Ingo Langner

Evaluating breast cancer mortality in the German mammography screening program with health insurance claims data requires the availability of claims data with information on causes of death. This work aimed to determine the proportions of successful cause-of-death linkages between the second-largest German statutory health insurance fund and three federal cancer registries and to investigate whether linked proportions differed by region, year, and age. Women aged 40–90 years whose insurance was terminated between 2006 and 2018 were included. Proportions successfully linked to the official death certificate databases of all individuals (available in one registry) and of registered cancer cases (available in three registries) were calculated. Of 150,369 women whose insurance was terminated due to death, 90.0% were linked to the database including all deceased women. Regarding the databases including only registered cancer cases, 35.9% of 150,369, 38.6% of 47,472, and 20.1% of 65,893 deceased women were linked. Linked proportions increased from 2006 to 2018 and peaked in age group 60–69 years. The data will be used for the evaluation of the German Mammography screening program. Since causes of death were not linked for all deceased women and the proportions of linkages differed by region, year, and age, claims-based algorithms will also be considered to complement claims data with causes of death.

Background: In Germany, all women aged 50–69 have been invited to biennial mammography screening since 2009. We aimed to assess longitudinal adherence over ten years in women aged 50 in 2009 and characterize the different adherence groups. Methods: Using the German Pharmacoepidemiological Research Database (GePaRD, ~ 20% of the German population), we included women aged 50 in 2009 (baseline) with continuous health insurance coverage and without breast cancer or in-situ-carcinoma. We followed them until age 59 and categorized them according to mammography screening participation into the following groups: never, 1–2, 3–4, 5–6 times. We characterized these groups, inter alia, regarding the use of other preventive measures, non-screening mammography (i.e., mammography outside the organized screening program) and menopausal hormone therapy. Results: Overall, 82,666 women were included. Of these, 27.6% never participated in the screening program, 15.1% participated 1–2 times, 31.7% participated 3–4 times and 25.6% participated regularly (5–6 times). Among regular participants, 91% utilized other preventive measures (e.g., cervical cancer screening, general health checkup) before baseline as compared to 66% among non-participants. Menopausal hormone therapy was least common among non-participants (11% vs. 18% among regular participants). Among non-participants, the proportions using ≥ 1, ≥ 2, and ≥ 3 non-screening mammographies between age 50–59 were 25%, 18%, and 15%, respectively. Conclusions: Using a large cohort based on claims data, this study provides novel insights into longitudinal adherence to the mammography screening program and the use of mammography outside of the program in Germany. Between age 50–59, 57% of eligible women participated at least three times in the German mammography screening program and 28% (~ 3 in 10 women) never participated. Among non-participants, 15% had at least three non-screening mammographies during this period, indicating potential gray screening. Participants more often utilized other preventive measures as compared to non-participants.

Background: The efficacy of mammography screening in reducing breast cancer mortality has been demonstrated in randomized trials. However, treatment options - and hence prognosis – for advanced tumor stages as well as mammography techniques have considerably improved since completion of these trials. Consequently, the effectiveness of mammography screening under current conditions is unclear and controversial. The German mammography screening program (MSP), an organized population-based screening program, was gradually introduced between 2005 and 2008 and achieved nation-wide coverage in 2009. Objective: We describe in detail a study protocol for investigating the effectiveness of the German MSP in reducing breast cancer mortality in women aged 50 to 69 years based on health claims data. Specifically, the proposed study aims at estimating per-protocol effects of several screening strategies on cumulative breast cancer mortality. The first analysis will be conducted once 10-year follow-up data are available. Methods and Analysis: We will use claims data from five statutory health insurance providers in Germany, covering approximately 37.6 million individuals. To estimate the effectiveness of the MSP, hypothetical target trials will be emulated across time, an approach that has been demonstrated to minimize design-related biases. Specifically, the primary contrast will be in terms of the cumulative breast cancer mortality comparing the screening strategies of “never screen” versus “regular screening as intended by the MSP”. Ethics and Dissemination: In Germany, the utilization of data from health insurances for scientific research is regulated by the Code of Social Law. All involved health insurance providers as well as the responsible authorities approved the use of the health claims data for this study. The Ethics Committee of the University of Bremen determined that studies based on claims data are exempt from institutional review. The findings of the proposed study will be published in peer-reviewed journals.

