Bayesian Age-Period-Cohort Model of Lung Cancer Mortality

Tharu, Bhikhari P., University of South Florida
Kafle, Ram C., Sam Houston State University

2015

2010-2019

OBJECTIVES: Cancer is the second most common cause of death in the US. Lung cancer is the leading cause of cancer deaths. An analysis of the epidemiological situation, as a support tool for planning of public health, requires an understanding of lung cancer mortality rates. Mortality rate temporal trends may be assessed by using data derived from age (time between birth and death), period (Time of death) and birth cohorts (time of birth) of patients with lung cancer. METHODS: Data regarding lung cancer mortality and incidence rates in the US from 1971 to 2010 were used in the study and obtained from the National Cancer Institute. Age-period-cohort (APC) models are widely used for studying time trends of disease incidence or mortality. Model identifiability is less of a problem with the Bayesian APC models. Our study applied the Bayesian APC model fitted with histogram smoothing prior decomposing mortality rates into age, period, and birth-cohort. RESULTS: Based on the data from the National Cancer Institute it was determined that as age increased, mortality rates from lung cancer increased more rapidly for individuals over the age of 52. The average annual lung cancer deaths for individuals over the age of 52 appear to be 28 deaths and, there were 47 deaths for individuals who were over 57 years old. There was a total of 157 deaths annually for individuals who were over 82 years old. The mortality of younger cohorts was lower than older cohorts. The relative risk of lung cancer lowered from period 1993 to recent periods. CONCLUSION: The fitted Bayesian Age-Period-Cohort model, with histogram smoothing prior, is capable of explaining the mortality rate of lung cancer. The reduction in carcinogens in cigarettes and the increase in smoking cessation from around 1960 may have led to the decreasing trend of lung cancer mortality that has taken place, since period 1993. KEYWORDS: Histogram smoothing, Multinomial, Multivariate normal, Logit parameters, Mortality

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Epidemiology Biostatistics and Public Health - 2015, Volume 12, Number 3

Department of Mathematics and Statistics,Department of Mathematics and Statistics, Huntsville

http://ebph.it/article/viewFile/11444/10636

http://hdl.handle.net/20.500.12322/sc.fac.pubs:2015_tharu_bhikhaari

http://rightsstatements.org/vocab/InC/1.0/