Spatio-temporal epidemiology: principles and opportunities.

Space-time analysis of disease data has historically involved the search for patterns in aggregated data to identify how regions of high and low risk change through time. Space-time analysis of aggregated data has great value, but represents only a subset of space-time epidemiologic applications. Technological advances for tracking and mapping individuals (e.g., global positioning systems) have introduced mobile populations as an important element in space-time epidemiology. We review five domains critical to the developing field of spatio-temporal epidemiology: (1) spatio-temporal epidemiologic theory, (2) selection of appropriate spatial scale of analysis, (3) choice of spatial/spatio-temporal method for pattern identification, (4) individual-level exposure assessment in epidemiologic studies, and (5) assessment and consideration of locational and attribute uncertainty. This review provides an introduction to principles of space-time epidemiology and highlights future research opportunities.

[1]  Beate Ritz,et al.  Historical pesticide exposure in California using pesticide use reports and land-use surveys: an assessment of misclassification error and bias. , 2003, Environmental health perspectives.

[2]  Thomas Webster,et al.  Method for mapping population-based case-control studies: an application using generalized additive models , 2006, International journal of health geographics.

[3]  Geoffrey M. Jacquez,et al.  Improving exposure assessment in environmental epidemiology: Application of spatio-temporal visualization tools , 2005, J. Geogr. Syst..

[4]  G. Jacquez,et al.  Visualization and exploratory analysis of epidemiologic data using a novel space time information system , 2004, International journal of health geographics.

[5]  Peter Boyle,et al.  Methods for investigating localized clustering of disease , 1996 .

[6]  G. Leung,et al.  Understanding the Spatial Clustering of Severe Acute Respiratory Syndrome (SARS) in Hong Kong , 2004, Environmental health perspectives.

[7]  M. Kulldorff A spatial scan statistic , 1997 .

[8]  D. Wheeler A comparison of spatial clustering and cluster detection techniques for childhood leukemia incidence in Ohio, 1996 – 2003 , 2007, International journal of health geographics.

[9]  John R. Nuckols,et al.  Using Geographic Information Systems for Exposure Assessment in Environmental Epidemiology Studies , 2004, Environmental health perspectives.

[10]  Linda Williams Pickle,et al.  The crossroads of GIS and health information: a workshop on developing a research agenda to improve cancer control , 2006, International journal of health geographics.

[11]  J. Fortenberry,et al.  International Journal of Health Geographics Open Access Using Gps-enabled Cell Phones to Track the Travel Patterns of Adolescents , 2022 .

[12]  Martin Kulldorff,et al.  Power evaluation of disease clustering tests , 2003, International journal of health geographics.

[13]  Verónica M. Vieira,et al.  Using Residential History and Groundwater Modeling to Examine Drinking Water Exposure and Breast Cancer , 2010, Environmental health perspectives.

[14]  I. McDowell,et al.  Conceptualizing the healthscape: contributions of time geography, location technologies and spatial ecology to place and health research. , 2010, Social science & medicine.

[15]  Gerard Rushton,et al.  Geocoding in cancer research: a review. , 2006, American journal of preventive medicine.

[16]  T. Tango,et al.  International Journal of Health Geographics a Flexibly Shaped Spatial Scan Statistic for Detecting Clusters , 2005 .

[17]  P. Reynolds,et al.  Residential Proximity to Agricultural Pesticide Use and Incidence of Breast Cancer in California, 1988–1997 , 2005, Environmental health perspectives.

[18]  Lin Liu,et al.  Reducing MAUP bias of correlation statistics between water quality and GI illness , 2008, Comput. Environ. Urban Syst..

[19]  L. Pickle A history and critique of U.S. mortality atlases. , 2009, Spatial and spatio-temporal epidemiology.

[20]  Jin Chen,et al.  A Visualization System for Space-Time and Multivariate Patterns (VIS-STAMP) , 2006, IEEE Transactions on Visualization and Computer Graphics.

[21]  Pierre Goovaerts,et al.  International Journal of Health Geographics Geostatistical Analysis of Disease Data: Accounting for Spatial Support and Population Density in the Isopleth Mapping of Cancer Mortality Risk Using Area-to-point Poisson Kriging , 2022 .

[22]  Stefan Leyk,et al.  Spatial modeling of personalized exposure dynamics: the case of pesticide use in small-scale agricultural production landscapes of the developing world , 2009, International journal of health geographics.

[23]  S. Dearwent,et al.  Locational uncertainty in georeferencing public health datasets , 2001, Journal of Exposure Analysis and Environmental Epidemiology.

[24]  J. Cerhan,et al.  Residential proximity to industrial facilities and risk of non-Hodgkin lymphoma. , 2008, Environmental research.

