Potential Medication-Related Problems in Older Breast, Colon, and Lung Cancer Patients in the United States Free

As the prevalence of multiple chronic conditions increases with age, older adults (age 65+ years) and their healthcare providers often must manage the use of multiple prescription medications. At the same time, age-related changes in body composition and organ function can alter the way the body processes and reacts to drugs, making older adults more sensitive to both the intended and unintended effects of medications (1). A recent study reported that nearly 40% of older Americans were taking five or more prescription drugs (i.e., polypharmacy) in the prior 30 days (2). This is concerning given that polypharmacy is associated with an increased risk of drug–drug interactions and adverse drug events (ADEs; ref. 3). In addition, polypharmacy increases the chances that an older adult will be prescribed a potentially inappropriate medication (PIM), i.e., a drug that has a high risk of an ADE relative to its potential benefit, when safer, more effective and well-tolerated options are available (4, 5). Taken together, exposure to polypharmacy, drug–drug interactions, and PIM have serious consequences for the healthcare system, increasing the use of avoidable healthcare services and costs, but also for older adults, decreasing functional capacity and quality of life (6–10).

As the proportion of cancer patients diagnosed at age 65 years and older is expected to reach 70% by 2030 (11), medication management among this population is a growing public health concern (12). Compounding the medication management complexities relevant to all older adults is the fact that older adults with cancer are also exposed to cancer-directed treatments, including chemotherapy, which have the potential to interact with concomitant medications used to manage other acute and chronic conditions (13). Furthermore, cancer patients also frequently use supportive care medications, some of which are considered PIMs, to manage cancer symptoms (e.g., pain and insomnia) and treatment-related side effects (e.g., nausea and diarrhea). As such, individualized assessment and scrutiny of these medications and their benefit–risk balance, considering life expectancy, cancer aggressiveness, and other coexisting conditions, is necessary to optimize medication use in this unique patient population.

At the population level, documentation of the prevalence of cancer-related PIM use and drug–drug interactions could help alert oncology providers to these problems and highlight subgroups of patients who have high exposure and for whom targeted intervention and medication reviews may be warranted. To generate such knowledge, we conducted a large, population-based study of older adults newly diagnosed with breast (I–III), colon (stage II–III), and lung (stage I–II) cancer to: (i) describe the monthly prevalence of PIM use from 6 months before through 23 months following cancer diagnosis, with a specific emphasis on cancer-related PIM and (ii) quantify the 12-month period prevalence of potential drug interactions among patients treated with specific chemotherapeutic agents.

We drew upon the Surveillance, Epidemiology, and End Results program (SEER)–Medicare database (14), a linkage of cancer registry and Medicare enrollment and claims data. This linked database includes cancer cases through 2011 and Medicare claims through 2013. Medicare Part A and B claims provide information on diagnoses and procedures in the hospital and outpatient setting and Part D claims provide information on prescription drug dispensing (available from 2007 to 2012).

For this study, we identified adults ages 66 years and older who were diagnosed with a first, primary cancer of the colon [American Joint Commission on Cancer 6th Edition (AJCC) stage II or III], breast (AJCC stage I–III), or lung (AJCC stage I–II) from 2007 to 2011. These cancer sites and stages were selected to identify populations that might receive chemotherapy, excluding older adults diagnosed with advanced stage disease, where the risk–benefit assessment of PIM use is less clear. To be included in the study cohort, individuals had to have: (i) at least 12 months of continuous Medicare enrollment in Parts A and B prior to their diagnosis date (set to the first day of the month of diagnosis) to assess relevant comorbid conditions, (ii) Medicare Part D (prescription drug) coverage during the month of diagnosis, and (iii) at least one prescription medication dispensed in the month of diagnosis. Individuals who were diagnosed at autopsy, did not survive throughout the month of diagnosis, or had a missing month of diagnosis were excluded.

