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 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 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.

The Social Impacts of Wetland Mitigation Policies in the United States

2008 ◽  
Vol 22 (4) ◽  
pp. 341-357 ◽  
Author(s):  
Todd BenDor ◽  
Nicholas Brozović ◽  
Varkki George Pallathucheril
Keyword(s):  
United States ◽  
The United States ◽  
Social Impacts ◽  
Wetland Mitigation ◽  
The Social ◽  
Mitigation Policies

scholarly journals Social Capital Dimensions are Differentially Associated with COVID-19 Vaccinations, Masks, and Physical Distancing

2021 ◽  
Author(s):  
Ibtihal Ferwana ◽  
Lav R. Varshney
Keyword(s):  
Public Health ◽  
United States ◽  
Social Capital ◽  
Community Health ◽  
Collective Efficacy ◽  
Residential Mobility ◽  
The United States ◽  
Social Distancing ◽  
The Social ◽  
Family Unity

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.

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

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260818
Author(s):  
Ibtihal Ferwana ◽  
Lav R. Varshney
Keyword(s):  
Public Health ◽  
United States ◽  
Social Capital ◽  
Community Health ◽  
Collective Efficacy ◽  
Residential Mobility ◽  
The United States ◽  
Social Distancing ◽  
The Social ◽  
Family Unity

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.

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.

scholarly journals Social Responsibilities in COVID-19: Pandemic

2021 ◽  
pp. 149-153
Author(s):  
Mayur B. Wanjari ◽  
Deeplata Mendhe ◽  
Pratibha Wankhede ◽  
Sagar Alwadkar
Keyword(s):  
Death Rate ◽  
Respiratory Infections ◽  
The United States ◽  
Social Distancing ◽  
Social Responsibilities ◽  
The Social ◽  
The World ◽  
Coronavirus Infection ◽  
Novel Coronavirus ◽  
Day By Day

Recent coronavirus discovered causes the coronavirus infection COVID-19 is also an infectious disease known to cause severe respiratory infections. This most recent virus and infection were unidentified until the epidemic in Wuhan in December 2019, China. Coronavirus has spread around the world and been declared a pandemic by the WHO. The disease has infected several nations, including Italy, Spain, and the United States, with brutality as the death rate rises day by day. The illness may transmit to cough or sneezes via small droplets. Therefore, social distancing is the only way to prevent the transmission as There is no vaccine available for prevention from thecoronavirus. One can reduce the chances of being infected by taking some social distancing measures which will reduce COVID-19 transmission. In the pandemic COVID-19, every individual’s responsibility is to follow all the social distancing measures, to follow the lockdown without being casual about the disease, to save our self, our family, community, and nation from novel coronavirus.

scholarly journals Efforts to Protect Violence in the Households during Covid-19 in Indonesia

2021 ◽  
Vol 10 ◽  
pp. 548-553
Author(s):  
Nelvitia Purba ◽  
◽  
Reynaldi Putra Rosihan ◽  
Ali Mukti Tanjung ◽  
Rudy Pramono ◽  
...  
Keyword(s):  
Domestic Violence ◽  
Teaching And Learning ◽  
Economic Security ◽  
Social Distancing ◽  
The Social ◽  
Psychological Pressure ◽  
Women And Children ◽  
The Government ◽  
Consequences Of Violence ◽  
At Home

The social distancing appeal that the government encourages is not matched by the state's efforts to provide economic security to the community. PSBB will directly or indirectly limit the movement of the community. The teaching and learning process at schools and residents who work will be limited to working or studying at home. This limitation of activities in public spaces will have an impact on people's income, especially those in the middle to lower economy. The implementation of social distancing during the Covid-19 outbreak has increased the risk of violence against women, complicates women's economic conditions, and affirms women's social status as subordinate, or women are in a lower position than men. The formulation of the problem in this research is what is the cause of domestic violence during the covid-19 period in Indonesia, what are the prevention efforts against domestic violence during the covid-19 period. Causes of Domestic Violence During the Covid-19 Period, namely the government's appeal to the community 'at home alone', causing a separate polemic for women and children, especially those who experience economic and psychological pressure at home from extraordinary isolation measures, has prompted increasing instances of reports of domestic violence, especially women who are forced to live for months in abusive relationships. causes and consequences of violence and to prevent the occurrence of violence through primary prevention programs, policy intervention and advocacy as well as information programs and supporting initiatives through all mass media TV, social networks, cell phones.

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