scholarly journals Distancing the socially distanced: Racial/ethnic composition’s association with physical distancing in response to COVID-19 in the U.S.

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0251960
Author(s):  
Joseph Gibbons
Keyword(s):  
Hierarchical Linear Modeling ◽  
The United States ◽  
Grocery Stores ◽  
Linear Modeling ◽  
Social Distancing ◽  
Healthy Behaviors ◽  
Mobility Data ◽  
Point Increase ◽  
Black Populations ◽  
Racial Ethnic

Social distancing prescribed by policy makers in response to COVID-19 raises important questions as to how effectively people of color can distance. Due to inequalities from residential segregation, Hispanic and Black populations have challenges in meeting health expectations. However, segregated neighborhoods also support the formation of social bonds that relate to healthy behaviors. We evaluate the question of non-White distancing using social mobility data from Google on three sites: workplaces, grocery stores, and recreational locations. Employing hierarchical linear modeling and geographically weighted regression, we find the relation of race/ethnicity to COVID-19 distancing is varied across the United States. The HLM models show that compared to Black populations, Hispanic populations overall more effectively distance from recreation sites and grocery stores: each point increase in percent Hispanic was related to residents being 0.092 percent less likely (p< 0.05) to visit recreational sites and 0.127 percent less likely (p< 0.01) to visit grocery stores since the onset of COVID-19. However, the GWR models show there are places where the percent Black is locally related to recreation distancing while percent Hispanic is not. Further, these models show the association of percent Black to recreation and grocery distancing can be locally as strong as 1.057 percent (p< 0.05) and 0.989 percent (p< 0.05), respectively. Next, the HLM models identified that Black/White residential isolation was related to less distancing, with each point of isolation residents were 11.476 percent more likely (p< 0.01) to go to recreational sites and 7.493 percent more likely (p< 0.05) to visit grocery stores compared to before COVID-19. These models did not find a measurable advantage/disadvantage for Black populations in these places compared to White populations. COVID-19 policy should not assume disadvantage in achieving social distancing accrue equally to different racial/ethnic minorities.

scholarly journals Socioeconomic Disparities in Social Distancing During the COVID-19 Pandemic in the United States: Observational Study (Preprint)

2020 ◽  
Author(s):  
Romain Garnier ◽  
Jan R Benetka ◽  
John Kraemer ◽  
Shweta Bansal
Keyword(s):  
United States ◽  
Black People ◽  
The United States ◽  
Control Measures ◽  
Mobility Patterns ◽  
Poverty Level ◽  
Social Distancing ◽  
Mobility Data ◽  
Black Populations ◽  
The Impact

BACKGROUND 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. OBJECTIVE We aimed to assess how mobility patterns have varied across the United States during the COVID-19 pandemic and to identify associations with socioeconomic factors of populations. METHODS We used anonymized mobility data from tens of millions of devices to measure the speed and depth of social distancing at the county level in the United States between February and May 2020, the period during which social distancing was widespread in this country. Using linear mixed models, we assessed the associations between social distancing and socioeconomic variables, including the proportion of people in the population below the poverty level, the proportion of Black people, the proportion of essential workers, and the population density. RESULTS We found that the speed, depth, and duration of social distancing in the United States are 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; in contrast, we show that social distancing is intensely adopted in counties with higher population densities and larger Black populations. CONCLUSIONS Socioeconomic inequalities appear to be associated with the levels of adoption of social distancing, potentially resulting in wide-ranging differences in the impact of the COVID-19 pandemic in communities across the United States. These inequalities are likely to amplify existing health disparities and must be addressed to ensure the success of ongoing pandemic mitigation efforts.

scholarly journals Socio-economic disparities in social distancing during the COVID-19 pandemic in the United States

2020 ◽  
Author(s):  
Romain Garnier ◽  
Jan R. Benetka ◽  
John Kraemer ◽  
Shweta Bansal
Keyword(s):  
United States ◽  
Black People ◽  
The United States ◽  
Control Measures ◽  
Mobility Patterns ◽  
Poverty Level ◽  
Social Distancing ◽  
Mobility Data ◽  
Black Populations ◽  
The Impact

AbstractImportanceEliminating 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.ObjectiveTo assess how mobility patterns have varied across the United States during the COVID-19 pandemic, and identify associations with socio-economic factors of populations.Design, Setting, and ParticipantsWe 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.Main outcomes and ResultsWe 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.Conclusions and relevanceSocio-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.

