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Keywords:

  • herbals;
  • complementary medicine;
  • older adults;
  • cancer. chemotherapy

Abstract

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. FUNDING SOURCES
  9. REFERENCES

BACKGROUND:

Little is known about complementary medication use among older adults with cancer, particularly those who are receiving chemotherapy. The objective of this study was to evaluate the prevalence of complementary medication use and to identify the factors associated with its use among older adults with cancer.

METHODS:

The prevalence of complementary medication use (defined as herbal agents, minerals, or other dietary supplements, excluding vitamins) was evaluated in a cohort of adults aged ≥65 years who were about to start chemotherapy for their cancer. The associations between complementary medication use and patient characteristics (sociodemographics; comorbidities; and functional, nutritional, psychological, and cognitive status), medication use (number of medications and concurrent vitamin use), and cancer characteristics (type and stage) were analyzed.

RESULTS:

The cohort included 545 patients (mean age, 73 years; range, 65-91 years; 52% women) with cancer (61% stage IV). Seventeen percent of these patients (N = 93) reported using ≥1 complementary medication; the mean number of complementary medications among users was 2 (range, 1-10 medications). Complementary medication use was associated with 1) earlier cancer stage (29% had stage I-II disease vs 17% with stage III-IV disease; odds ratio [OR], 2.05; 95% confidence interval [CI], 1.21-3.49) and 2) less impairment with instrumental activities of daily living (OR, 1.39; 95% CI, 1.12-1.73).

CONCLUSIONS:

Complementary medication use was reported by 17% of older adults with cancer and was more common among those who had less advanced disease (i.e., those receiving adjuvant, potentially curative treatment) and higher functional status. Further studies are needed to determine the association between complementary medication use and cancer outcomes among older adults. Cancer 2012. © 2012 American Cancer Society.


INTRODUCTION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. FUNDING SOURCES
  9. REFERENCES

The use of complementary medications (eg, herbal agents, high-dose vitamins, and other health supplements) is common among adults with cancer, with a prevalence ranging from 54% to 78%.1-6 Prior studies in adults with cancer have demonstrated consistently that younger patients are more likely to use complementary medications than older patients with cancer.2, 7-16 However, complementary medication use among community-dwelling older adults (without cancer) in the United States has been rising over the last decade,17-19 with a prevalence ranging from 13% to 33%.17, 18, 20-22 Yet little is known about the prevalence and types of complementary medication use specifically among older adults with cancer, particularly those receiving chemotherapy.

Complementary medication use among older adults with cancer may have important and unrecognized clinical implications. Older adults are at increased risk for polypharmacy and potential drug interactions.19, 21 The use of complementary medications may compound this risk. Furthermore, complementary medications, in addition to more traditional medications, can interact with chemotherapy and, thus, lead to increased risk of toxicity5, 23, 24 or diminished efficacy.23, 24 Despite these potential concerns, few studies have focused on the prevalence of complementary medications in older adults with cancer or the characteristics of those individuals who are more likely to use complementary medications. The objective of this study was to determine the prevalence of complementary medication use among a cohort of older adults with cancer who were scheduled to begin a new chemotherapy regimen and to identify the factors associated with complementary medication use in this setting.

MATERIALS AND METHODS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. FUNDING SOURCES
  9. REFERENCES

Study Design and Patient Eligibility

This study is a secondary cross-sectional analysis of data from a multicenter, longitudinal study evaluating the utility of a comprehensive geriatric assessment in predicting chemotherapy toxicity among a cohort of older adults with cancer.25 Patients were eligible for the study if they were aged ≥65 years, had a diagnosis of cancer (excluding nonmelanoma skin cancer), were about to receive a new chemotherapy regimen, were English-speaking, and were able to provide informed consent. Informed consent was obtained from all study participants. This study was approved by the institutional review board at all 7 participating sites.

Patients completed a baseline comprehensive geriatric assessment, which included an evaluation of their functional, nutritional, cognitive, and psychological status as well as their comorbid medical conditions.26, 27 Also, as part of this baseline assessment before the initiation of chemotherapy, patients were asked to provide a list of their medications (including nonprescription medications, herbs, or supplements) and the number of medications taken daily. The dosages of medications were not captured.

