scholarly journals Using Demographic Pattern Analysis to Predict COVID-19 Fatalities on the US County Level

2020 ◽  
Vol 2 (1) ◽  
pp. 1-11
Author(s):  
Klaus Mueller ◽  
Eric Papenhausen
Keyword(s):  
Pattern Analysis ◽  
County Level ◽  
The Us

A County-Level Analysis of the US Radiologist Workforce: Physician Supply and Subspecialty Characteristics

2018 ◽  
Vol 15 (4) ◽  
pp. 601-606 ◽  
Author(s):  
Andrew B. Rosenkrantz ◽  
Wenyi Wang ◽  
Danny R. Hughes ◽  
Richard Duszak
Keyword(s):  
County Level ◽  
Physician Supply ◽  
The Us ◽  
Level Analysis

scholarly journals 4536 Identification of the most salient risk factors of preterm birth in the US using geospatial mapping

2020 ◽  
Vol 4 (s1) ◽  
pp. 31-32
Author(s):  
Alexander J Layden ◽  
Janet Catov
Keyword(s):  
Public Health ◽  
Risk Factors ◽  
Health Insurance ◽  
Preterm Birth ◽  
Insurance Coverage ◽  
Health Insurance Coverage ◽  
County Level ◽  
Obesity Prevalence ◽  
The Us ◽  
Geospatial Mapping

OBJECTIVES/GOALS: Preterm birth is the most common birth complication in the United States. To date, there are no effective public health strategies to reduce the burden of prematurity. Using geospatial information system (GIS) mapping, we identified the most salient risk factors of preterm birth across US counties targetable for future interventions. METHODS/STUDY POPULATION: Risk factors of preterm birth were identified from the perinatal health nonprofit organization, March of Dimes, and included factors such as obesity, smoking, insurance coverage and poverty. US 2013 county-level data on sociodemographic characteristics, behavioral risk factors and preterm birth were extracted and combined from the American Census, Center for Disease Control, and US Health Resources and Services Administration. Spatial autocorrelation and multivariate spatial regression were used to determine the risk factors most strongly associated with preterm birth. These models were adjusted for race, given well-documented race disparities for preterm birth. As a case-study comparison, we mapped risk factors in the two states with the highest and lowest proportion of preterm births in 2013. RESULTS/ANTICIPATED RESULTS: In our preliminary analysis, obesity was the factor most strongly associated with preterm birth (ß = 7.32, SE: 1.13, p<0.001) at the US county-level. Surprisingly, smoking was not found to be significantly associated with preterm birth. In 2013, Vermont had the lowest prevalence of preterm birth at 7.6% and Mississippi had the highest prevalence of preterm birth at 13.1%. Health insurance coverage and obesity were the two risk factors that differed between Vermont and Mississippi. The median proportion of uninsured individuals in Mississippi counties was four times higher than that of Vermont counties (26.3% vs 10.9%, p<0.01). Similarly, the median obesity prevalence in Mississippi counties was significantly higher than the median obesity prevalence in Vermont counties (38.8% vs. 25.2%). DISCUSSION/SIGNIFICANCE OF IMPACT: Public health efforts aimed at reducing obesity and increasing health insurance coverage may have the greatest impact at addressing the US burden of preterm birth. Further, geospatial mapping is a powerful analytic tool to identify regions in the US where preterm birth interventions would be most beneficial.

scholarly journals Risk factors for increased COVID-19 case-fatality in the United States: A county-level analysis during the first wave

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258308
Author(s):  
Jess A. Millar ◽  
Hanh Dung N. Dao ◽  
Marianne E. Stefopulos ◽  
Camila G. Estevam ◽  
Katharine Fagan-Garcia ◽  
...  
Keyword(s):  
Generalized Linear Model ◽  
Ecological Study ◽  
Negative Binomial ◽  
Case Fatality ◽  
The United States ◽  
Asthma Prevalence ◽  
County Level ◽  
Increased Risk ◽  
The Us ◽  
Level Analysis

The ongoing COVID-19 pandemic is causing significant morbidity and mortality across the US. In this ecological study, we identified county-level variables associated with the COVID-19 case-fatality rate (CFR) using publicly available datasets and a negative binomial generalized linear model. Variables associated with decreased CFR included a greater number of hospitals per 10,000 people, banning religious gatherings, a higher percentage of people living in mobile homes, and a higher percentage of uninsured people. Variables associated with increased CFR included a higher percentage of the population over age 65, a higher percentage of Black or African Americans, a higher asthma prevalence, and a greater number of hospitals in a county. By identifying factors that are associated with COVID-19 CFR in US counties, we hope to help officials target public health interventions and healthcare resources to locations that are at increased risk of COVID-19 fatalities.

scholarly journals Does Halting Refugee Resettlement Reduce Crime? Evidence from the US Refugee Ban

2018 ◽  
Author(s):  
Daniel Masterson ◽  
Vasil Yasenov
Keyword(s):  
Asylum Seekers ◽  
Natural Experiment ◽  
Crime Rates ◽  
Refugee Resettlement ◽  
County Level ◽  
Executive Order ◽  
Security Risk ◽  
Discernible Effect ◽  
The Us ◽  
Refugees And Asylum Seekers

