scholarly journals A higher ratio of green spaces means a lower racial disparity in severe acute respiratory syndrome coronavirus 2 infection rates: A nationwide study of the United States

2020 ◽  
Author(s):  
Yi Lu ◽  
Long Chen ◽  
Xueming Liu ◽  
Yuwen Yang ◽  
Wenyan Xu ◽  
...  
Keyword(s):  
United States ◽  
Chronic Disease ◽  
Severe Acute Respiratory Syndrome ◽  
Racial Disparity ◽  
Green Space ◽  
The United States ◽  
Green Spaces ◽  
County Level ◽  
Infection Rates ◽  
Nationwide Study

AbstractThere is striking racial disparity in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection rates in the United States. We hypothesize that the disparity is significantly smaller in areas with a higher ratio of green spaces at the county level. This study used the 135 most urbanized counties across the United States as sample sites. County level data on the SARS-CoV-2 infection rates of black and white individuals in each county were collected. The ratio of green spaces by land-cover type at the county level was calculated from satellite imagery. An ecological hierarchical regression analysis measured cross-sectional associations between racial disparity in infection rates and green spaces, after controlling for socioeconomic, demographic, pre-existing chronic disease, and built-up area factors. We found significantly higher infection rate among black individuals compared to white individuals. More importantly, a higher ratio of green spaces at the county level is significantly associated with a lower racial disparity in the SARS-CoV-2 infection rate. Further, we identified four green space factors that have significant negative associations with the racial disparity in SARS-CoV-2 infection rates, including open space in developed areas, forest, shrub and scrub, and grassland and herbaceous. We suggest that green spaces are an equalizing salutogenic factor, modifying infection exposure.HighlightsThe first study to identify significant relationships between green spaces and the racial disparity of SARS-CoV-2 infection rates.A nationwide study of the 135 most urbanized counties of the United States.A within-subject study: The black-white racial disparity of SARS-CoV-2 infection rates was measured within each county.A higher ratio of green spaces in a county is associated with a lower racial disparity of SARS-CoV-2 infection rates after controlling for socio-economic, demographic, pre-existing chronic disease, and built-up area factors.Four green space factors are significantly associated with a lower racial disparity of SARS-CoV-2 infection rates.

scholarly journals Social Disadvantage, Politics, and Severe Acute Respiratory Syndrome Coronavirus 2 Trends: A County-level Analysis of United States Data

2020 ◽  
Author(s):  
Ahmad Mourad ◽  
Nicholas A Turner ◽  
Arthur W Baker ◽  
Nwora Lance Okeke ◽  
Shanti Narayanasamy ◽  
...  
Keyword(s):  
United States ◽  
Severe Acute Respiratory Syndrome ◽  
Metropolitan Areas ◽  
Belief Systems ◽  
The United States ◽  
Social Disadvantage ◽  
County Level ◽  
The Us ◽  
Black Residents ◽  
Social Demographic

Abstract Background Understanding the epidemiology of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is essential for public health control efforts. Social, demographic, and political characteristics at the United States (US) county level might be associated with changes in SARS-CoV-2 case incidence. Methods We conducted a retrospective analysis of the relationship between the change in reported SARS-CoV-2 case counts at the US county level during 1 June–30 June 2020 and social, demographic, and political characteristics of the county. Results Of 3142 US counties, 1023 were included in the analysis: 678 (66.3%) had increasing and 345 (33.7%) nonincreasing SARS-CoV-2 case counts between 1 June and 30 June 2020. In bivariate analysis, counties with increasing case counts had a significantly higher Social Deprivation Index (median, 48 [interquartile range {IQR}, 24–72]) than counties with nonincreasing case counts (median, 40 [IQR, 19–66]; P = .009). Counties with increasing case counts were significantly more likely to be metropolitan areas of 250 000–1 million population (P < .001), to have a higher percentage of black residents (9% vs 6%; P = .013), and to have voted for the Republican presidential candidate in 2016 by a ≥10-point margin (P = .044). In the multivariable model, metropolitan areas of 250 000–1 million population, higher percentage of black residents, and a ≥10-point Republican victory were independently associated with increasing case counts. Conclusions Increasing case counts of SARS-CoV-2 in the US during June 2020 were associated with a combination of sociodemographic and political factors. Addressing social disadvantage and differential belief systems that may correspond with political alignment will play a critical role in pandemic control.

scholarly journals Estimating Unreported COVID-19 Cases with a Time-Varying SIR Regression Model

2021 ◽  
Vol 18 (3) ◽  
pp. 1090
Author(s):  
Zhenghong Peng ◽  
Siya Ao ◽  
Lingbo Liu ◽  
Shuming Bao ◽  
Tao Hu ◽  
...  
Keyword(s):  
United States ◽  
Transmission Rate ◽  
Fixed Time ◽  
The United States ◽  
Ordinary Least Squares ◽  
Time Varying ◽  
County Level ◽  
Infection Rates ◽  
Level Data ◽  
The Us

