https://orcid.org/0000-0001-9244-5709
Figures
Abstract
Background
Complementary and alternative medicine (CAM) is a commonly used approach among cancer patients, with a reported prevalence of 14.8 to 73.1% depending on multiple factors. Even though a typical patient-reported reason for using CAM is symptom control, no conclusive evidence could be established for specific symptoms being a predictor for CAM use. Symptom clusters are a novel approach for assessing a multidimensional symptom burden, reflecting the impact of diseases on the patient’s quality of life and considering the tendency of symptoms to occur in groups.
Material and methods
A single-center cross-sectional study on cancer patients during oncological or palliative care was conducted to identify socio-demographical and clinical characteristics, including symptom clusters of CAM users and non-users. Symptom clusters were defined using latent profile analysis, and multivariable analyses were performed to assess significant factors influencing CAM use.
Results
Of 171 cancer patients in this study, 63.7% used CAM alongside oncological treatment or palliative care. The most common CAM therapies were biological and physical therapies, including homeopathy (30.3%), supplements (27.4%), herbs (26.6%), massage (24.8%), and acupuncture (22.0%). Four distinct symptom clusters were identified, of which the cluster drowsiness-depression-anxiety was associated with a 3.83-fold increased chance of using CAM compared to low-symptomatic cancer patients. Multivariate analysis did not show any additional significant predictors of socio-demographical factors.
Conclusion
Using the concept of symptom clusters revealed a significant predictor for CAM use, suggesting to be a more conclusive method for assessing symptom burden in cancer patients. In addition, understanding why and how cancer patients turn to CAM can enhance the quality of multidisciplinary communication about its use.
Citation: Huemer M, Pansi A, Hofmann G, Terbuch A, Sciri E, Lipp R, et al. (2023) Symptom clusters associated with complementary and alternative medicine use by cancer patients: A cross-sectional study. PLoS ONE 18(12): e0294641. https://doi.org/10.1371/journal.pone.0294641
Editor: Jae-Mahn Shim, Korea University, REPUBLIC OF KOREA
Received: January 4, 2023; Accepted: October 4, 2023; Published: December 14, 2023
Copyright: © 2023 Huemer et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: The authors received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Introduction
The use of complementary and alternative medicine (CAM) is a common practice among cancer patients, with an average prevalence of 51% and a range of 14.8 to 73.1% depending on geographical factors and the definition of CAM [1–3]. Currently, the most widely used definition of complementary therapies defines any health intervention that is not fully implemented into a public health care system as CAM, resulting in varying incidences of CAM use among cancer patients depending on the country and used definition of CAM in previous studies [1].
Current data identifies young females of higher educational levels as the main CAM users [1, 2], while also highlighting that psychological, cultural, and spiritual factors influence cancer patients’ tendency to use CAM [4, 5]. Patients commonly report using CAM to control and ease disease and treatment-related symptoms, but when evaluated using patient-reported outcome measures, the presence of specific symptoms and their severity did not correlate with CAM use [6, 7]. However, conflicting results exist regarding depressive symptoms as a predictor for spiritual CAM modalities such as prayers and spiritual healing in breast cancer patients [8].
Cancer patients’ symptoms often appear in groups or symptom clusters [9]. Currently, various definitions of symptom clusters exist, primarily defining them as two to three or more symptoms related to each other and appearing together [9]. The most common symptoms in cancer patients are pain, fatigue, and anorexia [10]. Each of these can be significantly disabling and create a self-perpetuating cycle when appearing together, therefore enhancing the perceived symptom burden for the patient. In contrast to single symptoms, such clusters interfere strongly with the patient’s quality of life [11]. A higher symptom impact in the quality of life was a predictor of CAM use in a population of cancer survivors [7].
Discussing CAM use with all cancer patients is essential to explore their potential needs, guide them in their choice of CAM, and also identify risks during cancer treatment, such as interactions between herbal therapies and tumor-specific remedies, which can either enhance their toxic side effects or impair the effectiveness of cancer treatment [12, 13]. However, not all patients report their CAM use to their oncologist in the course of their disease trajectory, making it difficult to estimate the risks of a changing behavior of patients that did not report the use of CAM in the past [14]. Finding patient characteristics associated with a higher chance of using CAM could raise oncologists’ awareness to actively assess CAM use in relevant patient populations. Symptoms are commonly evaluated during clinical practice and could be an easy screening tool for patients needing directed guidance concerning CAM if new symptoms occur or worsen in their severity which may change the tendency to use CAM of cancer patients [15]. Therefore, we aimed to investigate the relationships between symptom clusters and CAM use in a cross-sectional study of cancer patients.
