Evidence map›Paper›PMID 16205695›Full record

ArticleBritish journal of cancer2005

Modelling the impact of population-based cytologic screening on cervical cancer incidence and mortality in Hong Kong: an age--period--cohort approach.

P P S Woo, T Q Thach, S T B Choy, S M McGhee, G M Leung

Abstract read
In one paragraph

Article in British journal of cancer, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
  2. Article
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  5. Article
  6. Age-period-cohort models in cancer surveillance research: ready for prime time?Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2011
    Article
  7. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

P P S WooDepartment of Community Medicine and School of Public Health, University of Hong Kong, 21 Sassoon Road, Pokfulam, Hong Kong, China.
T Q Thach
S T B Choy
S M McGhee
G M Leung

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cervical cancer incidence and mortality statistics in Hong Kong during 1972-2001 were examined to estimate the potential number of cancer cases that can be averted and years of life saved after the launch of an organised, population-based cytologic screening recall programme in 2004 with projections to 2016. Incidence rates under the status quo of opportunistic screening were projected by an age-period-cohort model, using maximum likelihood and Bayesian methods. Modelled rates were translated into numbers of cancer cases and deaths using mid-year population figures and age-period-specific mortality to incidence ratios. We applied International Agency for Research on Cancer risk reduction estimates for different screening strategies to these base case figures to estimate the number of incident cancers potentially averted and years of life saved attributable to organised screening incremental to the current status quo. The estimated numbers of cases projected to be preventable by the maximum likelihood (Bayesian) approach from 2002 to 2016 were 4226 (4176), 3778 (3728) and 2334 (2287) with organised screening every 1, 3 and 5 years, compared to haphazard screening currently. Correspondingly, 33,000 (32,800), 29,500 (29,300) and 18,200 (17,900) years of life could potentially be saved.

Indexed as

Mass ScreeningModels, TheoreticalAdultAgedAge DistributionCohort StudiesFemaleHong KongHumansIncidenceMiddle AgedMortalityUterine Cervical DysplasiaUterine Cervical Neoplasms

Identifiers

PMID16205695
PMCPMC2361667

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.