Background: Potential unreported infection might impair and mislead policymaking for COVID-19, and the contemporary spread of COVID-19 varies in different counties of the United States. It is necessary to estimate the cases that might be underestimated based on county-level data, to take better countermeasures against COVID-19. We suggested taking time-varying Susceptible-Infected-Recovered (SIR) models with unreported infection rates (UIR) to estimate factual COVID-19 cases in the United States. Methods: Both the SIR model integrated with unreported infection rates (SIRu) of fixed-time effect and SIRu with time-varying parameters (tvSIRu) were applied to estimate and compare the values of transmission rate (TR), UIR, and infection fatality rate (IFR) based on US county-level COVID-19 data. Results: Based on the US county-level COVID-19 data from 22 January (T1) to 20 August (T212) in 2020, SIRu was first tested and verified by Ordinary Least Squares (OLS) regression. Further regression of SIRu at the county-level showed that the average values of TR, UIR, and IFR were 0.034%, 19.5%, and 0.51% respectively. The ranges of TR, UIR, and IFR for all states ranged from 0.007–0.157 (mean = 0.048), 7.31–185.6 (mean = 38.89), and 0.04–2.22% (mean = 0.22%). Among the time-varying TR equations, the power function showed better fitness, which indicated a decline in TR decreasing from 227.58 (T1) to 0.022 (T212). The general equation of tvSIRu showed that both the UIR and IFR were gradually increasing, wherein, the estimated value of UIR was 9.1 (95%CI 5.7–14.0) and IFR was 0.70% (95%CI 0.52–0.95%) at T212. Interpretation: Despite the declining trend in TR and IFR, the UIR of COVID-19 in the United States is still on the rise, which, it was assumed would decrease with sufficient tests or improved countersues. The US medical system might be largely affected by severe cases amidst a rapid spread of COVID-19.

scholarly journals Estimating Unreported COVID-19 Cases in the United States Based on the tvSIRu Model

2020 ◽  
Author(s):  
Lingbo Liu ◽  
Tao Hu ◽  
Shuming Bao ◽  
Hao Wu ◽  
Zhenghong Peng ◽  
...  
Keyword(s):  
United States ◽  
Transmission Rate ◽  
Fixed Time ◽  
The United States ◽  
Rate Equations ◽  
Time Varying ◽  
County Level ◽  
Infection Rates ◽  
Country Level ◽  
Level Data

Abstract BackgroundThe potential unreported infection may impair and mislead policymaking for COVID-19，and the contemporary spread of COVID-19 varies in different counties of the United States. It is necessary to estimate the cases that may be underestimated based on county-level data to take better countermeasures against COVID-19. We suggested taking time-varying SIR models with unreported infection rates (UIR)to estimate the factual COVID-19 cases in the United States.MethodsSIR integrated with unreported infection rates (SIRu) of fixed time effect and SIR with time-varying parameters (tvSIRu)were applied to estimate and compare the value of transmission rate(TR), UIR, and infection fatality rate (IFR) based on US county-level COVID-19 data. ResultsBased on US county-level COVID-19 data from January 22 (T1) to August 20 (T212) in 2020, SIRu was first tested and verified by a general OLS regression. The further regression of SIRu at the country-level showed that the average values of TR, UIR, and IFR were 0.034,19.5, 0.51% respectively. The range of TR, UIR, IFR of all states ranged were 0.007-0.157 (mean=0.048) ,7.31-185.6 (mean=38.89), and 0.04%-2.22% (mean=0.22%). Among time-varying transmission rate equations, the power function showed better fitness, which indicated a decline in TR decreasing from 227.58 (T1) to 0.022 (T212). The general equation of tvSIRu showed that both the UIR and IFR were gradually increasing, wherein, the UIR has an estimate of 9.1(95%CI = 5.7-14.0), and IFR was 0.70% (0.52%-0.95%) at T212.InterpretationDespite the decline in TR and IFR, the UIR of the United States is still on the rise, which had been supposed to decrease with sufficient tests or improved countersues. The US medical system may be largely affected by severe cases in the rapid spread of COVDI-19.

scholarly journals Factors Associated With County-Level Variation in Premature Mortality Due to Noncommunicable Chronic Disease in the United States, 1999-2017

2020 ◽  
Vol 3 (2) ◽  
pp. e200241
Author(s):  
Suhang Song ◽  
Michael G. Trisolini ◽  
Kenneth A. LaBresh ◽  
Sidney C. Smith ◽  
Yinzi Jin ◽  
...  
Keyword(s):  
United States ◽  
Chronic Disease ◽  
Premature Mortality ◽  
The United States ◽  
County Level ◽  
Level Variation ◽  
Factors Associated

Greenness and the Potential Resilience to Sexual Violence: “Your Neighborhood Is Being Neglected Because People Don’t Care. People With Power Don’t Care”

