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 Social Disadvantage, Politics, and SARS-CoV-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 ◽  
Voting Behavior ◽  
Social Deprivation ◽  
Belief Systems ◽  
Univariate Analysis ◽  
Social Disadvantage ◽  
County Level ◽  
The Us ◽  
Black Residents ◽  
Social Demographic

Background: Understanding the epidemiology of SARS-CoV-2 is essential for public health control efforts. Social, demographic, and political characteristics at the US county level might be associated with the trajectories of SARS-CoV-2 case incidence. Objective: To understand how underlying social, demographic, and political characteristics at the US county level might be associated with the trajectories of SARS-CoV-2 case incidence. Design: Retrospective analysis of the trajectory of reported SARS-CoV-2 case counts at the US county level during June 1, 2020 - June 30,2020 and social, demographic, and political characteristics of the county. Setting: United States. Participants: Reported SARS-CoV-2 cases. Exposures: Metropolitan designation, Social Deprivation Index (SDI), 2016 Republican Presidential Candidate Victory. Main Outcomes and Measures: SARS-CoV-2 case incidence. Results: 1023/3142 US counties were included in the analysis. 678 (66.3%) had increasing SARS-CoV-2 case counts between June 1 - June 30, 2020. In univariate analysis, counties with increasing case counts had a significantly higher SDI (median 48, IQR 24 - 72) than counties with non-increasing case counts (median 40, IQR 19 - 66; p=0.009). In the multivariable model, metropolitan areas of 250,000 - 1 million population, higher percentage of Black residents and a 10-point or greater Republican victory were independently associated with increasing case counts. Limitations: The data examines county-level voting patterns and does not account for individual voting behavior, subjecting this work to the potential for ecologic fallacy. Conclusion: Increasing case counts of SARS-CoV-2 in the US are likely driven by a combination of social disadvantage, social networks, and behavioral factors. Addressing social disadvantage and differential belief systems that may correspond with political alignment will be essential for pandemic control.

scholarly journals The Influence of Metropolitan Statistical Areas on Direct-to-consumer Agricultural Sales of Local Food in the Northeast

2016 ◽  
Vol 45 (3) ◽  
pp. 539-562 ◽  
Author(s):  
Jeffrey K. O'Hara ◽  
Sarah A. Low
Keyword(s):  
United States ◽  
Panel Data ◽  
Income Elasticity ◽  
Metropolitan Areas ◽  
The United States ◽  
County Level ◽  
Ceteris Paribus ◽  
Metropolitan Statistical Areas ◽  
Direct To Consumer ◽  
Market Saturation

Direct-to-consumer (DTC) agricultural sales doubled in the United States between 1992 and 2007 and then plateaued between 2007 and 2012. It is not clear whether the plateau in sales was attributable to the recession, market saturation, an aging population, or other factors. We estimate the influence of socioeconomic factors in metropolitan areas on DTC agricultural sales between 1992 and 2012 in thirteen Northeast states using county-level panel data. We find that the income elasticity of DTC agricultural purchases ranged from 2.2 to 2.7 and that counties in metropolitan areas did not have higher DTC agricultural sales than other counties, ceteris paribus.

scholarly journals Rural-Urban Disparities in Place of Death in Hematologic Malignancies in the US from 2003 to 2019

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 4137-4137
Author(s):  
Syed M. Qasim Hussaini ◽  
Arjun Gupta
Keyword(s):  
United States ◽  
Metropolitan Area ◽  
Rural Areas ◽  
Hospice Care ◽  
Metropolitan Areas ◽  
Hematologic Malignancies ◽  
The United States ◽  
Medical Facility ◽  
Nursing Facility ◽  
The Us

