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The American Journal of Managed Care March 2018
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False-Positive Mammography and Its Association With Health Service Use
Christine M. Gunn, PhD; Barbara Bokhour, PhD; Tracy A. Battaglia, MD, MPH; Rebecca A. Silliman, MD, PhD; and Amresh Hanchate, PhD
Development and Implementation of an Academic Cancer Therapy Stewardship Program
Amir S. Steinberg, MD; Anish B. Parikh, MD; Sara Kim, PharmD; Damaris Peralta-Hernandez, RPh; Talaat Aggour, BPharm; and Luis Isola, MD
Overuse and Insurance Plan Type in a Privately Insured Population
Meredith B. Rosenthal, PhD; Carrie H. Colla, PhD; Nancy E. Morden, MD; Thomas D. Sequist, MD; Alexander J. Mainor, JD; Zhonghe Li, MS; and Kevin H. Nguyen, MS
Patients Discharged From the Emergency Department After Referral for Hospitalist Admission
Christopher A. Caulfield, MD; John Stephens, MD; Zarina Sharalaya, MD; Jeffrey P. Laux, PhD; Carlton Moore, MD, MS; Daniel E. Jonas, MD, MPH; and Edmund A. Liles Jr, MD
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Veronica Fassio, PharmD; Sherrie L. Aspinall, PharmD, MSc; Xinhua Zhao, PhD; Donald R. Miller, ScD; Jasvinder A. Singh, MD, MPH; Chester B. Good, MD, MPH; and Francesca E. Cunningham, PharmD
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Dina Hafez, MD; Laurence F. McMahon Jr, MD, MPH; Linda Balogh, MD; Floyd John Brinley III, MD; John Crump, MD; Mark Ealovega, MD; Audrey Fan, MD; Yeong Kwok, MD; Kristen Krieger, MD; Thomas O'Connor, MD; Elisa Ostafin, MD; Heidi Reichert, MA; and Jennifer Meddings, MD, MSc
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Lonnie Wen, RPh, PhD; Christine Divers, PhD; Melissa Lingohr-Smith, PhD; Jay Lin, PhD, MBA; and Scott Ramsey, MD, PhD
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Nathan D. Shippee, PhD; Michael Finch, PhD; and Douglas R. Wholey, PhD
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Denise D. Quigley, PhD; Amelia M. Haviland, PhD; Jacob W. Dembosky, MPM; David J. Klein, MS; and Marc N. Elliott, PhD

False-Positive Mammography and Its Association With Health Service Use

Christine M. Gunn, PhD; Barbara Bokhour, PhD; Tracy A. Battaglia, MD, MPH; Rebecca A. Silliman, MD, PhD; and Amresh Hanchate, PhD
This study demonstrated that a false-positive mammogram was associated with increases in outpatient visits, but not provider referrals, for 1 year post mammogram.

Objectives: A false-positive mammogram can result in anxiety, distress, and increased perceptions of breast cancer risk, potentially changing how women utilize healthcare. This study examined whether having an abnormal mammogram, considered a proxy for elevated risk perception, was associated with greater future health service use (outpatient visits and referrals).

Study Design: A retrospective cohort study using electronic health record data, spanning 2008 to 2012, from Boston Medical Center, a safety-net hospital.

Methods: We grouped 3920 women aged 40 to 75 years receiving primary care and who had a mammogram between 2010 and 2011 into 3 categories: false-positive mammogram at index date; previous false positive, but normal index mammogram; and no history of false-positive mammograms. We contrasted the longitudinal changes in outpatient visits and provider referrals, before versus after the index mammogram, between women with false-positive mammogram and those without using Poisson regression models with a difference-in-differences specification. Clinical, visit, and demographic data were obtained from the institutional clinical data warehouse.

Results: Adjusting for baseline differences in sociodemographic characteristics across risk groups and for secular changes between pre- and postindex periods, a current false-positive mammogram was associated with an 18% increase in overall outpatient visits (incidence rate ratio [IRR], 1.18; 95% CI, 1.07-1.51), but no corresponding increase in provider referrals (IRR, 1.15; 95% CI, 0.99‑1.34), relative to never having a false positive. A previous false-positive mammogram had no associated change in outpatient utilization (IRR, 0.99; 95% CI, 0.91-1.07).

Conclusions: Providers should discuss the implications of mammography findings at the time of screening to help mitigate potential detrimental effects and promote appropriate engagement in health services.

