Background: Older adults are often exposed to multiple medications, some of which could be inappropriate or have the potential to interact with each other. Older cancer patients may be at increased risk for medication-related problems due to exposure to cancer-directed treatment.

Methods: We described patterns of potentially inappropriate medication (PIM) use and potential drug–chemotherapy interactions among adults age 66+ years diagnosed with stage I–III breast, stage II–III colon, and stage I to II lung cancer. Within the Surveillance, Epidemiology, and End Results–Medicare database, patients had to have Medicare Part D coverage with 1+ prescription in the diagnosis month and Medicare Parts A/B coverage in the prior 12 months. We estimated monthly prevalence of any and cancer-related PIM from 6 months pre- to 23 months postcancer diagnosis and 12-month period prevalence of potential drug–chemotherapy interactions.

Results: Overall, 19,318 breast, 7,283 colon, and 7,237 lung cancer patients were evaluated. Monthly PIM prevalence was stable prediagnosis (37%–40%), but increased in the year following a colon or lung cancer diagnosis, and decreased following a breast cancer diagnosis. Changes in PIM prevalence were driven primarily by cancer-related PIM in patients on chemotherapy. Potential drug–chemotherapy interactions were observed in all cohorts, with prevalent interactions involving hydrochlorothiazide, warfarin, and proton-pump inhibitors.

Conclusions: There was a high burden of potential medication-related problems among older cancer patients; future research to evaluate outcomes of these exposures is warranted.

Impact: Older adults diagnosed with cancer have unique medication management needs. Thus, pharmacy specialists should be integrated into multidisciplinary teams caring for these patients. Cancer Epidemiol Biomarkers Prev; 27(1); 41–49. ©2017 AACR.

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.

Data source and study population

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.

Patient demographic, clinical, and cancer treatment characteristics

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.

Assessment of PIM use

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.

Assessment of drug interactions with chemotherapeutic agents

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.

Statistical analysis

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.

Study population

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%).

Table 1.

