From smoking scenarios to lung cancer mortality: sequential bayesian APC projections

Manuel Flores ()

No 426, Documentos de Trabajo (working papers) from Department of Economics - dECON

Abstract: This working paper develops a sequential Bayesian age–period–cohort (APC) framework for projecting cancer mortality under alternative smoking scenarios using aggregate population data. The framework estimates smoking-prevalence surfaces, reconstructs cohort smoking states, maps effective exposure into incidence through external relative risks and risk-reversion schedules, and translates projected incidence into mortality using post-diagnosis mortality schedules. Simulation results evaluate recovery of known scenario effects, and the empirical illustration uses lung-cancer data from Uruguay with an extension to nine smoking-related cancer sites.

Keywords: age–period–cohort models; Bayesian methods; cancer projection; smoking; epidemiological modelling. (search for similar items in EconPapers)
JEL-codes: C11 C53 I10 I18 (search for similar items in EconPapers)
References: Add references at CitEc
Citations:

Downloads: (external link)
https://hdl.handle.net/20.500.12008/56099 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ude:wpaper:0426

Access Statistics for this paper

More papers in Documentos de Trabajo (working papers) from Department of Economics - dECON Contact information at EDIRC.
Bibliographic data for series maintained by Andrea Doneschi () and ().