Spatial Analysis in Health Geography PDF
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Spatial analySiS in HealtH GeoGrapHy Geographies of Health Series editors allison Williams, associate professor, School of Geography and earth Sciences, McMaster University, Canada Susan elliott, professor, Department of Geography and environmental Management and School of public Health and Health Systems, University of Waterloo, Canada there is growing interest in the geographies of health and a continued interest in what has more traditionally been labeled medical geography. the traditional focus of ‘medical geography’ on areas such as disease ecology, health service provision and disease mapping (all of which continue to reflect a mainly quantitative approach to inquiry) has evolved to a focus on a broader, theoretically informed epistemology of health geographies in an expanded international reach. as a result, we now find this subdiscipline characterized by a strongly theoretically-informed research agenda, embracing a range of methods (quantitative; qualitative and the integration of the two) of inquiry concerned with questions of: risk; representation and meaning; inequality and power; culture and difference, among others. Health mapping and modeling has simultaneously been strengthened by the technical advances made in multilevel modeling, advanced spatial analytic methods and GIS, while further engaging in questions related to health inequalities, population health and environmental degradation. This series publishes superior quality research monographs and edited collections representing contemporary applications in the field; this encompasses original research as well as advances in methods, techniques and theories. The Geographies of Health series will capture the interest of a broad body of scholars, within the social sciences, the health sciences and beyond. Also in the series The Afterlives of the Psychiatric Asylum Recycling Concepts, Sites and Memories edited by Graham Moon, robin Kearns and alun Joseph Geographies of Health and Development Edited by Isaac Luginaah and Rachel Bezner Kerr Soundscapes of Wellbeing in popular Music Gavin J. Andrews, Paul Kingsbury and Robin Kearns Mobilities and Health Anthony C. Gatrell Spatial analysis in Health Geography Edited by pavloS KanaroGloU McMaster University, Canada eriC DelMelle University of North Carolina at Charlotte, USA antonio páez McMaster University, Canada © Pavlos Kanaroglou, Eric Delmelle and Antonio Páez 2015 all rights reserved. no part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise without the prior permission of the publisher. Pavlos Kanaroglou, Eric Delmelle and Antonio Páez have asserted their right under the Copyright, Designs and Patents Act, 1988, to be identified as the editors of this work. published by ashgate publishing limited ashgate publishing Company Wey Court East 110 Cherry Street Union Road Suite 3-1 Farnham Burlington, VT 05401-3818 Surrey, GU9 7pt USa england www.ashgate.com British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library. The Library of Congress has cataloged the printed edition as follows: Spatial analysis in health geography / [edited] by pavlos Kanaroglou, eric Delmelle and Antonio Páez. pages cm. -- (Ashgate’s geographies of health series) includes bibliographical references and index. ISBN 978-1-4724-1619-3 (hardback) -- ISBN 978-1-4724-1620-9 (ebook) -- ISBN 978-1-4724-1621-6 (epub) 1. Medical geography--Statistical methods. I. Kanaroglou, Pavlos. II. Delmelle, Eric. III. Páez, Antonio. RA791.S64 2015 614.4'2--dc23 2014036510 ISBN 9781472416193 (hbk) ISBN 9781472416209 (ebk – PDF) ISBN 9781472416216 (ebk – ePUB) printed in the United Kingdom by Henry ling limited, at the Dorset press, Dorchester, Dt1 1HD Contents List of Figures vii List of Tables xi Notes on Contributors xiii Acknowledgements xxi 1 Introduction: Spatial Analysis and Health 1 Eric Delmelle and Pavlos S. Kanaroglou SECTION 1 METhOdS 2 Effective Use of GIS for Spatial Epidemiology 15 Linda Beale 3 An Assessment of Online Geocoding Services for Health Research in a Mid-Sized Canadian City 31 Patrick DeLuca and Pavlos S. Kanaroglou 4 Clustering and Co-occurrence of Cancer Types: A Comparison of Techniques with an Application to Pediatric Cancer in Murcia, Spain 47 Antonio Páez, Fernando A. López-Hernández, Juan A. Ortega-García, Manuel Ruiz SECTION 2 INfECTIOuS dISEaSE 5 Spatio-Temporal Characteristics of the Medieval Black Death 71 Brian H. Bossak and Mark R. Welford 6 Space-Time Visualization of Dengue Fever Outbreaks 85 Eric Delmelle, Meijuan Jia, Coline Dony, Irene Casas, and Wenwu Tang 7 Disease at the Molecular Scale: Methods for Exploring Spatial Patterns of Pathogen Genetics 101 Margaret Carrel SECTION 3 ChrONIC dISEaSE 8 Modeling Spatial Variation in Disease