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Basic characteristics and representativeness of the German Disease Analyzer database
Rathmann W, Bongaerts B, Carius HJ, Kruppert S, Kostev K.. International journal of clinical pharmacology and therapeutics, 2018-10-01
Topic: Risk, prevention and care in population studies
Purpose The aim of this study was to evaluate the representativeness of diagnoses in the Disease Analyzer (DA) database for major chronic diseases (cancer, dementia, diabetes). Materials and methods DA contains anonymized longitudinal data on drug prescriptions, diagnoses as well as medical and demographic data directly obtained from the computer system of a representative sample of practices throughout Germany. DA contains data from 2,498 practices with 7.8 million patients (2017). The distribution and sex-specific incidence of various cancer subsites among new cancer cases, the age- and sex-specific prevalence of dementia, and the prevalence of diabetes were assessed. National reference data were obtained from official sources. Results Mean age (43 years) and sex distribution (47% men) of primary care patients in DA were similar to the German population. Among incident cancer cases, there was good agreement between DA data and national data with respect to the various cancer subsites (e.g., breast cancer: DA 17%; reference: 15%). Furthermore, sex distribution was largely similar. The age distribution of prevalent dementia was similar to national reference data, both in men (80 - 84 years: DA: 26.8%; reference: 27.0%) and in women (80 - 84 years: DA: 24.6%; reference: 24.1%). Diabetes prevalence in the DA (10.7%) was higher than in claims data from physicians (9.8%) or patients from statutory health insurances (9.9%). Conclusion There was a good agreement of the incidence or prevalence of major chronic diseases in the outpatient DA with German reference data. The higher diabetes prevalence in the DA is due to the increased number of outpatient visits of diabetes patients. .
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APA 7th
Rathmann, W., Bongaerts, B., Carius, H. J., Kruppert, S., & Kostev, K. (2018). Basic characteristics and representativeness of the German Disease Analyzer database. International journal of clinical pharmacology and therapeutics. https://doi.org/10.5414/cp203320
Vancouver
Rathmann W, Bongaerts B, Carius HJ, Kruppert S, Kostev K. Basic characteristics and representativeness of the German Disease Analyzer database. International journal of clinical pharmacology and therapeutics. 2018. doi:10.5414/cp203320. PMID: 30168417.
BibTeX
@article{rathmann2018-30168417,
author = {Rathmann, W. and Bongaerts, B. and Carius, H. J. and Kruppert, S. and Kostev, K.},
title = {{Basic characteristics and representativeness of the German Disease Analyzer database}},
journal = {International journal of clinical pharmacology and therapeutics},
year = {2018},
doi = {10.5414/cp203320},
pmid = {30168417},
url = {https://doi.org/10.5414/cp203320}
}
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Key findings
- Results Mean age (43 years) and sex distribution (47% men) of primary care patients in DA were similar to the German population.
Why it matters
This study evaluated the representativeness of the German Disease Analyzer database and found that the age distribution of prevalent dementia was similar to national reference data in both men and women. It describes a data source for research, not a treatment, cure, or prevention for Alzheimer's disease.
New to this subject? Start with our plain-English guide: Alzheimer's in numbers →
Study details
- Study designlongitudinal study
- Effect size26.8%; 27.0%; 24.6%
- Evidence strengthModerate
How this study scored 56/100
Published in a peer-reviewed journal, and it is a longitudinal study following the same people over time. Scored zero for sample size, studied in people and randomised / blinded because the record does not state it. We have no data on institution's track record, so that was left out: it earned 54 of the 97 points we could judge it on, which is 56 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (International journal of clinical pharmacology and therapeutics) |
| Journal's citation record | 0 / 4 | International Journal of Clinical Pharmacology and Therapeutics: journal h-index 72 (OpenAlex, 2026-10-29) |
| Study design | 7 / 12 | A longitudinal study following the same people over time |
| Strength of evidence | 3 / 5 | Rated “moderate” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 2 / 5 | 2018 — within 10 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 2 / 3 | Effect reported as a number, without an interval or test |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 6 / 7 | Cited 397 times (OpenAlex, 2026-10-10) |
| Citations per year | 4 / 5 | 39.7 citations a year over 10 year(s) since 2018 |
| Impact for its field and year | 5 / 6 | Cited 38.3× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 3 / 4 | Cited 135 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Karel Kostev: h-index 55 (OpenAlex, whole career) |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | 0 / 3 | OpenAlex lists 0 institution(s) in 0 countries — scores zero (affiliation lists can be incomplete) |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 30168417) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 54 / 97 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 54 of the 97 points we could judge it on is 56 out of 100. |
| Total | 56 / 100 | — |
What each of these 20 components measures, and what this score is not →
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