scholarly journals Differential impact of mitigation policies and socioeconomic status on COVID-19 prevalence and social distancing in the United States

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hsien-Yen Chang ◽  
Wenze Tang ◽  
Elham Hatef ◽  
Christopher Kitchen ◽  
Jonathan P. Weiner ◽  
...  
Keyword(s):  
Impact Analysis ◽  
The United States ◽  
Relative Increase ◽  
Socioeconomic Disadvantage ◽  
Area Deprivation ◽  
Interrupted Time Series Analysis ◽  
Differential Impact ◽  
Social Distancing ◽  
Mitigation Policies ◽  
At Home

Abstract Background The spread of COVID-19 has highlighted the long-standing health inequalities across the U.S. as neighborhoods with fewer resources were associated with higher rates of COVID-19 transmission. Although the stay-at-home order was one of the most effective methods to contain its spread, residents in lower-income neighborhoods faced barriers to practicing social distancing. We aimed to quantify the differential impact of stay-at-home policy on COVID-19 transmission and residents’ mobility across neighborhoods of different levels of socioeconomic disadvantage. Methods This was a comparative interrupted time-series analysis at the county level. We included 2087 counties from 38 states which both implemented and lifted the state-wide stay-at-home order. Every county was assigned to one of four equally-sized groups based on its levels of disadvantage, represented by the Area Deprivation Index. Prevalence of COVID-19 was calculated by dividing the daily number of cumulative confirmed COVID-19 cases by the number of residents from the 2010 Census. We used the Social Distancing Index (SDI), derived from the COVID-19 Impact Analysis Platform, to measure the mobility. For the evaluation of implementation, the observation started from Mar 1st 2020 to 1 day before lifting; and, for lifting, it ranged from 1 day after implementation to Jul 5th 2020. We calculated a comparative change of daily trends in COVID-19 prevalence and Social Distancing Index between counties with three highest disadvantage levels and those with the least level before and after the implementation and lifting of the stay-at-home order, separately. Results On both stay-at-home implementation and lifting dates, COVID-19 prevalence was much higher among counties with the highest or lowest disadvantage level, while mobility decreased as the disadvantage level increased. Mobility of the most disadvantaged counties was least impacted by stay-at-home implementation and relaxation compared to counties with the most resources; however, disadvantaged counties experienced the largest relative increase in COVID-19 infection after both stay-at-home implementation and relaxation. Conclusions Neighborhoods with varying levels of socioeconomic disadvantage reacted differently to the implementation and relaxation of COVID-19 mitigation policies. Policymakers should consider investing more resources in disadvantaged counties as the pandemic may not stop until most neighborhoods have it under control.

scholarly journals Differential Impact of Mitigation Policies and Socioeconomic Status on COVID-19 Prevalence and Social Distancing in the United States

2020 ◽  
Author(s):  
Hsien-Yen Chang ◽  
Wenze Tang ◽  
Elham Hatef ◽  
Christopher Kitchen ◽  
Jonathan P. Weiner ◽  
...  
Keyword(s):  
Impact Analysis ◽  
The United States ◽  
Socioeconomic Disadvantage ◽  
Area Deprivation ◽  
Interrupted Time Series Analysis ◽  
Differential Impact ◽  
Social Distancing ◽  
The Social ◽  
Mitigation Policies ◽  
At Home

