Methodological challenges in using point-prevalence versus cohort data in risk factor analyses of nosocomial infections.

Purpose: To explore the impact of length-biased sampling on the evaluation of risk factors of nosocomial infections (NIs) in point-prevalence studies.

Methods: We used cohort data with full information including the exact date of the NI and mimicked an artificial 1-day prevalence study by picking a sample from this cohort study. Based on the cohort data, we studied the underlying multistate model which accounts for NI as an intermediate and discharge/death as competing events. Simple formulas are derived to display relationships between risk, hazard, and prevalence odds ratios.

Results: Due to length-biased sampling, long stay and thus sicker patients are more likely to be sampled. In addition, patients with NIs usually stay longer in hospital. We explored mechanisms that are-due to the design-hidden in prevalence data. In our example, we showed that prevalence odds ratios were usually less pronounced than risk odds ratios but more pronounced than hazard ratios.

Conclusions: Thus, to avoid misinterpretation, knowledge of the mechanisms from the underlying multistate model is essential for the interpretation of risk factors derived from point-prevalence data.