Abstract
In 2001-2002, a multicenter, prospective case-control study involving 1,714 participants > or =5 years of age was conducted in Australia to identify risk factors for Campylobacter infection. Adjusted population-attributable risks (PARs) were derived for each independent risk factor contained within the final multivariable logistic regression model. Estimated PARs were combined with adjusted (for the > or =5 years of age eligibility criterion) notifiable disease surveillance data to estimate annual Australian Campylobacter case numbers attributable to each risk factor. Simulated distributions of "credible values" were then generated to model the uncertainty associated with each case number estimate. Among foodborne risk factors, an estimated 50,500 (95% credible interval 10,000-105,500) cases of Campylobacter infection in persons > or =5 years of age could be directly attributed each year to consumption of chicken in Australia. Our statistical technique could be applied more widely to other communicable diseases that are subject to routine surveillance.
Highlights
In 2001–2002, a multicenter, prospective case-control study involving 1,714 participants >5 years of age was conducted in Australia to identify risk factors for Campylobacter infection
Prospective casecontrol study, we aimed to develop a multivariable logistic regression model that identified independent foodborne and nonfoodborne risk factors for Campylobacter infection for this sample [7] and calculate population-attributable risk (PAR) proportions
These PARs were combined with annual Campylobacter infection surveillance data to estimate the total number of infections among persons >5 years of age attributable to specific risk factors that occur in the community each year in Australia
Summary
In 2001–2002, a multicenter, prospective case-control study involving 1,714 participants >5 years of age was conducted in Australia to identify risk factors for Campylobacter infection. Prospective casecontrol study, we aimed to develop a multivariable logistic regression model that identified independent foodborne and nonfoodborne risk factors for Campylobacter infection for this sample [7] and calculate population-attributable risk (PAR) proportions. These PARs were combined with annual Campylobacter infection surveillance data to estimate the total number of infections (with associated CrIs) among persons >5 years of age attributable to specific risk factors that occur in the community each year in Australia
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