Journal of General Internal Medicine

, Volume 31, Issue 5, pp 561–572 | Cite as

Benefits and Harms of Screening Mammography by Comorbidity and Age: A Qualitative Synthesis of Observational Studies and Decision Analyses

  • Dejana Braithwaite
  • Louise C. Walter
  • Monika Izano
  • Karla Kerlikowske
Review Paper

Abstract

Objective

We conducted a systematic review to assess the quality and limitations of published studies examining benefits and harms of screening mammography in relation to comorbidity and age.

Methods

We searched MEDLINE and EMBASE from January 1980 through June 2013 for studies that examined benefits or harms of screening mammography in women aged 65 years or older in relation to comorbidity. For each study, we extracted data regarding setting, design, quality, screening schedule, measure of comorbidity, and estimates of benefits and/or harms. We reviewed 1760 titles, identifying 7 articles that met the inclusion criteria: prospective cohort (two studies), retrospective cohort (two studies), and decision analyses (three studies). No randomized controlled trials were identified.

Results

At least one measure of life expectancy or reduction in the risk of breast cancer death as a marker of benefit was examined in four studies, whereas three studies addressed the harms of screening mammography, including false-positive results. Both cohort studies and decision analyses showed that screening benefits decreased with increasing age and comorbidity burden.

Conclusions

The limited evidence currently available suggests that, apart from older women with severe comorbidity, women 65 and older may experience improvements in life expectancy from screening. Given the potential for harm, it is unclear whether the magnitude of the benefit is sufficient to warrant regular screening. Women, clinicians and policymakers should consider these factors in deciding whether continue screening.

KEY WORDS

breast cancer screening comorbidity aging 

Introduction

Almost half of new invasive breast cancer cases diagnosed each year in the United Sates occur among women aged 65 years and older (hereafter referred to as older women), and rates rise with advancing age.1 With the increasing life expectancy and aging of women in the U.S. and globally, the absolute number of breast cancer cases among older women is expected to increase over the coming decades. These dual demographic and epidemiologic forces, coupled with heterogeneity in health and the lack of direct evidence for screening efficacy among women aged 70 and older, create a clinical and policy conundrum: is there a combination of comorbidity and age when women should stop screening because the harms outweigh the benefits?2,3 Age-related differences in comorbidity and tumor biology, variance in women’s preferences for health outcomes associated with breast cancer screening, and increasing health care costs add to the challenge in answering this question.2, 3, 4, 5, 6, 7, 8

Numerous factors including tumor size, involvement of regional lymph nodes, histologic grade, expression of hormone receptors (estrogen and progesterone), and human epidermal growth factor receptor 2 (HER2) amplification are used to determine which women with early-stage breast cancer should be treated with adjuvant systemic therapy, including endocrine therapy, chemotherapy, and HER2-directed treatments.9 Importantly, older patients with comorbidities often experience complications from virtually all treatment modalities.10, 11, 12 Although one of the advantages of early diagnosis includes identifying tumors with favorable prognostic markers and risk assessment scores,9 such benefits may not be realized in older women with substantial comorbidity due to their short life expectancy.4 The harms of screening are often immediate, and include false-positive results and overdiagnosis.13, 14, 15, 16, 17 Given the increasing comorbidity burden and attendant decline in life expectancy, many older women are unlikely to have a favorable ratio of benefits and harms. Additionally, rates of clinically indolent invasive tumors and ductal carcinoma in situ (DCIS) increase with age, raising the concern that older women are likely to be harmed from overdiagnosis and unnecessary treatment.16 Robust evidence regarding the efficacy of screening mammography in older women is lacking because randomized controlled trials have not included women over age 74 years and those with substantial comorbidity.18

The extent to which benefits and harms of breast cancer screening in older women vary according to comorbidity and age is not well established. To better target health services to those who may benefit, it is important that screening mammography practices in older women incorporate patient factors such as comorbidity and age, which are important predictors of life expectancy. Our purpose here is to report the results of a systematic review of the literature examining the impact of comorbidity and age on screening mammography outcomes in older women. Limitations of previous studies and future directions are also discussed.

METHODS

Search Strategy and Selection Criteria

Our research question was: Do the benefits and harms of screening mammography in older women vary according to comorbidity and age? We performed a systematic search of the literature using PubMed and EMBASE (January 1, 1980, to July 1, 2013) to identify relevant studies in all languages. The term “breast neoplasms” was combined with the permutations, variations, and abbreviations of the relevant MeSH keywords and non-MeSH key terms for mammography, age, and comorbidity, including specific conditions (e.g., cardiovascular diseases, cognition disorders, diabetes mellitus, health status, heart diseases, hypertension, myocardial infarction, stroke) or comorbidity summary scores. Severe comorbidity was defined as a Charlson score of ≥ 3 and the presence of AIDS, mild or severe liver disease, chronic obstructive pulmonary disease, chronic renal failure, dementia, or congestive heart failure. A Charlson score of 3 or higher also represents severe comorbidity.

Additional studies were obtained through citations of review articles or by contacting experts in the field regarding any unpublished articles that might be suitable for inclusion in the systematic review.

For each study, two authors (MI and DB) independently abstracted data regarding study eligibility and outcomes to determine relevance. We set a priori broad inclusion criteria permitting any study design, including decision analyses that (i) included women aged 65 and older, (ii) assessed women’s comorbidity (either as a specific condition or a summary score), and (iii) reported at least one of the following outcomes: (a) tumor stage at diagnosis, (b) reassurance about negative results, (c) life expectancy and/or quality-adjusted life expectancy, (d) mortality, and (e) number needed to screen to gain one life-year. We also evaluated studies that assessed harms as outcomes, specifically (a) false-positive results, (b) false-positive biopsy, and (c) overdiagnosis. We excluded studies of women with a history of breast cancer.

To evaluate the quality of observational studies, we used the Newcastle-Ottawa Scale (NOS),19 in which a study is judged within three broad perspectives: (i) the selection of the study groups (representativeness of the exposed cohort, selection of the non-exposed cohort, and ascertainment of the exposure and demonstration that the outcome of interest was present); (ii) the comparability of the groups (comparability of cohorts on the basis of the design or analysis); and (iii) the ascertainment of either the exposure or outcome of interest (assessment of outcome, whether follow-up was long enough for outcomes to occur, and adequacy of follow-up of cohorts). Whereas a study can receive one star for meeting each criterion (*), the exception is comparability, for which a study receives one star if the study controlled for age, and two stars if the study also controlled for other important factors. Studies with a score of 5 and above (of a total of 9) are considered of moderate to high quality.