Definition of Complementary Medication Use

Our definition of complementary medications consisted of herbal agents, dietary supplements, and minerals for which no clear clinical indication could be derived from the initial patient assessment. This definition was adapted from the National Cancer Institute definition of complementary medication use, which includes megadose vitamins, dietary supplements, and herbal preparations.28 We did not classify vitamins as complementary medications, because vitamin dosages were not captured as part of the original assessment; therefore, we could not determine whether such vitamins constituted “megadose” vitamins. However, the frequencies and types of vitamins were evaluated. Our operational definition of complementary medication use also excluded the use of agents that may have a clinical indication: 1) particular mineral salts (ie, iron, calcium, magnesium, and bicarbonate), 2) aspirin and nonsteroidal anti-inflammatory agents, and 3) omega-3 fatty acids/fish oil (ie, may be available in a prescription formulation and possibly used in the treatment of dyslipidemia). The determination of such agents as potentially being used for a clinical indication was made through consensus attained among the study coauthors.

Factors Associated With Complementary Medication Use

A review of prior geriatrics-based studies17, 18, 20, 29, 30 and oncology-based studies2, 3, 7, 8, 11-16, 31-36 was performed to identify the factors associated with complementary medicine use and to guide our analysis plan. These factors also have been used and evaluated in prior geriatric oncology-based studies.25, 27, 37 We evaluated the association between complementary medication use and the following factors: 1) sociodemographics (age, sex, ethnicity, race, presence of a living companion, and educational status), 2) cancer type and stage, 3) medication use (number of prescription medications), and 4) the following geriatric assessment variables (Table 1): a) functional status as measured by i) the ability to perform instrumental activities of daily living (IADLs) as assessed by the Older Americans Resources and Services (OARS) subscale38; ii) Karnofsky performance status (KPS) scale (both physician-rated and patient-rated)39, 40; and iii) history of falls in the 6 months before study41-43; b) comorbidity number and type was captured by the OARS subscale38; c) psychological status (ie, depression/anxiety) was assessed by the Hospital Anxiety and Depression Scale (HADS)44, 45; d) nutritional status was measured by percent unintentional weight loss in the 6 months before study; and e) cognitive status was assessed by the Blessed Orientation-Memory-Concentration (BOMC) scale.46, 47

Table 1. Description of Geriatric Assessment Measures
 MeasureDescriptionScoring
Functional statusInstrumental Activities of Daily Living (IADL) (Older Americans Resources and Services subscale)Seven items evaluating the ability to complete activities required to maintain independence in the communityScore, 0-14; a score of 14 indicates no impairment in the completion of IADL
 Karnofsky performance statusGlobal evaluation of physical function as determined by patient self-report or by the physician, ranging from “normal” to “severely disabled”Score, 0%-100%; a score of 100% indicates normal physical function (ie, no level of disability)
 FallsSelf-reported no. of falls in the 6 mos. before the studyScore is variable, depending on the no. of falls reported
ComorbidityNumber, type and severity of comorbidity“Yes-or-no” checklist of 13 comorbid conditions with a 3-point Likert scale rating the impact of each condition on daily function.Scored as the no. and type of comorbid illnesses
Psychological statusHospital Anxiety/Depression Scale (HADS)Fourteen items on a Likert scale, with 7 items evaluating for anxiety symptoms and 7 items evaluating for depressive symptomsScore, 0-42; scores ≥15 indicate having significant underlying anxiety or depression
Nutritional statusUnintentional weight lossSelf-reported weight loss in the 6 mos. before the studyScored as the percentage of weight loss from baseline weight 6 mos. before the study
Cognitive statusBlessed Orientation-Memory- Concentration (BOMC) scaleSix weighted items evaluating 3 components of cognitive function (ie, orientation, memory, concentration)Score, 0-28; scores ≥11 indicate having significant underlying cognitive impairment

2

Table 2. Baseline Patient Characteristics and Geriatric Assessment Measures
Patient CharacteristicNo. of Patients%
Age, y  
 65-6918634
 70-7414927
 75-7911221
 ≥809818
Sex  
 Women28252
 Men26348
Ethnicity  
 Hispanic265
 Non-Hispanic51594
 Unknown41
Race  
 African American438
 Asian265
 Caucasian47387
 Native American/Pacific Islander/other31
Highest educational level  
 Grade school275
 High school19536
 College20538
 Graduate/professional school11721
 Missing10
Living situation  
 Live alone11020
 Live with spouse/partner/children40975
 Other255
 Missing10
Cancer type  
 Gastrointestinal15929
 Lung15929
 Gynecologic6512
 Breast5410
 Genitourinary5310
 Lymphoma295
 Other265
Cancer stage  
 I244
 II6612
 III11721
 IV and extensive-stage SCLC33261
 Limited-stage SCLC10
 Acute myeloid leukemia41
 Chronic lymphocytic leukemia10
Geriatric Assessment MeasuresMean±SDMedian [Range]
  1. Abbreviations: BOMC, Blessed Orientation-Memory-Concentration test; HADS, Hospital Anxiety and Depression Score; IADL OARS, Instrumental Activities of Daily Living, Older Americans Resources and Services subscale; KPS, Karnofsky performance status; MOS, Medical Outcomes Study; SD, standard deviation.

