Spatiotemporal statistical analysis of influenza mortality risk in the State of California during the period 1997–2001

Original Paper

Abstract

Using the Bayesian maximum entropy (BME) method of spatiotemporal statistics, the present study examines the geographical risk pattern of influenza mortality in the state of California during the time period 1997–2001. BME risk analysis is of considerable value, since influenza is the largest contributing factor to wintertime mortality increases in the US. By incorporating age-adjusted mortality data collected at the county level, informative influenza mortality maps were generated and composite space-time influenza dependences were assessed quantitatively. On the basis this analysis, essential risk patterns and correlations were detected across the state during wintertime. It was found that significantly high risks initially occurred during December in the west-central part of the state; in the following two weeks the risk distribution extended in the south and east-central parts of the state; in late February significant influenza mortalities were detected mainly in the west-central part of the state. These findings, combined with the results of earlier works, can lead to useful conclusions regarding influenza risk assessment in a space-time context and, also, point toward promising future research directions.

Keywords

Influenza Mapping Mortality Risk Spatiotemporal statistics BME California 

Notes

Acknowledgments

We would like to thank Dr. RA Olea of USGS for his valuable suggestions. This research was supported by grants from the Oak Ridge National Lab (OR7865-001.01), the Fred J. Hansen Institute, SDSU Foundation, California (54266A P3590), the Office of the Vice President for Research at the University of Michigan and the National Oceanographic and Atmospheric Administration (NA16GP23361).