AbstractBackgroundThe spread of COVID-19 has highlighted the long-standing health inequalities across the U.S. as neighborhoods with fewer resources were associated with higher rates of COVID-19 transmission. Although the stay-at-home order was one of the most effective methods to contain its spread, residents in lower-income neighborhoods faced barriers to practicing social distancing. We aimed to quantify the differential impact of stay-at-home policy on COVID-19 transmission and residents’ mobility across neighborhoods of different levels of socioeconomic disadvantage.MethodsThis was a comparative interrupted time-series analysis at the county level. We included 2,087 counties from 38 states which both implemented and lifted the state-wide stay-at-home order. Every county was assigned to one of four equally-sized groups based on its levels of disadvantage, represented by the Area Deprivation Index. Prevalence of COVID-19 was calculated by dividing the daily number of cumulative confirmed COVID-19 cases by the number of residents from the 2010 Census. We used the Social Distancing Index, derived from the COVID-19 Impact Analysis Platform, to measure the social distancing practice. For the evaluation of implementation, the observation started from Mar 1 St 2020 to one day before lifting; and, for lifting, it ranged from one day after implementation to Jul 5 th 2020. We calculated a comparative change of daily trends in COVID-19 prevalence and Social Distancing Index between counties with three highest disadvantage levels and those with the least level before and after the implementation and lifting of the stay-at-home order, separately.ResultsOn both stay-at-home implementation and lifting dates, COVID-19 prevalence was much higher among counties with the highest or lowest disadvantage level, while mobility decreased as the disadvantage level increased. Mobility of the most disadvantaged counties was least impacted by stay-at-home implementation and relaxation compared to counties with the most resources; however, disadvantaged counties experienced the largest relative increase in COVID-19 infection after both stay-at-home implementation and relaxation.ConclusionsNeighborhoods with varying levels of socioeconomic disadvantage reacted differently to the implementation and relaxation of COVID-19 mitigation policies. Policymakers should consider investing more resources in disadvantaged counties as the pandemic may not stop until most neighborhoods have it under control.

scholarly journals Differential Impact of Mitigation Policies and Socioeconomic Status on COVID-19 Prevalence and Social Distancing in the United States

2020 ◽  
Author(s):  
Hsien-Yen Chang ◽  
Wenze Tang ◽  
Elham Hatef ◽  
Christopher Kitchen ◽  
Jonathan P. Weiner ◽  
...  
Keyword(s):  
Impact Analysis ◽  
The United States ◽  
Socioeconomic Disadvantage ◽  
Area Deprivation ◽  
Interrupted Time Series Analysis ◽  
Differential Impact ◽  
Social Distancing ◽  
The Social ◽  
Mitigation Policies ◽  
At Home

Abstract BackgroundThe spread of COVID-19 has highlighted the long-standing health inequalities across the U.S. as neighborhoods with fewer resources were associated with higher rates of COVID-19 transmission. Although the stay-at-home order was one of the most effective methods to contain its spread, residents in lower-income neighborhoods faced barriers to practicing social distancing. We aimed to quantify the differential impact of stay-at-home policy on COVID-19 transmission and residents’ mobility across neighborhoods of different levels of socioeconomic disadvantage.MethodsThis was a comparative interrupted time-series analysis at the county level. We included 2,087 counties from 38 states which both implemented and lifted the state-wide stay-at-home order. Every county was assigned to one of four equally-sized groups based on its levels of disadvantage, represented by the Area Deprivation Index. Prevalence of COVID-19 was calculated by dividing the daily number of cumulative confirmed COVID-19 cases by the number of residents from the 2010 Census. We used the Social Distancing Index, derived from the COVID-19 Impact Analysis Platform, to measure the social distancing practice. For the evaluation of implementation, the observation started from Mar 1st 2020 to one day before lifting; and, for lifting, it ranged from one day after implementation to Jul 5th 2020. We calculated a comparative change of daily trends in COVID-19 prevalence and Social Distancing Index between counties with three highest disadvantage levels and those with the least level before and after the implementation and lifting of the stay-at-home order, separately.ResultsOn both stay-at-home implementation and lifting dates, COVID-19 prevalence was much higher among counties with the highest or lowest disadvantage level, while mobility decreased as the disadvantage level increased. Mobility of the most disadvantaged counties was least impacted by stay-at-home implementation and relaxation compared to counties with the most resources; however, disadvantaged counties experienced the largest relative increase in COVID-19 infection after both stay-at-home implementation and relaxation.ConclusionsNeighborhoods with varying levels of socioeconomic disadvantage reacted differently to the implementation and relaxation of COVID-19 mitigation policies. Policymakers should consider investing more resources in disadvantaged counties as the pandemic may not stop until most neighborhoods have it under control.

scholarly journals Social Distancing Interventions in the United States: An Exploratory Investigation of Determinants and Impacts

2020 ◽  
Author(s):  
Shenyang Guo ◽  
Ruopeng An ◽  
Timothy D. McBride ◽  
Danlin Yu ◽  
Linyun Fu ◽  
...  
Keyword(s):  
United States ◽  
Impact Analysis ◽  
Random Effect ◽  
The United States ◽  
Mitigation Measures ◽  
Optimal Timing ◽  
Washington Dc ◽  
Spatial Error ◽  
Social Distancing ◽  
At Home