Cognitive status  
 BOMC: Scale 0-284.6±4.04 [0-28]
Comorbidity  
 No. of comorbidities2.4±1.62 [0-9]
 No. of prescription medications4.4±3.14 [0-20]
Functional status  
 KPS: Scale 0-100  
  Patient-rated85.9±13.790 [40-100]
  Physician-rated84.9±11.290 [50-100]
 IADL OARS: Scale 0-1413.0±1.714 [4-14]
 No. of falls in past 6 mos.0.3±0.70 [0-4]
Nutritional status  
 Unintentional weight loss in prior 6 mos., %4.9±6.32.2 [0-32.3]
Psychological status  
 HADS: Scale 0-428.2±5.87 [0-35]

Statistical Considerations

Descriptive analyses were performed to determine means, medians, standard deviations (SDs), and ranges for continuous variables, and frequencies and percentages for categorical variables. KPS, HADS, and BOMC scores were dichotomized at clinically meaningful thresholds: a KPS <70% indicated poor functional status, a HADS score ≥15 indicated anxiety/depression, and a BOMC score ≥11 indicated cognitive impairment.43-46 Other continuous variables, except for OARS IADL scores and the number of prescription medications, also were categorized because of their nonlinear relations with the logarithm of the odds.

Bivariate analysis was then conducted to assess the association between each of the variables and complementary medication use (yes or no) using unconditional logistic regression models and Fisher exact tests when data were sparse. Variables that reached a P value < 0.1 in the bivariate analysis were examined further by using multivariate logistic regression models. Interactions were examined by adding an interaction term in multivariate models. Two-sided tests with a significance level of 0.05 were used. All statistical analyses were performed using the SAS statistical software package (version 9.2; SAS Institute, Inc., Cary, NC).

RESULTS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. FUNDING SOURCES
  9. REFERENCES

Of the 550 eligible patients, 545 (99%) had sufficient medication data evaluable for analysis; 1 patient had missing medication data, and 4 patients had withdrawn from the original study. Baseline patient characteristics and geriatric assessment scores were examined (Table 2). Thirty-three patients (6%) had BOMC scores ≥11, indicating potentially clinically significant cognitive impairment; 79 patients (15%) had HADS scores ≥15, indicating potentially clinically significant anxiety or depression; and 75 patients (14%) and 27 patients (5%) had patient-rated and physician-rated KPS scores <70%, respectively, indicating potentially clinically significant functional impairment. The mean ± SD number of total medications was 6.9 ± 4.2 (range, 0-25 medications), with 5.1 ± 3.6 medications (range, 0-23 medications) taken daily. The mean ± SD number of prescription medications per patient was 4.4 ± 3.1 (range, 0-20 prescription medications per patient).

Table 3. Prevalence and Patterns of Complementary Medication Use
VariableNo. of Patients (%)
  • a

    Alone or in combination with chondroitin.

  • b

    Frequencies = 1 each.

  • c

    % reflect percent of total medications.

  • d

    N and % indicate number of complementary medications identified and percentage of total complementary medications used by the study cohort, respectively.

