Factors Influencing Global Health Related Quality of Life in Elderly Cancer Patients: Results of a Secondary Data Analysis
by
and
Heike Schmidt
1,*, 
Thomas Nordhausen
1,
Stephanie Boese
1,
Dirk Vordermark
2,
Sally Wheelwright
3,
Andreas Wienke
4 and
Colin D. Johnson
5 1
Institute of Health and Nursing Sciences, Medical Faculty, Martin Luther University Halle-Wittenberg, Magdeburger Str. 8, 06112 Halle (Saale), Germany
2
Department of Radiation Oncology, University Hospital Halle (Saale), Medical Faculty, Martin Luther University Halle-Wittenberg, Ernst-Grube-Str. 40 06120 Halle (Saale), Germany
3
Health Sciences, University of Southampton, SO17 1BJ Southampton, UK
4
Institute for Medical Epidemiology, Biostatistics, and Informatics, Medical Faculty, Martin Luther University Halle-Wittenberg, Magdeburger Str. 8, 06112 Halle (Saale), Germany
5
Cancer Sciences, University of Southampton, SO17 1BJ Southampton, UK
*
Author to whom correspondence should be addressed.
Geriatrics 2018, 3(1), 5; https://doi.org/10.3390/geriatrics3010005
Submission received: 14 November 2017
/
Revised: 23 January 2018
/
Accepted: 24 January 2018
/
Published: 30 January 2018
(This article belongs to the Special Issue Oncology Care and Research in the Elderly)
Abstract
:Cancer treatment for elderly patients is often complicated by poor physical condition, impaired functioning and comorbidities. Patient reported health related quality of life (HRQOL) can contribute to decisions about treatment goals and supportive therapy. Knowledge about factors influencing HRQOL is therefore needed for the development of supportive measures and care pathways. An exploratory secondary data analysis on 518 assessments of the European Organisation for Research and Treatment of Cancer (EORTC) core questionnaire (EORTC QLQ-C30) and the elderly module (EORTC QLQ-ELD14) was performed to identify factors predictive for global HRQOL. Preliminary simple and multivariable regression analyses were conducted resulting in a final model comprising sociodemographic and disease specific variables and scales of the QLQ-C30 and QLQ-ELD14. Age, sex and disease related variables explained only part of the variance of global HRQOL (adjusted R2 = 0.203). In the final model (adjusted R2 = 0.504) fatigue, social function, burden of illness and joint stiffness showed possible influence on global HRQOL. Fatigue, social function and burden of illness seem to have the largest impact on global HRQOL of elderly cancer patients. Further prospective studies should examine these domains. Actionable symptoms should be given special attention to initiate targeted supportive measures aiming to maximize HRQOL of older cancer patients.
1. Introduction
As new therapeutic options improve survival rates in cancer [1], the maintenance of quality of life (QOL) has become a major therapeutic goal. In this context, it is important to recognize that elderly cancer patients as a group are more heterogeneous than younger patients with respect to their physiological reserve, functional impairments and comorbidities [2,3]. Therefore, in order to detect relevant risk factors and resources and to adjust the therapy accordingly, a comprehensive geriatric assessment (CGA) comprising mobility, cognition, nutritional status and psychosocial aspects is recommended [3]. In addition to the medical condition, patients’ individual preferences should be taken into account in the process of decision-making [4]. In this context, maintenance of health related quality of life (HRQOL), which encompasses the effects of health, illness and treatment on QOL [5], independence and the ability to perform normal activities are key issues reported by cancer patients of all age groups [6]. In childhood, good self-esteem and scholarship are identified as key elements, in adulthood social support becomes fundamental and in the elderly activities assume a significant value [7,8,9]. In comparison to younger people with cancer, older people with cancer tend to prefer a better HRQOL to an increased length of life [9]. Therefore, maintenance of HRQOL and functioning are integral parts of cancer care for older patients [10] and factors like the fear of side effects, treatment discomfort and the fear of losing independence are important aspects for accepting or declining cancer therapy [11]. These issues should be considered in the decision-making process and throughout the treatment trajectory. Targeted interventions and adequate measurement tools assessing the effects of supportive therapy and these interventions on HRQOL should be developed and utilized. To achieve these goals, a sound understanding of HRQOL in elderly cancer patients, of possible influencing factors and of differences in comparison to younger patients is required.
The HRQOL model developed by Ferrans et al. [5] that is based on Wilson and Cleary’s model [12] includes the possible influence of biological and demographic factors and characteristics of the social environment on HRQOL. The model underlines the subjective nature of health perceptions and describes the impact of symptoms on levels of activity and functioning [5]. This individual perception may vary considerably. For some patients, symptoms or functional restrictions may cause a reduction in perceived global HRQOL but not all patients with poor global HRQOL might experience these problems. There may be interdependencies like depressed mood influencing the perception of HRQOL and poor HRQOL possibly influencing the state of mind [13]. Furthermore, in the process of coping with their disease, patients might adapt their assessment of HRQOL to their changed condition, a phenomenon described as response shift [14,15,16,17], which might explain the finding, that global HRQOL scores of cancer patients are often comparable to the general population [18]. Although global HRQOL scores may be less precise than specific scales in detecting group differences over time [19] they are still informative if the aim is to optimize cancer care and to support patients’ adjustment and coping with the disease. Therefore, it is of interest to explore which factors predict global HRQOL to optimize supportive therapy for elderly cancer patients.
Factors which could influence HRQOL may be potentially modifiable (e.g., patient-reported symptoms like pain or functional impairments), or unmodifiable like sociodemographic variables (e.g., age, education, gender) or disease related variables (e.g., treatment intention, disease stage, and comorbidities). Recent studies have revealed for instance the impact of comorbidities on HRQOL in elderly patients with multiple myeloma [20] and the negative impact of cancer related fatigue on global HRQOL in cancer patients [21]. For patients with advanced cancer, emotional functioning, pain, appetite loss [22] and social support [23] have been shown to influence HRQOL. However, there are few studies comparing age related differences of HRQOL [24,25,26] and examining HRQOL in older cancer patients specifically. Therefore, this study aimed to explore which factors are predictive of global HRQOL in older cancer patients in order to formulate hypotheses for future prospective studies and to gather information to inform the development of supportive measures targeting modifiable factors and care pathways intended to improve HRQOL.
