This article was downloaded by: [Emory University] On: 28 July 2015, At: 23:31 Publisher: Routledge Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: 5 Howick Place, London, SW1P 1WG
Behavioral Medicine Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/vbmd20
Psychosocial Predictors of Quality of Life in Hematological Cancer a
b
a
Ángeles Pulgar , Antonio Alcalá & Gustavo A. Reyes del Paso a
University of Jaén
b
Hospital Médico Quirúrgico Ciudad de Jaén Accepted author version posted online: 02 Sep 2013.Published online: 03 Sep 2014.
Click for updates To cite this article: Ángeles Pulgar, Antonio Alcalá & Gustavo A. Reyes del Paso (2015) Psychosocial Predictors of Quality of Life in Hematological Cancer, Behavioral Medicine, 41:1, 1-8, DOI: 10.1080/08964289.2013.833083 To link to this article: http://dx.doi.org/10.1080/08964289.2013.833083
PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http:// www.tandfonline.com/page/terms-and-conditions
BEHAVIORAL MEDICINE, 41: 1–8, 2015 Copyright Ó Taylor & Francis Group, LLC ISSN: 0896-4289 print / 1940-4026 online DOI: 10.1080/08964289.2013.833083
Psychosocial Predictors of Quality of Life in Hematological Cancer Angeles Pulgar University of Ja en
Antonio Alcala Hospital M edico Quir urgico Ciudad de Ja en
Downloaded by [Emory University] at 23:31 28 July 2015
Gustavo A. Reyes del Paso University of Ja en
The improvement of health related Quality of Life (QOL) has become one of the main objectives of psychological interventions in cancer. The aim of this study was to analyze sociodemographic and psychosocial variables that predict the different components of QOL in a sample of 69 hemato-oncological patients. Depression, social support, disease-related stress situations, coping strategies and optimism were taken as psychosocial predictors. QOL was evaluated with the Short-Form Health Survey (SF-36). With respect to sociodemographic variables, results showed that age and time from the diagnosis were associated with a decrease in QOL, while educational level and having a partner were associated with less pain and better mental health. With respect to negative-affecting psychosocial variables, depression was associated with general health and social functioning, the coping strategy of stoicism was associated with physical and emotional roles, the number of disease-related stress situations was associated with pain, and the feeling of negative emotions associated with the illness was associated with mental health. Social support and optimism were positively associated with vitality. These results have clear clinical implications for psychological interventions aimed to improve QOL in hemato-oncological patients.
Keywords: coping strategies, depression, health related quality of life, hematological cancer, stress social support The measure of health-related Quality of Life (QOL) enables the estimation of an illness’s interference in the welfare and adaptive functioning of a person, using a global, inclusive and multi-disciplinary approach to health.1 The rise in life expectancy has made it essential to focus on maintaining QOL in patients with chronic pathologies.1 Specifically, cancer can damage some aspects of QOL through the psychosocial impact of its diagnosis, prognosis, and treatment.2 Anxiety, depression, lack of energy, sexual dysfunctions, work difficulties, and feelings of loneliness are just some of the problems cancer patients have. Advances in oncology Correspondence should be addressed to Gustavo A. Reyes del Paso, PhD, Departament of Psychology, University of Jaen, 23071 Jaen, Spain. E-mail: [email protected]
have led to treatments with great curative potential. These medical treatments, however, also affect patients` lives at physical, psychological, and social levels, necessitating a considerable adaptive effort. The current article is focused on the study of QOL in hematological cancer (leukemia, lymphoma, and multiple myeloma), the fifth most common and the second most life-threatening type of cancer (see www.cdc.gov/cancer/ hematologic). Leukemias are proliferations of malignant cells of hematopoietic origin, whose progressive accumulation involves a decrease in the production of normal myeloid elements. Lymphomas can be classified into Hodgkin and non-Hodgkin types. The former are tumors formed by lymphatic tissues that frequently affect most axial lymphs. Non-Hodgkin lymphomas bear a
Downloaded by [Emory University] at 23:31 28 July 2015
2
PULGAR ET AL.
