Methods for analysing recurrent events in health care data. Examples from admissions in Ebeltoft Health Promotion Project.

BACKGROUND: Evaluation of health care contacts from first events alone often misses large amounts of potentially important data and may produce different results than evaluation of all data including recurrent events.
OBJECTIVE: We aim to bring the different methodological approaches for analysing longitudinal health care data to the attention of researchers in primary care.
METHODS: We used hospital admission data from the Ebeltoft Health Promotion Project, a randomized trial in primary care examining the effect of preventive health checks. Comparisons included three randomized groups: an intervention group receiving health checks, a group where intervention consisted of a health check followed by a health discussion with the GP and one control group.
RESULTS: Both intervention groups had approximately 20% fewer hospital admissions than the control group over a 6 year period. If dependence among recurrent events is excluded, such a reduction amounts to a highly significant effect. Use of the standard Poisson distribution for analysing recurrent events and exclusion of their dependent structure causes data interpretation to be incorrect, because the model does not account for the extra variability between persons; the resulting 95% CIs would therefore be too small.
CONCLUSION: Analysis of health care contacts should embrace both first and recurrent events and it should use a model appropriate to these data. An individual rate model that includes a parameter of an unspecified individual event distribution frailty may be a natural choice when analysing longitudinal data of contacts to the health care system in broad terms.

RCT Entities:   Population Interventions Outcomes