2022
DOI: 10.1186/s12885-022-10187-1
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Trends and predictions of lung cancer incidence in Jiangsu Province, China, 2009–2030: a bayesian age-period-cohort modelling study

Abstract: Background Lung cancer is currently the most frequent cancer in Jiangsu Province, China, and the features of cancer distribution have changed continuously in the last decade. The aim of this study was to analyse the trend of the incidence of lung cancer in Jiangsu from 2009 to 2018 and predict the incidence from 2019 to 2030. Methods Data on lung cancer incidence in Jiangsu from 2009 to 2018 were retrieved from the Jiangsu Cancer Registry. The aver… Show more

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Cited by 6 publications
(4 citation statements)
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“…No presente estudo, homens e ex-consumidores de tabaco apresentaram a maior proporção de câncer de pulmão. Estes resultados são semelhantes aos encontrados no Marrocos (Erefai et al, 2022) e estão de acordo com o que foi relatado pelo Estudo da Carga Global de Doenças (Chen;Mo;Yi, 2022), bem como em estudo realizado em Jiangsu, na China (Jiang et al, 2022). Acredita-se que uma das razões para as diferenças entre os sexos sejam as taxas de tabagismo entre homens e mulheres.…”
Section: Discussionunclassified
“…No presente estudo, homens e ex-consumidores de tabaco apresentaram a maior proporção de câncer de pulmão. Estes resultados são semelhantes aos encontrados no Marrocos (Erefai et al, 2022) e estão de acordo com o que foi relatado pelo Estudo da Carga Global de Doenças (Chen;Mo;Yi, 2022), bem como em estudo realizado em Jiangsu, na China (Jiang et al, 2022). Acredita-se que uma das razões para as diferenças entre os sexos sejam as taxas de tabagismo entre homens e mulheres.…”
Section: Discussionunclassified
“…In Bayesian inference, uncertainty about all unknown parameters is considered, and for this study, non-informative priors were employed [ 21 ]. The BAPC model is generally based on Poisson regression models, treating the age effect as a row variable, the period effect as a column variable, and the cohort effect as a cross-effect of the age effect and the period effect to construct a three-factor model of age, period, and cohort [ 22 ]. Integrated nested Laplacian approximation (INLA) was employed within the Bayesian age period cohort model to approximate the edge posterior distribution.…”
Section: Methodsmentioning
confidence: 99%
“…BAPC models are generally based on Poisson regression models, in which age effects are considered as row variables, period effects as column variables, and cohort effects as crosseffects of age and period effects. Three-factor models of age, period, and cohort were constructed and analyzed, and Markov chain Monte Carlo (MCMC) simulations were performed [13]. The population data required for the projections in this study are derived from the GBD2019 demographic data on the number of people by gender in China and India (https://ghdx.healthdata.org/record/ihme-data/global-populationforecasts-2017-2100).…”
Section: Bayesian Age-period-cohort Modelmentioning
confidence: 99%