AbstractBackgroundTo combat the Covid-19 pandemic in the United States, many states and Washington DC enacted Stay-at-Home order and nonpharmaceutical mitigation interventions. This study examined the determinants of the timing to implement an intervention. Through an impact analysis, the study explored the effects of the interventions and the potential risks of removing them under the context of reopening the economy.MethodA content analysis identified nine types of mitigation interventions and the timing at which states enacted these strategies. A proportional hazard model, a multiple-event survival model, and a random-effect spatial error panel model in conjunction with a robust method analyzing zero-inflated and skewed outcomes were employed in the data analysis.FindingsTo our knowledge, we provided in this article the first explicit analysis of the timing, determinants, and impacts of mitigation interventions for all states and Washington DC in the United States during the first five weeks of the pandemic. Unlike other studies that evaluate the Stay-at-Home order by using simulated data, the current study employed the real data of various case counts of Covid-19. The study obtained two meritorious findings: (1) states with a higher prevalence of Covid-19 cases per 10,000 population reacted more slowly to the outbreak, suggesting that some states may have missed the optimal timing to prevent the wide spread of the Covid-19 disease; and (2) of nine mitigation measures, three (non-essential business closure, large-gathering bans, and restaurant/bar limitations) showed positive impacts on reducing cumulative cases, new cases, and death rates across states.InterpretationThe opposite direction of the prevalence rate on the timing of issuing the mitigation interventions partially explains why the Covid-19 caseload in the U.S. remains high. A swift implementation of social distancing is crucial— the key is not whether such measures should be taken but when. Because there is no preventive vaccine and because there are few potentially effective treatments, recent reductions in new cases and deaths must be due, in large part, to the social interventions delivered by states. The study suggests that the policy of reopening economy needs to be implemented carefully.

scholarly journals That Woman from Michigan: How Gender Resentment Shapes the Efficacy of Stay-at-Home Policies

2020 ◽  
Vol 16 (4) ◽  
pp. 983-990
Author(s):  
Nicole Kalaf-Hughes ◽  
Debra Leiter
Keyword(s):  
Government Policy ◽  
The United States ◽  
Election Study ◽  
Female Leaders ◽  
Political Leaders ◽  
Social Distancing ◽  
National Election Study ◽  
American National Election Study ◽  
Using Data ◽  
At Home

AbstractThe United States has the highest number of COVID-19 cases, yet many Americans have responded indifferently toward policies designed to combat the spread of the virus. While nearly all 50 states have implemented some type of stay-at-home policy to encourage social distancing, there has been high variation in the degree of compliance. We argue that this variance is partly driven by gender resentment. Gender resentment reduces trust in female political leaders and thus decreases compliance with government policy and recommendations. Using data from SafeGraph and the 2016 American National Election Study, we demonstrate that the effect of stay-at-home policies on social distancing is reduced when gender resentment increases in states with female leaders. However, when gender resentment is low, there is no difference in the effect of policies on behavior. This research has important implications for understanding unseen barriers that can mediate the efficacy of female political leaders.

scholarly journals Predictive Modeling on the Number of Covid-19 Death Toll in the United States Considering the Effects of Coronavirus-Related Changes and Covid-19 Recovered Cases

2020 ◽  
Vol 5 (6) ◽  
pp. 1140-1155 ◽  
Author(s):  
Hoang Pham
Keyword(s):  
United States ◽  
United Kingdom ◽  
Policy Decision ◽  
The United States ◽  
Social Distancing ◽  
Death Toll ◽  
The United Kingdom ◽  
First Case ◽  
Staying At Home ◽  
At Home

