Statistical Methods for Global Health and Epidemiology: Principles, Methods and Applications (ICSA Book Series in Statistics)

✍ Scribed by Xinguang Chen (editor), (Din) Ding-Geng Chen (editor)

• Publisher: Springer
• Year: 2020
• Tongue: English
• Leaves: 420
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Preface
Contents
List of Contributors
List of Reviewers
About the Editors
Part I Data Acquisition and Management
1 Existent Sources of Data for Global Health and Epidemiology
1.1 Introduction
1.2 Country Codes, Population and Geographic Area Data
1.2.1 Standard Country Codes
1.2.2 Population Data by Country
1.2.3 Geographic Area Data by Country
1.2.4 Data from the Internet World Stats
1.2.5 Data from Wikipedia
1.3 Data for Socioeconomic Status and Vital Statistics
1.3.1 Data from the World Health Organization
1.3.2 Data from the World Bank
1.4 Data on Important Social, Legal and Religious Factors by Country
1.4.1 Data for Measuring Press Freedom
1.4.2 World Index of Moral Freedom
1.4.3 Country Profile of Religions
1.5 Data on Disease Statistics
1.5.1 Data for Global Cancer Statistics
1.5.2 Data for Global Cardiovascular Disease Statistics
1.5.3 Data for Global Infectious Disease Statistics
1.5.4 Data for Causes of Death in the United States
1.6 Data on Global Tobacco and Substance Use
1.6.1 Tobacco Use and Prevention
1.6.2 Alcohol Use
1.7 Data for Measuring Suicide by Countries in the World
1.8 Data on Physicians, Nurses and Hospital Beds
1.9 Important Surveys with International and Global Coverages
1.9.1 The Demographic and Health Surveys
1.9.2 Global School-Based Student Health Survey
1.9.3 Health Behavior in School-Aged Children
1.9.4 International Social Survey Program
1.9.5 Multiple Indicator Cluster Survey
1.9.6 World Health Survey
1.9.7 The World Mental Health Survey Initiative
1.9.8 World Value Survey
1.10 Summary
References
2 Satellite Imagery Data for Global Health and Epidemiology
2.1 Introduction
2.2 USGS Data
2.2.1 Introduction of Earth Explorer (EE)
2.2.2 Steps to Access USGS Data Using EE
2.3 UNEP Data of United Nations Environmental Program (UNEP)
2.3.1 Introduction of the Environmental Data Explorer of UNEP
2.3.2 Steps to Access UNEP Data
2.4 NASA Earth Science Data
2.4.1 Introduction of the Earth Science Data
2.4.2 Steps to Access Earth Science Data
2.5 Sentinel Satellite Data
2.5.1 Introduction of the Sentinel Satellite Data
2.5.2 Steps to Access the Sentinel Satellite Data
2.6 Global ALOS 3D World Data
2.6.1 Introduction of the Global ALOS 3D World
2.6.2 Steps to Access the ALOS 3D World Data
2.7 Earth Online Data
2.7.1 Introduction to the EO Data
2.7.2 The Steps of Access Earth Online Data
2.8 Additional Sources of Data
2.8.1 Comprehensive Large Array-Data Stewardship System (CLASS)
2.8.2 National Institute for Space Research (INPE)
2.8.3 Himawari Monitor Data of Japanese Meteorological Agency (JMA)
2.8.4 The AErosol RObotic NETwork (AERONET)
2.8.5 Bhuvan India Geo-Platform of ISRO
2.9 Conclusion Remark
References
3 GIS/GPS-Assisted Probability Sampling in Resource-Limited Settings
3.1 Study Population and Samples
3.2 Non-probability Sampling
3.2.1 Purposeful Sampling
3.2.2 Convenience Sampling
3.3 Probability Sampling
3.3.1 Know the Probability for Sampling
3.3.2 Independent Identical Sample Distribution
3.3.3 Generalizability to the Study Population
3.4 Challenges to the Classic Probability Sampling Methods and Alternatives
3.4.1 Methodology Barriers
3.4.2 Hard-to-Reach or Hidden Populations
3.4.3 Urgency to Know Study Results
