519470 research-article2014
CNRXXX10.1177/1054773813519470Clinical Nursing ResearchAhmad et al.
Article
Quality of Life for Patients in Medical–Surgical Wards
Clinical Nursing Research 2015, Vol. 24(4) 375–387 © The Author(s) 2014 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/1054773813519470 cnr.sagepub.com
Muayyad M. Ahmad, PhD, RN1, Laila Ismae’l Al-Daken, MSc, RN2, and Huthaifa M. Ahmad1
Abstract The purpose of this study was to examine the quality of life (QoL) for patients in medical–surgical wards in Jordanian hospitals. A cross-sectional design was performed. The data were collected between January and April 2011 through individual interviews (n = 746) using the Medical Outcome Study 36-item Short-Form (MOS-SF-36) and Charlson’s Co-morbidity Index (CCI). The private and public hospitals in the three largest cities in Jordan were represented. The MANOVA test was used to examine the health status based on patients’ co-morbidity level. The results showed that QoL for patients with severe co-morbidity has been affected negatively in many aspects more than both patients with no co-morbidity and patients with mild co-morbidity. However, although it is difficult to change the demographic characteristics, it is possible to improve the health status of patients with multiple co-morbidities, which will result in having a better QoL. Keywords quality of life, nursing care, medical–surgical wards Patients with moderate or severe co-morbid diseases may be sicker and have a worse quality of life (QoL) than other patients (Valderas, Starfield, Sibbald, Salisbury, & Roland, 2009). Patients who are successfully treated and reach 1The
University of Jordan, Amman, Jordan Private University, Amman, Jordan
2Zarqa
Corresponding Author: Muayyad M. Ahmad, Clinical Nursing Department, The University of Jordan, Amman 11942, Jordan. Email: [email protected]
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a state of good general health will result in having a high QoL (Ritchie, 2007). Comorbidity is significantly related to a severe impact on the QoL and survival rate among patients in medical and surgical wards (Gourin et al., 2005). Many studies have examined the effect of specific disease on the QoL. Diabetes mellitus was the most common health problem studied in relation to QoL (Nichols & Brown, 2004; Stone, Khunti, Squire, & Paul, 2008). The QoL is not only an outcome of the co-morbidity levels, but it is also affected by other variables. Patients’ demographics are considered an integral part in every study when examining QoL. Inconsistency was found in the literature concerning the relationships between patient characteristics and perception of the QoL (Dardas & Ahmad, 2015; Ervin, 2006). No correlations were found between patients’ QoL and age, gender, or education (Hasson & Arnetz, 2009). Ritchie (2007) found that individuals with lower socioeconomic status tend to have worse QoL. However, one study noted that factors such as age and living alone accounted for some of the variation in physical limitation scores (Stone et al., 2008). Nurses play a significant role in enhancing the QoL through the care they provide to their patients (Alasad & Ahmad, 2003). Nurses need to verify the quality of care to ensure that patients are satisfied with their QoL. Nurses in medical and surgical wards usually work with patients with different diseases and they are obliged to provide holistic care to their patients. Many of the admitted patients have multiple health problems, thus examining only the effect of one medical diagnosis rather than all of the health problems that each patient has may result in limited explanations for the QoL of those patients. There are many indices developed to measure patients’ QoL with different diseases; however, the most common index was developed by Charlson and colleagues to classify co-morbidity by assigning weights for diseases (Charlson, Pompei, Ales, & MacKenzie, 1987). Charlson’s Co-morbidity Index (CCI) was used to classify co-morbid conditions because the weighted index of co-morbidity is unambiguous and closely related to the coexistent diseases in this study. According to CCI, diabetes mellitus or cardiovascular disease if occurred alone, it gives a score of 1 for co-morbidity, and if occurred with other disorders, it may raise the score to 2 or 3 (de Visser, Bilo, Groenier, de Visser, & Jong Meyboom-de, 2002). In most of these cases, it is considered a mild or moderate level of co-morbidity, but it rarely reaches a severe level of co-morbidity. No previous studies have examined patients’ QoL based on different comorbidity levels while considering patients’ demographics in Jordan yet. Thus, this study was necessary to examine these issues with adult patients in medical–surgical wards in Jordanian hospitals. The main purpose of this study was to examine patients’ QoL in medical and surgical wards.
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Method Design and Sampling A cross-sectional design was used to assess the QoL among hospitalized patients in medical and surgical wards. Data were collected through individual interviews with patients using the Medical Outcome Study 36-item ShortForm (MOS-SF-36) health survey and patients’ demographics. Co-morbidity levels were classified based on CCI. Sample size was calculated using G* power 3.0 software (Faul, Erdfelder, Lang, & Buchner, 2007). In this study, which undertook comparison among three groups using MANOVA with eight dependent variables, and a small effect size (0.10), a total sample of 315 participants was needed to provide 80% power to detect difference at the 0.05 significance level. The actual sample size in this study was 746 participants. The convenience sample of eligible patients at the medical and surgical wards in each of the selected hospitals was targeted between January and April 2011. The eligibility criteria for the participants were (a) being an adult with 18 years of age or older (b) being oriented, and (c) agreeing to give an informed consent to be interviewed.
