A (non exhaustive) list of references to accompany the tutorial on
Introduction to Spatial Epidemiology by Sylvia Richardson
 Abellan JJ, Richardson S, Best N. Use of space-time models to
investigate the stability of patterns of disease. Environmental Health
Perspectives, 2008, 116(8): 1111-1119.
 Bernardinelli L, Clayton D, Pascutto C, Montomoli C, Ghislandi M,
Songini M. Bayesian analysis of space-time variation in disease risk.
Statistics in Medicine, 1995, 14(21-22):2433–2443.
 Besag J, Newell J. The Detection Of Clusters In Rare Diseases. Journal
of the Royal Statistical Society Series A-Statistics in Society, 1991,
154:143-155.
 Besag J., York J., Mollié A. A Bayesian image restoration with two
applications in spatial statistcs. Annals of the Institute of Statistical
Mathematics, 1991, 43:1–59.
 Best N, Richardson S, Thomson A. A comparison of bayesian spatial
models for disease mapping. Statistical Methods in Medical Research,
2005, 14(1):35–59.
 Best N, Cockings S, Bennett J, Wakefield J, Elliott P. Ecological
regression analysis of environmental benzene exposure and childhood
leukaemia: sensitivity to data inaccuracies, geographical scale and
ecological bias. Journal of the Royal Statistical Society Series A Statistics In Society, 2001, 164:155-174.
 Best N; Hansell AL. Geographic variations in risk: adjusting for
unmeasured confounders through joint modeling of multiple diseases.
Epidemiology. 2009, 20:400-410
 Brewer, M.J. and Nolan, A.J. Variable smoothing in Bayesian intrinsic
autoregressions. Environmetrics, 2007, 18(8), 841-857.
 Clayton, D. and Kaldor, J. Empirical Bayes estimates of agestandardized relative risks for use in disease mapping. Biometrics, 1987,
43(3):671–681
 Clayton, D. and Bernardinelli, L. Bayesian methods for mapping disease
risk, In Geographical and environment epidemiology: methods for small
areas studies (P. Elliott, J.Cuzick, D. English, and R. Stern ed.), Oxford
University Press, 1992, 205–220.
 Clayton, D., Bernardinelli, L and Montomoli, C. Spatial correlation in
ecological analysis, International Journal of Epidemiology, 1993,
22:1193–1202.
 Cuzick J, Edwards R . Spatial Clustering For Inhomogeneous
Populations. Journal of The Royal Statistical Society Series BMethodological, 1990, 52(1):73-104.
 Denison DGT, Holmes CC. Bayesian partitioning for estimating disease
risk. Biometrics, 2001, 57(1):143-149.
 Devine, O. J., Louis, T. A., and Halloran, M. E. Empirical Bayes
estimators for spatially correlated incidence rates. Environmetrics, 1994
5(4):381–398.
 Diggle, PJ. A kernel method for smoothing point process data. Applied
Statistics, 1985, 138-147.
 Diggle P, Moyeed RA, Tawn JA. Model-based geostatistics. Applied
Statistics, 1998, 47:299–350.
 Diggle PJ. (2003). Statistical analysis of spatial point patterns. Arnold.
 Diggle PJ, Ribeiro PJ, Christensen OF. (2003). An introduction to modelbased geostatistics. Springer.
 Elliott P, Richardson S, Abellan JJ, et al. Geographic density of landfill
sites and risk of congenital anomalies in England. Occupational and
Environmental Medicine, 2009, 66(2):81-89.
 Fernandez C, Green PJ. Modelling spatially correlated data via mixtures:
a Bayesian approach. Journal of the Royal Statistical Society Series BStatistical Methodology, 2002, 64:805-826.
 Gangnon RE, Clayton MK. Bayesian detection and modeling of spatial
disease clustering. Biometrics, 2000, 56(3):922-935.
 Gelfand AE, Vounatsou P. Proper multivariate conditional autoregressive
models for spatial data análisis. Biostatistics, 2003, 4(1): 11-25.
 Green PJ, Richardson S. Hidden Markov models and disease mapping.
Journal of the American Statistical Association, 2002, 97(460):10551070.
 Greenland S and Morgenstern H. Ecological bias, confounding and effect
modification. International Journal of Epidemiology, 1989, 18: 269-274.
