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Disaggregating the mortality reductions due to cancer screening: model-based estimates from population-based data

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Abstract

The mortality impact in cancer screening trials and population programs is usually expressed as a single hazard ratio or percentage reduction. This measure ignores the number/spacing of rounds of screening, and the location in follow-up time of the averted deaths vis-a-vis the first and last screens. If screening works as intended, hazard ratios are a strong function of the two Lexis time-dimensions. We show how the number and timing of the rounds of screening can be included in a model that specifies what each round of screening accomplishes. We show how this model can be used to disaggregate the observed reductions (i.e., make them time-and screening-history specific), and to project the impact of other regimens. We use data on breast cancer screening to illustrate this model, which we had already described in technical terms in a statistical journal. Using the numbers of invitations different cohorts received, we fitted the model to the age- and follow-up-year-specific numbers of breast cancer deaths in Funen, Denmark. From November 1993 onwards, women aged 50–69 in Funen were invited to mammography screening every two years, while those in comparison regions were not. Under the proportional hazards model, the overall fitted hazard ratio was 0.82 (average reduction 18%). Using a (non-proportional-hazards) model that included the timing information, the fitted reductions ranged from 0 to 30%, being largest in those Lexis cells that had received the greatest number of invitations and where sufficient time had elapsed for the impacts to manifest. The reductions produced by cancer screening have been underestimated by inattention to their timing. By including the determinants of the hazard ratios in a regression-type model, the proposed approach provides a way to disaggregate the mortality reductions and project the reductions produced by other regimes/durations.

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Acknowledgements

The work was supported by a grant “Measuring the mortality reductions produced by cancer screening” to James Hanley from the Canadian Institutes of Health Research (CIHR). CIHR had no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the article for publication; i.e., these activities were carried out by the authors, independently of the funder. Both authors had access to the Lexis-cell-level data used in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

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Authors and Affiliations

  1. Department of Epidemiology, Biostatistics and Occupational Health, McGill University, 1020 Pine Ave. West, Montréal, QC, H3A 1A2, Canada

    James Anthony Hanley

  2. Department of Public Health Programmes, Randers Regional Hospital, Randers, Denmark

    Sisse Helle Njor

  3. Department of Clinical Epidemiology, Aarhus University, Aarhus, Denmark

    Sisse Helle Njor

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  1. James Anthony Hanley
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Authors’ contribution

James Hanley suggested the re-analysis; the authors jointly presented preliminary results, based on data to 2009, at the International Biometric Conference in Victoria, British Columbia, Canada in July 2016. Both drafted the manuscript, and both are guarantors for the study.

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Correspondence to James Anthony Hanley.

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Conflicts of interests

Both authors declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

Ethical approval

The study was approved by Region Midt under their umbrella permission form the Danish Data Inspection Board. According to Danish legislation this serves as ethical approval of register-based research projects (journal number 1-16-02-90-17).

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Hanley, J.A., Njor, S.H. Disaggregating the mortality reductions due to cancer screening: model-based estimates from population-based data. Eur J Epidemiol 33, 465–472 (2018). https://doi.org/10.1007/s10654-017-0339-7

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  • DOI: https://doi.org/10.1007/s10654-017-0339-7

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