Am J Manag Care. 2018;24(3):131-138
Takeaway Points

We used a difference-in-differences approach to measure changes in healthcare utilization associated with false-positive mammography. A current false positive was associated with an 18% increase in outpatient visits, but no increase in provider referrals, relative to women with no false positive.
  • Trends encouraging mammography adherence increase opportunities for false positives, which we have shown to impact short-term health service use.
  • Counseling about potential false positives and their meaning prior to screening may mitigate the anxiety experienced at the time of an abnormal mammogram.
  • Interventions to ensure appropriate utilization are needed to address women’s concerns while promoting evidence-based care.
Despite debate about the benefits and harms of screening mammography,1-3 it remains the best available tool to detect breast cancer early and prevent morbidity and mortality. As such, the majority of women participate in mammography screening. In 2013, the CDC estimated that 69.1% of women older than 50 years underwent mammography4 as recommended by the United States Preventive Services Task Force.5 Identified harms of mammography include a high prevalence of false positives, need for additional imaging and biopsies, potential overdiagnosis of breast cancer, radiation exposure, and masking bias in dense breast tissue.1 These issues have received extensive attention in the literature and are important concepts for women to understand when considering screening.

False-positive mammograms and the resulting psychosocial harms have garnered attention in recent years.6,7 Over a 10-year screening period, the cumulative probability of a false-positive mammogram is 42% to 61%, depending on screening interval.8,9 Anxiety and distress are common among those experiencing a false-positive mammogram, with effects persisting in the months following the test.6,10 The prevalence of false positives and documented negative psychosocial outcomes are, in part, the basis for not routinely screening women younger than 50 years, who are more frequently affected by these outcomes.5,11 Guidelines instead promote shared decision making for these women, although this ideal is not often realized in practice.12-14 Research has shown that women are not often aware of the harms of mammography, including the potential for false-positive findings and future testing.15,16

Having a false-positive mammogram is further associated with increases in individuals’ risk perceptions, despite no actual change in risk or health threat,10,17 that can persist long after the abnormal test and affect health service use.18,19 Although there is interest in determining the multidimensional benefits and harms associated with screening and risk communication, the effects of designating individuals as “at risk” on healthcare utilization measures beyond the downstream diagnostic workup are understudied. This is a critical gap in knowledge at a time when improving cancer screening rates is emphasized through payment schemes intended to improve quality of care.20 Yet the process of undergoing a mammogram and receiving results may change the way a woman conceptualizes her health and engages with the medical system. Understanding the impact of these tests on how women utilize healthcare services over time is needed to improve the quality of care.

This study examined whether having an abnormal mammogram, considered a proxy for elevated risk perception,10,18 was associated with greater future health service use. Previous studies have examined breast-specific health service use in relation to other clinical markers of risk status, such as genetic mutations or family history.21-23 This study expanded the measure of healthcare utilization beyond breast-specific services among a population of women undergoing mammography. We hypothesized that women who experienced a recent or previous false positive would have higher postmammogram visit and referral rates relative to women without false-positive mammograms. Although we were unable to fully characterize the purpose and content of these outpatient visits and referrals, this study sought to deliver an initial assessment of healthcare utilization changes following a false-positive test result, establishing the basis for a detailed exploration of such visits and assessment of whether additional service use represents value-based care.


This retrospective cohort study examined whether experiencing a false-positive mammogram recently or in the past was associated with healthcare utilization by comparing rates of outpatient visits and referrals in the 2 years prior to an index mammogram with 1 year following. This study was determined to be exempt by the Boston University Medical Campus Institutional Review Board.


Utilization data came from Boston Medical Center (BMC), an urban safety-net hospital. Included women: 1) were aged 40 to 75 years, 2) received primary care at BMC, 3) had a screening mammogram at BMC in 2010 or 2011 that did not result in a cancer diagnosis, and 4) had their first mammogram at 40 years or older. Women receiving primary care at BMC were identified by a documented visit with a designated provider in the preceding 2 years. Prior work has demonstrated that these patients use the hospital system as their main source of care for all conditions, including emergency and specialist care.24 The first mammogram during 2010 or 2011 was defined as the index mammogram. Women with a prior cancer or cancer diagnosed during the study period or those who died during the study were excluded.

