Oversimplifying overdiagnosis

Estimating Breast Cancer Overdiagnosis After Screening Mammography Among Older Women in the United States

BACKGROUND

Overdiagnosis is increasingly recognized as a harm of breast cancer screening, particularly for older women.

OBJECTIVE

To estimate overdiagnosis associated with breast cancer screening among older women by age.

DESIGN

Retrospective cohort study comparing the cumulative incidence of breast cancer among older women who continued screening in the next interval with those who did not. Analyses used competing risk models, stratified by age.

SETTING

Fee-for-service Medicare claims, linked to the SEER (Surveillance, Epidemiology, and End Results) program.

PATIENTS

Women 70 years and older who had been recently screened.

MEASUREMENTS

Breast cancer diagnoses and breast cancer death for up to 15 years of follow-up.

RESULTS

This study included 54 635 women. Among women aged 70 to 74 years, the adjusted cumulative incidence of breast cancer was 6.1 cases (95% CI, 5.7 to 6.4) per 100 screened women versus 4.2 cases (CI, 3.5 to 5.0) per 100 unscreened women. An estimated 31% of breast cancer among screened women were potentially overdiagnosed. For women aged 75 to 84 years, cumulative incidence was 4.9 (CI, 4.6 to 5.2) per 100 screened women versus 2.6 (CI, 2.2 to 3.0) per 100 unscreened women, with 47% of cases potentially overdiagnosed. For women aged 85 and older, the cumulative incidence was 2.8 (CI, 2.3 to 3.4) among screened women versus 1.3 (CI, 0.9 to 1.9) among those not, with up to 54% overdiagnosis. We did not see statistically significant reductions in breast cancer-specific death associated with screening.

LIMITATIONS

This study was designed to estimate overdiagnosis, limiting our ability to draw conclusions on all benefits and harms of screening. Unmeasured differences in risk for breast cancer and differential competing mortality between screened and unscreened women may confound results. Results were sensitive to model specifications and definition of a screening mammogram.

CONCLUSION

Continued breast cancer screening was associated with greater incidence of breast cancer, suggesting overdiagnosis may be common among older women who are diagnosed with breast cancer after screening. Whether harms of overdiagnosis are balanced by benefits and for whom remains an important question.

PRIMARY FUNDING SOURCE

National Cancer Institute.

[The development of early cancer detection in Germany]

Cancer is one of the most relevant chronic diseases in the German population, but not all neoplastic entities are eligible for early cancer detection (ECD) programs. In 1971, ECDs were introduced as population-wide screenings for the first time in the catalogue of benefits of the West German statutory health insurance funds. However, the implementation at that time was rarely systematic. Concurrently, a discussion on the perspectives of ECD arose in the former German Democratic Republic, where a structured program was not prepared in the country until the late 1980s.A national cancer plan (NCP) was initiated in 2008 and its area of action #1 was titled "Further development of ECD". In April 2013, the law for the development of early cancer detection and quality assurance by clinical cancer registries was passed, which adopted major suggestions of the NCP. Consequently, the pertinent recommendations of the EU guidelines for the screening of the breast, cervix, and colon-rectum are currently being implemented.Public opinion in Germany with regard to ECDs has changed in recent years from unanimous consent to a rather critical distance. While ineffective and inefficient preventive action is being replaced by quality-assured screening procedures, public discussion about the fundamental reasonability of ECDs is controversial as never before.

Defining, Estimating, and Communicating Overdiagnosis in Cancer Screening

The toll of inadequate health care is well-substantiated, but recognition is mounting that "too much" is also possible. Overdiagnosis represents one harm of too much medicine, but the concept can be confusing: It is often conflated with related harms (such as overtreatment, misclassification, false-positive results, and overdetection) and is difficult to measure because it cannot be directly observed. Because the U.S. Preventive Services Task Force (USPSTF) issues screening recommendations aimed largely at healthy persons, it has a particular interest in understanding harms related to screening, especially but not limited to overdiagnosis. In support of the USPSTF, the authors summarize the knowledge and provide guidance on defining, estimating, and communicating overdiagnosis in cancer screening. To improve consistency, thinking, and reporting about overdiagnosis, they suggest a specific definition. The authors articulate how variation in estimates of overdiagnosis can arise, identify approaches to estimating overdiagnosis, and describe best practices for communicating the potential for harm due to overdiagnosis.

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.

Missteps in Current Estimates of Cancer Overdiagnosis

The balance between the benefits and harms of imaging-based cancer screening continues to be an area of controversy and widespread media attention. Of the potential harms, overdiagnosis from screening is likely the most elusive in estimating and quantifying. This article describes the major methodological issues with recently reported estimates of overdiagnosis that are based on excess cancer incidence, and suggests that modeling focused on tumor lead-time can serve as a complementary method for excess incidence-based overdiagnosis estimates. Radiologists should be conversant on the topic of overdiagnosis and understand the limitations of different methods used to estimate its magnitude.