Purpose: Epidemiological and health care research frequently rely on diagnoses from routine care, but the intra‐individual stability of diagnoses of Alzheimer's disease (AD), vascular dementia (VD) or other forms of dementia (oD) in patients over time is understudied. More data on the diagnostic stability is needed to appraise epidemiological findings from such studies.Methods: Using health claims data of the years 2004–2016 from the German Pharmacoepidemiological Research Database, 160 273 patients aged ≥50 with incident dementia were identified and followed for 4 years. According to the incident ICD‐10 codes patients were assigned to the categories AD, VD or oD. Changes between categories during follow‐up were calculated.Results: Overall, 18.8% had incident AD (VD: 21.5%, oD: 59.7%). Fifteen thousand eight hundred forty‐two patients had only one dementia diagnosis during 4 years (AD: 7.4%, VD: 12.4%, oD: 9.8%). Among those with more than one diagnosis, the incident diagnosis matched the last diagnosis in 65.1% (AD), 53.9% (VD) and 73.8% (oD) of patients. Changes in the diagnostic category were higher in patients with AD (mean: 5.1) than in patients with VD (3.6) or oD (3.3). Patients with stable AD diagnoses during the observation period were younger (median: 76 vs. 79 years) and had less inpatient treatment days (median: 14 days) than patients with changes from an AD diagnosis to another category or from another category to AD (27 days).Conclusions: While health claims data are feasible for estimating the incidence of dementia in general, the substantial number of changes in dementia diagnoses during the course of the disease warrant caution on the interpretation of epidemiological data on specific dementia types.

Zusammenfassung: Routinedaten haben ein hohes Potenzial für die epidemiologische und Versorgungsforschung, doch fehlen beispielsweise Informationen zu Todesursachen und es mangelt häufig an detaillierten Informationen u. a. zu Lebensstilfaktoren. In Deutschland ist v. a. aufgrund der strengen Datenschutzauflagen eine unmittelbare Ergänzung dieser Informationen durch andere Datenquellen („Linkage“) mit einigen Herausforderungen verbunden. So besteht eine Herausforderung darin, dass die Routinedatennutzer üblicherweise keinen Zugriff auf Personenidentifikatoren besitzen, die für ein Linkage erforderlich sind. Darüber hinaus sollten sensible Informationen (z. B. die Todesursache) nicht an Institutionen übermittelt werden, die selbst über Personenidentifikatoren verfügen. In diesem Artikel veranschaulichen wir diese zentralen Herausforderungen und stellen entsprechende Lösungen anhand eines Praxisbeispiels vor, bei dem die Abrechnungsdaten von gesetzlichen Krankenkassen mit einem epidemiologischen Krebsregister verknüpft werden, um Informationen zur Todesursache zu erhalten. Wir beschreiben die für das Linkage notwendigen Genehmigungsverfahren, den Datenfluss zwischen den beteiligten Institutionen und erläutern die Gründe für den Datenfluss im Hinblick auf die wesentlichen Herausforderungen. Schließlich verallgemeinern wir die Fragen, die bei der Planung eines Linkage-Verfahrens zu klären sind und zeigen mögliche weitere Herausforderungen auf. Mit diesem praktischen Beispiel zeigen wir, dass ein Abgleich zwischen Routinedaten und anderen Datenquellen in Deutschland umsetzbar ist, dabei aber bestimmte Beschränkungen und Hindernisse berücksichtigt werden müssen.

Objective: To adapt a Canadian algorithm for the identification of female cases of breast cancer (BC) deaths to German health insurance claims data and to test and validate the algorithm by comparing results with official cause of death (CoD) data on the individual and the population level. Design: Validation study, secondary data, medical claims. Setting: Claims data of two statutory health insurance providers (SHIs) for inpatient and outpatient care, CoD added via record linkage with epidemiological cancer registry (ECR). Participants All women insured with the two SHIs and who deceased in the period 2006–2013, were residents of North Rhine Westphalia (NRW) and were linked with ECR data: n=22 413. Main outcome measures: Based on inpatient and outpatient diagnoses in the year before death, six algorithms were derived and the accordance of the algorithm-based CoD with the official CoD was evaluated calculating specificity, sensitivity, negative and positive predictive values (NPV, PPV). Furthermore, algorithm-based age-specific BC mortality rates covering several calendar years were calculated for the entire insured female population and compared with official national rates. Results: Our final algorithm, derived from the NRW subsample, comprised codes indicating the presence of BC, metastases, a terminal illness phase and the absence of codes for other tumours. Overall, specificity, sensitivity, NPV and PPV of this algorithm were 97.4%, 91.3%, 98.9% and 81.7%, respectively. In the age range 40–80 years, sensitivity and PPV slightly decreased with increasing age. Algorithm-based age-specific BC mortality rates agreed well with official rates except for the age group 85 years and older. Conclusions: The algorithm-based identification of BC deaths in German claims data is feasible and valid, except for higher ages. The algorithm to ascertain BC mortality rates in an epidemiological study seems applicable when information on the official CoD is not available in the original database.