The included decision analyses were critically appraised according to criteria outlined by Richardson and Detsky20 and Justice et al.,21 with modification to include the suggestion of Justice et al.21—that models should be assessed for their transportability between populations, and that if valid, they should accurately predict events in populations other than the one in which the model was developed. This appraisal method was previously used in a systematic review of benefits and harms of screening mammography in older women.22

RESULTS

We identified 1760 potentially relevant abstracts through EMBASE and 398 through MEDLINE (see PRISMA flowchart in Fig. 1). After excluding studies with participants whose average age was less than 65 years, those that did not evaluate breast cancer or mammography screening, those that evaluated outcomes other than benefits or harms of screening, and those that did not report on comorbidity, there were 21 remaining studies published between 1980 and 2013,3,15,17,23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39 with one article in the process of publication at the time of the literature search, which has since been published.40 Reviews of the full texts of these studies resulted in the exclusion of 14 studies,3,17,23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34 leaving 7 studies15,35, 36, 37, 38, 39, 40 (Fig. 1). Characteristics of the included studies are shown in Table 1. All four cohort studies15,35, 36, 37 involved U.S. study populations, and all three decision analyses38, 39, 40 employed U.S.-based population estimates. None of the studies were clinical trials.
Figure 1

PRISMA flow diagram: description of the literature search.

Table 1

Characteristics of Studies Identified in Literature Search

Source

Setting

No. enrolled

Study design

Years of accrual

Age range, years

Length of follow-up

Measures of comorbidity

Screening regimens compared

Outcome(s) of interest reported

Cohort studies

McPherson, 2002

USA

5186

Retrospective cohort

1986–1994

65–101

1 month to 10.9 years

Charlson Comorbidity Score

Mammographic vs. clinical (palpation) diagnosis

Risk of death

Fleming, 2005

USA

17,468

Retrospective cohort

1993–1995

≥67

--

24 conditions (listed in Table 4)

Diagnostic mammography vs. screening mammography

Late-stage (regional and distant) vs. early-stage (in situ and local) breast cancer

Yasmeen, 2012

USA

149,045

Prospective cohort

1998–2006

≥67

1–6 years

Unstable (life-threatening conditions such as severe heart failure, cardiac arrhythmias, end-stage liver disease), stable (conditions that could affect daily function such as diabetes, depression, arthritis, osteoporosis), or none

1-year interval vs. 2-year interval vs. 3-year interval vs. >3 years or first screening mammography vs. >3 years or first diagnostic mammography

Advanced- (stages IIB–IV) vs. early-stage (stages I–IIA) breast cancer

Braithwaite, 2013

USA

140,942

Prospective cohort

1999–2006

66–89

1–10 years

Charlson Comorbidity Score

1-year interval vs. 2-year interval

1. Invasive breast cancer vs. ductal carcinoma in situ (DCIS)

2. Advanced- (stages IIB–IV) vs. early-stage (stages I–IIA) breast cancer

3. Large (>20 mm) vs. small (≤20 mm) tumors

4. Lymph node involvement vs. no

5. False-positive recall

6. False-positive biopsy recommendation

Decision-analytic models

Mandelblatt, 1992

USA

--

Decision-analytic model

1975–1984

≥65

--

Average comorbidity (mortality equal to that of the general population), mild hypertension (mild comorbidity), congestive heart failure (major comorbidity)

Screening vs. no screening

1. Marginal savings in life expectancy

2. Long-term quality-adjusted marginal savings in life expectancy

3. Long- and short- term adjusted marginal savings in life expectancy

Messecar, 2000

USA

--

Decision-analytic model

--

≥75

10 years

Cognitive impairment vs. no cognitive impairment

One additional screening following regular biennial screening vs. no prior screening

Quality-adjusted savings in life expectancy

Lansdorp-Vogelaar, 2014

USA

--

Decision-analytic models

--

50–90

--

None, mild (history of myocardial infarction [MI], acute MI, ulcer or rheumatologic disease), moderate (cardiovascular disease, paralysis, diabetes), or severe comorbidity (AIDS, chronic obstructive pulmonary disease, mild/severe liver disease, renal failure, dementia, congestive heart failure)

Biennial screening from age 50 to cessation age ranging from 66 to 90

1. Incremental life-years gained (LYG)

2. Cancer deaths prevented

3. Incremental number of screening tests

4. False-positive screens

5. Over-diagnosed cases

6. Number needed to screen to gain one life-year (NNS/LYG) in the population

Quality Assessment

All four cohort studies scored 5 points or more based on the NOS criteria, indicating moderate to good study quality.19 A summary of the quality scoring criteria for cohort studies is provided in Table 2. In all cohort studies, downgrading of the evidence was due to a lack of adjustment for important confounding factors. Table 3 presents a critical appraisal of the decision analyses estimating life expectancy gains from screening mammography in older U.S. women. All decision-analytic studies conducted sensitivity analyses, and used U.S. estimates of prior probabilities, utilities, and other parameters in models, and were considered of good quality.
Table 2

Critical Evaluation of the Quality and Limitations of the Cohort Studies Evaluating Benefits and Harms of Screening Mammography According to Comorbidity

 

Selection

Comparability of cohorts

Outcome

NOS

Source

Exposed cohort representative

Non-exposed cohort representative

Exposure ascertainment

Demonstration that outcome of interest was not present at start

Assessment

Follow-up length

Follow-up adequacy

McPherson, 2002

*

*

*

 

*

*

*

*

7

Fleming, 2005

*

*

*

*

*

*

*

*

8

Yasmeen, 2012

*

*

*

*

*

*

*

*

8

Braithwaite, 2013

*

*

*

*

*

*

*

*

8

†Newcastle-Ottawa Quality Assessment Scale: study can have one star (*) for meeting each criterion in the selection and outcome categories. Comparability has a maximum of two stars. In this review, one star was given if a study controlled for age and two stars if it controlled for other important factors

Table 3

Critical Evaluation of the Quality and Limitations of the Decision-Analytic Models Evaluating Benefits and Harms of Screening Mammography According to Comorbidity

Source

Were important strategies included?

Was the potential impact of uncertainty in the evidence determined?

How strong is the evidence?

Do the probabilities fit the U.S. population?

Do the utilities* reflect the values of older women in the U.S.?