 Hegarty A, Barry D. Bayesian disease mapping using product partition
models. Statistics in Medicine, 2008, 27(19): 3868-3893.
 Heikkinen J, Arjas E. Non-parametric Bayesian estimation of a spatial
Poisson intensity. Scandinavian Journal of Statistics, 1998, 25(3):435450.
 Hogan JW, Tchernis R. Bayesian factor analysis for spatially correlated
data, with application to summarizing area-level material deprivation from
census data. Journal of the American Statistical Association, 2004,
99(466):314-324.
 Jackson C, Best N and Richardson S. Improving ecological inference
using individual-level data. Statistics in Medicine, 2006, 25: 2136-2159.
 Jackson C, Best N and Richardson S. Hierarchical related regression for
combining aggregate and individual data in studies of socio-economic
disease risk factors. Journal of the Royal Statistical Society, Series A:
Statistics In Society, 2008, 171(1):1-20.
 Jarup L, Briggs D, de Hoogh C, et al. Cancer risks in populations living
near landfill sites in Great Britain. British Journal of Cancer, 2002,
86(11):1732-1736.
 Jarup L, Best N, Toledano MB, Wakefield J, Elliott P. Geographical
epidemiology of prostate cancer in great britain. International Journal of
Cancer, 2002, 97(5):695–699.
 Jin X, Banerjee S, Carlin B. Order-free co-regionalized areal data models
with application to multiple disease mapping. Journal of the Royal
Statistical Society, Series B:, 2007, 69(5):817-838.
 Kelsall J and Diggle PJ. Spatial variation in risk of disease: a non
parametric binary regression approach. Journal of the Royal Statistical
Society, Series A: Statistics In Society, 1998, 47(4):559-573.
 Kelsall J and Wakefield J. Modeling spatial variation in risk: a
geostatistical approach. Journal of the American Statistical Association,
2002, 97:692-701.
 Knorr-Held L, Besag J. Modelling risk from a disease in time and space.
Statistics in Medicine, 1998, 17(18):2045–2060.
 Knorr-Held L, Rasser G. Bayesian detection of clusters and
discontinuities in disease maps. Biometrics, 2000, 56(1):13–21.
 Knorr-Held L. Bayesian modelling of inseparable space-time variation in
disease risk. Statistics in Medicine, 2000, 19(17-18):2555–2567.
 Knorr-Held L, Best NG. A shared component model for detecting joint
and selective clustering of two diseases. Journal of the Royal Statistical
Society Series A-Statistics in Society, 2001, 164:73-85.
 Knorr-Held L, Rue H. On block updating in Markov random field models
for disease zapping. Scandinavian Journal of Statistics, 2002, 29(4):597614.
 Kottas A, Duan J and Gelfand, A. Modeling disease incidence data with
spatial and spatio-temporal dirichlet process mixtures. Biometrical
Journal, 2008, 50: 29-42.
 Kulldorff M, Nagarwalla N. Spatial disease clusters - detection and
inference. Statistics in Medicine, 1995, 14(8):799-810.
 Kulldorff M . Tests of spatial randomness adjusted for an inhomogeneity:
A general framework. Journal of the American Statistical Association,
2006, 101(475):1289-1305.
 Lasserre V, Guihenneuc-Jouyaux C, and Richardson S. Biases in
ecological studies: utility of including within-area distribution of
confounders. Statistics in Medicine, 2000, 19, 45-59.
 Lawson AB, Clark A. Spatial mixture relative risk models applied to
disease mapping. Statistics in Medicine, 2002, 21(3):359–70.
 Lawson AB. Disease cluster detection: a critique and a bayesian
proposal. Statistics in Medicine, 2006, 25(5):897–916.
 Lee DJ and Durban M. Smooth-CAR mixed models for spatial count
data. Computational Statistics and Data Analysis, 2009, 53:2968-2979.
 Leyland AH, Langford IH, Rasbash J, et al. Multivariate spatial models
for event data. Statistics in Medicine, 2000, 19(17-18):2469-2478
 Leyland, A. H. and Davies, C. A. Empirical Bayes methods for dsease
mapping. Stat Methods Med Res, 2005, 17–34.
