The three data sources included: 1) the Lausanne Cohort 65+ (Lc65+), designed to advance epidemiological and public health research on ageing, with a focus on the determinants, manifestations, and outcomes of frailty; 2) the CoLaus|PsyCoLaus (CoLaus) study, initiated in 2003 to improve understanding of the epidemiology and genetic determinants of cardiovascular risk factors and diseases in the Swiss population; and 3) the 2022 Swiss Health Survey (SHS), which provided representative data on health behaviors, lifestyle, and healthcare use among residents aged 15 years and older in Switzerland, using data from the canton of Vaud (of which Lausanne is the capital). These cohorts were selected for their data availability and complementarity, enabling a comprehensive assessment of screening eligibility [21, 22].

The Lc65+ study is an ongoing longitudinal cohort of community-dwelling adults aged 65 years and older living in Lausanne [21].

The Lc65+ study comprises three samples recruited at 5-year intervals (2004, 2009, and 2014) through simple random sampling from the population register of the canton of Vaud, Switzerland. Eligible participants were community-dwelling residents of Lausanne aged 65–69 years at the beginning of each recruitment year, while institutionalized individuals and those unable to respond due to cognitive impairment were excluded. Invitations were mailed with study information and a questionnaire, followed by two reminders, and participants were subsequently invited to in-person assessments at the study center. For the present analysis, we used data from the third recruitment wave (2014), which included 1,678 participants (participation rate 45.9%) after exclusion of 141 ineligible individuals who met the study exclusion criteria, namely, institutionalization or inability to respond due to cognitive impairment.

Variables available for subsequent comparisons between eligible and non-eligible participants included sex, age, country of birth, monthly income, indicators of financial vulnerability (financial assistance, financial difficulties, health insurance subsidy, or additional welfare), smoking status (current, former or never), alcohol problems, and major comorbidities (overweight, hypertension, hypercholesterolemia, coronary heart disease, stroke, diabetes, previous cancer, depression, arthrosis, or none of the above). Self-reported fatigue over the previous 4 weeks (no at all, a little, a lot) and educational level (from basic compulsory schooling to university or college degree) were also assessed.

The Lc65+ study protocol (no. 19/04), along with periodic updates, received approval from the Ethics Committee for Human Research of the Canton of Vaud. All participants provided written informed consent.

A detailed description of the recruitment procedure and follow-up process for the CoLaus study has been previously published [22]. Briefly, between 2003 and 2006, a representative, non-stratified sample of residents aged 35–75 years was recruited. The complete list of city residents (n = 56,694 in 2003) was obtained from the municipal population register, which includes all individuals residing in Lausanne for more than 90 days. A simple, non-stratified random sample of 19,830 residents (35% of the source population) was drawn, and invitation letters were mailed between June 2003 and May 2006. After two reminder letters and follow-up phone calls, 8,121 individuals agreed to participate, corresponding to a participation rate of 41% among those sampled and 57% among eligible responders. Of these, 6,738 attended the clinic and completed the baseline assessment; 549 (8.1%) non-Caucasian participants were excluded, resulting in a final baseline sample of 6,189 participants meeting all inclusion criteria.

The baseline examination was conducted between June 2003 and May 2006, followed by follow-up waves held from April 2009 to September 2012 (n = 5,064) and from May 2014 to April 2017 (n = 4,881). The median follow-up duration was 5.4 years (mean 5.6, range 4.5–8.8) for the first wave and 10.7 years (mean 10.9, range 8.8–13.6) for the second.

As the initial consent did not cover this type of analysis, we used data from the second follow-up, conducted between May 2014 and April 2017.

Variables available for subsequent comparisons between eligible and non-eligible participants included sex, age, and country of birth, as well as smoking status and alcohol consumption, along with clinical parameters and self-reported health measures. Clinical parameters comprised body mass index (BMI), hypertension, hypercholesterolemia, metabolic syndrome, diabetes, and sleep apnea. Additional health indicators included abdominal obesity (WHO definition), HDL and LDL cholesterol, triglycerides, and systolic and diastolic blood pressure. Self-reported measures encompassed overall health rating (very good, good, bad, or very bad), perceived fatigue during the previous 4 weeks (not at all, a little, or a lot), self-care difficulty (no difficulty, some difficulty, or requiring help), and weight loss over the previous 12 months.

