Computing hospitalization rates in presence of repeated events: impact and countermeasures to avoid misinterpretation

The admission rate, including both first and recurrent events, is a clear overall measure of hospital utilization, its variability accounting for individual propensity to disease recurrence.

In this paper, we compared two variance estimators derived from the Poisson and negative binomial distribution of directly and indirectly age/gender-standardized hospitalization rates allowing for multiple events. The latter approach accommodates departures from the assumption of randomness of repeated events required by the Poisson distribution. We apply these methods to a retrospective cohort based on hospital discharge data in 2001 of Piedmont (north-western Italy) residents.

Estimated standard errors under the negative binomial for both directly and indirectly standardized rates result in almost twice those under the Poisson distribution.

Our analysis confirms that ignoring the typical non-random nature of repeated events underestimates the true variance of rates and can lead to biased optimistic interpretation of study results.