Uncertainty of population risk estimates for pathogens based on QMRA or epidemiology : a case study of campylobacter in the Netherlands
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<subfield code="a">Uncertainty of population risk estimates for pathogens based on QMRA or epidemiology</subfield>
<subfield code="b">: a case study of campylobacter in the Netherlands</subfield>
<subfield code="c">Martijn Bouwknegt...[et.al]</subfield>
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<subfield code="a">Epidemiology and quantitative microbiological risk assessment are disciplines in which the same public health measures are estimated, but results differ frequently. If large, these differences can confuse public health policymakers. This article aims to identify uncertainty sources that explain apparent differences in estimates for Campylobacter spp. incidence and attribution in the Netherlands, based on four previous studies (two for each discipline). An uncertainty typology was used to identify uncertainty sources and the NUSAP method was applied to characterize the uncertainty and its influence on estimates. Model outcomes were subsequently calculated for alternative scenarios that simulated very different but realistic alternatives in parameter estimates, modeling, data handling, or analysis to obtain impressions of the total uncertainty. For the epidemiological assessment, 32 uncertainty sources were identified and for QMRA 67. Definitions (e.g., of a case) and study boundaries (e.g., of the studied pathogen) were identified as important drivers for the differences between the estimates of the original studies. The range in alternatively calculated estimates usually overlapped between disciplines, showing that proper appreciation of uncertainty can explain apparent differences between the initial estimates from both disciplines. Uncertainty was not estimated in the original QMRA studies and underestimated in the epidemiological studies. We advise to give appropriate attention to uncertainty in QMRA and epidemiological studies, even if only qualitatively, so that scientists and policymakers can interpret reported outcomes more correctly. Ideally, both disciplines are joined by merging their strong respective properties, leading to unified public health measures.</subfield>
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<subfield code="t">Risk analysis : an international journal</subfield>
<subfield code="d">McLean, Virginia : Society for Risk Analysis, 1987-2015</subfield>
<subfield code="x">0272-4332</subfield>
<subfield code="g">05/05/2014 Volumen 34 Número 5 - mayo 2014 </subfield>
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