Procedure Data were collected using a questionnaire that includes the MOS-SF-36, CCI, and patients’ demographics. The three cities of Amman, Zarqa, and Irbid were selected for the sample of this study because the majority of the Jordanian population (71%) is residing in these areas (Department of Statistics, 2012). Ten hospitals with bed capacity more than 200 were contacted for the study purpose. A total of seven hospitals representing the private (n = 163), public (n = 281), and university (n = 302) hospitals gave permissions and were included in the study. Ten research assistants from outside the selected hospitals were trained on conducting interviews, and an interrater reliability of 95% was achieved. The research assistants have followed a standardized procedure while interviewing the participating patients. The time for each interview was between 15 and 20 min.
Ethical Considerations Formal application was submitted for approval to the Institutional Review Board at the relevant hospitals. The purpose of this study was explained to potential participants, and those who gave informed consent were interviewed individually. All gathered data and information were strictly confidential and could not be
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accessed by any other party without prior permission of the participants and involved institute. Patients were interviewed in their rooms and when there was more than one patient in the room, curtains were closed around the participant. The interviewers were sitting within a short distance (1-1.5 m) from the participants to assure the confidentiality of the interview.
Measurement Rigor Health status perception is one of the main health outcomes that reflects QoL (Ware, 1996). Therefore, the MOS-SF-36 was used to assess patients’ QoL. The choice to use the MOS-SF-36 in this study is due to the following: (a) it is composed of eight health concepts commonly represented in widely used surveys, (b) it has been successfully used in cross-sectional and longitudinal studies with different populations, and (c) it allows for easy interpretation and presentation of results (McDowell & Newell, 2006). The MOS-SF-36 is a multidimensional scale for health concepts, which includes physical functioning, role limitations due to physical health problems, bodily pain, general health, vitality (energy/fatigue), social functioning, role limitations due to emotional problems, and general mental health (Ware, 1996). The subscale comprises 10 items with three levels of responses (1 = yes, limited a lot, 2 = yes, limited a little, and 3 = no, not limited at all). Possible scores range from 10 to 30. A higher score indicates better physical functioning. The role limitation due to physical health dimension is a four-item scale. Participants responded in dichotomous options (1 = yes, 2 = no). Possible scores ranged from 4 to 8, and a higher score indicates better physical functioning. The subscale is composed of three items. Subjects responded in a dichotomous response option (1 = yes, 2 = no). The average is taken for these three items and used in analysis, ensuring that a higher score indicates better emotional functioning. The bodily pain dimension is composed of two items and is used to measure the intensity of bodily participants’ responses for the first item ranged from “none” (1) to “very severe” (6) and from “not at all” (1) to “extremely” (5) for the second item. For analytic purposes, the coding for both items was reversed. Thus, a higher score indicates a lack of bodily pain. The vitality dimension is a four-item scale that measures the subjective feelings of fatigue and energy level. Scale items are rated from “all the time” (1) to “none of the time” (6). A higher score for the four items of vitality indicates less fatigue and higher energy. The mental health dimension is a five-item scale to measure different aspects of mental health including depression, anxiety, and psychological well-being. Participants responses
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ranged from “all of the time” (1) to “none of the time” (6). A higher score indicates better mental health. The social functioning dimension measures the extent to which health problems interfered with social activities during the past 4 weeks. The scale is composed of two items, which are rated from “not at all” (1) to “extremely” (5); and “all of the time” (1) to “none of the time” (5). The general health perception dimension is composed of five items used to measure participants’ perceived current health, comparing health status with others, and resistance to illness. A higher score represents better health. In this study, the reliability coefficient for the eight subscales of MOS-SF-36 ranged between .71 and .93. Cronbach’s alphas were .93 for physical health, .84 for role limitations due to physical problems, .87 for bodily pain, .71 for social functioning, .79 for mental health, .83 for role limitations due to emotional problems, .80 for vitality/energy, and .72 for general health perception. The validity of the instrument was preserved following a translation and back-translation procedure by bilingual experts in English and Arabic languages (Maneesriwongul & Dixon, 2004). By comparing the MOS-SF-36 with other widely used general health surveys, content validity was established. Data on co-morbidity were classified according to Charlson scoring system (Charlson et al., 1987). This tool has a weighted index that takes into account the number and the seriousness of co-morbid diseases. The respondents were assessed for their primary diagnosis and the presence of any other co-morbid conditions by checking the patients’ charts. For patients who had two or more diseases, their scores of co-morbidity were the total of the assigned weight for each disease. For the purpose of this study, co-morbidity scores were coded into three groups. The group with no co-morbidity was coded as (0), the group with scores 1 and 2 in CCI was coded as (1), and the group with scores 3 and above in CCI was coded as (2). The reason for recoding the CCI scores in this format is the low frequency of patients with scores of 3 and above.
Data Analysis All univariate and multivariate statistics were computed using the Statistical Package for Social Sciences (IBM, 2012, Version 21). Frequency distributions, means, and standard deviations were used to describe the data. Preliminary screening for the data did not show any serious violations of the assumptions of multivariate analyses. An 8 × 3 MANOVA was performed on the co-morbidity data using the eight scales of health in MOS-SF-36 as
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Table 1. Patients’ Demographics (n = 746). Demographics Age (years)
Gender Education
Family income
Classification
Frequency (%)
18-39 40-59 60-80 Male Female 9th grade or less 10th grade to high school More than high school Insufficient Sometimes sufficient Sufficient
332 (44.5) 327 (43.8) 87 (11.7) 376 (50.4) 370 (49.6) 198 (26.5) 233 (31.2) 315 (42.2) 357 (47.9) 229 (30.7) 160 (21.4)
dependent variables. Type III sums of squares in MANOVA were used to adjust the possible confounding between uneven group sizes (Polit, 2010).