Mandelblatt, 1992

Yes - compared screening for women ≥65 years with no screening

Conducted sensitivity analyses by varying quality of life, breast cancer incidence rates, perioperative death rate, sensitivity and specificity of mammography test, stage distribution of detected breast cancer

The evidence is strong, as the model assumes U.S. breast cancer stage distribution and stage-specific survival data

All measures used in models were based on U.S. population estimates

Yes

Messecar, 2000

Yes - compared 1 mammography screening in women ≥75 years with and without cognitive impairment, who (a) underwent regular screening, or (b) had no prior screening

Conducted sensitivity analyses by varying prior probabilities, quality of life, costs of recurrence, sensitivity and specificity of mammography test

The evidence is strong, as the model assumes U.S. breast cancer stage distribution and stage-specific survival data

All measures used in models were based on U.S. population estimates

Yes

Lansdorp-Vogelaar, 2014

Yes - compared biennial mammography screening from age 50 to a range of cessation ages from 66 to 90

Assessed the robustness of choice of metric by considering other harms (false-positive tests, over-diagnosed cancers) and benefits (cancer deaths prevented). Also varied method of extrapolating comorbidity-specific life tables

The evidence is strong, as the models assume U.S. breast cancer stage distribution and stage-specific survival data

All measures used in models were based on U.S. population estimates

Yes

* Weights used to adjust life expectancy gains for impact on quality of life

Estimates of Screening Mammography Benefits from Cohort Studies

Benefit Estimates by Breast Cancer Mortality (Table 4)

Table 4

Summary of Findings from Studies Evaluating the Benefits, Harms and the Balance of Benefits Versus Harms of Screening Mammography

Source

Subgroups

Outcomes Reported

Benefits

McPherson, 2002

 

Relative risk (RR) of death and 95 % confidence interval (CI)

 

Screening groups

Mammographic vs. clinical (palpation) diagnosis

 

Comorbidity

No comorbidity

Moderate

Severe

 
 

Ages: 65–69

0.44 (0.32–0.59)

0.32 (0.15–0.69)

0.41 (0.11–1.48)

 
 

Ages: 70–74

0.32 (0.23–0.44)

0.45 (0.22–0.91)

0.30 (0.11–0.79)

 
 

Ages: 75–79

0.36 (0.26–0.49)

0.47 (0.25–0.88)

0.53 (0.20–1.36)

 
 

Ages: ≥80

0.66 (0.52–0.83)

0.52 (0.33–0.80)

0.64 (0.30–1.87)

 

Fleming, 2005

 

Odds ratio (and p value) of late-stage (regional and distant) vs. early-stage (in situ and local) disease, by comorbid condition

 

Screening groups

All patients were screened

 

Comorbidity

Cardiovascular disease

Benign hypertension

Malignant hypertension

Other vascular disease

  

0.87 (P < 0.01)

0.98 (P < 0.05)

1.02 (P > 0.05)

1.04 (P > 0.05)

  

Diabetes

Endocrine disease

Neurological disease

Psychiatric disease

  

1.19 (P < 0.01)

1.11 (P < 0.05)

1 (P > 0.05)

1.2 (P < 0.01)

  

Musculoskeletal disease

Pulmonary disease, mild/moderate

Pulmonary disease, severe

Gastrointestinal disease

  

0.93 (P < 0.01)

1.08 (P > 0.05)

0.99 (P > 0.05)

0.86 (P < 0.01)

  

Benign breast disease, nonmalignant

Genital-urinary disease

Obesity

AIDS

  

0.76 (P < 0.01)

0.91 (P > 0.05)

1.18 (P > 0.05)

1.41 (P > 0.05)

  

Cerebrovascular disease

Renal disease

Gastrointestinal disease, severe

Hematologic disease

  

1.03 (P > 0.05)

1.15 (P > 0.05)

0.94 (P > 0.05)

1.19 (P < 0.01)

  

Osteoarthritis

Osteoporosis

Rheumatologic disease

Other cancers

  

0.96 (P > 0.05)

1.16 (P > 0.05)

1.02 (P > 0.05)

1.04 (P > 0.05)

Yasmeen, 2012

 

Rates (per 1000 mammograms) and 95 % confidence intervals for advanced (stages IIB–IV) vs. early-stage (stages I–IIA) breast cancer

 

Screening groups

One additional screening

 

Comorbidity

All

No comorbidities

Stable comorbidities

Unstable comorbidities

 

Time since prior screening

    
 

4–18 months (1 year)

0.7 (0.6–0.8)

0.3 (0.2–0.6)

0.7 (0.6–0.8)

0.9 (0.7–1.2)

 

19–30 months (2 years)

0.9 (0.7–1.2)

0.4 (0.1–1.5)

0.9 (0.6–1.3)

1.0 (0.6–1.6)

 

31–42 months (3 years)

1.6 (1.1–2.4)

2.2 (0.8–5.9)

1.0 (0.5–1.9)

2.7 (1.6–4.7)

 

>42 months/first screen

1.7 (1.2–2.4)

1.3 (0.4–3.9)

1.5 (0.9–2.5)

2.2 (1.3–3.9)

Braithwaite, 2013

 

Odds ratio (OR) and 95 % confidence interval (CI) for invasive breast cancer vs. ductal carcinoma in situ (DCIS)

 

Screening groups

2-year vs. 1-year interval

   
 

Comorbidity

CCS = 0

CCS ≥ 1

  
 

Ages: 66–74

0.83 (0.59–1.17)

0.92 (0.54–1.56)

  
 

Ages: 75–89

1.07 (0.71–1.60)

1.02 (0.51–2.03)

  
  

Odds ratio (OR) and 95 % confidence interval (CI) for advanced-stage (stages IIB–IV) vs. early-stage (stages I–IIA) breast cancer

 

Screening groups

2-year vs. 1-year interval

   
 

Comorbidity

CCS = 0

CCS ≥ 1

  
 

Ages: 66–74

0.75 (0.46–1.22)

0.99 (0.48–2.04)

  
 

Ages: 75–89

1.27 (0.72–2.25)

0.37 (0.13–1.04)

  
  

Odds ratio (OR) and 95 % confidence interval (CI) for large tumors (>20 mm) vs. small (≤20 mm)

 

Screening groups

2-year vs. 1-year interval

   
 

Comorbidity

CCS = 0

CCS ≥ 1

  
 

Ages: 66–74

0.83 (0.55–1.24)

0.91 (0.50–1.65)

  
 

Ages: 75–89

1.30 (0.83–2.05)

1.38 (0.70–2.73)

  
  

Odds ratio (OR) and 95 % confidence interval (CI) for positive lymph node involvement

 

Screening groups

2-year vs. 1-year interval

   
 

Comorbidity

CCS = 0

CCS ≥ 1

  
 

Ages: 66–74

0.84 (0.57–1.23)

0.76 (0.41–1.43)

  
 

Ages: 75–89

0.83 (0.51–1.33)

0.62 (0.29–1.34)

  

Mandelblatt, 1992

 

Long-term quality-adjusted marginal savings in life expectancy (in days) and 95 % confidence intervals (CI)

 

Screening groups

Screening vs. no screening

 

Comorbidity

Average health

Mild hypertension

Congestive heart failure

Average health (black)

 

Ages: 65–69

2.19 (1.97, 2.41)

1.97 (1.77, 2.16)

1.17 (1.06, 1.28)

2.17 (1.95, 2.39)

 

