Who participates in LDCT screening for the early detection of lung cancer after long-term occupational exposure to asbestos dust? Results of the analysis of administrative and medical routine data from the German Social Accident Insurance

Our plain-English summary of Who participates in LDCT screening for the early detection of lung cancer after long-term occupational exposure to asbestos dust? Results of the analysis of administrative and medical routine data from the German Social Accident Insurance, by N. Oubaid, F. Greiner, H. Keller, et al (BMC Pulmonary Medicine, 2026).

The study

• Authors: N. Oubaid, F. Greiner, H. Keller, M.A. Horn, A.M. Preisser, V. Harth
• Journal: BMC Pulmonary Medicine
• Published: 2026;26(1)
• Read it: PubMed · Publisher · Free full text

Abstract

Background: Lung cancer is one of the most common cancers in Germany and is predominantly detected at an advanced stage. To ensure early detection, the German Social Accident Insurance (DGUV) offers lung cancer screening including a low-dose computed tomography (LDCT) for individuals with long-term occupational exposure to asbestos dust and a smoking history of at least 30 pack years. However, evidence remains scarce about what encourages this group of people to participate in LDCT screening after medical consultation.

Methods: A secondary data analysis using routinely collected medical and administrative data between 2014 and 2024 was conducted in a cross-sectional design. This study aimed to investigate factors that could be used to predict the uptake of LDCT screening following a medical consultation. Exploratory logistic regression analysis was used to identify and assess possible predictors.

Results: The sample included n = 14,491 individuals (97% male; average age 67 years; average asbestos dust exposure 21 years; average cigarette consumption 43 pack years). 74% of individuals underwent LDCT screening after medical consultation. 13 predictors were included in the regression model. The model demonstrated a low level of variance explanation (Nagelkerkes R2 = 0.026). Younger individuals (OR: 0.96, p < 0.001), individuals with prolonged exposure to asbestos dust (OR: 1.01, p = 0.011) and with a history of pneumonia (OR: 1.16, p = 0.011) were more likely to undergo LDCT screening.

Conclusion: The predictors showed only a low explanatory power. Further research should include non-participants and expand the exploratory model to include theoretically grounded predictors.

© 2026. The Author(s).

In plain English

This study looked at people offered lung cancer screening by the German Social Accident Insurance (DGUV) for long-term occupational asbestos dust exposure and heavy smoking. The authors analysed routine medical and administrative data from 2014 to 2024. Of 26,353 eligible people, 16,822 (63.8%) attended at least an initial medical consultation, and 14,491 met the study's criteria. These were 97% male, with an average age of 67 years. After the consultation, 10,714 of the 14,491 (73.9%) had a low-dose CT (LDCT) scan. In a model of 14,020 people and 13 predictors, three were linked to having the scan. The first was age (odds ratio 0.96 per additional year, 95% confidence interval (CI) 0.95 to 0.97), meaning younger people were more likely to be scanned. The second was years of asbestos exposure (1.01 per year, 95% CI 1.00 to 1.01). The third was past pneumonia (1.16, 95% CI 1.03 to 1.29). Current versus former smoking showed no statistically significant association. The authors concluded that the predictors had only low explanatory power. They noted that people who dropped out before the consultation could not be included, so the sample may lean toward healthier people.

We wrote this plain-English summary from the full article with the help of AI, and a second AI model checked each sentence against the article. It is not the authors' wording; the abstract and the article are their own account.

What the study did and found

• Design: Cross-sectional secondary analysis of routinely collected medical and administrative data, using exploratory binary logistic regression
• Who was studied: Insured people in Germany eligible for the DGUV lung cancer screening programme (EVA-LCS) from 2014 to mid-2024. Of 26,353 eligible individuals, 16,822 (63.8%) attended at least the initial medical consultation. 14,491 met the study's inclusion criteria (97% male, average age 67 years). Programme inclusion criteria were at least 10 years of occupational asbestos dust exposure starting before 1985, at least 30 pack years of tobacco use, and age 55 or older.
• Exposure: Occupational asbestos dust exposure, averaging 21.2 calendar years (median 20) among study participants, plus an average of 43.0 pack years of smoking. 42.6% were active smokers. The most common former field of work was insulation. No fiber types are named.
• Main results:
  • Of the 14,491 study participants who had their initial medical consultation, 10,714 (73.9%) went on to have an LDCT scan.
  • In the regression model (n = 14,020), three of 13 predictors were statistically significantly associated with having an LDCT scan. Age: OR 0.96 per year, 95% CI 0.95 to 0.97, p less than 0.001, meaning younger participants were more likely to be scanned. Years of asbestos exposure: OR 1.01, 95% CI 1.00 to 1.01, p = 0.011. Past pneumonia: OR 1.16, 95% CI 1.03 to 1.29, p = 0.011.
  • The model explained little of the variation in who had a scan (Nagelkerke R2 = 0.026), which the authors rated as low.
  • Smoking status (current vs. former smoker) was not statistically significantly associated with having a scan (OR 0.97, p = 0.432). Neither were sex, current symptoms, or pre-existing lung conditions other than pneumonia.
  • Pack years were dropped from the model because the linearity assumption was close to failing. Current versus former smoking status was used instead.
• The authors' conclusion: The authors concluded that the predictors had only low explanatory power. They said further research should include non-participants and add theoretically grounded predictors.
• Limitations the authors state:
  • The data were administrative routine data, which limited the choice of variables, data quality and the testing of specific hypotheses.
  • Eligible people who opted out before the medical consultation could not be included.
  • As a result, the sample may be biased toward healthier individuals.
  • Some factors were rare in the data (for example symptoms, pre-existing conditions and female sex), which might have slightly biased the results.
  • LDCT scans done outside the programme were not recorded, so results on symptoms and pre-existing conditions could be slightly biased.
  • There was no information on how long people had stopped smoking.
  • The analyses cannot show how individuals and physicians actually decided whether to scan.

Topics

The National Library of Medicine indexes this study under subjects our site covers: Lung cancer.

Medical terms in this study, explained in our Lexicon: Low-Dose CT Screening, Lung Cancer, Occupational Exposure.

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