Results Selected participants’ demographics are presented in Table 1. The study sample was distributed almost equally between both genders. The participants’ ages ranged from 18 to 80 years (M = 41.6 years, SD = 14.2 years). The majority of the participants (42%) have college education or higher. Almost half of the participants considered their family income as insufficient. The most frequent medical diagnoses under the current admissions of the participants were abdominal pain, urinary tract infections, renal colic, asthma, heart diseases, diabetes mellitus, vertebral disc prolapse, leg fractures, hypertension, trauma, wound infections, and pneumonia. Having three groups as independent variables and eight types of health statuses as dependent variables supports the MANOVA statistics. The MANOVA is a statistical test used for comparing two or more dependent variables’ means of several groups at once (Polit, 2010). Running MANOVA has advantage on running multiple ANOVA tests in saving time and reducing the risk of Type I statistical errors. The MANOVA results showed significant differences in the total scores across the three groups of co-morbidity in physical functioning, the role limitation due to physical health, and vitality. To determine where the significant differences among groups lie, post hoc analysis using Scheffé test was used (Polit, 2010). Patients with severe co-morbid conditions demonstrated worse
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Ahmad et al. Table 2. MANOVA Testing QoL Based on Three Co-morbidity Levels. No co-morbid conditions (n = 268) Dependent variables
M (SE)
Physical functioning The role limitation due to physical health The role limitations due to emotional problems Social functioning Bodily pain Mental health Vitality
Moderate Severe co-morbid co-morbid conditions conditions (n = 277) (n = 201)
M (SE)
M (SE)
SS^
Scheffé’s post hoc analysisa
F
20.5 (.37) 20.8 (.36) 18.9 (.42) 419.58 5.85** 0* and 1** >2 5.2 (.09) 5.2 (.09) 4.8 (.10) 32.63 7.74*** 0** and 1** >2 4.1 (.08)
4.1 (.08)
4.1 (.09)
5.6 (.07) 5.8 (.07) 5.8 (.08) 7.4 (.16) 7.1 (.16) 7.2 (.18) 14.9 (.14) 15.0 (.14) 15.2 (.16) 11.6 (.10) 11.6 (.10) 12.0 (.12)
General health 14.9 (.14) 15.1 (.14) 15.1 (.16) perception MANOVA Test Wilks’ lamda = .93
.20
.06
6.80 2.64 9.68 .71 10.75 1.04 24.56 4.34* 4.95
.49
2* > 0 and 1
3.62***
Note. QoL = quality of life. ^ SS = Sum of Squares a0 = no co-morbid conditions, 1 = moderate co-morbid conditions, 2 = severe co-morbid conditions. *p ≤ .05. **p ≤ .01. ***p ≤ .001.
QoL than other patients with regard to “physical functioning” and “role limitation due to physical health.” The patients with severe co-morbidity were having less vitality/energy than the other two groups. There were no significant differences among the three groups of co-morbidity with regard to the remaining health subscales (Table 2). The relationship of QoL indicators and the demographic characteristics of patients were examined (Table 3). The “physical functioning” and “the role limitation due to physical health” are the only health indicators that correlated significantly with the selected demographics. However, the “social functioning” indicator did not show any significant relationship with any of
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Table 3. Correlations Between Demographics and Indicators of QoL (n = 746). QoL indicators Physical functioning The role limitation due to physical health The role limitations due to emotional problems Social functioning Bodily pain Mental health Vitality General health perception
Agea
Genderb
Educationa
Incomea
−.13*** −.11**
−.24*** −.09*
.18*** .13***
−.19*** −.10**
.03
−.10**
.20***
−.19***
.07 −.01 .08* .10** .01
−.02 .18*** −.10** −.11** −.14***
−.06 −.16*** .11** .04 .17***
.07 .16*** −.10** −.07 −.10**
Note. QoL = quality of life. aSpearman’s rank correlation. bPoint biserial correlation. *p ≤ .05. **p ≤ .01. ***p ≤ .001.
the demographics. Being a female shows less QoL than being a male on seven of the health status indicators. The “age” was correlated negatively with physical functioning and positively with mental health and vitality.
Discussion This cross-sectional study was conducted to examine adult patients’ QoL under different co-morbidity levels. Managing patients with co-morbid conditions frequently coexists in patients admitted to hospitals. The results confirm the cumulative impact of having multiple chronic diseases on the QoL. Nevertheless, the likelihood that people with multiple conditions may have difficulty in attributing the limitations to specific conditions when they have multiple chronic diseases. Caring for individuals with chronic medical conditions is frequently aimed to maximize QoL rather than to “cure” the disease. Furthermore, interventions to improve nursing care for patients often assess domains of health such as physical functioning, overall health status, and emotional well-being. These health domains are mainly subjective in their assessment because they are self-reported. Therefore, the values assigned to these health domains are most meaningful to the patients themselves. A negative impact on QoL has been shown both for diabetes mellitus (Wandell, 2005) and for congestive heart disease (Gravely-Witte, De Gucht, Heiser, Grace, & Van Elderen, 2007) with higher rates of depression in people with diabetes mellitus (Ali, Stone, Peters, Davies, & Khunti, 2006).