proliferation of B-type lymphoid cells and usually affect peripheral lymphs. Finally, multiple myeloma is a neoplassic process characterized by the uncontrolled proliferation of plasmatic cells of the bone marrow with local destruction of the bone.3 Since these processes spread through different parts of the skeleton, the illness is called “multiple myeloma.” These hemato-oncological diseases were chosen due to their similarity in diagnosis method, treatment, cells and/or affected areas and their relationship to the lymphatic and immunological system. In psychooncology, much interest has been focused on stress perception, stress-coping strategies, social support, optimism, personality traits and emotional responses to cancer, such as depression and anxiety.4 Depression is the emotional state more often associated with cancer.5 High levels of anxiety have also been found at all stages of the oncological disease, including finding non-diagnosed breast tumors, biopsy and reappearance of the illness.6 Depression and anxiety can affect QOL in cancer patients and the treatment of these negative emotional states is associated with an improvement in QOL.7 Stress-related psychosocial factors have been associated with cancer outcomes and subjective well-being. Metaanalysis studies have shown that high levels of stress are associated with higher cancer incidence, poorer survival, and higher cancer mortality.8 However, these associations could be modulated by stress-coping mechanisms. It has been proposed that differences in coping may be the main source of patients´ variability in adjustment to illness and its treatment, and one of the main factors determining QOL in chronic disease.9,10 In cancer patients, coping style is an important factor influencing QOL, depression and hopelessness.11 Social support has been considered a generator of general health benefits,12,13 and with regard to cancer, it has been suggested that it can positively affect the prognosis of the disease.14 Social support is positively associated with physical, functional and emotional well-being in breast cancer patients.15 Dispositional optimism is defined as the presence of generally positive expectations, and the belief that the future will bring more success than failure.16 Several studies have shown the positive influence of optimism on psychological variables and health.17 For example, optimistic breast cancer patients have shown better mental health and social functioning.18 Contrarily, dispositional pessimism predicts emotional morbidity and poorer well-being in cancer patients.19 The aim of this study is to evaluate QOL in patients with hemato-oncological diseases and analyze their associations with the aforementioned psychosocial variables, controlling previously for the effect of sociodemographic variables. We predicted that negative emotional states (anxiety and depression), low dispositional optimism and social support, higher stress in disease-related situations and the use of
passive coping strategies would be associated with poorer QOL indicators in hematological cancer patients. The results of the study may point to the most relevant psychosocial factors for psychological interventions to increase QOL in this population.
METHODS Participants Participants were 69 patients with hematological cancer. The middle phase of the disease corresponded to stage 3. Of the sample, 84% was currently under chemotherapy treatment, and 11.6% received combined chemotherapy with radiotherapy or surgery. Main demographic and clinical features of the sample are shown in Table 1. The inclusion criterion was to have a diagnosis of hematological cancer. Exclusion criteria were: (1) to be in remission, (2) to be admitted to the hospital, and (2) presence of cognitive deficits. Patients were recruited through the Hematology Service of the Medical-Surgery Hospital of Ciudad de Jaen. TABLE 1 Sociodemographic and Clinical Characteristics of the Sample Variables Age 70 years Sex Men Women Diagnosis Hodgkin’s lymphoma Non-Hodgkin’s lymphoma Multiple myeloma Acute myeloid Leukemia Level of education Primary education Secondary education University studies Marital status Single Married Divorced Widowed Time of diagnosis Less than one year More than one year More than two years Received treatment Without current treatment Chemotherapy Radiotherapy Chemotherapy C surgery Chemotherapy C radiotherapy
N
12 37 20 39 30 17 19 17 16 58 9 2 5 57 1 6 37 11 21 2 58 1 4 4
QUALITY OF LIFE IN HEMATOLOGICAL CANCER
The Bioethics Committee of the hospital approved the study protocol.
