Background: Although the COVID-19 pandemic has left an unprecedented impact worldwide, countries such as the United States have reported the most substantial incidence of COVID-19 cases worldwide. Within the United States, various sociodemographic factors have played a role in the creation of regional disparities. Regional disparities have resulted in the unequal spread of disease between US counties, underscoring the need for efficient and accurate predictive modeling strategies to inform public health officials and reduce the burden on health care systems. Furthermore, despite the widespread accessibility of COVID-19 vaccines across the United States, vaccination rates have become stagnant, necessitating predictive modeling to identify important factors impacting vaccination uptake.
Objective: This study aims to determine the association between sociodemographic factors and vaccine uptake across counties in the United States.
Methods: Sociodemographic data on fully vaccinated and unvaccinated individuals were sourced from several online databases such as the US Centers for Disease Control and Prevention and the US Census Bureau COVID-19 Site. Machine learning analysis was performed using XGBoost and sociodemographic data.
Results: Our model predicted COVID-19 vaccination uptake across US counties with 62% accuracy. In addition, it identified location, education, ethnicity, income, and household access to the internet as the most critical sociodemographic features in predicting vaccination uptake in US counties. Lastly, the model produced a choropleth demonstrating areas of low and high vaccination rates, which can be used by health care authorities in future pandemics to visualize and prioritize areas of low vaccination and design targeted vaccination campaigns.
Conclusions: Our study reveals that sociodemographic characteristics are predictors of vaccine uptake rates across counties in the United States and, if leveraged appropriately, can assist policy makers and public health officials to understand vaccine uptake rates and craft policies to improve them.
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http://dx.doi.org/10.2196/33231 | DOI Listing |
Appl Clin Inform
January 2025
Department of Internal Medicine, Division of Infectious Diseases and Geographic Medicine, The University of Texas Southwestern Medical Center, Dallas, United States.
Background: Global efforts aimed at ending human immunodeficiency virus (HIV) incidence have adapted and evolved since the turn of the century. The utilization of machine learning incorporated into an electronic health record (EHR) can be refined into prediction models that identify when an individual is at greater HIV infection risk. This can create a novel and innovative approach to identifying patients eligible for preventative therapy.
View Article and Find Full Text PDFSci Rep
January 2025
Virginia Commonwealth University School of Medicine, 1201 E Marshall St #4-100, 23298, Richmond, VA, USA.
Routine preventive care (RPC) services are recommended for people with HIV, who have higher risk of certain preventable conditions. We used a pooled cross-section of patient-years to examine receipt of 5 annual RPC services among Medicaid enrollees in the US South. Data were person-level administrative claims (Medicaid Analytic eXtract, 2008-2012) and county-level characteristics for 16 Southern states plus District of Columbia.
View Article and Find Full Text PDFPhysiol Rep
January 2025
Division of Clinical Physiology, Department of Laboratory Medicine, Karolinska Institutet, Stockholm, Sweden.
Maximal oxygen uptake (VOmax) in healthy subjects is primarily limited by systemic oxygen delivery. In chronic kidney disease (CKD), VOmax is potentially reduced by both central and peripheral factors. We aimed to investigate the effect on VOpeak of adding arm exercise to leg exercise.
View Article and Find Full Text PDFBMC Health Serv Res
January 2025
Te Aka Whai Ora (Māori Health Authority), Auckland, New Zealand.
Background: Breast cancer screening in Aotearoa New Zealand (NZ) still has persistent inequitable coverage by ethnicity, especially for Indigenous Māori women. This project aimed to undertake systematic data linkage to identify and invite eligible Māori women to participate in breast screening.
Methods: This is a cross-sectional observational study conducted in Northern New Zealand between 1/01/2020 and 30/06/2021.
Mol Psychiatry
January 2025
Turku PET Centre, University of Turku, Turku, Finland.
Anorexia nervosa (AN) is a severe psychiatric disorder, characterized by restricted eating, fear to gain weight, and a distorted body image. Mu-opioid receptor (MOR) functions as a part of complex opioid system and supports both homeostatic and hedonic control of eating behavior. Thirteen patients with AN and thirteen healthy controls (HC) were included in this study.
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