2. Material and Methods
An exploratory secondary data analysis of 518 one-time assessments of the European Organization for Research and Treatment of Cancer (EORTC) Quality of life Questionnaire (EORTC QLQ-C30) and the elderly module (EORTC QLQ-ELD14) was performed to examine which unmodifiable and modifiable factors predicted global HRQOL. Based on the models of Ferrans [5] and Wilson and Cleary [12] unmodifiable factors are sociodemographic characteristics of the individual e.g., age, sex and clinician-reported disease related factors like disease stage, treatment intention and comorbidities. Modifiable factors comprise patient-reported symptoms and modifiable functional restrictions. While there may be interactions and interdependencies between symptoms and functional restrictions, definite hierarchical causal relationships have not yet been comprehensively proven. Therefore, we used an exploratory non-hierarchical regression model including sociodemographic and disease related factors and subscales of the EORTC QLQ-C30 and QLQ-ELD14 representing mostly modifiable factors.
The primary data had been collected for the validation of the EORTC QLQ-ELD14 [27]. Patients eligible for this study were aged >70 years and had a confirmed diagnosis of any primary, recurrent or metastatic cancer. Further details are given in the results paragraph and are described in the publication by Wheelwright et al. [27]. For the original study, ethical and research governance approvals were obtained at each centre in accordance with local requirements and all patients provided written informed consent. For the secondary data analysis, the data set was provided by the data repository of the EORTC Quality of Life Group.
2.1. HRQOL Questionnaires
The EORTC QLQ-C30 comprises five functioning scales (physical, role, cognitive, emotional and social), three symptom scales (fatigue, pain, nausea and vomiting), six single items assessing frequent symptoms (dyspnea, appetite loss, sleep disturbance, constipation, diarrhea and perceived financial burden) and a global health status/quality of life scale [28]. All scale and item scores are linearly transformed to a 0–100 scale, higher scores representing better functioning but worse symptom burden [29]. The EORTC QLQ-ELD14 questionnaire comprises five scales (mobility, worries about others, future worries, maintaining purpose and burden of illness) and two single items (joint stiffness and family support). As for the QLQ-C30 all scale and item scores are transformed to a 0–100 scale, higher scores representing a worse outcome except for maintaining purpose and family support [27].
2.2. Statistical Analyses
Regression analyses were carried out to investigate the associations between global HRQOL and sociodemographic factors, clinician-reported disease related factors and patient-reported factors (subscales of HRQOL). Comparable to other studies [22,30,31], subscales of the HRQOL questionnaires were included to examine the possible impact of patient-reported and potentially modifiable factors e.g., symptoms and functioning on global HRQOL in this sample of elderly cancer patients.
The analyses comprised preliminary simple regression analyses and multivariable regression analyses. Preliminary simple analyses were performed in order to identify unmodifiable socio-demographic and disease related factors possibly influencing global HRQOL. Global HRQOL, as assessed by questions 29 and 30 of the EORTC QLQ-C30, was defined as the dependent variable. Based on theoretical considerations, 41 variables of the available socio-demographic and medical characteristics were analysed. None of these variables were considered as mediators in the sense of Ferrans et al. [5]. In order to facilitate analyses, ordinal variables were dichotomized (e.g., education low vs. high, ECOG good (0–2) vs. poor (3, 4). In addition to the preselected variables age and sex, all variables with p < 0.05 were included into the multivariable regression analyses. Variables representing items of the G8 screening tool, a validated screening tool considering eight questions for identifying problems of older patients [32], are labelled (G8) accordingly, as the instrument was used for data collection in the primary work [27].
Unmodifiable factors like sociodemographic characteristics of the individual e.g., age, sex and clinician-reported disease related factors like disease stage, treatment intention and comorbidities and patient-reported potentially modifiable factors e.g., symptoms and functioning scales were included stepwise into the model to examine possible influence on global HRQOL.
The first step (model 1) comprised the following socio-demographic and disease related factors identified in the preliminary simple analyses: age, sex, disease stage, disease progression, treatment intention, ECOG-Status, toxicity level, food intake (G8), weight loss (G8), >three medications/day (G8) and Charlson Comorbidity Index.
In the second step (model 2), the EORTC QLQ-C30 symptom scales were added to the model. In the third step EORTC QLQ-C30 functioning scales were added (model 3) and in the fourth and final step the EORTC QLQ-ELD14 scales were added (model 4).
After visual inspection of the distribution of global HRQOL data, no major deviations from symmetry were observed and therefore linear regression was deemed appropriate. We investigated the correlation between the variables based on their tolerance values (appendix A and B, available online). In all models the tolerance values were >0.2. Consequently, multicollinearity is not a major issue in the models [33]. As missing values were not imputed and only patients with complete data with respect to all variables were included in the multivariable regression analyses n = 341 complete data sets remained for the final analysis. For better comparability of the results, all models were carried out with the final sample of n = 341 patients. Sociodemographic, medical characteristics and HRQOL scores of both samples were descriptively contrasted to examine comparability of the reduced sample with the study population.
3. Results
Study Population
The mean age of the participants of the whole sample (n = 518) was 77.3 years (SD: 4.9), 48.8% were male and 83.3% had a good ECOG performance status between 0 and 2. The subsample included in the multivariable regression comprised the reduced number of n = 341 participants. Mean age of these participants was 77.0 years (SD: 4.7), 50.7% were male and 91.5% had an ECOG status between 0 and 2. Sociodemographic data for both samples are summarized in Table 1, disease and therapy-related data in Table 2. Unadjusted mean values of all HRQOL scales for both samples are shown in Table 3.
The results of the multivariable regression models are described briefly in the following paragraphs.
The results of all models are summarized in Table 4.
Model 1 regression analysis including unmodifiable and disease related variables with respect to global HRQOL (EORTC QLQ-C30)
In this model EGOG status regression coefficient (β = −8.78, 95%CI: −16.704; −0.855), food intake (G8) (β = 13.915, 95%CI: 8.398; 19.431) and (more than) three medications (G8) (β = 8.239, 95%CI: 3.879; 12.599) showed a possible influence on global HRQOL. Adjusted R2 of this model is 0.203, indicating that 20,3% of the variation of global HRQOL could be explained with the variables included.