Ages: 70–74

1.85 (1.67, 2.03)

1.68 (1.51, 1.84)

1.08 (0.98, 1.18)

2.22 (1.99, 2.44)

 

Ages: 75–79

1.43 (1.30, 1.57)

1.32 (1.20, 1.44)

0.91 (0.83, 0.98)

1.76 (1.59, 1.94)

 

Ages: 80–84

1.08 (0.98, 1.18)

1.01 (0.92, 1.10)

0.76 (0.69, 0.82)

1.65 (1.49, 1.80)

 

Ages: ≥85

0.80 (0.73, 0.87)

0.76 (0.69, 0.83)

0.59 (0.54, 0.65)

1.16 (1.05, 1.27)

  

Long- and short- term quality adjusted marginal savings in life expectancy and 95 % confidence intervals (CI)

 

Screening groups

Screening vs. no screening

 

Comorbidity

Average health

Mild hypertension

Congestive heart failure

Average health (black)

 

Ages: 65–69

1.44 (1.22, 1.66)

1.22 (1.03, 1.42)

0.43 (0.31, 0.54)

1.42 (1.20, 1.64)

 

Ages: 70–74

1.10 (0.92, 1.28)

0.93 (0.77, 1.09)

0.33 (0.23, 0.44)

1.47 (1.25, 1.69)

 

Ages: 75–79

0.69 (0.55, 0.82)

0.57 (0.45, 0.70)

0.16 (0.08, 0.24)

1.01 (0.84, 1.19)

 

Ages: 80–84

0.34 (0.24, 0.44)

0.27 (0.17, 0.36)

0.01 (-0.06, 0.07)

0.90 (0.74, 1.06)

 

Ages: ≥85

0.05 (-0.02, 0.12)

0.01 (-0.06, 0.08)

−0.15 (-0.20, -0.10)

0.42 (0.31, 0.56)

Messecar, 2000

 

Quality-adjusted savings in life expectancy, quality-adjusted life-years (days)

 

Screening groups

One additional screening in women following regular biennial screening vs. no prior screening

 

Subgroups

Following regular biennial screening

No prior screening

 

Comorbidity

Cognitive impairment

Healthy

Cognitive impairment

Healthy

 

Ages: 75–79

0.004 (1.5)

0.009 (3.3)

0.055 (20)

0.119 (43.4)

 

Ages: 80–84

0.002 (0.7)

0.007 (2.5)

0.025 (9.1)

0.089 (32.5)

 

Ages: ≥85

0.001 (0.4)

0.006 (2.2)

0.015 (5.5)

0.071 (25.9)

Lansdorp-Vogelaar 2015

Incremental life-years gained (LYG) per 1000 individuals screened according to guidelines since age 50 in populations with average comorbidity, by model and age of screening cessation

 

Screening groups

Age of screening cessation

   
 

Comorbidity

Average comorbidity

 

Model

MISCAN-Fadia*

SPECTRUM

  
 

Age of cessation 74 (vs. 72)

7.6

5.8

  
 

Age of cessation 76 (vs. 74)

6.9

5.1

  
  

Deaths prevented per 1000 individuals screened according to guidelines since age 50 in populations with average comorbidity, by model and age of screening cessation

 

Screening groups

Age of screening cessation

   
 

Comorbidity

Average comorbidity

 

Model

MISCAN-Fadia*

SPECTRUM

  
 

Age of cessation 74 (vs. 72)

0.9

0.7

  
 

Age of cessation 76 (vs. 74)

0.9

0.7

  

Harms

Braithwaite, 2013

 

% of false-positive recalls at first mammography

 

Screening groups

First mammography for all women

   
 

Comorbidity

CCS = 0

CCS ≥ 1

  
 

Ages: 66–74

8.6 (8.3–8.8)

8.9 (8.5–9.3)

  
 

Ages: 75–89

8.0 (7.6–8.4)

8.8 (8.2–9.4)

  
  

% of women with at least one false-positive recall after 10 years of subsequent mammography, by screening interval

 

Screening groups

All women were screened annually

All women were screened biennially

 

Comorbidity

CCS = 0

CCS ≥ 1

CCS = 0

CCS ≥ 1

 

Ages: 66–74

49.7 (47.8–51.5)

48.0 (46.1–49.9)

30.2 (29.4–31.1)

29.0 (28.1–29.9)

 

Ages: 75–89

47.2 (44.9–49.5)

48.4 (46.1–50.8)

26.6 (25.7–27.5)

27.4 (26.5–28.4)

  

% of false-positive biopsy recommendations at first mammography

 

Screening groups

First mammography for all women

   
 

Comorbidity

CCS = 0

CCS ≥ 1

  
 

Ages: 66–74

1.2 (1.1–1.3)

1.7 (1.5–1.9)

  
 

Ages: 75–89

1.2 (1.1–1.4)

1.7 (1.4–2.0)

  
  

% of women with at least one false-positive biopsy recommendation after 10 years of subsequent mammography, by screening interval

 

Screening groups

All women were screened annually

All women were screened biennially

 

Comorbidity

CCS = 0

CCS ≥ 1

CCS = 0

CCS ≥ 1

 

Ages: 66–74

9.8 (8.4–11.3)

11.8 (10.1–13.8)

4.6 (4.2–5.1)

5.6 (5.1–6.2)

 

Ages: 75–89

9.2 (7.5–11.2)

11.3 (9.3–13.6)

4.1 (3.7–4.6)

5.1 (4.5–5.7)

Lansdorp-Vogelaar (in press)

False-positive tests per 1000 individuals screened according to guidelines since age 50 in populations with average comorbidity, by model and age of screening cessation

 

Screening groups

Age of screening cessation

   
 

Comorbidity

Average comorbidity

 

Model

MISCAN-Fadia*

SPECTRUM

  
 

Age of cessation 74 (vs. 72)

79

96

  
 

Age of cessation 76 (vs. 74)

77

96

  
  

Over-diagnosed cases per 1000 individuals screened according to guidelines since age 50 in populations with average comorbidity, by model and age of screening cessation

 

Screening groups

Age of screening cessation

   
 

Comorbidity

Average comorbidity

 

Model

MISCAN-Fadia*

SPECTRUM

  
 

Age of cessation 74 (vs. 72)

0.8

0.5

  
 

Age of cessation 76 (vs. 74)

1

0.6

  

Balance of benefits versus harms

Landsdorp-Vogelaar (2014)

Number needed to screen to gain one life-year (NNS/LYG), by model and age of screening cessation

 

Screening groups

Age of screening cessation

   
 

Comorbidity

Average comorbidity

 

Model

MISCAN-Fadia*

SPECTRUM

  
 

Age of cessation 74 (vs. 72)

132

173

  
 

Age of cessation 76 (vs. 74)

146

198

  