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Patients who experienced good health outcome on the MOS-SF-36 were significantly more satisfied with the QoL, which is related to health condition than patients who experienced a poor health outcome (Sofaer & Firminger, 2005). The MOS-SF-36 has been used with 1,440 patients with congestive heart failure, hypertension, and type 2 diabetes (Ware, 1996). Caring for patients with chronic disease is complex, as multiple chronic conditions frequently exist in such patients. Tailoring care to each patient while still adhering to pertinent guidelines becomes increasingly problematic as the number of co-morbid conditions increases. The findings in this study revealed that patients with severe co-morbidity were having more physical health problems, more role limitations due to physical health, and less vitality than other patients. Several studies agreed with the findings of this study that people with multiple conditions may have difficulty in attributing their physical limitations to specific conditions (Deaton et al., 2006; Yasein, Ahmad, Matrook, Nasir, & Froelicher, 2010). Furthermore, the findings of this study support the notion that the number of coexisting medical disorders has a significant impact on the decline in the physical and the mental domains of health-related QoL (Baune, Adrian, & Jacobi, 2007; Dardas & Ahmad, 2014). One of the essential aims of nursing care is to improve patients’ health outcome; hence, it is reflected on their QoL. Patients with more co-morbid conditions need to have a higher intensity of care than patients with fewer co-morbid conditions (Min, Wenger, Reuben, & Saliba, 2008). Thus, the results of this study confirm the cumulative impact of having more than one illness condition, particularly in relation to physical functioning and vitality on the QoL. This result is consistent with other studies that reported an association between perceptions of the severity of health problems and healthrelated QoL (Min et al., 2008; Stone et al., 2008). The QoL is affected not only by having multiple coexisting diseases but also by factors such as age, gender, education, and income. Seven out of the eight health domains that reflect the QoL have shown significant correlation with gender. Except in “social functioning,” males demonstrated better health status than females, thus experiencing better QoL. This finding agrees with several studies that reported differences in health status based on gender (Ahmad, Alasad, & Nawafleh, 2010; Findik, Unsar, & Sut, 2010). Regarding the correlation between QoL and age, education, and income, the findings were almost alike. Becoming older has a negative relationship with health status, thus a decrease in the perception of the QoL. In this study, we have found that there is an inversely significant relationship between the QoL of patients and their income as demonstrated in Table 3. This result is consistent with a study by Peterson, Lowe, Peterson, and Janz
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(2006), which used income as a moderator model to examine the physical components of health-related QoL among residents from the Midwestern United States. The results showed that active living was associated with greater increases in health-related QoL than those reporting lower income (Peterson, Lowe, et al., 2006). There is a large and persistent association between education and health that has been well documented in many countries and for a wide variety of health measures (Hasson & Arnetz, 2009). Cutler and Lleras-Muney (2010) have demonstrated in their article on education and health that better educated people have lower morbidity rates from the most common acute and chronic diseases. Furthermore, they found that better educated individuals are less likely to self-report a past diagnosis of an acute or chronic disease and less likely to die from the most common acute and chronic diseases. The finding of this study is consistent with the literature that higher education level is significantly related to high QoL. However, the magnitude of the relationship between education and health varies across conditions, but it is generally large. Based on data analyses, we have shown in Table 3 a positively significant (p < .001) relationship between the education of patients and each of the following QoL indicators: physical functioning, the role limitation due to physical health, the role limitations due to emotional problems, general health perception, and mental health. Participants who reside in small cities and urban areas were under-represented in this study. However, the advantage of using data of a relatively large sample from the three major cities in Jordan supports the sample selection. The heterogeneous sample of the study with different co-morbidity levels indicates that the reduction in the QoL occurs with the increase in the coexisting morbidity. Cases with CCI scores of 4, 5, and 6 were considered in this study, as well as 20 different medical diagnoses for patients in medical– surgical wards. Although we used well-trained interviewers in collecting the data, there was a possibility of lacking of some information for patients who have been medically diagnosed in other hospitals. Thus, possibly, not all data were transferred to the research setting, which might have affected the accuracy of assessing the presence of other co-morbid conditions. This could be a potential limitation that might have affected this study. However, the heterogeneity of the sample and the sample size support the generalizability of the findings.
Conclusion This study demonstrated the feasibility of collecting information from hospitalized patients about their QoL. The findings shed the light on important
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aspects that affect the QoL among hospitalized patients in medical and surgical wards. It is recommended when examining QoL to include the small cities and urban areas to attain better representations of people with low socioeconomic status. Furthermore, using qualitative approach to explore QoL will contribute to a better understanding of this phenomenon.
Implications The importance of identifying factors that affect patient’ QoL has a crucial role in our care. Thus, nurses can verify the quality of care they provide to ensure that patients have acceptable QoL. As the patients’ perception of the QoL increases, their compliance with provided care increases as well. Acknowledgments The authors acknowledge the partial funding from the University of Jordan, and the input from Dr. Jafar Alasad and Dr. Manar Nabolsi.
Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors acknowledge the partial funding from the University of Jordan.
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Author Biographies Muayyad M. Ahmad, PhD, RN, is a professor at the Clinical Nursing Department at the University of Jordan with research interest in quality of nursing care, psychometric properties of instruments, and psychosocial care of cancer patients. Laila Ismae’l Al-Daken, MSc, RN, is a lecturer at the Faculty of Nursing at Zarqa Private University with clinical experience in medical-surgical cases. Huthaifa M. Ahmad is a medical student at the Faculty of Medicine at the University of Jordan with research interest in quality of life.
