Health in Megacities and Urban Areas pp 243-261

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

Spatial Epidemiological Applications in Public Health Research: Examples from the Megacity of Dhaka

  • Oliver Gruebner
  • Md. Mobarak Hossain Khan
  • Patrick Hostert
Chapter

Abstract

Public health researchers are increasingly shifting their focus from models of disease epidemiology that focus exclusively on individual risk factors to models that also consider the complex and important effects of the socio-physical environment (Geanuracos et al. 2007). The application of spatial analysis in the context of epidemiological surveillance and research has increased exponentially (Pfeiffer et al. 2009). Geographic information systems (GIS), global positioning systems (GPS) and remote sensing (RS) have been increasingly used in public health research since the 1990s (Kaiser et al. 2003). At the same time, geographers have started to extend their collaborations with public health researchers leading to the still young discipline of health geography that uses geographical concepts and techniques to investigate health-related topics (Meade and Earickson 2005; Gatrell and Elliott 2009).

References

  1. Alberti, M. (2009). Advances in Urban Ecology: Integrating Humans and Ecological Processes in Urban Ecosystems. New YorkGoogle Scholar
  2. Anselin, L. (1995). “Local Indicators of Spatial Association – LISA.” Geographical Analysis 27(2): 93–115CrossRefGoogle Scholar
  3. Anselin, L., I. Syabri, et al. (2002). Visualizing multivariate spatial correlation with dynamically linked windows. New Tools for Spatial Data Analysis: Proceedings of the Specialist Meeting, Santa Barbara, Center for Spatially Integrated Social Science (CSISS), University of CaliforniaGoogle Scholar
  4. Barton, D. E., F. N. David, et al. (1965). “A criterion for testing contagion in time and space.” Annals of Human Genetics 29: 97–103CrossRefGoogle Scholar
  5. Besag, J. and J. Newell (1991). “The Detection of Clusters in Rare Diseases.” Journal of the Royal Statistical Society. Series A (Statistics in Society) 154(1): 143–155CrossRefGoogle Scholar
  6. Bithel, J. F. (1995). “The choice of test for detecting a raised disease risk near a point source.” Statistics in Medicine 14: 2309–2322CrossRefGoogle Scholar
  7. Bithel, J. F., S. J. Dutton, et al. (1994). “Distribution of childhood Leukemias and non-Hodgkin's lymphomas near nuclear installations in England and Wales.” British Medical Journal 309: 501–505CrossRefGoogle Scholar
  8. Bivand, R. S., E. J. Pebesma, et al. (2008). Applied Spatial Data Analysis with R. Berlin, New York, SpringerGoogle Scholar
  9. Boulos, M. N. K. (2004). “Towards evidence-based, GIS-driven national spatial health information infrastructure and surveillance services in the United Kingdom.” International Journal of Health Geographics 3(1): 1PubMedCrossRefGoogle Scholar
  10. Boulos, M. N. K., A. V. Roudsari, et al. (2001). “Health Geomatics: An Enabling Suite of Technologies in Health and Healthcare.” Journal of Biomedical Informatics 34: 195–219PubMedCrossRefGoogle Scholar
  11. Cliff, A. D. and J. K. Ord (1973). Spatial Autocorrelation. LondonGoogle Scholar
  12. Cliff, A. D. and J. K. Ord (1981). Spatial Processes: Models and Applications. LondonGoogle Scholar
  13. Cromley, E. K. (2003). “GIS and Disease.” Annual Review of Public Health 24(1): 7–24PubMedCrossRefGoogle Scholar
  14. Cromley, E. K. and S. L. McLafferty (2002). GIS and Public Health. New YorkGoogle Scholar
  15. Cuzick, J. and R. Edwards (1990). “Spatial Clustering for Inhomogeneous Populations.” Journal of the Royal Statistical Society. Series B (Methodological) 52(1): 73–104Google Scholar
  16. Diggle, P. (1990). “A point process modelling approach to raised incidence of a rare phenomenon in the vicinity of a prespecified point.” Journal of the Royal Statistical Society Series A 153: 349–362Google Scholar
  17. Diggle, P., A. G. Chetwynd, et al. (1995). “Second-order analysis of space-time clustering.” Statistical Methods in Medical Research 4: 124–136PubMedCrossRefGoogle Scholar
  18. Dormann, C., F., J. McPherson, M., et al. (2007). “Methods to account for spatial autocorrelation in the analysis of species distributional data: a review.” Ecography 30(5): 609–628CrossRefGoogle Scholar