Risk in Epidemiologic Studies 121 David C. Wheeler and Umaporn Siangphoe vi Spatial Analysis in Health Geography 9 The Spatial Epidemiology of Mental Well-being in Dhaka’s Slums 139 Oliver Gruebner, Mobarak Hossain Khan, Sven Lautenbach, Daniel Müller, Alexander Krämer, Tobia Lakes, Patrick Hostert and Sandro Galea 10 Space-Time Analysis of Late-Stage Breast Cancer Incidence in Michigan 161 Pierre Goovaerts and Maxime Goovaerts SECTION 4 ExpOSurE 11 A Method for Reducing Classical Error in Long-Term Average Air Pollution Concentrations from Incomplete Time-Series Data 183 Matthew D. Adams and Pavlos S. Kanaroglou 12 The Geographic Distribution of Metal Dust Exposure in Syracuse, NY 197 Daniel A. Griffith 13 Participatory and Ubiquitous Sensing for Exposure Assessment in Spatial Epidemiology 219 Michael Jerrett, Colleen E. Reid, Thomas E. McKone, and Petros Koutrakis SECTION 5 aCCESSIbIlITy aNd hEalTh 14 Locational Planning of Health Care Facilities 243 Alan T. Murray and Tony H. Grubesic 15 Planning Towards Maximum Equality in Accessibility of NCI Cancer Centers in the U.S. 261 Fahui Wang, Cong Fu and Xun Shi 16 Spatial Dimensions of Access to Health Care Services 275 Daniel J. Lewis 17 Nature and Death: An Individual Level Analysis of the Relationship Between Biophilic Environments and Premature Mortality in Florida 295 Christopher J. Coutts and Mark W. Horner Appendix 313 Index 315 List of Figures 1.1 Dengue fever cases for the city of Colombia, 2010 (geocoded at the street intersection level), in (a). Kernel density estimation in (b), aggregated dengue cases per neighborhood in (c), population density in (d) and dengue fever rates in (e) 3 2.1 MAUP: scale and aggregation problem 20 2.2 The effect of classification: Directly standardized mortality rates for 2011, London, UK 23 3.1 The City of Hamilton, indicating Urban and Rural Census Tracts 33 3.2 Observed geocodes versus what is expected based on the parcel fabric centroids for (A) Geocoder.ca, (B) Yahoo API and (C) ArcGIS Online 37 3.3 Average distance separating geocode and parcel fabric centroid by census tract for (A) Geocoder.ca, (B) Yahoo API and (C) ArcGIS Online 39 3.4 Average distances based on the interactive rematch in ArcGIS 42 4.1 Three examples of spatial patterns using simulated data. The x- and y-axis represent geographical coordinates (easting and northing). Triangles, squares, and circles represent different types of diseases. The scale in the maps has been normalized to create a unit square 50 4.2 Multinomial spatial scan statistic and Q(m) applied to simulated examples 55 4.3 Empirical frequency of symbols (co-occurrence patterns) under k=5 and m=3, and bootstrapped confidence intervals (90% and 95%) 58 4.4 Empirical frequency of symbols (co-occurrence patterns) under k=2 (leukemia and other) and m=3, and bootstrapped confidence intervals (90% and 95%) 59 4.5 Co-occurring leukemia triads in Murcia (dots are cases of pediatric cancer). Triad identifiers as in Table 4.5. 61 5.1 Medieval Black Death Outbreaks (By Year) and Medieval Transportation Networks 74 5.2 Geographic Expansion of the Medieval Black Death. Contours represent one-year intervals 76 5.3 Medieval Black Death Localities, Buffers (8/16 km), and Medieval Transportation Networks in the UK 77 5.4 Epidemic initialization of the Medieval Black Death (ordinary kriging) 78 5.5 Epidemic termination of the Medieval Black Death (ordinary kriging) 78 6.1 STKDE computing framework. Boxes in bold (2) and (4) denote computing intensive procedures, while the dashed box (3) is optional 89 viii Spatial Analysis in Health Geography 6.2 Running time for the STKDE on one CPU (a) 96 CPUs (b) based on different cell sizes. Note the values along the Y-axis are different 91 6.3 Sensitivity of the STKDE running with variation in spatial bandwidth, on one CPU (a) 96 CPUs (b) Note the values along the Y-axis are different 92 6.4 Transparency scheme adopted in Voxler for visualizing STKDE values ranging from 0 to 1 (on the map) 93 6.5 Space-time distribution of dengue fever cases for the first seven month of the year 2010 in Cali, Columbia in (a) STKDE using a cell size of 125 meters with shaded isovolumes in (b) and isovolumes in a triangulated fashion in (c) Clusters numbers (1)-(5) are discussed in the text 94 7.1 Example of a phylogenetic tree indicating the relationships between viruses (or bacteria) B-J, from an ancestral or root virus (A). Viruses G and H have the greatest degree of genetic change from virus A, while B and C are closely related to each other and to A 103 7.2 A Mantel spatial correlogram indicating genetic similarity or dissimilarity at various distance thresholds. Points above the zero line indicate similiarity, points below indicate dissimilarity. Filled circles indicate statistical significance. In this case, an