3.4.4 Application of GIS/GPS Technologies in Probability Sampling
3.5 Challenges to the Existing GIS/GPS-Assisted Probability Sampling Methods
3.5.1 Challenges to Determine Sample Size Before Sampling
3.5.2 Challenges to Distinguishing Residential from Non-residential Housing
3.5.3 Challenges Due to Heterogeneity in Population Density
3.5.4 Challenges to Determine the Geographic Sample Weights
3.6 GIS/GPS Assisted Multi-stage Probability Sampling
3.6.1 Introduction to the Method
3.6.2 Stage 1 Sampling: Random Selection of Geographic Units
3.6.3 Stage 2 and 3: Random Selection of Geographic Segments and Households
3.6.4 Stage 4: Random Selection of Participants from Households
3.6.5 Complementary Data Collection
3.7 Methods to Determine Residential Areas
3.7.1 Method 1. Estimate Residential Area with Collected Data
3.7.2 Method 2. Estimate Residential Area with Monte Carlo Method
3.8 Estimate of Sample Weights
3.9 Practical Test of the Method in an NIH Funded Project
3.9.1 Geographic Sampling Frame and Geounits
3.9.2 Sampling Geographic Segments, Households and Participants
3.9.3 Determination of Residential Area with Imagery Data and GPS-Tracking File
3.10 Strengths and Recommendation
3.10.1 Strengths
3.10.2 Recommendations for Application
Appendix 1: R Program Codes for a Semi-automatic, Computer-Assisted, and Step-Wise Algorithm for Geounit Sampling
Appendix 2: R Program Codes for Monte Carlo Method to Determine Residential Area
References
4 Construal Level Theory Supported Method for Sensitive Topics: Applications in Three Different Populations
4.1 Introduction
4.1.1 Factors Affecting the Quality of Survey Data
4.1.2 Cognitive Censoring and Social Desirability Bias
4.1.3 Existing Methods to Reduce Social Desirability Bias
4.2 Theoretical and Analytical Foundations
4.2.1 Construal-Level Theory and Social Desirability Bias
4.2.2 The Measurement Theory Underpinning of CLT-Based Survey
4.2.3 Statistical Modeling of CLT-Based Survey Data
4.2.4 Bifactor and Tri-factor Modeling Analysis of CLT-Based Data
4.3 Detecting the Sensitive of a Question Using CLT-Based Method
4.3.1 Participants and Procedures
4.3.2 Statistical Analysis and Results
4.3.3 Summary
4.4 Application of CLT-Based Method in an Urban Population
4.4.1 Participants and Procedures
4.4.2 Conventional and CLT-Based Brief Sexual Openness Scale
4.4.3 Variable for Predictive Validity Analysis
4.4.4 Statistical Analysis
4.4.5 Sample Characteristics
4.4.6 Performance of the BSOS as a Conventional Scale
4.4.7 Performance of the CLT-Based Method for Assessing Single Questions
4.4.8 Construct Validity of CLT-Based Method as a Multi-contents Instrument
4.4.9 Separation of Three Factors Based on CLT-Based Data
4.4.10 Bias Assessment
4.4.11 Predictive Validity
4.4.12 Summary
4.5 Application of the CLT-Based Method in an Rural Sample
4.5.1 Data Sources and Participants
4.5.2 BSOS as Conventional and CLT-Based Scale
4.5.3 Variables for Validity Assessment
4.5.4 Statistical Analysis
4.5.5 Sample Characteristics
4.5.6 Results from Tri-factor Analysis
4.5.7 Predictive Validity
4.5.8 Summary
4.6 Discussion and Conclusions
4.6.1 Theoretical Framework of the CLT-Based Method
4.6.2 Empirical Support for the CLT-Based Method
4.6.3 Recommendations and Future Research
References
5 Integrative Data Analysis and the Study of Global Health
5.1 Pooled Data Analysis and Global Health Research
5.2 Integrative Data Analysis
5.2.1 Defining Integrative Data Analysis
5.2.2 Research Questions Suitable for IDA
5.3 Measurement Harmonization
5.3.1 Need for Measurement Harmonization