2021 ◽  
pp. 088626052110280
Author(s):  
Gibran C. Mancus ◽  
Andrea N. Cimino ◽  
Md Zabir Hasan ◽  
Jacquelyn C. Campbell ◽  
Phyllis Sharps ◽  
...  
Keyword(s):  
Quantitative Analysis ◽  
Violent Crime ◽  
Green Space ◽  
A Priori ◽  
Community Resilience ◽  
The United States ◽  
Green Spaces ◽  
Traffic Density ◽  
Black Communities ◽  
Black Neighborhoods

There is increasing evidence that green space in communities reduces the risk of aggression and violence, and increases wellbeing. Positive associations between green space and resilience have been found among children, older adults and university students in the United States, China and Bulgaria. Little is known about these associations among predominately Black communities with structural disadvantage. This study explored the potential community resilience in predominately Black neighborhoods with elevated violent crime and different amounts of green space. This embedded mixed-methods study started with quantitative analysis of women who self-identified as “Black and/or African American.” We found inequality in environments, including the amount of green space, traffic density, vacant property, and violent crime. This led to 10 indepth interviews representing communities with elevated crime and different amounts of green space. Emergent coding of the first 3 interviews, a subset of the 98 in the quantitative analysis, led to a priori coding of barriers and facilitators to potential green space supported community resilience applied to the final 7 interview data. Barriers were a combination of the physical and social environment, including traffic patterns, vacant property, and crime. Facilitators included subjective qualities of green space. Green spaces drew people in through community building and promoting feelings of calmness. The transformation of vacant lots into green spaces by community members affords space for people to come together and build community. Green spaces, a modifiable factor, may serve to increase community resilience and decrease the risk of violence.

scholarly journals Patterns and correlates of mis-implementation in state chronic disease public health practice in the United States

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Margaret M. Padek ◽  
Stephanie Mazzucca ◽  
Peg Allen ◽  
Emily Rodriguez Weno ◽  
Edward Tsai ◽  
...  
Keyword(s):  
Public Health ◽  
United States ◽  
Decision Making ◽  
Chronic Disease ◽  
Multinomial Logistic Regression ◽  
Public Health Practice ◽  
State Level ◽  
The United States ◽  
Organizational Level ◽  
Effective Programs

Abstract Background Much of the disease burden in the United States is preventable through application of existing knowledge. State-level public health practitioners are in ideal positions to affect programs and policies related to chronic disease, but the extent to which mis-implementation occurring with these programs is largely unknown. Mis-implementation refers to ending effective programs and policies prematurely or continuing ineffective ones. Methods A 2018 comprehensive survey assessing the extent of mis-implementation and multi-level influences on mis-implementation was reported by state health departments (SHDs). Questions were developed from previous literature. Surveys were emailed to randomly selected SHD employees across the Unites States. Spearman’s correlation and multinomial logistic regression were used to assess factors in mis-implementation. Results Half (50.7%) of respondents were chronic disease program managers or unit directors. Forty nine percent reported that programs their SHD oversees sometimes, often or always continued ineffective programs. Over 50% also reported that their SHD sometimes or often ended effective programs. The data suggest the strongest correlates and predictors of mis-implementation were at the organizational level. For example, the number of organizational layers impeded decision-making was significant for both continuing ineffective programs (OR=4.70; 95% CI=2.20, 10.04) and ending effective programs (OR=3.23; 95% CI=1.61, 7.40). Conclusion The data suggest that changing certain agency practices may help in minimizing the occurrence of mis-implementation. Further research should focus on adding context to these issues and helping agencies engage in appropriate decision-making. Greater attention to mis-implementation should lead to greater use of effective interventions and more efficient expenditure of resources, ultimately to improve health outcomes.

scholarly journals Effect of specific non-pharmaceutical intervention policies on SARS-CoV-2 transmission in the counties of the United States

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Bingyi Yang ◽  
Angkana T. Huang ◽  
Bernardo Garcia-Carreras ◽  
William E. Hart ◽  
Andrea Staid ◽  
...  
Keyword(s):  
United States ◽  
Home Visiting ◽  
Leisure Activities ◽  
The United States ◽  
Causal Effects ◽  
Basic Reproductive Number ◽  
Reproductive Number ◽  
County Level ◽  
Metapopulation Model ◽  
Pharmaceutical Interventions

AbstractNon-pharmaceutical interventions (NPIs) remain the only widely available tool for controlling the ongoing SARS-CoV-2 pandemic. We estimated weekly values of the effective basic reproductive number (Reff) using a mechanistic metapopulation model and associated these with county-level characteristics and NPIs in the United States (US). Interventions that included school and leisure activities closure and nursing home visiting bans were all associated with a median Reff below 1 when combined with either stay at home orders (median Reff 0.97, 95% confidence interval (CI) 0.58–1.39) or face masks (median Reff 0.97, 95% CI 0.58–1.39). While direct causal effects of interventions remain unclear, our results suggest that relaxation of some NPIs will need to be counterbalanced by continuation and/or implementation of others.

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