As an example for the interpretation of the regression coefficient of dichotomous variables, for ECOG status β = −8.78 means that the mean value of global HRQOL of patients with poor ECOG status (3,4) is 8.8 less compared to patients with good ECOG status. As β is a point estimate of the true effect in the population, the confidence interval shows the true value is located within this interval with 95% probability.
Model 2 regression analysis including unmodifiable and disease related variables and EORTC QLQ-C30 symptom scales with respect to global HRQOL (EORTC QLQ-C30)
In this model (more than) three medications (G8) (β = 4.941, 95%CI: 1.286; 8.596), fatigue (β = −0.365, 95%CI: −0.463; −0.267), pain (β = −0.097, 95%CI: −0.180; −0.014) and financial problems (β = −0.116, 95%CI: −0.198; −0.034) showed a possible influence on global HRQOL. Adjusted R2 of this model is 0.460, indicating that 46% of the variation of global HRQOL could be explained with the variables included.
As an example for the interpretation of the regression coefficient of continuous variables, for fatigue β = −0.365 means that an increase of fatigue by one point is associated with a mean decrease of global HRQOL by 0.4 points.
Model 3 regression analysis including unmodifiable and disease related variables, EORTC QLQ-C30 symptom scales and EORTC QLQ-C30 functioning scales with respect to global HRQOL (EORTC QLQ-C30)
In this model fatigue (β = −0.243, 95%CI: −0.353; −0.134), pain (β = −0.083 95%CI: −0.164; −0.002), physical function (β = 0.173, 95%CI: 0.041; 0.305) and social function (β = 0.137, 95%CI: 0.054; 0.221), showed a possible influence on global HRQOL. Adjusted R2 of this model is 0.497, indicating that 49.7% of the variation of global HRQOL could be explained with the variables included.
Model 4 regression analysis including unmodifiable and disease related variables, EORTC QLQ-C30 symptom scales, EORTC QLQ-C30 functioning scales and EORTC QLQ-ELD14 scales with respect to global HRQOL (EORTC QLQ-C30)
In this final model, only fatigue (β = −0.223, 95%CI: −0.334; −0.112), social function (β = 0.099, 95%CI: 0.008; 0.191), joint stiffness (β = −0.064, 95%CI: −0.126; −0.002), and burden of illness (β = −0.071, 95%CI: −0.141; −0.001) showed a possible influence on global HRQOL. In conclusion, it can be stated that in this model fatigue prevailed, the regression coefficient β of -0.223 meaning, that an increase of fatigue by a clinically relevant amount of 10 points on a 0–100 scale [34] will be correlated with a decrease of global HRQOL of 2.2 points. Adjusted R2 of this model is 0.504, indicating that 50,4% of the variation of global HRQOL could be explained with the variables included.
4. Discussion
This study explored factors that might influence global HRQOL in elderly cancer patients in a large international data set representative of elderly cancer patients with a wide range of cancers [27]. Based on the model of Ferrans [5], unmodifiable factors and potentially modifiable factors like symptoms were included stepwise into the analyses. It is important to note that the chosen method can only examine associations and causal relationships cannot be claimed. The stepwise approach for multivariable regression analyses resulted in a final model showing that fatigue, social functioning and burden of illness had the strongest association and possible influence on global HRQOL. These findings are in line with other research describing fatigue and social functioning as important contributors to HRQOL in cancer patients [21] and support the models of Ferrans [5] and Wilson and Cleary [12] that describe symptoms and functioning as factors possibly influencing HRQOL although causality cannot be proven.
Although the final model has the best fit, explaining 50.4% of the variance of global HRQOL, the preceding models provide useful insights. In model 2, when EORTC QLQ-C30 symptom scales were added, fatigue, pain and financial problems were identified as factors possibly influencing global HRQOL. The use of more than three medications remained in the model. For older cancer patients, the use of more than three medications per day might represent health problems with a possible impact on overall wellbeing and HRQOL and a possible risk for polypharmacy. This finding underlines the importance of a medication review for older patients including all self-administered medication [35].
Adding EORTC QLQ-C30 functioning scales in model 3, fatigue, physical function, social function and pain prevailed. The importance of these symptoms and functioning domains is also supported by previous studies examining HRQOL of cancer patients [21,22,31]. In the final model, when the EORTC QLQ-ELD14 scales were added, fatigue still showed the strongest possible impact on global HRQOL followed by social function, burden of illness and joint stiffness. With respect to pain and physical function, it can be presumed that the more comprehensive questions of the EORTC QLQ-ELD14 about burden of illness and burden of treatment encompass the content of these items. The finding that joint stiffness prevailed while pain left the model might be understandable as trouble with joints e.g., stiffness or pain can be incapacitating and hamper mobility, while pain in general might be represented by burden of illness. This finding is supported by a study on HRQOL in elderly multiple myeloma patients describing bone aches, and pain in hips, arms and shoulders having a negative impact on global health status [30].
As the interference of symptoms with functioning is especially burdensome for elderly patients and can lead to distress [25], regular assessment of symptoms and functioning is advisable to trigger timely targeted supportive care. In addition, attention should be given to other factors like social support, particularly in the case of limited functional capacity. In patients with advanced cancer, Rodriguez et al. found social support to be the most important contributor of overall HRQOL [23]. The availability of social support can play a major role in the upkeep of medical appointments and social relations for patients with disease related impairments. Therefore, an assessment of psychosocial risk factors and the development of targeted interventions e.g., psychological interventions on families could prove beneficial in the endeavor to optimize supportive care.
In conclusion, it can be stated that the association between fatigue, social functioning and burden of illness with global HRQOL in our data underlines the significance of patient-reported outcomes for the treatment of older cancer patients. Particular attention should be given to symptoms that affect HRQOL which are sensitive to treatment or supportive measures to facilitate targeted supportive care. Special attention should also be given to individual and environmental characteristics that might influence social function and the use of health services.