No. of complementary medications 
 156 (10)
 ≥237 (7)
 Total93 (17)
Medication typecN (%)d
 Glucosaminea37 (19)
 Flaxseed oil14 (7)
 Coenzyme Q1012 (6)
 Zinc10 (5)
 Garlic7 (3.5)
 Lutein7 (3.5)
 Selenium7 (3.5)
 Probiotics6 (3)
 Lecithin5 (2.5)
 Other minerals5 (2.5)
 Saw palmetto5 (2.5)
 Chromium4 (2)
 Cod liver oil4 (2)
 Mushrooms4 (2)
 Other arthritis supplements4 (2)
 Other herbal preparations3 (1.5)
 Aloe3 (1.5)
 Cinnamon3 (1.5)
 Green tea extract3 (1.5)
 Other cancer supplements3 (1.5)
 Preservation supplements3 (1.5)
 Bilberry2 (1)
 Curcumin2 (1)
 Echinacea2 (1)
 Ginkgo biloba2 (1)
 Grapeseed oil2 (1)
 Melatonin2 (1)
 Milk thistle2 (1)
 Zyflamend2 (1)
 Other dietary supplementsb33 (17)

Ninety-three patients (17%) were taking at least 1 complementary agent regularly, and 37 of these patients (40%) were taking more than 1 such agent concurrently (Table 3). Among the patients who took complementary medications, the mean ± SD number of complementary medications taken was 2 ± 2.2 medications per patient (range, 1-13 medications per patient). The 3 most commonly encountered complementary medications were glucosamine (with or without chondroitin), flaxseed oil, and coenzyme Q10 (Table 3). In addition to complementary medication use, vitamin use also was common, and 236 patients (43%) were taking any vitamin. Almost one-third (31%) of vitamin users were concurrently taking complementary medications. Among the types of vitamins used, multivitamins were the most frequently encountered (37% of all patients); and vitamins C, B complex, and E were the most common vitamin subtypes encountered (data not shown).

In the bivariate logistic regression analysis, the following factors reached a P value < 0.1: less advanced cancer stage (stages I-II), higher scores on individual measures of functional and nutritional status (ie, higher patient-rated or physician-rated KPS scores, higher OARS IADL scores, and no falls or lower unintentional weight loss in the last 6 months), and cancer type (ie, higher use among older adults with lymphomas, genitourinary cancer, and gynecologic cancer vs gastrointestinal cancer as a reference group given its lower prevalence of complementary medication use) (Table 4). There was no association between HADS scores (total score or subscales measuring anxiety and depression) and complementary medication use.

Table 4. Associations of Patient Factors With Complementary Medication Use
 No. of Patients (%)  
  CAMOR [95% CI]
VariableTotalPositiveNegativeUnadjustedaAdjustedb
  • Abbreviations: AML, acute myeloid leukemia; BOMC, Blessed Orientation-Memory-Concentration test; CAM, complementary medicine; CI, confidence interval; CLL, chronic lymphocytic leukemia; HADS, Hospital Anxiety and Depression Score; KPS, Karnofsky performance status; IADL OARS, Instrumental Activities of Daily Living, Older Americans Resources and Services subscale; OR, odds ratio; Rx meds, prescription medications; SCLC, small cell lung cancer; SD, standard deviation.

  • a

    Determined with bivariate logistic regression analysis/Fisher exact test (patients who had missing values were excluded from the analysis).

  • b

    Determined with multivariate logistic regression analysis (patients who had missing values were excluded from the analysis).

  • c

    P < 0.05 in bivariate analysis.

  • d

    P < 0.1 in bivariate analysis.