5.3.2 Logical Harmonization
5.4 Harmonization
5.4.1 Psychometric Harmonization and IDA
5.4.2 Psychometric Harmonization Model
5.5 Illustrative Example
5.5.1 Logical Harmonization of Individual Items
5.5.2 Steps 1 and 2: Descriptive Analysis
5.5.3 Step 3: Iterative MNLFA
5.5.4 Step 4: Examine MNLFA Scores
5.6 Hypothesis Testing in IDA
5.6.1 Challenges to Hypothesis Testing
5.6.2 Another Example of IDA in Global Health Research
5.7 Advances in IDA Methods
5.7.1 New Regularization Method to Identify DIF Items
5.7.2 Trifactor Modeling Method
5.7.3 Summary and Conclusions
Technical Appendix: Moderated Nonlinear Factor Analysis (MNLFA)
Initial Models
Simultaneous Model
Final Model
References
6 Introduction to Privacy-Preserving Data Collection and Sharing Methods for Global Health Research
6.1 Introduction
6.2 Randomized Response Technique and Its Extensions
6.3 Warner's Method
6.3.1 Principles and Method
6.3.2 Application of Warner's Method in Study Risky Behaviors Among College Students
6.3.3 Limitations of Warner's Method
6.3.3.1 Other Extensions of Warner's Method
6.4 More Sophisticated Randomized Response Techniques: RAPPOR
6.5 Random Orthogonal Matrix Masking (ROMM)for Data Sharing
6.5.1 Basic Principles and Methodology
6.5.2 Examples of Random Transformation
6.6 Triple Matrix-Masking (TM2) Methods
6.6.1 Principles and Methodology
6.6.2 Extensions of TM2 Methods
6.7 Conclusion Remarks
References
Part II Essential Statistical Methods
7 Geographic Mapping for Global Health Research
7.1 Importance of Global Mapping
7.2 Preparation for Geographic Mapping
7.2.1 Brief Introduction to R and R Studio
7.2.2 Download and Install R
7.2.3 Download and Install R Studio
7.2.4 Work Around R Studio
7.3 R Packages for Geographic Mapping
7.3.1 R Packages Needed
7.3.2 Download and Install the Related R Packages
7.4 Mapping the World Using R
7.4.1 Creating a Base World Map
7.4.2 Change Map Projections for Best View
7.4.3 Map Rotation for a Different Central View
7.4.4 An Example with Both Projection and Rotation
7.5 Geographic Mapping of the World Population: A Practical Example
7.5.1 Steps to Map a Subject Matter
7.5.2 Data Preparation
7.5.3 Mapping Your Data
7.6 Mapping the Density of World Population by Country
7.7 Conclusion Remarks
Appendix
References
8 A 4D Indicator System of Count, P Rate, G Rate and PG Rate for Epidemiology and Global Health
8.1 Introduction
8.2 Ending the HIV/AIDS Epidemic by 2030
8.3 Four-Dimensional Measurement System
8.3.1 Two Conventional Measure of Headcount and P Rate
8.3.2 Two New Measures of G Rate and P Rate
8.4 An Example of Global HIV Epidemic
8.4.1 Materials and Method
8.4.2 Estimation of P Rate, G Rate and PG Rate
8.4.3 Geographic Mapping
8.5 Results
8.5.1 The Global HIV Epidemic Measured by Headcounts of PLWH
8.5.2 The Global HIV Epidemic Measured by P Rates of PLWH
8.5.3 The Global HIV Epidemic Measured by G Rates of PLWH
8.5.4 The Global HIV Epidemic Measured by PG Rates of PLWH
8.6 Discussion and Conclusion Remarks
A.1 Appendix 1. List of countries with population, land area, total PLWH, P rate, G rate and PG rate
References
9 Historical Trends in Mortality Risk over 100-Year Period in China with Recent Data: An Innovative Application of Age-Period-Cohort Modeling
9.1 Introduction
9.1.1 Learn from History
9.1.2 Challenges for Quantitative Historical Research
9.2 Timeline of Significant Events in China Since 1900
9.2.1 Overthrow of the Feudalistic Society
9.2.2 Early Period After Independence
9.2.3 The Period of Open Policy and Economic Reform