3
the problem). The inter-rater reliability of this instrument is .80 and it has showed a good predictive validity in predicting anxiety, depression and functioning scores.22
Psychological Measures
Downloaded by [Emory University] at 23:31 28 July 2015
Short-Form Health Survey (SF-36, Version 1) The Short-Form Health Survey (SF-36, Version 1),20 Spanish version by Alonso and colleagues (1998),21 consists of 36 items referring to 8 levels of functioning: (1) Physical Function (limitations in physical activities because of health problems), (2) Physical Role (limitations in usual role activities because of physical health problems), (3) Bodily Pain, (4) General Health perception, (5) Vitality (energy and fatigue), (6) Social Function (limitations in social activities because of health or emotional problems), (7) Emotional Role (limitations in usual role activities because of emotional problems) and (8) Mental Health (psychological distress and well-being). The internal consistency of the different subscales (coefficients of Cronbach’s a) range between 0.7 and 0.94. Stressors and Coping Strategies for Cancer Inventory (ISEAC) The Stressors and Coping Strategies for Cancer Inventory (ISEAC)22 is a structured guide to specifically evaluate the areas of mayor worry and stress in cancer patients and the way they cope with them. Three variables were obtained from this instrument: (1) Number of diseaserelated stress situations, classified as worry about the treatment (discomfort that the treatment produces and/or long stays at hospital), dread of the prognosis (fear concerning the reappearance and/or worsening progression), worry about functional problems (concern about work, social and/ or personal interference caused by the disease and its treatment), negative emotions (irritability, apathy and/or sadness) and social relationships (feelings of loneliness and/or the worry about relatives’ distress). (2) A total score for the number of stressful situations and level of stress experienced. (3) Coping strategies used for handling the diseaserelated stress situations, classified as follows: (a) passivity (the patient doesn’t do anything), (b) cognitive distraction (the patient uses something else to distract himself from the stressful situation), (c) irrational desire (the patient deals with the stress by wishing for an opposite or unreal situation), (d) avoidance (the patient avoids facing the stressful situation), (e) emotional support (the patient looks for social-emotional contacts), (f) stoicism (the patient stoically accepts the stressful situation), (g) denial (the patient denies the stressful situation or some parts of it), (h) catharsis (the patient responds for example through weeping), (i) relaxing-easing (for the patient seeks physically comfortable and calm conditions), (j) direct action (the patient looks for objective solutions), and (k) redefinition of the situation (the patient tries to look for other more positive views about
Hospital Anxiety and Depression Scale (HAD) The Hospital Anxiety and Depression Scale (HAD),23 Spanish version by Caro and Iba~nez (1992)24 evaluates anxiety and depression, minimizing the influence of somatic symptoms. It has a high internal consistency, with a-coefficients of 0.82 (anxiety) and 0.84 (depression). Social Support Scale (AS-25) The Social Support Scale (AS-25)25 quantifies the perception of availability of social support. It consists of 25 items punctuated from 1 to 4. Its internal consistency is 0.87. Life Orientation Test (LOT) The Life Orientation Test (LOT),26 Spanish version by Otero, Luengo, Romero, Gomez, and Castro (1998)27 evaluates dispositional optimism. The 12-item reduced revised version was used. Its internal consistency is 0.87. Procedure The nursing staff assessed patients with the required inclusion/exclusion criteria and asked them to take part in the study. The evaluation was carried out in a room next to the hematology surgery before the patients went to a previously scheduled medical appointment. The objectives and protocol were explained individually to each patient and an informed consent form was signed. No patient refused to participate in the study and all of them fully carried out the interviews. Completion of the above cited questionnaires were made through individual interviews with the patients performed by an expert clinical psychologist. For an interpretation of the SF-36 values, direct scores were transformed into standardized scores according to available norms for the general Spanish population21 and afterwards standardized (mean D 50, DT D 10). Statistical Analysis Analysis of the predictive capacity of the variables considered predictors over the variables defined as dependent (QOL) was carried out using hierarchical multiple regression analysis. Sociodemographic variables (age, educational level and marital status–categorized as having a partner or not), along with the time elapsed since cancer diagnosis, were introduced in a first regression model. In a second model, psychosocial predictors were entered in a step-wise multiple regression analysis.28 Before regression analysis, collinearity statistics were obtained, and predictors with
4
PULGAR ET AL.
Downloaded by [Emory University] at 23:31 28 July 2015
tolerance values 0.6. The regression analysis provided an adjusted r2, as index of the predictive capacity of the model, and standardized b coefficients, as value of the slope of the regression line.
