Combining QMRA and Epidemiology to Estimate Campylobacteriosis Incidence. Issue 10 (17th February 2016)

• Record Type: Journal Article
• Authors: Evers, Eric G.; Bouwknegt, Martijn
• Is Part Of: Risk analysis. Volume 36:Issue 10(2016)
• Page Start: 1959
• Page End: 1968
• Publication Date: 2016-02-17
• Subjects: Campylobacter -- disease burden -- epidemiology -- QMRA; Technology -- Risk assessment -- Periodicals; 658.403
• DOI: 10.1111/risa.12538
• Languages: English
• ISSNs: 0272-4332

Abstract

Abstract : The disease burden of pathogens as estimated by QMRA (quantitative microbial risk assessment) and EA (epidemiological analysis) often differs considerably. This is an unsatisfactory situation for policymakers and scientists. We explored methods to obtain a unified estimate using campylobacteriosis in the Netherlands as an example, where previous work resulted in estimates of 4.9 million (QMRA) and 90, 600 (EA) cases per year. Using the maximum likelihood approach and considering EA the gold standard, the QMRA model could produce the original EA estimate by adjusting mainly the dose‐infection relationship. Considering QMRA the gold standard, the EA model could produce the original QMRA estimate by adjusting mainly the probability that a gastroenteritis case is caused by Campylobacter . A joint analysis of QMRA and EA data and models assuming identical outcomes, using a frequentist or Bayesian approach (using vague priors), resulted in estimates of 102, 000 or 123, 000 campylobacteriosis cases per year, respectively. These were close to the original EA estimate, and this will be related to the dissimilarity in data availability. The Bayesian approach further showed that attenuating the condition of equal outcomes immediately resulted in very different estimates of the number of campylobacteriosis cases per year and that using more informative priors had little effect on the results. In conclusion, EA was dominant in estimating the burden of campylobacteriosis in the Netherlands. However, it must be noted that only statistical uncertainties were taken into account here. Taking all, usually difficult to quantify, uncertainties into account might lead to a different conclusion.