Categorical variables     
Age, y     
 65-6918632 (17)154 (83)1.00 
 70-7414927 (18)122 (82)1.07 [0.61-1.87] 
 75-7911220 (18)92 (82)1.05 [0.57-1.94] 
 ≥809814 (14)84 (86)0.80 [0.41-1.59] 
Sex     
 Women28245 (16)237 (84)1.00 
 Men26348 (18)215 (82)1.18 [0.75-1.84] 
Ethnicity     
 Non-Hispanic51590 (17)425 (83)1.00 
 Hispanic263 (12)23 (88)0.62 [0.18-2.10] 
Race     
 Nonwhite728 (11)64 (89)1.00 
 White47385 (18)388 (82)1.75 [0.81-3.79] 
Education level     
 Grade school/high school22232 (14)190 (86)1.00 
 College/graduate school32261 (19)261 (81)1.39 [0.87-2.21] 
Living situation     
 Live with someone43476 (18)358 (82)1.00 
 Live alone11017 (15)93 (85)0.86 [0.49-1.53] 
Cancer type     
 Gastrointestinal15917 (11)142 (89)1.00 
 Breast5410 (19)44 (82)1.90 [0.81-4.45] 
 Genitourinaryc5314 (26)39 (74)3.00 [1.36-6.61] 
 Gynecologicc6513 (20)52 (80)2.09 [0.95-4.60] 
 Lung15926 (16)133 (84)1.63 [0.85-3.15] 
 Lymphomad298 (28)21 (72)3.18 [1.22-8.29] 
 Other265 (19)21 (81)1.99 [0.66-5.96] 
Cancer stage     
 III-IV/extensive SCLC/AML/CLL45467 (15)387 (85)1.001.00
 I-II/limited SCLC9126 (29)65 (71)2.31 [1.37-3.90]2.05 [1.21-3.49]
BOMC     
 <1151091 (18)419 (82)1.00 
 ≥11332 (6)31 (94)0.30 [0.07-1.26] 
No. of comorbid illnesses     
 0-117728 (16)149 (84)1.00 
 213225 (19)107 (81)1.24 [0.69-2.25] 
 311120 (18)91 (82)1.17 [0.62-2.20] 
 ≥412420 (16)104 (84)1.02 [0.55-1.91] 
Patient-rated KPS, %c     
 <70756 (8)69 (92)1.00 
 ≥7046386 (19)377 (81)2.62 [1.10-6.24] 
Physician-rated KPS, %d     
 <70271 (4)26 (96)1.00 
 ≥7051089 (17)421 (83)5.50 [0.74-41.03] 
No. of falls in past 6 mos.c     
 045184 (19)367 (81)1.00 
 ≥1898 (9)81 (91)0.43 [0.20-0.93] 
Unintentional weight loss in prior 6 mos., %     
 025453 (21)201 (79)1.00 
 0.01-4.99d8010 (13)70 (88)0.54 [0.26-1.12] 
 5-9.9910020 (20)80 (80)0.95 [0.53-1.69] 
 ≥10c10910 (9)99 (91)0.38 [0.19-0.79] 
HADS     
 ≤1445583 (18)372 (82)1.00 
 ≥157910 (13)69 (87)0.65 [0.32-1.31] 
Continuous variables     
 IADL OARS: Mean±SDd54213.5±1.012.9±1.81.42 [1.15-1.77]1.39 [1.12-1.73]
 No. of Rx meds: Mean±SD5454.3±3.14.4±3.10.98 [0.91-1.06] 

In the multivariate analysis, only less advanced cancer stage (stages I-II) and higher functional status (higher OARS IADL scores) were associated significantly with a higher likelihood of complementary medication use (P < .01) (Table 4). The results indicate that patients with stage I or II cancer are approximately 2 times as likely to be using at least 1 complementary medication as patients with more advanced cancer stage (odds ratio [OR], 2.05; 95% confidence interval [CI], 1.21-3.49). Moreover, the likelihood of complementary medication use increases with increasing IADL score (OR, 1.39; 95% CI, 1.12-1.73). OARS IADL scores also were evaluated categorically, because there is only a 1-unit difference among scores (1 for every IADL that is impaired); nevertheless, this association remained statistically significant when the analysis was adjusted for covariates (data not shown). Furthermore, adjusting for cancer type or study site did not substantially alter these estimates (data not shown). In addition, a sensitivity analysis was performed by excluding patients with leukemia or lymphoma, and no substantial change in the estimates was observed (data not shown).

DISCUSSION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. FUNDING SOURCES
  9. REFERENCES

Our reported prevalence of complementary medication use (17%) among older adults with cancer is comparable to that reported in geriatrics-based studies of older patients without cancer.17, 18, 20-22 This estimate is lower than that reported in cancer-based studies2-6; however, the majority of cancer-based studies include a relatively younger cohort of patients (ie, with a mean/median age ranging from 51 to 54 years). Our current work is 1 of the few studies to specifically focus on complementary medication use among older adults with cancer.

Complementary medication use in older adults with cancer raises several clinical concerns. First, there are potential interactions between complementary medications and cancer therapies. Therefore, knowing the most frequently used types of complementary medications would help stratify patients according to risk in terms of their relative risk for drug interactions. Some herbal-chemotherapy combinations can give rise to well associated adverse drug events,24 and some studies in relatively younger adults with cancer have suggested that 25% to 27% of these individuals are taking an herbal or similar agent that is known to interact significantly with chemotherapy.5, 6 Second, older adults may be at increased risk for such interactions given the increase in age-related comorbidity and attendant polypharmacy. For example, several patients who were included in our study were using some of the more “high-risk” agents, such as garlic and ginkgo, which have multiple effects on the cytochrome P450-mediated and P-glycoprotein-mediated metabolic pathways. Moreover, patients receiving chemotherapy who are using such medications, on average, can have at least 3 potential herbal-chemotherapy interactions, and up to 50% can have potential interactions that have yet to be defined.23 The majority of complementary medications encountered in our patients potentially fall into this category. The potential for drug interactions is of particular concern in our cohort, because the patients were more likely to be receiving treatment for early stage, potentially curable disease. Of the potentially clinically significant drug-drug interactions that we identified using LexiComp (http://www.lexi.com [accessed on September 1, 2011]), herbal-related interactions represented the third most common type of drug-drug interaction and approximately 7.5% of all potential interactions encountered (data not shown).