  19. Edelmann, L. S. (2007). “Using geographic information systems in injury research.” Journal of Nursing Scholarship 39(4): 306–311CrossRefGoogle Scholar
  20. Ederer, F., M. H. Myers, et al. (1964). “A statistical problem in space and time: Do Leukemia cases come in clusters?” Biometrics 20: 626–638CrossRefGoogle Scholar
  21. Elliott, P., J. C. Wakefield, et al., Eds. (2006). Spatial epidemiology: methods and applications. Oxford, Oxford University PressGoogle Scholar
  22. ESRI (Environmental Systems Research Institute) (2009). ArcGIS 9.3.1. Redlands, USA, www.esri.com
  23. ESRI (Environmental Systems Research Institute) (2011). ArcGIS 10. In. Redlands, USA: www.esri.com
  24. Fortin, M.-J. and M. Dale (2006). Spatial Analysis. A Guide for Ecologists. CambridgeGoogle Scholar
  25. FOSSGIS e.V. (2010). “FreeGIS.org.” Retrieved April, 8., 2010, from http://www.freegis.org/
  26. Galea, S., N. Freudenberg, et al. (2005). “Cities and population health.” Social Science & Medicine 60(5): 1017–1033CrossRefGoogle Scholar
  27. Galea, S., Freudenberg, N., & Vlahov, D. (2005). Cities and population health. Social Science & Medicine, 60, 1017–1033Google Scholar
  28. Gatrell, A. C. and S. J. Elliott (2009). Geographies of Health: An Introduction. OxfordGoogle Scholar
  29. Geanuracos, C. G., D. D. Cunningham, et al. (2007). “Use of Geographic Information Systems for Planning HIV Prevention Interventions for High-Risk Youths.” American Journal of Public Health 97(11): 1974–1981PubMedCrossRefGoogle Scholar
  30. Geary, R. C. (1954). “The contiguity ratio and statistical mapping.” The Incorporated Statistician 5: 115–145CrossRefGoogle Scholar
  31. Getis, A. and J. K. Ord (1992). “The Analysis of Spatial Association by Use of Distance Statistics.” Geographical Analysis 24: 189–206CrossRefGoogle Scholar
  32. Griffiths, P., P. Hostert, et al. (2010). “Mapping megacity growth with multi-sensor data.” Remote Sensing of Environment 114(2): 426–439CrossRefGoogle Scholar
  33. Gruebner, O., Khan, M.M., Lautenbach, S., Muller, D., Kramer, A., Lakes, T., & Hostert, P. (2011). A spatial epidemiological analysis of self-rated mental health in the slums of Dhaka. International Journal of Health Geographics, 10, 36Google Scholar
  34. Hostert, P. and O. Gruebner (2010). Geographic Information Systems. Modern infectious disease epidemiology - Concepts, methods, mathematical models, public health. A. Krämer, M. Kretzschmar and K. Krickeberg. Berlin, Springer: 177192Google Scholar
  35. Jacquez, G. (1996). “A k-nearest neighbour test for space-time interaction.” Statistics in Medicine 15: 1935–1949PubMedCrossRefGoogle Scholar
  36. Kaiser, R., P. B. Spiegel, et al. (2003). “The Application of Geographic Information Systems and Global Positioning Systems in Humanitarian Emergencies: Lessons Learned, Programme Implications and Future Research.” Disasters 27(2): 127–140PubMedCrossRefGoogle Scholar
  37. Kawachi, I., & Berkman, L.F. (Eds.) (2003). Neighborhoods and health. Oxford, New York: Oxford University PressGoogle Scholar
  38. Kearns, R.A. (1993). Place and Health: Towards a Reformed Medical Geography. The Professional Geographer, 45, 139–147Google Scholar
  39. Kearns, R., & Moon, G. (2002). From medical to health geography: novelty, place and theory after a decade of change. Progress in Human Geography, 26, 605–625Google Scholar
  40. Kulldorff, M. (1997). “A spatial scan statistic.” Communications in Statistics - Theory and Methods 26(6): 1481–1496CrossRefGoogle Scholar
  41. Kulldorff, M. (2001). “Prospective time periodic geographical disease surveillance using a scan statistic.” Journal of the Royal Statistical Society Series A 164: 61–72Google Scholar
  42. Lawson, A. (1993). “On the analysis of mortality events associated with a prespecified fixed point.” Journal of the Royal Statistical Society Series A 156: 363–377Google Scholar
  43. Lawson, A. (2009). Bayesian disease mapping: hierarchical modeling in spatial epidemiology, CRC PressGoogle Scholar
  44. Lawson, A., W. Browne, et al. (2003). Disease mapping with WinBUGS and MLwiN, J. WileyGoogle Scholar
  45. Likert, R. (1932). “A Technique for the Measurement of Attitudes.” Archives of Psychology 140 Google Scholar
  46. Lillesand, T. M., R. W. Kiefer, et al. (2003). Remote Sensing and Image Interpretation. New York, WileyGoogle Scholar