[25]  J. Leavitt,et al.  Typhoid Mary: Captive to the Public’s Health , 1997, Nursing History Review.

[26]  David Ozonoff,et al.  Cluster detection methods applied to the Upper Cape Cod cancer data , 2005, Environmental health : a global access science source.

[27]  Gerard Rushton,et al.  Analyzing Geographic Patterns of Disease Incidence: Rates of Late-Stage Colorectal Cancer in Iowa , 2004, Journal of Medical Systems.

[28]  Pierre Goovaerts,et al.  Assessment of the production and economic risks of site‐specific liming using geostatistical uncertainty modelling , 2001 .

[29]  Pavlos S. Kanaroglou,et al.  Effects of alternative point pattern geocoding procedures on first and second order statistical measures , 2008 .

[30]  S. Openshaw A million or so correlation coefficients : three experiments on the modifiable areal unit problem , 1979 .

[31]  Geoffrey M. Jacquez,et al.  Design and implementation of a Space-Time Intelligence System for disease surveillance , 2005, J. Geogr. Syst..

[32]  J. Nuckols,et al.  Neurobehavioral effects of exposure to trichloroethylene through a municipal water supply. , 2003, Environmental research.

[33]  L. Parker,et al.  Environmental factors and childhood acute leukemias and lymphomas , 2006, Leukemia & lymphoma.

[34]  E. Hofer HOW TO ACCOUNT FOR UNCERTAINTY DUE TO MEASUREMENT ERRORS IN AN UNCERTAINTY ANALYSIS USING MONTE CARLO SIMULATION , 2008, Health physics.

[35]  Soumya Mazumdar,et al.  Spatial clustering of the failure to geocode and its implications for the detection of disease clustering. , 2008, Statistics in medicine.

[36]  Geoffrey M. Jacquez,et al.  Spatial Statistics When Locations Are Uncertain , 1999, Ann. GIS.

[37]  Shih-Lung Shaw,et al.  Exploring potential human activities in physical and virtual spaces: a spatio‐temporal GIS approach , 2008, Int. J. Geogr. Inf. Sci..

[38]  G. Jacquez,et al.  Local clustering in breast, lung and colorectal cancer in Long Island, New York , 2003, International journal of health geographics.

[39]  Geoffrey M Jacquez,et al.  Local indicators of geocoding accuracy (LIGA): theory and application , 2009, International journal of health geographics.

[40]  Bryan L. Williams,et al.  Comparison of spatial scan statistic and spatial filtering in estimating low birth weight clusters , 2005, International journal of health geographics.

[41]  P. Diggle Applied Spatial Statistics for Public Health Data , 2005 .

[42]  M. Wilson,et al.  Lifetime exposure to arsenic in drinking water and bladder cancer: a population-based case–control study in Michigan, USA , 2010, Cancer Causes & Control.

[43]  J. Cuzick,et al.  Methods for investigating localized clustering of disease. Clustering methods based on k nearest neighbour distributions. , 1996, IARC scientific publications.

[44]  Scott M. Berry,et al.  Bayesian Smoothing and Regression Splines for Measurement Error Problems , 2002 .

[45]  Albert-László Barabási,et al.  Understanding individual human mobility patterns , 2008, Nature.

[46]  Geoffrey M Jacquez,et al.  Cluster morphology analysis. , 2009, Spatial and spatio-temporal epidemiology.

[47]  G. Pershagen,et al.  Using geographic information systems to assess individual historical exposure to air pollution from traffic and house heating in Stockholm. , 2001, Environmental health perspectives.

[48]  David Ozonoff,et al.  Spatial analysis of lung, colorectal, and breast cancer on Cape Cod: An application of generalized additive models to case-control data , 2005, Environmental health : a global access science source.

[49]  Pilar Loreto Iglesias,et al.  Semiparametric Bayesian measurement error modeling , 2010, J. Multivar. Anal..

[50]  M. Kulldorff,et al.  Breast cancer clusters in the northeast United States: a geographic analysis. , 1997, American journal of epidemiology.

[51]  Russell D Wolfinger,et al.  Spatial prediction of counts and rates , 2003, Statistics in medicine.

[52]  P. Goovaerts,et al.  Individual lifetime exposure to inorganic arsenic using a space–time information system , 2007, International archives of occupational and environmental health.

[53]  R. Lynch,et al.  Use of global positioning system technology to track subject's location during environmental exposure sampling , 2001, Journal of Exposure Analysis and Environmental Epidemiology.

[54]  Martin Kulldorff,et al.  Cancer map patterns: are they random or not? , 2006, American journal of preventive medicine.

