Related Experiment Video
Updated: Mar 2, 2026

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
Modelling lung cancer mortality rates from smoking prevalence: Fill in the gap
Juan Carlos Martín-Sánchez1, Usama Bilal2, Ramon Clèries3
1Group of Evaluation of Health Determinants and Health Policies, Universitat Internacional de Catalunya, Sant Cugat del Vallès, Spain.
Background:
The objective of this study is to estimate the gap between smoking prevalence and lung cancer mortality and provide predictions of lung cancer mortality based on previous smoking prevalence.
Materials And Methods:
We used data from the Spanish National Health Surveys (2003, 2006 and 2011) to obtain information about tobacco use and data from the Spanish National Statistics Institute to obtain cancer mortality rates from 1980 to 2013. We calculated the cross-correlation among the historical series of smoking prevalence and lung cancer mortality rate (LCMR) to estimate the most likely time gap between both series. We also predicted the magnitude and timing of the LCMR peak.
Results:
All cross-correlations were statistically significant and positive (all above 0.8). For men, the most likely gap ranges from 20 to 34 years. The age-adjusted LCMR increased by 3.2 deaths per 100,000 people for every 1 unit increase in the smoking prevalence 29 years earlier. The highest rate for men was observed in 1995 (55.6 deaths). For women, the most likely gap ranges from 10 to 37 years. The age-adjusted LCMR increased by 0.28 deaths per 100,000 people for every 1 unit increase in the smoking prevalence 32 years earlier. The maximum rate is expected to occur in 2026 (10.3 deaths).
Conclusion:
The time series of prevalence of tobacco smoking explains the mortality from lung cancer with a distance (or gap) of around 30 years. According to the lagged smoking prevalence, the lung cancer mortality among men is declining while in women continues to rise (maximum expected in 2026).
Related Concept Videos
Statistical Methods for Analyzing Epidemiological Data
Cancer Survival Analysis
Actuarial Approach
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Life Tables
Observational Studies
There are three types of observational studies – Prospective, retrospective, and cross-sectional.
Prospective Study
Prospective studies, also known as longitudinal or cohort studies, are carried out by collecting future data from groups sharing similar characteristics. One...
Assumptions of Survival Analysis

