Coronary artery disease (CAD) remains the leading cause of hospitalization in the United States, although the rate of hospitalization for CAD has declined from 77% in 2000 to 44% in 2010.1 This decline may be due to control of risk factors, including serum cholesterol levels, hypertension, and smoking; increased adoption of evidence-based medication use and timeliness of diagnosis and treatment of patients with CAD have also contributed to the decline in hospitalizations.2,3 According to the Healthcare Cost and Utilization Project (HCUP), CAD accounted for inpatient expenditures of nearly $9.5 billion (2010).4 The per-capita hospital expenditures were estimated at $22,700 in 2014, accounting for approximately 62% of total direct healthcare expenditures,5,6 suggesting that hospitalizations are the primary drivers of total direct medical care expenditures.
Existing evidence also suggests that individuals aged ≥65 years with incident cancer may be at risk for non–cancer-related hospitalizations. Although not specific to CAD-related hospitalizations, men with incident prostate cancer and cardiometabolic conditions were more likely to experience hospitalization in the period immediately after cancer diagnosis.7 The risk of first hospitalization for CAD increases during the 6 months after cancer diagnosis,8 perhaps due to cardiotoxicity associated with cancer treatments, such as radiotherapy, chemotherapy, and hormone therapy.9–11 Some studies have reported an increased risk of cardiovascular events, complications, and mortality in women who were treated with radiotherapy12–14 or were exposed to cardiotoxic drugs (trastuzumab and anthracycline).15–17
Adherence to angiotensin-converting enzyme inhibitors (ACEIs), angiotensin II receptor blockers (ARBs), β-blockers, and statins have been shown to not only improve survival,18,19 but also to reduce the risk of CAD-related hospitalizations.18,20 Specifically, statins have been reported to reduce cancer-specific mortality in those with breast, colorectal, and prostate cancers,21–24 suggesting that adherence to statins may be even more important for patients with cancer.
However, to date, no study has examined the relationship between adherence to statins and ACEIs/ARBs/β-blockers, and CAD-related hospitalizations in older individuals (age ≥65 years) with preexisting CAD and incident cancer. Because CAD is the most common preexisting condition among older adults (age ≥65 years) diagnosed with cancer,25–27 it is important to analyze the relationship between adherence to both statins and ACEIs/ARBs/β-blockers, and CAD-related hospitalizations among patients with cancer. Therefore, we evaluated the relationship between adherence to both statins and ACEIs/ARBs/β-blockers, and CAD-related hospitalizations among elderly fee-for-service (FFS) Medicare beneficiaries with preexisting CAD and incident breast, colorectal, or prostate cancer, after controlling for cardiotoxic cancer treatments and other risk factors that may affect CAD-related hospitalizations.
Patients and Methods
Conceptual Framework
This study adapted Andersen's Behavioral Model of Health Services Utilization to select independent variables that may influence CAD-related hospitalizations (health outcome).28,29 The sixth iteration of Andersen's model posits that predisposing (eg, age, sex, race/ethnicity), enabling (eg, education, poverty status), and need factors (eg, chronic conditions; cancer type, stage, and treatment; CAD severity), and external environment affect health behavior or healthcare use, which in turn affects health outcomes. Because these factors affect both healthcare use and health outcomes, they were included as covariates in the analysis.
Study Design
The study used a retrospective observational longitudinal cohort design with baseline (24–36 months before cancer diagnosis), prediagnosis (12 months before cancer diagnosis), and postdiagnosis (12 months after cancer diagnosis) periods.
Data Sources
Data were obtained from the SEER registries, Medicare claims, the American Community Survey (ACS), and the Area Health Resource Files (AHRF). The SEER program is an epidemiologic surveillance system consisting of population-based tumor registries that collects data on all incident cases of cancer that occur in persons residing in 18 SEER areas.30 The ACS census tract files provided information on the census tract poverty status,31 and the AHRF was used to derive county-level information.32
Study Population
Individuals with CAD were identified using a validated algorithm developed by the Centers for Medicare & Medicaid Services (CMS)33; the algorithm used ICD-9-CM codes. Individuals with at least 1 inpatient or 2 outpatient claims (institutional outpatient, physician office, and home health agency claims) with a primary or secondary diagnosis of CAD during the baseline period were classified as having preexisting CAD. Individuals with incident cancer were identified from the SEER registry for the period between January 2008 and December 2011.
Other inclusion criteria were age ≥68 years at cancer diagnosis, no missing data for type and stage of cancer, alive with continuous FFS Medicare Parts A and B enrollment during the entire study period, continuous Part D enrollment during the prediagnosis and postdiagnosis period, no missing information on county, and having ≥2 prescriptions for either statins or ≥2 prescriptions for any one of the following medication types: ACEIs, ARBs, or β-blockers (Figure 1).
