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  • Research article
  • Open Access
  • Open Peer Review

Prevalence and predictors of depression among hemodialysis patients: a prospective follow-up study

BMC Public Health201919:531

https://doi.org/10.1186/s12889-019-6796-z

  • Received: 1 September 2018
  • Accepted: 10 April 2019
  • Published:
Open Peer Review reports

Abstract

Background

Even though depression is one of the most common psychiatric disorders, it is under-recognized in hemodialysis (HD) patients. Existing literature does not provide enough information on evaluation of predictors of depression among HD patients. The objective of the current study was to determine the prevalence and predictors of depression among HD patients.

Methods

A multicenter prospective follow-up study. All eligible confirmed hypertensive HD patients who were consecutively enrolled for treatment at the study sites were included in the current study. HADS questionnaire was used to assess the depression level among study participants. Patients with physical and/or cognitive limitations that prevent them from being able to answer questions were excluded.

Results

Two hundred twenty patients were judged eligible and completed questionnaire at the baseline visit. Subsequently, 216 and 213 patients completed questionnaire on second and final follow up respectively. The prevalence of depression among patients at baseline, 2nd visit and final visit was 71.3, 78.2 and 84.9% respectively. The results of regression analysis showed that treatment given to patients at non-governmental organizations (NGO’s) running HD centers (OR = 0.347, p-value = 0.039) had statistically significant association with prevalence of depression at final visit.

Conclusions

Depression was prevalent in the current study participants. Negative association observed between depression and hemodialysis therapy at NGO’s running centers signifies patients’ satisfaction and better depression management practices at these centers.

Keywords

  • Depression
  • HADS
  • Hemodialysis
  • Hypertension

Background

According to the guidelines of the World Health Organization (WHO), “depression is a common mental disorder, characterized by sadness, loss of interest or pleasure, feelings of guilt or low self-worth, disturbed sleep or appetite, feelings of tiredness and poor concentration” [1]. Among end stage renal disease (ESRD) patient’s depression is one of the most common psychiatric disorders [2]. The prevalence of depression is known to be much higher in HD patients as compared to other individuals of normal population [3]. Like in other chronic disease conditions and in general population, evidence does exist that depression in patients on hemodialysis is associated with mortality [4, 5]. It is under-recognized in HD patients because healthcare providers giving facilities, treatment and routinely work with these patients cannot give attention to control depression due to the nature of their illness [2]. There is a need of regular implementation of screening of depression among this population. Depression and anxiety both are strongly associated with patient’s quality of life (QOL). One study suggests that depression among divorced and widowed women strongly affected patient’s QOL [6].

Different questionnaires have been compiled and tested to investigate and measure the problems of ‘anxiety and depression’ commonly found in ESRD patients, including to those of “Hospital Anxiety and Depression Scale (HADS)” and “Beck’s Depression Inventory (BDI)”, both being properly validated in chronic kidney disease (CKD) patients [7]. The former questionnaire (HADS) was developed with the objective to investigate anxiety and depression associated fresh cases in an adult population. The later (HADS) one is different than the former one as it was developed to address the symptomatic position with respect to anxiety and depression. It is known that HD patients have higher rates of depression prevalence in contrast to the PD patients. The possible reasons are because HD patients usually stay connected with the machine during dialysis procedure which directly affects their daily activities and independence. It has also been reported that among the HD patients suicide rates are much higher. Moreover, it is found that due to the give flexibility and because of limited restrictions in their diet and social activities PD patients live with better quality of life [810].

To identify the factors associated with depression and anxiety, there is need of to conduct appropriate longitudinal studies. The instant research work was carried out to determine the contributing action of such factors in causing depression among HD population. Moreover, the expected outcomes of this study will give a comparative information on better management practices of depression at different dialysis facilities.

Methods

HADS questionnaire

HADS has been used for numerous studies among HD patients and showed acceptable reliability and validity [7]. Zigmond and Snaith are the original developers of HADS [11]. This scale cannot be used as a clinical diagnostic tool [12]. HADS has many advantages in terms of its interpretability (the results are very easy to interpret), in terms of its acceptability (widely accepted and can be used in a number of populations), in terms of its feasibility (the patients can complete the questionnaire within few minutes, no need of specialized training as the patients themselves can easily completed the questionnaire).

In the current study, we used the official validated Malay version of HADS provided by the original authors of the published Malay version of HADS from the department of psychiatry, Hospital Universiti Sains Malaysia (HUSM) [13].

Administration of the HADS

The total time required to complete the questionnaire is 2–5 min. Some cautions should be taken into consideration, for instance, the patients should be literate to read it. It can be a reasonable practice for the administrators of the HADS to ask patients first to read it once loudly and then fill it accordingly. HADS is comprised of 14 questions and have two domains: Anxiety (7 items) and depression (7 items). For Anxiety (HADS-A) this gave a specificity and sensitivity of 0.78 and 0.9 respectively. For depression (HADS-D) it gave a specificity and sensitivity of 0.79 and 0.83 respectively [14].

