Epidemiology and Geography: Principles, Methods and Tools of Spatial Analysis

✍ Scribed by Marc Souris

• Publisher: Wiley-ISTE
• Year: 2019
• Tongue: English
• Leaves: 284
• Edition: 1
• Category: Library

✦ Synopsis

Localization is involved everywhere in epidemiology: health phenomena often involve spatial relationships among individuals and risk factors related to geography and environment. Therefore, the use of localization in the analysis and comprehension of health phenomena is essential. This book describes the objectives, principles, methods and tools of spatial analysis and geographic information systems applied to the field of health, and more specifically to the study of the spatial distribution of disease and health–environment relationships. It is a practical introduction to spatial and spatio-temporal analysis for epidemiology and health geography, and takes an educational approach illustrated with real-world examples.

Epidemiology and Geography presents a complete and straightforward overview of the use of spatial analysis in epidemiology for students, public health professionals, epidemiologists, health geographers and specialists in health–environment studies.

✦ Table of Contents

Cover
Half-Title Page
Title Page
Copyright Page
Contents
Foreword
Preface
Introduction: Software and Databases
I.1. Software
I.1.1. QGIS
I.1.2. ArcGIS
I.1.3. SavGIS
I.1.4. R
I.1.5. GeoDA
I.1.6. SaTScanTM
I.1.7. GWR4
I.1.8. Gama
I.2. Data for the examples
1. Methodological Context
1.1. A systemic approach to health
1.2. Risk and public health
1.3. Epidemiology
1.4. Health geography
1.5. Spatial analysis for epidemiology and health geography
1.6. Geographic information systems
1.7. Book structure
2. Spatial Analysis of Health Phenomena: General Principles
2.1. Spatial analysis in epidemiology and health geography
2.1.1. Spatial distribution of a health phenomenon
2.1.2. Spatial analysis in epidemiology
2.1.3. Spatial and statistical dependence
2.1.4. Causal relationships, explanatory factors, confounding factors
2.1.5. Uncertainty in event localization
2.1.6. Health data are often aggregated into geographical units
2.2. Spatial analysis terminology and formalism
2.2.1. Objects, attributes, events
2.2.2. Localization and spatial domain
2.2.3. The formalism of descriptive analysis
2.2.4. The formalism of the explanatory analysis
2.3. General approach of spatial analysis in epidemiology
2.3.1. The approach of descriptive analysis
2.3.2. The approach of explanatory analysis
2.3.3. Spatial analysis methods
2.3.4. Spatial analysis and health geography
2.4. Required knowledge on epidemiology and statistics
2.4.1. Epidemiology
2.4.2. Statistical analysis
2.4.3. Methods for model adjustment
2.4.4. Several distributions and models
3. Spatial Data in Health
3.1. Introduction
3.2. Health data
3.2.1. Various types of data for individuals
3.2.2. Individual and aggregated health data
3.2.3. Description of the healthcare system
3.3. Spatialization of epidemiological data
3.3.1. Localization in space
3.3.2. Localization in time
3.3.3. Localization in time and space
3.3.4. Data aggregated according to a spatial criterion
3.3.5. Ethics and localization
3.4. Sources of data
3.4.1. Epidemiological data
3.4.2. Geographical and environmental data
3.4.3. Access to geographical data
4. Cartographic Representations and Synthesis Tools
4.1. Introduction
4.1.1. Why use mapping methods?
4.1.2. How to use mapping?
4.2. Cartographic representations
4.2.1. Mapping events or health status
4.2.2. Mapping rates: prevalence, incidence, risk and odds ratio
4.2.3. Mapping flows and spatial relationships
4.2.4. Mapping limitations
4.2.5. Mapping rate significance
4.2.6. Rate adjustment
4.3. Descriptive statistics and visual synthesis tools
4.3.1. Average points, median points
4.3.2. Standard deviational ellipses
4.4. Interpolations and trend surfaces
4.4.1. Interpolations and continuous representation
4.4.2. Directions and gradients
4.4.3. Anamorphoses
4.5. Spatio-temporal animations
4.5.1. What and how
4.5.2. Animated mapping
5. Spatial Distribution Analysis
5.1. Introduction
5.1.1. “Direct” methods for spatial analysis
5.1.2. Continuous space, point pattern, subsets
5.2. Global spatial analyses
5.2.1. Geographical location, extent, orientation
5.2.2. Centrality
5.2.3. Spatial dependence of values
5.2.4. Bivariate spatial analysis
5.2.5. Global pattern of the phenomenon and search for a geometric model
5.3. Local spatial analyses
5.3.1. Local indicators of spatial association (LISA)
5.3.2. Spatial scan-based search for singularities
5.3.3. Analyses around a source point
5.4. Example: emergence and diffusion of avian influenza
5.4.1. Introduction
5.4.2. Mapping
5.4.3. Analysis of the spatial distribution of cases
5.4.4. Spatio-temporal analyses
5.4.5. Analyses of risk factors
6. Spatial Analysis of Risk
6.1. Introduction
6.2. Aggregation-based spatial analyses
6.2.1. Spatial aggregation operation
6.2.2. Statistical analysis
6.2.3. Spatial analysis of aggregation
6.2.4. Spatial belonging
6.3. Statistical modeling of spatial data
6.3.1. Statistical correlations and spatial relationships
6.3.2. Statistical modeling
6.3.3. Spatial models
6.3.4. Spatial heterogeneity of parameters
6.4. An example: analysis of tuberculosis risk factors
6.4.1. Epidemiological and socio-economic data
6.4.2. Analysis of the statistical and spatial distribution of rates
6.4.3. Statistical modeling of SMR and incidence
7. Space–time Analyses and Modeling
7.1. Time–distance relationships
7.2. Mobile mean points
7.3. Spatio-temporal autocorrelation and clusters
7.3.1. Global spatio-temporal autocorrelation
7.3.2. Local spatio-temporal autocorrelation
7.3.3. Spatio-temporal clusters
7.3.4. Statistical modeling: GTWR
7.4. Emergence, diffusion, pathway
7.5. Spatio-temporal modeling of health phenomena
7.5.1. Process modeling and simulation
7.5.2. The deterministic approach of SEIR models
7.5.3. SEIR models and localization
7.5.4. Non-deterministic approach of multi-agent models
Glossary
References
Index
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