Methods
Questionnaire design
We designed a paper-based self-completion questionnaire to engage patients using CAM in addition to their oncological care. The questionnaire was divided into three sections, including demographical data and patterns of CAM use, attitude towards CAM and holistic health, and symptom burden.
Demographic questions were collected to describe the diversity of patients participating in the study and to find potential sociodemographic factors associated with CAM use. The section included questions about sex, age, living status, educational level, yearly income. Data about the histologically confirmed cancer diagnosis, presence of metastasis, and current tumor-specific treatment were collected from the patients’ medical record. Further, we included a list of 24 treatment modalities used in previous studies to assess the most prevalent therapies of CAM, including homeopathy, traditional Chinese medicine, ayurveda, anthroposophical medicine, yoga, hypnosis, herbs, medical teas, vitamin/mineral supplements, massages, osteopathy, chiropractic, acupuncture, acupressure, qi gong/tai chi, meditation, prayers, dietary change, spiritual healing, shamanism, reiki/healing touch, healing stones/crystals, mistletoe therapy, shiatsu, and kinesiology [2]. This list was complemented by additional questions about the source of information about personal reasons for using CAM, its perceived positive and negative effects, and if its use was discussed with the responsible oncologist.
The following two sections included validated questionnaires exploring the attitude towards CAM and holistic health and symptom burden. First, we used the Holistic Complementary and Alternative Medicine Questionnaire (HCAMQ) to measure the attitude toward CAM [16, 17]. This two-factored patient-reported outcome measurement tool includes eight items creating two subscales, the holistic health (HH) subscale (four items) and the CAM subscale (four items). The HH factor describes a mental model consistent with the desire to avoid iatrogenic effects of conventional medicine and the belief that various lifestyle factors affect health. The items of HH assess the believe in how lifestyle factors including mood, stress and conflicts affect health. Additionally, the CAM subscale assesses the general attitude towards complementary therapies including the lack of its scientific evidence, whether CAM might be dangerous, when CAM should be used, how CAM might work and for which health conditions it should be used [16]. A higher score on each subscale reflects a greater positive belief in HH and CAM with acceptable psychometric properties of a Cronbach’s alpha of 0.75 and 0.83, respectively [17]. To evaluate symptom burden, we used the Edmonton Symptom Assessment Scale (ESAS), a widely used tool for assessing individual symptoms and their severity in cancer patients [18]. It includes 12 individual symptoms including pain, fatigue, drowsiness, nausea, anorexia, breathlessness, depression, anxiety, well-being, sleep, constipation, and emesis, rated on a numerical scale of 0 to 10, with higher scores reflecting greater symptom severity. To receive the ESAS total score, we summarized the single symptom scores of all 12 symptoms giving it a range of 0 to 120 [19].
Study participants and survey delivery.
Data were collected in the clinics of the Division of Oncology and its associated Palliative Care Unit at the Department of Internal Medicine, Medical University of Graz, Austria. After obtaining the written informed consent of each participant, we handed out the paper-based questionnaire, which the participants completed on their own without further assistance by members of the study staff during their stay at the clinic. Eligible patients had histologically confirmed cancer, were at least 18 years old and should be able to read and complete the provided questions. Patients were excluded if there was a significant language barrier or were physically or mentally not able to complete the questionnaire. The completed questionnaires were returned to the study staff on the same day of participation. This study was performed in line with the principles of the Declaration of Helsinki. The local ethics committee approved the study (Ethics Committee of the Medical University of Graz, Austria; document number 32–665 ex 19/20).
Statistical analysis.
The proportion of CAM users in Austrian oncological patients was previously reported to be 27% [3]. Therefore, to draw a representative sample with a comparable incidence with a margin of error of 6%, the required sample size is 171 patients. The sample size was calculated using G*Power (Version 3.1.9.6, 2020) [20].
The survey data was manually entered into a spreadsheet in Microsoft Excel format (Microsoft Corporation, 2019, version 2306, 64 bit) [21, 22]. For further analysis and data visualization, we used the statistical program R (RStudio, 2020, version 4.0.3) [23]. Differences in socio-demographical data between CAM users and non-users were calculated using the Chi2-test or Fishers’s exact test for categorical and the Wilcoxon-rank-sum-test for continuous variables. Finally, we used logistic regression for multivariable analysis using symptom clusters and the previously described predictors for CAM use sex, age and education as covariates.