scholarly journals Mining Google and Apple mobility data: temporal anatomy for COVID-19 social distancing

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Corentin Cot ◽  
Giacomo Cacciapaglia ◽  
Francesco Sannino
Keyword(s):  
Infection Rate ◽  
The United States ◽  
Strong Decrease ◽  
Social Distancing ◽  
Mobility Data ◽  
Actual Measure ◽  
The Us ◽  
The Impact ◽  
Political Decisions ◽  
Mobility Reduction

AbstractWe employ the Google and Apple mobility data to identify, quantify and classify different degrees of social distancing and characterise their imprint on the first wave of the COVID-19 pandemic in Europe and in the United States. We identify the period of enacted social distancing via Google and Apple data, independently from the political decisions. Our analysis allows us to classify different shades of social distancing measures for the first wave of the pandemic. We observe a strong decrease in the infection rate occurring two to five weeks after the onset of mobility reduction. A universal time scale emerges, after which social distancing shows its impact. We further provide an actual measure of the impact of social distancing for each region, showing that the effect amounts to a reduction by 20–40% in the infection rate in Europe and 30–70% in the US.

scholarly journals Disproportionate Impact of COVID-19 Pandemic on Racial and Ethnic Minorities

2020 ◽  
pp. 000313482097335
Author(s):  
Brad Boserup ◽  
Mark McKenney ◽  
Adel Elkbuli
Keyword(s):  
Ethnic Groups ◽  
Social Vulnerability ◽  
Negative Binomial ◽  
Demographic Data ◽  
National Level ◽  
The United States ◽  
Death Rates ◽  
Social Distancing ◽  
The Impact ◽  
Racial Ethnic

Background Health disparities are prevalent in many areas of medicine. We aimed to investigate the impact of the COVID-19 pandemic on racial/ethnic groups in the United States (US) and to assess the effects of social distancing, social vulnerability metrics, and medical disparities. Methods A cross-sectional study was conducted utilizing data from the COVID-19 Tracking Project and the Centers for Disease Control and Prevention (CDC). Demographic data were obtained from the US Census Bureau, social vulnerability data were obtained from the CDC, social distancing data were obtained from Unacast, and medical disparities data from the Center for Medicare and Medicaid Services. A comparison of proportions by Fisher’s exact test was used to evaluate differences between death rates stratified by age. Negative binomial regression analysis was used to predict COVID-19 deaths based on social distancing scores, social vulnerability metrics, and medical disparities. Results COVID-19 cumulative infection and death rates were higher among minority racial/ethnic groups than whites across many states. Older age was also associated with increased cumulative death rates across all racial/ethnic groups on a national level, and many minority racial/ethnic groups experienced significantly greater cumulative death rates than whites within age groups ≥ 35 years. All studied racial/ethnic groups experienced higher hospitalization rates than whites. Older persons (≥ 65 years) also experienced more COVID-19 deaths associated with comorbidities than younger individuals. Social distancing factors, several measures of social vulnerability, and select medical disparities were identified as being predictive of county-level COVID-19 deaths. Conclusion COVID-19 has disproportionately impacted many racial/ethnic minority communities across the country, warranting further research and intervention.

A Novel Intervention for System-Involved Intimate Partner Violence Survivors: Changes in Parenting

2017 ◽  
Vol 29 (3) ◽  
pp. 268-280
Author(s):  
Christopher J. Wretman ◽  
Cynthia Fraga Rizo ◽  
Rebecca J. Macy ◽  
Shenyang Guo ◽  
Dania Ermentrout
Keyword(s):  
Intimate Partner Violence ◽  
Hierarchical Linear Modeling ◽  
Partner Violence ◽  
Parenting Practices ◽  
The United States ◽  
Preliminary Evidence ◽  
Intimate Partner ◽  
Future Research ◽  
Court System ◽  
Linear Modeling

Purpose: A growing subpopulation of intimate partner violence (IPV) victims comprises mothers who have been mandated to services by either the court system or child protective services (CPS). Two human service agencies in the United States developed a 13-week novel intervention to address these women. All participants were assigned to the intervention, which featured group psychoeducation sessions, social events, and childcare. Method: This quasi-experimental study gathered preliminary evidence regarding whether the intervention promoted participants’ ( N = 70) parenting practices. Specifically, growth curve analyses using hierarchical linear modeling examined outcomes at completion (3 months) and follow-up (6 months). Results: Participants reported statistically significant improvements on key parenting practices at both postintervention time points. Conclusions: This study provides preliminary support for engaging court- and CPS-involved female IPV survivors in specialized, group-based interventions such as that investigated herein. Future research should investigate similar programs using larger samples and more robust designs.

