Spatial and Syndromic Surveillance for Public Health

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1 Spatial and Syndromic Surveillance for Public Health Edited by Andrew B. Lawson Department of Epidemiology and Biostatistics University of South Carolina, USA Ken Kleinman Department of Ambulatory Care and Prevention Harvard Medical School, USA

3 Spatial and Syndromic Surveillance for Public Health

5 Spatial and Syndromic Surveillance for Public Health Edited by Andrew B. Lawson Department of Epidemiology and Biostatistics University of South Carolina, USA Ken Kleinman Department of Ambulatory Care and Prevention Harvard Medical School, USA

6 Copyright 2005 John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, England Telephone (+44) (for orders and customer service enquiries): Visit our Home Page on 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, scanning or otherwise, except under the terms of the Copyright, Designs and Patents Act 1988 or under the terms of a licence issued by the Copyright Licensing Agency Ltd, 90 Tottenham Court Road, London W1T 4LP, UK, without the permission in writing of the Publisher. Requests to the Publisher should be addressed to the Permissions Department, John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, England, or ed to permreq@wiley.co.uk, or faxed to (+44) This publication is designed to provide accurate and authoritative information in regard to the subject matter covered. It is sold on the understanding that the Publisher is not engaged in rendering professional services. If professional advice or other expert assistance is required, the services of a competent professional should be sought. Other Wiley Editorial Offices John Wiley & Sons Inc., 111 River Street, Hoboken, NJ 07030, USA Jossey-Bass, 989 Market Street, San Francisco, CA , USA Wiley-VCH Verlag GmbH, Boschstr. 12, D Weinheim, Germany John Wiley & Sons Australia Ltd, 33 Park Road, Milton, Queensland 4064, Australia John Wiley & Sons (Asia) Pte Ltd, 2 Clementi Loop #02-01, Jin Xing Distripark, Singapore John Wiley & Sons Canada Ltd, 22 Worcester Road, Etobicoke, Ontario, Canada M9W 1L1 Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic books. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library ISBN (HB) Typeset in 10/12pt Photina by Integra Software Services Pvt. Ltd, Pondicherry, India Printed and bound in Great Britain by TJ International Ltd, Padstow, Cornwall This book is printed on acid-free paper responsibly manufactured from sustainable forestry in which at least two trees are planted for each one used for paper production.

7 Contents Preface List of Contributors xi xiii 1 Introduction: Spatial and syndromic surveillance for public health 1 Andrew B. Lawson and Ken Kleinman 1.1 What is public health surveillance? Spatial surveillance Syndromic surveillance The increased importance of public health surveillance Geographic information, cluster detection and spatial surveillance Surveillance and screening Overview of process control and mapping Process control methodology The analysis of maps and surveillance The purpose of this book Statistical surveillance and methodological development in a public health context The statistician s role in surveillance The contents of this book 8 Part I Introduction to Temporal Surveillance 11 2 Overview of temporal surveillance 13 Yann Le Strat 2.1 Introduction Surveillance systems Surveillance attributes Early detection of unusual health events Statistical methods Historical limits method Process control charts Time-series analysis Conclusion 28

8 vi Contents 3 Optimal surveillance 31 Marianne Frisén and Christian Sonesson 3.1 Introduction Optimality for a fixed sample and for on-line surveillance Specification of the statistical surveillance problem Evaluations of systems for surveillance Measures for a fixed sample situation adopted for surveillance False alarms Delay of the alarm Predictive value Optimality criteria Minimal expected delay Minimax optimality Average run length Optimality of some standard methods The likelihood ratio method The Shewhart method The CUSUM method Moving average and window-based methods Exponentially weighted moving average methods Special aspects of optimality for surveillance of public health Gradual changes during outbreaks of diseases Change between unknown incidences Spatial and other multivariate surveillance Concluding remarks 51 Acknowledgment 52 Part II Basic Methods for Spatial and Syndromic Surveillance 53 4 Spatial and spatio-temporal disease analysis 55 Andrew B. Lawson 4.1 Introduction Disease mapping and map reconstruction Disease map restoration Simple statistical representations Basic models A simple overdispersion model Advanced Bayesian models Residuals and goodness of fit Spatio-temporal analysis Surveillance issues 75 5 Generalized linear models and generalized linear mixed models for small-area surveillance 77 Ken Kleinman 5.1 Introduction Surveillance using small-area modeling 78

9 Contents vii Example Using the model results Alternate model formulations Fixed effects logistic regression Poisson regression models Practical variations Data Developing and defining syndromes Evaluation Fixed and random effects monthly models Daily versus monthly modeling Conclusion 93 6 Spatial surveillance and cumulative sum methods 95 Peter A. Rogerson 6.1 Introduction Statistical process control Shewhart charts Cumulative sum charts Cumulative sum methods for spatial surveillance Maintaining a cumulative sum chart for each region Maintaining cumulative sum charts for local neighborhoods around each region Cumulative sum charts for global spatial statistics Multivariate cumulative sum methods Summary and discussion 112 Acknowledgments 113 Appendix Scan statistics for geographical disease surveillance: an overview 115 Martin Kulldorff 7.1 Introduction Geographical disease surveillance Tests for spatial randomness Scan statistics Scan statistics for geographical disease surveillance Probability models Likelihood ratio test Scanning window Adjustments Secondary clusters Null and alternative hypotheses The null hypothesis Spatial autocorrelation The alternative hypothesis Power Visualizing the detected clusters A Sample of applications 127

