Literature DB >> 28600946

The completeness and timeliness of cancer registration and the implications for measuring cancer burden.

Conan Donnelly1, Victoria Cairnduff2, Jingwen Jessica Chen3, Therese Kearney2, Deirdre Fitzpatrick2, Colin Fox2, Anna Gavin2.   

Abstract

BACKGROUND: Population based cancer registration provides a critical role in disease surveillance in terms of incidence, survival, cancer cluster investigations and prevalence trends, and therefore high levels of completeness and timeliness are required. This study estimates completeness and variation between early and late registrations in the N. Ireland Cancer Registry (NICR) and assesses the implications for reporting cancer incidence and for registry-based research.
METHODS: Two main approaches assessed completeness. For the period 2010-2012, incidence reported in the first year of data publication was compared to incidence reported in subsequent years until 2015. Demographic characteristics and survival of incident cases ascertained before the first publication year were compared to those ascertained in subsequent years. The flow method approach was used to estimate completeness annually after the incident year.
RESULTS: Overall incidence for all cancers increased between the first year of data publication and subsequent years up to 2015, irrespective of year of diagnosis. Late registrations had poorer survival. The flow method approach estimated the completeness of case ascertainment of NICR data to be 96% complete at five years for all cancers combined.
CONCLUSION: The estimated completeness levels for the NICR are comparable to other high quality cancer registries internationally. While data timeliness has little impact on incidence estimates, delays in registration may have implications for specific research studies into incidence and survival. This means that improvements in the timeliness of reporting should be a target for all registries but not at the expense of completeness.
Copyright © 2017. Published by Elsevier Ltd.

Entities:  

Keywords:  Cancer registry completeness; Data timeliness; Incidence; Validation

Mesh:

Year:  2017        PMID: 28600946     DOI: 10.1016/j.canep.2017.05.007

Source DB:  PubMed          Journal:  Cancer Epidemiol        ISSN: 1877-7821            Impact factor:   2.984


  4 in total

1.  Research Strategies for Low-Survival Cancers.

Authors:  Caroline Conway; Denis M Collins; Amanda McCann; Kellie Dean
Journal:  Cancers (Basel)       Date:  2021-01-30       Impact factor: 6.639

2.  Conceptual Framework to Guide Early Diagnosis Programs for Symptomatic Cancer as Part of Global Cancer Control.

Authors:  Minjoung Monica Koo; Karla Unger-Saldaña; Amos D Mwaka; Marilys Corbex; Ophira Ginsburg; Fiona M Walter; Natalia Calanzani; Jennifer Moodley; Greg P Rubin; Georgios Lyratzopoulos
Journal:  JCO Glob Oncol       Date:  2021-01

3.  The Intervention of Data Mining in the Allocation Efficiency of Multiple Intelligent Devices in Intelligent Pharmacy.

Authors:  Xiaohua Li; Benren Tan; Jinkun Zheng; Xiaomei Xu; Jian Xiao; Yanlin Liu
Journal:  Comput Intell Neurosci       Date:  2022-08-22

Review 4.  Indicators of Data Quality at the Cancer Registry Zurich and Zug in Switzerland.

Authors:  Miriam Wanner; Katarina L Matthes; Dimitri Korol; Silvia Dehler; Sabine Rohrmann
Journal:  Biomed Res Int       Date:  2018-06-13       Impact factor: 3.411

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.