4.1. Limitations of the Study
This study has several limitations. The adjusted R2 value of around 0.5 indicates that there are additional factors (not measured or possibly not yet known), which may influence the global HRQOL. The health status of the participants was generally high, as indicated by the limited number of participants with poor ECOG status, meaning generalizability might be limited. However, with the exception of emotional function and pain, which were better for the study population, the unadjusted mean scores of the EORTC QLQ-C30 did not differ significantly (defined as >10 point difference) in comparison to reported scores of cancer patients of a comparable age cohort [24]. With respect to comorbidities, the data on the use of more than three medications indicate the presence of more health problems, than captured by the Charlson comorbidity index, as might be expected in older cancer patients. These health problems might have been captured more comprehensively by the cumulative illness rating scale (CIRS). In addition, the documentation of the exact amount of prescribed and self-administered medication would have been of interest. With respect to dementia and depression, the available information as given by the respective single item of the G8 screening tool is not suitable for analyzing the influence of either impairment on HRQOL. In addition, this study has some limitations due to the data of the original study. For the original study [27] data of people who declined to take part in the study were not recorded, due to ethical considerations. A considerable number of patients (n = 177) had to be excluded from the analysis due to incomplete or missing data of the original study. Except for the ECOG status which was better for the subsample, the descriptive comparison of the percentages did not reveal any major differences between both groups.
4.2. Implications for Future Research
The variables associated with HRQOL in elderly cancer patients should be investigated in further prospective studies, which could also examine the development of HRQOL over time including response shift in connection with different diagnoses and disease trajectories. In addition to symptoms and functioning, social support [36], self-efficacy [37] and ways of coping could be investigated as possible influencing factors on adjustment to the illness and the individual rating of HRQOL. Findings should feed into the design of supportive measures and multicomponent interventions including psychosocial interventions for spouses and families aimed at maximizing the HRQOL of elderly cancer patients.
Acknowledgements
The data for this analysis were collected in an EORTC funded project for the validation of the EORTC Elderly 14 questionnaire [27] and were used with permission of the EORTC. The secondary data analysis was funded by the Wilhelm-Roux-Program of the Martin Luther University Halle-Wittenberg (Grant No. 28/20). We thank Anja Broda, Institute for Health and Nursing Science, Martin Luther University Halle-Wittenberg and Corneel Coens, EORTC quality of life department, Brussels, for fruitful discussion.
Author Contributions
Study concepts: Schmidt, Boese, Wienke, Vordermark, Johnson; Study design: Schmidt, Boese, Wienke, Johnson; Data acquisition: Wheelwright, Johnson; Quality control of data and algorithms: Nordhausen, Boese; Data analysis and interpretation: all authors; Statistical analysis: Schmidt, Nordhausen, Boese, Wienke; Manuscript preparation: Schmidt, Nordhausen; Manuscript editing: all authors; Manuscript review: all authors.
Conflicts of Interest
The authors declare no conflict of interest.
References
- Eichhorst, B.; Hallek, M.; Goede, V. New treatment approaches in CLL: Challenges and opportunities in the elderly. J. Geriatr. Oncol. 2016, 7, 375–382. [Google Scholar] [CrossRef] [PubMed]
- Vigano, A.; Morais, J.A. The elderly patient with cancer: A holistic view. Nutrition 2015, 31, 587–589. [Google Scholar] [CrossRef] [PubMed]
- Burhenn, P.S.; McCarthy, A.L.; Begue, A.; Nightingale, G.; Cheng, K.; Kenis, C. Geriatric assessment in daily oncology practice for nurses and allied health care professionals: Opinion paper of the Nursing and Allied Health Interest Group of the International Society of Geriatric Oncology (SIOG). J. Geriatr. Oncol. 2016, 7, 315–324. [Google Scholar] [CrossRef] [PubMed]
- National Comprehensive Cancer Network (NCCN). Older Adult Oncology Version 2.2016; National Comprehensive Cancer Network: Fort Washington, PA, USA, 2016. [Google Scholar]
- Ferrans, C.E.; Zerwig, J.J.; Wilbur, J.E.; Larson, J.L. Conceptual Model of Health-Related Quality of Life. J. Nurs. Scholarsh. 2005, 37, 336–342. [Google Scholar] [CrossRef] [PubMed]
- Hollen, P.J.; Msaouel, P.; Gralla, R.J. Determining issues of importance for the evaluation of quality of life and patient-reported outcomes in breast cancer: Results of a survey of 1072 patients. Breast Cancer Res. Treat. 2015, 151, 679–686. [Google Scholar] [CrossRef] [PubMed]
- Tremolada, M.; Taverna, L.; Bonichini, S.; Basso, G.; Pillon, M. Self-Esteem and Academic Difficulties in Preadolescents and Adolescents Healed from Paediatric Leukaemia. Cancers 2017, 9, 55. [Google Scholar] [CrossRef] [PubMed]
