Spatial Clustering and Autocorrelation in Health Events

  • Geoffrey Jacquez
Reference work entry


Spatial autocorrelation in health events may be the signature of underlying causal factors of direct scientific and practical interest but may also be due to pedestrian or nuisance factors that obscure meaningful spatial patterns. The problem is to discern spatial patterns that inform our understanding of the health events themselves from those that are of little interest. This chapter provides a framework for advancing knowledge when the causes of observed health event clusters are unknown.


Spatial Autocorrelation West Nile Virus Lyme Disease Behavioral Risk Factor Surveillance System Neutral Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The author’s efforts were funded in part by grants 2R44CA112743, 5R44CA135818, and 1R21LM011132 from the National Cancer Institute and the National Library of Medicine. The perspectives are those of the author and do not necessarily represent those of the funding agencies.


  1. Adams SA (2011) Sourcing the crowd for health services improvement: the reflexive patient and “share-your-experience” websites. Soc Sci Med 72(7):1069–1076CrossRefGoogle Scholar
  2. Aldstad J (2010) Spatial clustering. In: Fischer MM, Getis A (eds) Handbook of applied spatial analysis. Software tools, methods and applications. Springer, Berlin/Heidelberg/New York, pp 270–300Google Scholar
  3. Cuzick J, Edwards R (1990) Spatial clustering for inhomogeneous populations. J R Stat Soc B 52(1):73–104Google Scholar
  4. Ecker DJ, Massire C, Blyn LB, Hofstadler SA, Hannis JC, Eshoo MW, Hall TA, Sampath R (2009) Molecular genotyping of microbes by multilocus PCR and mass spectrometry: a new tool for hospital infection control and public health surveillance. In: Molecular epidemiology of microorganisms. Springer, Berlin/Heidelberg/New York, pp 71–87CrossRefGoogle Scholar
  5. Enayati A, Hemingway J (2010) Malaria management: past, present, and future. Annu Rev Entomol 55(1):569–591. doi:10.1146/annurev-ento-112408-085423CrossRefGoogle Scholar
  6. Fornalski KW, Dobrzyński L (2010) The healthy worker effect and nuclear industry workers. Dose-Response 8(2):125–147CrossRefGoogle Scholar
  7. Funk S, Salathé M, Vincent A, Jansen A (2010) Modelling the influence of human behaviour on the spread of infectious diseases: a review. J R Soc Interface 7(50):1247–1256. doi:10.1098/rsif.2010.0142CrossRefGoogle Scholar
  8. Gallagher CM, Goovaerts P, Jacquez GM, Hao Y, Jemal A, Meliker JR (2009) Racial disparities in lung cancer mortality in U.S. congressional districts, 1990-2001. Spat Spattemporal Epidemiol 1(1):41–47. doi:10.1016/j.sste.2009.07.007CrossRefGoogle Scholar
  9. Getis A (2010) Spatial autocorrelation. In: Fischer MM, Getis A (eds) Handbook of applied spatial analysis. Software tools, methods and applications. Springer, Berlin/Heidelberg/New York, pp 255–278CrossRefGoogle Scholar
  10. Goodchild MF, Alan Glennona J (2010) Crowdsourcing geographic information for disaster response: a research frontier. Int J Digit Earth 3(3):231–241CrossRefGoogle Scholar
  11. Goovaerts P (2009) Medical geography: a promising field of application for geostatistics. Math Geol 41(3):243–264Google Scholar
  12. Greenland S (2004) Model-based estimation of relative risks and other epidemiologic measures in studies of common outcomes and in case-control studies. Am J Epidemiol 160(4):301–305. doi:10.1093/aje/kwh221CrossRefGoogle Scholar
  13. Höhle M, Paul M (2008) Count data regression charts for the monitoring of surveillance time series. Comput Stat Data Anal 52(9):4357–4368. doi:10.1016/j.csda.2008.02.015CrossRefGoogle Scholar
  14. Jacobson M, Earle CC, Newhouse JP (2011) Geographic variation in physicians’ responses to a reimbursement change. N Eng J Med 365(22):2049–2052. doi:10.1056/NEJMp1110117CrossRefGoogle Scholar
  15. Jacquez GM, Slotnick MJ, Meliker JR, AvRuskin G, Copeland G, Nriagu J (2011) Accuracy of commercially available residential histories for epidemiologic studies. Am J Epidemiol 173(2):236–243CrossRefGoogle Scholar
  16. Johnson G, Buckeridge D, Dearth S, Ditty J, Finelli L, Hopkins RS, Hummel J, et al. (2012) Draft guidelines for syndromic surveillance using inpatient and ambulatory clinical care EHR data. A report from the international society for disease surveillance: international society for disease surveillanceGoogle Scholar
  17. Kingsley BS, Schmeichel KL, Rubin CH (2007) An update on cancer cluster activities at the centers for disease control and prevention. Environ Health Perspect 115(1):165–171CrossRefGoogle Scholar
  18. Kulldorff M, Mostashari F, Duczmal L, Katherine Yih W, Kleinman K, Platt R (2007) Multivariate scan statistics for disease surveillance. Stat Med 26(8):1824–1833CrossRefGoogle Scholar
  19. Lawson AB, Banerjee S (2009) Bayesian spatial analysis. In: Fotheringham S, Rogerson P (eds) The handbook of spatial analysis. Sage, London, pp 321–342CrossRefGoogle Scholar
  20. Lu H, Carlin BP (2005) Bayesian areal wombling for geographical boundary analysis. Geogr Anal 37(3):265–285CrossRefGoogle Scholar
  21. Neutra RR (1990) Counterpoint from a cluster buster. Am J Epidemiol 132(1):1–8Google Scholar
  22. Platt JR (1964) Strong inference. Science 146:347–353CrossRefGoogle Scholar
  23. Rimoin AW, Mulembakani PM, Johnston SC, Lloyd JO, Smith NK, Kisalu TL, Kinkela SB et al (2010) Major increase in human monkeypox incidence 30 years after smallpox vaccination campaigns cease in the Democratic Republic of Congo. Proc Natl Acad Sci 107(37):16262–16267. doi:10.1073/pnas.1005769107CrossRefGoogle Scholar
  24. Rogerson PA (2006) Statistical methods for the detection of spatial clustering in case-control data. Stat Med 25(5):811–823CrossRefGoogle Scholar
  25. Sattenspiel L, Lloyd A (2010) The geographic spread of infectious diseases: models and applications. Princeton University Press, PrincetonGoogle Scholar
  26. Spielman SE, Yoo E (2009) The spatial dimensions of neighborhood effects. Soc Sci Med 68(6):1098–1105. doi:10.1016/j.socscimed.2008.12.048CrossRefGoogle Scholar
  27. Tango T, Takahashi K (2005) A flexibly shaped spatial scan statistic for detecting clusters. Int J Health Geogr 7(14):11CrossRefGoogle Scholar
  28. Turnbull BW, Iwano EJ, Burnett WS, Howe HL, Clark LC (1990) Monitoring for clusters of disease: application to leukemia incidence in upstate New York. Am J Epidemiol 132(1 Suppl):S136–S143Google Scholar
  29. Waller LA, Gotway CA (2004) Applied spatial statistics for public health data. Wiley, HobokenCrossRefGoogle Scholar
  30. Weissman JS, Hasnain-Wynia R (2011) Advancing health care equity through improved data collection. N Eng J Med 364(24):2276–2277CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.SUNY at BuffaloBuffaloUSA
  2. 2.BioMedwareAnn ArborUSA

Personalised recommendations