 MacNab, Y and Gustafson P. Regression B-spline smoothing in
Bayesian diease mapping: with an application to patient safety
surveillance. Statistics in Medicine, 2007, 26:4455-4474
 Martinez-Beneito, M. A., Lopez-Quilez, A., and Botella-Rocamora, P. An
autoregressive approach to spatio-temporal disease mapping. Statistics
in Medicine, 2008, 27(15):2874–2889.
 Moller, J; Syversveen, AR; Waagepetersen, RP. Log Gaussian Cox
processes. Scandinavian Journal of Statistics, 1998, 25(3):451-482.
 Moller, J. A comparison of spatial point process models in
epidemiological applications. In Green PJ, Hjort N and Richardson S
(eds) Highly Structured Stochastic Systems. Oxford University Press,
2003:260-265.
 Ogawa T, Kobayashi E, Okubo Y, Suwazono Y, Kido T, Nogawa K.
Relationship among prevalence of patients with Itai-itai disease,
prevalence of abnormal urinary findings, and cadmium concentrations in
rice of individual hamlets in the Jinzu River basin, Toyama prefecture of
Japan. Int J Environ Health Res. 2004,14(4):243-52.
 Openshaw S. An automated geographical analysis system. Environment
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 Openshaw S, Charlton M, Craft AW, et al. Investigation of leukemia
clusters by use of a geographical analysis machine. Lancet, 1988,
1(8580): 272-273.
 Richardson ST, Stücker I and Hémon, D. Comparison of relative risks
obtained in ecological and individual studies : some methodological
considerations. Int. J. of Epidemiol, 1987, 16(1), 111-119
 Richardson S and Monfort C. Ecological correlation studies. In: Spatial
Epidemiology. P. Elliott, J. Wakefield, N. Best, D. Briggs eds. Oxford
University Press, (2000).
 Richardson S. Spatial models in epidemiological applications. In Green
PJ, Hjort N and Richardson S (eds) Highly Structured Stochastic
Systems. Oxford University Press, 2003, 237-255.
 Richardson S, Thomson A, Best N, Elliott P. Interpreting posterior
relative risk estimates in disease-mapping studies. Environmental Health
Perspectives, 2004, 112(9):1016–1025.
 Richardson S, Abellan JJ, Best N. Bayesian spatio-temporal analysis of
joint patterns of male and female lung cancer risks in Yorkshire (UK).
Statistical Methods in Medical Research, 2006, 15(4):385–407.
 Ripley BD. Spatial statistics. 1981, John Wiley Sons, New York.
 Rue H, Martino S, Chopin N. Approximate Bayesian inference for latent
Gaussian models by using integrated nested Laplace approximations.
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 Tango, T . A class of tests for detecting general and focused clustering of
rare diseases. Statistics in Medicine, 1995, 14(21-22): 2323-2334.
 Tango, T . A test for spatial disease clustering adjusted for multiple
testing. Statistics in Medicine, 2000, 19(2):191-204.
 Ugarte L, Ibanez B and Militino AF. Testing for zero inflation in disease
mapping. Biometrical Journal, 2004, 5:526-539.
 Ugarte, A. D., Militino, A. F., and Goicoa, T. Prediction error estimators
in empirical bayes disease mapping. Environmetrics, 2008, 19(3):287–
300
 Wakefield, JC Sensitivity analyses for ecological regression. Biometrics,
2003, 59: 9-17.
 Wakefield, JC Ecological inference for 2 x 2 tables (with discussion).
Journal of the Royal Statistical Society, Series A., 2004, 167:385-445.
 Wakefield, JC and Shaddick, G. Health-exposure modeling and the
ecological fallacy. Biostatistics, 2006, 7:438-455.
 Waller LA, Carlin BP, Xia H, Gelfand AE. Hierarchical spatio-temporal
mapping of disease rates. Journal of the American Statistical
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 Waller LA, Hill EG and Rudd RA. The geography of power: Statistical
performance of tests of clusters and clustering in heterogeneous
populations. Statistics in Medicine,. 2006; 25:853–865.
 Wolpert RL, Ickstadt K. Poisson/gamma random field models for spatial
statistics. Biometrika, 1998, 85(2):251-267.
 Zhan Y, Hodges, J and Banerjee B. Smoothed ANOVA with spatial
effects as a competitor to MCAR in multivariate spatial smoothing. To
appear in the Annals of Applied Probability, 2009.