Biostatistics

Bayesian Age-Period-Cohort Model of Lung Cancer Mortality


Abstract


Background The objective of this study was to analyze the time trend for lung cancer mortality in the population of the USA by 5 years based on most recent available data namely to 2010. The knowledge of the mortality rates in the temporal trends is necessary to understand cancer burden.
Methods Bayesian Age-Period-Cohort model was fitted using Poisson regression with histogram smoothing prior to decompose mortality rates based on age at death, period at death, and birth-cohort.
Results Mortality rates from lung cancer increased more rapidly from age 52 years. It ended up to 325 deaths annually for 82 years on average. The mortality of younger cohorts was lower than older cohorts. The risk of lung cancer was lowered from period 1993 to recent periods.
Conclusions The fitted Bayesian Age-Period-Cohort model with histogram smoothing prior is capable of explaining mortality rate of lung cancer. The reduction in carcinogens in cigarettes and increase in smoking cessation from around 1960 might led to decreasing trend of lung cancer mortality after calendar period 1993.


Full Text:

PDF


DOI: https://doi.org/10.2427/11444

NBN: http://nbn.depositolegale.it/urn%3Anbn%3Ait%3Aprex-15677

References



Article Metrics

Metrics Loading ...

Metrics powered by PLOS ALM

Refbacks

  • There are currently no refbacks.


Copyright (c) 2015 Epidemiology, Biostatistics and Public Health

We use cookies to ensure that we give you the best experience on our website. If you continue to use this site we will assume that you are happy with it (Read more).
Ok


EBPH Epidemiology, Biostatistics and Public Health | ISSN 2282-0930

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.