Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Long term absence of invasive breast cancer diagnosis in 2,402,672 pre and postmenopausal women: A systematic review and meta-analysis

  • Winnifred Cutler ,

    Contributed equally to this work with: Winnifred Cutler, James Kolter, Catherine Chambliss, Heather O’Neill, Hugo M. Montesinos-Yufa

    Roles Conceptualization, Data curation, Funding acquisition, Investigation, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing

    Cutler@Athenainstitute.com

    Affiliation Department of Research, Athena Institute for Women’s Wellness, Chester Springs, Pennsylvania, United States of America

  • James Kolter ,

    Contributed equally to this work with: Winnifred Cutler, James Kolter, Catherine Chambliss, Heather O’Neill, Hugo M. Montesinos-Yufa

    Roles Conceptualization, Data curation, Investigation, Writing – original draft, Writing – review & editing

    Affiliation Office of the Physician Director, Department of Obstetrics and Gynecology, Bryn Mawr Hospital, Bryn Mawr, Pennsylvania, United States of America

  • Catherine Chambliss ,

    Contributed equally to this work with: Winnifred Cutler, James Kolter, Catherine Chambliss, Heather O’Neill, Hugo M. Montesinos-Yufa

    Roles Conceptualization, Investigation, Project administration, Resources, Writing – original draft, Writing – review & editing

    Affiliation Department of Psychology, Ursinus College, Collegeville, Pennsylvania, United States of America

  • Heather O’Neill ,

    Contributed equally to this work with: Winnifred Cutler, James Kolter, Catherine Chambliss, Heather O’Neill, Hugo M. Montesinos-Yufa

    Roles Conceptualization, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing

    Affiliation Department of Economics, Ursinus College, Collegeville, Pennsylvania, United States of America

  • Hugo M. Montesinos-Yufa

    Contributed equally to this work with: Winnifred Cutler, James Kolter, Catherine Chambliss, Heather O’Neill, Hugo M. Montesinos-Yufa

    Roles Conceptualization, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing

    Affiliation Department of Mathematics and Computer Science, Ursinus College, Collegeville, Pennsylvania, United States of America

Abstract

Background

Invasive Breast Cancer (IBC) risk estimates continue to be based on data collated from cancer registries, i.e., retrospective research that excludes disease-free women. For women without a prior diagnosis, these estimates inflate both risk and screening frequency recommendations and inadvertently increase recently recognized harms from overdiagnosis and overtreatment.

Objective

To estimate the likelihood that pre or postmenopausal women with no prior diagnosis will remain free of IBC in order to enable evidence-based screening recommendations.

Methods

Prospective data from 21 studies of 2,402,672 women were analyzed, updating our previously published systematic search of 19 studies. This second systematic search included PubMed and The Cochrane Library from 2012 through April 2019. Inclusion criteria: only studies reporting the number of women enrolled, length of follow-up, and number of women diagnosed with IBC. Linear regression was used to estimate the percentage of women expected to remain free from an IBC diagnosis based on follow-up duration. To minimize non-response bias and selective outcome bias, only studies reporting outcomes for all enrolled women followed for similar, specific lengths of time were included. Sensitivity analyses confirm that the overall findings were unchanged by age at enrollment, menopausal status, screened women, variation in sample size, duration of follow-up, and heteroskedasticity.

Results

The calculated percentage of women remaining IBC-free after follow-ups of 5, 10, 15, 20 and 25 years decreases uniformly by about one-fourth of one percent per year, i.e., 0.255% (95% CI: -0.29, -0.22; p < .0001). At 25 years, the expected percentage of women with no invasive breast cancer is 93.41% (95% CI: 92.75, 94.07).

Conclusions

Over 99.7% of pre/postmenopausal women with no prior diagnosis continued with no IBC each year, with 93.41% still free after 25 years. Our study supports the medical justification for reducing the frequency of mammograms for menopausal women with no prior IBC diagnosis.

Introduction

Optimal healthcare decision-making depends upon patients and providers having accurate information about the potential benefits and risks of diagnostic and treatment procedures. Breast cancer screening pursuing early detection has harms as well as possible benefits [16] The harms justify careful selection of when and which candidates might best be excluded from such screening. This study examines prospectively the likelihood that pre and postmenopausal women not previously diagnosed with IBC will remain free of IBC for given durations.

In 2015, a multi-step systematic literature review through 2012, identifying and evaluating diagnostic outcomes of 2,305,427 pre and postmenopausal women with no prior diagnosis of breast cancer, was performed [1]. The percentage of women in each study that eventually was diagnosed with a first IBC was surprisingly low. Since that 2015 work was completed, the medical context has changed. The introduction of new medical technologies, such as 3D breast cancer screening, as well as the aging demographics of many developed countries, has increased the number of women at risk of overdiagnosis. Revised IBC screening guidelines have created greater ambiguity about best practices and a desire for more precise quantification of the screening needs of particular groups of women. Furthermore, the availability of new data from two long term studies now enables calculation of the likelihood of long-term absence of IBC diagnosis for pre and postmenopausal women with no prior history of breast cancer. Sensitivity analyses that correct for the inevitable heteroskedasticity permit estimation of the percentage of 2,402,672 women that are expected to remain free from a diagnosis of IBC over their next 25 years of life.

This study has achieved its aim of providing and interpreting accurate new data facilitating optimal decision-making for screening and treating invasive breast cancer. These findings should help to inform patients’ and providers’ decisions about the value of screening as well as avoiding it.

