Applied spatial statistics for public health data

• Main Authors: Waller, Lance A, Gotway, Carol A
• Format: eBook Book
• Language: English
• Published: Hoboken WILEY 2004; Wiley-Interscience; Wiley; John Wiley & Sons, Incorporated; Wiley-Blackwell
• Edition: 1
• Series: Wiley series in probability and statistics
• Subjects: Experimental Design; Public health; Public health > Statistical methods; Spatial analysis (Statistics); Statistical methods

While mapped data provide a common ground for discussions between the public, the media, regulatory agencies, and public health researchers, the analysis of spatially referenced data has experienced a phenomenal growth over the last two decades, thanks in part to the development of geographical info...

Table of Contents:

• Applied spatial statistics for public health data -- Contents -- Preface -- Acknowledgments -- Chapter 1: Introduction -- Chapter 2: Analyzing Public Health Data -- Chapter 3: Spatial Data -- Chapter 4: Visualizing Spatial Data -- Chapter 5: Analysis of Spatial Point Patterns -- Chapter 6: Spatial Clusters of Health Events: Point Data for Cases and Controls -- Chapter 7: Spatial Clustering of Health Events: Regional Count Data -- Chapter 8: Spatial Exposure Data -- Chapter 9: Linking Spatial Exposure Data to Health Events -- References -- Author Index -- Subject Index.
• 5.4.1 Poisson Cluster Processes -- 5.4.2 Contagion/Inhibition Processes -- 5.4.3 Cox Processes -- 5.4.4 Distinguishing Processes -- 5.5 Additional Topics and Further Reading -- 5.6 Exercises -- 6 Spatial Clusters of Health Events: Point Data for Cases and Controls -- 6.1 What Do We Have? Data Types and Related Issues -- 6.2 What Do We Want? Null and Alternative Hypotheses -- 6.3 Categorization of Methods -- 6.4 Comparing Point Process Summaries -- 6.4.1 Goals -- 6.4.2 Assumptions and Typical Output -- 6.4.3 Method: Ratio of Kernel Intensity Estimates -- DATA BREAK: Early Medieval Grave Sites -- 6.4.4 Method: Difference between K Functions -- DATA BREAK: Early Medieval Grave Sites -- 6.5 Scanning Local Rates -- 6.5.1 Goals -- 6.5.2 Assumptions and Typical Output -- 6.5.3 Method: Geographical Analysis Machine -- 6.5.4 Method: Overlapping Local Case Proportions -- DATA BREAK: Early Medieval Grave Sites -- 6.5.5 Method: Spatial Scan Statistics -- DATA BREAK: Early Medieval Grave Sites -- 6.6 Nearest-Neighbor Statistics -- 6.6.1 Goals -- 6.6.2 Assumptions and Typical Output -- 6.6.3 Method: q Nearest Neighbors of Cases -- CASE STUDY: San Diego Asthma -- 6.7 Further Reading -- 6.8 Exercises -- 7 Spatial Clustering of Health Events: Regional Count Data -- 7.1 What Do We Have and What Do We Want? -- 7.1.1 Data Structure -- 7.1.2 Null Hypotheses -- 7.1.3 Alternative Hypotheses -- 7.2 Categorization of Methods -- 7.3 Scanning Local Rates -- 7.3.1 Goals -- 7.3.2 Assumptions -- 7.3.3 Method: Overlapping Local Rates -- DATA BREAK: New York Leukemia Data -- 7.3.4 Method: Turnbull et al.'s CEPP -- 7.3.5 Method: Besag and Newell Approach -- 7.3.6 Method: Spatial Scan Statistics -- 7.4 Global Indexes of Spatial Autocorrelation -- 7.4.1 Goals -- 7.4.2 Assumptions and Typical Output -- 7.4.3 Method: Moran's I -- 7.4.4 Method: Geary's c
