Space–time mixture model of infant mortality in peninsular Malaysia from 1990 to 2000

Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 11)


Disease mapping is a method used to display the geographical distribution of disease occurrence. Some traditional methods of classification for detection of high- or low-risk area such as traditional percentiles method and significant method have been used in disease mapping for map construction. However, as described by several authors, the classification based on these traditional methods has some disadvantages for describing the spatial distribution of the risk of the disease concerned. To overcome these limitations, an approach using space–time mixture model within an empirical Bayes framework is described in this chapter. The aim of this chapter is to investigate the geographical distribution of infant mortality in peninsular Malaysia from 1991 to 2000. The analysis showed that in the early 1990s the spatial heterogeneity effect was more prominent; however, toward the end of 1990s this pattern tends to disappear. Indirectly, this may indicate that the provisions of health services throughout peninsular Malaysia are uniformly distributed over the period of the study, particularly toward the year 2000.


Disease mapping Space–time mixture model Infant mortality Geographical distribution Spatial heterogeneity 


  1. 1.
    Adebayo SB, Fahrmeir L, Klasen S (2004) Analyzing infant mortality with geoadditive categorical regression model: a case study for Nigeria. Economics and Human Biology 2:229–244CrossRefGoogle Scholar
  2. 2.
    Biggeri A, Dreassi E, Lagazio C, Bohning D (2003) A transitional non-parametric maximum pseudo-likelihood estimator for disease mapping. Computational Statistics & Data Analysis 41:617–629CrossRefMathSciNetGoogle Scholar
  3. 3.
    Clayton D, Kaldor J (1987) Empirical Bayes estimates of age-standardized relative risks for use in disease mapping. Biometrics 43:671–681CrossRefGoogle Scholar
  4. 4.
    Estimates of Malaysia Federal Revenue and Expenditure (1970–2000) Ministry of Finance MalaysiaGoogle Scholar
  5. 5.
    Everitt BS, Hand DJ (1981) Finite mixture distributions. Chapman and Hall, New YorkMATHGoogle Scholar
  6. 6.
    Everitt BS, Hand DJ (1993)Google Scholar
  7. 7.
    Langford IH, Leyland AH, Rasbash J, Goldstein H (1999) Multilevel modeling of the geographical distributions of diseases. Applied Statistics 48:253–268MATHGoogle Scholar
  8. 8.
    Lawson AB, Browne WJ, Rodeiro CLV (2003) Disease mapping with WinBUGS and MlwiN. Wiley, New YorkCrossRefGoogle Scholar
  9. 9.
    Lawson AB, Williams FLR (2001) An introductory guide to disease mapping. Wiley, New YorkCrossRefGoogle Scholar
  10. 10.
    Mantel N, Stark CR (1968) Computation of indirect-adjusted rates in the presence of confounding. Biometrics 24:997–1005CrossRefGoogle Scholar
  11. 11.
    Marshall RJ (1991) Mapping disease and mortality rates using empirical Bayes estimators. Applied Statistics 40:283–294CrossRefMATHGoogle Scholar
  12. 12.
    Meza JL (2003) Empirical Bayes estimation smoothing of relative risks in disease mapping. Journal of Statistical Planning and Inference 112:43–62CrossRefMATHMathSciNetGoogle Scholar
  13. 13.
    Mohamed WN, Diamond I, Smith WFP (1998) The determinants of infant mortality in Malaysia: a graphical chain modeling approach. Journal of the Royal Statistical Society A 161:349–366CrossRefGoogle Scholar
  14. 14.
    Pollard AH, Yusuff F, Pollard GN (1981) Demographic techniques. Pergamon Press, SydneyGoogle Scholar
  15. 15.
    Rattanasiri S, Bohning D, Rojanavipart P, Athipanyakom S (2004) A mixture model application in disease mapping of malaria. Southeast Asian Journal Trop Med Public Health 35:38–47Google Scholar
  16. 16.
    Robbins H (1964) The empirical Bayes approach to statistical decision problems. Annals of Mathematical Statistics 35:1–20CrossRefMATHMathSciNetGoogle Scholar
  17. 17.
    Rutstein SO (2005) Effects of preceding birth intervals and neonatal, infant and under-five years mortality and nutritional status in developing countries: evidence from the demographic and health survey. International Journal of Gynecology and Obstetrics 89:7–24CrossRefGoogle Scholar
  18. 18.
    Schlattmann P, Bohning D (1993) Mixture models and disease mapping. Statistics in Medicine 12:1943–1950CrossRefGoogle Scholar
  19. 19.
    Schlattmann P, Dietz E, Bohning D (1996) Covariate adjusted mixture models and disease mapping wih the program Dismapwin. Statistics in Medicine 15:919–929CrossRefGoogle Scholar
  20. 20.
    Social Statistics Bulletin Malaysia (1965–2000) Department of Statistics MalaysiaGoogle Scholar
  21. 21.
    Turrell G, Mengersen K (2000) Socioeconomic status and infant mortality in Australia: a national study of small urban areas, 1985–1989. Social Science & Medicine 50:1209–1225CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media LLC 2009

Authors and Affiliations

  1. 1.School of Mathematical SciencesFaculty of Science & Technology, Universiti Kebangsaan MalaysiaSelangorMalaysia

Personalised recommendations