RESULTS Levels of Quality of Life Means and standard deviations of the different components of SF-36 appear in Table 2. QOL levels in our sample are far below those obtained in the general Spanish population, especially in mental health, vitality, physical function, social function, and physical role. Prediction of Quality of Life Sociodemographic and Clinical Variables The results of the first step in the regression analysis show that time elapsed since cancer diagnosis is negatively associated with general health (b D ¡0.34, r2 D 0.11, p D .003) and emotional role (b D ¡0.27, r2 D 0.06, p D .028). Age is negatively associated with physical function (b D ¡0.28, r2 D 0.07, p D .018), and social function (b D ¡0.26, r2 D 0.05, p D .032). Marital status (ie, to have a partner) is marginally associated with better mental health
(b D 0.228, r2 D 0.07, p D .057). Finally, educational level is associated with pain (b D 0.29, r2 D 0.07, p D 0.015). As an inverse item, it indicates lower perception of pain at higher educational levels. Psychosocial Variables The results of the multiple regression analysis for prediction of QOL from psychosocial variables, once the effect of sociodemographic variables and time since the diagnosis are controlled for, are shown in Table 3. Results can be summarized as follows: General health is negatively associated with depression, explaining 29% of the variance. Physical function is negatively associated with passivity in a first regression model, explaining 16% of the variance; depression is incorporated in a second model, both variables explaining 22% of the variance. Physical role is negatively associated with the coping strategy of stoicism, explaining 10% of the variance. Emotional role is associated in a first model with stoicism, explaining 12% of the variance; in a second model the strategy of relaxation is incorporated, both variables explaining 19% of the variance. The two variables are associated with a decrease in emotional role. Social function is negatively associated with depression, explaining 16% of the variance. Pain is negatively associated with the total number of disease-related stress situations, explaining 17% of the variance. Being a negative item, an increase in stressful situations entails greater perception of pain. With respect to Vitality, four regression models were obtained. In the first model, social support is positively associated with vitality, explaining 12% of the variance. In the second model (with a negative direction), total number of disease-related stress situations is incorporated, both variables explaining 17% of the variance. In a third model optimism is incorporated with a positive association, the three variables together explaining 23% of the variance. Finally, passivity, with a negative association, is added to the previous variables, this fourth model predicting 28% of the variance. Finally,
TABLE 2 Mean and Standard Deviations (SD) of SF-36 Components Direct punctuations
Normalized punctuations
Spanish population
Quality of Life dimension
Mean
SD
Mean
SD
Mean
SD
Physical Function Physical Role Body Pain General Health Vitality Social Function Emotional Role Mental Health
48.45 32.89 54.56 50.45 32.78 66.37 55.02 41.13
34.52 40.54 33.42 26.67 24.45 29.78 44.47 24.39
34.89 35.68 41.16 41.91 34.52 38.15 38.78 33.87
14.32 11.51 11.87 12.03 11.02 14.86 14.72 12.10
84.75 83.23 79.01 68.32 66.93 90.12 88.61 73.31
24.01 35.17 27.86 22.29 22.14 20.01 30.10 20.09
Notes. Results are presented both in direct and standardized scores (M D 50, SD D 10). General Spanish population values (direct scores)16 are also included.
5
QUALITY OF LIFE IN HEMATOLOGICAL CANCER TABLE 3 Significant Regression Analysis Models for the Predictions of SF-36 Components from Psychosocial Predictors Dependent variable General Health Physical Function
Physical Role Emotional Role
Social Function Pain
Downloaded by [Emory University] at 23:31 28 July 2015
Vitality
Mental Health
Predictor variable
b
r2
t
p
Depression 1st Model: Passivity 2nd Model: Passivity Depression Stoicism 1st Model: Stoicism 2nd Model: Stoicism Relaxation Depression No. of disease-related stress situations 1st Model: Social Support 2nd Model: Social Support No. of disease-related stress situations 3rd Model: Social Support No. of disease-related stress situations Optimism 4th Model: Social Support No. of disease-related stress situations Optimism Passivity 1st Model: Negative Emotions 2nd Model: Negative Emotions Depression
¡0.55
0.30
¡4.14
Behavioral Medicine Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/vbmd20
Psychosocial Predictors of Quality of Life in Hematological Cancer a
b
a
Ángeles Pulgar , Antonio Alcalá & Gustavo A. Reyes del Paso a
University of Jaén
b
Hospital Médico Quirúrgico Ciudad de Jaén Accepted author version posted online: 02 Sep 2013.Published online: 03 Sep 2014.
