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The American Journal of Managed Care January 2014
Patient-Centered Medical Home Transformation With Payment Reform: Patient Experience Outcomes
Leonie Heyworth, MD, MPH; Asaf Bitton, MD, MPH; Stuart R. Lipsitz, ScD; Thad Schilling, MD, MPH; Gordon D. Schiff, MD; David W. Bates, MD, MSc; and Steven R. Simon, MD, MPH
Process of Care Compliance Is Associated With Fewer Diabetes Complications
Felicia J. Bayer, PhD; Deron Galusha, MS; Martin Slade, MPH; Isabella M. Chu, MPH; Oyebode Taiwo, MBBS, MPH; and Mark R. Cullen, MD
Evidence-Based Guidelines to Determine Follow-up Intervals: A Call for Action
Emilia Javorsky, MPH; Amanda Robinson, MD; and Alexa Boer Kimball, MD, MPH
Electronic Health Risk Assessment Adoption in an Integrated Healthcare System
Diana S. M. Buist, PhD, MPH; Nora Knight Ross, MA; Robert J. Reid, MD, PhD; and David C. Grossman, MD, MPH
Currently Reading
Comorbidities and Cardiovascular Disease Risk in Older Breast Cancer Survivors
Reina Haque, PhD, MPH; Marianne Prout, MD; Ann M. Geiger, PhD; Aruna Kamineni, PhD; Soe Soe Thwin, PhD; Chantal Avila, MA; Rebecca A. Silliman, MD, PhD; Virginia Quinn, PhD; and Marianne Ulcickas Yood, DSc
Evaluation of Electronic Medical Record Administrative Data Linked Database (EMRALD)
Karen Tu, MD, MSc; Tezeta F. Mitiku, BSc, MSc; Noah M. Ivers, MD; Helen Guo, BSc, MSc; Hong Lu, PhD; Liisa Jaakkimainen, MD, MSc; Doug G. Kavanagh, BSEng, MD; Douglas S. Lee, MD, PhD; and Jack V. Tu, MD, PhD
Specialist Participation in Healthcare Delivery Transformation: Influence of Patient Self-Referral
Oluseyi Aliu, MD, MS; Gordon Sun, MD, MS; James Burke, MD, MS; Kevin C. Chung, MD, MS; and Matthew M. Davis, MD, MAPP
Optimal Management of Diabetes Among Overweight and Obese Adults
Denison S. Ryan, MPH; Karen J. Coleman, PhD, MS; Jean M. Lawrence, ScD, MPH, MSSA; Teresa N. Harrison, SM; and Kristi Reynolds, PhD, MPH
Why Are Medicare and Commercial Insurance Spending Weakly Correlated?
Laurence C. Baker, PhD; M. Kate Bundorf, PhD; and Daniel P. Kessler, JD, PhD

Comorbidities and Cardiovascular Disease Risk in Older Breast Cancer Survivors

Reina Haque, PhD, MPH; Marianne Prout, MD; Ann M. Geiger, PhD; Aruna Kamineni, PhD; Soe Soe Thwin, PhD; Chantal Avila, MA; Rebecca A. Silliman, MD, PhD; Virginia Quinn, PhD; and Marianne Ulcickas Yood, DSc
Effective management of the comorbidities of diabetes and hypertension may increase survival in older breast cancer survivors.
Objective: To evaluate cardiovascular disease (CVD) risk factors in older breast cancer survivors compared with a group of women without breast cancer.

Study Design: The retrospective study included (1) women aged 65 or more years who were initially diagnosed with stage I or II breast cancer from 1990 to 1994 in 6 US health plans and who survived at least 5 years post-diagnosis (cases) and (2) a matched comparison group. They were followed for a maximum of 15 years.

Methods: Data sources included medical charts and electronic health records. Cases (n = 1361) were matched on age, health plan site, and enrollment year to women in the comparison group (n = 1361). Subjects were followed to the first CVD outcome, health plan disenrollment, death, or study end. We compared rates of CVD in these 2 groups and used Cox proportional hazard models to estimate the hazard ratio (HR), considering body mass index, smoking history, diabetes, and hypertension.

Results: The strongest predictors of CVD were smoking history (HR = 1.29; 95% confidence interval [CI], 1.15-1.46), diabetes (HR = 1.72; 95% CI, 1.48-1.99), and hypertension (HR = 1.48; 95% CI, 1.31-1.67) rather than breast cancer case-comparison status (HR = 0.97; 95% CI, 0.87-1.09).

Conclusion: Results suggest that long-term prognosis in breast cancer patients is affected by management of preexisting conditions. Assessment of comorbid conditions and effective management of diabetes and hypertension in older breast cancer survivors may lead to longer overall survival.

Am J Manag Care. 2014;20(1):86-92
Our results suggest that older long-term breast cancer survivors (initially diagnosed with early-stage disease) have a cardiovascular disease (CVD) risk similar to that of otherwise healthy women of comparable ages.
  • The established risk factors (very old age, smoking history, diabetes, and hypertension) were more predictive of CVD risk than breast cancer history status.
 