Study design and setting

This was a prospective follow-up study among HD patients conducted at HUSM and its affiliated dialysis centers. All eligible (> 18 years of age, literate and able to understand Malay) confirmed hypertensive HD patients who were consecutively enrolled for treatment at the study sites from 1st April 2017 to 31st December 2017 were included in the study. Patients with physical and/or cognitive limitations that prevent them from being able to answer questions were excluded.

Data collection

During the study period, all eligible HD patients who agreed to participate in the study by giving a written consent were asked to self-complete HADS questionnaire at three-time points: i) at baseline visit (initial evaluation), ii) after 3 months’ interval (second follow up) and iii) at 6 months’ interval (third follow up). Enrolled subjects who were unable to participate at the second follow up were not asked to take the questionnaire on third follow up. Using a standardized data collection form, socio-demographic and clinical data were collected from the regularly updated Advanced Dialysis Nephrology Application Network (ADNAN) at study sites. Height, weight and blood pressure were measured during a physical examination. Patient’s interview and data abstraction tool designed by principal investigator based on an input from advisory committee, extensive literature review, hypothetical possible association and nephrologist’s suggestions. At each interview session, the data collector evaluated the questionnaire for completion and asked the subject to provide missing response unless it was intentionally left unchecked.

Scoring

Grading on HADS questionnaire score sheet was used for scoring of questionnaires. Each question has 4 options; i) yes definitely (3), ii) yes sometimes (2), iii) No, not much (1), iv) No, not at all (0). For items 7 & 10 the scoring is reversed. Scores ranging on HADS from 0 to 7 are considered as non-case, score ranging from 8 to 10 is considered as borderline case and a score of > 11 points were considered as case according to grading system of HADS. For the sake of analysis, we excluded borderline cases and only considered cases and non-cases.

Statistical analysis

Statistical Package for Social Sciences (SPSS 21) was used for data analysis. Means and standard deviations were calculated for continuous variables, whereas categorical variable are presented as frequencies and percentages. Chi-squared test was used to observe significance between categorical variables. Multivariate logistic regression analysis with the Wald statistical criteria was used to obtain a final model. A p-value of < 0.05 was considered statistically significant. Relevant variables with a p-value < 0.25 in the univariate analysis were included in the multivariate analysis [15]. We confirmed the correlations among variables entered in the multivariate analysis. The results of multivariate analysis were presented as beta, standard error, P-value, adjusted odds ratio and 95% confidence interval. The fit of the model was assessed by Hosmer Lemeshow and overall classification percentage.

Results

During the study recruitment period, a total of 272 HD patients were enrolled for the treatment at the study sites. Fifty-two patients did not meet the eligibility criteria and were excluded. 220 patients were judged eligible and completed questionnaire at the baseline visit. Subsequently, 216 and 213 patients completed questionnaire on second and final follow up respectively (Fig. 1).
Fig. 1
Fig. 1

Flow diagram of patient screened, included and evaluated for depression level

Socio-demographic characteristics of patients evaluated for depression level

The mean patient age was 56.58 ± 11.09 years. The majority of the patients were male (55.5%), 41–60 years old (59.1%), of a normal BMI (67.3%), on dialysis for more than 5 years (36.4%) and suffering from hypertension (91.8%) “Table 1”.
Table 1

Sociodemographics and clinical characteristics of patients (N = 220)

Variables

No. (%)

Gender

 Female

98 (45.5)

 Male

122 (55.5)

Age mean (±SD)

56.58 (± 11.09)

Age group (years)

  < 40

17 (7.7)

 41–60

130 (59.1)

  > 60

73 (33.2)

BMI mean (±SD)

23.57 (± 4.31)

BMI Classification

 Underweight

12 (5.5)

 Normal

148 (67.3)

 Overweight

46 (20.9)

 Obese

14 (6.4)

Socioeconomic Status

 Low

39 (17.7)

 Middle

155 (70.5)

 High

26 (11.8)

Education Level

 Uneducated

74 (33.6)

 Educated

146 (66.4)

Marital Status

 Single

18 (8.2)

 Married

202 (91.8)

Race

 Malay

212 (96.4)

 Others

8 (3.6)

Smoking Status

 Current Smoker

73 (33.2)

 Non-Smoker

147 (66.8)

Alcohol

 Current drinker

18 (8.2)

 Non-drinker

202 (91.8)

Drug Addiction

 Current Drug Addiction

35 (15.9)

 No Drug Addiction

185 (84.1)

Employment

 Unemployed

120 (54.5)

 Employed

100 (45.5)

Dialysis Years

 1 year

62 (28.2)

 2–4 years

78 (35.5)

  > 5 years

80 (36.4)

Hemodialysis Centers

 Private

129 (58.6)

 NGO

33 (15)

 Governmental

58 (26.4)

Vascular access

 Fistula

204 (92.7)

 Others

16 (7.3)

Hypertension

 No

18 (8.2)

 Yes

202 (91.8)

Diabetes Mellitus

 No

81 (36.8)

 Yes

139 (63.2)

Cardiovascular Diseases

 No

185 (84.1)

 Yes

35 (15.9)

Other Comorbidities including: Blood clots, depression, asthma, osteoarthritis, pregnancy losses/birth defects and osteoporosis. Low socioeconomic status (≤ RM 2300 or 531 USD), Middle socioeconomic status (RM 2301–5600 or 531–1294 USD) and High socioeconomic status (> RM 5600 or 1294 USD)

SD Standard deviation, BMI Body Mass Index

Predictors of prevalence of depression among hemodialysis patients at baseline visit

Table 2 shows that patients gender (OR = 0.690, p-value = 0.224), socioeconomic status (high) (OR = 0.500, p-value = 0.182), patients receiving treatment at NGO running HD centers (OR = 0.508, p-value = 0.105), patients receiving treatment at governmental HD centers (OR = 0.475, p-value = 0.030) and multitherapy (OR = 0.659, p-value = 0.164) are the variables with p-value < 0.25 and will be included in the multivariate analysis.
Table 2

Predictors of prevalence of depression among hemodialysis patients at baseline visit (n = 220)

Variables

Prevalence of Depression (No. %)

Univariate analysis OR (95% CI)

P-value

Multivariate analysis OR (95% CI)

P-value

No

Yes

Gender

 Female

23 (23.4)

75 (76.6)

Referent

 

Referent

 

 Male

40 (32.8)

82 (67.2)

0.690 (0.380–1.254)

0.224

0.742 (0.399–1.383)

0.348

Age (years)

  < 40

4 (23.5)

13 (76.5)

Referent

   

 41–60

40 (30.8)

90 (69.2)

0.692 (0.213–2.255)

0.542

  

  > 60

19 (26)

54 (74)

0.874 (0.254–3.012)

0.832

  

BMI

 Underweight

4 (33.3)

8 (66.7)

Referent

   

 Normal

41 (27.7)

107 (72.3)

1.305 (0.373–4.568)

0.677

  

 Overweight

11 (23.9)

35 (76.1)

1.591 (0.401–6.313)

0.509

  

 Obese

7 (50)

7 (50)

0.500 (0.102–2.460)

0.394

  

Socioeconomic Status

 Low

13 (33.3)

26 (66.7)

Referent

 

Referent

 

 Middle

37 (23.9)

118 (76.1)

1.595 (0.745–3.414)

0.230

1.826 (0.816–4.086)

0.143

 High

13 (50)

13 (50)

0.500 (0.181–1.382)

0.182

0.570 (0.194–1.677)

0.307

Marital Status

 Single

4 (22.2)

14 (77.8)

Referent

   

 Married

59 (29.2)

143 (70.8)

0.692 (0.219–2.191)

0.532

  

Race

 Malay

62 (29.2)

150 (70.8)

Referent

   

 Others

1 (12.5)

7 (87.5)

2.893 (0.349–24.011)

0.325

  

Smoking status

 Current Smoker

21 (28.8)

52 (71.2)

Referent

   

 Non-Smoker

42 (28.6)

105 (71.4)

1.010 (0.543–1.877)

0.976

  

Alcohol

 Current drinker

6 (33.3)

12 (66.7)

Referent

   

 Non-drinker

57 (28.2)

145 (71.8)

1.272 (0.456–3.551)

0.646

  

Drug Addiction

 Current Drug Addiction

8 (22.9)

27 (77.1)

Referent

   

 No Drug Addiction

55 (29.7)

130 (70.3)

0.700 (0.299–1.638)

0.411

  

Employment

 Unemployed

32 (26.7)

88 (73.3)

Referent

   

 Employed

31 (31)

69 (69)

0.809 (0.451–1.454)

0.479

  

Dialysis Years

 1 year

23 (37.1)

39 (62.9)

Referent

   

 2–4 years

21 (26.9)

57 (73.1)

1.601 (0.781–3.283)

0.299

  

  > 5 years

19 (23.8)

61 (76.3)

1.893 (0.914–3.923)

0.686

  

Hemodialysis Centers

 Private

29 (22.5)

100 (77.5)

Referent

 

Referent

 

 NGO

12 (36.4)

21 (63.6)

0.508 (0.223–1.153)

0.105

0.413 (0.173–0.985)

0.046

 Governmental

22 (37.9)

36 (62.1)

0.475 (0.242–0.930)

0.030

0.522 (0.248–1.100)

0.087

Vascular access

 Fistula

59 (28.9)

145 (71.1)

Referent

   

 Others

4 (25)

12 (75)

1.221 (0.378–3.938)

0.739

  

Diabetes Mellitus

 No

23 (28.4)

58 (71.6)

Referent

0.952

  

 Yes

40 (28.8)

99 (71.2)

0.981 (0.535–1.800)

   

Cardiovascular Diseases

 No

55 (29.7)

130 (70.3)

Referent

   

 Yes

8 (22.9)

27 (77.1)

1.428 (0.611–3.339)

0.411

  

Gouty Arthritis

 No

56 (29.3)

135 (70.7)

Referent

   

 Yes

7 (24.1)

22 (75.9)

1.304 (0.527–3.225)

0.566

  

Other Comorbidities

 No

44 (28.2)

112 (71.8)

Referent

   

 Yes

19 (29.7)

45 (70.3)

0.930 (0.491–1.764)

0.825

  

Type Therapy

 Mono-therapy

30 (24.8)

91 (75.2)

Referent

 

Referent

 

 Multi-therapy

33 (33.3)

66 (66.7)

0.659 (0.366–1.186)

0.164

0.553 (0.293–1.043)

0.067

Analysis: Univariate and Multivariate binary logistic regression analysis. All variables with p-value < 0.25 are included in the multivariate analysis

Low socioeconomic status (≤ RM 2300 or 531 USD), Middle socioeconomic status (RM 2301–5600 or 531–1294 USD) and High socioeconomic status (> RM 5600 or 1294 USD)

OR Odds ratio, CI confidence interval, BMI Body mass index, NGO Non-governmental organization

Other comorbidities: Blood clots, depression, asthma, osteoarthritis, pregnancy losses/birth defects and osteoporosis

In Multivariate logistic regression analysis, the only variable which had statistically significant association with prevalence of depression at baseline visit was treatment given to patients at NGO’s running HD centers (OR = 0.413, p-value = 0.046) (Table 2).

Predictors of prevalence of depression among hemodialysis patients at 2nd visit

Table 3 shows that patients’ gender (OR = 0.676, p-value = 0.245), treatment at NGO’s running HD centers (OR = 0.519, p-value = 0.139), Diabetes (OR = 0.646, p-value = 0.219) and multi-therapy (OR = 0.653, p-value = 0.198) are the variables with p-value < 0.25 and will be included in the multivariate analysis.
Table 3

Predictors of prevalence of depression among hemodialysis patients at 2nd visit (n = 216)

Variables

Prevalence of Depression (No. %)

Univariate analysis OR (95% CI)

P-value

Multivariate analysis OR (95% CI)

P-value

No

Yes

Gender

 Female

17 (17.3)

81 (82.7)

Referent

   

 Male

30 (25.4)

88 (74.6)

0.676 (0.351–1.336)

0.245

0.699 (0.357–1.370)

0.297

Age (years)

  < 40

3 (17.6)

14 (82.4)

Referent

   

 41–60

28 (22)

99 (78)

0.758 (0.203–2.824)

0.679

  

  > 60

16 (22.2)

56 (77.8)

0.750 (0.192–2.937)

0.680

  

BMI

 Underweight

4 (33.3)

8 (66.7)

Referent

   

 Normal

28 (19.2)

118 (80.8)

2.107 (0.592–7.496)

0.250

  

 Overweight

10 (22.2)

35 (77.8)

1.750 (0.436–7.032)

0.430

  

 Obese

5 (38.5)

8 (61.5)

0.800 (0.155–4.123)

0.790

  

Socioeconomic Status

 Low

10 (25.6)

29 (74.4)

Referent

   

 Middle

29 (19.1)

123 (80.9)

1.463 (0.641–3.337)

0.366

  

 High

8 (32)

17 (68)

0.733 (0.243–2.214)

0.582

  

Marital Status

 Single

3 (16.7)

15 (83.3)

Referent

   

 Married

44 (22.2)

154 (77.8)

0.700 (0.194–2.528)

0.586

  

Race

 Malay

47 (22.6)

161 (77.4)

Non-computable

   

 Others

8 (100)

 

  

Smoking status

 Current Smoker

15 (21.1)

56 (78.9)

Referent

   

 Non-Smoker

32 (22.1)

113 (77.9)

0.946 (0.474–1.889)

0.875

  

Alcohol

 Current drinker

4 (23.5)

13 (76.5)

Referent

   

 Non-drinker

43 (21.6)

156 (78.4)

1.116 (0.346–3.598)

0.854

  

Drug Addiction

 Current Drug Addiction

7 (20.6)

27 (79.4)

Referent

   

 No Drug Addiction

40 (22)

142 (78)

0.920 (0.373–2.269)

0.857

  

Employment

 Unemployed

23 (19.7)

94 (80.3)

Referent

   

 Employed

24 (24.2)

75 (75.8)

0.765 (0.400–1.461)

0.417

  

Dialysis Years

 1 year

15 (24.6)

46 (75.4)

Referent

   

 2–4 years

18 (23.7)

58 (76.3)

1.051 (0.478–2.308)

0.902

  

  > 5 years

14 (17.7)

65 (82.3)

1.514 (0.667–3.439)

0.322

  

Hemodialysis Centers

 Private

23 (18.4)

102 (81.6)

Referent

 

Referent

 

 NGO

10 (30.3)

23 (69.7)

0.519 (0.217–1.237)

0.139

0.580 (0.238–1.412)

0.580

 Governmental

14 (24.1)

44 (75.9)

0.709 (0.334–1.504)

0.370

0.646 (0.295–1.417)

0.276

Vascular access

 Fistula

44 (22)

156 (78)

Referent

   

 Others

3 (18.8)

13 (81.3)

1.222 (0.333–4.481)

0.762

  

Diabetes Mellitus

 No

14 (17.3)

67 (82.7)

Referent

 

Referent

 

 Yes

33 (24.4)

102 (75.6)

0.646 (0.322–1.297)

0.219

0.688 (0.335–1.413)

0.309

Cardiovascular Diseases

 No

40 (22)

142 (78)

Referent

   

 Yes

7 (20.6)

27 (79.4)

1.087 (0.441–2.679)

0.857

  

Gouty Arthritis

 No

43 (22.9)

145 (77.1)

Referent

   

 Yes

4 (14.3)

24 (85.7)

1.779 (0.585–5.409)

0.310

  

Other Comorbidities

 No

35 (22.9)

118 (77.1)

Referent

   

 Yes

12 (19)

51 (81)

1.261 (0.605–2.625)

0.536

  

Type Therapy

 Mono-therapy

22 (18.5)

97 (81.5)

Referent

 

Referent

 

 Multi-therapy

25 (25.8)

72 (74.2)

0.653 (0.341–1.250)

0.198

0.628 (0.319–1.237)

0.178

Analysis: Univariate and Multivariate binary logistic regression analysis. All variables with p-value < 0.25 are included in the multivariate analysis

Low socioeconomic status (≤ RM 2300 or 531 USD), Middle socioeconomic status (RM 2301–5600 or 531–1294 USD) and High socioeconomic status (> RM 5600 or 1294 USD)

OR Odds ratio, CI confidence interval, BMI Body mass index, NGO Non-governmental organization

Other comorbidities: Blood clots, depression, asthma, osteoarthritis, pregnancy losses/birth defects and osteoporosis

In multivariate logistic regression analysis, no significant association was found between depression and any sociodemographic and clinical factors (Table 3).

Predictors of prevalence of depression among hemodialysis patients at final visit

Analysis of prevalence of depression at final visit presented in (Table 4) revealed that BMI (normal) (OR = 4.133, p-value = 0.039), BMI (overweight) (OR = 5.333, p-value = 0.037), treatment given at NGO’s running HD centers (OR = 0.334, p-value = 0.030), treatment given at governmental HD centers (OR = 0.485, p-value = 0.105), gouty arthritis (OR = 2.630, p-value = 0.203) are the variables with p-value < 0.25 and will be included in the multivariate analysis.
Table 4

Predictors of prevalence of depression among hemodialysis patients at final visit (n = 213)

Variables

Prevalence of Depression (No. %)

Univariate analysis OR (95% CI)

P-value

Multivariate analysis OR (95% CI)

P-value

No

Yes

Gender

 Female

13 (13.7)

82 (86.3)

Referent

   

 Male

19 (16.1)

99 (83.9)

0.826 (0.385–1.773)

0.624

  

Age (years)

  < 40

2 (11.8)

15 (88.2)

Referent

   

 41–60

20 (15.6)

108 (84.4)

0.720 (0.153–3.394)

0.678

  

  > 60

10 (14.7)

58 (85.3)

0.773 (0.153–3.911)

0.756

  

BMI

 Underweight

4 (40)

6 (60)

Referent

 

Referent

 

 Normal

20 (13.9)

124 (86.1)

4.133 (1.071–15.951)

0.039

3.339 (0.833–13.376)

0.089

 Overweight

5 (11.1)

40 (88.9)

5.333 (1.110–25.636)

0.037

4.205 (0.834–21.187)

0.082

 Obese

< 5

11 (78.6)

2.444 (0.405–14.748)

0.330

1.907 (0.300–12.123)

0.494

Socioeconomic Status

 Low

6 (15.8)

32 (84.2)

Referent

   

 Middle

20 (13.4)

129 (86.6)

1.209 (0.449–3.258)

0.707

  

 High

6 (23.1)

20 (76.9)

0.625 (0.177–2.208)

0.465

  

Marital Status

 Single

2 (11.1)

16 (88.9)

Referent

   

 Married

30 (15.4)

165 (84.6)

0.688 (0.150–3.145)

0.629

  

Race

 Malay

32 (15.6)

173 (84.4)

Non-computable

   

 Others

8 (100)

 

  

Smoking status

 Current Smoker

12 (16.4)

61 (83.6)

Referent

   

 Non-Smoker

20 (14.3)

120 (85.7)

1.180 (0.541–2.573)

0.677

  

Alcohol

 Current drinker

2 (11.1)

16 (88.9)

Referent

   

 Non-drinker

30 (15.4)

165 (84.6)

0.688 (0.150–3.145)

0.629

  

Drug Addiction

 Current Drug Addiction

5 (14.3)

30 (85.7)

Referent

   

 No Drug Addiction

27 (15.2)

151 (84.8)

0.932 (0.332–2.615)

0.894

  

Employment

 Unemployed

17 (14.4)

101 (85.6)

Referent

   

 Employed

15 (15.8)

80 (84.2)

0.898 (0.422–1.908)

0.779

  

Dialysis Years

 1 year

10 (16.9)

49 (83.1)

Referent

   

 2–4 years

13 (17.6)

61 (82.4)

0.958 (0.387–2.370)

0.925

  

  > 5 years

9 (11.3)

71 (88.8)

1.610 (0.610–4.253)

0.337

  

Hemodialysis Centers

 Private

13 (10.4)

112 (89.6)

Referent

 

Referent

 

 NGO

8 (25.8)

23 (74.2)

0.334 (0.124–0.897)

0.030

0.347 (0.127–0.949)

0.039

 Governmental

11 (19.3)

46 (80.7)

0.485 (0.203–1.162)

0.105

0.487 (0.196–1.205)

0.120

Vascular access

 Fistula

29 (14.6)

169 (85.4)

Referent

   

 Others

3 (20)

12 (80)

0.686 (0.182–2.583)

0.578

  

Diabetes Mellitus

 No

9 (11.7)

68 (88.3)

Referent

   

 Yes

23 (16.9)

113 (83.1)

0.650 (0.284–1.487)

0.308

  

Cardiovascular Diseases

 No

29 (16.2)

150 (83.8)

Referent

   

 Yes

3 (8.8)

31 (91.2)

1.998 (0.572–6.973)

0.278

  

Gouty Arthritis

 No

30 (16.3)

154 (83.7)

Referent

 

Referent

 

 Yes

2 (6.9)

27 (93.1)

2.630 (0.594–11.653)

0.203

2.637 (0.577–12.056)

0.211

Other Comorbidities

 No

24 (16)

126 (84)

Referent

   

 Yes

8 (19)

55 (87.3)

1.310 (0.554–3.096)

0.539

  

Type Therapy

 Mono-therapy

16 (13.8)

100 (86.2)

Referent

   

 Multi-therapy

16 (16.5)

81 (83.5)

0.810 (0.382–1.719)

0.583

  

Analysis: Univariate and Multivariate binary logistic regression analysis. All variables with p-value < 0.25 are included in the multivariate analysis

Low socioeconomic status (≤ RM 2300 or 531 USD), Middle socioeconomic status (RM 2301–5600 or 531–1294 USD) and High socioeconomic status (> RM 5600 or 1294 USD)

OR Odds ratio, CI confidence interval, BMI Body mass index, NGO Non-governmental organization

Other comorbidities: Blood clots, depression, asthma, osteoarthritis, pregnancy losses/birth defects and osteoporosis

Table 4 shows that in multivariate logistic regression analysis, treatment given to patients at NGO’s running HD centers (OR = 0.347, p-value = 0.039) had statistically significant association with prevalence of depression at final visit.

Discussion

To the best of our knowledge, this is the first follow up study which evaluated the prevalence and factors associated with depression among HD patients in Malaysia. In the current study, 220 eligible patients filled the HADS questionnaire on baseline and 213 filled it at the end of the study.

In our study 157 (71.3%) patients suffered from depression at baseline, 169 (78.2%) on 2nd evaluation and 181 (84.9%) on the final visit respectively. However, the previously published literature has reported a comparatively low rate of depression among HD patients, ranging from 23.3 to 60.5% [2, 1625].

In our study the rate of depression worsened with the passage of time, a linear increase was found from baseline (71.3%) to final visit (84.9%) among patients. The possible reasons for this finding could be the lifelong dialysis therapy with at least 3 dialysis procedures per week, patients taking too much medicine at once, economic burden on patients and their families and altered family and social relationships. Similar findings were reported in various studies where depression was noted to increase from baseline towards the end of the study period [18, 26, 27]. Keskin et al. revealed that depression is a risk factor for suicidal ideation and the chances of suicide attempts increasing with the severity of depression. Therefore, HD patients should be under regular psychiatric evaluation and all risk factors should be properly evaluated [28]. Depressive symptoms were linearly increasing in a population of chronic HD patients and there was a significant association of poor sleep quality, unemployment, pruritus, hypoalbuminemia and diabetes with depressive symptoms. Women were at increased risk of depression [29].