10 viii Contents Cancer surveillance Infectious diseases Other human diseases Veterinary medicine Plant diseases Software 131 Acknowledgment Distance-based methods for spatial and spatio-temporal surveillance 133 Laura Forsberg, Marco Bonetti, Caroline Jeffery, Al Ozonoff and Marcello Pagano 8.1 Introduction Motivation Distance-based statistics for surveillance MEET statistic The interpoint distribution function and the M statistic Spatio-temporal surveillance: an example Temporal component Bivariate test statistic Power calculations Locating clusters Conclusion 151 Acknowledgments Multivariate surveillance 153 Christian Sonesson and Marianne Frisén 9.1 Introduction Specifications Approaches to multivariate surveillance Reduction of dimensionality Reduction to one scalar statistic for each time Parallel surveillance Vector accumulation methods Simultaneous solution Evaluation of the properties of multivariate surveillance methods Concluding discussion 164 Part III Database Mining and Bayesian Methods Bayesian network approaches to detection 169 Weng-Keen Wong and Andrew W. Moore 10.1 Introduction Association rules WSARE Creating the baseline distribution Finding the best one-component rule 174

11 Contents ix Two-component rules Obtaining the p-value for each rule Evaluation The simulator Algorithms Results Conclusion Efficient scan statistic computations 189 Daniel B. Neill and Andrew W. Moore 11.1 Introduction The spatial scan statistic Randomization testing The naive approach Overlap-multiresolution partitioning Score bounds Results Comparison to SaTScan Conclusions and future work Bayesian data mining for health surveillance 203 David Madigan 12.1 Introduction Probabilistic graphical models Hidden Markov models for surveillance: illustrative examples Hidden Markov models for surveillance: further exploration Beyond normality How many hidden states? Label switching Multivariate extensions Random observation time hidden Markov models Interpretation of hidden Markov models for surveillance Discussion 221 Acknowledgments Advanced modeling for surveillance: clustering of relative risk changes 223 Andrew B. Lawson 13.1 Introduction Cluster concepts Cluster modeling Spatial modeling of case event data Spatial modeling of count data Spatio-temporal modeling of case and count data Syndromic cluster assessment The Bayesian posterior distribution Bayesian version of the optimal surveillance alarm function 239

12 x Contents Clustering and f x u A simple real-time biohazard model Computational issues Conclusions and future directions 243 References 245 Index 267

13 Preface This volume hopes to fill a growing need for the description of current methodology in public health surveillance. Recent advances in syndromic surveillance and, more generally, in spatial and multivariate surveillance have never been collected in a single volume. The field of syndromic surveillance now attracts a wide audience due to the perceived need to implement wide-ranging monitoring systems to detect possible health-related bioterrorism activity. In addition, many computer systems have been, and are being, developed that have the capability to store and display large volumes of health data and to link dynamically between different data streams. This capability has not been matched with extensive statistical research into the properties of the methods used in these systems. The ability of these systems to correctly sound health alarms when needed is of paramount importance. It is the task of statisticians to develop and evaluate the methodologies and to ensure that the correct interpretation is made of evaluated data. This volume seeks to provide a synopsis of current practice as well as a starting point for the development and evaluation of methods. In the production of this volume we have been helped by a great range of people. First we would like to thank our families who, throughout this venture, have been a great source of support. In addition we would like to thank the contributors for their timely submission of interesting articles, as well as our colleagues in respective departments for helping with evaluation and criticism. Finally we would like to thank the staff of Wiley Europe for their continual help during the sometimes fraught stages of production. In particular, we thank Kathryn Sharples the Statistics sub-editor and Lucy Bryan in production, as well as Richard Leigh, the copy-editor. Andrew Lawson (Columbia) Ken Kleinman (Boston)

15 List of Contributors Marco Bonetti Instituto di Metodi Quantitativi Univerisita Bocconi Viale Isonzo, 25 Milano Italy Laura Forsberg Department of Biostatistics Harvard School of Public Health 655 Huntington Ave Boston MA USA Marianne Frisén Statistical Research Unit Göteborg University Box 660 SE Göteborg Sweden Caroline Jeffery Department of Biostatistics Harvard School of Public Health 655 Huntington Ave Boston MA USA Ken Kleinman Department of Ambulatory Care and Prevention Harvard Medical School & Harvard Pilgrim Health Care 133 Brookline Avenue Boston MA USA Martin Kulldorff Department of Ambulatory Care and Prevention Harvard Medical School and Harvard Pilgrim Health Care 133 Brookline Avenue 6th Floor Boston MA USA Andrew B. Lawson Department of Epidemiology and Biostatistics Arnold School of Public Health University of South Carolina Columbia SC USA David Madigan Faculty of Arts and Sciences Rutgers University 77 Hamilton Street New Brunswick NJ USA Andrew W. Moore Carnegie Mellon University School of Computer Science 5000 Forbes Avenue Pittsburgh PA USA

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