Skip to main content
SpringerLink
Log in

Geographical Disparities in Mortality Rates: Spatial Data Mining and Bayesian Hierarchical Modeling

  • Chapter
  • First Online:
Statistical Methods for Spatial Planning and Monitoring

Part of the book series: Contributions to Statistics ((CONTRIB.STAT.))

  • 1719 Accesses

Abstract

Achieving health equity has been identified as a major international challenge since the 1978 declaration of Alma Ata. Disease risk maps provide important clues concerning many aspects of health equity, such as etiology risk factors involved by occupational and environmental exposures, as well as gender-related and socioeconomic inequalities. This explains why epidemiological disease investigation should always include an assessment of the spatial variation of disease risk, with the objective of producing a representation of important spatial effects while removing any noise. Bearing in mind this goal, this review covers basic and more advanced aspects of Bayesian models for disease mapping, and methods to analyze whether the spatial distribution of the disease risk closely follows that of underlying population at risk, or there exist some nonrandom local patterns (disease clusters) which may suggest a further explanation for disease etiology. We provide a practical illustration by analyzing the spatial distribution of liver cancer mortality in Apulia, Italy, during the 2000–2005 quinquennial. (Massimo Bilancia wrote Sects. 1.1.2, 1.1.4, 1.1.6, 1.2.1, 1.2.3, 1.2.5. Giusi Graziano wrote Sects. 1.1.1, 1.1.3, 1.1.5, 1.2.2, 1.2.4, 1.2.6. Giacomo Demarinis wrote the software for data analysis. Section 1.3 was written jointly. The three authors read and approved the final manuscript. We wish to thank Maria Rosa Debellis, Department of Neuroscience and Sense Organs, University of Bari, Italy, and Claudia Monte PhD, Department of Physics, University of Bari, Italy, for their valuable support.)

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    SaTScan™ is a trademark of Martin Kulldorff. The SaTScan™ software was developed under the joint auspices of (1) Martin Kulldorff, (2) the National Cancer Institute, and (3) Farzad Mostashari of the New York City Department of Health and Mental Hygiene.

References

  1. Banerjee, S., Carlin, B.P., Gelfand, A.E.: Hierarchical Modeling and Analysis for Spatial Data. Chapman & Hall/CRC, Boca Raton (2004). ISBN 978-0-203-48780-8

    MATH  Google Scholar 

  2. Besag, J.E., York, J.C., Mollié, A.: Bayesian image restoration with two applications in spatial statistics (with discussion). Ann. Inst. Stat. Math. 43, 1–59 (1991). doi:10.1007/BF00116466

    Article  MATH  Google Scholar 

  3. Bernardinelli, L., Pascutto, C., Best, N.G., Gilks, W.R.: Disease mapping with errors in covariates. Stat. Med. 16, 741–752 (1997). doi:10.1002/(SICI)1097-0258(19970415)16:7<741::AID-SIM501>3.0.CO;2-1

    Article  Google Scholar 

  4. Besag, J., Kooperberg, C.: On conditional and intrinsic autoregression. Biometrika 82, 733–746 (1995). http://www.jstor.org/stable/2337341

    MathSciNet  MATH  Google Scholar 

  5. Bilancia, M., Demarinis, G.: Bayesian scanning of spatial disease rates with INLA (submitted)

    Google Scholar 

  6. Browne, W., Draper, D.: A comparison of Bayesian and likelihood-based methods for fitting multilevel models. Bayesian Anal. 1(3), 473–514 (2006). doi:10.1214/06-BA117

    MathSciNet  Google Scholar 

  7. Caranci, N., Costa, G.: Un indice di deprivazione a livello aggregato da utilizzare su scala nazionale: giustificazioni e composizione dell”indice. In: Costa, G., Cislaghi, C., Caranci, N. (eds) Disuguaglianze sociali di salute. Problemi di definizione e di misura. “Salute e Società” VIII, 1 (2009). doi:10.3280/SES2009-001006

  8. Carlin, B.P., Louis, T.A.: Bayesian Methods for Data Analysis. Chapman & Hall/CRC, Boca Raton (2008). ISBN 978-1-58488-697-6