Methods

Literature search

Two multi-step, systematic search protocols exhaustively identified all qualified peer-reviewed published studies through 2012 and April 2019. See Online Supplements: S2 and S3 Tables, and S1 Text which provide the specific details of each search encompassing 687,886 and 18,033 studies, respectively.

Every study met five inclusion criteria: 1) All the women were prospectively studied during their pre/postmenopausal years and had no invasive breast cancer history at enrollment; 2) The study specified the number of women enrolled; 3) The study specified the number of women diagnosed with a first invasive breast cancer as well as the number of women not diagnosed; 4) Study subjects were counted only once; and, 5) The duration of follow-up was clearly defined.

The primary outcome of interest for this review and subsequent data analysis was the total cumulative percentage of women still free of a diagnosis of invasive breast cancer via prospectively recorded data sets. To minimize potential sources of non-response and selective outcome bias, we included only studies where outcomes were reported for all enrolled women who were followed up for a similar, specific length of time.

The prospective method used in this study allows the estimation of long-term incidence-of, and absence-of, the first ever IBC diagnosis. In contrast, the CDC/SEER’s retrospective methods using data from cancer registries are unable to estimate these crucial statistics because they lack the necessary information. Hospital registries collect data on cases of cancer, and they do not track women that move across medical systems, making the necessary raw data unavailable and therefore the calculation impossible using retrospective methods from cancer registry data. To correctly estimate the incidence of the first ever IBC diagnosis and unveil the absence of IBC, a prospective method is necessary.

Because of the way the data are collected and the difference in approaches, our prospective method and the retrospective registry method are not parallel to each other. Only IBC-free women enter our studies; only post-diagnosed women enter the CDC/SEER cancer registries.

To highlight the differences, Table 1 presents a side by side comparison of the two methods.

thumbnail
Table 1. Two ways of estimating the risk of invasive breast cancer diagnosis.

https://doi.org/10.1371/journal.pone.0237925.t001

Results

Twenty-one studies met all five inclusion criteria with each providing high-quality evidence for its duration of follow-up and its percentage of women who remained free of a diagnosis of IBC. We used regression analysis to model the relationship between the percentage of women who remained free from a diagnosis of IBC (the “survival rate”) and the duration of study follow-up. Table 2 summarizes the design of 21 studies noting all interventions and separate outcomes when relevant to IBC diagnosis.

thumbnail
Table 2. The design of each of the 21 published studies enrolling ONLY women without a prior diagnosis of invasive breast cancer.

https://doi.org/10.1371/journal.pone.0237925.t002

Description of statistical analyses

The number of follow-up years and the sample size varied from one study to the next. The heterogeneity in the sample size across studies induces heteroskedasticity in the regression. In our sensitivity analysis, we solve the heteroskedasticity problem by complementing the ordinary least squares (OLS) regression analysis with a generalized least squares (GLS) analysis. Statistically, both OLS and GLS provide unbiased and consistent estimates of the relationship between the “survival rate” and the follow-up time; but GLS is more efficient [28]. These efficiency gains translate into higher precision, that is higher t-ratios, higher r-squares, lower p-values, and narrower confidence intervals.

The 21 studies covering 2,402,672 different women provided 24 separate outcomes. Studies 7, 8, and 19 each offered 2 qualifying outcomes because of each having a treatment and a control group. Each outcome states the percentage of the women in each study that remained free of a diagnosis of invasive breast cancer (IBC) at the end of that study’s follow-up period. Table 3 provides the outcome data for each study.

thumbnail
Table 3. The results of each of the 21 published studies enrolling only women without a prior diagnosis.

https://doi.org/10.1371/journal.pone.0237925.t003

Fig 1 plots each of these 24 outcomes as one dot showing the duration and the percentage of women who remained free of a diagnosis of IBC at the conclusion of the study. Fig 1 illustrates that the percentages of women with no IBC diagnosis ranged from a high of over 99% for studies #1 (UK), #13 (Italy), and #20 (Spain) with follow up durations between 2.6 and 4.8 years, to lows of 92.77% and 94.52% for the longest studies #19 (Canada), #16 (Sweden) with respective 21.9- and 25-years of follow up.

thumbnail
Fig 1. Percentage of women still free from IBC diagnosis relative to the duration of clinical study (years of follow-up) in 21 studies (24 outcomes) comprising 2,402,672 women.

Scatterplot and regression (solid line) with 95% CI (dotted lines) for the relationship between the probability of remaining free from IBC diagnosis and the follow-up duration based on all 21 studies of pre/postmenopausal women. Each point represents the percentage of women still free from IBC diagnosis at the end of the follow-up period in each study. Each dot describes one observation, i.e., one outcome, from one study. The dashed lines show the 95% confidence interval bounds for the solid regression line.

https://doi.org/10.1371/journal.pone.0237925.g001

The linear regression statistical methods confirm what simply looking at the way the data array on the graph reveals: studies that lasted longer produced a linear (straight line) pattern of a slightly declining percentage of women still free of an IBC diagnosis. There is no obvious curving pattern that would occur if older pre and postmenopausal women were substantially more likely to receive the first diagnosis than younger women.