Click for updates To cite this article: Ángeles Pulgar, Antonio Alcalá & Gustavo A. Reyes del Paso (2015) Psychosocial Predictors of Quality of Life in Hematological Cancer, Behavioral Medicine, 41:1, 1-8, DOI: 10.1080/08964289.2013.833083 To link to this article: http://dx.doi.org/10.1080/08964289.2013.833083
PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http:// www.tandfonline.com/page/terms-and-conditions
BEHAVIORAL MEDICINE, 41: 1–8, 2015 Copyright Ó Taylor & Francis Group, LLC ISSN: 0896-4289 print / 1940-4026 online DOI: 10.1080/08964289.2013.833083
Psychosocial Predictors of Quality of Life in Hematological Cancer Angeles Pulgar University of Ja en
Antonio Alcala Hospital M edico Quir urgico Ciudad de Ja en
Downloaded by [Emory University] at 23:31 28 July 2015
Gustavo A. Reyes del Paso University of Ja en
The improvement of health related Quality of Life (QOL) has become one of the main objectives of psychological interventions in cancer. The aim of this study was to analyze sociodemographic and psychosocial variables that predict the different components of QOL in a sample of 69 hemato-oncological patients. Depression, social support, disease-related stress situations, coping strategies and optimism were taken as psychosocial predictors. QOL was evaluated with the Short-Form Health Survey (SF-36). With respect to sociodemographic variables, results showed that age and time from the diagnosis were associated with a decrease in QOL, while educational level and having a partner were associated with less pain and better mental health. With respect to negative-affecting psychosocial variables, depression was associated with general health and social functioning, the coping strategy of stoicism was associated with physical and emotional roles, the number of disease-related stress situations was associated with pain, and the feeling of negative emotions associated with the illness was associated with mental health. Social support and optimism were positively associated with vitality. These results have clear clinical implications for psychological interventions aimed to improve QOL in hemato-oncological patients.
Keywords: coping strategies, depression, health related quality of life, hematological cancer, stress social support The measure of health-related Quality of Life (QOL) enables the estimation of an illness’s interference in the welfare and adaptive functioning of a person, using a global, inclusive and multi-disciplinary approach to health.1 The rise in life expectancy has made it essential to focus on maintaining QOL in patients with chronic pathologies.1 Specifically, cancer can damage some aspects of QOL through the psychosocial impact of its diagnosis, prognosis, and treatment.2 Anxiety, depression, lack of energy, sexual dysfunctions, work difficulties, and feelings of loneliness are just some of the problems cancer patients have. Advances in oncology Correspondence should be addressed to Gustavo A. Reyes del Paso, PhD, Departament of Psychology, University of Jaen, 23071 Jaen, Spain. E-mail: [email protected]
have led to treatments with great curative potential. These medical treatments, however, also affect patients` lives at physical, psychological, and social levels, necessitating a considerable adaptive effort. The current article is focused on the study of QOL in hematological cancer (leukemia, lymphoma, and multiple myeloma), the fifth most common and the second most life-threatening type of cancer (see www.cdc.gov/cancer/ hematologic). Leukemias are proliferations of malignant cells of hematopoietic origin, whose progressive accumulation involves a decrease in the production of normal myeloid elements. Lymphomas can be classified into Hodgkin and non-Hodgkin types. The former are tumors formed by lymphatic tissues that frequently affect most axial lymphs. Non-Hodgkin lymphomas bear a
Downloaded by [Emory University] at 23:31 28 July 2015
2
PULGAR ET AL.
