Extrapolation of pre-screening trends: Impact of assumptions on overdiagnosis estimates by mammographic screening

Data-driven overdiagnosis definitions: A scoping review

INTRODUCTION

Adequate methods to promptly translate digital health innovations for improved patient care are essential. Advances in Artificial Intelligence (AI) and Machine Learning (ML) have been sources of digital innovation and hold the promise to revolutionize the way we treat, manage and diagnose patients. Understanding the benefits but also the potential adverse effects of digital health innovations, particularly when these are made available or applied on healthier segments of the population is essential. One of such adverse effects is overdiagnosis.

OBJECTIVE

to comprehensively analyze quantification strategies and data-driven definitions for overdiagnosis reported in the literature.

METHODS

we conducted a scoping systematic review of manuscripts describing quantitative methods to estimate the proportion of overdiagnosed patients.

RESULTS

we identified 46 studies that met our inclusion criteria. They covered a variety of clinical conditions, primarily breast and prostate cancer. Methods to quantify overdiagnosis included both prospective and retrospective methods including randomized clinical trials, and simulations.

CONCLUSION

a variety of methods to quantify overdiagnosis have been published, producing widely diverging results. A standard method to quantify overdiagnosis is needed to allow its mitigation during the rapidly increasing development of new digital diagnostic tools.

Overdiagnosis in the population-based organized breast cancer screening program estimated by a non-homogeneous multi-state model: a cohort study using individual data with long-term follow-up

Background

Overdiagnosis, defined as the detection of a cancer that would not become clinically apparent in a woman’s lifetime without screening, has become a growing concern. Similar underlying risk of breast cancer in the screened and control groups is a prerequisite for unbiased estimates of overdiagnosis, but a contemporary control group is usually not available in organized screening programs.

Methods

We estimated the frequency of overdiagnosis of breast cancer due to screening in women 50–69 years old by using individual screening data from the population-based organized screening program in Stockholm County 1989–2014. A hidden Markov model with four latent states and three observed states was constructed to estimate the natural progression of breast cancer and the test sensitivity. Piecewise transition rates were used to consider the time-varying transition rates. The expected number of detected non-progressive breast cancer cases was calculated.

Results

During the study period, 2,333,153 invitations were sent out; on average, the participation rate in the screening program was 72.7% and the average recall rate was 2.48%. In total, 14,648 invasive breast cancer cases were diagnosed; among the 8305 screen-detected cases, the expected number of non-progressive breast cancer cases was 35.9, which is equivalent to 0.43% (95% confidence interval (CI) 0.10%–2.2%) overdiagnosis. The corresponding estimates for the prevalent and subsequent rounds were 15.6 (0.87%, 95% CI 0.20%–4.3%) and 20.3 (0.31%, 95% CI 0.07%–1.6%), respectively. The likelihood ratio test showed that the non-homogeneous model fitted the data better than an age-homogeneous model (P <0.001).

Conclusions

Our findings suggest that overdiagnosis in the organized biennial mammographic screening for women 50–69 in Stockholm County is a minor phenomenon. The frequency of overdiagnosis in the prevalent screening round was higher than that in subsequent rounds. The non-homogeneous model performed better than the simpler, traditional homogeneous model.

Electronic supplementary material

The online version of this article (10.1186/s13058-018-1082-z) contains supplementary material, which is available to authorized users.

[Discrepancies and overdiagnosis in breast cancer organized screening. A "methodology" systematic review]

BACKGROUND

The risk-benefit ratio of breast cancer organized screening is the focus of much scientific controversy, especially about overdiagnosis. The aim of this study was to relate methodological discrepancies to variations in rates of overdiagnosis to help build future decision aids and to better communicate with patients.

METHODS

A systematic review of methodology was conducted by two investigators who searched Medline and Cochrane databases from 01/01/2004 to 12/31/2016. Results were restricted to randomized controlled trials (RCTs) and observational studies in French or English that examined the question of the overdiagnosis computation.