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CNRXXX10.1177/1054773813519470Clinical Nursing ResearchAhmad et al.
Article
Quality of Life for Patients in Medical–Surgical Wards
Clinical Nursing Research 2015, Vol. 24(4) 375–387 © The Author(s) 2014 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/1054773813519470 cnr.sagepub.com
Muayyad M. Ahmad, PhD, RN1, Laila Ismae’l Al-Daken, MSc, RN2, and Huthaifa M. Ahmad1
Abstract The purpose of this study was to examine the quality of life (QoL) for patients in medical–surgical wards in Jordanian hospitals. A cross-sectional design was performed. The data were collected between January and April 2011 through individual interviews (n = 746) using the Medical Outcome Study 36-item Short-Form (MOS-SF-36) and Charlson’s Co-morbidity Index (CCI). The private and public hospitals in the three largest cities in Jordan were represented. The MANOVA test was used to examine the health status based on patients’ co-morbidity level. The results showed that QoL for patients with severe co-morbidity has been affected negatively in many aspects more than both patients with no co-morbidity and patients with mild co-morbidity. However, although it is difficult to change the demographic characteristics, it is possible to improve the health status of patients with multiple co-morbidities, which will result in having a better QoL. Keywords quality of life, nursing care, medical–surgical wards Patients with moderate or severe co-morbid diseases may be sicker and have a worse quality of life (QoL) than other patients (Valderas, Starfield, Sibbald, Salisbury, & Roland, 2009). Patients who are successfully treated and reach 1The
University of Jordan, Amman, Jordan Private University, Amman, Jordan
2Zarqa
Corresponding Author: Muayyad M. Ahmad, Clinical Nursing Department, The University of Jordan, Amman 11942, Jordan. Email: [email protected]
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a state of good general health will result in having a high QoL (Ritchie, 2007). Comorbidity is significantly related to a severe impact on the QoL and survival rate among patients in medical and surgical wards (Gourin et al., 2005). Many studies have examined the effect of specific disease on the QoL. Diabetes mellitus was the most common health problem studied in relation to QoL (Nichols & Brown, 2004; Stone, Khunti, Squire, & Paul, 2008). The QoL is not only an outcome of the co-morbidity levels, but it is also affected by other variables. Patients’ demographics are considered an integral part in every study when examining QoL. Inconsistency was found in the literature concerning the relationships between patient characteristics and perception of the QoL (Dardas & Ahmad, 2015; Ervin, 2006). No correlations were found between patients’ QoL and age, gender, or education (Hasson & Arnetz, 2009). Ritchie (2007) found that individuals with lower socioeconomic status tend to have worse QoL. However, one study noted that factors such as age and living alone accounted for some of the variation in physical limitation scores (Stone et al., 2008). Nurses play a significant role in enhancing the QoL through the care they provide to their patients (Alasad & Ahmad, 2003). Nurses need to verify the quality of care to ensure that patients are satisfied with their QoL. Nurses in medical and surgical wards usually work with patients with different diseases and they are obliged to provide holistic care to their patients. Many of the admitted patients have multiple health problems, thus examining only the effect of one medical diagnosis rather than all of the health problems that each patient has may result in limited explanations for the QoL of those patients. There are many indices developed to measure patients’ QoL with different diseases; however, the most common index was developed by Charlson and colleagues to classify co-morbidity by assigning weights for diseases (Charlson, Pompei, Ales, & MacKenzie, 1987). Charlson’s Co-morbidity Index (CCI) was used to classify co-morbid conditions because the weighted index of co-morbidity is unambiguous and closely related to the coexistent diseases in this study. According to CCI, diabetes mellitus or cardiovascular disease if occurred alone, it gives a score of 1 for co-morbidity, and if occurred with other disorders, it may raise the score to 2 or 3 (de Visser, Bilo, Groenier, de Visser, & Jong Meyboom-de, 2002). In most of these cases, it is considered a mild or moderate level of co-morbidity, but it rarely reaches a severe level of co-morbidity. No previous studies have examined patients’ QoL based on different comorbidity levels while considering patients’ demographics in Jordan yet. Thus, this study was necessary to examine these issues with adult patients in medical–surgical wards in Jordanian hospitals. The main purpose of this study was to examine patients’ QoL in medical and surgical wards.
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Method Design and Sampling A cross-sectional design was used to assess the QoL among hospitalized patients in medical and surgical wards. Data were collected through individual interviews with patients using the Medical Outcome Study 36-item ShortForm (MOS-SF-36) health survey and patients’ demographics. Co-morbidity levels were classified based on CCI. Sample size was calculated using G* power 3.0 software (Faul, Erdfelder, Lang, & Buchner, 2007). In this study, which undertook comparison among three groups using MANOVA with eight dependent variables, and a small effect size (0.10), a total sample of 315 participants was needed to provide 80% power to detect difference at the 0.05 significance level. The actual sample size in this study was 746 participants. The convenience sample of eligible patients at the medical and surgical wards in each of the selected hospitals was targeted between January and April 2011. The eligibility criteria for the participants were (a) being an adult with 18 years of age or older (b) being oriented, and (c) agreeing to give an informed consent to be interviewed.
