Understanding Complex Patterns in Social, Geographic, and Economic Inequities in COVID-19 Mortality at the County Level in the US Using Generalized Additive Models
I present three types of applications of generalized additive models (GAMs) to COVID-19 mortality rates in the US for the purpose of advancing methods to document inequities with respect to which communities suffered disproportionate COVID-19 mortality rates at specific times during the first three years of the pandemic. First, GAMs can be used to describe the changing relationship between COVID-19 mortality and county-level covariates (sociodemographic, economic, and political metrics) over time. Second, GAMs can be used to perform spatiotemporal smoothing that pools information over time and space to address statistical instability due to small population counts or stochasticity resulting in a smooth, dynamic latent risk surface summarizing the mortality risk associated with geographic locations over time. Third, estimation of COVID-19 mortality associations with county-level covariates conditional on a smooth spatiotemporal risk surface allows for more rigorous consideration of how socio-environmental contexts and policies may have impacted COVID-19 mortality. Each of these approaches provides a valuable perspective to documenting inequities in COVID-19 mortality by addressing the question of which populations have suffered the worst burden of COVID-19 mortality taking into account the nonlinear spatial, temporal, and social patterning of disease.
A Hierarchical Multilevel Inference Framework for Structural Cardiovascular Risk Modeling: County-Scale Analysis of Cardiovascular Mortality in Ohio and Pennsylvania (1999-2020)
Howard University（霍华德大学）；UC Riverside（加州大学河滨分校）；California Institute of Technology（加州理工学院）；Dalian University of Technology（大连理工大学）
Identifying Spatiotemporal Patterns in Opioid Vulnerability: Investigating the Links Between Disability, Prescription Opioids and Opioid-Related Mortality
Spatiotemporal dynamic of COVID-19 mortality in the city of Sao Paulo, Brazil: shifting the high risk from the best to the worst socio-economic conditions
Comparative Evaluation of Explainable Machine Learning Versus Linear Regression for Predicting County-Level Lung Cancer Mortality Rate in the United States
University of Tennessee Graduate School（田纳西大学研究生院）
Jointly Estimating Subnational Mortality for Multiple Populations
University of Toronto（多伦多大学）；University of Washington（华盛顿大学）；University of California, Berkeley（加州大学伯克利分校）；Institut National d’Études Démographiques（法国国家人口研究所）
Socio-Spatial Patterns of Suicide Mortality in the United States
University of Pittsburgh（匹兹堡大学）；Old Dominion University（老道明大学）；Kaiser Permanente Northern California（凯撒永久北加州医疗中心）；University of California Berkeley（加州大学伯克利分校）；Boston Children’s Hospital（波士顿儿童医院）；Sorbonne Université（索邦大学）；INSERM（法国国家健康与医学研究院）
Social Determinants of Health and Fentanyl Overdose Mortality Across US Counties: An XGBoost and SHAP Analysis Identifying Silent Risk Counties and Treatment Deserts
A Model Predictive Control Functional Continuous Time Bayesian Network for Self-Management of Multiple Chronic Conditions
Northwestern University（西北大学）；The University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）；Florida State University（佛罗里达州立大学）；The University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心）
