Evidence map›Paper›PMID 42158552›Full record

ArticleBMJ public health2026

Real-world evidence of mammography screening from the Hong Kong Breast Cancer Registry: a cost minimisation analysis of 15 144 patients with breast cancer.

Ziyu Hao, Amy Lam, Yaxin Hu, Cassie Lee, Winnie Yeo, Carol Kwok, Inda Soong, Ting Ying Ng, Joanne Chiu, Miranda Chan and 9 more

Abstract read
In one paragraph

Article in BMJ public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

19 authors.

Ziyu HaoJC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, Hong Kong.ORCID https://orcid.org/0000-0001-5209-9856
Amy LamJC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, Hong Kong.
Yaxin HuJC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, Hong Kong.
Cassie LeeJC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, Hong Kong.
Winnie YeoDepartment of Clinical Oncology, The Chinese University of Hong Kong, Hong Kong, Hong Kong.
Carol KwokDepartment of Clinical Oncology, Princess Margaret Hospital, Hong Kong, Hong Kong.
Inda SoongDepartment of Clinical Oncology, Pamela Youde Nethersole Eastern Hospital, Hong Kong, Hong Kong.
Ting Ying NgTuen Mun Hospital Department of Clinical Oncology, Hong Kong, Hong Kong.
Joanne ChiuDepartment of Medicine, Queen Mary Hospital, Hong Kong, Hong Kong.
Miranda ChanDepartment of Surgery, Kwong Wah Hospital, Hong Kong, Hong Kong.
Sharon ChanDepartment of Surgery, United Christian Hospital, Hong Kong, Hong Kong.
Ting Ting WongHong Kong Breast Cancer Foundation, Hong Kong, Hong Kong.
Lawrence LiHong Kong Breast Cancer Foundation, Hong Kong, Hong Kong.
Wai Ka HungHong Kong Breast Cancer Foundation, Hong Kong, Hong Kong.
Benjamin YipJC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, Hong Kong.
Karen YiuStanley Ho Big Data Decision Analytics Research Centre, The Chinese University of Hong Kong, Hong Kong, Hong Kong.
Chun Chung YauHong Kong Breast Cancer Foundation, Hong Kong, Hong Kong.
Polly CheungHong Kong Breast Cancer Foundation, Hong Kong, Hong Kong.ORCID https://orcid.org/0000-0002-6297-7779
Kelvin K F TsoiJC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, Hong Kong.ORCID https://orcid.org/0000-0001-5580-7686

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Breast cancer poses substantial public health and economic burdens worldwide, particularly in Hong Kong, where incidence has risen fivefold in the last 30 years. While mammography (MMG) screening facilitates prognosis, early cancer detection has the potential to better use limited healthcare resources within a hospital system. Methods: A retrospective cohort study was conducted using data from the Hong Kong Breast Cancer Registry, which included 15 144 cases (13 502 self-detected and 1642 MMG-detected breast cancers) diagnosed between 2006 and 2018. A subgroup of 6359 cases (diagnosed 2006-2011) underwent Kaplan-Meier survival analysis to compare 10-year overall survival between detection methods. Population-based Markov models were developed to simulate 100 000 average-risk women aged 40 and to assess clinical outcomes, treatment costs and cost-minimisation analysis of MMG screening from the healthcare provider's perspective. Results: MMG-detected breast cancers presented significantly earlier at diagnosis, with higher proportions of stage 0 (33.2% vs 5.4%, p<0.001) and stage I (48.3% vs 31.5%, p<0.001) cancers, smaller tumour sizes (mean: 1.3 cm vs 2.3 cm, p<0.001) and less aggressive biological subtypes compared with self-detected cases. 10-year overall survival was notably higher in MMG-detected patients (95.7% vs 88.4%, log-rank p<0.001). Treatment costs per patient were 28.4% lower for MMG-detected vs self-detected cancers, driven primarily by stage downshifting. The Markov model demonstrated that MMG screening starting at age 40 in 100 000 women could prevent 27 932 life-years lost and save $98.8 million in treatment costs compared with self-detection. Benefits decreased if screening commenced at a later age. Conclusions: MMG screening in Hong Kong can significantly reduce mortality and healthcare expenditures due to earlier cancer detection. Policymakers and healthcare providers should consider expanding MMG screening programmes, particularly targeting women aged 40 and above, to optimise clinical outcomes and resource utilisation.

Indexed as

Community HealtheconomicsPreventive Medicine

Identifiers

PMID42158552
PMCPMC13182497

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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.