Procedure Data were collected using a questionnaire that includes the MOS-SF-36, CCI, and patients’ demographics. The three cities of Amman, Zarqa, and Irbid were selected for the sample of this study because the majority of the Jordanian population (71%) is residing in these areas (Department of Statistics, 2012). Ten hospitals with bed capacity more than 200 were contacted for the study purpose. A total of seven hospitals representing the private (n = 163), public (n = 281), and university (n = 302) hospitals gave permissions and were included in the study. Ten research assistants from outside the selected hospitals were trained on conducting interviews, and an interrater reliability of 95% was achieved. The research assistants have followed a standardized procedure while interviewing the participating patients. The time for each interview was between 15 and 20 min.
Ethical Considerations Formal application was submitted for approval to the Institutional Review Board at the relevant hospitals. The purpose of this study was explained to potential participants, and those who gave informed consent were interviewed individually. All gathered data and information were strictly confidential and could not be
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accessed by any other party without prior permission of the participants and involved institute. Patients were interviewed in their rooms and when there was more than one patient in the room, curtains were closed around the participant. The interviewers were sitting within a short distance (1-1.5 m) from the participants to assure the confidentiality of the interview.
Measurement Rigor Health status perception is one of the main health outcomes that reflects QoL (Ware, 1996). Therefore, the MOS-SF-36 was used to assess patients’ QoL. The choice to use the MOS-SF-36 in this study is due to the following: (a) it is composed of eight health concepts commonly represented in widely used surveys, (b) it has been successfully used in cross-sectional and longitudinal studies with different populations, and (c) it allows for easy interpretation and presentation of results (McDowell & Newell, 2006). The MOS-SF-36 is a multidimensional scale for health concepts, which includes physical functioning, role limitations due to physical health problems, bodily pain, general health, vitality (energy/fatigue), social functioning, role limitations due to emotional problems, and general mental health (Ware, 1996). The subscale comprises 10 items with three levels of responses (1 = yes, limited a lot, 2 = yes, limited a little, and 3 = no, not limited at all). Possible scores range from 10 to 30. A higher score indicates better physical functioning. The role limitation due to physical health dimension is a four-item scale. Participants responded in dichotomous options (1 = yes, 2 = no). Possible scores ranged from 4 to 8, and a higher score indicates better physical functioning. The subscale is composed of three items. Subjects responded in a dichotomous response option (1 = yes, 2 = no). The average is taken for these three items and used in analysis, ensuring that a higher score indicates better emotional functioning. The bodily pain dimension is composed of two items and is used to measure the intensity of bodily participants’ responses for the first item ranged from “none” (1) to “very severe” (6) and from “not at all” (1) to “extremely” (5) for the second item. For analytic purposes, the coding for both items was reversed. Thus, a higher score indicates a lack of bodily pain. The vitality dimension is a four-item scale that measures the subjective feelings of fatigue and energy level. Scale items are rated from “all the time” (1) to “none of the time” (6). A higher score for the four items of vitality indicates less fatigue and higher energy. The mental health dimension is a five-item scale to measure different aspects of mental health including depression, anxiety, and psychological well-being. Participants responses
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ranged from “all of the time” (1) to “none of the time” (6). A higher score indicates better mental health. The social functioning dimension measures the extent to which health problems interfered with social activities during the past 4 weeks. The scale is composed of two items, which are rated from “not at all” (1) to “extremely” (5); and “all of the time” (1) to “none of the time” (5). The general health perception dimension is composed of five items used to measure participants’ perceived current health, comparing health status with others, and resistance to illness. A higher score represents better health. In this study, the reliability coefficient for the eight subscales of MOS-SF-36 ranged between .71 and .93. Cronbach’s alphas were .93 for physical health, .84 for role limitations due to physical problems, .87 for bodily pain, .71 for social functioning, .79 for mental health, .83 for role limitations due to emotional problems, .80 for vitality/energy, and .72 for general health perception. The validity of the instrument was preserved following a translation and back-translation procedure by bilingual experts in English and Arabic languages (Maneesriwongul & Dixon, 2004). By comparing the MOS-SF-36 with other widely used general health surveys, content validity was established. Data on co-morbidity were classified according to Charlson scoring system (Charlson et al., 1987). This tool has a weighted index that takes into account the number and the seriousness of co-morbid diseases. The respondents were assessed for their primary diagnosis and the presence of any other co-morbid conditions by checking the patients’ charts. For patients who had two or more diseases, their scores of co-morbidity were the total of the assigned weight for each disease. For the purpose of this study, co-morbidity scores were coded into three groups. The group with no co-morbidity was coded as (0), the group with scores 1 and 2 in CCI was coded as (1), and the group with scores 3 and above in CCI was coded as (2). The reason for recoding the CCI scores in this format is the low frequency of patients with scores of 3 and above.
Data Analysis All univariate and multivariate statistics were computed using the Statistical Package for Social Sciences (IBM, 2012, Version 21). Frequency distributions, means, and standard deviations were used to describe the data. Preliminary screening for the data did not show any serious violations of the assumptions of multivariate analyses. An 8 × 3 MANOVA was performed on the co-morbidity data using the eight scales of health in MOS-SF-36 as
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Table 1. Patients’ Demographics (n = 746). Demographics Age (years)
Gender Education
Family income
Classification
Frequency (%)
18-39 40-59 60-80 Male Female 9th grade or less 10th grade to high school More than high school Insufficient Sometimes sufficient Sufficient
332 (44.5) 327 (43.8) 87 (11.7) 376 (50.4) 370 (49.6) 198 (26.5) 233 (31.2) 315 (42.2) 357 (47.9) 229 (30.7) 160 (21.4)
dependent variables. Type III sums of squares in MANOVA were used to adjust the possible confounding between uneven group sizes (Polit, 2010).