    MATH  Google Scholar 

  9. Clayton, D., Kaldor, J.: Empirical Bayes estimates of age-standardized relative risks for use in disease mapping. Biometrics 43(3), 671–681 (1987). http://www.jstor.org/stable/253200

    Article  Google Scholar 

  10. Cowles, M.K., Carlin, B.P.: Markov Chain Monte Carlo convergence diagnostics: a comparative review. J. Am. Stat. Assoc. 91, 883–904 (1996). http://www.jstor.org/stable/2291683

    Article  MathSciNet  MATH  Google Scholar 

  11. Cramb, S., Mengersen, K.L., Baade, P.: Developing the atlas of cancer in Queensland: methodological issues. Int. J. Health Geogr. 10, 9 (2011). doi:10.1186/1476-072X-10-9

    Article  Google Scholar 

  12. Dabney, A.R., Wakefiled, J.C.: Issues in the mapping of two disease. Stat. Methods Med. Res. 14, 1–30 (2005). doi:10.1191/0962280205sm340oa

    Article  Google Scholar 

  13. Gelfand, A.E., Sahu, S.K.: Identifiability, improper priors and Gibbs sampling for generalized linear models. JASA 94, 247–253 (1999). http://www.jstor.org/stable/2669699

    MathSciNet  MATH  Google Scholar 

  14. Gelman, A.: Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper). Bayesian Anal. 1(3), 515–534 (2006). doi:10.1214/06-BA117A

    MathSciNet  Google Scholar 

  15. Ghosh, M., Natarajan, K., Waller, L.A., Kin, D.: Hierarchical Bayes GLMs for the analysis of spatial data: an application to disease mapping. J. Stat. Plan. Inference 75(2), 305–318 (1999). doi:10.1016/S0378-3758(98)00150-5

    Article  MATH  Google Scholar 

  16. Gómez-Rubio, V., Ferrándiz-Ferragud, J., López-Quílez, A.: Detecting clusters of disease with R. J. Geogr. Syst. 7, 189–206 (2005). doi:10.1007/s10109-005-0156-5

    Article  Google Scholar 

  17. Kulldorff, M., Nagarwalla, N.: Spatial disease clusters: detection and inference. Stat. Med. 14, 799–810 (1995). doi:10.1002/sim.4780140809

    Article  Google Scholar 

  18. Kulldorff, M.: A spatial scan statistic. Commun. Stat. Theory Methods 26(6), 1481–1496 (1997). doi:10.1080/03610929708831995

    Article  MathSciNet  MATH  Google Scholar 

  19. La Vecchia, C., Negri, E., Decarli, A., et al.: Risk factors for hepatocellular carcinoma in Northern Italy. Int. J. Cancer 42, 872–876 (1988)

    Article  Google Scholar 

  20. Levi, F., Lucchini, F., Negri, E., Boyle, P., La Vecchia, C.: Cancer mortality in Europe, 1995–1999, and an overview of trends since 1960. Int. J. Cancer 110, 155–169 (2004). doi:10.1002/ijc.20097

    Article  Google Scholar 

  21. Loh, J.M., Zhu, Z.: Accounting for spatial correlation in the scan statistics. Ann. Appl. Stat. 1(2), 560–584 (2007). doi:10.1214/07-AOAS129

    Article  MathSciNet  MATH  Google Scholar 

  22. Marshall, R.J.: Mapping disease and mortality rates using empirical Bayes estimators. J. R. Stat. Soc. C Appl. Stat. 40(2), 283–294 (1991). http://www.jstor.org/stable/2347593

    MATH  Google Scholar 

  23. McGlynn, K.A., Tsao, L., Hsing, A.W., Devesa, S.S., Fraumeni Jr., J.F.: International trends and patterns of primary liver cancer. Int. J. Cancer 94, 290–296 (2001). doi:10.1002/ijc.1456