Many countries have reduced refugee admissions in recent years, in part due to fears that refugees and asylum seekers increase crime rates and pose a national security risk. Existing research presents ambiguous expectations about the consequences of refugee resettlement on crime. We leverage a natural experiment in the US, where an Executive Order by the president in January 2017 halted refugee resettlement. This policy change was sudden and significant – it resulted in the lowest number of refugees resettled on US soil since 1977 and a 66% drop in resettlement from 2016 to 2017. In this letter we find that there is no discernible effect on county-level property or violent crime rates.

scholarly journals State-Level Income Inequality and County-Level Social Capital in Relation to Individual-Level Depression in Middle-Aged Adults: A Lagged Multilevel Study

2020 ◽  
Vol 17 (15) ◽  
pp. 5386
Author(s):  
Saloni Dev ◽  
Daniel Kim
Keyword(s):  
Social Capital ◽  
Income Inequality ◽  
State Level ◽  
Social Determinant ◽  
County Level ◽  
Middle Aged ◽  
Effective Prevention ◽  
Individual Level ◽  
The Us ◽  
Middle Aged Adults

In the US, the incidence of depression and suicide have followed escalating trends over the past several years. These trends call for greater efforts towards identifying their underlying drivers and finding effective prevention strategies and treatments. One social determinant of health that plausibly influences the risk of depression is income inequality, the gap between the rich and poor. However, research on this association is still sparse. We used data from the National Longitudinal Survey of Youth 1979 and the US Census to investigate the multilevel lagged associations of state-level income inequality with the individual-level odds of depression in middle-aged adults, controlling for state- and individual-level factors. We also examined the independent associations of county-level social capital with depression and explored whether it mediated the income inequality relationship. Higher income inequality at the state level predicted higher odds of individual-level depression nearly 2 decades later [OR for middle vs. lowest tertile of income inequality = 1.35 (95% CI: 1.02, 1.76), OR for highest vs. lowest tertile = 1.34 (95% CI: 1.01, 1.78)]. This association was stronger among men than women. Furthermore, there was evidence that county-level social capital independently predicted depression and that it mediated the income inequality association. Overall, our findings suggest that policies attenuating levels of income inequality at the US state level and that leverage social capital may protect against one’s likelihood of developing depression.

scholarly journals Predicting County-Level Overdose Death Rates Amid the Escalating Overdose Crisis in the United States: A Statistical Modeling Approach Predicting Deaths Through 2022

2022 ◽  
Author(s):  
Charles Marks ◽  
Daniela Abramowitz ◽  
Christl A. Donnelly ◽  
Daniel Ciccarone ◽  
Natasha Martin ◽  
...  
Keyword(s):  
Negative Binomial ◽  
The United States ◽  
Similar Efficacy ◽  
Original Model ◽  
Mortality Data ◽  
County Level ◽  
Online Application ◽  
Death Rates ◽  
Overdose Death ◽  
The Us

Aims. U.S. overdose (OD) deaths continue to escalate but are characterized by geographic and temporal heterogeneity. We previously validated a predictive statistical model to predict county-level OD mortality nationally from 2013 to 2018. Herein, we aimed to: 1) validate our model’s performance at predicting county-level OD mortality in 2019 and 2020; 2) modify and validate our model to predict OD mortality in 2022.Methods. We evaluated our mixed effects negative binomial model’s performance at predicting county-level OD mortality in 2019 and 2020. Further, we modified our model which originally used data from the year X to predict OD deaths in the year X+1 to instead predict deaths in year X+3. We validated this modification for the years 2017 through 2019 and generated future-oriented predictions for 2022. Finally, to leverage available, albeit incomplete, 2020 OD mortality data, we also modified and validated our model to predict OD deaths in year X+2 and generated an alternative set of predictions for 2022.Results. Our original model continued to perform with similar efficacy in 2019 and 2020, remaining superior to a benchmark approach. Our modified X+3 model performed with similar efficacy as our original model, and we present predictions for 2022, including identification of counties most likely to experience highest OD mortality rates. There was a high correlation (Spearman’s ρ = 0.93) between the rank ordering of counties for our 2022 predictions using our X+3 and X+2 models. However, the X+3 model (which did not account for OD escalation during COVID) predicted only 62,000 deaths nationwide for 2022, whereas the X+2 model predicted over 87,000.Conclusion. We have predicted county-level overdose death rates for 2022 across the US. These predictions, made publicly available in our online application, can be used to identify counties at highest risk of high OD mortality and support evidence-based OD prevention planning.

Poverty and Obesity as Political Indicators

2017 ◽  
Vol 14 (1) ◽  
Author(s):  
Adam Drewnowski
Keyword(s):  
Presidential Election ◽  
Medical Condition ◽  
Health Indicators ◽  
County Level ◽  
Political Force ◽  
Socioeconomic Variables ◽  
The Us ◽  
Univariate Predictor

AbstractObesity in the US is treated as a medical condition, while its socioeconomic roots are all but ignored. Yet obesity was a univariate predictor of voting patterns in the 2016 presidential election at both state and county level. Health indicators, linked to socioeconomic variables, may be the new political force.

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