For modeling symptom clusters, we performed a latent profile analysis (LPA) using the tidyLPA package in R [24]. LPA, as a form of exploratory factor analysis, is used to discover latent groups within a study sample, based on a certain set of variables [24]. In our study, we used LPA to discover groups of patients with similar response patterns on the symptoms list of the ESAS. The advantage of LPA compared to other clustering algorithms is that the algorithm also provides the mean severity scores of each item per group allowing a clear allocation of patients to each of the found clusters. The algorithm divides the population into different groups of patients with a specific symptom cluster, which we used for further analysis. We based our choice of the best LPA model on the Akaike information criterion (AIC), Bayesian information criterion (BIC), entropy, and clinical interpretability to achieve a parsimonious solution for the number of clusters [11].
Because LPA does not allow variables of different lengths, we had to impute missing ESAS values to perform the analysis without list-wise deletion and loss of statistical power. We analyzed the pattern of missing ESAS values and found that 30 were incomplete, with participants reporting only one or more symptoms with scores >0. We treated the missing values among these 30 patients as structurally missing and deduced the unreported symptoms as “not present.” We chose this approach assuming that patients, which did not report a value to one of the symptoms on the ESAS, where also not experiencing this specific symptom. Hence, we replaced missing ESAS values with 0. All other variables with missing data were analyzed with the data available.
Results
Sociodemographic and clinical characteristics
Between October 2020 and March 2021, 199 patients were asked to participate in the survey, of which 171 (50.9% female) completed the questionnaire. Most patients were at the age of 51–65 (45.0%), married (59.6%), completed primary education and apprenticeship (36.3%), and had a yearly income of € 10,001 to 20,000 (26.3%). The most frequent cancer diagnoses were breast cancer (24.0%), cancer of the lower gastrointestinal tract (23.4%), and pancreobiliary area (19.3%). A high proportion of the total sample had metastasized disease (87.7%) and received palliative tumor-specific treatment (67.8%). The sample had a moderate symptom burden of a mean ESAS total score of 27.2 (standard deviation [SD] 16.6). Detailed characteristics are listed in Table 1.
Use of complementary and alternative medicine
An unexpectedly high proportion of 63.7% within our sample used CAM during their cancer treatment. Detailed socio- and clinical demographics of CAM users and non-users are provided in Table 1. However, only 42.2% of CAM users discussed the topic with their oncologist before this survey, while 19.3% did not answer this question. The most common CAM modalities used were homeopathy (30.3%), vitamin/mineral supplementation (28.4%), herbs (26.6%), massage (24.8%), acupuncture (22.0%), and medical teas (22.0%). Most participants used CAM to strengthen their body (47.7%) and reported a subjective improvement in cancer treatment side effects (34.9%). Side effects related to CAM therapy were reported by 15.6%, with nausea being the most commonly addressed adverse reaction. Patients mostly got their information about CAM from family members (37.6%) and friends (45.0%). Detailed information on reasons for CAM use, perceived effects, and source of CAM are listed in Table 2.
Symptom cluster analysis
To achieve an optimal symptom cluster solution, we performed exploratory LPA on increasing numbers of clusters and compared their model fit indices until the AIC and BIC plateaued (Table 3). This resulted in 1 to 8 possible clusters for further analysis. The model with the lowest BIC had 5 clusters. However, this solution would create two clusters that are very similar in terms of included symptoms and their severity. Because these would be hard to distinguish in a clinical setting, we selected the solution with 4 clusters to achieve a parsimonious model with balanced fit indices and clinical interpretability. Our model also showed a sufficient entropy greater than 0.8, indicating a correct group assignment of individual cases [25].
Our model yielded four clearly distinguishable symptom clusters (Fig 1). Clusters 2 and 4 are characterized by moderate fatigue-drowsiness-sleep and all-low scores, respectively. The remaining clusters 1 and 3 distinguish between drowsiness-depression-anxiety and anorexia-nausea-constipation-emesis scores, respectively, while both clusters had comparable high fatigue scores.
a Cluster 1: drowsiness-depression-anxiety-cluster, Cluster 2: moderate fatigue-drowsiness-sleep-cluster, Cluster 3: anorexia-nausea-breathlessness-constipation-emesis-cluster, Cluster 4: all-low-cluster; b Mean symptom scores of each cluster.
Bivariate analysis
For using CAM, we found strong and statistically significant (p<0.05) bivariate associations for cluster membership (χ2 = 10.07, df = 3, p = 0.018) but no significant differences in symptom burden between CAM users and non-users (ESAS total score CAM 28.3±16.5 vs. non-CAM 25.5±16.9, p = 0.23). Further, both groups reported comparable symptom prevalence (CAM median 8 symptoms, interquartile range [IQR] 6, 10 vs. non-CAM median 7 symptoms, IQR 4.25, 9, p = 0.13). The attitudes towards CAM and HH did also not differ significantly between CAM users and non-users (CAM subscale 12.2±4.4 vs. 11.0±4.5, p = 0.08; HH subscale 21.4±2.4 vs. 21.1±3.2, p = 0.65).