Characteristics of patients at diagnosis by cancer site

BreastColonLung
CharacteristicN = 19,318 (%)N = 7,283 (%)N = 7,237 (%)
Age at cancer diagnosis, years 
 66–69 4,319 (22) 1,083 (15) 1,571 (22) 
 70–74 4,845 (25) 1,555 (21) 1,981 (27) 
 75–79 4,207 (22) 1,543 (21) 1,755 (24) 
 80–84 3,253 (17) 1,504 (21) 1,247 (17) 
 85+ 2,694 (14) 1,598 (22) 683 (9) 
AJCC stage 
 I 10,552 (55) — 6,048 (84) 
 II 6,693 (35) 3,934 (54) 1,189 (16) 
 III 2,073 (11) 3,349 (46) — 
Sex 
 Female 19,318 (100) 4,507 (62) 4,163 (58) 
Race 
 White 16,230 (84) 5,865 (81) 6,134 (85) 
 Black 1,682 (9) 638 (9) 572 (8) 
 Other 1,406 (7) 780 (11) 531 (7) 
Marital status 
 Married 7,655 (40) 3,128 (43) 3,367 (47) 
 Single 1,842 (10) 775 (11) 667 (9) 
 Other 9,821 (51) 3,380 (46) 3,203 (44) 
Year of cancer diagnosis 
 2007 3,909 (20) 1,571 (22) 1,394 (19) 
 2008 3,826 (20) 1,518 (21) 1,481 (21) 
 2009 3,901 (20) 1,465 (20) 1,479 (20) 
 2010 3,799 (20) 1,363 (19) 1,463 (20) 
 2011 3,883 (20) 1,366 (19) 1,420 (20) 
Charlson comorbidity score 
 0 10,572 (55) 3,434 (47) 2,291 (32) 
 1 5,076 (26) 1,995 (27) 2,343 (32) 
 2+ 3,670 (19) 1,854 (26) 2,603 (36) 
Predicted probability of frailty 
 0%–<10% 12,905 (67) 4,305 (59) 4,458 (62) 
 10%–<20% 2,977 (15) 1,244 (17) 1,164 (16) 
 20%–<50% 2,110 (11) 1,007 (14) 1,021 (14) 
 50%+ 1,326 (7) 727 (10) 594 (8) 
Number of prescription fills 
 0–2 6,660 (34) 2,244 (31) 1,992 (27) 
 3–5 7,727 (40) 2,899 (40) 2,835 (39) 
 6–10 4,201 (22) 1,834 (25) 1,927 (27) 
 11+ 731 (4) 306 (4) 483 (7) 
Chemotherapy receipt 4,130 (21) 2,453 (34) 1,460 (20) 
Surgical resection 18,090 (94) 7,147 (98) 3,350 (46) 
Radiation receipt 8,581 (44) 135 (2) 2,129 (29) 
BreastColonLung
CharacteristicN = 19,318 (%)N = 7,283 (%)N = 7,237 (%)
Age at cancer diagnosis, years 
 66–69 4,319 (22) 1,083 (15) 1,571 (22) 
 70–74 4,845 (25) 1,555 (21) 1,981 (27) 
 75–79 4,207 (22) 1,543 (21) 1,755 (24) 
 80–84 3,253 (17) 1,504 (21) 1,247 (17) 
 85+ 2,694 (14) 1,598 (22) 683 (9) 
AJCC stage 
 I 10,552 (55) — 6,048 (84) 
 II 6,693 (35) 3,934 (54) 1,189 (16) 
 III 2,073 (11) 3,349 (46) — 
Sex 
 Female 19,318 (100) 4,507 (62) 4,163 (58) 
Race 
 White 16,230 (84) 5,865 (81) 6,134 (85) 
 Black 1,682 (9) 638 (9) 572 (8) 
 Other 1,406 (7) 780 (11) 531 (7) 
Marital status 
 Married 7,655 (40) 3,128 (43) 3,367 (47) 
 Single 1,842 (10) 775 (11) 667 (9) 
 Other 9,821 (51) 3,380 (46) 3,203 (44) 
Year of cancer diagnosis 
