We explored the feasibility, suitability, and reliability of using controls recruited among members of a non-probabilistic online panel (‘panel controls’) in a case–control study (CCS) to investigate a Salmonella Braenderup outbreak in Germany. For comparison, another control group was recruited via random digit dialling (‘classical controls’). Panel members received questionnaires by email; classical controls were interviewed by phone. Both control groups were frequency-matched to cases by age and sex; the classical controls also by federal state. Cases and controls were queried mainly about fruit consumption since melons were the suspected infection vehicle. We calculated adjusted odds ratios (aOR) and 95% confidence intervals (CIs) using single-variable and multivariable logistic regression. The study included 32 cases, 81 panel controls and 110 classical controls. Analyses identified melons, particularly Galia melons, as the most likely infection vehicle using either control group (panel controls – aOR 12, CI 2.7–66; classical controls – aOR 55, CI 8–1100). Recruitment of panel versus classical controls required substantially less person-time (8 vs. 111 hours) and was about 10 times less expensive. We recommend this timely and reliable control recruitment method when investigating diffuse foodborne outbreaks with CCS.

Established methods of recruiting population controls for case–control studies in infectious disease outbreak investigations are resource- and time-intensive, and are often subject to bias. The online panel have recently gained interest as an easy and timely method to select controls. We examined the feasibility, suitability and reliability of using an online panel to select controls for case–control studies as part of investigations of diffuse food and waterborne outbreaks. In January 2019, we deployed a web survey by email to the 277 members of a non-probabilistic online panel in Lower Saxony, Germany. We questioned them on basic sociodemographic characteristics and eating habits. They were frequency matched to cases on sex and age. Their food exposures were compared to those of traditionally recruited controls of four historical case–controls studies, which successfully investigated food and waterborne outbreaks. We used logistic regressions to assess the association between the food exposures and the disease (odds ratios). The use of a control panel successfully led to the identification of the food items in three of the four historical outbreak investigations, and their recruitment benefitted from increased speed and limited costs. Timely outbreak investigations would enable rapidly implementing control measures. We recommend the further evaluation of using panellists as controls in parallel case–control studies and case–panel studies.