Demographic and tumor characteristics were obtained in the month of diagnosis, including age, sex, race (white, black, other), marital status (married, single, divorced/widowed/separated), year of diagnosis, and AJCC stage. Using Medicare claims data, we assessed comorbidity using the Charlson Comorbidity Index (15; categorized as 0, 1, and 2+) during the 12 months prior to the month of diagnosis and medication burden using a count of the number of unique prescriptions (generic name level) in the month prior to the month of diagnosis (categorized as 0–2, 3–5, 6–9, and 10+). Using the same 12-month period, we estimated each individual's predicted probability of being frail based on an internally (16) and externally (17) validated Medicare claim-based model, including 20 unique variables (e.g., diagnosis of weakness, wheelchair claim, and home oxygen claim). The resulting predicted probabilities were categorized as 0%–<10% (low probability of frailty), 10%–<20% (low–intermediate), 20%–<50% (intermediate–high), and 50%+ (high). Finally, we constructed a variable for whether an individual underwent surgical resection or received chemotherapy or radiation in the 6 months following diagnosis. In addition, we constructed indicators for the use of specific chemotherapeutic agents in each month from diagnosis through month 11. Codes used to define cancer treatments from Medicare claims are listed in the Supplementary materials.

We identified PIMs according to the 2012 Beers criteria (4), a medication-screening tool developed to help healthcare providers optimize medication use in older adults. The Beers criteria, originally developed in 1991, have been regularly updated by the American Geriatrics Society. The 2012 Beers criteria include 34 drugs to avoid in older adults and 18 drugs that should be avoided as they could exacerbate a coexisting disease (i.e., drug–disease interactions). Prevalence of any PIM dispensing was evaluated monthly, starting 6 months before and going through 23 months following the diagnosis month (month 0). The precancer diagnosis period (months −6 to −1) was used to establish baseline PIM use patterns prior to a cancer diagnosis and to facilitate comparison with published prevalence estimates of PIM in the general older adult population. We selected the 23 months following the month of cancer diagnosis to evaluate patterns of PIM use during the initial treatment (month 0–11) and continuing (month 12–23) phases of cancer care (18, 19), as the transitions in PIM prevalence related to cancer treatment were of particular interest. All analyses were anchored at the month of cancer diagnosis (month 0).

Of particular interest were PIMs related to the alleviation of cancer symptoms and treatment-related side effects (referred to as cancer-related PIMs). We specifically examined the broad Beers criteria categories of “pain” and “central nervous system” to identify cancer-related PIMs for pain, anxiety/depression, and insomnia and then identified specific PIMs frequently used to manage nausea, diarrhea, and appetite in cancer patients. For presentation purposes, cancer-related PIM analyses were limited to specific PIMs that had a >1% prevalence in at least 1 month for at least one cancer site.

To be included in the denominator for a given month, individuals had to have (i) at least 12 months of continuous Medicare enrollment in Parts A and B prior to the given month of interest, (ii) Medicare Part D (prescription drug) coverage during the month, and (iii) at least one prescription medication dispensed (or days' supply carried over) in a given month. Dispensing of a prescription medication was a requirement for an individual to contribute to the denominator, consistent with prior studies (20–22), as an adult who is not receiving any prescription medications cannot be exposed to a PIM. Because eligibility was determined on a month-by-month basis, the number of individuals contributing to monthly prevalence measures changes over time.

PIMs were identified using Medicare Parts A, B, and D claims as described by Jiron and colleagues (22). We first used the Anatomical Therapeutic Chemical (ATC) classification system to identify all medications and classes of medications listed as part of the 2012 Beers criteria and then developed a crosswalk of these medications to their specific National Drug Codes (NDCs). For PIMs due to drug–disease interactions, we used International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9) codes to identify specific conditions in the 12 months prior to the given month of interest.

We identified potential drug–drug interactions involving chemotherapeutics through a review of the literature (13, 23–29) with confirmation by two clinical pharmacists with expertise in oncology and geriatrics. Our review was limited to potential interactions with chemotherapeutic agents used as initial treatment for stage I–III breast, II–III colon, or I–II lung cancer. We again used the ATC classification system to identify all medications included in our review and developed a crosswalk of these medications to their specific NDCs. A clinical pharmacist further classified each potential drug–chemotherapeutic interaction as minor (caution advised), moderate (monitor/modify therapy), or major (avoid/use alternative) using Micromedex Online (Micromedex, Inc.).