The estimated regression line indicates the predicted rate for women who remain free of an IBC diagnosis per year. The line’s predicted slope of -0.267 indicates that the percentage of women expected to remain free from an IBC diagnosis decreased annually by 0.27 percentage points (p < .0001), i.e., about one-fourth of one percent per year (95% CI: -0.31, -0.23). Consequently, in four years, the expected rate of absence of a diagnosis of IBC only declined by 1.1 percentage points. Overall, 93.25% of these 2,402,672 women would be expected to remain free of an IBC diagnosis after 25 years of follow-up (95% CI: 92.53, 93.97).

Sensitivity analyses

We performed several sensitivity analyses, summarized in Table 4 and two supplements to Table 4. See Online Supplements: S4 and S5 Tables. The reason for such an analysis is to consider whether variability in the sample sizes of different studies, or different subgroups, e.g., the screening or the postmenopausal women, yield different annual rate of change from absence of diagnosis of IBC. Given the heterogeneity of sample sizes across different studies, the error terms in our regression models have unequal variances. Therefore, statistical models that correct for this problem are necessary. The statistical term for this unequal variance problem is “heteroskedasticity”.

Table 4 shows the regression results using the generalized least squares (GLS) regression method, which corrects for heteroskedasticity by weighting each of the studies in this systematic review and meta-analysis by its corresponding sample size. Column 1 shows the results using the full sample of 24 outcomes corresponding to the 21 studies comprising 2,402,672 women. Columns 2 to 5 are additional regression models isolating important sub-groups of studies: those that focused on the subsample of women who were at least 50 years old or surgically menopausal at enrollment (Column 2, including only studies 1, 3, 4, 5, 6, 7a, 7b, 8a, 8b, 11, 12, 13, 14, 17, 20, and 21), and those that provided breast cancer screening at enrollment (Column 4, including only studies 1, 4, 5, 7a, 7b, 8a, 8b, 9, 10, 11, 14, 15, 16, 17, 18, 19a and 19b). For completeness, we also provide results for the studies that were not limited to menopausal women (Column 3) and not limited to screened women (Column 5).

The GLS regression line indicates a remarkable flat slope of -0.255 (p < .0001, 95% CI: -0.288,-0.221) for the average yearly decline in the absence of the first IBC diagnosis rate. See column 1 of Table 4. This result reinforces our previous estimate using OLS linear regression uncorrected for heteroskedasticity of about one-fourth of one percent. But this slope now is even slightly flatter (a decline in absence of IBC of 0.255 percent per year instead of a decline of 0.267 percent per year). Subsequently, after four years and accounting for heteroskedasticity, the rate of freedom from the first IBC diagnosis is expected to decline by 1.02% (95% CI: -1.15, -0.88). After 25 years of follow-up, the expected rate of absence of ever being diagnosed with IBC remains relatively high, at 93.41 percent (95% CI: 92.75, 94.07).

Table 5 summarizes the estimated probabilities and confidence intervals of remaining free from an IBC diagnosis for intermediate follow-up periods ranging from 0 to 25 years.

thumbnail
Table 5. Absence of IBC diagnosis probabilities and C.I. for the full sample of 21 studies.

https://doi.org/10.1371/journal.pone.0237925.t005

Postmenopausal women

The subset of 14 studies, which included only postmenopausal women, i.e., those who were either at least 50 years or who were surgically menopausal at enrollment, provided 16 outcomes representing 1,808,022 women. The 14 studies are: #1, #3, #4, #5, #6, #7, #8, #11, #12, #13, #14, #17, #20, and #21. The estimated regression line yields a predicted slope of -0.228 (p<0.001, 95% CI:-0.245,-0.212). See column 2 of Table 4. The slope indicates that the percentage of postmenopausal women expected to remain with no IBC diagnosis decreases by 0.228 per year, i.e., less than one-fourth of one percentage point per year. Thus 94.002% of the postmenopausal women in these 14 studies would be expected to remain free of an invasive breast cancer diagnosis after 25 years of follow-up (95% CI: 93.661, 94.343). Again, after correcting for heteroskedasticity, the estimated regression line is remarkably flat and slightly flatter and more precise than without accounting for heteroskedasticity (See Fig 2, and see also supplement S4, panel B, column 2, for details).

thumbnail
Fig 2. Percentage of post-menopausal women still free from IBC diagnosis relative to the duration of clinical study (years of follow-up) in 14 studies (16 outcomes) comprising 1,808,022 women.

This figure identifies the 14 studies (16 data points) that included only women who were at least 50 years old or surgically menopausal at enrollment. Studies 1, 3, 4, 5, 6, 7 (a and b), 8 (a and b), 11, 12, 13, 14, 17, 20, and 21. Each point represents the percentage of women still free from IBC diagnosis at the end of the follow-up period in each study. Each dot describes one observation, i.e., one outcome, from one study. The dashed lines show the 95% confidence interval bounds for the solid regression line.

https://doi.org/10.1371/journal.pone.0237925.g002

Screened women

The subset of 14 studies which included breast screening provided 17 outcomes representing 2,264,591 women: The 14 studies are: #1, #4, #5, #7, #8, #9, #10, #11, #14, #15, #16, #17, #18, and #19. The estimated regression line for the subsample of screened women indicates that for each additional year of study follow-up, the percentage of screened women expected to remain free from an IBC diagnosis decreased by 0.256 (p < .0001, 95% CI: -0.297, -0.214). This model predicts that 93.400% of these pre and postmenopausal screened women would remain IBC diagnosis-free after 25 years of follow-up (95% CI: 92.583, 94.218). See column 4 of Table 4. Again, after correcting for heteroskedasticity, the estimated regression line is remarkably shallow and slightly shallower and more precisely estimated than without accounting for heteroskedasticity (See Fig 3, and see also supplement 1, panel B, column 4, for details).

thumbnail
Fig 3. Percentage of screened women still free from IBC diagnosis relative to the duration of clinical study (years of follow-up) in 14 studies (17 outcomes) comprising 2,264,591 women.

