This book examines statistical methods and models used in the fields of global health and epidemiology. It includes methods such as innovative probability sampling, data harmonization and encryption, and advanced descriptive, analytical and monitory methods. Program codes using R are included as well as real data examples.
Contemporary global health and epidemiology involves a myriad of medical and health challenges, including inequality of treatment, the HIV/AIDS epidemic and its subsequent control, the flu, cancer, tobacco control, drug use, and environmental pollution. In addition to its vast scales and telescopic perspective; addressing global health concerns often involves examining resource-limited populations with large geographic, socioeconomic diversities. Therefore, advancing global health requires new epidemiological design, new data, and new methods for sampling, data processing, and statistical analysis. This book provides global health researchers with methods that will enable access to and utilization of existing data.
Featuring contributions from both epidemiological and biostatistical scholars, this book is a practical resource for researchers, practitioners, and students in solving global health problems in research, education, training, and consultation.
Tabella dei contenuti
Existent Data Sources for Global Health and Epidemiology.- Satellite Imagery Data for Global Health and Epidemiology.- GIS/GPS-Assisted Probability Sampling in Resource-Limited Settings.- Construal-Level Theory Supported Methods for Sensitive Topics: Applications in Three Different Populations.- Integrative Data Analysis and Application in Global Health.- Introduction to Privacy-Preserving Data Collection and Sharing Methods for Global Health Research.- Geographic Mapping for Global Health Research.- A 4D-Indicator System of Count, P Rate, G Rate and PG Rate for Epidemiology and Global Health.- Historical Trends in Mortality Risk over a 100-Year Period in China with Recent Data-An Innovative Application of APC Modeling.- Moore-Penrose Generalized-Inverse Solution to APC Modeling for Historical Epidemiology and Global Health.- Mixed Effects Modeling of Multi-Site Data-Health Behaviors among Adolescents in Hong Kong, Macao, Taipei, Wuhan and Zhuhai.- Geographically Weighted Regression for Global Epidemiological Research.- Bayesian Spatial-Temporal Disease Modeling With Application to Malaria.- ‘Efficient Biosurveillance By A Statistical Process Control Chart Using Covariates’.- Cusp Catastrophe Regression Analysis of Testosterone in Bifurcating the Age-Related Changes in PSA, a Biomarker for Prostate Cancer.- Logistic Cusp Catastrophe Regression for Binary Outcome: Method Development and Empirical Testing.
Circa l’autore
Professor Xinguang Chen, MD and Ph D is an elected fellow of American College of Epidemiology and currently a tenure professor of epidemiology at the University of Florida. He is also a chair professor at Wuhan University Global Health Institute, editor-in-chief for Global Health Research and Policy, associate editor for Global Health Journal, and an advisory board member of the WHO-China Information Collaboration Center at People’s Publication House of China. Professor Chen is well known for his international and intercultural research on challenging medical and health issues, including HIV/AIDS, substance use, physical activity, cardiovascular disease, and cancer. In 2014, Professor Chen published a paper with Yale Journal of Biology and Medicine on global health for medical and health education. His research is guided by cross-cultural, transdisciplinary, and global perspectives, and it is characterized by advanced statistical and epidemiological methods and models particularly suitable for resource-limited settings. He has received continuous NIH funding for his methods and epidemiological research since the 1990s, and his research extends from the United States to China, Southeast Asia, and Latin America. He has published over 280 manuscripts in peer-reviewed journals, four authored books, and several book chapters and encyclopedia entries.
Professor (Din) Ding-Geng Chen, Ph D is an elected fellow of the American Statistical Association, the Wallace Kuralt Distinguished Professor at the School of Social Work, and a professor of biostatistics at the Gillings School of Global Public Health, University of North Carolina at Chapel Hill, USA. He is also an extraordinary professor at the department of statistics, University of Pretoria, South Africa. Professor Chen was a professor in biostatistics at the University of Rochester and the Karl E. Peace Endowed Eminent Scholar Chair in Biostatistics at Georgia Southern University, USA. He is also a senior statistics consultant for biopharmaceuticals and government agencies with extensive expertise in Monte-Carlo simulations, clinical trial biostatistics and public health statistics. Professor Chen has more than 200 referred professional publications, and he has co-authored/co-edited 28 books on clinical trial methodology, meta-analysis and public health applications. He has been invited nationally and internationally to give speeches on his research.
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