% response rate (3045 of 90 320). Of the 2428 respondents who completed the survey, 1669 (75%) were 35 years and older, 1536 (69%) were women, and 1973 (89%) were White. After reweighting to match Missouri population demographic characteristics, the strongest preference was for the prohibition of large gatherings (mean preference, -1.43; 95% CI, -1.67 to -1.18), with relative indifference to the closure of social and lifestyle venues (mean preference, 0.05; 95% CI, -0.08 to 0.17). There were weak preferences to keep outdoor venues (mean preference, 0.50; 95% CI, 0.39 to 0.61) and schools (mean preference, 0.18; 95% CI, 0.05 to 0.30) open. Latent class analysis revealed 4 distinct preference phenotypes in the population: risk averse (48.9%), conflicted (22.5%), prosocial (14.9%), and back to normal (13.7%), with men twice as likely as women to belong to the back to normal group than the risk averse group (relative risk ratio, 2.19; 95% CI, 1.54 to 3.12).Conclusions and relevanceIn this survey study using a discrete choice experiment, public health policies that prohibited large gatherings, as well as those that closed social and lifestyle venues, appeared to be acceptable to the public. During policy implementation, these activities should be prioritized for first-phase closures. These findings suggest that policy messages that address preference heterogeneity (eg, focusing on specific preference subgroups or targeting men) could improve adherence to social distancing measures for COVID-19 and future pandemics.

Project description:BackgroundDue to the absence of a vaccine of SARS-CoV-2 virus pandemic, the most effective way to reduce transmission of the virus is by applying social distancing practices. Exploring factors that determine whether people adopt social distancing measures is, therefore, critical to reducing the spread of the virus. This study aimed to investigate people's intentions to socially distance based on the extended Theory of Planned Behavior.MethodsA questionnaire was distributed to the sample population and collected through social media online and WhatsApp groups from March 26, 2020 to March 29, 2020. There were 823 analyzed out of 1870 responses. The extended TPB variables and risk perception were measured using a 7-point scale (scored from 1 to 7). Data were analyzed using the partial least squares (PLS) structural equation modeling method.ResultsStudy found that the factors influencing the intention to perform social distancing were subjective norms and perceived behavior control. Risk perception affected attitudes, subjective norms, and perceived behavioral control. Media exposure was found to determine attitudes, subjective norms, and risk perceptions. The relationship between attitudes and intentions, and media use and perceived behavior control were not significant. The better and effective risk communication that can change the level of risk perception, raise family and religious leader as well as increase to control behavior are the keys to people's perform social distancing. Results from a multigroup analysis revealed that younger individuals showed a stronger relationship between the influence of risk perception and PBC and media use on risk perception. The predictive strength of subjective norms from risk perception and risk perception from media use was more strongly associated with rural populations.ConclusionsThe results of study provide an initial understanding of the level of the public's risk perception to reduce the spread of SARS-CoV-2. Findings also revealed the role of media use in determining risk perception, attitudes and subjective norms and, in turn, change people's intention to socially distance. This study may add to the literature of behavioral changes in pandemic and provide a framework for both policymakers and practitioners to formulate effective interventions in the future.

Dataset Information

Social distancing, population density, and spread of COVID-19 in England: a longitudinal study.

Publications

Social distancing, population density, and spread of COVID-19 in England: a longitudinal study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets