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Factors affecting quality of life in patients on haemodialysis: a cross-sectional study from Palestine

  • Sa’ed H. Zyoud1, 2, 3Email author,
  • Dala N. Daraghmeh4,
  • Diana O. Mezyed4,
  • Razan L. Khdeir4,
  • Mayas N. Sawafta4,
  • Nora A. Ayaseh4,
  • Ghada H. Tabeeb4,
  • Waleed M. Sweileh5,
  • Rahmat Awang3 and
  • Samah W. Al-Jabi2
BMC NephrologyBMC series – open, inclusive and trusted201617:44

https://doi.org/10.1186/s12882-016-0257-z

Received: 16 October 2015

Accepted: 22 April 2016

Published: 27 April 2016

Abstract

Background

Haemodialysis (HD) is a life-sustaining treatment for patients with end-stage renal disease (ESRD). HD can bring about significant impairment in health-related quality of life (HRQOL) and outcomes. Therefore, we sought to describe the patterns of HRQOL and determine the independent factors associated with poor HRQOL in Palestinian patients on HD.

Methods

A multicenter cross-sectional study was performed from June 2014 to January 2015 using the EuroQOL-5 Dimensions instrument (EQ-5D-5L) for the assessment of HRQOL. ESRD patients undergoing HD in all dialysis centres in the West Bank of Palestine were approached and recruited for this study. Multiple linear regression was carried out to identify factors that were significantly associated with HRQOL.

Results

Two hundred and sixty-seven patients were participated in the current study giving response rate of 96 %. Overall, 139 (52.1 %) were male, and the mean ± standard deviation age was 53.3 ± 16.2 years. The reported HRQOL as measured by mean EQ-5D-5L index value and Euro QOL visual analogue scale (EQ-VAS) score was 0.37 ± 0.44 and 59.38 ± 45.39, respectively. There was a moderate positive correlation between the EQ-VAS and the EQ-5D-5L index value (r = 0.42, p < 0.001). The results of multiple linear regression showed a significant negative association between HRQOL with age, total number of chronic co-morbid diseases and the total number of chronic medications. However, a significant positive association was found between HRQOL with male gender, university education level and patients who live in village.

Conclusion

Our results provided insight into a number of associations between patient variables and their HRQOL. Healthcare providers should be aware of low HRQOL among patients with no formal education, female gender, patient’s residents of refugee camps, multiple co-morbid diseases, multiple chronic medications, and elderly patients to improve their quality of life.

Keywords

Haemodialysis Risk factors Health-related quality of life Palestine

Background

Chronic kidney disease (CKD) is a growing worldwide public health concern [1, 2]. It is characterised by an irreversible worsening of renal function that could lead to end-stage renal disease (ESRD), which necessitates treatment with renal replacement therapy (RRT) such as renal transplant or haemodialysis (HD) [3, 4]. HD is one of the most effective therapeutic techniques for patients with ESRD second to renal transplantation, but is expensive and burdensome therapy for patients with ESRD [1, 2, 5].

In the United States of America (USA), the number of newly reported ESRD cases in 2013 was 117,162 corresponding to an unadjusted incidence rate of 363 per million per year [6, 7]. In the USA, 88.2 % of all incident cases started with RRT with HD, 9.0 % initiated with peritoneal dialysis, and 2.6 % got a pre-emptive kidney transplant [6, 8]. In the West Bank of Palestine, the total number of patients with ESRD has increased notably over the last several years [9]. In 2014, the total reported number of ESRD patients undergoing HD in the West Bank was 1104 patients, representing an increase of 77.5 % compared to reported numbers in 2011 [10].

Previous reports have confirmed that ESRD patients undergoing HD have lower health-related quality of life (HRQOL) compared with compared with a normative sample [1121]. Patients with ESRD undergoing HD are prone to several complications such as depression, inflammation, and malnutrition [21, 22]. Up to now, too little attention has been paid to document the HRQOL of patients with ESRD in the Middle East [12, 23, 24]. It has been shown that improving the quality of treatment in patients with ESRD can minimize the development and/or severity of complications and therefore improve patients’ HRQOL [2527].

Health-related quality of life is a cultural concept as revealed by the difference in association between HRQOL and clinical outcomes such as compliance, or patient survival [2830]. HRQOL is recognized as an essential health outcome for studies assessing the quality of healthcare, evaluating the impact of illness, and analyses of cost-effectiveness [3133]. In addition, it has been shown that HEQOL is clinically important for improving dialysis outcome in patients on HD [34, 35].

Although several studies were carried out and published about HRQOL in different disease populations in Palestine such as diabetic or hypertensive patients [36, 37], no such studies were carried out among HD patients in Palestine. Therefore, we performed the present study to describe the patterns of HRQOL and to determine the independent factors associated with poor HRQOL in Palestinian patients with HD.

Methods

Study design

A multicenter cross-sectional study was carried out from June 2014 to January 2015.

Study setting

Patients were recruited from all dialysis centres in West Bank, Palestine. We collated related information about the distribution of the population from the Palestinian Central Bureau of Statistics and Ministry of Health [38].

Study population, sampling procedure and sample size calculation

The Healthcare sector in Palestine is primarily managed by the Government through the Palestinian Ministry of Health (PMOH). There are 10 functioning dialysis centres in West-Bank, Palestine. All the dialysis centres are on hospital campus and varies in size between one-machine to 32-machine centres. The PMOH runs nine out of the 10 available dialysis facilities [10]. At the time of the study, there were 740 dialysis patients served on 160 HD machines [38]. For the purpose of this study, sample size was detrmined using a Raosoft sample size calculator, which is a web-based calculator [39]. A sample size of 254 patients was considered to achieve a 5 % margin of error and a 95 % confidence level assuming that 50 % of patients answered each question correctly. The sample size was increased by 5–10 % to account for the non-response rate. Two hundred and seventy seven patients were selected using a convenience quota sampling method proportional to the number of patients in each dialysis centre. The inclusion criteria were as follows: (1) patients 18 years of age or older; (2) confirmed diagnosis of ESRD by medical file; and (3) on regular HD therapy for a minimum of 3 months prior to the interview. Patients were excluded if they lacked the mental or physical capacity to communicate with interviewer.

Data collection instrument

Data were collected using a questionnaire containing two sections, a socio-demographic and clinical history section and a validated Arabic version of HRQOL section [40]. Details regarding age, gender, body mass index (BMI) quartiles calculated from height and weight, educational level, household monthly income were obtained, residency, living status, smoking status [“light smoker”: (1 to 9 cigarettes/day), “moderate smoker”: (10 to 19 cigarettes/day), and “heavy smoker”: (≥20 cigarettes/day) [41]], marital status, occupation, dialysis vintage (length of time on dialysis treatment), average duration of dialysis session, total number of medications for chronic use, and the total number of chronic diseases. We categorised BMI as obese (BMI ≥ 30 kg/m2), overweight (BMI = 25 to <30 kg/m2), normal (BMI = 18.5 to <25 kg/m2), or underweight (BMI < 18.5 kg/m2) [42]. To determine the health status, the 5-level EuroQoL Group’s 5-dimension (EQ-5D-5L) questionnaire was used. The EQ-5D instrument was developed by the Euro QOL Group. The EQ-5D includes a 5-item descriptive system to calculate the EQ-5D index score and the EQ visual analogue scale (EQ-VAS) that allows the patients to judge their current health status during intradialysis from 0 to 100. The measuring principle of the instrument was described in detail in the previous works by the investigators [36, 37, 43]. The Arabic version of EQ-5D [40] was provided by using Euro QOL guidelines [44]. The study was registered with Euro QOL and permission was given for its use (ID: 8537, approval date: April 7, 2014).

Data collection procedure

Two hundred and seventy seven patients were recruited and interviewed face to face. We tried to make each interviewee feel as comfortable as possible. Face-to-face interview was used to provide researchers with an opportunity to collect as complete data as possible and to overcome non-response by those who cannot read. Interviews were performed by trained clinical pharmacy students. Data collection method was pre-tested to check for the clarity of questions in a pilot study of 16 patients was tested on eight medical files that were not included in the final anslysis. All the comments noted in the pilot study were taken into consideration and a modified questionnaire was reviewed by experts in the field of quality of live (QOL) to ensure content validity of data collection form in relations to factors that might be associated with QOL in ESRD population. The internal consistency of the EQ-5D instrument was found to be 0.84 which showed high reliability of the EQ-5D instrument.

Ethical approval

The study protocol was approved by the Ethics Committee of An-Najah National University, and the local health authorities that had jurisdiction over the local study population. The interview content was described to respondents, and an informed verbal consent was obtained before the start of the interview.

Statistical analysis

Data were analysed using SPSS (SPSS Inc., Chicago, IL, USA) programme version 15. Results were reported as mean ± SDs or as frequencies and percentages or as a median with a range of values (lower-upper quartiles) wherever appropriate. Data that were not normally distributed were analysed using the Mann-Whitney U test or Kruskal-Wallis test according to the number of groups to compare. The Kolmogorov-Smirnov test was used to assess normality of distribution of data. The Pearson correlation coefficient was used to assess the correlation between the reported EQ-VAS scores and EQ-5D-5L index values. Multiple linear regression was carried out to identify factors that were significantly associated with HRQOL. Multiple linear regression was carried out to identify factors that were significantly associated with HRQOL (dependent factor). The independent factors were socio-demographic, and HD related clinical factors. A dummy coding of 0 and 1 was used to enter the nominal independent variables such as gender, BMI, educational level, residency, and occupation into the regression model. Variables with a p < 0.05 in univariate analysis were entered in the regression model. The significance level was predetermined at p level < of 0.05 for all tests. Variance inflation factors (VIF) and tolerance index were conducted to assess collinearity between independent variables. The internal consistency reliability of the study scale was assessed using Cronbach’s alpha values. EQ-5D was scored to calculate the index value using the value sets (weights) from the existing United Kingdom general population scoring algorithm (i.e. EQ-5D-5L Crosswalk Index Value Calculator [45]). We used the UK value set for three reasons; first, to present the health status as a continuous variable; second, due to absence of a locally or regionally appropriate set of values; and lastly to make the comparison more reasonable because most published studies at local or regional level used the UK value set as suggested by EuroQol Group the EQ-5D [12, 37, 46].

Results

Socio-demographic and clinical characteristics

Two hundred and sixty-seven patients were participated in the current study giving response rate of 96 %. Overall, 139 (52.1 %) were male, and the mean (standard deviation) age was 53.3 (16.2) years. 177 patients (66.3 %) were on dialysis for less than four years. There were 204 patients (76.4 %) who were dialysed three times weekly among which 198 patients (74.2 %) stayed on dialysis three hours. The mean duration of disease was 3.4 ± 3.7 year. The majority of patients 197 (73.8 %) took their medication by themselves, 116 (43.4 %) had three or more chronic co-morbid diseases, and 222 (83.1 %) were on four or more chronic medications. The mean number of chronic co-morbid diseases was 2.4 ± 1.6 and the mean number of chronic medications was 6.5 ± 2.8. Overall, 52.7 % of study participants were either overweight or obese (28.1 % were overweight and 24.6 % were obese). Being obese was significantly more prevalent in females (14.8 %) as compared to males (9.8 %); (p value = 0.042). The socio-demographic and clinical characteristics of the study participants are displayed in Table 1.
Table 1

Socio-demographic and clinical characteristics of the study sample

Variable

Frequency (%) N (267)

Age category

 

  < 30

34 (12.7)

 30–60

136 (50.9)

  > 60

97 (36.3)

Gender

 

 Male

139 (52.1)

 Female

128 (47.9)

BMIa

 

 Underweight

24 (9)

 Normal

97 (36.3)

 Overweight

72 (27)

 Obese

63 (23.6)

Education

 

 No formal education

40 (15.0)

 Primary

71 (26.6)

 Secondary

102 (38.2)

 Graduated

54 (20.2)

Household income (month)

 

 High (more than 1000 JDb)

10 (3.7)

 Moderate (400–1000 JD)

90 (33.7)

 Low (less than 400 JD)

167 (62.5)

Living

 

 Palestinian refugee camps

22 (8.2)

 Village

161 (60.3)

 City

84 (31.5)

Living status

 

 Alone

18 (6.7)

 With family

249 (93.3)

Marital status

 

 Single, divorced, widowed

79 (29.6)

 Married

188 (70.4)

Occupation

 

 Employed

35 (13.1)

 Unemployed

232 (86.9)

Current smoking status

 

 Non smoker

227 (85)

 Light smoker

19 (7.1)

 Moderate smoker

17 (6.4)

 Heavy smoker

4 (1.5)

Dialysis vintage (Years)

 

  < 4

177 (66.3)

  ≥ 4

90 (33.7)

Dialysis per week

 

  ≤ 2

26 (9.7)

 3

204 (76.4)

  ≥ 4

37 (13.9)

Dialysis session duration (hours)

 

  < 4

198 (74.2)

  ≥ 4

69 (25.8)

Transplantation history

 

 Yes

26 (9.7)

 No

241 (90.3)

Total chronic co-morbid diseases

 

 None

26 (9.7)

 1

62 (23.2)

 2

63 (23.6)

  ≥ 3

116 (43.4)

Chronic medications (per day)

 

  < 4

45 (16.9)

  ≥ 4

222 (83.1)

Abbreviations: BMI body mass index, JD Jordanian Dinar

aData were missing from 11 patients

b1 Jordanian Dinar (JD) equals 1.41 US Dollar

EQ-5D health status

The reported HRQOL as measured by mean EQ-5D-5L index value and EQ-VAS score was 0.37 ± 0.44 and 59.38 ± 45.39, respectively. There was a moderate positive correlation between the EQ-VAS and the EQ-5D-5L index value (r = 0.42, p < 0.001). The distribution of reported no problems across dimensions of QOL was as follows: mobility 73 (27.3 %), usual activities 100 (37.5 %), self-care 146 (54.7 %), pain/discomfort 68 (25.5 %) and anxiety/depression 94 (35.2 %); (Fig. 1). A total of 178 states of health were reported by the participants. We found that 17 (6.4 %) participants reported no problems for any dimension, and 9 (3.4 %) patients reported very severe difficulty for all five dimensions.
Fig. 1

Distribution of health-related quality of life measures in different European Quality of Life scale 5 (EQ-5D) dimensions

EQ-5D-5L index values

The median EQ-5D-5L index value was 0.41 (interquartile range: 0.06-0.77). Tables 2 showed that there were significant differences between participant groups according to age, BMI, education level, residency and total co-morbid disease, as well as gender, occupation, and total number of chronic medication (p-value < 0.05). No significant differences were found between participants according to income, living status, marital status, dialysis vintage, dialysis session duration, smoking status, and transplantation history.
Table 2

EQ-5D total score by socio-demographic and clinical variables (n = 267)

Variable

Frequency (%) N = 267

EQ-5D index score

P-value

Mean ± SD

Median [interquartile range]

Age category

    

  < 30

34 (12.7)

0.69 ± 0.28

0.79 [0.47–0.88]

0.001a

 30–60

136 (50.9)

0.44 ± 0.43

0.55 [0.18–0.77]

  > 60

97 (36.3)

0.17 ± 0.40

0.15 [0.13–0.56]

Gender

    

 Male

139 (52.1)

0.46 ± 0.41

0.85 [0.22–0.80]

0.001b

 Female

128 (47.9)

0.28 ± 0.45

0.21 [0.04–0.73]

BMIc

    

 Underweight

24 (9.0)

0.57 ± 0.42

0.69 [0.38–0.86]

0.047a

 Normal

97 (36.3)

0.41 ± 0.42

0.46 [0.10–0.78]

 Overweight

72 (27)

0.36 ± 0.45

0.43 [0.01–0.75]

 Obese

63 (23.6)

0.29 ± 0.43

0.26 [0.03–0.73]

Education

    

 No formal education

40 (15.0)

0.04 ± 0.41

0.06 [−0.21–0.22]

<0.001a

 Primary

71 (26.6)

0.36 ± 0.42

0.32 [0.08–0.74]

 Secondary

102 (38.2)

0.45 ± 0.42

0.55 [0.16–0.79]

 Graduated

54 (20.2)

0.50 ± 0.40

0.67 [0.23–0.84]

Household income (month)

    

 High (more than 1000 JDd)

10 (3.7)

0.34 ± 0.48

0.28 [0.12–0.84]

0.109a

 Moderate (400–1000 JD)

90 (33.7)

0.45 ± 0.43

0.64 [0.15–0.84]

 Low (less than 400 JD)

167 (62.5)

0.33 ± 0.44

0.36 [0.03–0.72]

Residency

   

0.004a

 Palestinian refugee camps

22 (8.2)

0.20 ± 0.39

0.15 [0.02–0.52]

 Village

161 (60.3)

0.44 ± 0.44

0.58 [0.15–0.78]

 City

84 (31.5)

0.29 ± 0.44

0.26 [0.02–0.73]

Living status

    

 Alone

18 (6.7)

0.31 ± 0.37

0.28 [0.10–0.67]

0.347b

 With family

249 (93.3)

0.38 ± 0.44

0.43 [0.07–0.77]

 

Marital status

    

 Single, divorced, widowed

79 (29.6)

0.42 ± 0.44

0.48 [0.12–0.79]

0.171b

 Married

188 (70.4)

0.35 ± 0.44

0.36 [0.07–0.75]

Occupation

    

 Employed

35 (13.1)

0.51 ± 0.38

0.66 [0.21–0.88]

0.041b

 Unemployed

232 (86.9)

0.35 ± 0.44

0.38 [0.06–0.75]

Smoking

    

 Non-smoker

227 (85)

0.36 ± 0.44

0.36 [0.04–0.76]

0.156a

 Light smoker

19 (7.1)

0.49 ± 0.48

0.64 [0.14–0.88]

 Moderate smoker

17 (6.4)

0.53 ± 0.38

0.66 [0.28–0.78]

 Heavy smoker

4 (1.5)

0.15 ± 0.46

0.10 [0.27–0.61]

Dialysis session duration (hours)

    

  < 4 h

198 (74.2)

0.37 ± 0.45

0.41 [0.05–0.77]

0.859b

  ≥ 4 h

69 (25.8)

0.39 ± 0.42

0.41 [0.14–0.73]

Dialysis vintage (years)

    

  < 4

177 (66.3)

0.37 ± 0.44

0.39 [0.08–0.76]

0.806b

  ≥ 4

90 (33.7)

0.38 ± 0.44

0.47 [0.04–0.77]

Transplantation

    

 Yes

26 (9.7)

0.52 ± 0.42

0.66 [0.33–0.84]

0.055b

 No

241 (90.3)

0.36 ± 0.44

0.35 [0.05–0.75]

Total chronic co-morbid diseases

    

 None

26 (9.7)

0.77 ± 0.29

0.81 [0.69–1.00]

<0.001a

 1

62 (23.2)

0.56 ± 0.39

0.69 [0.30–0.84]

 2

63 (23.6)

0.41 ± 0.34

0.38 [0.18–0.73]

  ≥ 3

116 (43.4)

0.16 ± 0.43

0.15 [0.17–0.52]

Chronic medications

    

  < 4 drugs/day

45 (16.9)

0.53 ± 0.37

0.67 [0.22–0.80]

0.010b

  ≥ 4 drugs/day

222 (83.1)

0.34 ± 0.44

0.33 [0.02–0.74]

Abbreviations: EQ-5D European Quality of Life scale 5 dimensions, JD Jordanian Dinar, BMI body mass index

aStatistical significance of differences calculated using the Kruskal-Wallis test

bStatistical significance of differences calculated using the Mann-Whitney U test

cData were missing from 11 patients

d1 Jordanian Dinar (JD) equals 1.41 US Dollar

EQ-VAS score

The median EQ-VAS score was 50 (interquartile range: 50–70). As seen in Tables 3, there were significant differences between participant groups according to age and total co-morbid diseases, as well as gender, and total number of chronic medications (p-value < 0.05). Patients older than 60 years had a lower EQ-VAS score than those younger than 60. In addition, male gender was associated with higher EQ-VAS value compared to female. The study found that EQ-VAS score decreased as the total number of chronic medication increased and as illustrated in Table 3 increased co-morbid diseases had the lowest EQ-VAS score.
Table 3

EQ-VAS by socio-demographic and clinical characteristics

Variable

EQ-VAS

P-value

Mean ± SD

Median [interquartile range]

Age category

   

  < 30

59.95 ± 21.53

60 [50–80]

0.010a

 30–60

60.28 ± 21.66

60 [50–75]

  > 60

51.46 ± 23.20

50 [30–70]

Gender

   

 Male

61.26 ± 19.94

60 [50–80]

0.002b

 Female

52.41 ± 24.28

50 [40–70]

BMIc

   

 Underweight

66.13 ± 16.44

65 [50–80]

0.198a

 Normal

55.98 ± 21.92

60 [45–70]

 Overweight

55.31 ± 24.68

50 [40–70]

 Obese

58.25 ± 20.81

50 [45–70]

Smoking

   

 Non smoker

55.96 ± 22.63

50 [45–70]

0.294a

 Light smoker

64.47 ± 21.07

70 [50–80]

 Moderate smoker

63.24 ± 22.29

60 [50–80]

 Heavy smoker

55.00 ± 20.82

55 [35–75]

Education level

   

 No formal education

50.63 ± 23.62

50 [40–70]

0.111a

 Primary

54.72 ± 24.20

50 [40–70]

 Secondary

59.79 ± 20.87

60 [50–70]

 Graduated

59.54 ± 21.77

63 [50–80]

Household income (month)

   

 High (more than 1000 JDd)

58.50 ± 20.00

55 [45–80]

0.968a

 Moderate (400–1000 JD)

56.63 ± 22.05

58 [49–70]

 Low (less than 400 JD)

57.14 ± 23.03

50 [50–72]

Residency

   

 Palestinian refugee camps

62.5 ± 21.92

55 [50–80]

0.145a

 Village

58.25 ± 21.99

60 [50–74]

 City

53.21 ± 23.38

50 [40–70]

Living statues

   

 Alone

57.50 ± 21.30

60 [50–70]

0.761b

 With family

56.98 ± 22.65

50 [48–72]

Marital status

   

 Single, divorced, widowed

55.78 ± 21.84

50 [50–70]

0.608b

 Married

57.54 ± 22.85

60 [50–71]

Occupation

   

 Employed

58.57 ± 21.54

60 [45–75]

0.509b

 Unemployed

56.78 ± 22.71

50 [50–70]

Dialysis session duration (hours)

   

  < 4 h

59.16 ± 51.11

50 [40–70]

0.163b

  ≥ 4 h

60.01 ± 22.13

60 [50–74]

Dialysis vintage (years)

   

  < 4

55.86 ± 22.45

50 [50–70]

0.275b

  ≥ 4

66.30 ± 71.33

60 [49–80]

Transplantation

   

 Yes

62.58 ± 19.54

68 [50–76]

0.212b

 No

59.04 ± 47.35

50 [45–70]

Total chronic co-morbid diseases

   

 None

63.15 ± 21.53

60 [50–80]

0.001a

 1

62.82 ± 21.75

63 [50–80]

 2

59.68 ± 21.04

65 [50–80]

  ≥ 3

56.53 ± 64.48

50 [40–70]

Chronic medications

   

  < 4 drugs/day

63.56 ± 19.96

60 [50–80]

0.042b

  ≥ 4 drugs/day

58.54 ± 48.95

50 [40–70]

Abbreviation: EQ-VAS European Quality visual analogue scale, JD Jordanian Dinar, BMI body mass index

aStatistical significance of differences calculated using the Kruskal-Wallis test

bStatistical significance of differences calculated using the Mann-Whitney U test

cData were missing from 11 patients

d1 Jordanian Dinar (JD) equals 1.41 US Dollar

After adjustment for covariates, regression coefficients indicated significant associations between some of the independent variables and EQ-5D index score in comparison to a reference category for categorical variables or with one unit increase of a continuous variable. This model explained about 37 % of the variance in EQ-5D scores. As shown in Table 4, age, total number of chronic comorbid diseases and the total number of chronic medications were negatively associated with EQ-5D scores, whereas male gender, university education level and patients who live in village were positively associated with the EQ-5D scores. The range of VIF was from 1.015 to 1.465 which indicated absence of multicollinearity between independent variables.
Table 4

Multiple linear regression analysis of association between factors and EQ-5D score

Variables

Unstandardised coefficients (B)

Standardised coefficients (Beta)

P value

95 % CI for B

Age

    

 Continuous (1-year units)

−0.007

−0.272

<0.001

−0.010– −0.004

Gender

    

 Male

0.154

0.177

0.001

0.064–0.244

 Female

Reference

BMI

    

 Underweight or normal

−0.034

−0.039

0.464

−0.124–0.057

 Overweight or obese

Reference

Education level

    

 University

0.119

0.109

0.042

0.005–0.233

 No formal, primary, secondary

Reference

Residency

    

 Village

0.121

0.136

0.007

0.033–0.210

 Palestinian refugee camps or city

Reference

Occupation

    

 Employed

0.009

0.007

0.892

−0.124–0.142

 Unemployed

Reference

Number of co-morbid diseases

    

 Continuous

−0.089

−0.324

<0.001

−0.121– −0.056

Number of chronic medications

    

 Continuous

−0.018

−0.113

0.039

−0.034– −0.001

Abbreviations: EQ-5D European quality of life scale, SE standard error, BMI body mass index, CI confidence interval

Discussion

This study provided a comprehensive analysis of HRQOL among ESRD patients undergoing HD in the West Bank of Palestine. HRQOL was assessed using the EQ-5D Overall, this study indicated that the main sociodemographic factors associated with HD-related QOL were old age, female gender, obesity, residency in a refugee camp, unemployment, low income, and having no formal education. Review of literature indicated that the EQ-5D has been used to measure HRQOL among ESRD patients undergoing HD in different countries [1119]. The construct validity, reliability, and responsiveness of the EQ-5D have been recognized widely in both specific and general disease populations [47, 48]. Furthermore, Wasserfallen et al. [49] showed that using a generic QOL instrument EQ-5D was well-accepted, and easy to use for assessing HRQOL among ESRD patients undergoing HD due to the shorter completion time compared with other generic instruments.

In the current study, we found that mean EQ-5D score among ESRD patients undergoing HD was 0.37 ± 0.44 while findings from studies that used the same instruments in Korean, Japanese, Taiwan, and Singaporean patients were 0.704 ± 0.199 [17], 0.75 ± 0.17 [18], 0.65 ± 0.23 [13], and 0.60 ± 0.21 [20], respectively. Several socioeconomic and healthcare system related factors could affect HRQOL among ESRD patients undergoing HD. Some of these variations in EQ-5D score could be explained by differences in the main sociodemographic and clinical characteristics of recruited participants such as; age, duration of HD and presence of comorbid diseases. Furthermore, many patients, particularly in developing countries, frequently do not seek medical advice until other debilitating symptoms or complications appeared, thus, delay in diagnosis and therapy can directly increase the number of complications and therefore leading to reduction in patient’s HRQOL [50].

In our study, there was a modest positive correlation between the EQ-5D index values and reported EQ-VAS scores. Several studies suggested that individual experiences that are assessed by different rating scales such as the EQ-5D-5L and EQ-VAS may result in slightly different outcomes [12, 51, 52]. In addition, Saffari et al. [12] declared that when contributors were asked to select their health status using five dimensions, accuracy in outcomes is probable than when using only one overall dimension of health status.

Our results demonstrated that increased age was associated with lower HRQOL. Similar previous studies have reported the same findings, for instance, Kang et al.’s [17] study using EQ-5D found age was a significant factor determining HRQOL of Korean HD patients. Younger patients (<30 years) in the current study reported significantly better HRQOL, possibly because of the short duration of disease, and minor complications [37]. According to another study, older age was the most important predictor of lower QOL and health status [12]. Our study found that female gender was significantly associated with lower mean EQ-5D scores than male gender. One possible explanation is that poor social life and physical inactivity of females in developing countries might contribute to lower QOL scores, thus, females tend to have poor QOL [50]. This observation is in agreement with Merom et al [53] findings which identified Palestinian women as being at the highest risk of physical inactivity. Furthermore, males were less likely to become anxious or depressed compared to females [12, 54], thus, patients presented with more symptoms of depression and anxiety indicated lower levels of QOL [5558]. The other possible explanation for this result may be that females were more obese in our study, which by itself worsens HRQOL, as reported by Bossola et al. [59] and Feroze et al. [54, 60]. According to our study, obese patients were significantly associated with lower EQ-5D scores. In the USA, a study conducted by Dwyer et al. [61] also mentioned obesity as one of the factors associated with impaired HRQOL and recommended the importance of keeping weight at healthier levels for improvement of QOL.

This study found significant associations between high education level and high HRQOL. This could be due to the fact that educated patients may have a better understanding of the illness, its effects, and will themselves benefit from the best management they can give [50], or they have more information about the treatments, greater self reported adherence, and a better relationship with their healthcare team [58]. Education was also confirmed in several studies as an important discriminator of HRQOL in HD patients [13, 6264]. Our data showed that being unemployed was significantly related to lower EQ-5D scores. These results were in agreement with the findings documented by Sakthong and Kasemsup [13]. Unemployment was also confirmed in several studies as an important factor associated with impaired HRQOL in HD patients [23, 32, 58].

According to our study, residency in a refugee camp was also associated with low QOL with those living in a village had the highest EQ-5D index. These results further support the idea of closer communications and stronger family ties among people in rural areas as found by Saffari et al. [12] among Iranian population. A Lebanese study demonstrated that rural residents had higher vitality scores than urban residents [65]. However, it is hard to compare our results with other studies since the health care systems are different in different countries. Most of Palestinians in refugees in the camps receive their care from the UNRWA, and according to Eljedi et al. [66], patients treated at the UNRWA clinics may have a poorer quality of health care than patients getting care from other providers. Residency in refugee camps, as an important factor associated with impaired HRQOL, was also confirmed in several previous studies among different populations from Palestine such as diabetic or hypertensive patients [36, 37, 43].

As for clinical factors, presence of co-morbid diseases and increasing in the total number of medications have been recognised as variables that were negatively associated with HRQOL. Presence of co-morbidity was negatively associated with HRQOL. Similar associations were observed in previous studies [12, 20, 24, 63]. Chronic illnesses, mainly DM, were strongly associated with impaired HRQOL in ESRD patients on dialysis [67]. The number of medications was significantly associated with lower EQ-5D scores. These results were in agreement with the findings reported by Chiu and colleagues whereby people who took a large number of medications rated their health as poorer than those who did not [68]. A negative impact of medication on HRQOL might be mediated by the effect of medication-taking on patients’ behaviour due to high expense or side effects.

Strengths and limitations

This study had many strengths such as including a generalised sample of all HD centres in the West Bank, as well as conducting face-to-face interviews to obtain more complete data and high reliability of data collection. Furthermore, our study is the first study assessing HRQOL among ESRD patients in the West Bank and to the best of our knowledge is the first one in Palestine that used the EQ-5D scale as a measure. However, there were a number of limitations that need to be noted. First, the cross-sectional nature of this study makes it difficult to interpret any cause–effect relationship. Second, we used a convenience sampling technique that could decrease the generalisability of the results to other HD patients. Third, data were collected via a face-to-face interview which might have introduced interviewer’s bias in the results. Lastly, additional clinical variables such as albumin, calcium, and creatinine would help to get a more complete view of possible dialysis outcome factors related to HRQOL of HD patients.

Conclusions

Our results provide insight into a number of associations between patient variables such as demographics, clinical factors, and their HRQOL. Our research study reveals a number of important results that can be taken into consideration when dealing with HD patients. Elderly patients, female gender, obese patients, patients with no formal education, and living in Palestinian refugee camps were all associated with poor HRQOL. In addition, this study showed that lower HRQOL was associated with higher numbers of chronic diseases as well as higher numbers of medications. These results are expected to be of interest to educators, pharmacists, and clinicians working with ESRD patients. Healthcare providers should be aware of low HRQOL among patients with no formal education, female gender, patient’s residents of refugee camps, multiple co-morbid diseases, multiple chronic medications, and elderly patients to improve their QOL.

Ethics approval and consent to participate

The study protocol was approved by the Ethics Committee of An-Najah National University. The interview content was described to respondents, and an informed verbal consent was obtained before the start of the interview.

Consent for publication

Not applicable.

Availability of supporting data

All data supporting the study is presented in the manuscript or available upon request from the corresponding author of this manuscript, Zyoud S. H.

Abbreviations

CKD: 

Chronic kidney disease

EQ-5D-5L: 

5-level EuroQoL Group’s 5-dimension

EQ-VAS: 

EuroQoL visual analogue scale

ESRD: 

end-stage renal disease

HD: 

haemodialysis

HRQOL: 

health-related quality of life, PMOH, Palestinian Ministry of Health, BMI, body mass index

QOL: 

quality of live

RRT: 

renal replacement therapy

SD: 

standard deviation

UNRWA: 

United Nations Relief and Works Agency for Palestine Refugees in the Near East

VIF: 

variance inflation factor

Declarations

Acknowledgements

We would like to thank all participants for their cooperation. Furthermore, we would like to express sincere thanks to An-Najah National University and the PMOH for their help and ethical approval to conduct this study.

Funding sources

No funding was received for writing this study.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Poison Control and Drug Information Center (PCDIC), College of Medicine and Health Sciences, An-Najah National University
(2)
Department of Clinical and Community Pharmacy, College of Medicine and Health Sciences, An-Najah National University
(3)
WHO Collaborating Centre for Drug Information, National Poison Centre, Universiti Sains Malaysia (USM)
(4)
PharmD program, College of Medicine and Health Sciences, An-Najah National University
(5)
Department of Pharmacology and Toxicology, College of Medicine and Health Sciences, An-Najah National University

References

  1. Karopadi AN, Mason G, Rettore E, Ronco C. Cost of peritoneal dialysis and haemodialysis across the world. Nephrol Dial Transplant. 2013;28(10):2553–69.View ArticlePubMedGoogle Scholar
  2. Mushi L, Marschall P, Flessa S. The cost of dialysis in low and middle-income countries: a systematic review. BMC Health Serv Res. 2015;15(1):506.View ArticlePubMedPubMed CentralGoogle Scholar
  3. Murtagh FE, Addington-Hall J, Higginson IJ. The prevalence of symptoms in end-stage renal disease: a systematic review. Adv Chronic Kidney Dis. 2007;14(1):82–99.View ArticlePubMedGoogle Scholar
  4. Levey AS, Coresh J. Chronic kidney disease. Lancet. 2012;379(9811):165–80.View ArticlePubMedGoogle Scholar
  5. Demir ME, Ercan Z, Karakas EY, Ulas T, Buyukhatipoglu H. Crohnic kidney disease: recurrent acute kidney failure in a patient with Crohn’s disease. N Am J Med Sci. 2014;6(12):648–9.View ArticlePubMedPubMed CentralGoogle Scholar
  6. Collins AJ, Foley RN, Chavers B, Gilbertson D, Herzog C, Ishani A, Johansen K, Kasiske BL, Kutner N, Liu J et al. US Renal Data System 2013 Annual Data Report. Am J Kidney Dis. 2014;63(1 Suppl):A7.View ArticlePubMedGoogle Scholar
  7. Foley RN, Collins AJ. The USRDS: what you need to know about what it can and can’t tell us about ESRD. Clin J Am Soc Nephrol. 2013;8(5):845–51.View ArticlePubMedGoogle Scholar
  8. Collins AJ, Foley RN, Gilbertson DT, Chen SC. United States Renal Data System public health surveillance of chronic kidney disease and end-stage renal disease. Kidney Int Suppl (2011). 2015;5(1):2–7.View ArticleGoogle Scholar
  9. Younis M, Jabr S, Al-Khatib A, Forgione D, Hartmann M, Kisa A. A cost analysis of kidney replacement therapy options in Palestine. Inquiry. 2015;52. doi: https://doi.org/10.1177/0046958015573494.
  10. Ministry of Health, Palestinian Health Information Center. Health Status, Palestine, 2014. 2015; Available from: http://www.moh.ps/Content/Books/kD3bquHr7jbwK9f6VQJAsLDCuckgEDlCZUFa9ssb62m9Eim2le562D. [Accessed 27 Dec 2015].
  11. Manns BJ, Johnson JA, Taub K, Mortis G, Ghali WA, Donaldson C. Dialysis adequacy and health related quality of life in hemodialysis patients. Asaio J. 2002;48(5):565–9.View ArticlePubMedGoogle Scholar
  12. Saffari M, Pakpour AH, Naderi MK, Koenig HG, Baldacchino DR, Piper CN. Spiritual coping, religiosity and quality of life: a study on Muslim patients undergoing haemodialysis. Nephrology (Carlton). 2013;18(4):269–75.View ArticleGoogle Scholar
  13. Sakthong P, Kasemsup V. Health utility measured with EQ-5D in Thai patients undergoing peritoneal dialysis. Value Health. 2012;15(1 Suppl):S79–84.View ArticlePubMedGoogle Scholar
  14. Yang F, Lau T, Lee E, Vathsala A, Chia KS, Luo N. Comparison of the preference-based EQ-5D-5L and SF-6D in patients with end-stage renal disease (ESRD). Eur J Health Econ. 2015;16(9):1019–26.View ArticlePubMedGoogle Scholar
  15. Ahlstrom A, Tallgren M, Peltonen S, Rasanen P, Pettila V. Survival and quality of life of patients requiring acute renal replacement therapy. Intensive Care Med. 2005;31(9):1222–8.View ArticlePubMedGoogle Scholar
  16. Faridah A, Jamaiyah H, Goh A, Soraya A. The validation of the EQ-5D in Malaysian dialysis patients. Med J Malaysia. 2010;65 Suppl A:114–9.PubMedGoogle Scholar
  17. Kang GW, Lee IH, Ahn KS, Lee J, Ji Y, Woo J. Clinical and psychosocial factors predicting health-related quality of life in hemodialysis patients. Hemodial Int. 2015;19(3):439–46.View ArticlePubMedGoogle Scholar
  18. Katayama A, Miyatake N, Nishi H, Uzike K, Sakano N, Hashimoto H, Koumoto K. Evaluation of physical activity and its relationship to health-related quality of life in patients on chronic hemodialysis. Environ Health Prev Med. 2014;19(3):220–5.View ArticlePubMedPubMed CentralGoogle Scholar
  19. Okubo R, Kai H, Kondo M, Saito C, Yoh K, Morito N, Usui J, Yamagata K. Health-related quality of life and prognosis in patients with chronic kidney disease: a 3-year follow-up study. Clin Exp Nephrol. 2014;18(5):697–703.View ArticlePubMedGoogle Scholar
  20. Yang F, Griva K, Lau T, Vathsala A, Lee E, Ng HJ,Mooppil N, Foo M, Newman SP, Chia KS et al. Health-related quality of life of Asian patients with end-stage renal disease (ESRD) in Singapore. Qual Life Res. 2015;24(9):2163–71.View ArticlePubMedGoogle Scholar
  21. Walters BA, Hays RD, Spritzer KL, Fridman M, Carter WB. Health-related quality of life, depressive symptoms, anemia, and malnutrition at hemodialysis initiation. Am J Kidney Dis. 2002;40(6):1185–94.View ArticlePubMedGoogle Scholar
  22. Pakpour AH, Saffari M, Yekaninejad MS, Panahi D, Harrison AP, Molsted S. Health-related quality of life in a sample of Iranian patients on hemodialysis. Iran J Kidney Dis. 2010;4(1):50–9.PubMedGoogle Scholar
  23. Al Wakeel J, Al Harbi A, Bayoumi M, Al-Suwaida K, Al Ghonaim M, Mishkiry A. Quality of life in hemodialysis and peritoneal dialysis patients in Saudi Arabia. Ann Saudi Med. 2012;32(6):570–4.PubMedGoogle Scholar
  24. Ayoub AM, Hijjazi KH. Quality of life in dialysis patients from the United Arab Emirates. J Family Community Med. 2013;20(2):106–12.View ArticlePubMedPubMed CentralGoogle Scholar
  25. Kimmel PL, Patel SS. Quality of life in patients with chronic kidney disease: focus on end-stage renal disease treated with hemodialysis. Semin Nephrol. 2006;26(1):68–79.View ArticlePubMedGoogle Scholar
  26. Cukor D, Ver Halen N, Asher DR, Coplan JD, Weedon J, Wyka KE, Saggi SJ, Kimmel PL. Psychosocial intervention improves depression, quality of life, and fluid adherence in hemodialysis. J Am Soc Nephrol. 2014;25(1):196–206.View ArticlePubMedPubMed CentralGoogle Scholar
  27. Turk S, Atalay H, Altintepe L, Guney I, Okudan N, Tonbul HZ, Gokbel H, Kucur R, Yeksan M, Yildiz A. Treatment with antidepressive drugs improved quality of life in chronic hemodialysis patients. Clin Nephrol. 2006;65(2):113–8.View ArticlePubMedGoogle Scholar
  28. Hicks LS, Cleary PD, Epstein AM, Ayanian JZ. Differences in health-related quality of life and treatment preferences among black and white patients with end-stage renal disease. Qual Life Res. 2004;13(6):1129–37.View ArticlePubMedGoogle Scholar
  29. Ramos EC, Santos ID, Zanini RV, Ramos JM. Quality of life of chronic renal patients in peritoneal dialysis and hemodialysis. J Bras Nefrol. 2015;37(3):297–305.View ArticlePubMedGoogle Scholar
  30. Saad MM, El Douaihy Y, Boumitri C, Rondla C, Moussaly E, Daoud M, El Sayegh SE. Predictors of quality of life in patients with end-stage renal disease on hemodialysis. Int J Nephrol Renovasc Dis. 2015;8:119–23.View ArticlePubMedPubMed CentralGoogle Scholar
  31. Fukuhara S, Yamazaki S, Hayashino Y, Green J. Measuring health-related quality of life in patients with end-stage renal disease: why and how. Nat Clin Pract Nephrol. 2007;3(7):352–3.View ArticlePubMedGoogle Scholar
  32. Wan EY, Chen JY, Choi EP, Wong CK, Chan AK, Chan KH, Lam CL. Patterns of health-related quality of life and associated factors in Chinese patients undergoing haemodialysis. Health Qual Life Outcomes. 2015;13:108.View ArticlePubMedPubMed CentralGoogle Scholar
  33. Dashti-Khavidaki S, Sharif Z, Khalili H, Badri S, Alimadadi A, Ahmadi F, Gatmiri M, Rahimzadeh S. The use of pharmaceutical care to improve health-related quality of life in hemodialysis patients in Iran. Int J Clin Pharm. 2013;35(2):260–7.View ArticlePubMedGoogle Scholar
  34. Preljevic VT, Osthus TB, Os I, Sandvik L, Opjordsmoen S, Nordhus IH, Dammen T. Anxiety and depressive disorders in dialysis patients: association to health-related quality of life and mortality. Gen Hosp Psychiatry. 2013;35(6):619–24.Google Scholar
  35. Makkar V, Kumar M, Mahajan R, Khaira NS. Comparison of Outcomes and Quality of Life between Hemodialysis and Peritoneal Dialysis Patients in Indian ESRD Population. J Clin Diagn Res. 2015;9(3):OC28–31.PubMedPubMed CentralGoogle Scholar
  36. Al‐Jabi SW, Zyoud SeH, Sweileh WM, Wildali AH, Saleem HM, Aysa HA, Badwan MA, Awang R. Relationship of treatment satisfaction to health‐related quality of life: findings from a cross‐sectional survey among hypertensive patients in Palestine. Health Expect. 2014. doi:https://doi.org/10.1111/hex.12324.
  37. Zyoud SH, Al-Jabi SW, Sweileh WM, Arandi DA, Dabeek SA, Esawi HH, Atyeh R, Abu–Ali HA, Sleet YI, Abd-Alfatah BM. Relationship of treatment satisfaction to health-related quality of life among Palestinian patients with type 2 diabetes mellitus: Findings from a cross-sectional study. J Clin Transl Endocrinol. 2015;2(2):66–71.Google Scholar
  38. Ministry of Health, Palestinian Health Information Center. Health Status, Palestine, 2013. 2014; Available from: http://www.moh.ps/Content/Books/YPsrDDHKyEaijhagiZaKXPOqLyttvfLvDBLhwMf5HkcxcpnSirQQWr. [Accessed 20 June 2014].
  39. Raosoft. Sample Size Calculator. Available from: http://www.raosoft.com/samplesize.html. [Accessed 20 April 2014]
  40. Horowitz E, Abadi-Korek I, Shani M, Shemer J. EQ-5D as a generic measure of health-related quality of life in Israel: reliability, validity and responsiveness. Isr Med Assoc J. 2010;12(12):715–20.PubMedGoogle Scholar
  41. Clair C, Chiolero A, Faeh D, Cornuz J, Marques-Vidal P, Paccaud F, Mooser V, Waeber G, Vollenweider P. Dose-dependent positive association between cigarette smoking, abdominal obesity and body fat: cross-sectional data from a population-based survey. BMC Public Health. 2011;11:23.View ArticlePubMedPubMed CentralGoogle Scholar
  42. Schmidt M, Johannesdottir SA, Lemeshow S, Lash TL, Ulrichsen SP, Botker HE, Sorensen HT. Obesity in young men, and individual and combined risks of type 2 diabetes, cardiovascular morbidity and death before 55 years of age: a Danish 33-year follow-up study. BMJ Open. 2013;3(4). doi: https://doi.org/10.1136/bmjopen-2013-002698.
  43. Al-Jabi SW, Zyoud SH, Sweileh WM, Wildali AH, Saleem HM, Aysa HA, Badwan MA, Awang R. Assessment of health-related quality of life among hypertensive patients: a cross-sectional study from Palestine. J Public Health. 2014;22(3):277–86.View ArticleGoogle Scholar
  44. Euro QOL Group. EQ-5D-5L User Guide Basic information on how to use the EQ-5D-5L instrument. 2015; Available from: http://www.euroqol.org/fileadmin/user_upload/Documenten/PDF/Folders_Flyers/EQ-5D-5L_UserGuide_2015.pdf. [Accessed 1 April 2015 ].
  45. EuroQol Group. EQ-5D-5L Crosswalk Index Value Calculator. 2015; Available from: http://www.euroqol.org/fileadmin/user_upload/Documenten/Excel/Crosswalk_5L/EQ-5D-5L_Crosswalk_Index_Value_Calculator.v2.xls. [Accessed 15 March 2015].
  46. Zyoud SH, Al-Jabi SW, Sweileh WM, Wildali AH, Saleem HM, Aysa HA, Badwan MA, Awang R, Morisky DE. Health-related quality of life associated with treatment adherence in patients with hypertension: a cross-sectional study. Int J Cardiol 2013;168(3):2981-3.Google Scholar
  47. Jia H, Lubetkin EI. Estimating EuroQol EQ-5D scores from Population Healthy Days data. Med Decis Making. 2008;28(4):491–9.View ArticlePubMedGoogle Scholar
  48. Sullivan PW, Ghushchyan V. Preference-Based EQ-5D index scores for chronic conditions in the United States. Med Decis Making. 2006;26(4):410–20.View ArticlePubMedPubMed CentralGoogle Scholar
  49. Wasserfallen JB, Halabi G, Saudan P, Perneger T, Feldman HI, Martin PY, Wauters JP. Quality of life on chronic dialysis: comparison between haemodialysis and peritoneal dialysis. Nephrol Dial Transplant. 2004;19(6):1594–9.View ArticlePubMedGoogle Scholar
  50. Javanbakht M, Abolhasani F, Mashayekhi A, Baradaran HR, Jahangiri noudeh Y. Health related quality of life in patients with type 2 diabetes mellitus in Iran: a national survey. PLoS One. 2012;7(8):e44526.View ArticlePubMedPubMed CentralGoogle Scholar
  51. Hauser K, Walsh D. Visual analogue scales and assessment of quality of life in cancer. J Support Oncol. 2008;6(6):277–82.PubMedGoogle Scholar
  52. Cleemput I, Kesteloot K, Moons P, Vanrenterghem Y, Van Hooff JP, Squifflet JP, De Geest S. The construct and concurrent validity of the EQ-5D in a renal transplant population. Value Health. 2004;7(4):499–509.View ArticlePubMedGoogle Scholar
  53. Merom D, Sinnreich R, Aboudi V, Kark JD, Nassar H. Lifestyle physical activity among urban Palestinians and Israelis: a cross-sectional comparison in the Palestinian-Israeli Jerusalem risk factor study. BMC Public Health. 2012;12:90.View ArticlePubMedPubMed CentralGoogle Scholar
  54. Lopes GB, Matos CM, Leite EB, Martins MT, Martins MS, Silva LF, Robinson BM, Port FK, James SA, Lopes AA. Depression as a potential explanation for gender differences in health-related quality of life among patients on maintenance hemodialysis. Nephron Clin Pract. 2010;115(1):c35–40.View ArticlePubMedGoogle Scholar
  55. Arenas MD, Alvarez-Ude F, Reig-Ferrer A, Zito JP, Gil MT, Carreton MA, Albiach B, Moledous A. Emotional distress and health-related quality of life in patients on hemodialysis: the clinical value of COOP-WONCA charts. J Nephrol. 2007;20(3):304–10.PubMedGoogle Scholar
  56. Drayer RA, Piraino B, Reynolds 3rd CF, Houck PR, Mazumdar S, Bernardini J, Shear MK, Rollman BL. Characteristics of depression in hemodialysis patients: symptoms, quality of life and mortality risk. Gen Hosp Psychiatry. 2006;28(4):306–12.View ArticlePubMedGoogle Scholar
  57. Hemati Z, Alidosti M, Sharifirad G, Kargar M. The relationship between depression and quality of life among hemodialysis patients in Chaharmahal and Bakhtiari province in the year 2011. J Educ Health Promot. 2013;2:6.View ArticlePubMedPubMed CentralGoogle Scholar
  58. Garcia-Llana H, Remor E, Selgas R. Adherence to treatment, emotional state and quality of life in patients with end-stage renal disease undergoing dialysis. Psicothema. 2013;25(1):79–86.PubMedGoogle Scholar
  59. Bossola M, Giungi S, Luciani G, Tazza L. Body mass index, comorbid conditions and quality of life in hemodialysis patients. J Nephrol. 2009;22(4):508–14.PubMedGoogle Scholar
  60. Feroze U, Noori N, Kovesdy CP, Molnar MZ, Martin DJ, Reina-Patton A, Benner D, Bross R, Norris KC, Kopple JD et al. Quality-of-life and mortality in hemodialysis patients: roles of race and nutritional status. Clin J Am Soc Nephrol. 2011;6(5):1100–11.Google Scholar
  61. Dwyer JT, Larive B, Leung J, Rocco M, Burrowes JD, Chumlea WC, Frydrych A, Kusek JW, Uhlin L, Hemodialysis Study G. Nutritional status affects quality of life in Hemodialysis (HEMO) Study patients at baseline. J Ren Nutr. 2002;12(4):213–23.Google Scholar
  62. Kao TW, Lai MS, Tsai TJ, Jan CF, Chie WC, Chen WY. Economic, social, and psychological factors associated with health-related quality of life of chronic hemodialysis patients in northern Taiwan: a multicenter study. Artif Organs. 2009;33(1):61–8.View ArticlePubMedGoogle Scholar
  63. Lopes AA, Bragg-Gresham JL, Goodkin DA, Fukuhara S, Mapes DL, Young EW, Young EW, Gillespie BW, Akizawa T, Greenwood RN, Andreucci VE et al. Factors associated with health-related quality of life among hemodialysis patients in the DOPPS. Qual Life Res. 2007;16(4):545–57.Google Scholar
  64. Manns B, Johnson JA, Taub K, Mortis G, Ghali WA, Donaldson C. Quality of life in patients treated with hemodialysis or peritoneal dialysis: what are the important determinants? Clin Nephrol. 2003;60(5):341–51.View ArticlePubMedGoogle Scholar
  65. Sabbah I, Drouby N, Sabbah S, Retel-Rude N, Mercier M. Quality of life in rural and urban populations in Lebanon using SF-36 health survey. Health Qual Life Outcomes. 2003;1:30.View ArticlePubMedPubMed CentralGoogle Scholar
  66. Eljedi A, Mikolajczyk RT, Kraemer A, Laaser U. Health-related quality of life in diabetic patients and controls without diabetes in refugee camps in the Gaza strip: a cross-sectional study. BMC Public Health. 2006;6:268.View ArticlePubMedPubMed CentralGoogle Scholar
  67. Bakewell AB, Higgins RM, Edmunds ME. Quality of life in peritoneal dialysis patients: decline over time and association with clinical outcomes. Kidney Int. 2002;61(1):239–48.View ArticlePubMedGoogle Scholar
  68. Chiu YW, Teitelbaum I, Misra M, de Leon EM, Adzize T, Mehrotra R. Pill burden, adherence, hyperphosphatemia, and quality of life in maintenance dialysis patients. Clin J Am Soc Nephrol. 2009;4(6):1089–96.View ArticlePubMedPubMed CentralGoogle Scholar

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© Zyoud et al. 2016

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