This figure identifies the 14 studies (17 data points) that included only women who were screened at enrollment. Studies 1, 4, 5, 7 (a and b), 8 (a and b), 9, 10, 11, 14, 15, 16, 17, 18, and 19 (a and b). Each point represents the percentage of women still free from IBC diagnosis at the end of the follow-up period in each study. Each dot describes one observation, i.e., one outcome, from one study. The dashed lines show the 95% confidence interval bounds for the solid regression line.

https://doi.org/10.1371/journal.pone.0237925.g003

It is worth noting that the subset of studies not limited to screened women (i.e., studies that allowed non-screened women) exhibited an even flatter regression line, i.e., a smaller decline, per year, in the “survival rate” or the rate at which women remain free from IBC. For this subsample of non-necessarily screened women, the estimated coefficient of -0.214 for the slope indicates that each additional year of follow-up is associated with an average decline in the “survival rate” of almost one-fifth of one percent (0.214 percent) per year. See column 5 of Table 4.

Despite the reduction in sample size in the analysis of the 4 subsamples shown, we obtain remarkably consistent results across different groups of studies, as can be seen with the naked eye by the similarity in the slopes of Figs 13. To test more rigorously that indeed there are no significant differences in the slopes across subsamples we conducted a binary-variable statistical analysis, the results of which are offered in Supplement 1 to Table 4. See Online Supplements: S4 Table. This analysis indicates that there are no statistically significant differences in the slopes across subsamples. This result essentially confirms that the flat slopes of Figs 13 are not statistically different from each other and not significantly different than the slope coefficients obtained in Table 4 (columns 1 to 5). In sum, these coefficients indicate a decline in the “survival rate” between one-fourth and one-fifth of one percent per year of follow-up.

Potential effects of outliers and the UK Million Women Study

Our results not only were consistent across subsamples but also robust to the exclusion of potentially influential outcomes. One such important potential concern could be the over-representativeness of the UK Million Women Study. With 1,084,110 women included in this single study, it represents 45.12% of our total sample of women. In supplement 2 to Table 4 (see Online Supplements: S5 Table) we present parallel results to those of Table 4 but excluding the UK Million Women Study. Using the new “full sample” of 20 studies (23 outcomes representing 1,318,562 women), the new model has an estimated slope coefficient of -0.252 (virtually unchanged from the model that included the UK Million Women Study which yielded a slope of -0.255). That means that each additional year of follow-up for women in studies other than the UK Million Women Study is associated with an average decline in the “survival rate” of nearly one-fourth of one percent (0.252 percentage points) per year. See column 1.

In models estimated on important subgroups of women that additionally excluded the UK Million Women Study, the flattest lines, i.e., the least decline per year, correspond to the subsamples of post-menopausal women (column 2) and non-necessarily screened women (column 5). The coefficient of -0.209 in column 2 indicates that each additional year of follow-up for post-menopausal women not in the UK Million Women Study is associated with an average decline in the “survival rate” of 0.209 percentage points per year, i.e., about one-fifth of one percent decline per year. These results indicate that the main findings of this research are not driven by the over-representation of women in the UK Million Women Study. These statistical tests corroborate the visual pattern of Figs 13, in which all outcomes are very close to the fitted OLS regression line and there are no curving patterns or outliers driving the results.

Potential bias due to age at enrollment, sample size, and study duration

Additional potential biases were tested considering the impact of differences in sample sizes or differences in the minimum calendar, i.e., chronological, age of participating women. We asked whether smaller or larger sample sizes distort the results and whether studies that enrolled only women at least 50 years of age yielded different results than the ones that included younger pre and postmenopausal women.

Tests for such bias showed either minimal or no differences. A multiple regression model using the full sample of 24 outcomes with the independent variables being the number of follow-up years and number of women enrolled in each study yielded virtually the same annual decline of -0.253 (95% CI: -0.296, 0.209; p < .000) in the percentage of women remaining free of an IBC diagnosis when holding the number of women enrolled constant. In turn, the impact of the number of women enrolled on the percentage of women expected to remain free from IBC diagnosis, holding the number of follow-up years constant, was statistically insignificant and negligible in magnitude: 0.0% (95% CI: 0.00, 0.00; p<0.80).

Similar results occurred for the multiple regression model incorporating the number of follow-up years and the minimum chronological age of the women at enrollment. There was no significant change in the estimated annual decline in percentage of IBC-free women holding the minimum chronological age constant. The estimated slope coefficient was -0.246 (95% CI: -0.280, -0.211; p < .0001). In turn, the impact on IBC diagnoses stemming from the minimum chronological age at enrollment, holding the number of follow-up years constant, was not statistically different from zero (95% CI: -0.009, 0.070, p>0.1). Our results indicate that no observable factor that could have altered the annual predicted rate of decline in IBC-free women did so.

