There was a continuous increase in the prevalence of diabetes in Germany during the 8-year period. Although there was only a modest increase in annual diabetes-related per-capita costs, total healthcare expenditure rose substantially due to the growing number of patients being treated for diabetes.
Over the course of the last few decades, statutory health insurance data have become increasingly important for health services research. Of particular interest in this context are diagnoses. Since all health insurance data are originally collected for billing purposes, secondary analyses should examine the completeness, plausibility, and validity of the information provided. While an external validation through, for example, a comparison with the physician's records or a second independent medical examination can be seen as a gold standard, this is often not feasible. For this reason, internal validation approaches are recommended for studies based upon diagnoses drawn from routine data. For such approaches, no established standards are currently available. The aim of this contribution is to introduce a generic internal validation concept for chronic diseases. Data employed in the present contribution stem from the health insuree sample of the AOK health insurance fund Hesse. Criteria for assessing the validity of diagnoses (e.g., repetitions, codes assigned by various physicians, prescriptions) are presented for three chronic diseases - heart failure, dementia, and tuberculosis. Building upon these criteria, algorithms for the definition of epidemiologically certain cases are developed and prevalence estimates formed on the basis of these algorithms are compared with other data sources (registers and surveys). Internal confirmation of the diagnoses of heart failure and dementia was possible in 97% and 80% of cases, respectively. The difference between the two diagnoses is due to the low rate of treatment with specific pharmaceuticals in the case of dementia. Prevalence estimates are comparable with those based on other sources. Inpatient discharge diagnoses of tuberculosis were internally confirmed in 100% and outpatient diagnoses in 40% of cases. For this reason, outpatient diagnoses were not considered for the case definition of tuberculosis. A comparison with tuberculosis surveillance data reveals a somewhat higher incidence in the insuree sample. In selecting and weighting criteria as well as employing a case definition, the research aim of the respective investigation must be taken into account. The adopted procedure is to be presented in a transparent manner.
A close relationship exists between diabetes related excess costs and the presence of microvascular and foot complications. It is important to identify these patients early in order to incorporate them into diabetes management programs. A better care of diabetes patients and subsequent prevention of these late complications promises not only to improve quality of life but also to be highly cost-effective.
Cost for diabetes is largely caused by management of complications. It is important to prevent complications by consequent management of diabetes as well as by primary prevention of its onset.
Objective
To analyze the impact of the length of disease‐free intervals on incidence estimation.
Data Source
Statutory health insurance sample in Germany.
Study Design
Overestimation of the incidence in the first quarter of 2008 for three selected diseases, diabetes mellitus, colorectal cancer, and heart failure, depending on different lengths of preceding disease‐free intervals.
Data Collection/Extraction Methods
Continuously insured from 2000 until 2008 ≥18 years (N = 144,907).
Principal Findings
Compared with an 8‐year disease‐free period, incidence overestimations for diabetes, colorectal cancer, and heart failure were 40, 23, and 43 percent defining a 1‐year, and 5, 9, and 5 percent defining a 5‐year disease‐free period, respectively.
Conclusions
Depending on the specific disease, caution has to be taken while using short disease‐free periods because incidence estimates may be extremely overestimated.
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