Food Safety Revisited

Frimodt‐Møller, Niels; Hammerum, Anette M.

doi:10.1086/508221

Cited by 2 publications

(2 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In summary, although several strong causal conclusions and interpretations of the case study data have previously been presented ( 17,18 ) that suggest increased human health risk from virginiamycin use in poultry, we could not reproduce these findings or conclusions using statistical methods that seek to minimize confirmation bias and dependence on a priori causal hypotheses. We believe that it would be valuable to expand the data set in the Marshfield study to include healthy poultry eaters and hospitalized nonpoultry eaters (categories currently of size 0 and 4, respectively).…”

Section: Discussion and Implications For Previous Conclusionmentioning

confidence: 94%

“…The study concluded that “the results of the present investigation suggest that virginiamycin use in poultry contributes to human carriage of Enterococcus faecium that contains streptogramin resistance genes with readily inducible resistance.” This appears to be a causal conclusion: that virginiamycin use in animals contributes to carriage of E. faecium with antibiotic resistance potential in humans. Based on this causal interpretation, an accompanying editorial ( 18 ) called for reduced use of virginiamycin use in food animals.…”

Section: Introduction: Confirmation Bias In Causal Inferencesmentioning

confidence: 99%

See 1 more Smart Citation

Overcoming Confirmation Bias in Causal Attribution: A Case Study of Antibiotic Resistance Risks

Cox

Popken

2008

Risk Analysis

View full text Add to dashboard Cite

When they do not use formal quantitative risk assessment methods, many scientists (like other people) make mistakes and exhibit biases in reasoning about causation, if-then relations, and evidence. Decision-related conclusions or causal explanations are reached prematurely based on narrative plausibility rather than adequate factual evidence. Then, confirming evidence is sought and emphasized, but disconfirming evidence is ignored or discounted. This tendency has serious implications for health-related public policy discussions and decisions. We provide examples occurring in antimicrobial health risk assessments, including a case study of a recently reported positive relation between virginiamycin (VM) use in poultry and risk of resistance to VM-like (streptogramin) antibiotics in humans. This finding has been used to argue that poultry consumption causes increased resistance risks, that serious health impacts may result, and therefore use of VM in poultry should be restricted. However, the original study compared healthy vegetarians to hospitalized poultry consumers. Our examination of the same data using conditional independence tests for potential causality reveals that poultry consumption acted as a surrogate for hospitalization in this study. After accounting for current hospitalization status, no evidence remains supporting a causal relationship between poultry consumption and increased streptogramin resistance. This example emphasizes both the importance and the practical possibility of analyzing and presenting quantitative risk information using data analysis techniques (such as Bayesian model averaging (BMA) and conditional independence tests) that are as free as possible from potential selection, confirmation, and modeling biases.

show abstract

Section: Discussion and Implications For Previous Conclusionmentioning

confidence: 94%

Section: Introduction: Confirmation Bias In Causal Inferencesmentioning

confidence: 99%