Discussion

Invasive breast cancer was uncommon in 21 rigorous studies that set out to search for it: 94 percent of postmenopausal women with no IBC prior history are estimated to remain IBC-diagnosis-free over their next 25 years; i.e., the end of life for many. When pre and postmenopausal women are included together, the regression statistic is just about half a percent lower: 93.41% are expected to remain IBC free over their next 25 years.

Despite the geographic and cultural heterogeneity of these samples, we saw a clear pattern in the data of 2,402,672 women from 21 diverse peer-reviewed studies. Plotted over a 25-year course, remaining IBC-free showed a persistent linear relationship for the entire group of women. Only about ¼ of 1% (0.255%) would be expected to be newly diagnosed per year after their entry and initial screening.

But even this finding may be too pessimistic because over-diagnosing did occur (e.g., Table 2, Study 16). “Overdiagnosis,” the finding of a real disease which would never have caused any problem, is now recognized as occurring in as high as 50% of these early detections [25, 29, 30]. Overdiagnosis leads to overtreatment that diminishes the quality of life without adding real benefit. A biopsy is psychologically damaging, even with a benign result [31].

Furthermore, the prospects for healthy women may be even better. Women in these 21 studies were not preselected for good health habits. Some studies included women with compromised health conditions, like the WHI (Table 2, studies 7 and 8) with its obese and overweight populations on statins. Therefore, the estimated probabilities of remaining free from an IBC diagnosis up to 25 years (see Table 5) may be interpreted as a lower boundary for the corresponding true probabilities.

In contrast to the ~6% 25-year-risk estimate here, prevailing calculations for the general population project much higher risk estimates. According to 2019 online NIH reporting, “the lifetime risk estimates for the general population, suggests 12.3 percent of women will develop breast cancer during their lives” [32]. Similarly, in 2019, the American Cancer Society posted this statement: “As reported in previous years, a female’s probability of developing invasive breast cancer in her lifetime continues to be 12.4 percent or 1 in 8 women” [33]. While lifetime estimates precede the premenopausal period we examined, women older than 39 accounted for 94.7% of all invasive breast cancer cases according to CDC data [34].

We have previously detailed how these lifetime predictions do not withstand scrutiny because of the way the data are collected and then interpreted [1]. First, the CDC considers each primary tumor and recurrence with a different histology to be a new case, which can yield multiple case counts in the same woman. Counting cases this way inflates the numerator of the fraction that is used to calculate the incidence rate. Second, the denominator of the fraction is deflated because it is based on census estimates which are skewed toward undercounting.

For example, in Marin County, California, the 2000 census-estimators made an error, underestimating the number of household members by 32%. This increased the calculated breast cancer rate among women 50–69 years by an astonishing 50% [35]. Although these undercounts and census-related estimation problems have been acknowledged, they continue [4, 3639]. Our results are very encouraging and suggest a much larger population of healthy postmenopausal women than is generally recognized.

Future corroboration of these findings using registry data, when available, would be valuable. Data from those registries that can specify the number of first IBC cases and the population size should enable calculation of cumulative total incidence or absence of IBC over a given span of years of age, by totaling the incidence in each age group. The expectation is that such alternative analysis would yield outcomes similar to those obtained in the current analysis of these 21 studies of 24 outcomes.

The recent changes in the current medical context have resulted in an increased need to accurately inform patients and providers about the true value of screening. The results of this paper represent an important milestone in that direction.

Limitations

The results of the prospective method we used apply only to those women who have no prior history of IBC diagnosis and meet all five selection criteria, including pre/postmenopausal status. We did not address risk estimates for women at high risk with known genetic markers. Nor can we evaluate groups of women not covered by these 21 studies, such as poor women in underserved populations. The present study also cannot specify how much greater freedom from a diagnosis is experienced by women with optimal health habits. A number of the 21 studies included women who were overweight or obese and none selected for women who practiced regular exercise and avoided excessive alcohol consumption.

The Clinical Practice Guidelines, used by different providers in 2019, are inconsistent with each other and vary depending on the source [40]. But they would be overprescribing mammogram frequency for menopausal women if based on the Retrospective Method of risk estimating used by the CDC and American Cancer Society.

This meta-analysis and systematic review did not exclude studies based on the prior diagnosis of ductal carcinoma in situ (DCIS). DCIS has been shown to elevate the subsequent findings of IBC [41]. However, if DCIS findings did elevate the subsequent findings of a 1st IBC, then such a bias would elevate the 1st IBC incidence found. For screened women, this would bias our results towards a larger incidence of IBC (or towards lower absence of IBC). But the incidence we are finding is already low (the “IBC absence” we are finding is very high). This is despite, not because of, this potential bias. Therefore, in this regard, our results provide a lower boundary for the “survival rate”. This limitation then is that absence of an IBC diagnosis would actually be a little bit higher than we could identify in the mammogram screened samples.

We recognize that each of the 21 studies was developed for a different purpose, but the stability of the outcome appears to be a strength of our investigation rather than a limitation. These 21 widely diverse studies allow us to form a striking picture, as shown in Figs 13 and statistic outcome Tables 4 and 5, of how women with no prior IBC actually fared as they were followed up.

The 21 studies did not systematically test their cohorts for IBC outcome as it related to some of the known risk factors for invasive breast cancer: high alcohol consumption, obesity, certain HRT regimens that include synthetic progestins rather than those regimens that use progesterone, inadequate daily exercise habits, deficient Vitamin D3, and cumulative total radiation doses to the breasts [42]. While these are each recognized, it remains for future researchers to examine whether behavioral change can increase the freedom from IBC diagnosis. Case control trials would be helpful but difficult to manage.