Racial and Ethnic Disparities in Prison Admissions Across Counties: An Evaluation of Racial/Ethnic Threat, Socioeconomic Inequality, and Political Climate Explanations

2017 ◽  
Vol 10 (2) ◽  
pp. 176-202 ◽  
Author(s):  
Katherine A. Durante
Keyword(s):  
Hierarchical Linear Modeling ◽  
Socioeconomic Inequality ◽  
Ethnic Disparities ◽  
State Level ◽  
Racial And Ethnic Disparities ◽  
Political Climate ◽  
Linear Modeling ◽  
Admission Rates ◽  
Racial Ethnic ◽  
Prison Admission

Previous macro-level studies of racial and ethnic disparities in prison admissions have focused narrowly on differences in offending and have limited their analyses to national- and state-level data. This study explores three alternative explanations for inequality in prison admissions for Blacks and Latinos compared to Whites: racial/ethnic threat, socioeconomic inequality, and the political and legal climate. I analyze data from multiple county- and state-level sources and employ hierarchical linear modeling techniques to examine the role of both county- and state-level factors in producing inequality in county-level prison admission rates. Findings indicate that Black–White disparities are lower in jurisdictions with greater shares of Black citizens; however, the reverse is true for Latino–White inequality. For both comparisons, political conservatism is associated with less inequality. Results also indicate that counties with greater parity in income and employment across race/ethnicity and that are located in the South have reduced racial/ethnic disparities in prison admissions. I argue that the presence of large shares of African Americans and of Republican voters, in addition to southern location, are likely better indicators of total prison admission rates than of racial/ethnic disparities in prison admissions.

scholarly journals Mining and Validating Social Media for COVID-19-Related Human Behaviors between January and July; An Infodemiology Study (Preprint)

2021 ◽  
Author(s):  
Ashlynn R. Daughton ◽  
Courtney Diane Shelley ◽  
Martha Barnard ◽  
Dax Gerts ◽  
Chrysm Watson Ross ◽  
...  
Keyword(s):  
Public Health ◽  
Social Media ◽  
Ground Truth ◽  
The United States ◽  
Training Data ◽  
Long Term Effects ◽  
Social Distancing ◽  
Human Behaviors ◽  
Mobility Data ◽  
The Impact

BACKGROUND Health authorities can minimize the impact of an emergent infectious disease outbreak through effective and timely risk communication, which can build trust and adherence to subsequent behavioral messaging. Monitoring the psychological impacts of an outbreak, as well as public adherence to such messaging is also important for minimizing long term effects of an outbreak. OBJECTIVE We used social media data to identify human behaviors relevant to COVID-19 transmission and the perceived impacts of COVID-19 on individuals as a first step toward real time monitoring of public perceptions to inform public health communications. METHODS We develop a coding schema for 6 categories and 11 subcategories, which includes both a wide number of behaviors, as well codes focused on the impacts of the pandemic (e.g., economic and mental health impacts). We use this to develop training data and develop supervised learning classifiers for classes with sufficient labels. Classifiers that perform adequately are applied to our remaining corpus and temporal and geospatial trends are assessed. We compare the classified patterns to ground truth mobility data and actual COVID-19 confirmed cases to assess the signal achieved here. RESULTS We apply our labeling schema to ~7200 tweets. The worst performing classifiers have F1 scores of only 0.18-0.28 when trying to identify tweets about monitoring symptoms and testing. Classifiers about social distancing, however, are much stronger with F1 scores of 0.64-0.66. We applied the social distancing classifiers to over 228 million tweets. We show temporal patterns consistent with real-world events, and show correlations of up to -0.5 between social distancing signals on Twitter and ground-truth mobility throughout the United States. CONCLUSIONS Behaviors discussed on Twitter are exceptionally varied. Twitter can provide useful information for parameterizing models that incorporate human behavior as well as informing public health communication strategies by describing awareness of and compliance with suggested behaviors. CLINICALTRIAL N/A

scholarly journals The Effects of Performance Budgeting and Funding Programs on Graduation Rate in Public Four-Year Colleges and Universities