[55]  Menno-Jan Kraak,et al.  Developing a Geovisual Analytics Environment for Investigating Archaeological Events: Extending the Space–Time Cube , 2009 .

[56]  A. Gelman,et al.  All maps of parameter estimates are misleading. , 1999, Statistics in medicine.

[57]  P. Goovaerts,et al.  Incorporating individual-level distributions of exposure error in epidemiologic analyses: an example using arsenic in drinking water and bladder cancer. , 2010, Annals of epidemiology.

[58]  J. J. Abellán,et al.  Methodologic Issues and Approaches to Spatial Epidemiology , 2008, Environmental health perspectives.

[59]  R. Colvile,et al.  Assessment of exposure to mercury from industrial emissions: comparing “distance as a proxy” and dispersion modelling approaches , 2006, Occupational and Environmental Medicine.

[60]  Michela Bertolotto,et al.  Towards a framework for mining and analysing spatio‐temporal datasets , 2007, Int. J. Geogr. Inf. Sci..

[61]  Bo Zhong,et al.  The use of a vest equipped with a global positioning system to assess water-contact patterns associated with schistosomiasis. , 2007, Geospatial health.

[62]  M. Monmonier How to Lie with Maps , 1991 .

[63]  F. Benjamin Zhan,et al.  A Comparison of Three Exploratory Methods for Cluster Detection in Spatial Point Patterns , 2010 .

[64]  Jonathan W. Pitchford,et al.  Changes in species diversity following habitat disturbance are dependent on spatial scale: theoretical and empirical evidence , 2008 .

[65]  Pierre Goovaerts,et al.  Case-control geographic clustering for residential histories accounting for risk factors and covariates , 2015 .

[66]  May Yuan,et al.  Understanding Dynamics of Geographic Domains , 2008 .

[67]  P. Goovaerts,et al.  Spatial cluster analysis of early stage breast cancer: a method for public health practice using cancer registry data , 2009, Cancer Causes & Control.

[68]  Max J. Egenhofer,et al.  Identity-based change: a foundation for spatio-temporal knowledge representation , 2000, Int. J. Geogr. Inf. Sci..

[69]  Karen M. Trifonoff How to Lie With Maps, 2nd ed. , 1996 .

[70]  Sylvia Richardson,et al.  A comparison of Bayesian spatial models for disease mapping , 2005, Statistical methods in medical research.

[71]  Peter J. Diggle,et al.  Statistical analysis of spatial point patterns , 1983 .

[72]  Pierre Goovaerts,et al.  Use of land surface remotely sensed satellite and airborne data for environmental exposure assessment in cancer research , 2010, Journal of Exposure Science and Environmental Epidemiology.

[73]  J. Freudenheim,et al.  Pesticide exposure and risk of breast cancer: a nested case-control study of residentially stable women living on Long Island. , 2004, Environmental research.

[74]  Kai Elgethun,et al.  Time-location analysis for exposure assessment studies of children using a novel global positioning system instrument. , 2003, Environmental health perspectives.

[75]  Keith C. Clarke,et al.  Interactive Visual Exploration of a Large Spatio-temporal Dataset: Reflections on a Geovisualization Mashup. , 2007, IEEE Transactions on Visualization and Computer Graphics.

[76]  Antonio Krüger,et al.  Applying indoor and outdoor modeling techniques to estimate individual exposure to PM2.5 from personal GPS profiles and diaries: a pilot study. , 2009, The Science of the total environment.

[77]  David W. S. Wong The Modifiable Areal Unit Problem (MAUP) , 2004 .

[78]  J Wakefield,et al.  Risk of adverse birth outcomes in populations living near landfill sites , 2001, BMJ : British Medical Journal.

[79]  J. Snow On the Mode of Communication of Cholera , 1856, Edinburgh medical journal.

[80]  Joanne S Colt,et al.  Positional Accuracy of Two Methods of Geocoding , 2005, Epidemiology.

[81]  I. Burstyn,et al.  Bayesian Method for Improving Logistic Regression Estimates under Group-Based Exposure Assessment with Additive Measurement Errors , 2009, Archives of environmental & occupational health.

[82]  Jaymie R Meliker,et al.  Space–time clustering of case–control data with residential histories: insights into empirical induction periods, age-specific susceptibility, and calendar year-specific effects , 2007, Stochastic environmental research and risk assessment : research journal.

[83]  S. Richardson,et al.  Interpreting Posterior Relative Risk Estimates in Disease-Mapping Studies , 2004, Environmental health perspectives.

[84]  Anders Skrondal,et al.  A simulation study of three methods for detecting disease clusters , 2006, International journal of health geographics.