proliferation of B-type lymphoid cells and usually affect peripheral lymphs. Finally, multiple myeloma is a neoplassic process characterized by the uncontrolled proliferation of plasmatic cells of the bone marrow with local destruction of the bone.3 Since these processes spread through different parts of the skeleton, the illness is called “multiple myeloma.” These hemato-oncological diseases were chosen due to their similarity in diagnosis method, treatment, cells and/or affected areas and their relationship to the lymphatic and immunological system. In psychooncology, much interest has been focused on stress perception, stress-coping strategies, social support, optimism, personality traits and emotional responses to cancer, such as depression and anxiety.4 Depression is the emotional state more often associated with cancer.5 High levels of anxiety have also been found at all stages of the oncological disease, including finding non-diagnosed breast tumors, biopsy and reappearance of the illness.6 Depression and anxiety can affect QOL in cancer patients and the treatment of these negative emotional states is associated with an improvement in QOL.7 Stress-related psychosocial factors have been associated with cancer outcomes and subjective well-being. Metaanalysis studies have shown that high levels of stress are associated with higher cancer incidence, poorer survival, and higher cancer mortality.8 However, these associations could be modulated by stress-coping mechanisms. It has been proposed that differences in coping may be the main source of patients´ variability in adjustment to illness and its treatment, and one of the main factors determining QOL in chronic disease.9,10 In cancer patients, coping style is an important factor influencing QOL, depression and hopelessness.11 Social support has been considered a generator of general health benefits,12,13 and with regard to cancer, it has been suggested that it can positively affect the prognosis of the disease.14 Social support is positively associated with physical, functional and emotional well-being in breast cancer patients.15 Dispositional optimism is defined as the presence of generally positive expectations, and the belief that the future will bring more success than failure.16 Several studies have shown the positive influence of optimism on psychological variables and health.17 For example, optimistic breast cancer patients have shown better mental health and social functioning.18 Contrarily, dispositional pessimism predicts emotional morbidity and poorer well-being in cancer patients.19 The aim of this study is to evaluate QOL in patients with hemato-oncological diseases and analyze their associations with the aforementioned psychosocial variables, controlling previously for the effect of sociodemographic variables. We predicted that negative emotional states (anxiety and depression), low dispositional optimism and social support, higher stress in disease-related situations and the use of
passive coping strategies would be associated with poorer QOL indicators in hematological cancer patients. The results of the study may point to the most relevant psychosocial factors for psychological interventions to increase QOL in this population.
METHODS Participants Participants were 69 patients with hematological cancer. The middle phase of the disease corresponded to stage 3. Of the sample, 84% was currently under chemotherapy treatment, and 11.6% received combined chemotherapy with radiotherapy or surgery. Main demographic and clinical features of the sample are shown in Table 1. The inclusion criterion was to have a diagnosis of hematological cancer. Exclusion criteria were: (1) to be in remission, (2) to be admitted to the hospital, and (2) presence of cognitive deficits. Patients were recruited through the Hematology Service of the Medical-Surgery Hospital of Ciudad de Jaen. TABLE 1 Sociodemographic and Clinical Characteristics of the Sample Variables Age 70 years Sex Men Women Diagnosis Hodgkin’s lymphoma Non-Hodgkin’s lymphoma Multiple myeloma Acute myeloid Leukemia Level of education Primary education Secondary education University studies Marital status Single Married Divorced Widowed Time of diagnosis Less than one year More than one year More than two years Received treatment Without current treatment Chemotherapy Radiotherapy Chemotherapy C surgery Chemotherapy C radiotherapy
N
12 37 20 39 30 17 19 17 16 58 9 2 5 57 1 6 37 11 21 2 58 1 4 4
QUALITY OF LIFE IN HEMATOLOGICAL CANCER
The Bioethics Committee of the hospital approved the study protocol.