COVID-19 is caused by a coronavirus called SARS-CoV-2. Many countries around the world implemented their own policies and restrictions designed to limit the spread of Covid-19 in recent months. Businesses and schools transitioned into working and learning remotely. In the United States, many states were under strict orders to stay home at least in the month of April. In recent weeks, there are some significant changes related restrictions include social-distancing, reopening states, and staying-at-home orders. The United States surpassed 2 million coronavirus cases on Monday, June 15, 2020 less than five months after the first case was confirmed in the country. The virus has killed at least 115,000 people in the United States as of Monday, June 15, 2020, according to data from Johns Hopkins University. With the recent easing of coronavirus-related restrictions and changes on business and social activity such as stay-at-home, social distancing since late May 2020 hoping to restore economic and business activities, new Covid-19 outbreaks are on the rise in many states across the country. Some researchers expressed concern that the process of easing restrictions and relaxing stay-at-home orders too soon could quickly surge the number of infected Covid-19 cases as well as the death toll in the United States. Some of these increases, however, could be due to more testing sites in the communities while others may be are the results of easing restrictions due to recent reopening and changed policies, though the number of daily death toll does not appear to be going down in recent days due to Covid-19 in the U.S. This raises the challenging question: • How can policy decision-makers and community leaders make the decision to implement public policies and restrictions and keep or lift staying-at-home orders of ongoing Covid-19 pandemic for their communities in a scientific way? In this study, we aim to develop models addressing the effects of recent Covid-19 related changes in the communities such as reopening states, practicing social-distancing, and staying-at-home orders. Our models account for the fact that changes to these policies which can lead to a surge of coronavirus cases and deaths, especially in the United States. Specifically, in this paper we develop a novel generalized mathematical model and several explicit models considering the effects of recent reopening states, staying-at-home orders and social-distancing practice of different communities along with a set of selected indicators such as the total number of coronavirus recovered and new cases that can estimate the daily death toll and total number of deaths in the United States related to Covid-19 virus. We compare the modeling results among the developed models based on several existing criteria. The model also can be used to predict the number of death toll in Italy and the United Kingdom (UK). The results show very encouraging predictability for the proposed models in this study. The model predicts that 128,500 to 140,100 people in the United States will have died of Covid-19 by July 4, 2020. The model also predicts that between 137,900 and 154,000 people will have died of Covid-19 by July 31, and 148,500 to 169,700 will have died by the end of August 2020, as a result of the SARS-CoV-2 coronavirus that causes COVID-19 based on the Covid-19 death data available on June 13, 2020. The model also predicts that 34,900 to 37,200 people in Italy will have died of Covid-19 by July 4, and 36,900 to 40,400 people will have died by the end of August based on the data available on June 13, 2020. The model also predicts that between 43,500 and 46,700 people in the United Kingdom will have died of Covid-19 by July 4, and 48,700 to 51,900 people will have died by the end of August, as a result of the SARS-CoV-2 coronavirus that causes COVID-19 based on the data available on June 13, 2020. The model can serve as a framework to help policy makers a scientific approach in quantifying decision-makings related to Covid-19 affairs.

StayWell at Home: A Text Messaging Intervention to Counteract Depression and Anxiety during COVID-19 Social Distancing (Preprint)

2021 ◽  
Author(s):  
Adrian Aguilera ◽  
Rosa Hernandez-Ramos ◽  
Alein Yaritza Haro ◽  
Claire Elizabeth Boone ◽  
Tiffany Luo ◽  
...  
Keyword(s):  
Mental Health ◽  
Text Messaging ◽  
The United States ◽  
Text Messages ◽  
Anxiety Symptoms ◽  
Anxiety And Depression ◽  
Depression And Anxiety ◽  
Negative Consequences ◽  
Social Distancing ◽  
At Home