Data on family history of breast cancer were not provided in any consistent way and, to the degree that women were more likely to be enrolled in a screening program because they are currently cancer free and concerned about their own risk, our findings would be biased as generating a higher incidence of IBC than would occur in women without a family history. Likewise, history of a genetic predisposition would also yield the same limitation.

Chronological age calculates the age in years from the date of birth. Biological age reflects the actual physiologic state: whether one is aging faster or slower than the chronologic age would imply. Data on the biological age of the participants, especially those rendered surgically menopausal, were unobservable and therefore unavailable to us.

Castration (ovariectomy) ages a 35 to 45-year-old woman into immediate menopause, making her biologically older than her intact peers. And even hysterectomy, with preservation of the ovaries, accelerates aging. For example, it triggers a demonstrable acceleration of kyphosis and related loss of bone [4345].

A rigorous study of the effects of biological age would be desirable but it is beyond the scope of this research. Nevertheless, the stability of our results across different subsamples of women suggests that variations in biological age may have, if any, a minimal effect on our findings. Also, using calendar, i.e., chronological, age as a proxy for biological age we observed an insignificant and negligible effect of age on the annual decline in the percentage of women who remained free from a first IBC diagnosis. This suggests that even though this limitation is worth exploring in future research, it is unlikely that the main findings will be altered.

Conclusions

We found that postmenopausal women with no prior history of IBC diagnosis have a ~94% chance of remaining IBC free over their next 25 years, and believe that, when guiding pre/postmenopausal patients, our numbers should replace the much higher estimates of breast cancer risk still in common use.

Risk estimates based on registries from cancer cases that exclude women without IBC do not appear to apply to the general population of menopausal women. Screening guidelines might be amended for women at low risk of breast cancer to avoid the dangers of overdiagnosis.

Potential clinical value

The ultimate goal of any screening test is to save lives. More clinicians are recognizing a lack of mortality benefit from mammogram screening in the normal population after age 40 [29, 30, 46]. Our data analysis demonstrates that the prevalence of breast cancer is not as great as the usual “1 in 8” mantra suggests. Our study should give comfort to those women and providers who choose to do fewer mammogram screenings. Health care leaders and women should reassess the value and frequency of any screening test as we learn more about its strengths and flaws over time [47]. These more precise estimates of a pre/postmenopausal woman’s likelihood of remaining free of invasive breast cancer should permit providers to offer more tailored guidance to these patients.

Supporting information

S2 Table. Steps 1, 2 and 3 of the first records search.

https://doi.org/10.1371/journal.pone.0237925.s002

(DOCX)

S3 Table. The steps of the 2012–2019 records search.

https://doi.org/10.1371/journal.pone.0237925.s003

(DOCX)

S4 Table. S1–S4 Tables.

Extended sensitivity analysis using binary variables.

https://doi.org/10.1371/journal.pone.0237925.s004

(DOCX)

S5 Table. S2–S4 Tables.

Regression analysis excluding the UK Million Women Study.

https://doi.org/10.1371/journal.pone.0237925.s005

(DOCX)

S1 Text. Detailed methods of successive literature searches.

https://doi.org/10.1371/journal.pone.0237925.s006

(DOCX)

Acknowledgments

The authors express their appreciation to Dr. Brent Mattingly, Chair of Psychology at Ursinus College for his statistical support and the student research interns Ryan Walker and Molly Divis for ongoing manuscript preparation and former student intern, now consultant, Paige Szmodis whose ongoing research assistance helped ferret out the updated search process and papers to review, and finally to Thomas Quay, Esq. for his iterative readings to help assure clarity in general English usage.

