Evidence map›Paper›PMID 40571910›Full record

ArticleBMC pulmonary medicine2025

Exploring the potential of German claims data to identify incident lung cancer patients.

Josephine Kanbach, Nikolaj Rischke, Sabine Luttmann, Ulrike Haug

Abstract read
In one paragraph

Article in BMC pulmonary medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Josephine KanbachDepartment of Clinical Epidemiology, Leibniz Institute for Prevention Research and Epidemiology- BIPS, Achterstr. 30, 28359, Bremen, Germany.
Nikolaj RischkeDepartment of Clinical Epidemiology, Leibniz Institute for Prevention Research and Epidemiology- BIPS, Achterstr. 30, 28359, Bremen, Germany.
Sabine LuttmannDepartment of Clinical Epidemiology, Leibniz Institute for Prevention Research and Epidemiology- BIPS, Achterstr. 30, 28359, Bremen, Germany.
Ulrike HaugDepartment of Clinical Epidemiology, Leibniz Institute for Prevention Research and Epidemiology- BIPS, Achterstr. 30, 28359, Bremen, Germany. haug@leibniz-bips.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundReal-world healthcare databases offer great potential for cancer research, but the valid identification of cancer patients is crucial for the suitability of a database in this regard. We aimed to assess the plausibility of an algorithm to identify incident lung cancer (LC) patients in German claims data.

methodsUsing the German Pharmacoepidemiological Research Database (GePaRD; claims data from ∼ 20% of the German population) we applied a previously developed algorithm which identifies incident LC patients and classifies them into advanced and non-advanced. We calculated age-standardized incidence rates (ASIRs) per 100,000 for the years 2013-2018. Further, we assessed the ASIRs stratified by the deprivation index of the district of residence and determined age-standardized five-year absolute and relative survival. We stratified all analyses by sex.

resultsOverall, we identified ∼ 9,500 - 10,500 incident LC patients per year. In 2018, (N = 10,625, mean age: 69.2 years in men) the proportion classified as advanced at diagnosis was 71.4%; the ASIRs of LC were 45 per 100,000 in men (9% lower than in 2013) and 27 per 100,000 persons in women (similar to 2013). ASIRs were lowest in persons living in areas with a low deprivation index. Age-standardized five-year absolute and relative survival rates, respectively, were 31% and 34% in women and 27% and 31% in men.

conclusionsThe algorithm we applied to identify incident LC patients in German claims data yielded plausible results, supporting its validity.

trial registrationNot applicable.

Indexed as

AlgorithmsDatabases, FactualLung NeoplasmsAdultAgedAged, 80 and overFemaleGermanyHumansIncidenceMaleMiddle AgedPharmacoepidemiologyCancer registry dataClaims dataIncidenceLung cancerSurvival

Identifiers

PMID40571910
PMCPMC12203718

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.