The 12-month period prevalence of potential drug–chemotherapy interactions was evaluated on the specific chemotherapy agent level during the initial treatment phase of care (month 0–11). To be included in the denominator for the period prevalence analyses, individuals had to have: (i) continuous Medicare enrollment in Parts A, B, and D (and no managed care coverage) during the entire initial treatment phase and (ii) at least one claim for the administration of a specific chemotherapeutic agent of interest in at least 1 month during this period. To be included in the numerator, patients had to have a prescription claim for a potentially interacting drug and overlapping days' supply with the administration of a specific chemotherapeutic agent of interest. For presentation purposes, we restricted our descriptive analyses to specific chemotherapies that had more than 100 patients in the denominator in an attempt to avoid imprecise estimates. All prevalence with a numerator of <11 were suppressed due to SEER-Medicare privacy rules. Specific chemotherapeutics included were 5-fluorouracil (5-FU)/capecitabine (colon), cyclophosphamide (breast), doxorubicin (breast), methotrexate (breast), paclitaxel (breast, lung), carboplatin (lung), cisplatin (lung), etoposide (lung), and gemcitabine (lung). Tamoxifen (breast) was also included in analysis, but is considered endocrine therapy.

We estimated the monthly point prevalence of any PIM among the 3 cancer cohorts from the 6 months before through the 23 months following the month of cancer diagnosis (month 0). Given the specific interest in the influence of chemotherapy on the use of PIMs as supportive care agents, month-level analyses were stratified by chemotherapy receipt (yes versus no). The monthly prevalence of specific cancer-related PIMs was computed among the three cohorts. Finally, we estimated the 12-month period prevalence of potential drug–chemotherapy interactions during the initial treatment phase, stratifying results by cancer site. All statistical analyses were performed in SAS version 9.4. This study was performed after approval by the University of North Carolina at Chapel Hill Institutional Review Board.

After applying all study inclusion criteria, there were 19,318 stage I–III breast, 7,283 stage II–III colon, and 7,237 stage I–II lung cancer patients included in our baseline cohorts (Supplementary Table S1). Demographic and clinical characteristics of these patients are reported in Table 1. Median age at cancer diagnosis was similar across cohorts at 75 years for breast and lung cancer to 78 years for colon cancer. Variation in the burden of comorbidity at the time of cancer diagnosis was observed across the three cohorts, where lung cancer patients had the highest proportion of patients with a Charlson comorbidity score of 2 or more (lung: 36%, colon: 26%, and breast: 19%) and were dispensed the greatest number of prescription medications in the month prior to cancer diagnosis (% receiving 6+ medications, lung: 33%, colon: 27%, and breast: 25%). However, colon cancer patients had the highest probability of being frail (using a claims-based model), while breast cancer patients had the lowest probability. During the 6 months following cancer diagnosis, 98% of all colon cancer patients and 94% of breast cancer patients underwent surgical resection, compared with only 46% of lung cancer patients. Chemotherapy was most common among colon cancer patients (34%), while radiation was frequent among breast cancer patients (44%).

The monthly prevalence of any PIM prior to cancer diagnosis was similar across all three cancer cohorts, hovering between 37% and 40% (Fig. 1), similar to general population estimates among Medicare beneficiaries (22). However, following cancer diagnosis, different patterns emerged. The prevalence of PIM among breast cancer patients consistently decreased over the period following diagnosis, whereas PIM prevalence sharply increased in the first few months following a colon or lung cancer diagnosis, and slowly decreased back to prediagnosis levels over the following 23 months. Decreases in PIM prevalence in the breast cancer cohort were attributable to decreased dispensing of estrogen following diagnosis (9% at 6 months prior to diagnosis to 0.5% at 1 year after diagnosis).

