Literature DB >> 12407687

Estimation and projections of cancer prevalence from cancer registry data.

Arduino Verdecchia1, Giovanni De Angelis, Riccardo Capocaccia.   

Abstract

A method, PIAMOD (Prevalence, Incidence, Analysis MODel), which allows the estimation and projection of cancer prevalence patterns by using cancer registry incidence and survival data is presented. As a first step the method involves the fit of incidence data by an age, period and cohort model to derive incidence projections. Prevalence is then estimated from modelled incidence and survival estimates. Cancer mortality is derived as a third step from modelled incidence, prevalence and survival. An application to female breast cancer is given for the Connecticut State by using data from the Connecticut Tumor Registry (CTR), 1973-1993. The age, period and cohort model fitted incidence quite well and allowed us to derive long-term projections up to 2030. Patients' survival was also projected to future years according to a scenario approach based on two extreme hypotheses: steady, that is, no more improvements after 1993 (conservative), and continuously improving at the same rate as during the observation period. Age-standardized estimated incidence shows a changing trend around the year 2005, when it starts decreasing. Age-standardized prevalence is expected to increase and change trend at a later date. Breast cancer mortality is projected as decreasing, as the combined result of no further increase in incidence and improving cancer patients' survival. An easy-to-use PIAMOD software package, on which work is in progress, will be made available to individual cancer registries and/or health planning institutions or authorities once it is developed. The use of the PIAMOD method for cancer registries will allow them to provide results of paramount importance for the whole community involved in the assessment of future disease burden scenarios in an evolving society. Copyright 2002 John Wiley & Sons, Ltd.

Entities:  

Mesh:

Year:  2002        PMID: 12407687     DOI: 10.1002/sim.1304

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  22 in total

1.  Modelling to estimate future trends in cancer prevalence.

Authors:  Francesca Fiorentino; Jacob Maddams; Henrik Møller; Martin Utley
Journal:  Health Care Manag Sci       Date:  2011-02-23

2.  Projections of the cost of cancer care in the United States: 2010-2020.

Authors:  Angela B Mariotto; K Robin Yabroff; Yongwu Shao; Eric J Feuer; Martin L Brown
Journal:  J Natl Cancer Inst       Date:  2011-01-12       Impact factor: 13.506

3.  Detection of CD133 expression in U87 glioblastoma cells using a novel anti-CD133 monoclonal antibody.

Authors:  Dongyang Wang; Yuanxu Guo; Yanqing Li; Weiling Li; Xiaojing Zheng; Haibin Xia; Qinwen Mao
Journal:  Oncol Lett       Date:  2015-03-27       Impact factor: 2.967

4.  Complete prevalence of malignant primary brain tumors registry data in the United States compared with other common cancers, 2010.

Authors:  Adah S Zhang; Quinn T Ostrom; Carol Kruchko; Lisa Rogers; David M Peereboom; Jill S Barnholtz-Sloan
Journal:  Neuro Oncol       Date:  2017-05-01       Impact factor: 12.300

5.  Cancer survivors in the United States: prevalence across the survivorship trajectory and implications for care.

Authors:  Janet S de Moor; Angela B Mariotto; Carla Parry; Catherine M Alfano; Lynne Padgett; Erin E Kent; Laura Forsythe; Steve Scoppa; Mark Hachey; Julia H Rowland
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2013-03-27       Impact factor: 4.254

6.  Projections of cancer prevalence by phase of care: a potential tool for planning future health service needs.

Authors:  Xue Qin Yu; Mark Clements; Dianne O'Connell
Journal:  J Cancer Surviv       Date:  2013-08-07       Impact factor: 4.442

7.  Anticipating the "Silver Tsunami": Prevalence Trajectories and Comorbidity Burden among Older Cancer Survivors in the United States.

Authors:  Shirley M Bluethmann; Angela B Mariotto; Julia H Rowland
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2016-07       Impact factor: 4.254

8.  Uncovering symptom progression history from disease registry data with application to young cystic fibrosis patients.

Authors:  Jun Yan; Yu Cheng; Jason P Fine; Huichuan J Lai
Journal:  Biometrics       Date:  2009-06-12       Impact factor: 2.571

9.  Estimating the number of colorectal cancer patients treated with anti-tumour therapy in 2015: the analysis of the Czech National Cancer Registry.

Authors:  Tomáš Pavlík; Ondřej Májek; Jan Mužík; Jana Koptíková; Lubomír Slavíček; Jindřich Fínek; David Feltl; Rostislav Vyzula; Ladislav Dušek
Journal:  BMC Public Health       Date:  2012-02-10       Impact factor: 3.295

10.  Projections of cancer prevalence in the United Kingdom, 2010-2040.

Authors:  J Maddams; M Utley; H Møller
Journal:  Br J Cancer       Date:  2012-08-14       Impact factor: 7.640

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.