References

  1. 1. Cutler W, Bürki R, Kolter J, Chambliss C, Friedmann E, Hart K. Invasive Breast Cancer Incidence in 2,305,427 Screened Asymptomatic Women: Estimated Long Term Outcomes during Menopause Using a Systematic Review. PLOSone 2015;10(6): e0128895.
  2. 2. Cutler WB, Burki RE, Kolter J, Chambliss C. Mammography for symptomless women. Climacteric 2013;16:313–315. pmid:23425505
  3. 3. Cutler WB, Burki RE. Climacteric commentaries: Overdiagnosis may further diminish mammographic breast cancer benefits minimized by elegant study. Climacteric 2011;14:296
  4. 4. Cutler W and Burki R. Climacteric commentaries: A public health paper on breast cancer incidence that does not with withstand scrutiny. Climacteric 2010;13:6, 607–608.
  5. 5. Kaplan R Breast cancer screening a case in point in More than Medicine: The Broken Promise of American Health Kaplan RM Harvard University Press 2019 pp 72–76. http://www.hup.harvard.edu/catalog.php?isbn=9780674975903
  6. 6. Bell RJ, Screening mammography–early detection or over-diagnosis? Contribution from Australian data. Climacteric 2014;17(Suppl 2):1–7
  7. 7. Beral V. Breast cancer-replacement therapy in the Million Women Study. Lancet 2003;362(9382):419–427. pmid:12927427
  8. 8. Stahlberg C, Lynge E, Andersen ZJ, Keiding N, Ottesen B, Rank F, et al. Breast cancer incidence, case-fatality and breast cancer mortality in Danish women using hormone replacement therapy–a prospective observational study. International Journal of Epidemiology 2005;34:931–935. pmid:15914504
  9. 9. Gertig DM, Fletcher AS, English DR, MacInnis RJ, Hopper JL, Giles GG, et al. Hormone therapy and breast cancer: what factors modify the association? Menopause 2006;13(2):178–184. pmid:16645531
  10. 10. Lyytinen H, Pukkala E, Ylikorkala O. Breast Cancer risk postmenopausal women using estrogen-only therapy. Obstet Gynecol 2006;108:1354–1360. pmid:17138766
  11. 11. Lyytinen H, Pukkala E, Ylikorkala O. Breast Cancer risk in postmenopausal women using estradiol-progestogen therapy. Obstet Gynecol 2009;113(1):65–73. pmid:19104361
  12. 12. De Lignieres B, de Vathaire F, Fournier S, Urbinelli R, Allaert F, Le MG, et al. Combined hormone replacement therapy and risk of breast cancer in a French cohort study of 3175 women. Climacteric 2002;5:332–340. pmid:12626212
  13. 13. Writing Group for the Women’s Health Initiative Investigators. Risks and benefits of estrogen plus progestin in healthy postmenopausal women: principal results from the Women’s Health Initiative randomized controlled trial. JAMA 2002;288:321–33. pmid:12117397
  14. 14. Stefanick M, Anderson GL, Margolis K, Hendrix SL, Rodabough RJ, Paskett ED, et al. Effects of conjugated equine estrogens on breast cancer and mammography screening in postmenopausal women with hysterectomy. JAMA 2006;295:1647–57. pmid:16609086
  15. 15. Bjurstam N, Björneld L, Warwick J, Sala E, Duffy SW, Nyström L,et al. The Gothenburg Breast Screening Trial. Cancer 2003;97(10):2387–2396. pmid:12733136
  16. 16. Moss SM, for the UK Trial of Early Detection of Breast Cancer Group: 16-year mortality from breast cancer in the UK trial of early detection of breast cancer. Lancet 1999;353:1909–1914. pmid:10371568
  17. 17. Dimitrakakis C, Jones RA, Liu A, Bondy CA. Breast cancer incidence in postmenopausal women using testosterone in addition to usual hormone therapy. Menopause 2004;11(5):531–535. pmid:15356405
  18. 18. Cauley JA, Lucas FL, Kuller LH, Stone K, Browner W, Cummings SR, et al. Elevated serum estradiol and testosterone concentrations are associated with a high risk for breast cancer. Study of Osteoporotic Fractures Research Group. Ann Intern Med 1999;130:270–277. pmid:10068384
  19. 19. Berrino F, Muti P, Micheli A, Boleli G, Krogh V, Sciajno R, et al. Serum sex hormone levels after menop ause and subsequent breast cancer. J Natl Cancer Inst 1996;88:291–296. pmid:8614008
  20. 20. Toniolo PG, Levitz M, Zeleniuch-Jacquotte A, Shore RE, Koenig KL, Banerjee S, et al. A prospective study of endogenous estrogens and breast cancer in postmenopausal women. J Natl Cancer Inst 1995;87:190–197. pmid:7707406
  21. 21. Lietzmann MF, Moore SC, Peters TM, Lacey JV Jr, Schatzkin A, Schairer C, et al. Prospective study of physical activity and risk of postmenopausal breast cancer. Breast Cancer Research 2008;10:1–11.
  22. 22. Zackrisson S, Andersson I, Janzon L, Manjer J, Garne JP. Rate of overdiagnosis of breast cancer 15 years after end of Malmo mammographic screening trial: follow-up study. BMJ 2006;332:689–692. pmid:16517548
  23. 23. Zahl PH, Maehlen J, Welch HG. The natural history of invasive breast cancers detected by screening mammography. Arch Intern Med 2008;168:2311–2316. pmid:19029493
  24. 24. Tabár L, Vitak B, and Chen TH, Yen AM, Yaffe MJ, Rosell J, et al. Swedish Two-County Trial: Impact of mammographic screening on breast cancer mortality during 3 decades. Radiology 2011;260:658–663. pmid:21712474
  25. 25. Miller AB, Wall C, Baines CJ, Sun P, To T, Narod SA, et al. Twenty-five-year follow-up for breast cancer incidence and mortality of the Canadian National Breast Screening Study: a randomized screening trial. BMJ 2014;348: 366. pmid:24519768