- Tremolada, M.; Bonichini, S.; Basso, G.; Pillon, M. Perceived social support and health-related quality of life in AYA cancer survivors and controls. Psychooncology 2016, 25, 1408–1417. [Google Scholar] [CrossRef] [PubMed]
- Meropol, N.J.; Egleston, B.L.; Buzaglo, J.S.; Benson, A.B.; Cegala, D.J.; Diefenbach, M.A.; Fleisher, L.; Miller, S.M.; Sulmasy, D.P.; Weinfurt, K.P. Cancer patient preferences for quality and length of life. Cancer 2008, 113, 3459–3466. [Google Scholar] [CrossRef] [PubMed]
- Baier, P.; Ihorst, G.; Wolff-Vorbeck, G.; Hull, M.; Hopt, U.; Deschler, B. Independence and health related quality of life in 200 onco-geriatric surgical patients within 6 months of follow-up: Who is at risk to lose? Eur. J. Surg. Oncol. 2016, 42, 1890–1897. [Google Scholar] [CrossRef] [PubMed]
- Puts, M.T.; Tapscott, B.; Fitch, M.; Howell, D.; Monette, J.; Wan-Chow-Wah, D.; Krzyzanowska, M.; Leighl, N.B.; Springall, E.; Alibhai, S.M. A systematic review of factors influencing older adults’ decision to accept or decline cancer treatment. Cancer Treat. Rev. 2015, 41, 197–215. [Google Scholar] [CrossRef] [PubMed]
- Wilson, I.B.; Cleary, P.D. Linking Clinical Variables with Health-Related Quality of Life: A Conceptual Model of Patient Outcomes. JAMA 1995, 273, 59–65. [Google Scholar] [CrossRef] [PubMed]
- Fayers, P.M.; Hand, D.J.; Bjordal, K.; Groenvold, M. Causal indicators in quality of life research. Qual. Life Res. 1997, 6, 393–406. [Google Scholar] [CrossRef] [PubMed]
- Hamidou, Z.; Dabakuyo, T.S.; Bonnetain, F. Impact of response shift on longitudinal quality-of-life assessment in cancer clinical trials. Expert Rev. Pharmacoecon. Outcomes Res. 2011, 11, 549–559. [Google Scholar] [CrossRef] [PubMed]
- Dabakuyo, T.S.; Guillemin, F.; Conroy, T.; Velten, M.; Jolly, D.; Mercier, M.; Causeret, S.; Cuisenier, J.; Graesslin, O.; Gauthier, M.; et al. Response shift effects on measuring post-operative quality of life among breast cancer patients: A multicenter cohort study. Qual. Life Res. 2013, 22, 1–11. [Google Scholar] [CrossRef] [PubMed]
- Donohoe, J.E. To what extent can response shift theory explain the variation in prostate cancer patients’ reactions to treatment side-effects? A review. Qual. Life Res. 2011, 20, 161–167. [Google Scholar] [CrossRef] [PubMed]
- Sprangers, M.A.G.; Schwartz, C.E. Integrating response shift into health-related quality of life research. Soc. Sci. Med. 1999, 48, 1507–1515. [Google Scholar] [CrossRef]
- Hinz, A.; Mehnert, A.; Dégi, C.; Reissmann, D.R.; Schotte, D.; Schulte, T. The relationship between global and specific components of quality of life, assessed with the EORTC QLQ-C30 in a sample of 2019 cancer patients. Eur. J. Cancer Care 2017, 26. [Google Scholar] [CrossRef] [PubMed]
- Giesinger, J.M.; Kieffer, J.M.; Fayers, P.M.; Groenvold, M.; Petersen, M.A.; Scott, N.W.; Sprangers, M.A.G.; Velikova, G.; Aaronson, N.K. Replication and validation of higher order models demonstrated that a summary score for the EORTC QLQ-C30 is robust. J. Clin. Epidemiol. 2016, 69, 79–88. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Efficace, F.; Rosti, G.; Breccia, M.; Cottone, F.; Giesinger, J.M.; Stagno, F.; Iurlo, A.; Russo Rossi, A.; Luciano, L.; Martino, B.; et al. The impact of comorbidity on health-related quality of life in elderly patients with chronic myeloid leukemia. Ann. Hematol. 2016, 95, 211–219. [Google Scholar] [CrossRef] [PubMed]
- McCabe, R.M.; Grutsch, J.F.; Braun, D.P.; Nutakki, S.B. Fatigue as a Driver of Overall Quality of Life in Cancer Patients. PLoS ONE 2015, 10, e0130023. [Google Scholar] [CrossRef] [PubMed]
- Cramarossa, G.; Chow, E.; Zhang, L.; Bedard, G.; Zeng, L.; Sahgal, A.; Vassiliou, V.; Satoh, T.; Foro, P.; Ma, B.B.; et al. Predictive factors for overall quality of life in patients with advanced cancer. Support. Care Cancer 2013, 21, 1709–1716. [Google Scholar] [CrossRef] [PubMed]
- Rodríguez, A.M.; Mayo, N.E.; Gagnon, B. Independent contributors to overall quality of life in people with advanced cancer. Br. J. Cancer 2013, 108, 1790–1800. [Google Scholar] [CrossRef] [PubMed]
- Quinten, C.; Coens, C.; Ghislain, I.; Zikos, E.; Sprangers, M.A.; Ringash, J.; Martinelli, F.; Ediebah, D.E.; Maringwa, J.; Reeve, B.B.; et al. The effects of age on health-related quality of life in cancer populations: A pooled analysis of randomized controlled trials using the European Organisation for Research and Treatment of Cancer (EORTC) QLQ-C30 involving 6024 cancer patients. Eur. J. Cancer 2015, 51, 2808–2819. [Google Scholar] [CrossRef] [PubMed]
- Mohile, S.G.; Heckler, C.; Fan, L.; Mustian, K.; Jean-Pierre, P.; Usuki, K.; Sprod, L.; Janelsins, M.; Purnell, J.; Peppone, L.; et al. Age-related Differences in Symptoms and Their Interference with Quality of Life in 903 Cancer Patients Undergoing Radiation Therapy. J. Geriatr. Oncol. 2011, 2, 225–232. [Google Scholar] [CrossRef] [PubMed]
- Waldmann, A.; Schubert, D.; Katalinic, A. Normative data of the EORTC QLQ-C30 for the German population: A population-based survey. PLoS ONE 2013, 8, e74149. [Google Scholar] [CrossRef] [PubMed]