  • Long-term prognosis in older breast cancer patients is affected by management of preexisting conditions, and these may be best managed by primary care providers.
 
  • Management of comorbidities in survivors should not be different from that in the general population of older patients.
More than half of the 2.6 million breast cancer survivors living in the United States are over age 65 years,1,2 and the fraction of older people with cancer is growing, partly attributable to the success of cancer screening and treatment. Consequently, the number of older cancer survivors at risk of developing other age-related conditions such as cardiovascular disease (CVD) is increasing. Many of these older breast cancer patients also have comorbid conditions or other CVD risk factors.3 Moreover, CVD is the leading cause of death in breast cancer survivors.4 As comorbidities impact prognosis and cardiovascular outcomes in breast cancer patients, the role of primary care physicians in the care of survivors is expanding to manage these preexisting conditions.

Despite CVD being the leading cause of morbidity in older breast cancer survivors, very few studies have examined CVD risk factors in such women, whether these factors differ from those in women in the general population, and the long-term impact of these risk factors on CVD outcomes.4 For example, prior studies on CVD risk in older cancer survivors were limited by cross-sectional designs; few included information on health status prior to cancer diagnosis; and even fewer included data from comparison subjects without a cancer history.5-20 Given these limitations, it is unclear whether there is excess risk of CVD among breast cancer survivors.

Examining CVD risk poses a challenge, as long-term observation periods are required. Further, CVD is more common in older adults in general and especially in those who have established risk factors other than cancer treatments. Because few older breast cancer survivors are treated with chemotherapy,21 particularly those agents known to be cardiotoxic, examining the impact of comorbidities on CVD risk is crucial. Therefore, a well-characterized comparison group with long follow-up is essential to determine whether there truly is excess morbidity in older women treated for breast cancer.

The purpose of this investigation was to determine whether incident CVD was greater in a group of older breast cancer survivors versus a cancer-free comparison group, and if the excess risk could be attributed to differences in comorbid conditions. To this end, we compared incident CVD in the 2 groups over a 15-year follow-up period, incorporating baseline risk factors such as race/ethnicity, body mass index (BMI), smoking history, diabetes, and hypertension.

METHODS

Design, Setting, and Subjects


We identified women 65 years or older who were diagnosed with early-stage breast cancer (American Joint Commission on Cancer  TNM stage I, IIA, or IIB) from January 1, 1990, through December 31, 1994, who survived at least 5 years after the initial breast cancer diagnosis. We selected 5-year survivors because this time period is most often used as a benchmark to define recovery.19 These women were participants in the BOWI study.21 Briefly, the BOWI multisite cohort study is a 10-year longitudinal study focusing on the effectiveness of treatment for breast cancer. Women in the BOWI cohort were identified through Cancer Research Network (CRN) managed care systems: Group Health Cooperative, Seattle, Washington; Kaiser Permanente, Southern California;

Lovelace Health System, New Mexico; Henry Ford Hospital and Health System, Detroit, Michigan; Health Partners, Minnesota; and Fallon Community Health Plan, Massachusetts. 21 These CRN sites were selected to achieve diversity in geography, system size, and patient populations.

The BOWII study extended data collection through 5 additional years of follow-up on the BOWI cohort and added a comparison group. The eligible BOWII case group for this analysis consisted of 1361 five-year breast cancer survivors. Comparison women were selected from the source population of each health plan. Comparisons included women who were cancer free at the time of the case’s year of diagnosis, and frequency matched (1:1) on age, health plan site, and enrollment year. These potential confounders were selected as matching variables because they are strongly associated either with survival or with treatments. To be eligible, comparison women also had to survive 5 years after enrollment into the study cohort. The final cohort consisted of 2722 women (1361 matched pairs). Women were followed from 5 years after the index date (breast cancer diagnosis date or matched enrollment  date) until first CVD event, disenrollment from the health plan, loss to clinical follow-up, death, or completion of 15 years of follow-up  (up to December 31, 2009), whichever occurred first. The protocol for this study was reviewed and approved by the institutional review board at each participating CRN site.

Data Source

Data on CVD outcomes, demographics, comorbidities, and other covariates were ascertained primarily from the women’s medical  records and were supplemented with electronic health records. Standardized medical record reviews were conducted at each site by trained medical record abstractors. A detailed description of the data collection system and the training procedures implemented to standardize data collection across sites has been published elsewhere.22 Mortality data (date of death and whether  the cause was related to breast cancer) were collected using the National Death Index. We used mortality information for censoring in the analysis.