3
the problem). The inter-rater reliability of this instrument is .80 and it has showed a good predictive validity in predicting anxiety, depression and functioning scores.22
Psychological Measures
Downloaded by [Emory University] at 23:31 28 July 2015
Short-Form Health Survey (SF-36, Version 1) The Short-Form Health Survey (SF-36, Version 1),20 Spanish version by Alonso and colleagues (1998),21 consists of 36 items referring to 8 levels of functioning: (1) Physical Function (limitations in physical activities because of health problems), (2) Physical Role (limitations in usual role activities because of physical health problems), (3) Bodily Pain, (4) General Health perception, (5) Vitality (energy and fatigue), (6) Social Function (limitations in social activities because of health or emotional problems), (7) Emotional Role (limitations in usual role activities because of emotional problems) and (8) Mental Health (psychological distress and well-being). The internal consistency of the different subscales (coefficients of Cronbach’s a) range between 0.7 and 0.94. Stressors and Coping Strategies for Cancer Inventory (ISEAC) The Stressors and Coping Strategies for Cancer Inventory (ISEAC)22 is a structured guide to specifically evaluate the areas of mayor worry and stress in cancer patients and the way they cope with them. Three variables were obtained from this instrument: (1) Number of diseaserelated stress situations, classified as worry about the treatment (discomfort that the treatment produces and/or long stays at hospital), dread of the prognosis (fear concerning the reappearance and/or worsening progression), worry about functional problems (concern about work, social and/ or personal interference caused by the disease and its treatment), negative emotions (irritability, apathy and/or sadness) and social relationships (feelings of loneliness and/or the worry about relatives’ distress). (2) A total score for the number of stressful situations and level of stress experienced. (3) Coping strategies used for handling the diseaserelated stress situations, classified as follows: (a) passivity (the patient doesn’t do anything), (b) cognitive distraction (the patient uses something else to distract himself from the stressful situation), (c) irrational desire (the patient deals with the stress by wishing for an opposite or unreal situation), (d) avoidance (the patient avoids facing the stressful situation), (e) emotional support (the patient looks for social-emotional contacts), (f) stoicism (the patient stoically accepts the stressful situation), (g) denial (the patient denies the stressful situation or some parts of it), (h) catharsis (the patient responds for example through weeping), (i) relaxing-easing (for the patient seeks physically comfortable and calm conditions), (j) direct action (the patient looks for objective solutions), and (k) redefinition of the situation (the patient tries to look for other more positive views about
Hospital Anxiety and Depression Scale (HAD) The Hospital Anxiety and Depression Scale (HAD),23 Spanish version by Caro and Iba~nez (1992)24 evaluates anxiety and depression, minimizing the influence of somatic symptoms. It has a high internal consistency, with a-coefficients of 0.82 (anxiety) and 0.84 (depression). Social Support Scale (AS-25) The Social Support Scale (AS-25)25 quantifies the perception of availability of social support. It consists of 25 items punctuated from 1 to 4. Its internal consistency is 0.87. Life Orientation Test (LOT) The Life Orientation Test (LOT),26 Spanish version by Otero, Luengo, Romero, Gomez, and Castro (1998)27 evaluates dispositional optimism. The 12-item reduced revised version was used. Its internal consistency is 0.87. Procedure The nursing staff assessed patients with the required inclusion/exclusion criteria and asked them to take part in the study. The evaluation was carried out in a room next to the hematology surgery before the patients went to a previously scheduled medical appointment. The objectives and protocol were explained individually to each patient and an informed consent form was signed. No patient refused to participate in the study and all of them fully carried out the interviews. Completion of the above cited questionnaires were made through individual interviews with the patients performed by an expert clinical psychologist. For an interpretation of the SF-36 values, direct scores were transformed into standardized scores according to available norms for the general Spanish population21 and afterwards standardized (mean D 50, DT D 10). Statistical Analysis Analysis of the predictive capacity of the variables considered predictors over the variables defined as dependent (QOL) was carried out using hierarchical multiple regression analysis. Sociodemographic variables (age, educational level and marital status–categorized as having a partner or not), along with the time elapsed since cancer diagnosis, were introduced in a first regression model. In a second model, psychosocial predictors were entered in a step-wise multiple regression analysis.28 Before regression analysis, collinearity statistics were obtained, and predictors with
4
PULGAR ET AL.
Downloaded by [Emory University] at 23:31 28 July 2015
tolerance values 0.6. The regression analysis provided an adjusted r2, as index of the predictive capacity of the model, and standardized b coefficients, as value of the slope of the regression line.