• 9.1.1 Estimation and Inference -- 9.1.2 Interpretation and Use with Spatial Data -- DATA BREAK: Raccoon Rabies in Connecticut -- 9.2 Linear Regression Models for Spatially Autocorrelated Data -- 9.2.1 Estimation and Inference -- 9.2.2 Interpretation and Use with Spatial Data -- 9.2.3 Predicting New Observations: Universal Kriging -- DATA BREAK: New York Leukemia Data -- 9.3 Spatial Autoregressive Models -- 9.3.1 Simultaneous Autoregressive Models -- 9.3.2 Conditional Autoregressive Models -- 9.3.3 Concluding Remarks on Conditional Autoregressions -- 9.3.4 Concluding Remarks on Spatial Autoregressions -- 9.4 Generalized Linear Models -- 9.4.1 Fixed Effects and the Marginal Specification -- 9.4.2 Mixed Models and Conditional Specification -- 9.4.3 Estimation in Spatial GLMs and GLMMs -- DATA BREAK: Modeling Lip Cancer Morbidity in Scotland -- 9.4.4 Additional Considerations in Spatial GLMs -- CASE STUDY: Very Low Birth Weights in Georgia Health Care District 9 -- 9.5 Bayesian Models for Disease Mapping -- 9.5.1 Hierarchical Structure -- 9.5.2 Estimation and Inference -- 9.5.3 Interpretation and Use with Spatial Data -- 9.6 Parting Thoughts -- 9.7 Additional Topics and Further Reading -- 9.7.1 General References -- 9.7.2 Restricted Maximum Likelihood Estimation -- 9.7.3 Residual Analysis with Spatially Correlated Error Terms -- 9.7.4 Two-Parameter Autoregressive Models -- 9.7.5 Non-Gaussian Spatial Autoregressive Models -- 9.7.6 Classical/Bayesian GLMMs -- 9.7.7 Prediction with GLMs -- 9.7.8 Bayesian Hierarchical Models for Spatial Data -- 9.8 Exercises -- References -- Author Index -- Subject Index
• Intro -- Applied Spatial Statistics for Public Health Data -- Contents -- Preface -- Acknowledgments -- 1 Introduction -- 1.1 Why Spatial Data in Public Health? -- 1.2 Why Statistical Methods for Spatial Data? -- 1.3 Intersection of Three Fields of Study -- 1.4 Organization of the Book -- 2 Analyzing Public Health Data -- 2.1 Observational vs. Experimental Data -- 2.2 Risk and Rates -- 2.2.1 Incidence and Prevalence -- 2.2.2 Risk -- 2.2.3 Estimating Risk: Rates and Proportions -- 2.2.4 Relative and Attributable Risks -- 2.3 Making Rates Comparable: Standardized Rates -- 2.3.1 Direct Standardization -- 2.3.2 Indirect Standardization -- 2.3.3 Direct or Indirect? -- 2.3.4 Standardizing to What Standard? -- 2.3.5 Cautions with Standardized Rates -- 2.4 Basic Epidemiological Study Designs -- 2.4.1 Prospective Cohort Studies -- 2.4.2 Retrospective Case-Control Studies -- 2.4.3 Other Types of Epidemiological Studies -- 2.5 Basic Analytic Tool: The Odds Ratio -- 2.6 Modeling Counts and Rates -- 2.6.1 Generalized Linear Models -- 2.6.2 Logistic Regression -- 2.6.3 Poisson Regression -- 2.7 Challenges in the Analysis of Observational Data -- 2.7.1 Bias -- 2.7.2 Confounding -- 2.7.3 Effect Modification -- 2.7.4 Ecological Inference and the Ecological Fallacy -- 2.8 Additional Topics and Further Reading -- 2.9 Exercises -- 3 Spatial Data -- 3.1 Components of Spatial Data -- 3.2 An Odyssey into Geodesy -- 3.2.1 Measuring Location: Geographical Coordinates -- 3.2.2 Flattening the Globe: Map Projections and Coordinate Systems -- 3.2.3 Mathematics of Location: Vector and Polygon Geometry -- 3.3 Sources of Spatial Data -- 3.3.1 Health Data -- 3.3.2 Census-Related Data -- 3.3.3 Geocoding -- 3.3.4 Digital Cartographic Data -- 3.3.5 Environmental and Natural Resource Data -- 3.3.6 Remotely Sensed Data -- 3.3.7 Digitizing -- 3.3.8 Collect Your Own!