* MISCAN-Fadia: The MISCAN-Fadia model is a computer simulation program which incorporates information on the natural history of the disease as described by tumor stage and fatal tumor diameter (the size at which cancer becomes fatal) to construct models that compare the (cost-)effectiveness of different screening policies. It consists of four major components that simulate the demography and breast cancer incidence in the population, the natural history of a breast cancer tumor, the dissemination of mammography screening and its effects, and the dissemination of adjuvant treatment and its effects

SPECTRUM: SPECTRUM is an event-driven continuous-time-state model which uses population-based estimates of breast cancer incidence and distribution of stage and other breast cancer characteristic (such as estrogen receptor status, response to treatment, and mortality) to estimate the efficacy of screening programs53

In a study by McPherson et al.37 reporting on 5186 women aged 65 years and older who were diagnosed with breast cancer between 1986 and 1994 through the Upper Midwest Tumor Registry system, women’s comorbidity was assessed via the Charlson Comorbidity Score (CCS).41 In this study, women aged  ≥  65 with no or moderate comorbidity and mammography-detected tumors were found to be at reduced risk of breast cancer death compared to those with clinically detected (palpable) tumors.37 Furthermore, among women with severe comorbidity, as defined by a CCS score of ≥ 3, mammography screening was associated with reduced breast cancer mortality among women aged 70–74 years, but not in those aged <70 or >74 years.37

Benefit Estimates by Tumor Stage

Of the three studies that evaluated the risk of early versus advanced tumor stage,15,35,36 two—Braithwaite et al. and Yasmeen et al.—used data from the Breast Cancer Surveillance Consortium (BCSC) mammography registries that participated in a linkage with Medicare claims between 1999 and 2006,15,36 where information on comorbidities was obtained from Medicare claims in the 2 years before screening mammography. In another cohort study, Flemming et al. merged data from the Surveillance, Epidemiology and End Results (SEER) program with Medicare claims for 17,468 women diagnosed with breast cancer between 1993 and 1995.35 Heterogeneous measures of comorbidity were utilized: Braithwaite et al. employed the CCS,15,37,41 while Flemming et al.35 and Yasmeen et al.36 reported on 24 individual conditions and severity-based categorizations of comorbidity, respectively.

Yasmeen et al. found that overall rates (per 1000 mammograms) of advanced breast cancer were lower among women with no comorbidity than among those with stable comorbidity in annually and biennially screened women and those that received their first screen.36 However, among women who had previously undergone mammography within 4 to 18 months of cancer diagnosis, the rates of advanced-stage cancer were higher among those with either stable or unstable comorbidities than among those without comorbidities.36 In contrast, in another BCSC study, Braithwaite et al.15 reported that adverse tumor characteristics, including advanced stage, did not differ significantly by CCS or screening interval.15

Finally, Fleming et al.35 reported that women with cardiovascular disease, musculoskeletal disorders, mild-to-moderate gastrointestinal disease, and non-malignant benign breast disease had 13, 7, 14, and 24 % lower odds, respectively, of being diagnosed with advanced breast cancer, while those with diabetes, other endocrine disorders, psychiatric disorders, or hematologic disorders had higher odds of advanced-stage diagnosis by 19, 11, 20, and 19 %, respectively, compared to women without these comorbidities.

Estimates of Screening Mammography Benefits from Decision Analyses

Benefit Estimates by Life Expectancy (Table 4)

Two decision analyses in this systematic review, Mandelblatt et al.39 and Lansdorp-Vogelaar et al.,40 employed well-established, independently developed models that are part of the Cancer Intervention and Surveillance Modeling Network (CISNET), with each model simulating the life histories of large U.S. cohorts, and assessing the underlying disease in the presence and absence of screening.

In the only contemporary study examining the harms and benefits of stopping mammography according to comorbidity, Lansdorp-Vogelaar et al. compared the number needed to screen per life-year gained at different stopping ages and estimated threshold stopping ages according to the level of comorbidity, at which the number needed to screen per life-year gained was the same as mammography until age 74 for women of average comorbidity.40 The authors evaluated biennial mammography screening from age 50 to a cessation age ranging from 66 to 90 by simulating U.S. cohorts of women who were 66–90 years of age and alive in 2010, and had no comorbidity, mild comorbidity (a history of myocardial infarction, acute myocardial infarction, ulcer, or rheumatologic disease), moderate comorbidity (the presence of vascular disease, cardiovascular disease, paralysis, or diabetes), or severe comorbidity (the presence of AIDS, mild or severe liver disease, chronic obstructive pulmonary disease, chronic renal failure, dementia, or congestive heart failure), as well as comparison cohorts of women aged 74 and 76 years with average comorbidity. The authors found that, with breast cancer screening through age 74, the number needed to screen to gain one life-year among women with no comorbidity was 117–149 across models, which was lower than in the entire population with average comorbidity; cessation of screening at age 76–78 years among women with no comorbidities was estimated to yield the same number needed to screen to gain one life-year as cessation at age 74 years in the entire population.40 Finally, this study pointed to the benefits of biennial mammography across models until median ages of 76–78, 74, 70–72, and 64–68 years for women with no comorbidity, mild comorbidity, moderate comorbidity, and severe comorbidity, respectively.40

In hypothetical cohorts examining the benefits of biennial screening in terms of life-years, Mandelblatt et al.39 found that long- and short-term quality-adjusted savings in life expectancy from screening compared to a non-screening strategy were greater for older women with mild hypertension than for those with heart disease, and the benefit in both groups decreased with increasing age.

In another decision analysis examining three hypothetical cohorts of women aged 75–79, 80–84, and ≥ 85 years, with and without cognitive impairment, Messecar et al. tested two models for each group, assuming no prior screening versus continued biennial screening. Whereas all older women benefited from biennial mammography screening, among women with no prior screening, the gain in quality-adjusted life-years was lower for cognitively impaired women (20, 9.1, and 5.5 days for ages 75–79, 80–84, and ≥ 80 years, respectively) than their healthy counterparts (43.4, 32.5, and 25.9 days for ages 75–79, 80–84, and ≥ 80 years, respectively).38

Estimates of Screening Mammography Harms from Cohort Studies

Harm Estimates by False-Positive Results (Table 4)

In the only cohort study to evaluate the harms of screening mammography, Braithwaite et al. reported that the 10-year cumulative probability of a false-positive mammography result was higher among annual than biennial screeners, irrespective of comorbidity: 48.0 % (95 % CI 46.1–49.9 %) of annual screeners aged 66 to 74 years had a false-positive result, compared with 29.0 % (95 % CI 28.1–29.9 %) of biennial screeners.15

Estimates of the Harms of Screening Mammography from Decision Analyses

Harm Estimates by False-Positive Results (Table 4)

In the only decision-analytic study to evaluate the harms of screening, Lansdorp-Vogelaar et al.40 showed that ending screening at age 74 versus 72 years resulted in 96 more false positive tests and 0.5 more over-diagnoses per 1000 screening tests.

