Health Surveillance Around Prespecified Locations Using Case-Control Data

  • Peter A. Rogerson
Part of the Advances in Spatial Science book series (ADVSPATIAL)


There are several approaches one may use to model or test for potential risk around point sources of interest. These approaches have been developed almost universally to (a) fit model parameters to estimate the nature and significance of decline in risk as one moves away from the point source, or (b) assess the significance of a test statistic based upon the null hypothesis of no raised incidence around the source. In this paper, I assume that the data on the locations of cases and controls often used for these questions may be arranged in temporal order (for example, data might consist of the date of diagnosis for both case and control diseases). I then illustrate how conventional modeling approaches may be adapted to use the dataset observation by observation, to detect as quickly as possible a change from one set of model parameters to another.


False Alarm Point Process Model Putative Source Prospective Monitoring Simulated Distance 
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.


  1. Chapeau-Blondeau F, Monir A (2002) Numerical evaluation of the Lambert W function and application to generation of generalized Gaussian noise with exponent 1/2. IEEE Trans Signal Process 50:2160–2165CrossRefGoogle Scholar
  2. Corless RM, Gonnet GH, Hare DEG, Jeffrey DJ, Knuth DE (1996) On the Lambert W function. Adv Comput Math 5:329–359CrossRefGoogle Scholar
  3. Corless RM, Jeffrey DJ, Knuth DE (1997) A sequence of series for the Lambert W function. In: Proceedings of the 1997 international symposium on Symbolic and algebraic computation. ACM, New York, pp 197–204Google Scholar
  4. Diggle PJ (1990) A point process modelling approach to raised incidence of a rare phenomenon in the vicinity of a prespecified point. J R Stat Soc A 153:349–362CrossRefGoogle Scholar
  5. Diggle PJ, Rowlingson BS (1994) A conditional approach to point process modelling of elevated risk. J R Stat Soc A 157:433–440CrossRefGoogle Scholar
  6. Hawkins D, Olwell D (1998) Cumulative sum charts and charting for quality improvement. Springer, BerlinGoogle Scholar
  7. Lawson A (1993) On the analysis of mortality events associated with a prespecified fixed point. J R Stat Soc A 156:363–377CrossRefGoogle Scholar
  8. Stone R (1988) Investigations of excess environmental risks around putative sources: statistical problems and a proposed test. Stat Med 7:649–660CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Departments of Geography and BiostatisticsUniversity at BuffaloBuffaloUSA

Personalised recommendations