 2007 3,909 (20) 1,571 (22) 1,394 (19) 
 2008 3,826 (20) 1,518 (21) 1,481 (21) 
 2009 3,901 (20) 1,465 (20) 1,479 (20) 
 2010 3,799 (20) 1,363 (19) 1,463 (20) 
 2011 3,883 (20) 1,366 (19) 1,420 (20) 
Charlson comorbidity score 
 0 10,572 (55) 3,434 (47) 2,291 (32) 
 1 5,076 (26) 1,995 (27) 2,343 (32) 
 2+ 3,670 (19) 1,854 (26) 2,603 (36) 
Predicted probability of frailty 
 0%–<10% 12,905 (67) 4,305 (59) 4,458 (62) 
 10%–<20% 2,977 (15) 1,244 (17) 1,164 (16) 
 20%–<50% 2,110 (11) 1,007 (14) 1,021 (14) 
 50%+ 1,326 (7) 727 (10) 594 (8) 
Number of prescription fills 
 0–2 6,660 (34) 2,244 (31) 1,992 (27) 
 3–5 7,727 (40) 2,899 (40) 2,835 (39) 
 6–10 4,201 (22) 1,834 (25) 1,927 (27) 
 11+ 731 (4) 306 (4) 483 (7) 
Chemotherapy receipt 4,130 (21) 2,453 (34) 1,460 (20) 
Surgical resection 18,090 (94) 7,147 (98) 3,350 (46) 
Radiation receipt 8,581 (44) 135 (2) 2,129 (29) 

Monthly prevalence of any PIM

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).

Figure 1.

Monthly prevalence of any PIM by cancer site from 6 months before through 23 months following the month of cancer diagnosis. The solid black line represents the stage I–III breast cancer cohort; the solid gray line represents the stage II–III colon cancer cohort; the dashed black line represents the stage I–II lung cancer cohort. The black vertical line denotes the month of cancer diagnosis.

Figure 1.

Monthly prevalence of any PIM by cancer site from 6 months before through 23 months following the month of cancer diagnosis. The solid black line represents the stage I–III breast cancer cohort; the solid gray line represents the stage II–III colon cancer cohort; the dashed black line represents the stage I–II lung cancer cohort. The black vertical line denotes the month of cancer diagnosis.

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Stratification by chemotherapy receipt

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.

Figure 2.

A–C, Monthly prevalence of PIM by cancer site from 6 months before through 23 months following the month of cancer diagnosis, stratified by chemotherapy receipt (dashed line) versus no chemotherapy receipt (solid line). Chemotherapy initiation was assessed during the 6 months following cancer diagnosis. The black vertical line denotes the month of cancer diagnosis. Monthly PIM prevalence is reported by cancer site for the breast (A), colon (B), and lung (C) cohorts.