RESULTS Levels of Quality of Life Means and standard deviations of the different components of SF-36 appear in Table 2. QOL levels in our sample are far below those obtained in the general Spanish population, especially in mental health, vitality, physical function, social function, and physical role. Prediction of Quality of Life Sociodemographic and Clinical Variables The results of the first step in the regression analysis show that time elapsed since cancer diagnosis is negatively associated with general health (b D ¡0.34, r2 D 0.11, p D .003) and emotional role (b D ¡0.27, r2 D 0.06, p D .028). Age is negatively associated with physical function (b D ¡0.28, r2 D 0.07, p D .018), and social function (b D ¡0.26, r2 D 0.05, p D .032). Marital status (ie, to have a partner) is marginally associated with better mental health
(b D 0.228, r2 D 0.07, p D .057). Finally, educational level is associated with pain (b D 0.29, r2 D 0.07, p D 0.015). As an inverse item, it indicates lower perception of pain at higher educational levels. Psychosocial Variables The results of the multiple regression analysis for prediction of QOL from psychosocial variables, once the effect of sociodemographic variables and time since the diagnosis are controlled for, are shown in Table 3. Results can be summarized as follows: General health is negatively associated with depression, explaining 29% of the variance. Physical function is negatively associated with passivity in a first regression model, explaining 16% of the variance; depression is incorporated in a second model, both variables explaining 22% of the variance. Physical role is negatively associated with the coping strategy of stoicism, explaining 10% of the variance. Emotional role is associated in a first model with stoicism, explaining 12% of the variance; in a second model the strategy of relaxation is incorporated, both variables explaining 19% of the variance. The two variables are associated with a decrease in emotional role. Social function is negatively associated with depression, explaining 16% of the variance. Pain is negatively associated with the total number of disease-related stress situations, explaining 17% of the variance. Being a negative item, an increase in stressful situations entails greater perception of pain. With respect to Vitality, four regression models were obtained. In the first model, social support is positively associated with vitality, explaining 12% of the variance. In the second model (with a negative direction), total number of disease-related stress situations is incorporated, both variables explaining 17% of the variance. In a third model optimism is incorporated with a positive association, the three variables together explaining 23% of the variance. Finally, passivity, with a negative association, is added to the previous variables, this fourth model predicting 28% of the variance. Finally,
TABLE 2 Mean and Standard Deviations (SD) of SF-36 Components Direct punctuations
Normalized punctuations
Spanish population
Quality of Life dimension
Mean
SD
Mean
SD
Mean
SD
Physical Function Physical Role Body Pain General Health Vitality Social Function Emotional Role Mental Health
48.45 32.89 54.56 50.45 32.78 66.37 55.02 41.13
34.52 40.54 33.42 26.67 24.45 29.78 44.47 24.39
34.89 35.68 41.16 41.91 34.52 38.15 38.78 33.87
14.32 11.51 11.87 12.03 11.02 14.86 14.72 12.10
84.75 83.23 79.01 68.32 66.93 90.12 88.61 73.31
24.01 35.17 27.86 22.29 22.14 20.01 30.10 20.09
Notes. Results are presented both in direct and standardized scores (M D 50, SD D 10). General Spanish population values (direct scores)16 are also included.
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QUALITY OF LIFE IN HEMATOLOGICAL CANCER TABLE 3 Significant Regression Analysis Models for the Predictions of SF-36 Components from Psychosocial Predictors Dependent variable General Health Physical Function
Physical Role Emotional Role
Social Function Pain
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Vitality
Mental Health
Predictor variable
b
r2
t
p
Depression 1st Model: Passivity 2nd Model: Passivity Depression Stoicism 1st Model: Stoicism 2nd Model: Stoicism Relaxation Depression No. of disease-related stress situations 1st Model: Social Support 2nd Model: Social Support No. of disease-related stress situations 3rd Model: Social Support No. of disease-related stress situations Optimism 4th Model: Social Support No. of disease-related stress situations Optimism Passivity 1st Model: Negative Emotions 2nd Model: Negative Emotions Depression
¡0.55
0.30
¡4.14
