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Modelling the impact of prior immunity, case misclassification and bias on case-control studies in the investigation of outbreaks of cryptosporidiosis

  • P. R. HUNTER (a1)
  • Published online: 01 March 2001

Cryptosporidiosis is the most common cause of outbreaks of disease linked to mains water supply in the United Kingdom and the second commonest in the United States. Recent evidence has suggested that prior population immunity may have an impact on the epidemiology of waterborne outbreaks and in particular prior immunity may reduce the power of case-control studies for demonstrating association between disease and water consumption behaviour. However, the degree of impact of prior immunity on the power of epidemiological studies is not yet clear. This paper reports the results of some simple mathematical models of outbreaks of waterborne disease in populations with varying levels of immunity due to prior water and non-water exposure. The basic outbreak model was run on a spreadsheet. To determine the impact of prior immunity on case-control studies, further analysis was done using a Monte Carlo method to simulate sampling from cases and controls. It was found that moderate degrees of prior immunity due to water associated disease could markedly reduce the relative risk of water consumption on illness in waterborne outbreaks. In turn this would reduce the power of case-control studies. In addition, this model was used to demonstrate the impact of case misclassification and recall bias on case-control studies. Again it was found that within the model, the results of case-control studies could be significantly affected by both these sources of error. Anyone conducting epidemiological investigations of potentially waterborne outbreaks of disease should be aware of the epidemiological problems. Mistakes from case-control studies will be minimized if the outbreak team pays considerable attention to the descriptive phase of the investigation and if case-control studies are conducted as soon as possible after an outbreak is detected.

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Epidemiology & Infection
  • ISSN: 0950-2688
  • EISSN: 1469-4409
  • URL: /core/journals/epidemiology-and-infection
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