The SHS are nationwide cross-sectional surveys conducted every 5 years since 1992 by the Federal Statistical Office. They collect nationally representative data on residents aged 15 years and older, including information on health status, lifestyle behaviors, substance use, physical activity, health insurance, and healthcare utilization. Data are gathered via computer-assisted telephone interviews conducted by trained personnel.

The SHS did not collect exact quit dates but asked participants to indicate the time since smoking cessation using predefined categories, with the longest being “10 years or more.” Additionally, pack-year history is unavailable for former smokers in the SHS dataset.

For this study, we used tobacco-related data from the 2022 SHS, restricted to respondents residing in the canton of Vaud. The use of anonymized SHS data is granted upon request by the Federal Statistical Office and does not require ethics committee approval.

Variables available for analyses comparing eligible and non-eligible participants included sociodemographic, lifestyle, and health-related characteristics. These comprised age, sex, nationality, civil status, household income, household size, education, employment status and health indicators. Health indicators comprised self-rated health, long-term illness, and self-reported comorbidities (hypertension, high blood glucose, hypercholesterolemia, depression, stroke, cancer, asthma, chronic bronchitis or chronic obstructive pulmonary disease [COPD], and arthritis), as well as overweight status, smoking behavior (current or former), and alcohol consumption risk level (abstinent, low, medium, or high). Regional variation analyses were also performed within the canton of Vaud, comparing rural, semi-rural, and urban areas, with stratification by sex.

The prevalence of individuals meeting lung cancer screening eligibility criteria was estimated separately for each dataset, with corresponding 95% confidence intervals (CIs). Confidence intervals were calculated based on observed proportions and sample sizes within each cohort. Analyses of SHS data incorporated survey weights provided by the Federal Statistical Office to ensure representativeness of the canton of Vaud population.

For the Lc65+ cohort, smoking-related variables were sufficiently complete to compute eligibility without additional assumptions.

In the CoLaus dataset, smoking-related variables were collected differently for current and former smokers, resulting in structurally incomplete information on either smoking duration or daily consumption. To derive complete indicators of tobacco exposure, including pack-years,a targeted single-imputation strategy was applied. For participants who were former smokers, information on smoking duration was available whereas daily consumption was not systematically collected; for participants who were currently smoking, the inverse applied. Missing value for the age at which participants who were currently smoking had started was estimated based on the distribution observed among participants who were former smokers. This distribution was skewed, with values concentrated around the median, and formal normality tests (e.g., sktest) confirmed a significant deviation from normality. To minimize the influence of extreme values and ensure a conservative, robust approach, the median age of initiation (18 years) observed among former smokers was used as a single imputed value for all current smokers with missing data. This strategy aimed to reduce potential bias while maintaining comparability between smoking-status groups. The variable representing the average number of cigarette packs smoked per day had a high proportion of missing data among participants who were former smokers. To preserve the coherence of tobacco exposure indicators and maintain empirical variability, a stochastic imputation approach was applied. Specifically, bootstrap sampling with replacement was performed using the observed distribution of the average number of cigarette packs smoked per day among participants who were currently smoking, whose data were complete and considered representative of all smokers. Each former smoker with missing data was assigned a randomly selected value drawn from this empirical distribution, truncated at three packs per day, the maximum observed among current smokers. All imputations were implemented without modifying of observed values, allowing consistent calculation of smoking exposure indicators, including cumulative duration, daily consumption, and pack-years.

Some assumptions were made for missing values in the SHS. When smokers did not report their daily consumption of a specific tobacco product (cigarette, pipe, cigarillo, cigar, etc.) this value was assumed to be zero. Missing data for other eligibility-related variables were rare (0.06% for smoking status; 2.6% for number years of smoking; 0.06% for time since quitting) and simply treated as non-eligible. For variables used in bivariate comparisons, missing values were grouped under an “unknown” category.

In each of the three datasets, participants were classified as eligible or non-eligible for lung cancer screening based on the 2021 USPSTF criteria. Within each cohort, eligible and non-eligible participants were compared for clinical and sociodemographic characteristics. Because data collection methods varied across sources, comparisons were performed separately for each dataset, as specified in the corresponding cohort descriptions.

Continuous variables were summarized as means with standard deviations or medians with interquartile ranges, as appropriate. Categorical variables were described using frequencies and percentages. For univariate analyses, associations between categorical variables were assessed using the chi-square test or Fisher’s exact test, depending on cell counts. Differences in means were evaluated using the unpaired t-test for normally distributed data, and differences in medians were assessed using the Mann–Whitney U test for non-normally distributed data.

