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

2021 ◽  
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

AbstractThe 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 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.

scholarly journals County-level exposures to greenness and associations with COVID-19 incidence and mortality in the United States

2020 ◽  
Author(s):  
Jochem O Klompmaker ◽  
Jaime E Hart ◽  
Isabel Holland ◽  
M Benjamin Sabath ◽  
Xiao Wu ◽  
...  
Keyword(s):  
United States ◽  
Population Density ◽  
Vegetation Index ◽  
Negative Binomial ◽  
The United States ◽  
Systems Science ◽  
County Level ◽  
Incidence And Mortality ◽  
The Us ◽  
At Home

AbstractBackgroundCOVID-19 is an infectious disease that has killed more than 246,000 people in the US. During a time of social distancing measures and increasing social isolation, green spaces may be a crucial factor to maintain a physically and socially active lifestyle while not increasing risk of infection.ObjectivesWe evaluated whether greenness is related to COVID-19 incidence and mortality in the United States.MethodsWe downloaded data on COVID-19 cases and deaths for each US county up through June 7, 2020, from Johns Hopkins University, Center for Systems Science and Engineering Coronavirus Resource Center. We used April-May 2020 Normalized Difference Vegetation Index (NDVI) data, to represent the greenness exposure during the initial COVID-19 outbreak in the US. We fitted negative binomial mixed models to evaluate associations of NDVI with COVID-19 incidence and mortality, adjusting for potential confounders such as county-level demographics, epidemic stage, and other environmental factors. We evaluated whether the associations were modified by population density, proportion of Black residents, median home value, and issuance of stay-at-home order.ResultsAn increase of 0.1 in NDVI was associated with a 6% (95% Confidence Interval: 3%, 10%) decrease in COVID-19 incidence rate after adjustment for potential confounders. Associations with COVID-19 incidence were stronger in counties with high population density and in counties with stay-at-home orders. Greenness was not associated with COVID-19 mortality in all counties; however, it was protective in counties with higher population density.DiscussionExposures to NDVI had beneficial impacts on county-level incidence of COVID-19 in the US and may have reduced county-level COVID-19 mortality rates, especially in densely populated counties.

scholarly journals Long-Term Physical Health Outcomes of Resettled Refugee Populations in the United States: A Scoping Review

2021 ◽  
Author(s):  
Gayathri S. Kumar ◽  
Jenna A. Beeler ◽  
Emma E. Seagle ◽  
Emily S. Jentes
Keyword(s):  
Health Outcomes ◽  
Physical Health ◽  
Public Health Practice ◽  
The United States ◽  
Inclusion Criteria ◽  
Physical Health Outcomes ◽  
Increased Risk ◽  
The Us ◽  
Bhutanese Refugee

AbstractSeveral studies describe the health of recently resettled refugee populations in the US beyond the first 8 months after arrival. This review summarizes the results of these studies. Scientific articles from five databases published from January 2008 to March 2019 were reviewed. Articles were included if study subjects included any of the top five US resettlement populations during 2008–2018 and if data described long-term physical health outcomes beyond the first 8 months after arrival in the US. Thirty-three studies met the inclusion criteria (1.5%). Refugee adults had higher odds of having a chronic disease compared with non-refugee immigrant adults, and an increased risk for diabetes compared with US-born controls. The most commonly reported chronic diseases among Iraqi, Somali, and Bhutanese refugee adults included diabetes and hypertension. Clinicians should consider screening and evaluating for chronic conditions in the early resettlement period. Further evaluations can build a more comprehensive, long-term health profile of resettled refugees to inform public health practice.

scholarly journals THU0442 GOUT AND HEART FAILURE IN THE US: A NATIONAL PERSPECTIVE

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 458.2-458
Author(s):  
G. Singh ◽  
M. Sehgal ◽  
A. Mithal
Keyword(s):  
Risk Factors ◽  
Heart Failure ◽  
Confidence Intervals ◽  
Burden Of Illness ◽  
Average Length ◽  
Private Insurance ◽  
Significant Risk ◽  
Case Fatality ◽  
The United States ◽  
The Us