  26. 26. Castro-Quezada I, Sanchez-Villegas A, Martinez-Gonzalez MA, Salas-Salvadó J, Corella D, Estruch R, et al. Glycemic index, glycemic load and invasive breast cancer incidence in postmenopausal women: The PREDIMED study Eur J Cancer Prev. 2016 Nov;25(6):524–32. pmid:26633163
  27. 27. Fourkala EO, Burnell M, Cox C, Ryan A, Manchanda R, Dawnay A, et al.Association of skirt size and postmenopausal breast cancer risk in older women: a cohort study within the UK Collaborative Trial of Ovarian Cancer Screening BMJ Open. 2014 Sep 24;4(9):e005400. pmid:25252818
  28. 28. Gujarati Damodar, and Porter Dawn. 2009. “Basic Econometrics.” 5th edition. McGraw-Hill.
  29. 29. Bleyer A MD, Welch G MD MPH. Effect of Three Decades of Screening Mammography on Breast Cancer Incidence. NEJM 2012;367(21):p.1998–2000. pmid:23171096
  30. 30. Autier P, Boniol M, Gavin A, Vatten LJ. Breast cancer mortality in neighboring European countries with different levels of screening but similar access to treatment: trend analysis of WHO mortality database. BMJ 2011;343:d4411. Available from: pmid:21798968
  31. 31. Sweeny, K, Christianson D, McNeill J. The Psychological Experience of Awaiting Breast Diagnosis. Annals of Behavioral Medicine, Volume 53, Issue 7, July 2019, Pages 630–641, https://doi.org/10.1093/abm/kay072
  32. 32. Screening for Breast Cancer: A Systematic Review to Update the 2009 U.S. Preventive Services Task Force Recommendation [Internet]. Available at https://www.ncbi.nlm.nih.gov/books/NBK343820/#ch1.s3. Accessed February 18, 2019
  33. 33. American Cancer Society; 2019. and American Cancer Society. Breast Cancer. Facts & Figures 2017–2018. Available at: https://www.cancer.org/research/cancer-facts-statistics/all-cancer-facts-figures/cancer-facts-figures-2019.html. Accessed February 18, 2019
  34. 34. Eheman CR, Shaw KM, Ryerson AB, Miller JW, Ajani UA, and White MC. The Changing Incidence of In situ and Invasive Ductal and Lobular Breast Carcinomas: United States, 1999–2004 Cancer Epidemiol Biomarkers Prev 2009; 18(6): 1763–9. pmid:19454615
  35. 35. Phipps AI, Clarke CA, Ereman RR. Impact of intercensal population projections and error of closure on breast cancer surveillance: examples from 10 California counties. Breast Cancer Res 2005; 7:R655–60. (Accessed at http://www.ncbi.nlm.nih.gov/pubmed/16168110). pmid:16168110
  36. 36. Ereman RR, Prebil LA, Mockus M, Koblick K, Orenstein F, Benz C, et al. Recent trends in hormone therapy utilization and breast cancer incidence rate in the high incidence population of Marin County, California BMC Public Health 2010, 10:228 pmid:20433756
  37. 37. Kissam E Differential undercount of Mexican immigrant families in the U.S. Census… Statistical Journal of the IAOS 33 (2017) 797–816 797 https://doi.org/10.3233/SJI-1 http://www.wkfamilyfund.org/docs/sji-33-sji170388.pdf
  38. 38. O’Hare W.P. Who Is Missing? Undercounts and Omissions in the U.S. Census. In: Differential Undercounts in the U.S. Census. (2019) Springer Briefs in Population Studies. Springer, Cham https://link.springer.com/chapter/10.1007/978-3-030-10973-8_1
  39. 39. Williams J The 2010 Decennial Census: Background and. Issues. Jennifer D. Williams. Specialist in American National Government. February 3, 2011. Congressional Research Service https://www.census.gov/history/pdf/2010-background-crs.pdf
  40. 40. Qaseem CA, Lin JS, Mustafa RA, et al. Screening for Breast Cancer in Average-Risk Women: A Guidance Statement From the American College of Physicians, CLINICAL GUIDELINES |16 APRIL 2019 https://annals.org/aim/fullarticle/2730520/screening-breast-cancer-average-risk-women-guidance-statement-from-american
  41. 41. Wapnir I, Dignam J, Fisher JJ. Long-term outcomes of invasive ipsilateral breast tumor…www.ncbi.nlm.nih.gov › pubmed › 21398619 J Natl Cancer Inst. 2011 Mar 16;103(6):478–88. https://doi.org/10.1093/jnci/djr027 Epub 2011 Mar 11. Long-term outcomes of invasive ipsilateral breast tumor recurrences after lumpectomy in NSABP B-17 and B-24 randomized clinical trials for DCIS. Wapnir IL(1)… pmid:21398619
  42. 42. Cutler WB. Chapter 10, Protecting the Breasts in Hormones and Your Health: The Smart Woman’s Guide to Hormonal and Alternative Therapies for Menopause., Wiley, NY, 2009
  43. 43. Cutler W Chapter 8 Protect Your Bones—with Exercise, Posture, Vitamin D, Calcium, and Hormones., in. Hormones and Your Health: The Smart Woman's Guide to Hormonal and Alternative Therapies for Menopause. © 2009 John Wiley & Sons, Inc.
  44. 44. Cutler WB, Genovese-Stone E Wellness in Women After 40 Years of Age: The Role of Sex Hormones and Pheromones: Part II Hormone Replacement Therapy; Part III Hysterectomy. Current Problems in Obstetrics, Gynecology and Fertility 2000; 23:33–88.
  45. 45. Cutler WB, Karp J, Stine R (1988) Single photon absorptiometry imaging as a screen for diminished dual photon density measures. Maturitas 10:143–55. pmid:3262192
  46. 46. Biller-Andorno N, Juhi P.Abolishing Mammography Screening Programs? A View from the Swiss Medical Board. n engl j med 370;21 nejm.org may 22, 2014 accessed at https://boris.unibe.ch/51602/7/Biller-Andorno%20NEnglJMed%202014.pdf
  47. 47. Morgan DJ, Dhruva SS, Coon ER, et al. 2019 Update on Medical Overuse: A Review. JAMA Internal Med. Published online September 09, 2019. pmid:31498374