A definition and ethical evaluation of overdiagnosis: response to commentaries

It is a privilege to have respected colleagues engage with our definition and ethical evaluation of overdiagnosis. In our response to the commentaries, we first deal with paradigmatic issues: the place of realism, the relationship between diagnostic standards and correctness and the distinction between overdiagnosis and both false-positives and medicalisation. We then discuss issues arising across the commentaries in turn. Our definition captures the range of different types of overdiagnosis, unlike a definition limited to diagnosis of harmless disease. Certain implications do flow from our definition, as noted by commentators, but we do not view them as problematic: overdiagnoses can become beneficial diagnoses as medical knowledge and practice changes over time; inadequate systems of healthcare can produce tragic overdiagnosis, and the effectiveness of treatment partly determines whether overdiagnosis occurs. Complexity and uncertainty in balancing benefits and harms is unfortunate, but not a reason to avoid making a judgement (ideally one that reflects multiple perspectives). We reaffirm that overdiagnosis, for the foreseeable future, must be estimated at a population level and defend the importance of good-quality risk communication for individuals. We acknowledge that a lot turns on the relevance of professional communities in our definition and expand our reasoning in this regard then conclude with a note on the difference between intentions and goals. We expect that it will be some time before these matters are settled and we look forward to continue debating these matters with our colleagues.

Conditions for Valid Empirical Estimates of Cancer Overdiagnosis in Randomized Trials and Population Studies

Cancer overdiagnosis is frequently estimated using the excess incidence in a screened group relative to that in an unscreened group. However, conditions for unbiased estimation are poorly understood. We developed a mathematical framework to project the effects of screening on the incidence of relevant cancers-that is, cancers that would present clinically without screening. Screening advances the date of diagnosis for a fraction of preclinical relevant cancers. Which diagnoses are advanced and by how much depends on the preclinical detectable period, test sensitivity, and screening patterns. Using the model, we projected incidence in common trial designs and population settings and compared excess incidence with true overdiagnosis. In trials with no control arm screening, unbiased estimates are available using cumulative incidence if the screen arm stops screening and using annual incidence if the screen arm continues screening. In both designs, unbiased estimation requires waiting until screening stabilizes plus the maximum preclinical period. In continued-screen trials and population settings, excess cumulative incidence is persistently biased. We investigated this bias in published estimates from the European Randomized Study of Screening for Prostate Cancer after 9-13 years. In conclusion, no trial or population setting automatically permits unbiased estimation of overdiagnosis; sufficient follow-up and appropriate analysis remain crucial.

Recognizing the Limitations of Cancer Overdiagnosis Studies: A First Step Towards Overcoming Them

Numerous studies have attempted to quantify the number of breast cancers that would never have been diagnosed in the absence of screening. Unfortunately, results are highly variable across studies and there is considerable disagreement about both the frequency of overdiagnosis and the validity of different methodologic approaches. In this Commentary, we review limitations of the two major approaches used in existing studies. Studies that use excess incidence as a proxy for overdiagnosis require a valid estimate of incidence in the absence of screening and sufficient follow-up to ensure the excess excludes relevant (ie, nonoverdiagnosed) cancers detected early. The requirement of sufficient follow-up applies to both population studies and clinical trials, but only certain clinical trial designs have the potential to yield unbiased results. Studies that model disease natural history to infer overdiagnosis must, in addition, examine whether their models produce valid estimates in the presence of nonprogressive cases. In this setting, limited follow-up could lead to a lack of identifiability of the parameters needed to accurately infer overdiagnosis. In a polarized research community, the excess incidence and modeling approaches are generally viewed as competitors, but we argue that they are complementary, with models being more complex but having greater potential to inform about disease natural history and the outcomes of candidate screening policies. Rather than arguing why one approach should be preferred to another, investigators should focus on developing studies that generate reliable estimates of overdiagnosis. Recognizing that both approaches have limitations, which existing studies rarely overcome, is a first step towards reconciling methodologic perspectives and achieving consensus about the real magnitude of the overdiagnosis problem.

Lung cancer screening overdiagnosis: reports of overdiagnosis in screening for lung cancer are grossly exaggerated

The National Lung Cancer Screening Trial (NLST) demonstrated a mortality reduction benefit associated with low-dose computed tomography (LDCT) screening for lung cancer. There has been considerable debate regarding the benefits and harms of LDCT lung cancer screening, including the challenges related to its practical implementation. One of the controversies regards overdiagnosis, which conceptually denotes diagnosing a cancer that, either because of its indolent, low-aggressiveness biologic behavior or because of limited life expectancy, is unlikely to result in significant morbidity during the patient's remainder lifetime. In theory, diagnosing and treating these cancers offer no measurable benefit while incurring costs and risks. Therefore, if a screening test detects a substantial number of overdiagnosed cancers, it is less likely to be effective. It has been argued that LDCT screening for lung cancer results in an unacceptably high rate of overdiagnosis. This article aims to defend the opposite stance. Overdiagnosis does exist and to a certain extent is inherent to any cancer-screening test. Nonetheless, the concept is less dualistic and more nuanced than it has been suggested. Furthermore, the average estimates of overdiagnosis in LDCT lung cancer screening based on the totality of published data are likely much lower than the highest published estimates, if a careful definition of a positive screening test reflecting our current understanding of lung cancer biology is utilized. This article presents evidence on why reports of overdiagnosis in lung cancer screening have been exaggerated.

Overdiagnosis: epidemiologic concepts and estimation

Overdiagnosis of thyroid cancer was propounded regarding the rapidly increasing incidence in South Korea. Overdiagnosis is defined as 'the detection of cancers that would never have been found were it not for the screening test', and may be an extreme form of lead bias due to indolent cancers, as is inevitable when conducting a cancer screening programme. Because it is solely an epidemiological concept, it can be estimated indirectly by phenomena such as a lack of compensatory drop in post-screening periods, or discrepancies between incidence and mortality. The erstwhile trials for quantifying the overdiagnosis in screening mammography were reviewed in order to secure the data needed to establish its prevalence in South Korea.