Measures
Dependent Variable: Any Hospitalization for CAD Events: The dependent variable was categorized into 3 mutually exclusive groups based on a hierarchy: any CAD-related hospitalization, other hospitalization, and no hospitalization. CAD-related hospitalizations were defined as inpatient admissions for any cardiovascular event (heart attack, heart failure, angina, or stroke) and were identified from primary and secondary diagnoses using ICD-9-CM codes.34 This measure was calculated every 120 days before cancer diagnosis and every 120 days during the post-diagnosis period. A 120-day interval before cancer diagnosis was a baseline measurement to ensure that the change in hospitalization was associated with adherence and not due to any other factors.
Key Independent Variable: Adherence to Statins, ACEIs/ARB/β-blockers: Adherence was calculated 120 days before cancer diagnosis and every 120 days during the postdiagnosis period. A 120-day interval before cancer diagnosis was a baseline measurement to account for change in adherence due to cancer diagnosis. Based on the type of drug regimen, individuals were classified into 5 mutually exclusive groups: adherent to both medication classes (statins and ACEIs/ARBs/β-blockers), not adherent to both medication classes, adherent to either medication class, use of one medication class and adherent, and use of one medication class and not adherent.
Schematic presentation of selection criteria for study cohort.
Abbreviations: ACEIs, angiotensin-converting enzyme inhibitors; ARBs, angiotensin II receptor blockers; BB, β-blockers; CAD, coronary artery disease; HMO, Health Maintenance Organization.
Citation: Journal of the National Comprehensive Cancer Network J Natl Compr Canc Netw 15, 11; 10.6004/jnccn.2017.0168
For those with a drug regimen, PDC includes days within a specific period when an individual is covered for both statins and β-blockers, ACEIs, or ARBs. PDC was dichotomized, wherein individuals with PDC ≥80% were considered as adherent.35
Other Independent Variables: Other independent variables included predisposing (age, sex, race/ethnicity), enabling (census tract education level and poverty status, marital status, Medicare Part D prescription drug coverage), need factors (chronic physical and mental health conditions, CAD severity, and cancer type, stage, and treatment), personal health practices (tobacco and alcohol use), healthcare use (visits to primary care/cardiologist), and external environment (SEER region, county metropolitan status, percent of cardiologists and oncologists in a county).
A proxy measure was used for CAD severity based on the CMS hierarchical condition category (HCC) classification system. Based on the CMS risk adjustment model, each of the HCC codes for CAD was assigned a specific score based on the risk and severity,36 ranging from 0.231 to 0.349. Thus, higher scores represented more severe manifestations of CAD.
Statistical Analyses
Unadjusted subgroup differences in time-invariant characteristics between medication adherence categories were tested with chi-square statistics. Given that medication adherence was measured every 120 days during the prediagnosis and postdiagnosis periods, each individual had 4 observations. These 4 observations were not independent; therefore, we analyzed the relationship between adherence categories and CAD-related hospitalizations using multinomial logistic regression for repeated measures. Marginal models (eg, population-averaged models) were selected to describe changes in CAD-related hospitalization given changes in adherence categories, while accounting for nonindependence of observations within individuals.37
Inverse Probability Treatment Weight: As shown in supplemental eAppendix 1 (available online with this article at JNCCN.org), there were significant associations between medication adherence categories and for other independent variables. Therefore, we derived inverse probability treatment weights (IPTWs) to balance the independent variables using a multinomial logistic regression on medication adherence categories during the 120-day period immediately after cancer diagnosis to take advantage of the availability of cancer-related variables (eg, stage, type, treatment). These IPTWs derived for each individual were then used as weights in CAD-related hospitalization analyses.
Results
Study Population Characteristics: Before and After IPTW Adjustment
Table 1 summarizes the study population characteristics. The study population comprised 12,096 elderly FFS Medicare beneficiaries with preexisting CAD and incident cancer diagnosis. Table 2 summarizes the characteristics of the cohort by adherence to statins and/or ACEIs/ARBs/β-blockers before and
Selected Characteristics
Selected Characteristics by Adherence to Statins and/or ACEIs/ARBs/β-Blockers
Adherence to both statins and ACEIs/ARBs/β-blockers was estimated at 31.2% during the 120-day period immediately after cancer diagnosis; 13.7% were not adherent to both medication classes during the same period. Supplemental eAppendix 2 summarizes the CAD-related hospitalizations over time. The prevalence of CAD-related hospitalizations was 27.4% during the 120-day period immediately after cancer diagnosis, which declined to 10.6% during the last 4 months of the postdiagnosis period.
