title: [Validation of SHI Claims Data Exemplified by Gender-specific Diagnoses].
abstract: Aim:Use of statutory health insurance (SHI) data in health services research is increasing steadily and questions of validity are gaining importance. Using gender-specific diagnosis as an example, the aim of this study was to estimate the prevalence of implausible diagnosis and demonstrate an internal validation strategy.Method:The analysis is based on the SHI data from Baden-Württemberg for 2012. Subject of validation are gender-specific outpatient diagnoses that mismatch with the gender of the insured. To uncover this implausibility, it is necessary to clarify whether the diagnosis or the gender is wrong. The validation criteria used were the presence of further gender-specific diagnoses, the presence of gender-specific settlement items, the specialization of the physician in charge and the gender assignment of the first name of the insured. To review the quality of the validation, it was verified if the gender was changed during the following year.Results:Around 5.1% of all diagnoses were gender-specific and there was a mismatch between diagnosis and gender in 0.04% of these cases. All validation criteria were useful to sort out implausibility, whereas the last one was the most effective. Only 14% remained unsolved. From the total of 1 145 insured with implausible gender-specific diagnoses, one year later 128 had a new gender (in the data). 119 of these cases were rightly classified as insured with wrong gender and 9 cases were in the unsolved group. This confirms that the validation works well.Conclusion:Implausibility in SHI data is relatively small and can be solved with appropriate validation criteria. When validating SHI data, it is advisable to question all data used critically, to use multiple validation criteria instead of just one and to abandon the idea that reality and the associated data conform to standardized norms. Keeping these aspects in mind, analysis of SHI data is a good starting point for research in health services.
authors.affiliation: AOK Baden-Württemberg, RSA/ Gesundheitsfonds, Stuttgart.
authors.forename: J
authors.initials: J
authors.lastname: Hartmann
authors.affiliation: AOK Baden-Württemberg, RSA/ Gesundheitsfonds, Stuttgart.
authors.forename: C
authors.initials: C
authors.lastname: Weidmann
authors.affiliation: AOK Baden-Württemberg, RSA/ Gesundheitsfonds, Stuttgart.
authors.forename: R
authors.initials: R
authors.lastname: Biehle
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000553:Ambulatory Care / Q000706:statistics & numerical data; D000068598:Data Accuracy; D003951:Diagnostic Errors / Q000517:prevention & control* / Q000706:statistics & numerical data*; D005260:Female; D005858:Germany; D006801:Humans; D007345:Insurance Claim Review / Q000706:statistics & numerical data; D007349:Insurance, Health, Reimbursement / Q000706:statistics & numerical data*; D008297:Male; D008875:Middle Aged; D009313:National Health Programs / Q000706:statistics & numerical data*; D011785:Quality Assurance, Health Care / Q000706:statistics & numerical data*; D017678:Sex Distribution; D055815:Young Adult