Bayesian Age-Period-Cohort Model of Lung Cancer Mortality
<p><em>Background</em> 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 underst...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Milano University Press
2015-09-01
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| Series: | Epidemiology, Biostatistics and Public Health |
| Online Access: | http://ebph.it/article/view/11444 |
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| Summary: | <p><em>Background</em> 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.<br /><em>Methods</em> 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.<br /><em>Results</em> 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.<br /><em>Conclusions</em> 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.</p> |
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| ISSN: | 2282-0930 |