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

• Published in: Risk analysis 2008-10, Vol.28 (5), p.1155-1172
• Main Authors: Cox, Jr, Louis Anthony (Tony), Popken, Douglas A
• Format: Article
• Language: English
• Subjects: Animals; Antibiotics; Antimicrobial risk assessment; Bayes Theorem; Bayesian analysis; Bayesian method; Bayesian model averaging; Bias; Bird Diseases - drug therapy; Case studies; causal analysis; Causality; conditional independence tests; confirmation bias; Consumption; Data analysis; Drug resistance; Drug Resistance, Microbial; Food Supply; Health care; Health economics; Health risk assessment; Health risks; Hospitalization; Humans; Models, Statistical; Poultry; Poultry - microbiology; Public policy; Quantitative analysis; Risk; Risk Assessment - methods; Risk management; Studies; Vegetarianism; Virginiamycin - therapeutic use
• ISSN: 0272-4332; 1539-6924
• DOI: 10.1111/j.1539-6924.2008.01122.x

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.