BACKGROUND Social distancing and stay-at-home orders are critical interventions to slow down person-to-person transmission of COVID-19. While these societal changes help to contain the pandemic, they also have unintended negative consequences, including anxiety and depression. We developed StayWell, a daily skills-based SMS text messaging program, to mitigate COVID-19 related depression and anxiety symptoms among people who speak English and Spanish in the United States. OBJECTIVE This paper describes the changes in the anxiety and depression levels of participants in the StayWell program after 60 days of exposure to skills-based SMS text messages. METHODS We used self-administered, empirically supported web-based questionnaires to assess the demographic and clinical characteristics of StayWell participants. Anxiety and depression were measured using the 2-item Generalized Anxiety Disorder (GAD-2) scale and the 8-item Patient Health Quesstionanire-8 (PHQ-8) scale at baseline and 60-day timepoints. We used paired t-tests to detect the change in PHQ-8 and GAD-2 scores from baseline to follow-up measured 60 days later. RESULTS The analytic sample includes 193 participants who completed both the baseline and 60-day exit questionnaires. At the 60-day time point, there were statistically significant reductions in both PHQ-8 and GAD-2 scores from baseline. We found an average reduction of -1.72 (95% CI: -2.35, -1.09) in PHQ-8 scores and -0.48 (95% CI: -0.71, -0.25) in GAD-2 scores. This translated to an 18.5% and 17.2% reduction in mean PHQ-8 scores and GAD-2, respectively. CONCLUSIONS StayWell is a low-intensity, cost-effective, and accessible population-level mental health intervention. Participation in StayWell focused on COVID-19 mental health coping skills and was related to improved depression and anxiety symptoms. In addition to improvements in outcomes, we found high levels of engagement during the 60-day intervention period. Text messaging interventions could serve as an important public health tool for disseminating strategies to manage mental health. CLINICALTRIAL ClinicalTrials.gov Identifier: NCT04473599 INTERNATIONAL REGISTERED REPORT RR2-10.2196/23592

scholarly journals The Immediate Effect of COVID-19 Policies on Social-Distancing Behavior in the United States

2021 ◽  
Vol 136 (2) ◽  
pp. 245-252 ◽  
Author(s):  
Rahi Abouk ◽  
Babak Heydari
Keyword(s):  
United States ◽  
Early Stage ◽  
Public Awareness ◽  
The United States ◽  
School Closure ◽  
Grocery Stores ◽  
Social Distancing ◽  
Percentage Points ◽  
Before And After ◽  
At Home

Objective Although anecdotal evidence indicates the effectiveness of coronavirus disease 2019 (COVID-19) social-distancing policies, their effectiveness in relation to what is driven by public awareness and voluntary actions needs to be determined. We evaluated the effectiveness of the 6 most common social-distancing policies in the United States (statewide stay-at-home orders, limited stay-at-home orders, nonessential business closures, bans on large gatherings, school closure mandates, and limits on restaurants and bars) during the early stage of the pandemic. Methods We applied difference-in-differences and event-study methodologies to evaluate the effect of the 6 social-distancing policies on Google-released aggregated, anonymized daily location data on movement trends over time by state for all 50 states and the District of Columbia in 6 location categories: retail and recreation, grocery stores and pharmacies, parks, transit stations, workplaces, and residences. We compared the outcome of interest in states that adopted COVID-19–related policies with states that did not adopt such policies, before and after these policies took effect during February 15–April 25, 2020. Results Statewide stay-at-home orders had the strongest effect on reducing out-of-home mobility and increased the time people spent at home by an estimated 2.5 percentage points (15.2%) from before to after policies took effect. Limits on restaurants and bars ranked second and resulted in an increase in presence at home by an estimated 1.4 percentage points (8.5%). The other 4 policies did not significantly reduce mobility. Conclusion Statewide stay-at-home orders and limits on bars and restaurants were most closely linked to reduced mobility in the early stages of the COVID-19 pandemic, whereas the potential benefits of other such policies may have already been reaped from voluntary social distancing. Further research is needed to understand how the effect of social-distancing policies changes as voluntary social distancing wanes during later stages of a pandemic.

scholarly journals Time Spent at Home and Alone in the United States, 2019

2021 ◽  
Author(s):  
Kelsey Jeanne Drotning
Keyword(s):  
United States ◽  
Time Use ◽  
Low Income ◽  
Black People ◽  
The United States ◽  
Foreign Born ◽  
Social Distancing ◽  
American Time Use Survey ◽  
Time Use Survey ◽  
At Home

Social distancing conditions implemented in response to the Covid-19 pandemic significantly altered where and with whom people were able to spend their time. By examining data from the 2019 American Time Use Survey, this study provides a baseline of how much time people spent at home, alone, and alone at home prior to the onset of the pandemic. Men, Black people, older adults, low-income households, foreign-born adults, people who live alone, and people who are unemployed spend more time alone than other groups. These findings highlight which groups in the United States already spent more time at home and more time alone pre-pandemic, forecasting how other groups time use may shift in response to Covid-19 pandemic social distancing regulations.

scholarly journals Expected Health Effects of Reduced Air Pollution from COVID-19 Social Distancing