Balance of Benefits Versus Harms from Decision Analyses

Lansdorp-Vogelaar et al.40 also estimated that extending breast cancer screening from age 72 to 74 years among individuals with average comorbidity required screening 132 to 174 women to gain one life-year; continuing screening until age 76 years required an additional 146–198 women screened to gain one life-year.40

DISCUSSION

As life expectancy continues to rise, it becomes increasingly important to determine the harms and benefits of preventive services such as screening mammography in older populations. The continuing controversy of whether to extend screening mammography to older women indicates a need to evaluate the extent to which benefits and harms of screening vary according to the extent and severity of comorbidity and age. The evidence currently available from both cohort studies and decision-analytic models19,57–62 indicates that, apart from older women with severe comorbidity, women 65 and older may experience improved life expectancy from screening. Because studies in this synthesis were conducted over a long period of time, ranging from the mid-1970s to today, it is possible that outcomes may have been affected by the screening modality used, specifically film-screen versus digital mammography. However, the evidence has shown similar cancer detection rates with digital versus film-screen mammography among U.S. women aged 50–79 in the Breast Cancer Surveillance Consortium cohort.42

Comorbidity and Benefits of Screening in Older Women

Evidence points to a complex relationship between comorbidity and screening outcomes such as tumor stage at diagnosis and mortality, with variation linked to multiple patient factors including heterogeneous comorbidity measures, age, and screening intervals. Whereas Yasmeen et al. found that overall rates of advanced breast cancer were generally lower among women with no comorbidity versus those with stable comorbidity, Braithwaite et al.57 reported that adverse tumor characteristics, including advanced stage, did not differ significantly based on Charlson score or screening interval in the population-based BCSC cohort57. Moreover, Fleming et al.58 reported that the odds of early versus advanced tumor stage varied across individual comorbid conditions, with diabetes and hematologic disorders showing the highest (19 % increased) odds of advanced-stage disease at diagnosis. Finally, in women with severe comorbidity, as defined by a Charlson score ≥ 3, mammography screening was associated with reduced breast cancer mortality among women aged 70–74 years, but not in those aged < 70 or > 74 years59. Consistent with observational data, decision-analytic models indicate that benefits were unlikely among women aged 65 years or older with severe comorbidity60–62. Specifically, in a decision-analytic model, Lansdorp-Vogelaar et al. showed that the benefits of biennial mammography existed across models until median ages of 76–78 years, 74 years, 70–72 years, and 64–68 years for women with no comorbidity, mild comorbidity, moderate comorbidity, and severe comorbidity, respectively.61

Comorbidity and Harms of Screening in Older Women

Overall, there is a dearth of evidence on the harms of screening mammography in older women: only one cohort study15 and one decision model40 in this systematic review assessed screening harms according to comorbidity. Braithwaite et al. demonstrated that the cumulative 10-year probability of a false-positive mammography result was approximately twice as high in biennially screened as in annually screened women aged 66 to 74 years, irrespective of comorbidity.15 While examining one of the hypothetical cohorts, Lansdorp-Vogelaar et al.40 demonstrated that ending screening at age 74 versus 72 years resulted in 96 more false-positive tests and 0.5 more cases of overdiagnosis per 1000 screening tests. Because rates of clinically indolent tumors and ductal carcinoma in situ (DCIS) increase with age, older women are more likely to be harmed from overdiagnosis,16 defined as detection of tumors by screening that would not become clinically apparent during a woman’s life or would not affect overall survival. Given the steeper rise in competing causes of mortality in women older than 74, evidence suggests that rates of overdiagnosis are likely to be greater for older than for younger women.16,43

Decision Making Regarding Benefits and Harms of Screening in Older Women

Given the limited available evidence, the communication of potential benefits and harms to women in their 70s and 80s also poses a challenge.4,17,44, 45, 46 Clinical decisions among older populations about whether to undergo mammography may benefit from life expectancy-based screening strategies, especially given the evidence showing that screening mammography may not be targeted to the women who are most likely to benefit.47 One meta-analysis demonstrated that 10.7 years (4.4 to 21.6) on average was required before one death from breast cancer was prevented per 1000 women screened, which supports the notion that screening should be targeted to women with a life expectancy greater than 10 years.48 If these findings are replicated and confirmed with large-scale cohort data, women and their providers might consider the use of decision aids that accurately predict life expectancy in order to estimate a woman’s risk of 10-year mortality and facilitate informed decisions about screening mammography.49, 50, 51

Evidence Gaps

This review has identified many areas related to screening mammography outcomes in older women that require additional research. Without randomized controlled trials, the benefits of continued screening mammography in women aged 75 and older will need to be ascertained from cohort data and simulation models. As noted in the recent Journal of the National Cancer Institute (JNCI) editorial,52 it will be important to eschew the pseudo-precision that direct application of microsimulation models can offer by combining empirical evidence with modeling. Moreover, moving the field forward will necessitate modeling screening performance (false-positive rates, detection rates) and breast cancer survival as a function of comorbidity status and life expectancy, as well as the cost-effectiveness of various screening strategies according to comorbidity.

Strengths and Limitations

An important strength of this systematic review is that, to our knowledge, this is the first synthesis evaluating the extent to which benefits and harms of screening mammography vary according to comorbidity and age. It is important to recognize that observational data on screening mammography in older populations are subject to selection bias as well as lead-time and length bias.5 In observational studies evaluating screening mammography, the study populations of older women have self-selected to undergo mammography screening, and are thus likely healthier than the general U.S. population. Moreover, this systematic review identified heterogeneous studies with differing endpoints, which precluded us from synthesizing our results and estimating effects and bias quantitatively.

Conclusions

In summary, the limited evidence currently available suggests that, apart from the oldest women and those with severe comorbidity, women aged 65 and older may experience a slight increase in life expectancy from screening. Given the potential for harm, it is unclear whether the magnitude of the benefit is sufficient to warrant regular screening. Women, clinicians, and policymakers should consider these factors in deciding whether to continue screening. Because Medicare is required under the Affordable Care Act to pay for yearly mammography screening at no cost to women age ≥ 40, with no upper age limit,1,2 screening harms may increase among older women with multiple comorbid conditions as a result of inappropriate screening utilization. Given that a randomized controlled trial of mammography in older women is unlikely, more high-quality observational research examining innovative measures of life expectancy and contextual factors may facilitate an improved understanding of the benefits and harms of different screening mammography cessation ages and frequencies among older women and, ultimately, inform clinical and policy decisions about the appropriate use of screening in this growing population.