    Article  Google Scholar 

  24. Mollié, A.: Bayesian mapping of Hodgkin’s disease in France. In: Elliot, P., Wakefield, J., Best, N., Briggs, D. (eds.) Spatial Epidemiology Methods and Applications, pp. 267–285. Oxford University Press, Oxford (2001). ISBN 978-0198515326

    Chapter  Google Scholar 

  25. Ocaña-Riola, R.: Common errors in disease mapping. Geospat. Health 4(2), 139–154 (2010). http://www.geospatialhealth.unina.it/articles/v4i2/gh-v4i2-02-ocana-riola.pdf

    Google Scholar 

  26. Osservatorio Epidemiologico Regionale Puglia: Atlante delle cause di Morte della Regione Puglia. Anni 2000–2005 (2008). http://www.oerpuglia.org/Atlante.asp

  27. Parkin, D.M., Bray, F., Ferlay, J., Pisani, P.: Estimating the world cancer burden: Globocan 2000. Int. J. Cancer 94, 153–156 (2001). doi:10.1002/ijc.1440

    Article  Google Scholar 

  28. Pericchi, L.R.: Model selection and hypothesis testing based on objective probabilities and Bayes factors. In: Dey, D.K., Rao, C.R. (eds.) Handbook of Statistics 25. Bayesian Thinking: Modeling and Computation, pp. 115–149. Amsterdam, Elsevier (2005). ISBN 9780444515391

    Chapter  Google Scholar 

  29. Pfeiffer, D.U., Robinson, T.U., Stevenson, M., Stevens, K.B., Rogers, D.J., Clements, A.C.A.: Spatial Analysis in Epidemiology. Oxford University Press, Oxford (2008). ISBN 978-0-19-85098-82

    Book  MATH  Google Scholar 

  30. Potthoff, R.F., Whittinghill, M.: Testing for homogeneity: I. The binomial and multinomial distribution. Biometrika 53, 167–182 (1966). http://www.jstor.org/stable/2334062

    MathSciNet  Google Scholar 

  31. Potthoff, R.F., Whittinghill, M.: Testing for homogeneity: II. The Poisson distribution. Biometrika 53, 183–190 (1966). http://www.jstor.org/stable/2334063

    MathSciNet  Google Scholar 

  32. Rao, C.R.: Advanced Statistical Methods in Biometric Research. Wiley, New York (1952)

    MATH  Google Scholar 

  33. Rue, H., Held, L.: Gaussian Markov Random Fields. Theory and Applications. Chapman & Hall/CRC, Boca Raton (2005). ISBN 978-1-58488-432-3

    Book  MATH  Google Scholar 

  34. Rue, H., Martino, S., Chopin, N.: Approximate Bayesian inference for latent Gaussian models using Integrated Nested Laplace approximations (with discussion). J. R. Stat. Soc. B 71, 319–392 (2009). doi:10.1111/j.1467-9868.2008.00700.x

    Article  MathSciNet  MATH  Google Scholar 

  35. Richardson, S., Guihenneuc, C., Lasserre, V.: Spatial linear models with autocorrelated error structure. Statistician 41, 539–557 (1992). http://www.jstor.org/stable/2348920

    Article  Google Scholar 

  36. Rogerson, P.A.: The detection of clusters using a spatial version of the chi-square goodness-of-fit statistic. Geogr. Anal. 31(1), 130–147 (1999). doi:10.1111/j.1538-4632.1999.tb00973.x

    Google Scholar 

  37. Spiegelhalter, D.J., Best, N.G., Carlin, B.P., Linde, A.: Bayesian measures of model complexity and fit (with discussion). J. R. Stat. Soc. B 64, 583–639 (2002). doi:10.1111/1467-9868.00353

    Article  MATH  Google Scholar 

  38. Strachan, R.W., van Dijk, H.K.: Divergent priors with well defined Bayes factors. Tinbergen Institute Discussion Paper, TI 2011-006/4 (2011). http://papers.ssrn.com/sol3/DisplayAbstractSearch.cfm

  39. Tabor, E., Kobayashi, K.: Hepatitis C virus, a causative infectious agent of non-A, non-B hepatitis: prevalence and structure – summary of a Conference on Hepatitis C virus as a cause of hepatocellular carcinoma. J. Natl. Cancer Inst. 84, 86–90 (1992). doi:10.1093/jnci/84.2.86