Atmosphere ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 951
Author(s):  
Steve Cicala ◽  
Stephen P. Holland ◽  
Erin T. Mansur ◽  
Nicholas Z. Muller ◽  
Andrew J. Yates
Keyword(s):  
Air Pollution ◽  
Health Effects ◽  
Electricity Consumption ◽  
The United States ◽  
Time Frame ◽  
Social Distancing ◽  
Mobility Data ◽  
Premature Deaths ◽  
The Impact ◽  
At Home

The COVID-19 pandemic resulted in stay-at-home policies and other social distancing behaviors in the United States in spring of 2020. This paper examines the impact that these actions had on emissions and expected health effects through reduced personal vehicle travel and electricity consumption. Using daily cell phone mobility data for each U.S. county, we find that vehicle travel dropped about 40% by mid-April across the nation. States that imposed stay-at-home policies before March 28 decreased travel slightly more than other states, but travel in all states decreased significantly. Using data on hourly electricity consumption by electricity region (e.g., balancing authority), we find that electricity consumption fell about 6% on average by mid-April with substantial heterogeneity. Given these decreases in travel and electricity use, we estimate the county-level expected improvements in air quality, and, therefore, expected declines in mortality. Overall, we estimate that, for a month of social distancing, the expected premature deaths due to air pollution from personal vehicle travel and electricity consumption declined by approximately 360 deaths, or about 25% of the baseline 1500 deaths. In addition, we estimate that CO2 emissions from these sources fell by 46 million metric tons (a reduction of approximately 19%) over the same time frame.

scholarly journals The Impact of COVID-19 Lockdowns on Mental Health Patient Populations: Evidence from Medical Claims Data

2021 ◽  
Author(s):  
Ibtihal Ferwana ◽  
Lav R. Varshney
Keyword(s):  
Mental Health ◽  
Mental Health Treatment ◽  
Claims Data ◽  
School Closures ◽  
Health Treatment ◽  
Medical Claims ◽  
Social Distancing ◽  
Mental Health Patients ◽  
Mitigation Policies ◽  
At Home

Background Social distancing policies were enacted during March 2020 to limit the spread of COVID-19. Lockdowns and movement restrictions increased the potential of negative impact on population mental health, in which depression and anxiety symptoms were frequently reported by different population groups during COVID-19 lockdown. However, the causal relationship of mitigation policies on national-wide mental health treatment is lacking. Objective This study investigates the effect of COVID-19 mitigation measures on mental health across the UnitedStates, on county and state levels. It examines the effect on the total mental health patients, different age and gender groups, and patients of selected mental health diagnoses. Methods We used large-scale medical claims data for mental health patients dated from September 1, 2019 to December31, 2020, with publicly available state- and county-specific COVID-19 cases from first case in January to December 31,2020, and used publicly available lockdown dates data for states and counties. We designed a difference-in-differences(DID) model, which infers the causal effect of a policy intervention by comparing pre-policy and post-policy periods indifferent regions. We mainly focused on two types of social distancing policies, stay-at-home and school closure orders. Results Based on common pre-treatment trend assumption of regions, we find that lockdown has significantly and causally increased seeking medical treatment for mental health across counties and states. Mental health patients inregions with lockdown orders have significantly increased by 18% compared to 1% decline in regions without a lockdown.Also, female populations have been exposed to a larger lockdown effect on their mental health with 24% increase in regions with lockdowns compared to 3% increase in regions without. While male mental health patients decreased by 5% in regions without lockdowns. Patients diagnosed with panic disorders and reaction to severe stress both were significantly exposed to a significant large effect of lockdowns. Also, life management difficulty patients doubled in regions with stay-at-home orders but increased less with school closures. Contrarily, attention-deficit hyperactivity patients declined in regions without stay-at-home orders. The number of mental health patients older than 80 decreased in regions with lockdowns. Adults between (21-40) years old were exposed to the greatest lockdown effect with patient number increasing between 20% to 30% in regions with lockdown. Adolescent patients under 21 increased in regions with school closures. Conclusion Although non-pharmaceutical intervention policies were effective in containing the spread of COVID-19, our results show that mitigation policies led to population-wide increase in mental health patients. Our results suggest the need for greater mental health treatment resources in the face of lockdown policies

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