Designing and evaluating risk-based surveillance systems: potential unwarranted effects of applying adjusted risk estimates

Abstract

Risk-based surveillance systems reveal occurrence of disease or infection in a sample of population units, which are selected on the basis of risk factors for the condition under study. The purpose of such systems for supporting practical animal disease policy formulations and management decisions are: A: to detect an emerging disease or infection, if it becomes introduced into a population; or B: to substantiate freedom from a condition in a population; or C: to detect cases and estimate the prevalence of an endemic condition in a population. In risk-based surveillance these aims should be met with prudent use of resources while maintaining acceptable system performance. High-risk category units are selected for testing by identification of the presence of specific high-risk factor(s), while disregarding other factors that might also influence the risk. On this basis we argue that the most applicable risk estimate for use in designing and evaluating a risk-based surveillance system would be a crude (unadjusted) relative risk, odds ratio or apparent prevalence. Risk estimates found in the published literature, however, are often the results of multivariable analyses implicitly adjusting the estimates for confounding from other risk factors. We describe some potential unintentional effects when using adjusted risk estimates in evaluating the efficacy and sensitivity of risk-based surveillance systems (SSe). In two examples, we quantify and compare the efficacy and SSe using adjusted and crude risk estimates. The examples use Danish surveillance data from previously published studies to evaluate systems aimed at risk-based detection of new cases of an endemic infection, i.e. Salmonella in dairy cattle herds (Example 1), and for substantiating the absence of a specific infection, i.e. Trichinella in the national slaughter pig population (Example 2), respectively.