- Wheelwright, S.; Darlington, A.S.; Fitzsimmons, D.; Fayers, P.; Arraras, J.I.; Bonnetain, F.; Brain, E.; Bredart, A.; Chie, W.C.; Giesinger, J.; et al. International validation of the EORTC QLQ-ELD14 questionnaire for assessment of health-related quality of life elderly patients with cancer. Br J. Cancer 2013, 109, 852–858. [Google Scholar] [CrossRef] [PubMed]
- Aaronson, N.K.; Ahmedzai, S.; Bergman, B.; Bullinger, M.; Cull, A.; Duez, N.J.; Filiberti, A.; Flechtner, H.; Fleishman, S.B.; Haes, J.C.; et al. The European Organization for Research and Treatment of Cancer QLQ-C30: A quality-of-life instrument for use in international clinical trials in oncology. J. Natl. Cancer Inst. 1993, 85, 365–376. [Google Scholar] [CrossRef] [PubMed]
- EORTC Quality of Life Group. EORTC QLQ-C30 Scoring Manual, 3rd ed.; EORTC Quality of Life Group: Brussels, Belgium, 2001. [Google Scholar]
- Van der Poel, M.W.M.; Oerlemans, S.; Schouten, H.C.; van de Poll-Franse, L.V. Elderly multiple myeloma patients experience less deterioration in health-related quality of life than younger patients compared to a normative population: A study from the population-based PROFILES registry. Ann. Hematol. 2015, 94, 651–661. [Google Scholar] [CrossRef] [PubMed]
- Gray, N.M.; Hall, S.J.; Browne, S.; Macleod, U.; Mitchell, E.; Lee, A.J.; Johnston, M.; Wyke, S.; Samuel, L.; Weller, D.; et al. Modifiable and fixed factors predicting quality of life in people with colorectal cancer. Br. J. Cancer 2011, 104, 1697–1703. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Liuu, E.; Canoui-Poitrine, F.; Tournigand, C.; Laurent, M.; Caillet, P.; Le Thuaut, A.; Vincent, H.; Culine, S.; Audureau, E.; Bastuji-Garin, S.; et al. Accuracy of the G-8 geriatric-oncology screening tool for identifying vulnerable elderly patients with cancer according to tumour site: The ELCAPA-02 study. J. Geriatr. Oncol. 2014, 5, 11–19. [Google Scholar] [CrossRef] [PubMed]
- Scott, M. Applied Logistic Regression Analysis, 2nd ed.; Series: Quantitative Applications in the Social Sciences; Sage Publications: Thousand Oaks, CA, USA, 1997; Volume 106. [Google Scholar]
- Osoba, D. Interpreting the meaningfulness of changes in health-related quality of life scores: Lessons from studies in adults. Int. J. Cancer Suppl. 1999, 12, 132–137. [Google Scholar] [CrossRef]
- Sharma, M.; Loh, K.P.; Nightingale, G.; Mohile, S.G.; Holmes, H.M. Polypharmacy and potentially inappropriate medication use in geriatric oncology. J. Geriatr. Oncol. 2016, 7, 346–353. [Google Scholar] [CrossRef] [PubMed]
- Haugland, T.; Wahl, A.K.; Hofoss, D.; DeVon, H.A. Association between general self-efficacy, social support, cancer-related stress and physical health-related quality of life: A path model study in patients with neuroendocrine tumors. Health Qual. Life Outcomes 2016, 14. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Shelby, R.A.; Edmond, S.N.; Wren, A.A.; Keefe, F.J.; Peppercorn, J.M.; Marcom, P.K.; Blackwell, K.L.; Kimmick, G.G. Self-efficacy for coping with symptoms moderates the relationship between physical symptoms and well-being in breast cancer survivors taking adjuvant endocrine therapy. Support. Care Cancer 2014, 22, 2851–2859. [Google Scholar] [CrossRef] [PubMed]
Table 1.
Sociodemographic data (n = 518; n = 341 complete data sets included in regression analyses) All values are number (%) unless stated otherwise.
Table 1.
Sociodemographic data (n = 518; n = 341 complete data sets included in regression analyses) All values are number (%) unless stated otherwise.
| Sociodemographic Data | n = 518 | n = 341 |
|---|---|---|
| Sex | ||
| Male | 253 (48.8) | 173 (50.7) |
| Female | 264 (51.0) | 168 (49.3) |
| Missing | 1 (0.2) | 0 (0) |
| Age | Mean: 77.3; SD: 4.9 | Mean: 77.0; SD: 4.7 |
| <80 | 365 (70.5) | 246 (72.1) |
| 80–85 | 118 (22.8) | 78 (22.9) |
| >85 | 35 (6.8) | 17 (5.0) |
| Education | ||
| No education/primary education | 187 (36.1) | 136 (39.9) |
| Secondary education | 184 (35.5) | 119 (34.9) |
| College | 88 (17.0) | 50 (14.9) |
| University | 50 (9.7) | 34 (10.0) |
| Missing | 9 (1.7) | 2 (0.6) |
| Employment Level | ||
| Unskilled | 130 (25.1) | 102 (29.9) |
| Skilled | 155 (29.9) | 97 (28.4) |
| Admin | 125 (24.1) | 85 (24.9) |
| Professional | 85 (16.4) | 56 (16.4) |
| Missing | 23 (4.4) | 1 (0.3) |
| Living | ||
| Alone | 131 (25.3) | 78 (22.9) |
| With Family | 359 (69.3) | 278 (75.7) |
| Supported | 8 (1.5) | 5 (1.5) |
| Missing | 20 (3.9) | 0 (0) |
| Children | ||
| No children | 41 (7.9) | 20 (5.9) |
| One or more children | 459 (88.6) | 321 (94.1) |
| Missing | 18 (3.5) | 0 (0) |
| Carer Support | ||
| At home | 273 (52.7) | 192 (56.3) |
| Easily available | 136 (26.3) | 95 (27.9) |
| Not available | 55 (10.6) | 32 (9.4) |
| Carer for other | 18 (3.5) | 12 (3.5) |
| Missing | 36 (7.0) | 10 (2.9) |
Table 2.
Disease- and therapy-related data (n = 518; n = 341 complete data sets included in regression analyses) All values are number (%) unless stated otherwise.
Table 2.
Disease- and therapy-related data (n = 518; n = 341 complete data sets included in regression analyses) All values are number (%) unless stated otherwise.