Abstract Background: more than 60,000 people die annually from hematologic malignancies in the united states (us). Patients with hematologic malignancies more frequently receive aggressive care toward the end-of-life and are more likely to die in a hospital compared to those with a solid tumor. Appropriate care of such patients is very dependent on an existing healthcare infrastructure. There are notable challenges to rural healthcare in the united states which contains less than 1/5th of all hospices in the us. In this study, we sought to investigate rural-urban disparities in place of death the us in individuals that died from hematologic malignancies. Methods: we utilized the us centers for disease control and prevention wide-ranging online data for epidemiologic research database to analyze all deaths from hematologic malignancies in the us from 2003 to 2019. A population classification utilizing the 2013 us census was made using the national center for health statistics urban-rural classification scheme. These classifications included: large metropolitan area (1 million), small- or medium-sized metropolitan area (50 000-999 999), and rural area (<50 000). We estimated deaths in a medical facility, hospice, home, or nursing care facility. We stratified the results by age, sex, and race/ethnicity. The annual percentage change (apc) in deaths was estimated. All data was publicly available and de-identified. Findings: from 2003-2019, there were a total 1,088,589 deaths form hematologic malignancies in the united states, predominantly in large metropolitan areas (50.2%), followed by small or medium sized metropolitan areas (31.7%) and rural areas (18.2%). All regions noted decreases in medical facility and nursing facility related deaths, and increase in hospice and home deaths. While rural areas demonstrated the quickest uptake of hospice care (apc 61.5), they had the lowest overall presence of hospice care (8.3% of all rural deaths in 2019 vs. 14.9% for small or medium metropolitan vs. 12% for large metropolitan) and larger share of nursing facility related deaths (15.8% of all rural deaths in 2019 vs 12.3% for small or medium metropolitan vs 10.6% for large metropolitan). Discussion: we demonstrate end-of-life disparities in hematologic malignancies based on where an individual resides in the us with rural areas having notably lower share of deaths in hospice facilities. Older infrastructure, inadequate access to care, and financial barriers add to the medical complexity of care for all patients, and especially hematologic patients with high needs and complex treatment planning. These have been aggravated by rural hospital closures in the previous 18 months. The us senate is currently debating a bipartisan infrastructure that may add billions in building rural healthcare infrastructure to state budgets. Our findings are timely in helping inform congressional policy. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.

scholarly journals Spatial Clustering of County-Level COVID-19 Rates in the U.S

2021 ◽  
Vol 18 (22) ◽  
pp. 12170
Author(s):  
Marcus R. Andrews ◽  
Kosuke Tamura ◽  
Janae N. Best ◽  
Joniqua N. Ceasar ◽  
Kaylin G. Battey ◽  
...  
Keyword(s):  
United States ◽  
Health Disparities ◽  
Rural Areas ◽  
Metropolitan Areas ◽  
Spatial Clustering ◽  
The United States ◽  
County Level ◽  
Communities Of Color ◽  
States Data ◽  
The U.S

Despite the widespread prevalence of cases associated with the coronavirus disease 2019 (COVID-19) pandemic, little is known about the spatial clustering of COVID-19 in the United States. Data on COVID-19 cases were used to identify U.S. counties that have both high and low COVID-19 incident proportions and clusters. Our results suggest that there are a variety of sociodemographic variables that are associated with the severity of COVID-19 county-level incident proportions. As the pandemic evolved, communities of color were disproportionately impacted. Subsequently, it shifted from communities of color and metropolitan areas to rural areas in the U.S. Our final period showed limited differences in county characteristics, suggesting that COVID-19 infections were more widespread. The findings might address the systemic barriers and health disparities that may result in high incident proportions of COVID-19 clusters.

scholarly journals Differential Effects of Intervention Timing on COVID-19 Spread in the United States

2020 ◽  
Author(s):  
Sen Pei ◽  
Sasikiran Kandula ◽  
Jeffrey Shaman
Keyword(s):  
United States ◽  
Disease Transmission ◽  
Metropolitan Areas ◽  
Human Mobility ◽  
The United States ◽  
Aggressive Response ◽  
Control Measures ◽  
County Level ◽  
Social Distancing ◽  
Us Counties

Assessing the effects of early non-pharmaceutical interventions1-5 on COVID-19 spread in the United States is crucial for understanding and planning future control measures to combat the ongoing pandemic6-10. Here we use county-level observations of reported infections and deaths11, in conjunction with human mobility data12 and a metapopulation transmission model13,14, to quantify changes of disease transmission rates in US counties from March 15, 2020 to May 3, 2020. We find significant reductions of the basic reproductive numbers in major metropolitan areas in association with social distancing and other control measures. Counterfactual simulations indicate that, had these same control measures been implemented just 1-2 weeks earlier, a substantial number of cases and deaths could have been averted. Specifically, nationwide, 61.6% [95% CI: 54.6%-67.7%] of reported infections and 55.0% [95% CI: 46.1%-62.2%] of reported deaths as of May 3, 2020 could have been avoided if the same control measures had been implemented just one week earlier. We also examine the effects of delays in re-implementing social distancing following a relaxation of control measures. A longer response time results in a stronger rebound of infections and death. Our findings underscore the importance of early intervention and aggressive response in controlling the COVID-19 pandemic.

scholarly journals Characterizing the spatiotemporal heterogeneity of the COVID-19 vaccination landscape

2021 ◽  
Author(s):  
Andrew Tiu ◽  
Zachary Susswein ◽  
Alexes Merritt ◽  
Shweta Bansal
Keyword(s):  
United States ◽  
Vaccination Coverage ◽  
Vaccination Campaign ◽  
The United States ◽  
County Level ◽  
Spatial Clusters ◽  
Southern Us ◽  
The Us ◽  
Target Locations ◽  
County Scale