Data Collection

Administrative and clinical data were obtained through the clinical data warehouse, a comprehensive database that aggregates data from multiple electronic hospital sources. The use of retrospective clinical data provided a measure of actual utilization rates, eliminating bias from self-reported data. Data collected spanned calendar years 2008 to 2012.

Study Design

We used a difference-in-differences (DID) approach, which measures the difference in utilization before and after the index mammogram for women who experienced a false-positive mammogram and contrasts this difference with the corresponding difference for women who experienced no false-positive mammogram.25 This approach adjusts for pre-index differences in utilization between the 2 groups and secular changes in utilization between pre-index and postindex periods. We defined the 2-year period before and 1-year period after the index mammogram visit as the pre-index and postindex periods, respectively. This provided a stable measure of utilization prior to the index mammogram and allowed for assessment of bias that may have threatened internal validity.26

Comparison Groups

An abnormal mammogram was defined using criteria of the Breast Imaging Reporting and Data System (BI-RADS) Atlas, 4th edition.27 A normal finding included BI-RADS 1 (normal) or 2 (benign) classifications. BI-RADS scores of 0 (incomplete), 3 (probably benign), 4 (suspicious), or 5 (highly suggestive of malignancy) were included in the abnormal mammogram group, as they required a follow-up or further testing. False positives were defined as those abnormal findings that did not result in a cancer diagnosis as assessed by the Tumor Registry.

BI-RADS results were used to create 3 groups of women that reflected levels of risk designation7,10,17,18: 1) current false positive (high risk): women experiencing a false-positive mammogram at the index date and indicative of high risk perception; 2) previous false positive (intermediate risk): women who had a normal mammogram result on the index date with a false-positive result in the past; and 3) no false positives (low risk): women who had a normal index mammogram result and no previous false-positive results. We distinguished current from previous false-positive mammograms to test if changes in utilization following a current false positive would persist over time.


We examined 2 utilization indicators as outcomes: referrals and outpatient visits. The number of referrals measured provider-initiated orders for health services and was totaled for each time period. Referrals were captured as “orders,” and all types (ie, specialty visits, laboratory) were compiled to measure the total count documented in the medical record over each time frame. Counts of all outpatient visits attended for each 12-month period were also measured and included all primary care, specialty, emergency department, laboratory, and procedure visits. Diagnostic tests included in the follow-up for an abnormal mammogram were excluded.

Demographic covariates included age, race/ethnicity, insurance, education, and primary language. Baseline comorbidities were measured by identifiying all unique diagnosis codes (International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM]) in the pre-index period and grouping them into 30 categories using the Elixhauser classification.28,29 A dichotomous indicator variable for time (pre-index vs post index) was created and later interacted with the 3 groups to assess differences over time by group.25

Comparison of Pre-Index Longitudinal Trends Between Comparison Cohorts

A critical assumption underlying the appropriateness of the DID design was that the longitudinal trends in utilization for the 2 comparison cohorts, women with a current or previous false-positive mammogram versus those without, diverged from a parallel trajectory only after the false-positive mammogram event; that is, prior to this event, the longitudinal trends between the 2 groups should have followed similar trends (ie, were parallel).30 We tested this “parallel trends” test by using only pre-index event data, dividing the 2 pre-index years of utilization into 2 single-year utilization periods; estimated a similarly specified DID model limited to only the pre-index data; and tested if the changes in utilization between the 2 years were similar between the 2 comparison groups (current vs never; previous vs never). Confirmation of parallel trends enhanced the ability of the main DID model (with complete data) to attribute postindex utilization changes to false-positive mammograms.

Propensity Score Weighting

Another potential source of confounding was systematic differences in the prevalence of the observed demographic and socioeconomic covariates and pre-index utilization between the groups of women compared. One approach to adjust for potential imbalance was the use of propensity score weighting known as standardized mortality ratio weighting (SMRW).31 In this 2-step method, we first estimated a logistic regression of the indicator of false-positive mammogram (0/1) on age, race, language, education, insurance, Elixhauser categories, and pre-index visits (or referrals) to obtain the predicted probability of false-positive mammogram (propensity score); in the second step, we estimated the main regression model using SMRW, wherein women with false-positive mammograms were assigned a weight of 1 and women without false-positive mammograms were assigned a weight equal to the ratio of the propensity score to 1 minus the propensity score.32 The propensity score weighting was used to produce comparable groups with parallel pre-visit trends.

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