9.3 Age-Specific Data and APC Modeling Analysis
9.3.1 Age-Specific Data as Digital Fossils
9.3.2 APC Model to Extract the Historical Information
9.3.3 Challenges to APC Modeling
9.3.4 New Data Selection Method to Correctly Estimate Cohort Effect
9.3.5 Using Single Year of Data 5 Years Apart as a Solution
9.4 Materials and Methods
9.4.1 Source of Data
9.4.2 APC Modeling Analysis
9.5 Main Study Findings
9.5.1 Visual Presentation of the Mortality Data
9.5.2 Comparison of Results from Four Different APC Models
9.5.3 Period Effect for Mortality Risk Change over 1990–2010
9.5.4 Changes in Cohort Effect Through Numerical Differentiation
9.5.5 Sunny Periods in Historical China
9.5.6 Cloudy Period in Historical China
9.6 Discussion and Conclusions
9.6.1 Findings and Implications for China
9.6.2 Economic and Technic Advancement Not Equal to Good Health
9.6.3 APC Modeling for Historical Epidemiology
9.6.4 Implication for Research in Other Countries
9.6.5 Limitations and Conclusion Remarks
References
10 Moore-Penrose Generalized-Inverse Solution to APC Modeling for Historical Epidemiology and Global Health
10.1 Introduction
10.2 APC Model and Its Estimation
10.2.1 An Introduction to APC Model
10.2.2 Solving APC Using MP Method
10.3 Application with Real Data
10.3.1 Data Source and Arrangement
10.3.2 Modeling Analysis with MP-APC
10.3.3 Comparison with Results from IE-APC
10.4 Relationship Between IE-APC and MP-APC
10.4.1 IE-APC Modeling
10.4.2 MP-APC Modeling
10.5 Discussion and Conclusions
Appendix: R Program for MP-APC
References
11 Mixed Effects Modeling of Multi-site Data-Health Behaviors Among Adolescents in Hong Kong, Macao, Taipei, Wuhan and Zhuhai
11.1 Introduction
11.2 Methodology Challenge and Alternatives
11.2.1 Heterogeneity Data for Global Health Research
11.2.2 Understand Multi-site and Multi-level Data
11.3 A Study Across Five Chinese Cities: An Example
11.3.1 Purposes and Rational
11.3.2 Participants and Procedure
11.3.3 Measurement of Lifestyle Behavior
11.3.4 Measurement of Addictive Behaviors
11.3.5 Measurement of Student-Level Factors
11.3.6 Measurement of Site-Level Factors
11.4 Statistical Analysis and Results
11.4.1 Data Analysis
11.4.2 Study Site and Sample
11.4.3 Prevalence of Life Style Variables
11.4.4 Prevalence of Addictive Behaviors
11.4.5 Intraclass Correlation for the Variable Time on Siting Position
11.4.6 Results from Mixed Effects Model and Linear Regression
11.5 Discussion and Conclusions
11.5.1 Significance of the Mixed Effects Modeling Methods
11.5.2 Implications of the Findings from This Study
References
12 Geographically Weighted Regression
12.1 Introduction
12.2 Theory
12.2.1 Basic Model Structure and Inference
12.2.2 Constructing Weights
12.2.3 Testing Spatial Nonstationarity
12.2.4 Geographically Weighted Generalized Linear Models
12.2.5 Colinearity and Remedies
Local Linear Estimation
Regularized Fitting
12.3 Software and Case Study
12.3.1 Data
12.3.2 Data Analysis with R Packages
12.3.3 Conclusion
References
Part III Advanced Statistical Methods
13 Bayesian Spatial-Temporal Disease Modeling with Application to Malaria
13.1 Introduction
13.2 Spatial-Temporal Data in Nigeria
13.2.1 Study Area
13.2.2 Country Profile
13.2.3 Ethical Approval
13.2.4 Predictor Variables
13.3 Statistical Methodology
13.3.1 Malaria Spatial-Temporal Modeling
Data Distribution
Spatial-Temporal Mixed-Effects Regression Model
13.4 Bayesian Spatial-Temporal Models with INLA
13.4.1 Goodness of Fit Statistics
13.5 Results
13.6 Conclusion and Summary of Findings
A.1 Appendix 1: R Program Codes for Analysis.
