Scenarios of future lung cancer incidence by educational level: Modelling study in Denmark.
OBJECTIVE
To model future trends in lung cancer incidence in Denmark by education under different scenarios for cigarette smoking.


METHODS
Lung cancer incidence until 2050 was modelled using Prevent software. We estimated lung cancer incidence under a baseline scenario and under four alternative scenarios for smoking reduction: decreasing initiation rates among the young, increasing cessation rates among smokers, a scenario combining both changes and a levelling-up scenario in which people with low and medium levels of education acquired the smoking prevalence of the highly educated. Danish National Health Interview Surveys (1987-2005) and cancer registry data combined with individual education status from Statistics Denmark were used for empirical input.


RESULTS
Under the baseline scenario, lung cancer rates are expected to decrease for most educational groups during the next few decades, but educational inequalities will increase further. Under the alternative scenarios, an additional decrease in lung cancer rates will be observed from 2030 onwards, but only from 2050 onwards it will be observed under the initiation scenario. The cessation and the combined scenarios show the largest decrease in lung cancer rates for all educational groups. However, in none of these scenarios would the relative differences between educational groups be reduced. A modest decrease in these inequalities will be observed under the levelling-up scenario.


CONCLUSIONS
Our analyses show that relative inequalities in lung cancer incidence rates will tend to increase. They may be reduced to a small extent if the smoking prevalence of people with a low level of education was to converge towards those more highly educated people. An important decrease in lung cancer rates will be observed in all educational groups, however, especially when focusing on both initiation and cessation strategies.
Menvielle
Gwenn
G
Inserm U1018, Center for Epidemiology and Population Health, Occupational and Social Determinants of Health, Villejuif, France. Gwenn.Menvielle@inserm.fr
Soerjomataram
Isabelle
I
de Vries
Esther
E
Engholm
Gerda
G
Barendregt
Jan J
JJ
Coebergh
Jan Willem
JW
Kunst
Anton E
AE
D000328:Adult; D017677:Age Distribution; D000368:Aged; D003198:Computer Simulation; D003718:Denmark / Q000453:epidemiology; D004522:Educational Status; D005260:Female; D005544:Forecasting; D006801:Humans; D015994:Incidence; D008175:Lung Neoplasms / Q000453:epidemiology* / Q000517:prevention & control; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D017678:Sex Distribution; D012907:Smoking / Q000453:epidemiology* / Q000639:trends; D016540:Smoking Cessation / Q000706:statistics & numerical data; D000074606:Smoking Prevention; D055815:Young Adult