There is a wealth of evidence that dialysis has negative impact on depression and the severe depression among patients is in turn associated with mortality among these patients. Fifteen large scales studies indicating the significant association of depression with mortality among dialysis patients [30]. Significantly higher mortality risks were observed with depressive symptoms in patients on dialysis therapy in various longitudinal studies that assessed the repeated measurement of depression [3133]. Studies indicated that depression is associated with initiation of early dialysis treatment [34, 35]. Other studies found relationship of depression with immune and inflammatory responses [36, 37]. Previous studies revealed that poor nutrition and nonadherence to treatment is significantly linked with depression among HD patients [38, 39]. The findings of one other systematic review showed 2-fold risk of dying in patients with depression [40]. Additionally, age is also a risk factor of increased mortality in depressive patients. Findings of another study indicated that in depressive patients with age of 65 years or above, there is 41% higher risk of mortality [41]. Depression is common and serious psychiatric disorder but underrecognized in patients undergoing dialysis therapy. It is reported elsewhere that only one-third of the HD patients with a diagnosis of depression were receiving treatment [42, 43]. Only few observational studies and clinical trials demonstrated the outcomes with pharmacologic and non-pharmacologic therapies in depressive patients [4448]. Two systematic reviews of antidepressants use in treatment of depression among chronic renal failure patients concluded that the evidence for effectiveness of these drugs is insufficient [49, 50].

In our study, comparable rates of depression were observed among female (86.3%) and male participants (83.9%). In contrast to our finding of no significant association between rate of depression among male and female patients, a study conducted in the University of Michigan, female gender was a significant risk factor for depression [51]. Similar positive association between female gender and high rate of depression among HD patients have been reported elsewhere [52, 53]. On the other hand, in line with our finding, no significant differences were observed in prevalence of depression and life event variables among males and females study participants in a study conducted in Turkey [54]. In our study 86.6% patients with middle socioeconomic status were having depression. In a study conducted elsewhere, an inverse relation was observed between depression and socioeconomic status [55]. Similarly, in another study, poor quality of life and depression was reported in study participants with middle and low socioeconomic status [56]. In another study where authors were interested to determine the association between socioeconomic status and depression among community residents and psychiatric patients, the authors concluded that study subjects holding jobs were more likely to have depression as compared to jobless participants [57].

Of the total 195 married patients, 165 (84.6%) were having depression in the current study. In contradiction to our study findings authors reported that depression was less common in married people which were undergoing dialysis therapy while divorced/widowed patients were at higher risk of depression [52]. Similar results were reported from a study in Taiwan where the status of marriage in HD patients was significantly associated with better quality of life [58]. On the other hand, Kimmel and colleagues reported that rate of depression is higher among divorced and widowed women and depression is associated with patient’s poor quality of life [6]. Supportive and peaceful family environment, happy married life and family support is associated with depression free and better quality of life in chronic HD patients [24]. These findings are in contradiction to the findings of the current study.

Out of the total 140 non-smokers in our study, 85.7% patients were having depression. This is in contradiction to the study findings where authors reported that more than half of the current smokers undergoing dialysis therapy were having depression [59]. Beside in dialysis patients, many epidemiological studies have shown that reciprocal relationship exists between smoking and depression [6062]. In some studies, it has been reported that health related quality of life (HRQoL) was not improved in patients by smoking cessation [63] and depressed smokers have very less chances to quit smoking [4466]. Hence, Smoking should be discouraged among HD patients to improve quality of life and to prevent cardiovascular events.