References
14 BCEWMA: A New and Effective Biosurveillance System for Disease Outbreak Detection
14.1 Introduction
14.2 Some Basic SPC Concepts and Methods
14.3 A New Biosurveillance System
14.3.1 A Baseline Model and Its Estimation
14.3.2 Sequential Monitoring of Disease Incidence Rates
14.4 Real Data Examples
14.4.1 The Hand, Foot and Mouth Disease Data
14.4.2 The Influenza-Like-Illness Data
14.5 Concluding Remarks
References
15 Cusp Catastrophe Regression Analysis of Testosterone in Bifurcating the Age-Related Changes in PSA, a Biomarker for Prostate Cancer
15.1 Introduction
15.1.1 Challenges to Using PSA as Prostate Cancer Screener
15.1.2 Age Pattern of PSA Changes
15.1.3 Relationship Between Testosterone and PSA
15.1.4 A Cusp Catastrophe Model of PSA as Function of Age and Testosterone
15.1.5 Purpose of This Study
15.2 Materials
15.2.1 Participants and Data
15.2.2 Variables and Measurement
15.3 Statistical Analysis and Cusp Modeling
15.3.1 Statistical Analysis
15.3.2 Cusp Catastrophe Modeling
15.4 Analytical Findings
15.4.1 Sample Characteristics
15.4.2 Results from Linear Correlation Analysis
15.4.3 Results from Linear Regression Modeling
15.4.4 Bimodality of the PSA Level in Men
15.4.5 Results from Cobb-Grasman Cusp Modeling
15.4.6 Results from Chen-Chen Cusp Regression Modeling
15.4.7 Cusp Point, Threshold Lines and Cusp Region
15.5 Discussion and Conclusions
15.5.1 PSA Dynamics Is Nonlinear and Discrete
15.5.2 Co-use of Testosterone and PSA for Screening
15.5.3 Limitations and Future Research
References
16 Logistic Cusp Catastrophe Regression for Binary Outcome: Method Development and Empirical Testing
16.1 Background
16.1.1 Cusp Catastrophe for Nonlinear Discrete Systems
16.1.2 Established Methods for Cusp Catastrophe Modeling
16.1.3 Need for Methods to Model Binary Data
16.2 An Overview of the Cusp Catastrophe Model
16.2.1 Deterministic Cusp Model
16.2.2 Characteristics of the Cusp Catastrophe Model
16.3 Implementation of a Cusp Catastrophe Model
16.3.1 Guastello's Polynomial Approach
16.3.2 Cobb-Grasman's Approach
16.3.3 Chen-Chen's Cusp Regression Approach
16.4 Cusp Catastrophe Modeling of Binary Data
16.4.1 The Binary Data Structure
16.4.2 The Binary Cusp Catastrophe Model
16.4.3 Maximun Likelihood Estimation
16.4.4 Cusp Catastrophe Conventions
16.4.5 Cusp Region Estimation
16.4.6 Numeric Search Algorithms for Parameter Estimates
16.5 Test the Logistic Cusp Catastrophe Model Through Monte-Carlo Simualtion
16.5.1 Model Settings for Simulation
16.5.2 Steps of Simulation Study
16.5.3 Results and Interpretation
16.6 Modeling Analysis with Real Data: Binge Drinking
16.6.1 Data Sources and Variables
16.6.2 Modeling Analysis
16.6.3 Parameter Estimates and Comparison
16.6.4 Comparison of the Estimated Cusp Regions
16.7 Discussion and Conclusions
References
Correction to: Statistical Methods for Global Health and Epidemiology
Index