Data Elements

Cardiovascular Outcomes. Study outcomes included the following CVD events and were examined as a single binary composite outcome (presence or absence of any event): myocardial infarction, congestive heart failure, coronary artery disease, arrhythmias, and cerebrovascular disease. The CVD events were identified by the first occurrence of a diagnosis in the women’s medical charts during the study follow-up period. Women with a diagnosis for multiple events on the same day were assigned a single outcome using a priority scale based on importance per the recommendation of one of the study clinicians (RAS): (1) myocardial infarction, (2) coronary artery disease, (3) cerebrovascular disease, (4) arrhythmia, and (5) congestive heart failure.23

Demographics and Cardiovascular Disease Risk Factors. We gathered information on date of birth, race, and ethnicity. We also collected information on diabetes, hypertension, smoking status, and BMI for both groups. Information closest to time of entry into the cohort was used in the analyses.

Statistical Analyses

Differences in demographic characteristics and CVD risk factors between case and comparison women were first examined by comparing frequency distributions (P values were based on x2 or Fisher exact tests). Cox proportional hazards models were used to estimate the hazard ratio (HR) of the association of CVD composite outcome with case-comparison status. Because we examined  only the first CVD outcome (and not multiple occurrences), and as this model is the standard approach for analyzing cohort data, we used the Cox model.

We tested the proportional hazards assumption using Schoenfeld residuals.25 The subjects’ entry into the analysis corresponded to 5 years after the initial breast cancer diagnosis for the cases and matched enrollment date for the comparisons. We examined the association with 2 models: a parsimonious model adjusted for the matching factors, age, and site, and another model that included hypertension, diabetes, smoking history, BMI, and race/ethnicity. Life table analysis was used to compare CVD incidence by case-comparison status.

RESULTS

Demographic characteristics, comorbidities, and CVD risk factors for cases and comparisons are displayed in Table 1. Although the majority of women in both groups were white non-Hispanic (81.9% and 84.3% for cases and comparisons, respectively), the race/ethnicity distribution was similar in both groups (P = .34). Case patients were more likely to be obese (BMI >30 kg/m2, P = .01) and have hypertension (P = .005) than comparison women. There were no significant differences observed in smoking history and diabetes between the 2 groups.

Table 2 displays follow-up characteristics of the case and comparison groups. The mean follow-up time among cases was 5.0 years (1828 days) after entry into the cohort compared with 5.3 years (1942 days) among comparisons. Nearly 20% of women in both groups completed 15 years of follow-up (17.4% and 19.7% for cases and comparisons, respectively). Nearly half of the women in both groups experienced a CVD event during the follow-up period, with a slightly higher proportion of comparisons experiencing a CVD event (47.7%) than cases (45.3%). The fraction of deaths was nearly 2- greater in cases (21.7%) than in comparisons (12.1%). As expected, there was a greater risk of death due to breast cancer among cases. Nearly one-third died of breast cancer in the  case group (97/295) versus 1% in the comparison group (2/165).

Of the 2722 total women in the entire cohort, 1266 (46.5%) experienced a CVD event. Of the 1266 women, 740 experienced 1 of the 5 CVD events constituting the composite outcome and 526 experienced 2 or more CVD events during follow-up (data not shown). Table 3 shows the incidence of first CVD event during the follow-up period (maximum 10 years) for the composite outcome as well  as the individual conditions. No overall differences were found with the composite CVD outcome (P = .40) nor with the individual outcomes in terms of risk. The rates of the composite CVD outcome and the individual outcomes were also similar in cases and comparisons (83.3/1000 person-years vs 82.90/1000 person years, respectively). We also examined the risk of CVD in the 2 groups  over time. The life table curves for CVD events by case-comparison status showed no difference in overall survival probabilities  between the 2 groups (data not shown). 

Table 4 presents the adjusted HR for the association between CVD and case-comparison status. Multivariable models were initially adjusted for matching factors (model 1 included age at diagnosis and health plan site). Cases were no more likely to experience a CVD event than comparisons (HR = 1.00; 95% confidence interval [CI], 0.90-1.12). In model 2, we further examined race/ethnicity, BMI, smoking history, diabetes, and hypertension. Interestingly, the strongest predictors of CVD were smoking history, diabetes, and hypertension rather than breast cancer case-comparison status (HR = 0.97; 95% CI, 0.87-1.09). For example, smoking history  increased CVD risk by nearly 30% (HR = 1.29; 95% CI, 1.15-1.46). Women with diabetes were 72% more likely to develop CVD (HR = 1.72; 95% CI, 1.48-1.99), and those with hypertension were 48% more likely to develop CVD after accounting for case-comparison status (HR = 1.48; 95% CI, 1.31-1.67). Also, white women were 50% (HR = 1.51; 95% CI, 1.28-1.77) more likely to develop CVD than minority women. As expected, CVD risk increased with older age (P for trend <.10). We did not find a trend with BMI categories, possibly due to missing values. We repeated the Cox regression analysis excluding the 121 survivors exposed to chemotherapy (and their matched comparisons). As the subset results were similar, we reported the HRs of the full cohort.

DISCUSSION

 
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