Advances in Public Health Statistics and Data Analytics

Erscheinungsjahr 2026
ISBN: 978-3-032-33811-2
Verlag: Springer

Buch, Englisch, 308 Seiten, Format (B × H): 155 mm x 235 mm

Reihe: ICSA Book Series in Statistics

This book presents the most advanced Public Health Statistical modeling and data analytics in essential component in evidence-based public health decision. Modeling such as Poisson Regression, Joinpoint Regression, SIRD Model and Game Theory in Public Health and Medical Studies.

is an essential component in evidence-based public health decision-making. To promote the research and development in public health statistics, this book is contributed from the leadership team at the Applied Public Health Statistics (APHS) Section from the American Public Health Association(APHA).

The primary aim of this book is to stimulate research, foster collaboration among statistical and public health researchers, and provide valuable opportunities for further academic and professional interactions. As a timely and authoritative resource, it serves as a reference for professionals, researchers, and graduate students in public health research and applications. The latest advancements presented in this volume are invaluable for both practitioners and academics seeking to navigate the evolving landscape of public health statistical science and data analytics.

Zielgruppe

Research

Autoren/Hrsg.

Chen, Ding-Geng

Wilson, Jeffrey

Wang, Jason J.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Mathematik | Informatik Mathematik Stochastik

Inhaltsverzeichnis

Chapter 1. Using Modified Poisson Regression to Estimate Prevalence Ratios for Multilevel Data Analysis.- Chapter 2. Using Modified Poisson Regression to Estimate Prevalence Ratios for Multilevel Data Analysis.- Chapter 3. Joinpoint Regression for Identifying Change Points in Nonclinical Outcomes Over Time.- Chapter 4. Modeling Feedback and Endogeneity in Longitudinal Binary Outcomes: A Bayesian Three-Step Framework.- Chapter 5. Dynamics of COIVID-19 Transmission during Lockdown Period Using SIRD Model: A Case of Ethiopian Data .- Chapter 6. Application of Statistical Techniques for Analyzing Obesity Prevalence and Trends Using Population-Based Surveillance Data.- Chapter 7. Small Area Estimation Techniques for County Level Obesity Prevalence Estimation Using State Survey Data.- Chapter 8. Using Game Theory in Public Health and Medical Studies.- Chapter 9. Beyond Aggregate Analysis: Addressing Health Disparities Through Disaggregated Premature Mortality Research Among Asian Americans.- Chapter 10. Cost Saved by Decreasing the Colorectal Cancer Incidence Rate in Louisiana.- Chapter 11. Bayesian Small Area Estimation for Spatial-Temporal Health Data: Methods and Applications.- Chapter 12. Large Language Models for Missing Data Imputation in Public Health Research.- Chapter 13. Changes to US federal data collection capacity in the first 100 days of the 2025 Trump administration.- Chapter 14. The Role of Measurement and Psychometrics in Public Health Statistics.- Chapter 15. Fallacies in Public Health and Biomedical Research.- Chapter 16. Measuring What Matters: The Power of in Evaluating Educator Sexual Misconduct Prevention Training.- Chapter 17. Teaching Statistics to Health Major Undergraduate Students: Strategies for Engagement and Understanding.- Chapter 18. An Introduction to Meta-Analysis: Concepts, Methods, and Applications.- Chapter 19. Distorted Truths: How Poor-Quality Survey Data Impacts Public Health Analyses.

Über Autor(innen)

Ding-Geng Chen (aka, Din Chen) is the executive director and professor in biostatistics at the College of Health Solutions, Arizona State University. He is an elected fellow of the American Statistical Association, an elected fellow of the Royal Society of South Africa, an elected member of the Academy of Science of South Africa, an extraordinary professor and the SARChI research chair in biostatistics at the University of Pretoria, and an honorary professor at the University of KwaZulu-Natal, South Africa. Dr. Chen was the Wallace H. Kuralt Distinguished professor in Biostatistics at the University of North Carolina at Chapel Hill,  a professor in biostatistics at the University of Rochester Medical School, and the Karl E. Peace Endowed Eminent Scholar Chair in Biostatistics at Georgia South-ern University. Dr. Chen has more than 300 referred professional publications, co-authored 12 books and co-edited 31 books on clinical trial methodology, meta-analysis, data science, causal inference, and public health statistics research and applications. This work is partially supported by the South African National Research Foundation and South African Medical Research Council (SARChI Research Chair in Biostatistics, Grant Number 114613).

 Jeffrey R. Wilson is Professor of Statistics and Biostatistics at Arizona State University and serves as Associate Dean for Research and Inclusive Excellence in the W. P. Carey School of Business. He is a Fellow of the American Statistical Association (ASA). His research focuses on Bayesian inference, longitudinal and correlated data analysis, generalized linear mixed models, and categorical outcomes. He has published more than 85 peer-reviewed articles in journals including Statistics in Medicine, American Journal of Public Health, Journal of Business and Economic Statistics, and Management Science.

Dr. Wilson is co-author of several statistical texts, including Statistical Analytics for Health Data Science with SAS and R (Springer, 2023), Modeling Correlated Binary Responses (2nd ed., Springer, 2024), and Marginal Models in Analysis of Correlated Binary Data with Time-Dependent Covariates (Springer, 2020). His methodological research has been supported by grants from the NIH, NSF, USDA, and the Arizona Department of Health Services. He has delivered invited talks and workshops internationally and has led short courses on Bayesian modeling, GMM methods, and correlated data analysis in the United States, South Africa, Trinidad, and Morocco. 

Jason J. Wang, PhD, is Professor and Health Economist at the Feinstein Institutes for Medical Research at Northwell Health. He also serves as Professor at the Zucker School of Medicine at Hofstra/Northwell and as Director of Data Analytics in Northwell Health's Department of Radiology. Prior to joining Northwell in 2016, he served as Senior Director of Evaluation, Research, and Analysis at the New York City Department of Health and Mental Hygiene and held academic appointments at SUNY Old Westbury and the Mount Sinai School of Medicine.

Dr. Wang is the Immediate Past Chair of the Applied Public Health Statistics Section of the American Public Health Association. With more than 30 years of experience in health economics, statistical analysis, and health services research, he has authored over 100 peer-reviewed publications and led numerous federally and foundation funded studies, including as principal investigator of an NINDS R01 (R01NS139190). His work integrates advanced analytics, artificial intelligence, and large-scale clinical data to inform policy, improve health system performance, and address disparities in population health.