Interestingly, we could not identify any bivariate associations between CAM use and socio-demographical characteristics, including the previously described predictors [1] sex (χ2 = 1.58, df = 1, p = 0.208), age (p = 0.909 Fisher’s exact test), and education (p = 0.259 Fisher’s exact test). Additionally, no other variables showed significant associations with the outcome variable (Table 4).
Multivariable analysis
We fitted a multiple logistic regression model using symptom cluster membership and the previously described factors of sex, age, and education as predictors for CAM use. Table 5 shows the fully adjusted model.
Cluster 1 (drowsiness-depression-anxiety-cluster) was the only significant predictor of all included variables for CAM use. Patients being part of this cluster had a 3.83-fold higher chance in using CAM (95% confidence interval [CI] 1.14, 15.48; p = 0.039) compared to cluster 4 (all-low-cluster). In contrast, Cluster 3 (anorexia-nausea-breathlessness-constipation-emesis-cluster) showed a 0.57-fold decreased chance of using CAM, although not significant. Female sex, lower age, and higher education showed a trend towards a higher chance of using CAM. However, these predictors did not reach statistical significance in our model.
Discussion
CAM is a highly relevant topic in cancer patients as it can have positive as well as harmful effects on the oncological treatment outcome. However, factors influencing its use are insufficiently understood. It should be noted that the circumstances increasing the chance of patients using CAM are undoubtedly multifactorial, encompassing bio-psycho-sociocultural factors. Our study identified a new factor contributing to a higher willingness to use CAM during oncological treatment.
Despite previous findings, our multivariable analysis showed a robust statistical association between a multidimensional symptom burden and the use of CAM. We found the chance of using CAM to be 3.83-fold higher for the symptom-cluster drowsiness-depression-anxiety compared to low-symptomatic cancer patients. This is in contrast to previous studies, finding no associations to single symptoms or summative scores like the ESAS total score [6, 7], but complements the findings of Wu et al. showing a higher chance for CAM use by symptom interference with the quality of life of patients [7].
Further, we could identify the relevant symptoms contributing to the higher willingness to use CAM. Cluster 1 and cluster 3 have comparable high symptom scores. However, compared to cluster 1, patients with cluster 3 membership showed an insignificant trend toward a decreased chance of using CAM. This could be explained by several factors, including better symptom management or the persistence of the cluster symptoms. Compared to depressive and exhausting symptoms, gastrointestinal symptoms like nausea (the most prominent symptom in cluster 3) may be temporary, self-limiting, or better manageable with pharmaceutical interventions. Fatigue and depressive symptoms are among the most distressing for cancer patients with limited treatment options, possibly explaining the higher prevalence of CAM use in our sample [26]. This is also reflected by the two most reported reasons for using CAM among all users: the wish to strengthen the body (47.7%) and the wish to improve quality of life (45.9%). In contrast, 33.9% of participants reported ‘to lower treatment side effects,’ and only 18.3% stated controlling their symptoms with complementary therapies. Symptoms associated to cancer and tumor-specific therapies change throughout the disease trajectory, may worsen in severity and impact the patient’s quality of life as the disease progresses. While the use of CAM should be discussed with every cancer patient as early as possible, a significant change in the symptom burden of patients such as the appearance of the drowsiness-depression-anxiety cluster may serve as an indicator for oncologists to reassess the use of CAM in this patient population. Schofield et al. published a guide for effectively discussing CAM in an oncology setting based on a systematic review [15]. In this review, the authors suggest to actively assess CAM use at critical points during a patient’s disease trajectory including the commencement of a new treatment regime and new occurring symptoms [15].
Other studies suggested using CAM as an active problem-solving coping behavior or as a result of dissatisfaction with their treating physician rather than psychological distress per se [27, 28]. Unmet needs in symptom management may urge certain patients to seek help from ‘outside’ the boundaries of ‘conventional medicine’ as far as omitting cancer treatment in favor of CAM. This may also explain the higher risk of death of CAM users because of treatment refusal [13]. The high prevalence of CAM in our sample and in numerous other countries might reflect the need for professional symptom management and an integrative approach compromising rehabilitative, early palliative, supportive care, and evidence-based complementary medicine during oncological treatment [2, 8].