We further plotted the monthly prevalence of any PIM dispensing stratified by chemotherapy receipt (Fig. 2A–C). Longitudinal patterns across cancer sites were consistent showing a sharp immediate increase in the monthly prevalence of both PIM dispensing among individuals who initiated chemotherapy within the first 6 months following cancer diagnosis. In contrast, the monthly prevalence of PIM remained relatively constant among those who do not initiate chemotherapy.

The monthly prevalence of cancer-related PIM use is presented in Fig. 3A–C. Across all three cancer cohorts, the prevalence of amitriptyline use (a tricyclic antidepressant) was high, but remained relatively constant over the study period from 1.5% to 2.5%. The monthly prevalence of cyclobenzaprine (a muscle relaxant) was also steady, but lower across cohorts ranging from 0.5% to 1.5%. The lowest cancer-related PIM prevalence was for dicyclomine (an antispasmodic), which was also stable across the trajectory of care, with the exception of the colon cancer cohort, where a small spike in the month of diagnosis was observed.

For breast cancer, promethazine (an antiemetic) was the most common cancer-related PIM, with a monthly prevalence that was elevated in the first 9 months following cancer diagnosis (1%–3%), but returned to prediagnosis levels (<1%) thereafter. In the lung cancer cohort, the prevalence of promethazine and megestrol (a drug indicated to increase appetite) use increased after cancer diagnosis and remained elevated throughout the following year (promethazine: 1%–3%; megestrol: 2%–4%). The colon cancer cohort had the most cancer-related PIM use, including a high prevalence of metoclopramide, a pro-motility drug used to speed transit after surgery, (3%–4%) and promethazine (3%) use during the initial months following diagnosis. Increasing prevalence in belladonna alkaloid use, an antispasmodic, (2%–3%) and megestrol (1%–3%) were largely sustained during the year following cancer diagnosis.

The 12-month period prevalence for selected potential drug–chemotherapeutic interactions is presented in Table 2 along with a brief description of the potential interaction outcome (e.g., increased chemotherapy effect). Overall, the reported period prevalence of potential drug–chemotherapy interactions ranged from 1% to 31%. The most prevalent potential interactions in the colon cancer cohort were for 5-FU/capecitabine and hydrochlorothiazide (a diuretic used to manage blood pressure, 22%) and warfarin (a drug used to treat blood clots, 15%), both classified as moderate potential drug interactions. In the breast cancer cohort, the most prevalent potential interactions classified as major included cyclophosphamide and hydrochlorothiazide (31%) and methotrexate and nonsteroidal anti-inflammatory drugs (drugs used to inflammation, pain, and fever, 16%), while the most prevalent moderate interaction was for methotrexate in combination with proton-pump inhibitors (drugs used for suppression of gastric acid, 29%). Finally, in the lung cancer cohort, warfarin was considered a major interaction when used together with etoposide (14%) and gemcitabine (15%).

Prior to cancer diagnosis, we observed that the vast majority of cancer patients used multiple prescription medications, many of which were considered to be potentially inappropriate according to the Beers criteria. However, following a cancer diagnosis, clear evidence of changes in patterns of medication use emerged, many as a direct result of cancer-related care. The prevalence of PIM dispensing increased following a diagnosis of colon and lung cancer, but decreased following a breast cancer diagnosis. When further stratified by chemotherapy receipt, changes in PIM prevalence were observed among those initiating chemotherapy in all cohorts. Further exploration of cancer-related PIM dispensing revealed that changes in PIM use were largely due to the addition of supportive care medications, in particular, antiemetics and antispasmodic drugs. In addition, we observed a range of prevalence of potential drug–chemotherapy interactions across cohorts, with the most prevalent interactions involving hydrochlorothiazide, warfarin, and proton-pump inhibitors.