| Disease and Therapy Related Data | n = 518 | n = 341 |
|---|---|---|
| Primary Cancer Localisation | ||
| Breast | 91 (17.6) | 58 (17.0) |
| Colorectal | 87 (16.8) | 63 (18.5) |
| Lung | 63 (12.2) | 52 (15.2) |
| Ovary | 23 (4.4) | 17 (5.0) |
| Prostate | 75 (14.5) | 53 (15.5) |
| Upper GI | 21 (4.1) | 14 (4.1) |
| Haematological | 54 (10.4) | 10 (2.9) |
| Other | 104 (20.1) | 74 (21.7) |
| Disease stage | ||
| Not metastatic | 306 (59.1) | 212 (62.2) |
| Metastatic | 176 (34.0) | 129 (37.8) |
| Missing | 36 (7.0) | 0 (0) |
| Disease Progression | ||
| Yes | 54 (10.4) | 39 (11.4) |
| No | 464 (89.6) | 302 (88.6) |
| Therapy | ||
| Surgery | 253 (48.8) | 190 (55.7) |
| Chemotherapy | 300 (57.9) | 211 (61.9) |
| Radiotherapy | 205 (39.6) | 133 (39.0) |
| Hormonal Therapy | 93 (18.0) | 61 (17.9) |
| Treatment Intention | ||
| Curative | 288 (55.6) | 208 (61.0) |
| Palliative | 189 (36.5) | 133 (39.0) |
| Missing | 41 (7.9) | 0 (0) |
| Toxicity Level of Therapy | ||
| Low | 484 (93.4) | 313 (91.8) |
| Severe | 34 (6.6) | 28 (8.2) |
| Missing | 36 (7.0) | 0 (0) |
| ECOG-Status | ||
| Good (0–2) | 434 (83.8) | 312 (91.5) |
| Poor (3,4) | 45 (8.7) | 29 (8.5) |
| Missing | 39 (7.5) | 0 (0) |
| Charlson Comorbidity Index | Mean: 0.7, SD: 1.1 | Mean: 0.6, SD: 1.0 |
| Number of comorbidities/patient | Min.: 0, Max.: 4 | Min.: 0, Max.: 4 |
| No Comorbidity (score 0) | 321 (62.0) | 213 (62.5) |
| At least one Comorbidity (score > 0) | 197 (38.0) | 128 (37.5) |
| Frequent Comorbidities | ||
| Cardiovascular disease | 104 (20.1) | 64 (18.8) |
| Diabetes | 72 (13.9) | 46 (13.5) |
| Pulmonary disease | 31 (6.0) | 18 (5.3) |
| Renal failure | 18 (3.5) | 9 (2.6) |
| Liver disease | 16 (3.1) | 11 (3.2) |
| G8 Items (dichotomized) | ||
| Food intake poor | 172 (33.2) | 118 (34.6) |
| Weight loss > 3 kg | 122 (23.6) | 88 (25.9) |
| Unable to leave the house | 87 (16.8) | 55 (16.1) |
| Dementia or depression | 52 (10.0) | 33 (9.7) |
| Malnutrition | 54 (10.4) | 40 (11.7) |
| >Three medications | 272 (52.5) | 190 (55.7) |
| Own health status perceived poor | 180 (34.7) | 128 (37.5) |
| Age > 80 | 161 (31.1) | 99 (29.0) |
| G8 total Score | M: 12.5, SD: 2.9 | M: 12.7, SD: 3.0 |
| Missing | 19 (3.7) | 0 (0) |
Table 3.
EORTC QLQC30 and QLQ-ELD14 scales (n = 518; n = 341 complete data sets included in regression analyses).
Table 3.
EORTC QLQC30 and QLQ-ELD14 scales (n = 518; n = 341 complete data sets included in regression analyses).
| Variable | Total Sample (n = 518) | Complete Data Sets Included in Analyses (n = 341) | ||
|---|---|---|---|---|
| n | Mean (SD) | n | Mean (SD) | |
| QLQ-C30 global health status | 513 | 65.2 (21.9) | 341 | 65.6 (21.6) |
| QLQ-C30 symptom scales | ||||
| Fatigue | 505 | 35.6 (27.5) | 341 | 35.1 (27.3) |
| Nausea/vomiting | 512 | 7.0 (15.8) | 341 | 7.0 (15.7) |
| Pain | 507 | 21.2 (27.6) | 341 | 20.0 (25.9) |
| Dyspnoea | 513 | 23.2 (30.6) | 341 | 22.1 (30.2) |
| Insomnia | 510 | 27.2 (33.1) | 341 | 26.4 (32.7) |
| Appetite loss | 513 | 18.7 (30.9) | 341 | 18.9 (30.7) |
| Constipation | 514 | 21.0 (29.0) | 341 | 22.0 (29.1) |
| Diarrhoea | 513 | 8.9 (20.5) | 341 | 9.5 (21.5) |
| Financial problems | 513 | 8.7 (21.1) | 341 | 9.9 (22.7) |
| QLQ-C30 functioning scales | ||||
| Physical Function | 508 | 73.3 (23.7) | 341 | 74.3 (32.9) |
| Role Function | 514 | 70.8 (32.8) | 341 | 71.6 (32.4) |
| Emotional Function | 506 | 82.2 (20.3) | 341 | 82.7 (19.6) |
| Cognitive Function | 509 | 83.3 (20.6) | 341 | 84.2 (20.3) |
| Social Function | 506 | 77.8 (29.4) | 341 | 77.6 (29.1) |
| QLQ-ELD14 scales | ||||
| Mobility | 503 | 28.4 (28.5) | 341 | 27.9 (29.0) |
| Joint Stiffness | 518 | 30.1 (32.4) | 341 | 28.6 (31.8) |
| Family Support | 484 | 70.7 (34.7) | 341 | 71.0 (35.2) |
| Worries about others | 493 | 39.6 (33.0) | 341 | 41.2 (33.6) |
| Future worries | 505 | 34.0 (31.9) | 341 | 31.6 (32.5) |
| Maintaining purpose | 511 | 64.6 (29.8) | 341 | 63.7 (30.9) |
| Burden of illness | 506 | 41.7 (32.7) | 341 | 42.2 (32.6) |
Table 4.
Four stepwise regression models with respect to global HRQOL (EORTC QLQ-C30) including fixed and disease related variables, EORTC QLQ-C30 symptom scales, EORTC QLQ-C30 functioning scales and EORTC QLQ-ELD14 scales (for better comparability all models were carried out with the final sample of n = 341 patients).
Table 4.
Four stepwise regression models with respect to global HRQOL (EORTC QLQ-C30) including fixed and disease related variables, EORTC QLQ-C30 symptom scales, EORTC QLQ-C30 functioning scales and EORTC QLQ-ELD14 scales (for better comparability all models were carried out with the final sample of n = 341 patients).