Associations Between Adherence
In unadjusted analyses (Table 3), patients not adherent to both medication classes (odds ratio [OR], 1.67; 95% CI, 1.58, 1.76; P<.0001) or those adherent to 1 (out of 2) medication class (OR, 1.22; 95% CI, 1.15, 1.30; P<.0001) were more likely to have CAD-related hospitalizations compared with those adherent to both medication classes. However, those using and adherent to 1 medication class were significantly less likely to have CAD-related hospitalizations compared with those adherent to both medication classes (OR, 0.76; 95% CI, 0.66, 0.86; P<.0001).
The adjusted model controlled for time and all other independent variables (Table 3). Findings from the adjusted model were consistent with the unadjusted model. For example, those adherent to both medication classes, those not adherent to both medication classes (adjusted OR [AOR], 1.82; 95% CI, 1.72, 1.92; P<.0001) and those adherent to 1 (out of 2) medication class (AOR, 1.26; 95% CI, 1.17, 1.34; P<.0001) were more likely to have CAD-related hospitalizations. In contrast to the unadjusted models, those using 1 medication class and not adherent (AOR, 1.20; 95% CI, 1.09, 1.31; P=.005) were more likely to have CAD-related hospitalizations.
Sensitivity Analyses
Instrumental Variable Regression: Instrumental variable regression was used to control for unobserved selection bias that can affect medication adherence and the relationship between medication adherence categories and CAD hospitalizations. We selected the percentage of oncologists at the county level as an instrumental variable under the assumption that the effect of oncologists on CAD-related hospitalization occurs only through medication adherence categories. For this analysis, we dichotomized adherence categories into adherent to both medications and other; CAD hospitalization was dichotomized into CAD-related hospitalizations and no CAD hospitalizations, including no CAD and other hospitalizations.
We found that the percent of oncologists at the county level was a strong and valid instrumental variable. The variable oncologists at the county level were significantly associated with medication adherence (OR, 4.27; 95% CI, 2.15, 8.47; P<.0001) and medication adherence was an endogenous variable (Wald test, P<.0001). Results from instrumental variable regression revealed that Medicare beneficiaries not adherent to 1 or both medication classes were more likely to have CAD-related hospitalizations compared with those who were adherent to both medication classes (β, 1.20; SE, 0.10; P<.0001).
Supplemental eAppendix 3 displays results from adjusted multinomial logistic regressions on CAD-related hospitalization identified from primary diagnosis. Some similarities and differences were present regarding hospitalizations identified from both primary and secondary diagnoses (primary analyses). These findings for hospitalizations identified from primary diagnosis were consistent with the primary analyses (adjusted model), except that no significant difference was observed in those using 1 medication class and adherent to that medication class compared with those adherent to both medication classes.
Associations Between Other Independent Variables and CAD Hospitalization
Supplemental eAppendix 4 displays results from adjusted generalized estimating equation models with IPTW on adherence for other independent variables. Other variables, such as cancer diagnosis categories, stage, and treatment were significantly associated with CAD-related hospitalization categories.
Discussion
To the best of our knowledge, this is the first study to examine the association between adherence to statins and ACEIs/ARBs/β-blockers on CAD hospitalizations in elderly FFS Medicare beneficiaries with preexisting CAD and incident cancer diagnosis. Consistent with the literature, our results suggest
Analyses of Multinomial Logistic Regression Models With Repeated Measures on Hospitalization Categories
Counterintuitively, our study findings indicated that those using a single medication class and adherent to that medication class were less likely to have CAD-related hospitalizations compared with those adherent to both medication classes. We speculate plausible reasons for this finding. We did not control for CAD severity with the “gold standard measure,” therefore, the lower likelihood of hospitalization by those with single medication class may partially reflect less-severe CAD. It is also plausible that individuals on a single class of medication may be adhering to Life's Simple 7 steps recommended by the American Heart Association: (1) managing blood pressure, (2) controlling cholesterol, (3) controlling blood sugar, (4) being active, (5) eating a healthy diet, (6) maintaining normal weight, and (7) not smoking.39 Unfortunately, none of these variables were available in our database.
Consistent with findings from a previous study on patients diagnosed with prostate cancer,7 we observed that CAD-related hospitalizations increased in the period immediately after cancer diagnosis for all medication adherence categories. It is possible that cardiotoxic cancer treatments increase the risk of CAD-related hospitalizations.9–11 Our study findings showed that those receiving cardiotoxic cancer treatment were more likely to have CAD-related hospitalizations.
A meta-analysis of clinical trials showed that the prophylactic use of β-blockers, ACEIs, ARBs, or statins reduced the risk of newly developed heart failure in patients administered anthracycline.40 Our study findings revealed that the interaction effect between adherence to both medication classes and cardiotoxic cancer treatment was not statistically significant. It is possible that the established CAD diminishes the cardioprotective effect of these medications. In addition, the meta-analysis did not specifically focus on the elderly population and the specific cancers included in our study. Further studies with other characteristics, such as physical activity, complementary medicine, and polypharmacy, are needed to provide conclusive evidence on the lack of interaction between adherence and cardiotoxic cancer treatment.
