Reply to Cameron et al.: Mistakes Were Made. Can They Be Corrected?

Cameron, Cameron, and Proctor (2017 Cameron, P., Cameron, K. M., &; Proctor, K. (2017). Children of homosexuals more apt to become homosexual and experience parental molestation: Surveys over three decades. Marriage &; Family Review, 53, 429–433. doi:10.1080/01494929.2017.1279942[Taylor &; Francis Online] , [Google Scholar]) have complained that Cameron and Cameron (1996 Cameron, P., &; Cameron, K. (1996). Homosexual parents. Adolescence, 31, 757–776.[PubMed], [Web of Science ®] , [Google Scholar]) were not cited in some articles authored by this journal’s editor. As part of this editor’s policy of welcoming critiques of the editor or articles published in Marriage &; Family Review, we are pleased to respond, as part of an assignment for a graduate course in statistics and research design. Although remarkable for its time in terms of sample design, Cameron and Cameron’s (1996 Cameron, P., &; Cameron, K. (1996). Homosexual parents. Adolescence, 31, 757–776.[PubMed], [Web of Science ®] , [Google Scholar]) research featured substantial limitations or ambiguity in sampling attrition, measurement of parental sexual orientation, and small sample size with respect to the number of children with same-sex parents. Although we cannot recall the exact reasons for which the editor neglected to cite Cameron and Cameron (1996 Cameron, P., &; Cameron, K. (1996). Homosexual parents. Adolescence, 31, 757–776.[PubMed], [Web of Science ®] , [Google Scholar]) in more frequency or detail, there were many scientifically reasonable possibilities. Cameron et al. (2017 Cameron, P., Cameron, K. M., &; Proctor, K. (2017). Children of homosexuals more apt to become homosexual and experience parental molestation: Surveys over three decades. Marriage &; Family Review, 53, 429–433. doi:10.1080/01494929.2017.1279942[Taylor &; Francis Online] , [Google Scholar]) are welcome to respond in detail to our concerns.     

Leveraging risk assessment for foodborne outbreak investigations: The Quantitative Risk Assessment-Epidemic Curve Prediction Model

Root cause analysis can be used in foodborne illness outbreak investigations to determine the underlying causes of an outbreak and to help identify actions that could be taken to prevent future outbreaks. We developed a new tool, the Quantitative Risk Assessment-Epidemic Curve Prediction Model (QRA-EC), to assist with these goals and applied it to a case study to investigate and illustrate the utility of leveraging quantitative risk assessment to provide unique insights for foodborne illness outbreak root cause analysis. We used a 2019 Salmonella outbreak linked to melons as a case study to demonstrate the utility of this model (Centers for Disease Control and Prevention [CDC], 2019). The model was used to evaluate the impact of various root cause hypotheses (representing different contamination sources and food safety system failures in the melon supply chain) on the predicted number and timeline of illnesses. The predicted number of illnesses varied by contamination source and was strongly impacted by the prevalence and level of Salmonella contamination on the surface/inside of whole melons and inside contamination niches on equipment surfaces. The timeline of illnesses was most strongly impacted by equipment sanitation efficacy for contamination niches. Evaluations of a wide range of scenarios representing various potential root causes enabled us to identify which hypotheses, were likely to result in an outbreak of similar size and illness timeline to the 2019 Salmonella melon outbreak. The QRA-EC framework can be adapted to accommodate any food–pathogen pairs to provide insights for foodborne outbreak investigations.