Strong Social Distancing Measures in the United States Reduced the COVID-19 Growth Rate

Bundy

et al. 2020

Preprint

Background: Countries have adopted different approaches, at different times, to reduce the transmission of coronavirus disease 2019 (COVID-19). Cross-country comparison could indicate the relative efficacy of these approaches. We assess various non-pharmaceutical interventions (NPIs) over time, comparing the effects of self-imposed (i.e. voluntary) behavior change and of changes enforced via official regulations, by statistically examining their impacts on subsequent death rates in 13 European countries. Methods and findings: We examine two types of NPI: the introduction of government-enforced closure policies over time; and self-imposed alteration of individual behaviors in response to awareness of the epidemic, in the period prior to regulations. Our proxy for the latter is Google mobility data, which captures voluntary behavior change when disease salience is sufficiently high. The primary outcome variable is the rate of change in COVID-19 fatalities per day, 16-20 days after interventions take place. Linear multivariate regression analysis is used to evaluate impacts. Voluntarily reduced mobility, occurring prior to government policies, decreases the percent change in deaths per day by 9.2 percentage points (95% CI 4.5-14.0 pp). Government closure policies decrease the percent change in deaths per day by 14.0 percentage points (95% CI 10.8-17.2 pp). Disaggregating government policies, the most beneficial are intercity travel restrictions, cancelling public events, and closing non-essential workplaces. Other sub-components, such as closing schools and imposing stay-at-home rules, show smaller and statistically insignificant impacts. Conclusions: This study shows that NPIs have substantially reduced fatalities arising from COVID-19. Importantly, the effect of voluntary behavior change is of the same order of magnitude as government-mandated regulations. These findings, including the substantial variation across dimensions of closure, have implications for the phased withdrawal of government policies as the epidemic recedes, and for the possible reimposition of regulations if a second wave occurs, especially given the substantial economic and human welfare consequences of maintaining lockdowns.

Section: Discussionmentioning

confidence: 99%

Comparing the impact on COVID-19 mortality of self-imposed behavior change and of government regulations across 13 countries

Bundy

et al. 2020

Preprint

World Med & Health Policy

“…In the United States, a variety of policies were implemented at the state level or the county level including shelter‐in‐place orders (SIPOs),10 closures of restaurants/bars/entertainment‐related businesses, bans on large events, and closures of public schools. The effectiveness of these policies varied widely; SIPOs and closures of nonessential businesses worked toward curtailing COVID‐19 while the prohibition of large events and closure of public schools did not show signs of slowing down COVID‐19 (C. Courtemanche et al, 2020; C. J. Courtemanche et al, 2020; Dave et al, 2020, 2021). Statewide SIPOs had the strongest effect, accounting for a 37% decrease in confirmed cases 15 days after implementation (Abouk & Heydari, 2020).…”

Section: Introductionmentioning

confidence: 99%

Death by political party: The relationship between COVID‐19 deaths and political party affiliation in the United States

Gao

Radford

2021

This study explored social factors that are associated with the US deaths caused by COVID‐19 after the declaration of economic reopening on May 1, 2020 by President Donald Trump. We seek to understand how county‐level support for Trump interacted with social distancing policies to impact COVID‐19 death rates. Overall, controlling for several potential confounders, counties with higher levels of Trump support do not necessarily experience greater mortality rates due to COVID‐19. The predicted weekly death counts per county tended to increase over time with the implementation of several key health policies. However, the difference in COVID‐19 outcomes between counties with low and high levels of Trump support grew after several weeks of the policy implementation as counties with higher levels of Trump support suffered relatively higher death rates. Counties with higher levels of Trump support exhibited lower percentages of mobile staying at home and higher percentages of people working part time or full time than otherwise comparable counties with lower levels of Trump support. The relative negative performance of Trump‐supporting counties is robust after controlling for these measures of policy compliance. Counties with high percentages of older (aged 65 and above) persons tended to have greater death rates, as did more populous counties in general. This study indicates that policymakers should consider the risks inherent in controlling public health crises due to divisions in political ideology and confirms that vulnerable communities are at particularly high risk in public health crises.

“…Given the focus of our current study, we restrict our review to studies that explore COVID-19 transmission and mortality rate at an aggregated spatial scale. To elaborate, these studies explored COVID-19 transmission and mortality rates at the national 4–6 , regional 7 , state 8 , county 9–14 , city 15 and zip code levels 16 . A majority of these studies considered transmission rate as the response variable (transmission rate per capita).…”

Section: Introductionmentioning

confidence: 99%

“…Regarding health indicators, earlier research found that smokers, obese and individuals with existing health conditions are more likely to be severely affected by COVID-19 11 . In terms of mobility trends, studies showed that staying at home and effective mobility restriction measures significantly lower the COVID-19 transmission rate 4,7,9,10,14 while increased mobility resulted in increased COVID-19 transmission 12,19 . Finally, among health care infrastructure attributes, testing rate is linked with reduced risk of COVID-19 transmission 4,5 .…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Analysis of COVID-19 Transmission and Fatality Rates at the County level in the United States considering Socio-Demographics, Health Indicators, Mobility Trends and Health Care Infrastructure Attributes

Bhowmik

Tirtha

Iraganaboina

et al. 2020

Preprint

Background: Several research efforts have evaluated the impact of various factors including a) socio-demographics, (b) health indicators, (c) mobility trends, and (d) health care infrastructure attributes on COVID-19 transmission and mortality rate. However, earlier research focused only on a subset of variable groups (predominantly one or two) that can contribute to the COVID-19 transmission/mortality rate. The current study effort is designed to remedy this by analyzing COVID-19 transmission/mortality rates considering a comprehensive set of factors in a unified framework. Method: We study two per capita dependent variables: (1) daily COVID-19 transmission rates and (2) total COVID-19 mortality rates. The first variable is modeled using a linear mixed model while the later dimension is analyzed using a linear regression approach. The model results are augmented with a sensitivity analysis to predict the impact of mobility restrictions at a county level. Findings: Several county level factors including proportion of African-Americans, income inequality, health indicators associated with Asthma, Cancer, HIV and heart disease, percentage of stay at home individuals, testing infrastructure and Intensive Care Unit capacity impact transmission and/or mortality rates. From the policy analysis, we find that enforcing a stay at home order that can ensure a 50% stay at home rate can result in a potential reduction of about 30% in daily cases. Interpretation: The model framework developed can be employed by government agencies to evaluate the influence of reduced mobility on transmission rates at a county level while accommodating for various county specific factors. Based on our policy analysis, the study findings support a county level stay at home order for regions currently experiencing a surge in transmission. The model framework can also be employed to identify vulnerable counties that need to be prioritized based on health indicators for current support and/or preferential vaccination plans (when available). Funding: None.