Figure 2.

A–C, Monthly prevalence of PIM by cancer site from 6 months before through 23 months following the month of cancer diagnosis, stratified by chemotherapy receipt (dashed line) versus no chemotherapy receipt (solid line). Chemotherapy initiation was assessed during the 6 months following cancer diagnosis. The black vertical line denotes the month of cancer diagnosis. Monthly PIM prevalence is reported by cancer site for the breast (A), colon (B), and lung (C) cohorts.

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Most common cancer-related PIM drugs to be avoided

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.

Figure 3.

A–C, Monthly prevalence of cancer-related PIM from 6 months before through 23 months following the month of cancer diagnosis by cancer site. All medications included in the analysis had a prevalence of >1% in at least 1 month for at least one cancer site. The black vertical line denotes the month of cancer diagnosis. Monthly cancer-related PIM prevalence is reported by cancer site for the breast (A), colon (B), and lung (C) cohorts.

Figure 3.

A–C, Monthly prevalence of cancer-related PIM from 6 months before through 23 months following the month of cancer diagnosis by cancer site. All medications included in the analysis had a prevalence of >1% in at least 1 month for at least one cancer site. The black vertical line denotes the month of cancer diagnosis. Monthly cancer-related PIM prevalence is reported by cancer site for the breast (A), colon (B), and lung (C) cohorts.

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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.

Period prevalence of potential drug–chemotherapeutic interactions

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%).

Table 2.

The 12-month period prevalence of potential drug interactions with specified chemotherapeutics by cancer site

SiteTherapy (n)Potential drug interactionInteraction outcomesRefSeverityExposedPeriod prevalence
Colon 5-FU/capecitabine (n = 2,199) Hydrochlorothiazide Increased myelosuppression due to thiazides 28 Moderate 490 22% 
  Warfarin Increased anticoagulant effect 13 Moderate 338 15% 
  Phenytoin Increased phenytoin effect 13 Moderate 25 1% 
Breast Cyclophosphamide (n = 2,796) Hydrochlorothiazide Increased myelosuppression due to thiazides 28 Major 856 31% 
  Warfarin Increased anticoagulant effect 24 Major 240 9% 
  Allopurinol Increased bone marrow suppression and toxicity 28 Major 63 2% 
  Phenytoin Increased phenytoin effect 28 Major 13 0.5% 
  Fluconazole Increased cyclophosphamide effect 24 Moderate 87 3% 
  Digoxin Reduced digoxin effect 24 Moderate 67 2% 
  Quinolones Reduced quinolone effect 26 Moderate 18 1% 
 Doxorubicin (n = 1,224) Quinolones Reduced quinolone effect 28 Major 18 1% 
  Digoxin Reduced digoxin effect 24 Mild 23 2% 
 Methotrexate (n = 255) NSAIDs Reduced methotrexate clearance, increased effect 13 Major 42 16% 
  Ciprofloxacin Increased methotrexate effect a Moderate 22 9% 
  PPIs Increased methotrexate effect 29 Mild 74 29% 
 Paclitaxel (n = 1,016) Warfarin Increased anticoagulant effect 13 Mild 109 11% 
 Tamoxifen (n = 2,304)b Warfarin Increased anticoagulant effect 13 Major 206 9% 
  Fluoxetine Reduced anticancer effect of tamoxifen 13 Major 36 2% 
  Paroxetine Reduced anticancer effect of tamoxifen 13 Major 53 2% 
Lung Carboplatin (n = 936) Warfarin Increased anticoagulant effect 13 Major 115 12% 
  Phenytoin Decreased phenytoin effect 24 Moderate 12 1% 
  Quinolones Reduced quinolone effect 28 Mild 18 2% 
 Cisplatin (n = 302) Furosemide Increased ototoxicity, unknown origin 24 Major 34 11% 
 Etoposide (n = 223) Warfarin Increased anticoagulant effect 13 Major 31 14% 
 Gemcitabine (n = 176) Warfarin Increased anticoagulant effect 13 Major 27 15% 
 Paclitaxel (n = 550) Warfarin Increased anticoagulant effect 13 Mild 70 13% 
SiteTherapy (n)Potential drug interactionInteraction outcomesRefSeverityExposedPeriod prevalence
Colon 5-FU/capecitabine (n = 2,199) Hydrochlorothiazide Increased myelosuppression due to thiazides 28 Moderate 490 22% 
  Warfarin Increased anticoagulant effect 13 Moderate 338 15% 
  Phenytoin Increased phenytoin effect 13 Moderate 25 1% 
Breast Cyclophosphamide (n = 2,796) Hydrochlorothiazide Increased myelosuppression due to thiazides 28 Major 856 31% 
  Warfarin Increased anticoagulant effect 24 Major 240 9% 
  Allopurinol Increased bone marrow suppression and toxicity 28 Major 63 2% 
  Phenytoin Increased phenytoin effect 28 Major 13 0.5% 
  Fluconazole Increased cyclophosphamide effect 24 Moderate 87 3% 
  Digoxin Reduced digoxin effect 24 Moderate 67 2% 
  Quinolones Reduced quinolone effect 26 Moderate 18 1% 
 Doxorubicin (n = 1,224) Quinolones Reduced quinolone effect 28 Major 18 1% 
  Digoxin Reduced digoxin effect 24 Mild 23 2% 
 Methotrexate (n = 255) NSAIDs Reduced methotrexate clearance, increased effect 13 Major 42 16% 
  Ciprofloxacin Increased methotrexate effect a Moderate 22 9% 
  PPIs Increased methotrexate effect 29 Mild 74 29% 
 Paclitaxel (n = 1,016) Warfarin Increased anticoagulant effect 13 Mild 109 11% 
 Tamoxifen (n = 2,304)b Warfarin Increased anticoagulant effect 13 Major 206 9% 
  Fluoxetine Reduced anticancer effect of tamoxifen 13 Major 36 2% 
  Paroxetine Reduced anticancer effect of tamoxifen 13 Major 53 2% 
Lung Carboplatin (n = 936) Warfarin Increased anticoagulant effect 13 Major 115 12% 
  Phenytoin Decreased phenytoin effect 24 Moderate 12 1% 
  Quinolones Reduced quinolone effect 28 Mild 18 2% 
 Cisplatin (n = 302) Furosemide Increased ototoxicity, unknown origin 24 Major 34 11% 
 Etoposide (n = 223) Warfarin Increased anticoagulant effect 13 Major 31 14% 
 Gemcitabine (n = 176) Warfarin Increased anticoagulant effect 13 Major 27 15% 
 Paclitaxel (n = 550) Warfarin Increased anticoagulant effect 13 Mild 70 13% 