  47. Longley, P. A., M. F. Goodchild, et al., Eds. (2007). Geographic Information Systems and Science. New York, Wiley & SonsGoogle Scholar
  48. Maguire, D., M. Batty, et al., Eds. (2005). GIS, Spatial Analysis, and Modeling. Redlands, California, ESRI PressGoogle Scholar
  49. Mantel, N. (1967). “The detection of disease clustering and a generalized regression approach.” Cancer Research 27: 209–220PubMedGoogle Scholar
  50. Mayer, J.D., & Meade, M.S. (1994). A Reformed Medical Geography Reconsidered. The Professional Geographer, 46, 103–106Google Scholar
  51. Meade, M. S. and R. J. Earickson (2005). Medical Geography. New YorkGoogle Scholar
  52. Moran, P. A. P. (1948). “The interpretation of statistical maps.” Journal of the Royal Statistical Society Series B 10: 243–251Google Scholar
  53. Moran, P. A. P. (1950). “Notes on Continuous Stochastic Phenomena.” Biometrika 37(1–2): 17–23PubMedGoogle Scholar
  54. Openshaw, S., M. Charlton, et al. (1987). “A Mark 1 Geographical Analysis Machine for the automated analysis of point data sets.” International journal of geographical information systems 1(4): 335–358CrossRefGoogle Scholar
  55. Pfeiffer, D. U., T. P. Robinson, et al. (2009). Spatial Analysis in Epidemiology. New York, Oxford University PressGoogle Scholar
  56. Richards, J. A. and X. Jia (2005). Remote Sensing Digital Image Analysis. Berlin, Germany, SpringerGoogle Scholar
  57. Ricketts, T. C. (2003). “Geographic information systems and Public health.” Annual Review of Public Health 24: 1–6PubMedCrossRefGoogle Scholar
  58. Ripley, B. D. (1977). “Modelling spatial patterns (with discussion).” Journal of the Royal Statistical Society Series B 39: 172–212Google Scholar
  59. Robertson, C., Nelson, T.A., MacNab, Y.C., & Lawson, A.B. (2010). Review of methods for space-time disease surveillance. Spatial and Spatio-temporal Epidemiology, 1, 105–116Google Scholar
  60. Rogerson, P. A. (1997). “Surveillance systems for monitoring the development of spatial patterns.” Statistics in Medicine 16: 2081–2093PubMedCrossRefGoogle Scholar
  61. Ruankaew, N. (2005). “GIS and epidemiology.” Journal of the Medical Association of Thailand 88(11): 1735–1738PubMedGoogle Scholar
  62. Rushton, G. and P. Lolonis (1996). “Exploratory spatial analysis of birth defect rates in an urban population.” Statistics in Medicine 15: 717–726PubMedCrossRefGoogle Scholar
  63. Stone, R. A. (1988). “Investigations of excess environmental risks around putative sources: statistical problems and a proposed test.” Statistics in Medicine 7: 649–660PubMedCrossRefGoogle Scholar
  64. Tango, T. (1995). “A class of tests for detecting 'general' and 'focused' clustering of rare diseases.” Statistics in Medicine 14: 2323–2334PubMedCrossRefGoogle Scholar
  65. Tobler, W. R. (1970). “A computer model simulation of urban growth in the Detroit region.” Economic Geography 46(2): 234–240CrossRefGoogle Scholar
  66. Turnbull, B. W., E. Iwano, et al. (1990). “Monitoring for clusters of disease: applications for leukemia incidence in upstate New York.” American Journal of Epidemiology 132: 136–143Google Scholar
  67. Waller, L. and C. Gotway (2004). Applied Spatial Statistics for Public Health Data. Hoboken, New JerseyGoogle Scholar
  68. Waller, L., B. W. Turnbull, et al. (1992). “Chronic disease surveillance and testing of clustering of disease and exposure: Application to Leukemia incidence and TCE-contaminated dumpsites in upstate New York.” Environmetrics 3: 281–300CrossRefGoogle Scholar
  69. Whittemore, A. S., N. Friend, et al. (1987). “A test to detect clusters of disease.” Biometrika 74(3): 631–635CrossRefGoogle Scholar
  70. WHO (World Health Organization) Collaborating Centre for Mental Health Frederiksborg General Hospital. (2010). “WHO-Five Well-being Index (WHO-5).” Retrieved 27.04.2010, from http://www.who-5.org/
  71. Zuur, A. F., E. N. Ieno, et al. (2009). Mixed Effects Models and Extensions in Ecology with R. New York, SpringerCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Oliver Gruebner
    • 1
  • Md. Mobarak Hossain Khan
    • 2
  • Patrick Hostert
    • 1
  1. 1.Geomatics Lab, Department of GeographyHumboldt-Universität zu BerlinBerlinGermany
  2. 2.Department of Public Health MedicineSchool of Public Health, Bielefeld UniversityBielefeldGermany

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