Funded by Health Data Research UK, Ethna is a Research Fellow in Health Economics and a cancer researcher with expertise in screening evaluation, population modelling, and equity analysis. Based at Queen’s University Belfast, working across cancer prevention, digital health, and health-system capacity planning. Her research focuses on translating complex modelling into policy-relevant evidence for cancer screening and early detection.


Background
Population-level smoking prevalence is declining in Northern Ireland, but historic exposure remains high and unevenly distributed. Current life-tables do not differentiate mortality risk by smoking status, limiting accurate estimation of eligible populations and health gains for targeted lung cancer screening. A calibrated smoking-stratified model provides a more realistic basis for planning, equity assessment, and long-term resource allocation.

Objectives
To develop dynamic smoking-adjusted life-tables for Northern Ireland, stratified by age, sex, smoking status, and pack-year history, and to project screening-eligible populations over 25–30 years to support evidence-based lung cancer screening policy.

Methods

• Combined population data, NI smoking prevalence surveys, and Eurobarometer 2017 microdata.
• Estimated age- and sex-specific probabilities of being a current, former, or never-smoker.
• Derived individual pack-year exposure distributions using calibrated models from Eurobarometer microdata.
• Applied smoking-related excess mortality risks to adjust baseline mortality rates, generating life-table transitions for each smoking group.
• Integrated projections with NISRA population estimates to produce forward 25–30-year estimates of screening-eligible cohorts.
• Conducted scenario tests for alternative smoking trends (declining, plateau, rebound).

Results

• Generated the first NI-specific smoking-adjusted life-tables incorporating pack-year distributions.
• Substantial differences emerged between adjusted and unadjusted estimates, with higher mortality among long-term smokers compressing eligibility windows for some cohorts while expanding them for former smokers with persistently elevated risk.
• Dynamic projections show evolving age structures of screening-eligible populations, with notable inequalities by deprivation and sex.
• Scenario modelling demonstrates the extent to which ongoing smoking cessation trends could alter future screening demand and programme efficiency.

Conclusions
Smoking-adjusted life-tables substantially refine estimates of the screening-eligible population and expected benefits of targeted lung cancer screening in Northern Ireland. This model supports capacity planning, equity analysis, and long-term programme sustainability assessments. It provides a robust analytic foundation for integration into cost-effectiveness and distributional modelling.

Implications for Public Health Action
Accurate smoking-stratified population projections are essential to:

• plan implementation pathways for lung cancer screening;
• quantify future workforce and diagnostic capacity needs;
• assess distributional effects on high-risk, deprived groups;
• inform policy decisions on eligibility thresholds and risk-based criteria.