RESULTS

Twenty-three observational studies and four RCTs were analyzed. The methods used comparisons of annual or cumulative incidence rates (age-cohort model) in populations invited to screen versus non-invited populations. Lead time and ductal carcinoma in situ (DCIS) were often taken into account. Some studies used statistical modeling based on the natural history of breast cancer and gradual screening implementation. Adjustments for lead time lowered the rate of overdiagnosis. Rate discrepancies, ranging from 1 to 15 % for some authors and around 30 % for others, could be explained by the hypotheses accepted concerning very slow growing tumors or tumors that regress spontaneously.

CONCLUSION

Apparently, research has to be centered on the natural history of breast cancer in order to provide responses concerning the questions raised by the overdiagnosis controversy.

Quantifying Overdiagnosis in Cancer Screening: A Systematic Review to Evaluate the Methodology

Background

Overdiagnosis is the main harm of cancer screening programs but is difficult to quantify. This review aims to evaluate existing approaches to estimate the magnitude of overdiagnosis in cancer screening in order to gain insight into the strengths and limitations of these approaches and to provide researchers with guidance to obtain reliable estimates of overdiagnosis in cancer screening.

Methods

A systematic review was done of primary research studies in PubMed that were published before January 1, 2016, and quantified overdiagnosis in breast cancer screening. The studies meeting inclusion criteria were then categorized by their methods to adjust for lead time and to obtain an unscreened reference population. For each approach, we provide an overview of the data required, assumptions made, limitations, and strengths.

Results

A total of 442 studies were identified in the initial search. Forty studies met the inclusion criteria for the qualitative review. We grouped the approaches to adjust for lead time in two main categories: the lead time approach and the excess incidence approach. The lead time approach was further subdivided into the mean lead time approach, lead time distribution approach, and natural history modeling. The excess incidence approach was subdivided into the cumulative incidence approach and early vs late-stage cancer approach. The approaches used to obtain an unscreened reference population were grouped into the following categories: control group of a randomized controlled trial, nonattenders, control region, extrapolation of a prescreening trend, uninvited groups, adjustment for the effect of screening, and natural history modeling.

Conclusions

Each approach to adjust for lead time and obtain an unscreened reference population has its own strengths and limitations, which should be taken into consideration when estimating overdiagnosis.

Estimation of overdiagnosis in breast cancer screening using a non-homogeneous multi-state model: A simulation study

Objectives

Overdiagnosis is regarded as a harm of screening. We aimed to develop a non-homogeneous multi-state model to consider the age-specific transition rates for estimation of overdiagnosis, to validate the model by a simulation study where the true frequency of overdiagnosis can be calculated, and to compare our estimate with the cumulative incidence method.

Methods

We constructed a four-state model to describe the natural history of breast cancer. The latent disease progression and the observed states for each individual were simulated in a trial with biennial screening of women aged 51–69 and a control group of the same size without screening. We performed 100 repetitions of the simulation with one million women to evaluate the performance of estimates. A sensitivity analysis with reduced number of controls was performed to imitate the data from the service screening programme.

Results

Based on the 100 repetitions, the mean value of the true frequency of overdiagnosis was 12.5% and the average estimates by the cumulative incidence method and the multi-state model were 12.9% (interquartile range: 2.46%) and 13.4% (interquartile range: 2.16%), respectively. The multi-state model had a greater bias of overdiagnosis than the cumulative incidence method, but the variation in the estimates was smaller. When the number of unscreened group was reduced, the variation of multi-state model estimates increased.

Conclusions

The multi-state model produces a proper estimate of overdiagnosis and the results are comparable with the cumulative incidence method. The multi-state model can be used in the estimation of overdiagnosis, and might be useful for the ongoing service screening programmes.

Geographic patterns of change over time in mammography: Differences between Black and White U.S. Medicare enrollees

U.S. Black women have higher breast cancer mortality compared to White women while their rate of ever having a mammogram has become equal to or slightly surpassed that of Whites. We mapped the distribution of change in screening mammography for Black and White female Medicare enrollees ages 67-69 from 2008 to 2012 by hospital referral region across the contiguous U.S., performed cluster analysis to assess spatial autocorrelation, and examined the screening differences between these groups in 2008 and 2012 respectively. Changes in screening mammography are not consistent across the U.S.: Black and White women have increased and decreased their use of mammography in different regions and Black women's change patterns vary more widely.