2004 ◽  
Vol 12 ◽  
pp. 22 ◽  
Author(s):  
Jung-cheol Shinn ◽  
Sande Milton
Keyword(s):  
Graduation Rates ◽  
Hierarchical Linear Modeling ◽  
Graduation Rate ◽  
Program Effectiveness ◽  
The United States ◽  
Institutional Performance ◽  
Linear Modeling ◽  
Performance Budgeting ◽  
Funding Programs ◽  
Study Population

This study was conducted to determine whether states with performance budgeting and funding (PBF) programs had improved institutional performance of higher education over the five years (1997 through 2001) considered in this study. First Time in College (FTIC) graduation rate was used as the measure of institutional performance. In this study, the unit of analysis is institution level and the study population is all public four-or-more-year institutions in the United States. To test PBF program effectiveness, Hierarchical Linear Modeling (HLM) growth analysis was applied. According to the HLM analysis, the growth of graduation rates in states with PBF programs was not greater than in states without PBF programs. The lack of growth in institutional graduation rates, however, does not mean that PBF programs failed to achieve their goals. Policy-makers are advised to sustain PBF programs long enough until such programs bear their fruits or are proven ineffective.

scholarly journals The Twitter Social Mobility Index: Measuring Social Distancing Practices With Geolocated Tweets (Preprint)

2020 ◽  
Author(s):  
Paiheng Xu ◽  
Mark Dredze ◽  
David A Broniatowski
Keyword(s):  
United States ◽  
South Carolina ◽  
Social Mobility ◽  
State Level ◽  
The United States ◽  
Mobility Index ◽  
Social Distancing ◽  
Mobility Data ◽  
Response Planning ◽  
Twitter Data

BACKGROUND Social distancing is an important component of the response to the COVID-19 pandemic. Minimizing social interactions and travel reduces the rate at which the infection spreads and “flattens the curve” so that the medical system is better equipped to treat infected individuals. However, it remains unclear how the public will respond to these policies as the pandemic continues. OBJECTIVE The aim of this study is to present the Twitter Social Mobility Index, a measure of social distancing and travel derived from Twitter data. We used public geolocated Twitter data to measure how much users travel in a given week. METHODS We collected 469,669,925 tweets geotagged in the United States from January 1, 2019, to April 27, 2020. We analyzed the aggregated mobility variance of a total of 3,768,959 Twitter users at the city and state level from the start of the COVID-19 pandemic. RESULTS We found a large reduction (61.83%) in travel in the United States after the implementation of social distancing policies. However, the variance by state was high, ranging from 38.54% to 76.80%. The eight states that had not issued statewide social distancing orders as of the start of April ranked poorly in terms of travel reduction: Arkansas (45), Iowa (37), Nebraska (35), North Dakota (22), South Carolina (38), South Dakota (46), Oklahoma (50), Utah (14), and Wyoming (53). We are presenting our findings on the internet and will continue to update our analysis during the pandemic. CONCLUSIONS We observed larger travel reductions in states that were early adopters of social distancing policies and smaller changes in states without such policies. The results were also consistent with those based on other mobility data to a certain extent. Therefore, geolocated tweets are an effective way to track social distancing practices using a public resource, and this tracking may be useful as part of ongoing pandemic response planning.

Outcome Discrepancies Among Racially/Ethnically Diverse Consumers of Vocational Rehabilitation Services: Summary and Critique of the Literature

2007 ◽  
Vol 38 (1) ◽  
pp. 3-11 ◽  
Author(s):  
Sandra LeBlanc ◽  
Julie F. Smart
Keyword(s):  
Vocational Rehabilitation ◽  
Ethnic Minority ◽  
Hierarchical Linear Modeling ◽  
Minority Groups ◽  
Ethnically Diverse ◽  
Linear Modeling ◽  
Vocational Rehabilitation Services ◽  
Parametric Statistics ◽  
Rehabilitation Act ◽  
Racial Ethnic

This article summarizes 27 studies that sought to investigate the experiences of various racial/ethnic minority groups in the public vocational rehabilitation agency. Spanning the years since the 1992 Amendments to the Rehabilitation Act, this body of research has identified and defined a significant question: are the experiences and outcomes of consumers who identify as racial/ethnic minority members different from consumers of the majority culture? Did the amendments to the Rehabilitation Act affect a change in outcome discrepancies? A critique of the various methodologies is presented, including: the use of archival data; the use of univariate, non parametric statistics; and the lack of precision in defining/operationalizing the independent variable of race/ethnicity. The use of hierarchical linear modeling is advocated since many variables of interest can be studied simultaneously. A brief summary of the researchers' recommendation of ways in which to provide higher quality outcomes is presented.

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