Results Selected participants’ demographics are presented in Table 1. The study sample was distributed almost equally between both genders. The participants’ ages ranged from 18 to 80 years (M = 41.6 years, SD = 14.2 years). The majority of the participants (42%) have college education or higher. Almost half of the participants considered their family income as insufficient. The most frequent medical diagnoses under the current admissions of the participants were abdominal pain, urinary tract infections, renal colic, asthma, heart diseases, diabetes mellitus, vertebral disc prolapse, leg fractures, hypertension, trauma, wound infections, and pneumonia. Having three groups as independent variables and eight types of health statuses as dependent variables supports the MANOVA statistics. The MANOVA is a statistical test used for comparing two or more dependent variables’ means of several groups at once (Polit, 2010). Running MANOVA has advantage on running multiple ANOVA tests in saving time and reducing the risk of Type I statistical errors. The MANOVA results showed significant differences in the total scores across the three groups of co-morbidity in physical functioning, the role limitation due to physical health, and vitality. To determine where the significant differences among groups lie, post hoc analysis using Scheffé test was used (Polit, 2010). Patients with severe co-morbid conditions demonstrated worse
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Ahmad et al. Table 2. MANOVA Testing QoL Based on Three Co-morbidity Levels. No co-morbid conditions (n = 268) Dependent variables
M (SE)
Physical functioning The role limitation due to physical health The role limitations due to emotional problems Social functioning Bodily pain Mental health Vitality
Moderate Severe co-morbid co-morbid conditions conditions (n = 277) (n = 201)
M (SE)
M (SE)
SS^
Scheffé’s post hoc analysisa
F
20.5 (.37) 20.8 (.36) 18.9 (.42) 419.58 5.85** 0* and 1** >2 5.2 (.09) 5.2 (.09) 4.8 (.10) 32.63 7.74*** 0** and 1** >2 4.1 (.08)
4.1 (.08)
4.1 (.09)
5.6 (.07) 5.8 (.07) 5.8 (.08) 7.4 (.16) 7.1 (.16) 7.2 (.18) 14.9 (.14) 15.0 (.14) 15.2 (.16) 11.6 (.10) 11.6 (.10) 12.0 (.12)
General health 14.9 (.14) 15.1 (.14) 15.1 (.16) perception MANOVA Test Wilks’ lamda = .93
.20
.06
6.80 2.64 9.68 .71 10.75 1.04 24.56 4.34* 4.95
.49
2* > 0 and 1
3.62***
Note. QoL = quality of life. ^ SS = Sum of Squares a0 = no co-morbid conditions, 1 = moderate co-morbid conditions, 2 = severe co-morbid conditions. *p ≤ .05. **p ≤ .01. ***p ≤ .001.
QoL than other patients with regard to “physical functioning” and “role limitation due to physical health.” The patients with severe co-morbidity were having less vitality/energy than the other two groups. There were no significant differences among the three groups of co-morbidity with regard to the remaining health subscales (Table 2). The relationship of QoL indicators and the demographic characteristics of patients were examined (Table 3). The “physical functioning” and “the role limitation due to physical health” are the only health indicators that correlated significantly with the selected demographics. However, the “social functioning” indicator did not show any significant relationship with any of
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Table 3. Correlations Between Demographics and Indicators of QoL (n = 746). QoL indicators Physical functioning The role limitation due to physical health The role limitations due to emotional problems Social functioning Bodily pain Mental health Vitality General health perception
Agea
Genderb
Educationa
Incomea
−.13*** −.11**
−.24*** −.09*
.18*** .13***
−.19*** −.10**
.03
−.10**
.20***
−.19***
.07 −.01 .08* .10** .01
−.02 .18*** −.10** −.11** −.14***
−.06 −.16*** .11** .04 .17***
.07 .16*** −.10** −.07 −.10**
Note. QoL = quality of life. aSpearman’s rank correlation. bPoint biserial correlation. *p ≤ .05. **p ≤ .01. ***p ≤ .001.
the demographics. Being a female shows less QoL than being a male on seven of the health status indicators. The “age” was correlated negatively with physical functioning and positively with mental health and vitality.
Discussion This cross-sectional study was conducted to examine adult patients’ QoL under different co-morbidity levels. Managing patients with co-morbid conditions frequently coexists in patients admitted to hospitals. The results confirm the cumulative impact of having multiple chronic diseases on the QoL. Nevertheless, the likelihood that people with multiple conditions may have difficulty in attributing the limitations to specific conditions when they have multiple chronic diseases. Caring for individuals with chronic medical conditions is frequently aimed to maximize QoL rather than to “cure” the disease. Furthermore, interventions to improve nursing care for patients often assess domains of health such as physical functioning, overall health status, and emotional well-being. These health domains are mainly subjective in their assessment because they are self-reported. Therefore, the values assigned to these health domains are most meaningful to the patients themselves. A negative impact on QoL has been shown both for diabetes mellitus (Wandell, 2005) and for congestive heart disease (Gravely-Witte, De Gucht, Heiser, Grace, & Van Elderen, 2007) with higher rates of depression in people with diabetes mellitus (Ali, Stone, Peters, Davies, & Khunti, 2006).