Although a handful of studies have evaluated the prevalence of PIM use in older cancer patients at diagnosis or before initiating treatment (30–34), only two prior studies have specifically used the 2012 Beers criteria to assess PIM prevalence. The first study by Maggiore and colleagues (32) included 500 older adults with cancer-initiating chemotherapy at seven academic medical centers in the United States. In this study, PIM prevalence, assessed via self-report and medical record verification, was 29%. This estimate is lower than that reported in our study, which may be due to the inclusion of: (i) only the Beers drugs to be avoided and not PIM drug–disease interactions and (ii) individuals who were initiating chemotherapy. When restricted to older adults' initiating chemotherapy, the PIM prevalence in our cohorts (prior to diagnosis) was lower (32%–35%), likely because these populations are healthier and have a lower overall medication burden. The second study by Nightingale and colleagues (33) was conducted among 248 patients who underwent a routine comprehensive geriatric assessment at an academic medical center and had generally not received any cancer treatment or supportive care. PIM was assessed through a pharmacist-led medication review with the patient and/or caregiver. The findings from this study are largely consistent with our results with a reported prevalence of PIM use (prior to cancer treatment) of 40%. Notably, neither of these studies evaluated patterns of PIM use over the trajectory of cancer care nor focused specifically on cancer-related PIM use. This information is important to clarify the unique medication-related issues facing older adults newly diagnosed with cancer and their healthcare providers.

It is important to recognize that the classification of a prescription drug as a PIM or cancer-related PIM only indicates that it might be inappropriate based on population-level data. There may be very good reasons for prescribing a medicine included on the Beers list, once the actual risks and benefits are considered within in the context of a particular individual and their cancer. Our findings indicate that changes in the prevalence of PIM dispensing over the course of cancer care are primarily driven by the use of supportive care medications among patients initiating chemotherapy. Although some of these medications may not be considered inappropriate when administered in the oncology setting because of a lack of alternatives with fewer adverse effects (e.g., megestrol for appetite stimulation), others (e.g., metoclopramide or promethazine) have therapeutic alternatives with better benefit–risk profiles for older adult populations. If a PIM is used for treating an older adult with cancer, increased efforts to monitor and manage side effects may be warranted.

Only a handful of small studies have investigated the frequency of potential drug–drug interactions involving chemotherapies in cancer patients and have found that drug interactions involving warfarin, quinolones, and antiepileptics are common (23–28), consistent with our findings. Yet no study evaluated the prevalence of these interactions among groups of older cancer patients truly at risk of an interaction (i.e., using a denominator of those patients receiving specific chemotherapies of interest). Thus, the estimates provided in this study fill an important gap clarifying the potential burden of these specific medication-related problems for cancer patients receiving chemotherapy. Still, caution is warranted when interpreting the potential drug–chemotherapy interaction analyses. Careful weighing of risks and benefits of specific medications in the context of a new cancer diagnosis and the expected benefits of cancer-directed treatment make these decisions particularly complex. In general, the severity of the potential drug–chemotherapy interaction may help to guide the level of concern and intervention or consultation with a pharmacy specialist.

We wholeheartedly concur with Nightingale and colleagues (33) that clinical pharmacists or pharmacologists can and should play a more prominent role in multidisciplinary teams caring for older adults with cancer. Pharmacy specialists are uniquely positioned to assess, plan, and optimize both oncology and nononcology medications prior to beginning new cancer or supportive care treatments, as well as following the completion of cancer treatment by ensuring continuity and coordination of care with patients' general practitioner and medical specialists. This broader review of medication quality and safety for older adults can ultimately lead to improved cancer- and noncancer outcomes. We found that the burden of PIM use varied by cancer site and chemotherapy receipt. Given resource constraints in busy oncology clinics, focused medication reviews, led by a pharmacy specialist, might target populations with the highest likelihood of being exposed to a medication-related problem. In this study, we found that changes in PIM prevalence were largely driven by the receipt of chemotherapy. Taken together with concerns about the potential for drug interactions with specific chemotherapeutic agents, this population might be a reasonable target for more in-depth intervention via medication reviews.

The primary strengths of this study include the large, diverse study population of older breast, colon, and lung cancer patients treated in real world settings, expanding upon the generalizability of prior studies focused on patients treated in academic medical centers. In addition, our work provides an expanded view of PIM use by describing longitudinal patterns of PIM prevalence over the course of the cancer care continuum, highlighting subgroups and time points where PIM prevalence is increased. This analysis used prescription medication dispensing information to identify PIM, and thus captures a more complete assessment of prescription medications dispensed across various healthcare settings and providers, which is not subject to recall bias.