| Variable | Model 1 | Model 2 | Model 3 | Model 4 (Final Model) | ||||
|---|---|---|---|---|---|---|---|---|
| Regression Coefficient | Confidence Interval (95%) | Regression Coefficient | Confidence Interval (95%) | Regression Coefficient | Confidence Interval (95%) | Regression Coefficient | Confidence Interval (95%) | |
| Fixed and disease related variables | ||||||||
| Age | 0.170 | −0.279; 0.619 | −0.102 | −0.478; 0.275 | −0.099 | −0.480; 0.281 | −0.100 | −0.481; 0.281 |
| Sex | −2.992 | −7.151; 1.167 | 1.153 | −2.425; 4.732 | 1.615 | −1.988; 5.218 | 1.980 | −1.663; 5.624 |
| Disease stage * | −1.951 | −7.385; 3.482 | 1.953 | −2.635; 6.540 | 2.134 | −2.358; 6.627 | 2.309 | −2.188; 6.805 |
| Disease progression * | −3.93 | −11.099; 3.239 | −2.291 | −8.258; 3.677 | −2.996 | −8.795; 2.802 | −3.105 | −8.888; 2.677 |
| Treatment intention * | −2.071 | −7.381; 3.238 | −2.569 | −6.972; 1.834 | −1.466 | −5.765; 2.832 | −2.097 | −6.411; 2.217 |
| ECOG status * | −8.78 | −16.704; −0.855 | −0.936 | −7.718; 5.845 | 4.087 | −3.260; 11.435 | 4.311 | −3.086; 11.708 |
| Toxicity level * | −5.749 | −13.739; 2.242 | 3.215 | −3.624; 10.054 | 3.592 | −3.022; 10.205 | 4.165 | −2.464; 10.794 |
| Food intake (G8) * | 13.915 | 8.398; 19.431 | 3.997 | −1.285; 9.280 | 2.323 | −2.843; 7.490 | 2.507 | −2.656; 7.669 |
| Weight loss (G8) * | −1.678 | −7.589; 4.234 | −1.166 | −6.088; 3.755 | −1.032 | −5.809; 3.744 | −1.053 | −5.890; 3.785 |
| >Three medications (G8) * | 8.239 | 3.879; 12.599 | 4.941 | 1.286; 8.596 | 3.435 | −0.236; 7.107 | 2.922 | −0.777; 6.622 |
| Charlson Comorbidity Index * | −2.302 | −6.705; 2.1 | −0.268 | −3.943; 3.407 | −0.228 | −3.838; 3.382 | 0.261 | −3.389; 3.911 |
| EORTC QLQ-C30 symptom scales | ||||||||
| Fatigue | −0.365 | −0.463; −0.267 | −0.243 | −0.353; −0.134 | −0.223 | −0.334; −0.112 | ||
| Nausea/vomiting | 0.027 | −0.100; 0.154 | 0.022 | −0.102; 0.146 | 0.035 | −0.090; 0.159 | ||
| Pain | −0.097 | −0.180; −0.014 | −0.083 | −0.164; −0.002 | −0.067 | −0.150; 0.015 | ||
| Dyspnoea | 0.047 | −0.021; 0.116 | 0.051 | −0.017; 0.119 | 0.048 | −0.020; 0.117 | ||
| Insomnia | −0.021 | −0.078; 0.037 | −0.002 | −0.059; 0.054 | 0.002 | −0.055; 0.059 | ||
| Appetite loss | −0.061 | −0.143; 0.020 | −0.038 | −0.119; 0.043 | −0.049 | −0.130; 0.033 | ||
| Constipation | −0.034 | −0.096; 0.029 | −0.024 | −0.085; 0.037 | −0.031 | −0.092; 0.029 | ||
| Diarrhoea | −0.066 | −0.149; 0.017 | −0.060 | −0.141; 0.021 | −0.059 | −0.140; 0.021 | ||
| Financial problems | −0.116 | −0.198; −0.034 | −0.011 | −0.102; 0.080 | −0.014 | −0.107; 0.079 | ||
| EORTC QLQ-C30 functioning scales | ||||||||
| Physical Function | 0.173 | 0.041; 0.305 | 0.114 | −0.034; 0.262 | ||||
| Role Function | 0.025 | −0.055; 0.105 | 0.022 | −0.059; 0.102 | ||||
| Emotional Function | 0.040 | −0.059; 0.140 | 0.015 | −0.086; 0.116 | ||||
| Cognitive Function | 0.020 | −0.080; 0.120 | −0.002 | −0.103; 0.099 | ||||
| Social Function | 0.137 | 0.054; 0.221 | 0.099 | 0.008; 0.191 | ||||
| EORTC QLQ-ELD14 scales | ||||||||
| Mobility | −0.035 | −0.140; 0.071 | ||||||
| Joint Stiffness | −0.064 | −0.126; −0.002 | ||||||
| Family Support | 0.005 | −0.050; 0.060 | ||||||
| Worries about others | −0.006 | −0.064; 0.052 | ||||||
| Future worries | −0.005 | −0.074; 0.063 | ||||||
| Maintaining purpose | 0.019 | −0.041; 0.079 | ||||||
| Burden of illness | −0.071 | −0.141; −0.001 | ||||||
| Model R2 (adjusted) | 0.203 | 0.460 | 0.497 | 0.504 | ||||
* Explanation of dichotomous variables: Disease stage (not metastatic vs. metastatic), disease progression (no vs. yes), treatment intention (curative vs. palliative), ECOG status (good 0–2 vs. poor 3–4), toxicity level (low vs. severe), food intake (G8) (appetite vs. no appetite loss), weight loss (G8) (weight loss > 3 kg vs. no weight loss/weight loss < 3 kg), >three medications (yes vs. no), Charlson Comorbidity Index (no comorbidity vs. at least one comorbidity). All other variables are metric.
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Schmidt, H.; Nordhausen, T.; Boese, S.; Vordermark, D.; Wheelwright, S.; Wienke, A.; Johnson, C.D. Factors Influencing Global Health Related Quality of Life in Elderly Cancer Patients: Results of a Secondary Data Analysis. Geriatrics 2018, 3, 5. https://doi.org/10.3390/geriatrics3010005
AMA Style
Schmidt H, Nordhausen T, Boese S, Vordermark D, Wheelwright S, Wienke A, Johnson CD. Factors Influencing Global Health Related Quality of Life in Elderly Cancer Patients: Results of a Secondary Data Analysis. Geriatrics. 2018; 3(1):5. https://doi.org/10.3390/geriatrics3010005
Chicago/Turabian StyleSchmidt, Heike, Thomas Nordhausen, Stephanie Boese, Dirk Vordermark, Sally Wheelwright, Andreas Wienke, and Colin D. Johnson. 2018. "Factors Influencing Global Health Related Quality of Life in Elderly Cancer Patients: Results of a Secondary Data Analysis" Geriatrics 3, no. 1: 5. https://doi.org/10.3390/geriatrics3010005
APA StyleSchmidt, H., Nordhausen, T., Boese, S., Vordermark, D., Wheelwright, S., Wienke, A., & Johnson, C. D. (2018). Factors Influencing Global Health Related Quality of Life in Elderly Cancer Patients: Results of a Secondary Data Analysis. Geriatrics, 3(1), 5. https://doi.org/10.3390/geriatrics3010005