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Patients who experienced good health outcome on the MOS-SF-36 were significantly more satisfied with the QoL, which is related to health condition than patients who experienced a poor health outcome (Sofaer & Firminger, 2005). The MOS-SF-36 has been used with 1,440 patients with congestive heart failure, hypertension, and type 2 diabetes (Ware, 1996). Caring for patients with chronic disease is complex, as multiple chronic conditions frequently exist in such patients. Tailoring care to each patient while still adhering to pertinent guidelines becomes increasingly problematic as the number of co-morbid conditions increases. The findings in this study revealed that patients with severe co-morbidity were having more physical health problems, more role limitations due to physical health, and less vitality than other patients. Several studies agreed with the findings of this study that people with multiple conditions may have difficulty in attributing their physical limitations to specific conditions (Deaton et al., 2006; Yasein, Ahmad, Matrook, Nasir, & Froelicher, 2010). Furthermore, the findings of this study support the notion that the number of coexisting medical disorders has a significant impact on the decline in the physical and the mental domains of health-related QoL (Baune, Adrian, & Jacobi, 2007; Dardas & Ahmad, 2014). One of the essential aims of nursing care is to improve patients’ health outcome; hence, it is reflected on their QoL. Patients with more co-morbid conditions need to have a higher intensity of care than patients with fewer co-morbid conditions (Min, Wenger, Reuben, & Saliba, 2008). Thus, the results of this study confirm the cumulative impact of having more than one illness condition, particularly in relation to physical functioning and vitality on the QoL. This result is consistent with other studies that reported an association between perceptions of the severity of health problems and healthrelated QoL (Min et al., 2008; Stone et al., 2008). The QoL is affected not only by having multiple coexisting diseases but also by factors such as age, gender, education, and income. Seven out of the eight health domains that reflect the QoL have shown significant correlation with gender. Except in “social functioning,” males demonstrated better health status than females, thus experiencing better QoL. This finding agrees with several studies that reported differences in health status based on gender (Ahmad, Alasad, & Nawafleh, 2010; Findik, Unsar, & Sut, 2010). Regarding the correlation between QoL and age, education, and income, the findings were almost alike. Becoming older has a negative relationship with health status, thus a decrease in the perception of the QoL. In this study, we have found that there is an inversely significant relationship between the QoL of patients and their income as demonstrated in Table 3. This result is consistent with a study by Peterson, Lowe, Peterson, and Janz
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(2006), which used income as a moderator model to examine the physical components of health-related QoL among residents from the Midwestern United States. The results showed that active living was associated with greater increases in health-related QoL than those reporting lower income (Peterson, Lowe, et al., 2006). There is a large and persistent association between education and health that has been well documented in many countries and for a wide variety of health measures (Hasson & Arnetz, 2009). Cutler and Lleras-Muney (2010) have demonstrated in their article on education and health that better educated people have lower morbidity rates from the most common acute and chronic diseases. Furthermore, they found that better educated individuals are less likely to self-report a past diagnosis of an acute or chronic disease and less likely to die from the most common acute and chronic diseases. The finding of this study is consistent with the literature that higher education level is significantly related to high QoL. However, the magnitude of the relationship between education and health varies across conditions, but it is generally large. Based on data analyses, we have shown in Table 3 a positively significant (p < .001) relationship between the education of patients and each of the following QoL indicators: physical functioning, the role limitation due to physical health, the role limitations due to emotional problems, general health perception, and mental health. Participants who reside in small cities and urban areas were under-represented in this study. However, the advantage of using data of a relatively large sample from the three major cities in Jordan supports the sample selection. The heterogeneous sample of the study with different co-morbidity levels indicates that the reduction in the QoL occurs with the increase in the coexisting morbidity. Cases with CCI scores of 4, 5, and 6 were considered in this study, as well as 20 different medical diagnoses for patients in medical– surgical wards. Although we used well-trained interviewers in collecting the data, there was a possibility of lacking of some information for patients who have been medically diagnosed in other hospitals. Thus, possibly, not all data were transferred to the research setting, which might have affected the accuracy of assessing the presence of other co-morbid conditions. This could be a potential limitation that might have affected this study. However, the heterogeneity of the sample and the sample size support the generalizability of the findings.
Conclusion This study demonstrated the feasibility of collecting information from hospitalized patients about their QoL. The findings shed the light on important
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aspects that affect the QoL among hospitalized patients in medical and surgical wards. It is recommended when examining QoL to include the small cities and urban areas to attain better representations of people with low socioeconomic status. Furthermore, using qualitative approach to explore QoL will contribute to a better understanding of this phenomenon.
Implications The importance of identifying factors that affect patient’ QoL has a crucial role in our care. Thus, nurses can verify the quality of care they provide to ensure that patients have acceptable QoL. As the patients’ perception of the QoL increases, their compliance with provided care increases as well. Acknowledgments The authors acknowledge the partial funding from the University of Jordan, and the input from Dr. Jafar Alasad and Dr. Manar Nabolsi.
Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors acknowledge the partial funding from the University of Jordan.
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Author Biographies Muayyad M. Ahmad, PhD, RN, is a professor at the Clinical Nursing Department at the University of Jordan with research interest in quality of nursing care, psychometric properties of instruments, and psychosocial care of cancer patients. Laila Ismae’l Al-Daken, MSc, RN, is a lecturer at the Faculty of Nursing at Zarqa Private University with clinical experience in medical-surgical cases. Huthaifa M. Ahmad is a medical student at the Faculty of Medicine at the University of Jordan with research interest in quality of life.
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