Literature DB >> 15044411

Multiple cancer sites incidence rates estimation using a multivariate Bayesian model.

Renato M Assunção1, Mônica S M Castro.   

Abstract

BACKGROUND: In Brazil cancer incidence rates have to be estimated from occasional surveys, due to lack of continuous cancer registries. Many estimated rates have very large variances, because only few years of data were collected. When dealing with a single cancer site, it is possible to adopt a Bayesian method which borrows information about the cancer rates from other geographical areas to estimate the cancer rate in a given area. We suggest an additional improvement to this method which explores the correlation between multiple cancer sites rates in a same area and in different areas.
METHODS: Our method works with a multivariate vector of different cancer sites rates in several areas and it borrows information from both, across geographical areas and across different cancer sites. We applied our method to data from a survey carried out in 18 Brazilian cities in São Paulo State in 1991. We estimated age and sex indirect standardized incidence rates for the six most common cancers in men and women, and calculated the 95% interval estimation for the incidence rates.
RESULTS: The usual indirect standardized incidence rates had very large confidence intervals for many cancers and cities due to small expected number of cases. The use of the multivariate Bayesian method led to more precise estimates.
CONCLUSIONS: More precise age-standardized cancer incidence rates can be calculated using data from other cancers. The method is conceptually simple, easy to perform, has low cost, and can improve substantially the estimation of cancer incidence and other vital rates.

Entities:  

Mesh:

Year:  2004        PMID: 15044411     DOI: 10.1093/ije/dyh040

Source DB:  PubMed          Journal:  Int J Epidemiol        ISSN: 0300-5771            Impact factor:   7.196


  6 in total

1.  Evaluating geographic variation in type 1 and type 2 diabetes mellitus incidence in youth in four US regions.

Authors:  Angela D Liese; Andrew Lawson; Hae-Ryoung Song; James D Hibbert; Dwayne E Porter; Michele Nichols; Archana P Lamichhane; Dana Dabelea; Elizabeth J Mayer-Davis; Debra Standiford; Lenna Liu; Richard F Hamman; Ralph B D'Agostino
Journal:  Health Place       Date:  2010-01-15       Impact factor: 4.078

2.  Joint Disease Mapping of Two Digestive Cancers in Golestan Province, Iran Using a Shared Component Model.

Authors:  Parisa Chamanpara; Abbas Moghimbeigi; Javad Faradmal; Jalal Poorolajal
Journal:  Osong Public Health Res Perspect       Date:  2015-02-19

3.  Joint spatial mapping of childhood anemia and malnutrition in sub-Saharan Africa: a cross-sectional study of small-scale geographical disparities.

Authors:  Rasheed A Adeyemi; Temesgen Zewotir; Shaun Ramroop
Journal:  Afr Health Sci       Date:  2019-09       Impact factor: 0.927

4.  Spatial Co-Morbidity of Childhood Acute Respiratory Infection, Diarrhoea and Stunting in Nigeria.

Authors:  Olamide Seyi Orunmoluyi; Ezra Gayawan; Samuel Manda
Journal:  Int J Environ Res Public Health       Date:  2022-02-06       Impact factor: 3.390

5.  Joint disease mapping using six cancers in the Yorkshire region of England.

Authors:  Amy Downing; David Forman; Mark S Gilthorpe; Kimberley L Edwards; Samuel Om Manda
Journal:  Int J Health Geogr       Date:  2008-07-28       Impact factor: 3.918

6.  Joint Spatio-Temporal Shared Component Model with an Application in Iran Cancer Data

Authors:  Behzad Mahaki; Yadollah Mehrabi; Amir Kavousi; Volker J Schmid
Journal:  Asian Pac J Cancer Prev       Date:  2018-06-25
  6 in total

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