Guiding oncology patients in terms of CAM, including open discussions about its use during oncological treatment, is of utmost importance to prevent interactions or even treatment refusal. Our sample mainly used safe CAM modalities, including physical therapies like massage and acupuncture [29]. However, the reported and prevalent therapies also included biological-based therapies like herbs and medical teas, prone to interactions with tumor-specific treatments [30]. Most patients get their information about CAM from their close social environment (friends 45.0%, family 37.6%) and by media (internet 16.5%, other media 18.3%), while substantial low proportions received guidance from medical trained professions (family physician 21.1%, CAM practitioner 11.9%, oncologist 9.2%, radiologist 0.9%). Additionally, only 42.2% of all CAM users discussed their CAM use with an oncologist. The nonnegligible 19.2% of denied answers to this question suggest a greater communication gap, as this item might be subject to a social desirability bias, and the outcome might be underreported. Keeping the skewness of the information-seeking behavior towards unprofessional sources in mind, risks associated with CAM might also result from this communication gap between physicians and patients. On the other hand, enhancing open communication about CAM can strengthen the physician-patient relationship and lead to a greater satisfaction with cancer care in general [2, 14]. The use of CAM should therefore be part of routine history taking, particularly at important changes within the disease trajectory. When discussing the topic of CAM, it is important to explain relevant concerns from a scientific point of view while considering and respecting the cultural diversity and epistemological believe systems of patients [15]. In some cases, it may also be useful to guide patients in their choice of CAM towards treatment modalities which are to some extend evidence-based and safe [2, 15]. Recently published clinical guidelines by leading oncological associations offer an overview of different CAM therapies, their possible benefits and harms in an oncology setting for various symptoms [31–36].
Our study was conducted during the COVID-19 pandemic, which led to a higher degree of uncertainty for cancer patients concerning the availability of tumor-specific treatment, the risk of COVID-19 infection, and its potential interference with the treatment plan. The resulting stress associated with the pandemic enhanced the perceived symptom burden and increased the incidence of anxiety and depression [37–40]. Additionally, the pandemic created a new barrier to access health care services for cancer patients causing a delayed start of tumorspecific treatment and reduced access to symptom management [41, 42]. This may have also changed the information seeking behavior of the patients in our study, leading to a greater motivation to manage their occuring symptoms themselves and using other sources including the family members, friends and media including the internet, which is also reflected by our results. In our study, we found a higher proportion of cancer patients using CAM compared to a survey among Austrian cancer patients before the COVID-19 pandemic (63.7% versus 27.3%). Further, we could show that the chances of using CAM were associated with the symptom cluster that included mainly psychological symptoms [3]. Hence, the pandemic could have influenced the results of our study. However, several factors could explain the higher incidence of CAM use, including its increasing popularity over time, in general.
Our study has certain other limitations, up-front the single-center design limiting the generalizability of our findings in terms of cultural and socio-demographical factors. Our results may therefore be not applicable in areas in which traditional and complementary medicine represents a significant proportion of the local public health care system [43]. However, we did assess the attitude towards CAM using a validated measurement tool (HCAMQ). We found no statistically significant difference between the users and non-users of CAM in our sample, suggesting no significant influence of the individual cultural preoccupation. Additionally, we only included patients currently in oncological or palliative care at our clinic. Hence, our associations are only applicable to patients using CAM complementary to conventional medicine and not alternatively, meaning if they quit oncological treatment in favor of CAM, which could be a completely different patient cohort with different socio-demographical, clinical, and motivational characteristics. Nevertheless, the study’s strengths, including a prospective study design, robust sample size, a diverse sample, and validated patient-reported outcome measures, support our finding that symptom clusters are a significant and measurable predictor of CAM in oncology patients.
Conclusion
For the first time, we could show the value of symptom burden using the concept of symptom clusters as a measurable predictor for CAM use in cancer patients. Our results indicate that patients with the symptom cluster “drowsiness-depression-anxiety” have a 3.83-fold higher chance to use CAM therapies than low-symptomatic patients, which is why we suggest to monitor the symptom burden of patients to detect significant changes of symptoms during the disease trajectory and reassess the use of CAM as early as possible in relevant patient populations. Further, we found the main source of information about CAM to be their family and friends and the media including the internet. This highlights the necessity to raise the awareness of clinical physicians to actively assess and discuss the use of CAM with their cancer patients.
Supporting information
S1 Checklist. STROBE statement—Checklist of items that should be included in reports of cross-sectional studies.
https://doi.org/10.1371/journal.pone.0294641.s001
(DOC)
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