AbstractIt is critical that we maximize vaccination coverage across the United States so that SARS-CoV-2 transmission can be suppressed, and we can sustain the recent reopening of the nation. Maximizing vaccination requires that we track vaccination patterns to measure the progress of the vaccination campaign and target locations that may be undervaccinated. To improve efforts to track and characterize COVID-19 vaccination progress in the United States, we integrate CDC and state-provided vaccination data, identifying and rectifying discrepancies between these data sources. We find that COVID-19 vaccination coverage in the US exhibits significant spatial heterogeneity at the county level and statistically identify spatial clusters of undervaccination, all with foci in the southern US. Vaccination progress at the county level is also variable; many counties stalled in vaccination into June 2021 and few recovered by July, with transmission of the Delta variant rapidly rising. Using a comparison with a mechanistic growth model fitted to our integrated data, we classify vaccination dynamics across time at the county scale. Our findings underline the importance of curating accurate, fine-scale vaccination data and the continued need for widespread vaccination in the US, especially in the wake of the highly transmissible Delta variant.

scholarly journals Investigating associations between COVID-19 mortality and population-level health and socioeconomic indicators in the United States: A modeling study

PLoS Medicine ◽  
2021 ◽  
Vol 18 (7) ◽  
pp. e1003693
Author(s):  
Sasikiran Kandula ◽  
Jeffrey Shaman
Keyword(s):  
United States ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Nursing Homes ◽  
Spatial Autocorrelation ◽  
Population Level ◽  
The United States ◽  
County Level ◽  
Socioeconomic Indicators ◽  
The Us

Background With the availability of multiple Coronavirus Disease 2019 (COVID-19) vaccines and the predicted shortages in supply for the near future, it is necessary to allocate vaccines in a manner that minimizes severe outcomes, particularly deaths. To date, vaccination strategies in the United States have focused on individual characteristics such as age and occupation. Here, we assess the utility of population-level health and socioeconomic indicators as additional criteria for geographical allocation of vaccines. Methods and findings County-level estimates of 14 indicators associated with COVID-19 mortality were extracted from public data sources. Effect estimates of the individual indicators were calculated with univariate models. Presence of spatial autocorrelation was established using Moran’s I statistic. Spatial simultaneous autoregressive (SAR) models that account for spatial autocorrelation in response and predictors were used to assess (i) the proportion of variance in county-level COVID-19 mortality that can explained by identified health/socioeconomic indicators (R2); and (ii) effect estimates of each predictor. Adjusting for case rates, the selected indicators individually explain 24%–29% of the variability in mortality. Prevalence of chronic kidney disease and proportion of population residing in nursing homes have the highest R2. Mortality is estimated to increase by 43 per thousand residents (95% CI: 37–49; p < 0.001) with a 1% increase in the prevalence of chronic kidney disease and by 39 deaths per thousand (95% CI: 34–44; p < 0.001) with 1% increase in population living in nursing homes. SAR models using multiple health/socioeconomic indicators explain 43% of the variability in COVID-19 mortality in US counties, adjusting for case rates. R2 was found to be not sensitive to the choice of SAR model form. Study limitations include the use of mortality rates that are not age standardized, a spatial adjacency matrix that does not capture human flows among counties, and insufficient accounting for interaction among predictors. Conclusions Significant spatial autocorrelation exists in COVID-19 mortality in the US, and population health/socioeconomic indicators account for a considerable variability in county-level mortality. In the context of vaccine rollout in the US and globally, national and subnational estimates of burden of disease could inform optimal geographical allocation of vaccines.

scholarly journals SARS surveillance case definition updated in United States

2003 ◽  
Vol 7 (18) ◽  
Author(s):  
Keyword(s):  
United States ◽  
Severe Acute Respiratory Syndrome ◽  
Laboratory Tests ◽  
Laboratory Data ◽  
The United States ◽  
Case Definition ◽  
Sars Coronavirus ◽  
Serum Samples ◽  
The Us

The surveillance case definition for Severe Acute Respiratory Syndrome (SARS) has been updated for the United States to take account of the results of laboratory tests for SARS-coronavirus (SARS-CoV) that are becoming increasingly available (1) (http://www.cdc.gov/mmwr/preview/mmwrhtml/mm52d429a1.htm). Reported cases of SARS in the US will continue to be classified as suspect or probable; however, these cases can be further classified as laboratory-confirmed or -negative if laboratory data are available and complete, or as laboratory-indeterminate if specimens are not available or testing is incomplete. Obtaining convalescent serum samples to make a final determination about infection with SARS-CoV is critical.

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.

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