Notes

Acknowledgments

This research was supported by grant no. 121891-MRSG-12-007-01-CPHPS from the American Cancer Society (to Dr. Braithwaite). We thank Min-Lin Fang, MLS, and Gloria Won, MLS, for their assistance with the literature search, and Jisu Shin for administrative assistance. We are also grateful to Iris Lansdorp-Vogelaar, PhD, for her helpful comments on an earlier version of this manuscript.

Compliance with ethical standards

Conflict of Interest

The funders had no role in the design or conduct of the study; collection, management, analysis, or interpretation of the data; or preparation, review, or approval of the manuscript. The authors declare that they do not have a conflict of interest.

References

  1. 1.
    American Cancer Society: Cancer Facts and Figures, 2014.Google Scholar
  2. 2.
    Walter LC, Covinsky KE. Cancer screening in elderly patients: a framework for individualized decision making. JAMA. 2001;285:2750–6.CrossRefPubMedGoogle Scholar
  3. 3.
    Mandelblatt JS, Schechter CB, Yabroff KR, et al. Toward optimal screening strategies for older women. Costs, benefits, and harms of breast cancer screening by age, biology, and health status. J Gen Intern Med. 2005;20:487–96.CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Braithwaite D, Mandelblatt JS, Kerlikowske K. To screen or not to screen older women for breast cancer: a conundrum. Future Oncol. 2013;9:763–6.CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Mandelblatt JS, Silliman R. Hanging in the balance: making decisions about the benefits and harms of breast cancer screening among the oldest old without a safety net of scientific evidence. J Clin Oncol. 2009;27:487–90.CrossRefPubMedGoogle Scholar
  6. 6.
    Berry DA, Baines CJ, Baum M, et al. Flawed inferences about screening mammography's benefit based on observational data. J Clin Oncol. 2009;27:639–40. author reply 641-632.CrossRefPubMedGoogle Scholar
  7. 7.
    Zappa M, Visioli CB, Ciatto S. Mammography screening in elderly women: Efficacy and cost-effectiveness. Crit Rev Oncol Hematol. 2003;46:235–9.CrossRefPubMedGoogle Scholar
  8. 8.
    Walter LC, Schonberg MA. Screening mammography in older women: a review. JAMA. 2014;311:1336–47.CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Cobain EF, Hayes DF. Indications for prognostic gene expression profiling in early breast cancer. Curr Treat Options Oncol. 2015;16:340.CrossRefGoogle Scholar
  10. 10.
    Extermann M. Interaction between comorbidity and cancer. Cancer Control. 2007;14:13–22.PubMedGoogle Scholar
  11. 11.
    Extermann M. Measurement and impact of comorbidity in older cancer patients. Crit Rev Oncol Hematol. 2000;35:181–200.CrossRefPubMedGoogle Scholar
  12. 12.
    Satariano WA, Silliman RA. Comorbidity: Implications for research and practice in geriatric oncology. Crit Rev Oncol Hematol. 2003;48:239–48.CrossRefPubMedGoogle Scholar
  13. 13.
    Etzioni R, Gulati R, Mallinger L, Mandelblatt J. Influence of study features and methods on overdiagnosis estimates in breast and prostate cancer screening. Ann Intern Med. 2013;158:831–8.CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Hubbard RA, Kerlikowske K, Flowers CI, Yankaskas BC, Zhu W, Miglioretti DL. Cumulative probability of false-positive recall or biopsy recommendation after 10 years of screening mammography: a cohort study. Ann Intern Med. 2011;155:481–92.CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Braithwaite D, Zhu W, Hubbard RA, et al. Screening Outcomes in Older US Women Undergoing Multiple Mammograms in Community Practice: Does Interval, Age or Comorbidity Score Affect Tumor Characteristics or False Positive Rates? J Natl Cancer Inst. 2013;105(5):334–41.CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Mandelblatt JS, Cronin KA, Bailey S, et al. Effects of mammography screening under different screening schedules: model estimates of potential benefits and harms. Ann Intern Med. 2009;151:738–47.CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Walter LC, Lewis CL, Barton MB. Screening for colorectal, breast, and cervical cancer in the elderly: a review of the evidence. Am J Med. 2005;118:1078–86.CrossRefPubMedGoogle Scholar
  18. 18.
    Nelson HD, Tyne K, Naik A, Bougatsos C, Chan BK, Humphrey L. Screening for breast cancer: an update for the U.S. Preventive Services Task Force. Ann Intern Med. 2009;151:727–37. W237-742.CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Wells GA, Shea B, O’Connell D, Peterson J, Welch V, Losos M, Tugwell P. The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. http://www.ohri.ca/programs/clinical˙epidemiology/oxford.asp (accessed 11 Nov 2015).
  20. 20.
    Richardson W, Detsky A. Users' Guides to the Medical Literature: VII. How to use a clinical decision analysis. JAMA. 1992;273:1292–5.CrossRefGoogle Scholar
  21. 21.
    Justice AC, Covinsky KE, Berlin JA. Assessing the generalizability of prognostic information. Ann Intern Med. 1999;130:515–24.CrossRefPubMedGoogle Scholar
  22. 22.
    Barratt A, Irwig L, Glasziou P, et al. Relative benefit of mammography reduces with age. Evid Based Healthc. 2002;6:156–7.CrossRefGoogle Scholar
  23. 23.
    Kajbaf S, Nichol G, Zimmerman D. Cancer screening and life expectancy of Canadian patients with kidney failure. Nephrol Dial Transplant. 2002;17:1786–9.CrossRefPubMedGoogle Scholar
  24. 24.
    LeBrun CJ, Diehl LF, Abbott KC, Welch PG, Yuan CM. Life expectancy benefits of cancer screening in the end-stage renal disease population. Am J Kidney Dis. 2000;35:237–43.CrossRefPubMedGoogle Scholar
  25. 25.
    Walter LC, Eng C, Covinsky KE. Screening mammography for frail older women: What are the burdens? J Gen Intern Med. 2001;16:779–84.CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Yasmeen S, Xing G, Morris C, Chlebowski RT, Romano PS. Comorbidities and mammography use interact to explain racial/ethnic disparities in breast cancer stage at diagnosis. Cancer. 2011;117:3252–61.CrossRefPubMedGoogle Scholar
  27. 27.
    Terret C, Castel-Kremer E, Albrand G, Droz JP. Effects of comorbidity on screening and early diagnosis of cancer in elderly people. Lancet Oncol. 2009;10:80–7.CrossRefPubMedGoogle Scholar
  28. 28.