Background:Heart failure (HF) is the eighth leading cause of death in the US, with a 38% increase in the number of deaths due to HF from 2011 to 2017 (1). Gout and hyperuricemia have previously been recognized as significant risk factors for heart failure (2), but there is little nationwide data on the clinical and economic consequences of these comorbidities.Objectives:To study heart failure hospitalizations in patients with gout in the United States (US) and estimate their clinical and economic impact.Methods:The Nationwide Inpatient Sample (NIS) is a stratified random sample of all US community hospitals. It is the only US national hospital database with information on all patients, regardless of payer, including persons covered by Medicare, Medicaid, private insurance, and the uninsured. We examined all inpatient hospitalizations in the NIS in 2017, the most recent year of available data, with a primary or secondary diagnosis of gout and heart failure. Over 69,800 ICD 10 diagnoses were collapsed into a smaller number of clinically meaningful categories, consistent with the CDC Clinical Classification Software.Results:There were 35.8 million all-cause hospitalizations in patients in the US in 2017. Of these, 351,735 hospitalizations occurred for acute and/or chronic heart failure in patients with gout. These patients had a mean age of 73.3 years (95% confidence intervals 73.1 – 73.5 years) and were more likely to be male (63.4%). The average length of hospitalization was 6.1 days (95% confidence intervals 6.0 to 6.2 days) with a case fatality rate of 3.5% (95% confidence intervals 3.4% – 3.7%). The average cost of each hospitalization was $63,992 (95% confidence intervals $61,908 - $66,075), with a total annual national cost estimate of $22.8 billion (95% confidence intervals $21.7 billion - $24.0 billion).Conclusion:While gout and hyperuricemia have long been recognized as potential risk factors for heart failure, the aging of the US population is projected to significantly increase the burden of illness and costs of care of these comorbidities (1). This calls for an increased awareness and management of serious co-morbid conditions in patients with gout.References:[1]Sidney, S., Go, A. S., Jaffe, M. G., Solomon, M. D., Ambrosy, A. P., & Rana, J. S. (2019). Association Between Aging of the US Population and Heart Disease Mortality From 2011 to 2017. JAMA Cardiology. doi:10.1001/jamacardio.2019.4187[2]Krishnan E. Gout and the risk for incident heart failure and systolic dysfunction. BMJ Open 2012;2:e000282.doi:10.1136/bmjopen-2011-000282Disclosure of Interests: :Gurkirpal Singh Grant/research support from: Horizon Therapeutics, Maanek Sehgal: None declared, Alka Mithal: None declared

Political Competition and Right-Wing Terrorism: A County-Level Analysis of the United States

2021 ◽  
pp. 106591292110067
Author(s):  
Stephen C. Nemeth ◽  
Holley E. Hansen
Keyword(s):  
Comparative Politics ◽  
Political Competition ◽  
The United States ◽  
County Level ◽  
Right Wing ◽  
Social Changes ◽  
Electoral Outcomes ◽  
Economic Anxiety ◽  
Political Economic ◽  
Level Analysis

While many previous studies on U.S. right-wing violence center on factors such as racial threat and economic anxiety, we draw from comparative politics research linking electoral dynamics to anti-minority violence. Furthermore, we argue that the causes of right-wing terrorism do not solely rest on political, economic, or social changes individually, but on their interaction. Using a geocoded, U.S. county-level analysis of right-wing terrorist incidents from 1970 to 2016, we find no evidence that poorer or more diverse counties are targets of right-wing terrorism. Rather, right-wing violence is more common in areas where “playing the ethnic card” makes strategic sense for elites looking to shift electoral outcomes: counties that are in electorally competitive areas and that are predominantly white.

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 694 Insufficient sleep linked with higher COVID-19 infection cases and deaths in the United States

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A271-A271
Author(s):  
Azizi Seixas ◽  
Nicholas Pantaleo ◽  
Samrachana Adhikari ◽  
Michael Grandner ◽  
Giardin Jean-Louis
Keyword(s):  
Regression Models ◽  
Negative Binomial ◽  
The United States ◽  
Poor Sleep ◽  
County Level ◽  
Infection Rates ◽  
Insufficient Sleep ◽  
Death Rates ◽  
Rural Counties ◽  
The Relationship