In our study in multivariate logistic regression analysis, treatment given to patients at NGO’s running HD centers (OR = 0.347, p-value = 0.039) had statistically significant negative association with prevalence of depression at final visit. Dalrymple et al. found that overall hospitalization rates of HD patients were remarkably higher (15% higher) for those patients which were receiving treatment in for-profit HD facilities as compared with nonprofit dialysis centers [67]. In Malaysia, the government is the main source of funding for new and existing patients on dialysis. There are 3 different sectors i.e. government, NGO’s and private dialysis centers that are providing dialysis treatment to patients in Malaysia. These funds provided by government are not only allocated for government dialysis facilities but also for NGOs running centers, for public pensioners, civil servants and their family members who are undergoing dialysis therapy in private dialysis facilities. Self-funding for dialysis treatment had dropped remarkably from 26% in 2006 to 17% in 2015. Increase in funding from NGO bodies from 12% in 2006 to 15% in 2015 was reported [68]. It is reported that in economically advanced states of Malaysia, patients were taking dialysis treatment in NGOs running centers and in private dialysis centers and the survival rates and quality of life of HD patients were better as compared to public dialysis centers. On the other hand, in states like Sabah, Sarawak, Kelantan and Terengganu 50% of patients were taking dialysis treatment in public sector dialysis facilities [69]. NGOs running programs like Syrian American Medical Society (SAMS) was initiated to help the Syrian patients in refugee camps and northern Syria during the crises in Syria. SAMS was basically a mission of Syrian American nephrologists for the direct observation, to treat psychological disorders and care of dialysis patients which was severely compromised due to destruction of health care facilities, loss of access to dialysis centers, lack of medications and sue to shortage of medical care professionals [70]. But in another study on assessment of ESRD during Syrian crises, the authors found that the aid from inexperienced NGOs and non-renal charities despite of their good will is insufficient and potentially dangerous. Regional and international renal teams should be involved in organizing aid in situations like Syrian crises [71]. A significant improvement in mortality rate over the years and reduce hospitalization rates due to providing adequate dialysis therapy, EPO and iron usage was reported in NGO based dialysis center. Moreover, the free supply of antihypertensive drugs was associated with better control of hypertension and reduced rates of cardiovascular mortality at this NGO funded dialysis facility in south India [72]. Authors of a study reported that Malaysian government reforms to encourage NGOs dialysis facilities and private facilities has brought a transformation and resulted in greatly expanded and an easy access to dialysis patients specially with low socioeconomic status to avail dialysis services [73]. Those dialysis patients who were receiving financial help from NGO’s, hospitals and other funding organizations were less depressed as compared to those who were not [74]. Most notably, the association of depression in NGOs and government sector dialysis centers has never been studied. Further studies are warranted to confirm this finding.

Strengths and limitations of the study

  • This study involved a group of patients from tertiary-level teaching hospital of Malaysia.

  • To the best of the authors’ knowledge, this is the first follow up study to assess the prevalence and predictors of depression among hemodialysis patients in a Malaysian setting.

  • For determining the factors associated with depression, multivariate analysis was conducted.

  • Being a prospective observational study, the findings of the present study need to be interpreted with caution since it is limited to only 6 months follow up.

  • Nevertheless, a multicenter study with a large sample size and longer follow up time is needed to confirm the findings of the current study.

Conclusion

The current study revealed that the negative association of depression with dialysis therapy at NGOs running dialysis facilities is an indication of better depression management practices at these centers. For better management of depression and to enhance the quality of life of HD patients, studies should be carried out on national level in government, private and NGOs running dialysis centers and strategies should be adopted on how to reduce the prevalence of depression where it is more prevalent.

Study limitations

The findings of the present study need to be interpreted with caution since it is limited to only 6 months follow up. Nevertheless, a multicenter study with a large sample size and longer follow-up time is needed to confirm the findings of the current study. As we have not correlated the depression scores of same individuals assessed on multiple times, our results should be interpreted with the limitation of separate analysis.

Abbreviations

ADNAN: 

Advanced Dialysis Nephrology Application Network

BDI: 

Beck’s Depression Inventory

CKD: 

Chronic kidney disease

ESRD: 

End stage renal disease

HADS: 

Hospital anxiety and depression scale

HD: 

Hemodialysis

HRQoL: 

Health related quality of life

HUSM: 

Hospital University Sains Malaysia

NGOs: 

Non-governmental organizations

PD: 

Peritoneal dialysis

QOL: 

Quality of life

WHO: 

World health organization

Declarations

Acknowledgments

We are grateful to the Institute of Postgraduate Studies (IPS) of Universiti Sains Malaysia (USM) for the fellowship support [Ref. no. P-FD0011/15(R)].

Funding

This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

Availability of data and materials

All data generated or analyzed during this study are included in this current article. The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Authors’ contributions

All authors (AK, AHK, ASA, SASS, SM) made substantial contributions to the conception and design of this study. AK and AHK made substantial contributions to the acquisition and analysis of the data. AK drafted the manuscript and ASA, SASS, and SM were involved in critical revision for important intellectual content. All authors read and approved the final manuscript.

Ethics approval and consent to participate

The current study was approved by the Human Resource Ethics Committee of Hospital Universiti Sains Malaysia (USM/JEPeM/16020058). Written informed consent was obtained from all individual participants included in the study. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

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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)
Discipline of Clinical Pharmacy, School of Pharmaceutical Sciences, Universiti Sains Malaysia, 11800 Penang, Malaysia
(2)
Chronic Kidney Disease Resource Centre, School of Medical Sciences, Health Campus, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia
(3)
Department of Pharmacy, Quaid-i-Azam University, Islamabad, 45320, Pakistan
(4)
Health Care Biotechnology Department, Atta ur Rahman School of Applied Biosciences, National University of Science & Technology, Islamabad, 44000, Pakistan
(5)
Management Science University, University Drive, Off Persiaran Olahraga, Section 13, 40100 Shah Alam, Selangor, Malaysia

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