Our study is subject to some important limitations. Medicare claims' data do not contain information on over-the-counter medications, herbal/supplements, or benzodiazepines (as they were not reimbursed by Medicare until 2013; ref. 35). As such, the monthly PIM prevalence presented is likely underestimated, especially among patients initiating chemotherapy, where benzodiazepine prescribing is common (36, 37). In addition, we did not attempt to identify all potential drug–chemotherapy interactions in our study cohort, but instead selected potential interactions for examination based on a literature review, indicating the most prevalent interactions. Finally, we did not evaluate specific outcomes of PIM dispensing or potential drug–chemotherapy interactions in older cancer patients (e.g., hospitalization, emergency department visits, mortality), however, we recognize this as an important area of future research.

Despite these limitations, this study has expanded our knowledge regarding potential medication-related problems among older adults newly diagnosed with breast, colon, and lung cancer over the course of cancer care. Our findings highlight the use of multiple supportive care medications and drug interactions that may be considered potentially inappropriate among older cancer patients receiving chemotherapy. Physician assessment of medication risks and benefits and consideration of possible treatment alternatives seems reasonable, given the potentially adverse profile of these mediations in older adult populations. Given the unique and complex aspects of cancer-directed treatment and medication management among older adults newly diagnosed with cancer, inclusion of clinical pharmacists or pharmacologists on multidisciplinary teams caring for older cancer patients is warranted.

J.L. Lund has an immediate family member employed at GlaxoSmithKline. T. Stürmer reports receiving commercial research grants from AstraZeneca and Amgen, as well as other commercial research support from GlaxoSmithKline, Merck, and UCB BioSciences, and has ownership interest in Novartis, Roche, BASF, AstraZeneca, and NovoNordisk. No potential conflicts of interest were disclosed by the other authors.

Conception and design: J.L. Lund, H.K. Sanoff, T. Stürmer

Development of methodology: J.L. Lund, H.K. Sanoff, H.B. Muss, V. Pate

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): J.L. Lund

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): J.L. Lund, H.K. Sanoff, S. Peacock Hinton, H.B. Muss, V. Pate, T. Stürmer

Writing, review, and/or revision of the manuscript: J.L. Lund, H.K. Sanoff, S. Peacock Hinton, H.B. Muss, V. Pate, T. Stürmer

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): J.L. Lund, S. Peacock Hinton

Study supervision: J.L. Lund

This work was supported by the National Cancer Institute K12CA120780 (to J.L. Lund) and through database infrastructure through the University of North Carolina Clinical and Translational Science Award (UL1TR001111).

This study used the linked SEER-Medicare database. The collection of cancer incidence data used in this study was supported by the California Department of Public Health as part of the statewide cancer reporting program mandated by California Health and Safety Code Section 103885; the National Cancer Institute's SEER Program under contract HHSN261201000140C awarded to the Cancer Prevention Institute of California, contract HHSN261201000035C awarded to the University of Southern California, and contract HHSN261201000034C awarded to the Public Health Institute; and the Centers for Disease Control and Prevention's National Program of Cancer Registries, under agreement #U58DP003862-01 awarded to the California Department of Public Health. The ideas and opinions expressed herein are those of the author(s) and endorsement by the State of California Department of Public Health, the National Cancer Institute, and the Centers for Disease Control and Prevention or their Contractors and Subcontractors is not intended nor should be inferred. The authors acknowledge the efforts of the National Cancer Institute; the Office of Research, Development and Information, CMS; Information Management Services, Inc.; and the SEER Program tumor registries in the creation of the SEER-Medicare database.

We would also like to acknowledge the UNC Lineberger Comprehensive Cancer Center and support from the University Cancer Research Fund via the State of North Carolina.

We would like to thank Dr. Aimee Faso and Dr. Jena Ivey-Burkhart for their assistance in identifying and classifying potential drug interactions with chemotherapy for this study. We would also like to thank our anonymous reviewers for their substantial contributions to refining the focus of the article.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.