Univariate logistic regression was used to explore associations between screening eligibility and selected covariates.

A p-value <0.05 was considered statistically significant. All analyses were conducted using SAS version 9.4 (SAS Institute, Cary, NC) and R version 4.2.0 (R Foundation for Statistical Computing, Vienna, Austria).

Eligibility for lung cancer screening varied by age across the three datasets (Figure 1; Supplementary Table S1). In Lc65+, which only included individuals aged 65 to 69, the overall eligible fraction was slightly higher than for the other data sources.

In CoLaus, eligibility fraction was highest among participants in their late 50s for both sexes, then declined progressively with age, reaching its lowest value in those aged 75 to 79. A similar pattern was seen in the SHS, although eligibility peaked slightly later, in the 60–64 age group, before decreasing in older age groups.

Across all age groups and datasets, men were consistently more likely to meet the screening criteria than women (Figure 2). In Lc65+, eligibility was slightly, but not statistically significantly higher in men (19.7% vs. 17%). In both CoLaus and SHS, however, the sex difference was more pronounced, with men showing significantly higher rates of eligibility (CoLaus: 17.7% vs. 12.2%, p = 0.011; SHS: 18.3% vs. 11.0%, p = 0.007).

The disparity was most pronounced in the youngest age group (50–54 years) in the SHS, where eligibility among men was nearly three times higher than among women (25.7% vs. 9.3%). Similar, though smaller, gaps were observed in the 55–59-year age group, with higher eligibility among men than women in both CoLaus (24.9% vs. 22.3%) and SHS (18.6% vs. 11.9%). This sex pattern reversed slightly in the oldest participants (75–79 years), with eligibility rates being marginally higher among women than men in both CoLaus (7.4% vs. 5.9%) and SHS (7.2% vs. 5.2%).

Several limitations must be acknowledged. Two datasets were collected several years ago (Lc65+ in 2014 and CoLaus in 2014–2017), and all relied on self-reported smoking data, which is subject to recall bias. In CoLaus, pack-years for current smokers were imputed using the median age at smoking initiation from former smokers, assuming similar distributions between groups; this simplification may reduce precision in eligibility estimates. In the SHS, the cessation window was limited to 10 years, which underestimates eligibility according to USPSTF criteria. Some community-dwelling adults—although technically eligible based on age and smoking history—may not be candidates for curative surgery because of significant comorbidities or impaired pulmonary function. This limitation is difficult to address without objective lung function assessments. Such individuals are often excluded from clinical trials, leaving the benefit of screening in this subgroup uncertain, despite encouraging outcomes reported with ablative radiotherapy [53, 54]. However, this limitation is likely mitigated by the fact that survey participants tend to be somewhat healthier than the general population, especially in the CoLaus cohort that had significant loss to follow-up. This may lead to underestimation of the number of individuals eligible for screening.

Due to the aforementioned data limitations, we could not verify eligibility rates based on individual risk prediction models (such as PLCOm2012 or LLP), neither compare them to the USPSTF eligibility criteria used here. Incorporating such models in future analyses could help better assess their applicability and potential utility in the Swiss context. Crude comparisons of eligibility rates between datasets are also potentially biased due to differing age ranges and inclusion criteria across cohorts, which should be considered when interpreting the results.

Future research should assess whether lung cancer patients in Vaud would have met current screening criteria and explore cost-effectiveness to guide locally adapted protocols. At the same time, the focus may also need to shift toward the thoughtful implementation of a lung cancer screening program—while continuing to monitor eligibility patterns and evaluate whether existing criteria should be adapted. If Switzerland were to move toward implementing lung cancer screening, particular attention should be given to promoting equitable access, especially for higher-risk populations who may face structural barriers to care.

Our study tackles the challenge of lung cancer screening eligibility in a Swiss population—an essential step toward implementing organized screening. Based on the 2021 USPSTF criteria, approximately one in six individuals aged 50 to 79 (14.4%–18.2%) would qualify for screening, most of whom are males and currently smoking. Across three independent data sources, our findings consistently show that eligible individuals face greater health risks and socioeconomic disadvantages. Taken together, these results reveal that a substantial portion of the population could benefit from lung cancer screening, and that their heightened medical and social vulnerability calls for a comprehensive, person-centered approach—combining screening with tobacco cessation support, health promotion, and equitable access to care.