    Smith-Bindman R, Quale C, Chu PW, Rosenberg R, Kerlikowske K. Can Medicare billing claims data be used to assess mammography utilization among women ages 65 and older? Med Care. 2006;44:463–70.CrossRefPubMedGoogle Scholar
  29. 29.
    Schousboe JT, Kerlikowske K, Loh A, Cummings SR. Personalizing mammography by breast density and other risk factors for breast cancer: analysis of health benefits and cost-effectiveness. Ann Intern Med. 2011;155:10–20.CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Kerlikowske K, Salzmann P, Phillips KA, Cauley JA, Cummings SR. Continuing screening mammography in women aged 70 to 79 years: impact on life expectancy and cost-effectiveness. JAMA. 1999;282:2156–63.CrossRefPubMedGoogle Scholar
  31. 31.
    Walter LC, Lindquist K, O'Hare AM, Johansen KL. Targeting screening mammography according to life expectancy among women undergoing dialysis. Arch Intern Med. 2006;166:1203–8.CrossRefPubMedGoogle Scholar
  32. 32.
    Chertow GM, Paltiel AD, Owen WF Jr, Lazarus JM. Cost-effectiveness of cancer screening in end-stage renal disease. Arch Intern Med. 1996;156:1345–50.CrossRefPubMedGoogle Scholar
  33. 33.
    Sennerstam RB, Wiksell H, Schassburger KU, Auer GU. Breast cancer and clinical outcome among women over 60 years of age a plead for more screening and alternative treatments. Anal Quant Cytol Histol. 2012;34:189–94.Google Scholar
  34. 34.
    Stout NK, Rosenberg MA, Trentham-Dietz A, Smith MA, Robinson SM, Fryback DG. Retrospective cost-effectiveness analysis of screening mammography. J Natl Cancer Inst. 2006;98:774–82.CrossRefPubMedGoogle Scholar
  35. 35.
    Fleming ST, Pursley HG, Newman B, Pavlov D, Chen K. Comorbidity as a predictor of stage of illness for patients with breast cancer. Med Care. 2005;43:132–40.CrossRefPubMedGoogle Scholar
  36. 36.
    Yasmeen S, Hubbard RA, Romano PS, et al. Risk of Advanced-Stage Breast Cancer among Older Women with Comorbidities. Cancer Epidemiol Biomarkers Prev. 2012.Google Scholar
  37. 37.
    McPherson CP, Swenson KK, Lee MW. The effects of mammographic detection and comorbidity on the survival of older women with breast cancer. J Am Geriatr Soc. 2002;50:1061–8.CrossRefPubMedGoogle Scholar
  38. 38.
    Messecar DC. Mammography screening for older women with and without cognitive impairment. J Gerontol Nurs. 2000;26:14–24. quiz 52-13.CrossRefPubMedGoogle Scholar
  39. 39.
    Mandelblatt JS, Wheat ME, Monane M, Moshief RD, Hollenberg JP, Tang J. Breast cancer screening for elderly women with and without comorbid conditions. A decision analysis model. Ann Intern Med. 1992;116:722–30.CrossRefPubMedGoogle Scholar
  40. 40.
    Lansdorp-Vogelaar I, Gulati R, Mariotto AB, et al. Personalizing age of cancer screening cessation based on comorbid conditions: model estimates of harms and benefits. Ann Intern Med. 2014;161:104–12.CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40:373–83.CrossRefPubMedGoogle Scholar
  42. 42.
    Kerlikowske K, Hubbard RA, Miglioretti DL, et al. Comparative effectiveness of digital versus film-screen mammography in community practice in the United States: a cohort study. Ann Intern Med. 2011;155:493–502.CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    Randolph WM, Goodwin JS, Mahnken JD, Freeman JL. Regular mammography use is associated with elimination of age-related disparities in size and stage of breast cancer at diagnosis. Ann Intern Med. 2002;137:783–90.CrossRefPubMedGoogle Scholar
  44. 44.
    Walter LC. What is the right cancer screening rate for older adults. Arch Intern Med. 2011;171:2037–9.CrossRefPubMedGoogle Scholar
  45. 45.
    Schonberg MA, McCarthy EP, York M, Davis RB, Marcantonio ER. Factors influencing elderly women's mammography screening decisions: implications for counseling. BMC Geriatr. 2007;7:26.CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Schonberg MA, Walter LC. Talking about stopping cancer screening-not so easy. JAMA Intern Med. 2013;173:532–3.CrossRefPubMedGoogle Scholar
  47. 47.
    Scinto JD, Gill TM, Grady JN, Holmboe ES. Screening mammography: Is it suitably targeted to older women who are most likely to benefit? J Am Geriatr Soc. 2001;49:1101–4.CrossRefPubMedGoogle Scholar
  48. 48.
    Lee SJ, Boscardin WJ, Stijacic-Cenzer I, Conell-Price J, O'Brien S, Walter LC. Time lag to benefit after screening for breast and colorectal cancer: meta-analysis of survival data from the United States, Sweden, United Kingdom, and Denmark. BMJ. 2012;346, e8441.CrossRefGoogle Scholar
  49. 49.
    Schonberg MA, Hamel MB, Davis RB, et al. Development and Evaluation of a Decision Aid on Mammography Screening for Women 75 Years and Older. JAMA Intern Med. 2013.Google Scholar
  50. 50.
    Cruz M, Covinsky K, Widera EW, Stijacic-Cenzer I, Lee SJ. Predicting 10-year mortality for older adults. JAMA. 2013;309:874–6.CrossRefPubMedPubMedCentralGoogle Scholar
  51. 51.
    Yourman LC, Lee SJ, Schonberg MA, Widera EW, Smith AK. Prognostic indices for older adults: a systematic review. JAMA. 2012;307:182–92.CrossRefPubMedPubMedCentralGoogle Scholar
  52. 52.
    Kramer BS, Elmore JG. Projecting the benefits and harms of mammography using statistical models: proof or proofiness? J Natl Cancer Inst. 2015;107.Google Scholar
  53. 53.
    Mandelblatt J, Schechter CB, Lawrence W, Yi B, Cullen J. The SPECTRUM population model of the impact of screening and treatment on U.S. breast cancer trends from 1975 to 2000: principles and practice of the model methods. J Natl Cancer Inst Monogr. 1975;2006:47–55.CrossRefGoogle Scholar

Copyright information

© Society of General Internal Medicine 2016

Authors and Affiliations

  • Dejana Braithwaite
    • 1
  • Louise C. Walter
    • 2
  • Monika Izano
    • 1
    • 3
  • Karla Kerlikowske
    • 1
    • 4
  1. 1.Department of Epidemiology and BiostatisticsUniversity of CaliforniaSan FranciscoUSA
  2. 2.Division of GeriatricsSan Francisco VA Medical Center and the University of CaliforniaSan FranciscoUSA
  3. 3.School of Public HealthUniversity of CaliforniaBerkeleyUSA
  4. 4.Division of General Internal MedicineVA Medical Center, University of CaliforniaSan FranciscoUSA

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