The Use Of Administrative Data To Determine Prevalence And Incidence Of Copd: A Systematic Review

Gothe, Holger; Rajsic, S; Gottfried, E. L.; Vukicevic, D; Siebert, U.

doi:10.1016/j.jval.2016.09.130

Cited by 1 publication

(1 citation statement)

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“…Health insurance claims as a big data Administrative data in healthcare primarily refer to the vast medical information available in the form of electronic health records through administrative or health claims data [1]. As the availability of digitized administrative records are increasing, health researchers are able to use these large longitudinal cohort datasets to estimate epidemiologic indicators, such as the incidence and prevalence of various conditions [2][3][4][5][6][7][8][9][10][11][12][13][14][15]. The strengths of this type of large population studies include having a large sample size and avoiding selection or participation bias [16].…”

Section: Introductionmentioning

confidence: 99%

The effect of the look-back period for estimating incidence using administrative data

Kim

Chae

Chung

et al. 2020

BMC Health Serv Res

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Background: The look-back period is needed to define baseline population for estimating incidence. However, short look-back period is known to overestimate incidence of diseases misclassifying prevalent cases to incident cases. The purpose of this study is to evaluate the impact of the various length of look-back period on the observed incidences of uterine leiomyoma, endometriosis and adenomyosis, and to estimate true incidences considering the misclassification errors in the longitudinal administrative data in Korea. Methods: A total of 319,608 women between 15 to 54 years of age in 2002 were selected from Korea National Health Insurance Services (KNHIS) cohort database. In order to minimize misclassification bias incurred when applying various length of look-back period, we used 11 years of claim data to estimate the incidence by equally setting the look-back period to 11 years for each year using prediction model. The association between the year of diagnosis and the number of prevalent cases with the misclassification rates by each look-back period was investigated. Based on the findings, prediction models on the proportion of misclassified incident cases were developed using multiple linear regression. Results: The proportion of misclassified incident cases of uterine leiomyoma, endometriosis and adenomyosis were 32.8, 10.4 and 13.6% respectively for the one-year look-back period in 2003. These numbers decreased to 6.3% in uterine leiomyoma and − 0.8% in both endometriosis and adenomyosis using all available look-back periods (11 years) in 2013. Conclusion: This study demonstrates approaches for estimating incidences considering the different proportion of misclassified cases for various length of look-back period. Although the prediction model used for estimation showed strong R-squared values, follow-up studies are required for validation of the study results.

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