Abbreviations: NSAIDs, nonsteroidal anti-inflammatory drugs; PPIs, proton-pump inhibitors.

aIdentified by clinical pharmacist upon review.

bIncluded in analysis, although tamoxifen is considered an endocrine therapy.

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.

1.
Cusack
BJ
. 
Pharmacokinetics in older persons
.
Am J Geriatr Pharmacother
2004
;
2
:
274
302
.
2.
Kantor
ED
,
Rehm
CD
,
Haas
JS
,
Chan
AT
,
Giovannucci
EL
. 
Trends in prescription drug use among adults in the United States from 1999–2012
.
JAMA
2015
;
314
:
1818
31
.
3.
Hanlon
JT
,
Pieper
CF
,
Hajjar
ER
,
Sloane
RJ
,
Lindblad
CI
,
Ruby
CM
, et al
Incidence and predictors of all and preventable adverse drug reactions in frail elderly persons after hospital stay
.
J Gerontol A Biol Sci Med Sci
2006
;
61
:
511
5
.
4.
American Geriatrics Society. updated Beers Criteria for potentially inappropriate medication use in older adults
.
J Am Geriatr Soc
2012
;
60
:
616
31
.
5.
O'Mahony
D
,
O'Sullivan
D
,
Byrne
S
,
O'Connor
MN
,
Ryan
C
,
Gallagher
P
. 
STOPP/START criteria for potentially inappropriate prescribing in older people: version 2
.
Age and Ageing
. 
2014
.
6.
Cahir
C
,
Fahey
T
,
Teeling
M
,
Teljeur
C
,
Feely
J
,
Bennett
K
. 
Potentially inappropriate prescribing and cost outcomes for older people: a national population study
.
Br J Clin Pharmacol
2010
;
69
:
543
52
.
7.
Fu
AZ
,
Liu
GG
,
Christensen
DB
. 
Inappropriate medication use and health outcomes in the elderly
.
J Am Geriatr Soc
2004
;
52
:
1934
9
.
8.
Hamilton
H
,
Gallagher
P
,
Ryan
C
,
Byrne
S
,
O'Mahony
D
. 
Potentially inappropriate medications defined by STOPP criteria and the risk of adverse drug events in older hospitalized patients
.
Arch Intern Med
2011
;
171
:
1013
9
.
9.
Klarin
I
,
Wimo
A
,
Fastbom
J
. 
The association of inappropriate drug use with hospitalisation and mortality: a population-based study of the very old
.
Drugs Aging
2005
;
22
:
69
82
.
10.
Lau
DT
,
Kasper
JD
,
Potter
DE
,
Lyles
A
,
Bennett
RG
. 
Hospitalization and death associated with potentially inappropriate medication prescriptions among elderly nursing home residents
.
Arch Intern Med
2005
;
165
:
68
74
.
11.
Smith
BD
,
Smith
GL
,
Hurria
A
,
Hortobagyi
GN
,
Buchholz
TA
. 
Future of cancer incidence in the United States: burdens upon an aging, changing nation
.
J Clin Oncol
2009
;
27
:
2758
65
.
12.
Sharma
M
,
Loh
KP
,
Nightingale
G
,
Mohile
SG
,
Holmes
HM
. 
Polypharmacy and potentially inappropriate medication use in geriatric oncology
.
J Geriatr Oncol
2016
;
7
:
346
53
.
13.
Lees
J
,
Chan
A
. 
Polypharmacy in elderly patients with cancer: clinical implications and management
.
Lancet Oncol
2011
;
12
:
1249
57
.
14.
National Cancer Institute
.
SEER-Medicare Data,
http://healthservices.cancer.gov/seermedicare/overview/seermed_fact_sheet.pdf, April 
2013
.
Accessed July 16, 2013
.
15.
Klabunde
CN
,
Potosky
AL
,
Legler
JM
,
Warren
JL
. 
Development of a comorbidity index using physician claims data
.
J Clin Epidemiol
2000
;
53
:
1258
67
.
16.
Faurot
KR
,
Jonsson Funk
M
,
Pate
V
,
Brookhart
MA
,
Patrick
A
,
Hanson
LC
, et al
Using claims data to predict dependency in activities of daily living as a proxy for frailty
.
Pharmacoepidemiology and Drug Safety
. 
2014
.
17.
Cuthbertson
CC
,
Lund
JL
,
Sturmer
T
,
Faurot
KR
,
Bandeen-Roche
KL
,
Funk
MJ
, et al
Validating a medicare claims-based model to classify phenotypic frailty in older adults
.
Innovation Aging
2017
;
1
:
381
.
18.
Brown
ML
,
Riley
GF
,
Potosky
AL
,
Etzioni
RD
. 
Obtaining long-term disease specific costs of care: application to Medicare enrollees diagnosed with colorectal cancer
.
Med Care
1999
;
37
:
1249
59
.
19.
Yabroff
KR
,
Davis
WW
,
Lamont
EB
,
Fahey
A
,
Topor
M
,
Brown
ML
, et al
Patient time costs associated with cancer care
.
J Nat Cancer Inst
2007
;
99
:
14
23
.
20.
Davidoff
AJ
,
Miller
GE
,
Sarpong
EM
,
Yang
E
,
Brandt
N
,
Fick
DM
. 
Prevalence of potentially inappropriate medication use in older adults using the 2012 Beers criteria
.
J Am Geriatr Soc
2015
;
63
:
486
500
.
21.
Holmes
HM
,
Luo
R
,
Kuo
YF
,
Baillargeon
J
,
Goodwin
JS
. 