References

  1. Anderson RM, May RM (1991) Infectious disease of humans: dynamics and control. Oxford University Press, OxfordGoogle Scholar
  2. Besag J, Newell J (1991) The detection of clusters in rare diseases. J R Stat Soc Ser A 154:143–155CrossRefGoogle Scholar
  3. Bogaert P (1996) Comparison of kriging techniques in a space-time context. Math Geol 28(1):73–86CrossRefGoogle Scholar
  4. Choi KM, Christakos G, Wilson ML (2006) El Niño effects on influenza mortality risks in the State of California. J Public Health 120:505–516CrossRefGoogle Scholar
  5. Chowell G, Ammonb CE, Hengartnera NW, Hymana JM (2006) Transmission dynamics of the great influenza pandemic of 1918 in Geneva, Switzerland: assessing the effects of hypothetical interventions. J Theoret Biol 241(2):193–204CrossRefGoogle Scholar
  6. Christakos G (1990) Random field modelling and its applications in stochastic data processing. Applied Sciences, PhD Thesis Harvard University, Cambridge, MAGoogle Scholar
  7. Christakos G (1991) On certain classes of spatiotemporal random fields with application to space-time data processing. IEEE Syst Man Cybern 21(4):861–875CrossRefGoogle Scholar
  8. Christakos G (1992) Random field models in earth sciences. Academic, San Diego, CA (Out of Print.) New edition, Dover, Mineola, NY, 2005Google Scholar
  9. Christakos G, Bogaert P (1996) Spatiotemporal analysis of springwater ion processes derived from measurements at the Dyle Basin in Belgium. IEEE Trans Geosci Remote Sens 34(3):626–642CrossRefGoogle Scholar
  10. Christakos G (1998) Spatiotemporal information systems in soil and environmental sciences. Geoderma 85(2–3):141–179CrossRefGoogle Scholar
  11. Christakos G (2000) Modern spatiotemporal geostatistics. Oxford University Press, New YorkGoogle Scholar
  12. Christakos G, Serre ML (2000) A spatiotemporal study of exposure-health effect associations. J Expos Anal Environ Epidemiol 10(2):168–187CrossRefGoogle Scholar
  13. Christakos G, Bogaert P, Serre ML (2002) Temporal GIS with CD-ROM. Springer, New YorkGoogle Scholar
  14. Christakos G, Hristopulos DT (1998) Spatiotemporal environmental health modelling: a tractatus stochasticus. Kluwer, BostonGoogle Scholar
  15. Christakos G, Olea RA, ML Serre, HL Yu, Wang L (2005) Interdisciplinary public health reasoning and epidemic modelling: the case of black death. Springer, New YorkGoogle Scholar
  16. Cliff AD (1995) Incorporating spatial components into models of epidemic models. In: Mollison D, Moffatt HK (eds) Epidemic models: their structure and relation to data. Cambridge Univ Press, Cambridge, pp 119–149Google Scholar
  17. Cliff AD, Haggett P (1988) Atlas of disease distributions: analytic approaches to epidemiological data. Blackwell, OxfordGoogle Scholar
  18. Cliff AD, Haggett P, Ord JK (1986) Spatial aspects of influenza epidemics. Pion, LondonGoogle Scholar
  19. Cressie N, Huang HC (1999) Classes of nonseparable, spatio-temporal stationary covariance functions. J Am Stat Assoc 94:1330–1340CrossRefGoogle Scholar
  20. Cuzick J, Edwards R (1990) Spatial clustering for inhomogeneous populations. J R Stat Soc Ser B 52:73–104Google Scholar
  21. Death Statistical Master Files (DSMF) (1997–2001) Center for Health Statistics, Sacramento, CAGoogle Scholar
  22. Earn, DJD, Dushoff J, Levin SA (2002) Ecology and evolution of the flu. TRENDS Ecol Evol 17(7):334–340CrossRefGoogle Scholar
  23. Ferguson MP, Neil A, Galvani R, Bush M (2003) Ecological and immunological determinants of influenza evolution. Nature 422:428–433CrossRefGoogle Scholar
  24. Golledge RG (2002) The nature of geographical knowledge. Ann Assoc Am Geogr 92(1):1–14CrossRefGoogle Scholar
  25. Goodall C, Mardia KV (1994) Challenges in multivariate spatio-temporal modeling. In: Proceedings of the XVIIth international biometric confererence, Hamilton, 8–12 August 1994, pp 1–17Google Scholar
  26. Haas TC (1995) Local prediction of spatio-temporal process with an application to wet sulfate deposition. J Am Stat Assoc 90:1189–1199CrossRefGoogle Scholar
  27. Haggett P (2000) The geographical structure of epidemics. Clarendon Press, OxfordGoogle Scholar
  28. Harper SA, Fukuda K, Uyeki TM, Cox NJ, Bridges CB (2004) Prevention and control of influenza recommendations of the advisory committee on immunization practices. Morbidity and Mortality Weekly Report 53:1–40Google Scholar
  29. Kyriakidis PC, Journel AG (1999) Geostatistical space-time models: a review. Math Geol 31(6):651–684CrossRefGoogle Scholar
  30. Lui KJ, Kendal AP (1987)119–149 Impact of influenza epidemics on mortality in the United States. Am J Public Health 77:712–716CrossRefGoogle Scholar
  31. May RM, Anderson RM (1984) Spatial heterogeneity and the design of immunization programs. Math Biosci 72:83–111CrossRefGoogle Scholar
  32. Openshaw S, Charlton M (1987) A mark 1 Geographical analysis machine for the automated analysis of point data sets. Int J Geogr Inform Syst 1:335–358CrossRefGoogle Scholar
  33. Openshaw S, Craft AW, Charlton M, Birch JM (1988) Investigation of leukaemia clusters by use of geographical analysis machine. The Lancet 272–273Google Scholar
  34. Viboud C, Boelle P-Y, Carrat F, Valleron A-J, Flahault A (2003) Prediction of the spread of influenza epidemics by the method of analogues. Am J Epidemiol 158: 996–1006CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  1. 1.Department of Environmental and Occupational HealthUniversity of North Texas Health Science CenterFort WorthUSA
  2. 2.Department of Bioenvironmental Systems EngineeringNational Taiwan UniversityTaipeiTaiwan
  3. 3.Department of Epidemiology, School of Public HealthUniversity of MichiganAnn ArborUSA

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