    Article  Google Scholar 

  40. Tango, T.: A class of test for detecting ‘general’ and ‘focused’ clustering of rare diseases. Stat. Med. 14, 2323–2334 (1995). doi:10.1002/sim.4780142105

    Article  Google Scholar 

  41. Tango, T.: A test for spatial disease clustering adjusted for multiple testing. Stat. Med. 19, 191–204 (1995). doi:10.1002/(SICI)1097-0258(20000130)19:2<191::AID-SIM281>3.0.CO;2-Q

    Article  Google Scholar 

  42. Vidal Roidero, C.L., Lawson, A.B..: An evaluation of edge effects in disease map modeling. Comput. Stat. Data Anal. 49, 45–62 (2005). doi:10.1016/j.csda.2004.05.012

    Article  Google Scholar 

  43. Wakefield, J.: Disease mapping and spatial regression with count data. Biostatistics 8(2), 158–183 (2007). doi:10.1093/biostatistics/kxl008

    Article  MathSciNet  MATH  Google Scholar 

  44. Waller, L.A., Carlin, B.P., Xia, H., Gelfand, A.E.: Hierarchical spatiotemporal mapping of disease rates. J. Am. Stat. Assoc. 92, 607–617 (1997). http://www.jstor.org/stable/10.2307/2965708

    Article  MATH  Google Scholar 

  45. Waller, L.A., Gotway, C.A.: Applied Spatial Statistics for Public Health Data. Wiley, New Jersey (2004). ISBN 0-471-38771-1

    Book  MATH  Google Scholar 

  46. Yiannakoulias, N., Rosychuk, R.J., Hodgson, J.: Adaptations for finding irregularly shaped disease clusters. Int. J. Health Geogr. 6, 28 (2007). doi:10.1186/1476-072X-6-28

    Article  Google Scholar 

  47. Yu, M.C., Mack, T., Hanisch, R., et al.: Hepatitis, alcohol consumption, cigarette smoking, and hepatocellular carcinoma in Los Angeles. Cancer Res. 43, 6077–6079 (1983). http://cancerres.aacrjournals.org/content/43/12_Part_1/6077.full.pdf

    Google Scholar 

  48. Zhang, T., Zhang, Z., Lin, G.: Spatial scan statistics with overdispersion. Stat. Med. 13;31(8):762–774 (2012). doi:10.1002/sim.4404

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dipartimento di Scienze Statistiche “Carlo Cecchi”, University of Bari “Aldo Moro”, Largo Abbazia di Santa Scolastica 53, 70124, Bari, Italy

    Massimo Bilancia & Giacomo Demarinis

  2. Laboratory of Lipid Metabolism and Cancer, Consorzio Mario Negri Sud, Via Nazionale 8/A, 66030, Santa Maria Imbaro (CH), Italy

    Giusi Graziano

Authors
  1. Massimo Bilancia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Giusi Graziano
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Giacomo Demarinis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Massimo Bilancia .

Editor information

Editors and Affiliations

  1. , Department of Statistical Sciences “Carl, University of Bari “A. Moro”, Bari, Ital, Via Camillo Rosalba 53, Bari, 70100, Bari, Italy

    Silvestro Montrone

  2. , Department of Statistical Sciences “Carl, University of Bari “A. Moro”, Bari, Ital, Via Camillo Rosalba 53, Bari, 70100, Bari, Italy

    Paola Perchinunno

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Italia

About this chapter

Cite this chapter

Bilancia, M., Graziano, G., Demarinis, G. (2013). Geographical Disparities in Mortality Rates: Spatial Data Mining and Bayesian Hierarchical Modeling. In: Montrone, S., Perchinunno, P. (eds) Statistical Methods for Spatial Planning and Monitoring. Contributions to Statistics. Springer, Milano. https://doi.org/10.1007/978-88-470-2751-0_1

Download citation

Publish with us

Policies and ethics

Search

Navigation