Association of potentially inappropriate medication use with patient and prescriber characteristics in Medicare Part D
.
Pharmacoepidemiol Drug Safety
2013
;
22
:
728
34
.
22.
Jiron
M
,
Pate
V
,
Hanson
LC
,
Lund
JL
,
Jonsson Funk
M
,
Sturmer
T
. 
Trends in prevalence and determinants of potentially inappropriate prescribing in the United States: 2007 to 2012
.
J Am Geriatr Soc
2016
;
64
:
788
97
.
23.
Lopez-Martin
C
,
Garrido Siles
M
,
Alcaide-Garcia
J
,
Faus Felipe
V
. 
Role of clinical pharmacists to prevent drug interactions in cancer outpatients: a single-centre experience
.
Int J Clin Pharmacy
2014
;
36
:
1251
9
.
24.
Popa
MA
,
Wallace
KJ
,
Brunello
A
,
Extermann
M
,
Balducci
L
. 
Potential drug interactions and chemotoxicity in older patients with cancer receiving chemotherapy
.
J Geriatr Oncol
2014
;
5
:
307
14
.
25.
Puts
MT
,
Costa-Lima
B
,
Monette
J
,
Girre
V
,
Wolfson
C
,
Batist
G
, et al
Medication problems in older, newly diagnosed cancer patients in Canada: How common are they? A prospective pilot study
.
Drugs Aging
2009
;
26
:
519
36
.
26.
Riechelmann
RP
,
Tannock
IF
,
Wang
L
,
Saad
ED
,
Taback
NA
,
Krzyzanowska
MK
. 
Potential drug interactions and duplicate prescriptions among cancer patients
.
J Nat Cancer Instit
2007
;
99
:
592
600
.
27.
Sokol
KC
,
Knudsen
JF
,
Li
MM
. 
Polypharmacy in older oncology patients and the need for an interdisciplinary approach to side-effect management
.
J Clin Pharm Therapeut
2007
;
32
:
169
75
.
28.
van Leeuwen
RW
,
Brundel
DH
,
Neef
C
,
van Gelder
T
,
Mathijssen
RH
,
Burger
DM
, et al
Prevalence of potential drug-drug interactions in cancer patients treated with oral anticancer drugs
.
Br J Cancer
2013
;
108
:
1071
8
.
29.
Voll
ML
,
Yap
KD
,
Terpstra
WE
,
Crul
M
. 
Potential drug-drug interactions between anti-cancer agents and community pharmacy dispensed drugs
.
Pharmacy World Sci
2010
;
32
:
575
80
.
30.
Lichtman
SM
,
Boparai
MK
. 
Geriatric medication management: Evaluation of pharmacist interventions and potentially inappropriate medication (PIM) use in older cancer patients
.
J Clin Oncol
2009
;
27
:
Abstract 9507 (on-line). Available at http://meetinglibrary.asco.org/content/35275--65 Accessed December 1, 2014
.
31.
Flood
KL
,
Carroll
MB
,
Le
CV
,
Brown
CJ
. 
Polypharmacy in hospitalized older adult cancer patients: experience from a prospective, observational study of an oncology-acute care for elders unit
.
Am J Geriatr Pharmacother
2009
;
7
:
151
8
.
32.
Maggiore
RJ
,
Dale
W
,
Gross
CP
,
Feng
T
,
Tew
WP
,
Mohile
SG
, et al
Polypharmacy and potentially inappropriate medication use in older adults with cancer undergoing chemotherapy: effect on chemotherapy-related toxicity and hospitalization during treatment
.
J Am Geriatr Soc
2014
;
62
:
1505
12
.
33.
Nightingale
G
,
Hajjar
E
,
Swartz
K
,
Andrel-Sendecki
J
,
Chapman
A
. 
Evaluation of a pharmacist-led medication assessment used to identify prevalence of and associations with polypharmacy and potentially inappropriate medication use among ambulatory senior adults with cancer
.
J Clin Oncol
2015
;
33
:
1453
9
.
34.
Prithviraj
GK
,
Koroukian
S
,
Margevicius
S
,
Berger
NA
,
Bagai
R
,
Owusu
C
. 
Patient characteristics associated with polypharmacy and inappropriate prescribing of medications among older adults with cancer
.
J Geriatr Oncol
2012
;
3
:
228
37
.
35.
Centers for medicare and medicaid services, department of health and human services
.
Transition to Part D Coverage of Benzodiazepines and Barbiturates Beginning in 2013
.
Available at
: https://www.cms.gov/Medicare/Prescription-Drug-Coverage/PrescriptionDrugCovContra/Downloads/BenzoandBarbituratesin2013.pdf.
Accessed on
November 4, 
2016
.
36.
Holland
JC
,
Hughes
MK
,
Greenberg
DB
(eds):
Quick reference for oncology clinicians: the psychiatric and psychological dimensions of cancer symptom management
.
Charlottesville, VA
,
American Psychosocial Oncology Society
, 
2006
.
37.
Basch
E
,
Prestrud
AA
,
Hesketh
PJ
,
Kris
MG
,
Feyer
PC
,
Somerfield
MR
, et al
Antiemetics: American Society of Clinical Oncology clinical practice guideline update
.
J Clin Oncol
2011
;
29
:
4189
98
.