Clinical and Policy Implications
Both CAD and cancer are life-threatening conditions and co-management of both conditions can be challenging. However, lack of effective management of CAD may not only increase related complications but also can have negative consequences on cancer prognosis. Current clinical practices recommend stabilizing preexisting CAD before initiating cancer treatment. For example, appropriate pharmacologic management (statins and β-blockers) of patients with preexisting CAD is recommended prior to cancer surgery.41 Given the complexity of interaction between CAD and cancer, it is important to routinely monitor medication adherence in general clinical practice and provide linkages to support services that can increase medication adherence. This may warrant integration of cardiovascular care in elderly patients diagnosed with incident cancer. An example of integrated care is the cardio-oncology clinics that provide an interdisciplinary and integrative management approach to patients with cardiovascular risks or conditions.42
It is possible that certain CAD-related hospitalizations in our study may be readmissions or preventable hospitalizations, which have policy implications. The Hospital Readmissions Reduction Program, under the Affordable Care Act, provided CMS the authority to reduce payments to certain hospital readmissions that were deemed avoidable.43 The emphasis was on Medicare readmissions related to heart failure, myocardial infarction, and pneumonia. Reducing avoidable readmissions can enhance patient quality of care and lower healthcare spending. In addition, based on the Agency for Healthcare Research and Quality's (AHRQ's) prevention quality indicators, hospitalizations related to conditions, such as angina without procedure and congestive heart failure, are considered preventable hospitalizations with good outpatient care. Therefore, it is important to implement interventions for improving medication adherence and to have integrated care for patients with preexisting CAD and incident cancer diagnosis for reducing avoidable hospitalizations.
Strengths and Limitations
Our study findings need to be interpreted in the context of its limitations. First, the study findings cannot be generalized to all Medicare beneficiaries because the study population is restricted to those aged ≥68 years and with continuous enrollment in Medicare Parts A, B, and D. It must be noted that not all Medicare beneficiaries were enrolled in Part D plans. Further, the population was restricted to those with breast, colorectal, or prostate cancer and preexisting CAD. Second, we were not able to control for many other variables, such as lifestyle health behaviors, knowledge, attitude, preferences, and other factors that may affect adherence, as well as hospitalizations.44 However, we controlled for a comprehensive list of variables to examine multivariable associations. We used a proxy measure for CAD severity, which may not reflect the precise CAD severity in the population. Gold standards for measuring CAD severity, such as stress tests, are not available in the Medicare database. Third, the migration of patients in and out of SEER registry geographic catchment areas makes it difficult to measure outcomes dependent on long-term follow-up, which leads to undercounting of the study population. Further, it is possible that CAD diagnosis may be undercoded or misclassified in claims data, as these data are dependent on professional ICD coding. Therefore, CAD-related diagnosis and CAD-related hospitalizations may be underestimated. Fourth, the need factor is a social construct and comprises perceived and evaluated need in the Andersen model. Evaluated need is measurable, whereas perceived need is partly determined by health beliefs. This study included “evaluated need” as it comprised measurable/objective factors that were available in the SEER-Medicare database. Lastly, we used prescription claims for measuring adherence. Because the Part D file contains only filled prescriptions, it is not known whether the patients actually used the medications or adhered to their providers' instructions.
Despite these limitations, our study adds to the nascent literature on cardio-oncology and has several strengths. First, the current study included a large cohort of patients over a long period across a variety of providers and focused on real-world practice patterns. Second, the study used a robust repeated measure design. Third, the study used a variety of statistical methods to control for both observed and unobserved selection bias to ensure robustness of findings.
Conclusions
Nonadherence to both statins and ACEIs/ARBs/β-blockers can increase the risk other CAD-related hospitalizations in preexisting CAD and incident breast, colorectal, or prostate cancer. Both nonadherence and hospitalizations are affected by a multitude of factors, comprising patient-related, clinical (disease- and therapy-related), socioeconomic, and healthcare system aspects. Understanding how and to what extent these factors affect nonadherence and hospitalization may shed light on the clinical practice and policy implementation. Our study included a comprehensive list of factors; however, it is limited by the scope of the SEER-Medicare linked database. Future studies need to examine the role of patient-related factors (lifestyle health behaviors, knowledge, attitude, and preferences) that were not measured in this study so that strategies to improve adherence in this population can be developed.
The authors have disclosed that they have no financial interests, arrangements, affiliations, or commercial interests with the manufacturers of any products discussed in this article or their competitors.
Research reported in this publication was supported by the National Institute of General Medical Sciences of the NIH, award number U54GM104942. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
See JNCCN.org for supplemental online content.
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