The effects of different lookback periods on the sociodemographic structure of the study population and on the estimation of incidence rates: analyses with German claims data

Abstract

Abstract Background Defining incident cases has always been a challenging issue for researchers working with routine data. Lookback periods should enable researchers to identify and exclude recurrent cases and increase the accuracy of the incidence estimation. There are different recommendations for lookback periods depending on a disease entity of up to 10 years. Well-known drawbacks of the application of lookback periods are shorter remaining observation period in the dataset or smaller number of cases. The problem of selectivity of the remaining population after introducing lookback periods has not been considered in the literature until now. Methods The analyses were performed with pseudonymized claims data of a German statutory health insurance fund with annual case numbers of about 2,1 million insured persons. Proportions of study population excluded due to the application of lookback periods are shown according to age, occupational qualification and income. Myocardial infarction and stroke were used to demonstrate changes in incidence rates after applying lookback periods of up to 5 years. Results Younger individuals show substantial dropouts after the application of lookback periods. Furthermore, there are selectivities regarding occupational qualification and income, which cannot be handled by age standardization. Due to selective dropouts of younger individuals, crude incidence rates of myocardial infarction and stroke increase after applying lookback periods. Depending on the income group, age-standardized incidence rates changed differentially, leading to a decrease and possible underestimation of the social gradient after applying lookback periods. Conclusions Selectivity analyses regarding age and sociodemographic structure should be performed for the study population after applying lookback periods since the selectivity can affect the outcome especially in health care research. The selectivity effects might occur not only in claims data of one health insurance fund, but also in other longitudinal data with left- or right-censoring not covering the whole population. The effects may also apply to health care systems with a mix of public and private health insurance. A trade-off has to be considered between selectivity effects and eliminating recurrent events for more accuracy in the definition of incidence.