Objective Claims data need to be validated to assess their use for epidemiological research. This study aimed to examine the validity of mortality information in the German Pharmacoepidemiological Research Database (GePaRD). Design Validation study, secondary data, medical claims. Setting Claims data of two German nationwide acting statutory health insurance providers (SHIs) contributing data for GePaRD; record linkage with epidemiological cancer registry providing individual official mortality information. Participants All women insured with the two SHIs whose insurance coverage ended in the period 2006–2013 and who were residents of North Rhine Westphalia. Measures Descriptive statistics were used to analyse the performance of the linkage procedure. Further, we calculated measures of agreement between the official and the GePaRD-based vital status and assessed differences between the official and the GePaRD-based date of death. Results Of the 256 111 women of the linkage sample, 25 528 were classified as ‘deceased’ in GePaRD and the others as ‘alive’. Compared with the official data, the GePaRD-based vital status showed a sensitivity of 95.9% and a specificity of 99.4%. The negative predictive value was 99.6% and the positive predictive value 94.3%. The date of death agreed in 96.3% between both data sources. Conclusions The vital status recorded in GePaRD was of high accuracy and discrepancies between dates of death in GePaRD and official dates were rare. This underlines the potential of the database for conducting large cohort studies with mortality as the endpoint.

Ziel der Studie Für die Evaluation von Krebsvorsorgeprogrammen stellen Daten der Gesetzlichen Krankenversicherung (GKV) eine wichtige Datenquelle dar, die jedoch nicht die benötigten Daten zum genauen Sterbedatum und zur Todesursache enthält. Diese Studie sollte prüfen, ob eine diesbezügliche Anreicherung individueller GKV-Daten über einen Abgleich mit einer geeigneten externen Datenquelle erfolgen kann. Methodik In der pharmako-epidemiologischen Forschungsdatenbank GePaRD identifizierten wir eine Versichertenstichprobe von 25 528 Frauen, die laut den Angaben in GePaRD im Zeitraum 2006–2013 verstorben waren und ihren Wohnsitz in Nordrhein-Westfalen (NRW) hatten. Datum und Ursache des Todes aller Einwohner von NRW seit 2005 liegen im Epidemiologischen Krebsregister von NRW vor. In Kooperation mit 2 gesetzlichen Krankenkassen wurde mit einem probabilistischen bzw. deterministischen Abgleichverfahren versucht, jeder Verstorbenen der Stichprobe einen Todesfall aus NRW und damit eine Todesursache zuzuordnen. Ergebnisse Für 94,72% der Verstorbenen der Versichertenstichprobe konnte probabilistisch und für 93,36% deterministisch ein Todesfall aus NRW zugeordnet werden. Schlussfolgerung Das probabilistische und das deterministische Verfahren erreichten vergleichbar hohe Trefferquoten. Nicht erfolgte Zuordnungen sind vermutlich größtenteils auf Fehler bei der Erfassung der Personendaten zurückzuführen. Aufgrund des geringeren technischen Aufwands erscheint das deterministische Verfahren als die Methode der Wahl für die Anreicherung von GKV-Daten mit amtlichen Todesursachen aus geeigneten externen Datenquellen. Background Claims data of the statutory health insurance (SHI) are an important data source for the evaluation of cancer prevention programs. However, this source does not contain relevant information on cause of death. This study examined whether individual claims data can be enriched with data on the required cause of death using record linkage procedures with suitable external data sources. Methods In the German pharmacoepidemiologic research database (GePaRD) we identified a sample of 25,528 deceased female residents of North Rhine Westphalia (NRW) who, according to GePaRD information, died between 2006 and 2013. Date and cause of all deaths among inhabitants of NRW since 2005 were available in the epidemiological cancer registry of NRW. In cooperation with 2 SHI companies, we tried to match each individual of the sample with a case of death in NRW and the corresponding cause of death using a probabilistic and, alternatively, a deterministic linkage procedure. Results Of the study sample, 94.72% were successfully matched by the probabilistic and 93.36% by the deterministic method. Conclusions The probabilistic and the deterministic record linkage approach produced comparably high matching rates. Cases without matches are probably due to errors occurring at the stage of personal data entry. Given the lower technical efforts, the deterministic approach appears to be the method of choice for the enrichment of claims data with cause of death information from suitable external data sources in Germany.

Cockpit crew in civil aviation are exposed to several potential health hazards, among them cosmic ionizing radiation. To assess the influence of occupational and other factors on mortality we conducted a cohort study among cockpit crew. All pilots and other cockpit personnel of two German airlines were traced through registries and other sources for the period 1960-1997. Standardized mortality ratios, with German population rates as the reference, were calculated. We estimated the individual radiation dose based on individual job histories and assessed dose-response trends in stratified and regression analyses. We compiled a cohort of 6061 male cockpit personnel, yielding 105,037 person-years of observation. The maximum estimated individual radiation dose was 80.5 mSv. Among 255 deaths overall (standardized mortality ratio [SMR] = 0.48; 95% confidence interval [CI] = 0.42-0.54) there were 76 cancer deaths (SMR = 0.56; CI = 0.43 - 0.74). Most cancer and cardiovascular SMRs were reduced. A slight increase was seen for brain cancer (SMR = 1.68; CI = 0.66-3.62). Employment duration was associated with the all-cancer mortality in Poisson regression analyses. No other dose-response relation was found. German cockpit crew have a low overall and cancer mortality. The role of occupational causes, and particularly cosmic radiation, appears limited.