• 7.5 Local Indicators of Spatial Association -- 7.5.1 Goals -- 7.5.2 Assumptions and Typical Output -- 7.5.3 Method: Local Moran's I -- 7.6 Goodness-of-Fit Statistics -- 7.6.1 Goals -- 7.6.2 Assumptions and Typical Output -- 7.6.3 Method: Pearson's c(2) -- 7.6.4 Method: Tango's Index -- 7.6.5 Method: Focused Score Tests of Trend -- 7.7 Statistical Power and Related Considerations -- 7.7.1 Power Depends on the Alternative Hypothesis -- 7.7.2 Power Depends on the Data Structure -- 7.7.3 Theoretical Assessment of Power -- 7.7.4 Monte Carlo Assessment of Power -- 7.7.5 Benchmark Data and Conditional Power Assessments -- 7.8 Additional Topics and Further Reading -- 7.8.1 Related Research Regarding Indexes of Spatial Association -- 7.8.2 Additional Approaches for Detecting Clusters and/or Clustering -- 7.8.3 Space-Time Clustering and Disease Surveillance -- 7.9 Exercises -- 8 Spatial Exposure Data -- 8.1 Random Fields and Stationarity -- 8.2 Semivariograms -- 8.2.1 Relationship to Covariance Function and Correlogram -- 8.2.2 Parametric Isotropic Semivariogram Models -- 8.2.3 Estimating the Semivariogram -- DATA BREAK: Smoky Mountain pH Data -- 8.2.4 Fitting Semivariogram Models -- 8.2.5 Anisotropic Semivariogram Modeling -- 8.3 Interpolation and Spatial Prediction -- 8.3.1 Inverse-Distance Interpolation -- 8.3.2 Kriging -- CASE STUDY: Hazardous Waste Site Remediation -- 8.4 Additional Topics and Further Reading -- 8.4.1 Erratic Experimental Semivariograms -- 8.4.2 Sampling Distribution of the Classical Semivariogram Estimator -- 8.4.3 Nonparametric Semivariogram Models -- 8.4.4 Kriging Non-Gaussian Data -- 8.4.5 Geostatistical Simulation -- 8.4.6 Use of Non-Euclidean Distances in Geostatistics -- 8.4.7 Spatial Sampling and Network Design -- 8.5 Exercises -- 9 Linking Spatial Exposure Data to Health Events -- 9.1 Linear Regression Models for Independent Data
• 3.4 Geographic Information Systems -- 3.4.1 Vector and Raster GISs -- 3.4.2 Basic GIS Operations -- 3.4.3 Spatial Analysis within GIS -- 3.5 Problems with Spatial Data and GIS -- 3.5.1 Inaccurate and Incomplete Databases -- 3.5.2 Confidentiality -- 3.5.3 Use of ZIP Codes -- 3.5.4 Geocoding Issues -- 3.5.5 Location Uncertainty -- 4 Visualizing Spatial Data -- 4.1 Cartography: The Art and Science of Mapmaking -- 4.2 Types of Statistical Maps -- MAP STUDY: Very Low Birth Weights in Georgia Health Care District 9 -- 4.2.1 Maps for Point Features -- 4.2.2 Maps for Areal Features -- 4.3 Symbolization -- 4.3.1 Map Generalization -- 4.3.2 Visual Variables -- 4.3.3 Color -- 4.4 Mapping Smoothed Rates and Probabilities -- 4.4.1 Locally Weighted Averages -- 4.4.2 Nonparametric Regression -- 4.4.3 Empirical Bayes Smoothing -- 4.4.4 Probability Mapping -- 4.4.5 Practical Notes and Recommendations -- CASE STUDY: Smoothing New York Leukemia Data -- 4.5 Modifiable Areal Unit Problem -- 4.6 Additional Topics and Further Reading -- 4.6.1 Visualization -- 4.6.2 Additional Types of Maps -- 4.6.3 Exploratory Spatial Data Analysis -- 4.6.4 Other Smoothing Approaches -- 4.6.5 Edge Effects -- 4.7 Exercises -- 5 Analysis of Spatial Point Patterns -- 5.1 Types of Patterns -- 5.2 Spatial Point Processes -- 5.2.1 Stationarity and Isotropy -- 5.2.2 Spatial Poisson Processes and CSR -- 5.2.3 Hypothesis Tests of CSR via Monte Carlo Methods -- 5.2.4 Heterogeneous Poisson Processes -- 5.2.5 Estimating Intensity Functions -- DATA BREAK: Early Medieval Grave Sites -- 5.3 K Function -- 5.3.1 Estimating the K Function -- 5.3.2 Diagnostic Plots Based on the K Function -- 5.3.3 Monte Carlo Assessments of CSR Based on the K Function -- DATA BREAK: Early Medieval Grave Sites -- 5.3.4 Roles of First- and Second-Order Properties -- 5.4 Other Spatial Point Processes