Abstract Introduction Causes of COVID-19 burden in urban, suburban, and rural counties are unclear, as early studies provide mixed results implicating high prevalence of pre-existing health risks and chronic diseases. However, poor sleep health that has been linked to infection-based pandemics may provide additional insight for place-based burden. To address this gap, we investigated the relationship between habitual insufficient sleep (sleep <7 hrs./24 hr. period) and COVID-19 cases and deaths across urban, suburban, and rural counties in the US. Methods County-level variables were obtained from the 2014–2018 American community survey five-year estimates and the Center for Disease Control and Prevention. These included percent with insufficient sleep, percent uninsured, percent obese, and social vulnerability index. County level COVID-19 infection and death data through September 12, 2020 were obtained from USA Facts. Cumulative COVID-19 infections and deaths for urban (n=68), suburban (n=740), and rural (n=2331) counties were modeled using separate negative binomial mixed effects regression models with logarithmic link and random state-level intercepts. Zero-inflated models were considered for deaths among suburban and rural counties to account for excess zeros. Results Multivariate regression models indicated positive associations between cumulative COVID-19 infection rates and insufficient sleep in urban, suburban and rural counties. The incidence rate ratio (IRR) for urban counties was 1.03 (95% CI: 1.01 – 1.05), 1.04 (95% CI: 1.02 – 1.05) for suburban, and 1.02 (95% CI: 1.00 – 1.03) rural counties.. Similar positive associations were observed with county-level COVID-19 death rates, IRR = 1.11 (95% CI: 1.07 – 1.16) for urban counties, IRR = 1.04 (95% CI: 1.01 – 1.06) for suburban counties, and IRR = 1.03 (95% CI: 1.01 – 1.05) for rural counties. Level of urbanicity moderated the association between insufficient sleep and COVID deaths, but not for the association between insufficient sleep and COVID infection rates. Conclusion Insufficient sleep was associated with COVID-19 infection cases and mortality rates in urban, suburban and rural counties. Level of urbanicity only moderated the relationship between insufficient sleep and COVID death rates. Future studies should investigate individual-level analysis to understand the role of sleep mitigating COVID-19 infection and death rates. Support (if any) NIH (K07AG052685, R01MD007716, R01HL142066, K01HL135452, R01HL152453

scholarly journals Clinical Characteristics and Outcomes of COVID-19–Infected Cancer Patients: A Systematic Review and Meta-Analysis

2020 ◽  
Author(s):  
Hua Zhang ◽  
Han Han ◽  
Tianhui He ◽  
Kristen E Labbe ◽  
Adrian V Hernandez ◽  
...  
Keyword(s):  
Risk Factors ◽  
Systematic Review ◽  
Case Fatality Rate ◽  
Meta Analysis ◽  
Univariate Analysis ◽  
Case Fatality ◽  
The United States ◽  
Fatality Rate ◽  
Patients With Cancer ◽  
Increased Risk

Abstract Background Previous studies have indicated coronavirus disease 2019 (COVID-19) patients with cancer have a high fatality rate. Methods We conducted a systematic review of studies that reported fatalities in COVID-19 patients with cancer. A comprehensive meta-analysis that assessed the overall case fatality rate and associated risk factors was performed. Using individual patient data, univariate and multivariable logistic regression analyses were used to estimate odds ratios (OR) for each variable with outcomes. Results We included 15 studies with 3019 patients, of which 1628 were men; 41.0% were from the United Kingdom and Europe, followed by the United States and Canada (35.7%), and Asia (China, 23.3%). The overall case fatality rate of COVID-19 patients with cancer measured 22.4% (95% confidence interval [CI] = 17.3% to 28.0%). Univariate analysis revealed age (OR = 3.57, 95% CI = 1.80 to 7.06), male sex (OR = 2.10, 95% CI = 1.07 to 4.13), and comorbidity (OR = 2.00, 95% CI = 1.04 to 3.85) were associated with increased risk of severe events (defined as the individuals being admitted to the intensive care unit, or requiring invasive ventilation, or death). In multivariable analysis, only age greater than 65 years (OR = 3.16, 95% CI = 1.45 to 6.88) and being male (OR = 2.29, 95% CI = 1.07 to 4.87) were associated with increased risk of severe events. Conclusions Our analysis demonstrated that COVID-19 patients with cancer have a higher fatality rate compared with that of COVID-19 patients without cancer. Age and sex appear to be risk factors associated with a poorer prognosis.

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