isolation-by-distance pattern is not observed. Instead, samples that are further apart geographically exhibit statistically significant similarity, while those at low geographic distances are genetically dissimilar 107 7.3 Sample points (black circles) are used to create a triangular network of neighbors (using Delaunay triangulation, black lines) and Voronoi tessellations (gray lines). In Monmonier’s algorithm the genetic distance or other measure of genetic relatedness is associated with each connection between points on the triangular network. Barriers are then drawn between points, along the Voronoi tessellation lines, that have the highest degree of genetic distance. Individual barrier segments, dividing points, are linked across the network to either form a closed loop or end at the edge of the study area 108 7.4 In this hypothetical example, the influence of an explanatory variable on genetic distance data varies across the dataset, with the strongest influence observed in the west and declining association in the east 110 8.1 Two simulation study scenarios and one realization from the alternative hypothesis for the first simulation study. The true risk area is either a circle or triangle with an odds ratio of 3.5 and a probability of disease of 0.2 outside the elevated risk area. Case (solid) and control (unfilled) locations are shown as circles for one realization. 127 8.2 One realization from the alternative hypothesis for the risk scenario in the second simulation study. Data were generated with a true elevated risk area at time 1 with an odds ratio of 3.5, a probability of disease of 0.2 outside the circle, and a 0.5 probability of moving at three later times: time 2 (upper right), 3 (lower left) and 4 (lower right). Case (unfilled square) and control (unfilled circle) locations are shown as circles. Cases located in the true risk area at time 1 are shaded black 128 List of Figures ix 8.3 Spatial log-odds estimated at time of diagnosis and at 20 years before diagnosis for models with individual spatial risk components and cumulative spatial log-odds estimated for a model with individual spatial risk components at time of diagnosis and 5, 10, 15, and 20 years before diagnosis. Statistically significant local areas of elevated (lowered) risk are outlined in black solid (white dashed) lines 132 9.1 Descriptive statistics for the 14 socio-ecological variables used in this study 143 9.2 Marginal effects for one predictor in the multivariable generalized linear regression model in Table 9.1, while keeping all others at a constant value 148 9.3 Marginal effects for one predictor in the multivariable generalized linear regression model in Table 9.1, while keeping all others at a constant value 149 9.4 Marginal effects for one predictor in the multivariable generalized linear regression model in Table 9.1, while keeping all others at a constant value 150 9.5 Mental well-being (WHO-5 scores) and housing quality of men in the slum settlement Bishil/Sarag 153 10.1 Two different representations of the Michigan space-time breast cancer dataset 162 10.2 Maps of three county-level quantities averaged over the entire time period 164 10.3 Annual proportions of breast cancer late-stage cases (white females 84 years and younger) that were diagnosed over the period 1985–2007 within Wayne County 167 10.4 Time series, with the joinpoint regression model fitted, for three quantities 169 10.5 Time series, with the joinpoint regression model fitted, for the annual number of cases that were diagnosed early and late over the Lower Peninsula 171 10.6 Flowchart describing the different steps of the space-time analysis of county-level rates of breast cancer late-stage diagnosis 172 10.7 Maps of two parameters of the joinpoint regression models fitted to Michigan county-level time series of proportions of breast cancer late- stage diagnosis 173 10.8 (A) Number of boundaries (that is, pairs of adjacent counties) with significant differences in kriged estimates or APC values as a function of time. (B) Average difference in travel distances to the closest screening facilities recorded for adjacent counties over time 174 10.9 Results of the space-time cluster analysis 176 11.1 Study area map of Paris, France with monitors identified by their ID. Circles with radii of 15, 30 and 60 km are included and center on the Paris Centre monitor. An inset is included of the downtown region 185 12.1 Flowchart articulating the data compilation methodology for this study 198 12.2 Sampling of the Syracuse, NY, geographic landscape 199 12.3 Zoom ins from Figure 12.2a: sample parcels in Syracuse, NY 200 12.4 A scatterplot of the relationship between Box-Cox power transformed soil (horizontal axis) and dust (vertical axis) measurements 204
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