Although our sample was restricted to patients aged ≥65 years, the use of complementary agents did vary substantially within our cohort. Therefore, we sought to identify patient characteristics associated with complementary medication use. The literature to date has demonstrated that older adults and adults with cancer who use complementary medications may share similar characteristics, including relatively younger age,2, 11-14, 16, 18, 29 female gender,2, 3, 13, 16, 17, 29, 31-33 higher income,2, 3, 13, 16, 18, 31-33 and higher education levels.7, 8, 13, 17, 18, 31, 32, 34 Among more oncology-focused studies, factors that also were associated with greater use of complementary medications included more advanced disease34, 36 and higher self-reported levels of cancer-related symptoms.11, 12, 16, 32, 35, 36 However, among our cohort of older adults, we observed that the aforementioned “traditional” factors associated with complementary medication use did not appear to be associated with use in our study. Rather, we observed that older adults with cancer who were receiving chemotherapy were more likely to use complementary medications if they had less advanced disease and higher functional status, both of which are markers of healthier individuals in general. Whether these findings suggest that the use of complementary medications in this group is more for “health maintenance” rather than for treatment of active symptoms related to cancer or comorbid conditions requires further study.

There are several limitations to this study. First, an accurate estimation of complementary medication use may be difficult to ascertain, because many adult patients with cancer do not readily disclose the use of such agents.6, 18 Second, our reported prevalence was based on a working definition derived from that set forth by the National Cancer Institute and not author-generated, as in several prior studies. This definition coincides with that established by the National Committee on Complementary and Alternative Medicine, which has been used in prior studies.20, 31 This more stringent definition, coupled with the finding that under reporting is a common issue in such studies,6, 18 may have led to an underestimation of prevalence in our study. However, if vitamin use had been included as part of this definition, then, in fact, the prevalence may have been 47% (N = 257; full data not shown), which is more consistent with the results from oncology-based studies, as discussed above. Third, the rationale for using complementary medications among our study cohort was not captured. Finally, we must consider these results as “hypothesis-generating,” because complementary medication use was the not the primary endpoint of the original study.

In conclusion, despite these limitations, our current study addresses several knowledge gaps. Few studies currently exist that have evaluated complementary medication use specifically among older adults with cancer,16, 48 although older adults represent the majority of individuals now affected by cancer in the United States.49 Our findings underscore key differences in the prevalence of, types of, and patient factors associated with complementary medication use among older adults with cancer who are receiving chemotherapy in contrast to what has been reported in either the geriatrics-based or oncology-based literature. These data suggest that this group of patients may be distinct in all these aspects of complementary medication use. More studies are needed to quantify the risks and benefits of complementary medication use among this growing yet vulnerable geriatric oncology population.

Acknowledgements

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. FUNDING SOURCES
  9. REFERENCES

We thank all of the patients and research coordinators who participated in the study.

FUNDING SOURCES

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. FUNDING SOURCES
  9. REFERENCES

R.J.M. was supported by grant T32 AG19134 from the National Institute on Aging (geriatric research training grant; principal investigator, Thomas Gill, M.D., Yale University, under the guidance of C.P.G. and A.H.). C.P.G. was supported by grant K08 AG24842 (Paul Beeson Career Development Award in Aging Research). A.H. was supported by grant K23 AG026749-01 (Paul Beeson Career Development Award in Aging Research; A.H., principal investigator) and by an American Society of Clinical Oncology, Association of Specialty Professors Junior Development Award in Geriatric Oncology (A.H., principal investigator).

CONFLICT OF INTEREST DISCLOSURES

A.H. is compensated as a consultant/advisor to GTX, Genentech, and Amgen and has received research funding from Celgene (prior Abraxis Bioscience) and GSK.

REFERENCES

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. Acknowledgements
  8. FUNDING SOURCES
  9. REFERENCES