Open Access

Malnutrition: Prevalence and its associated factors in People living with HIV/AIDS, in Dilla University Referral Hospital

  • Solomon Hailemariam1Email author,
  • Girma Tenkolu Bune1 and
  • Henok Tadesse Ayele1, 2
Archives of Public HealthThe official journal of the Belgian Public Health Association201371:13

DOI: 10.1186/0778-7367-71-13

Received: 28 October 2012

Accepted: 29 May 2013

Published: 8 June 2013

Abstract

Background

Literatures on prevalence and factors associated with malnutrition among peoples living with HIV/AIDS are limited in Ethiopia and not well documented either. The proper implementation of nutritional support and its integration with the routine highly active antiretroviral therapy package demands a clear picture of the magnitude and associated factors of malnutrition. The objective of this study is, therefore, to assess the prevalence and factors associated with malnutrition among peoples living with HIV/AIDS.

Methods

Institution based cross sectional study was conducted in Dilla University referral Hospital including adult HIV patients who were in highly active anti retroviral therapy. Interview administered questionnaires were used to collect data on socio demographic factors. Besides, HIV related clinical information was extracted from anti retro viral therapy data base and clinical charts. The nutritional status of the patients was determined by Body Mass Index (BMI) where BMI < 18kg/m2 was defined as malnutrition according to World Health Organization (WHO). Binary logistic regression was used to assess association between different risk factors and malnutrition. Confidence interval of 95% was considered to see the precision of the study and the level of significance was taken at α <0.05.

Results

A total of 520 patients were included in the analysis. The overall prevalence of malnutrition was 12.3% (95% CI 9.5–15.0). After full control of all variables; unemployment (OR = 3.61, 95% CI: 3.6 − 7.76), WHO clinical stage four (OR = 12.9, 95% CI: 2.49− 15.25), gastrointestinal symptoms (OR = 5.3, 95% CI: 2.56 − 10.78) and previous (one) opportunistic infection (OR = 3.1, 95% CI 2.06 − 5.46), and two & above previous opportunistic infections (OR = 4.5, 95% CI: 3.38 − 10.57) were significantly associated with malnutrition. However, moderately poor economic condition was found to be protective factor for malnutrition (OR = 0.4, 95% CI: 0.14 − 0.95).

Conclusion

Unemployment, WHO clinical AIDS stage four, one & more number of previous opportunistic infections and gastrointestinal symptoms were found to be important risk factors for malnutrition among People Living with HIV/AIDS. From this study it has been learnt that nutritional programs should be an integral part of HIV/AIDS continuum of care. Furthermore, it needs to improve household income of PLHIV with employment opportunity and to engage them in income generating activities as well.

Keywords

Prevalence Malnutrition HIV Ethiopia Dilla University Hospital

Background

Both Human Immunodeficiency Virus (HIV) and malnutrition can independently cause progressive damage to the immune system. The former increases susceptibility to infection, morbidity and mortality through opportunistic infections, fever, diarrhea, loss of appetite, nutrient malabsorption, and weight loss [13]. Furthermore, HIV specifically affects nutritional status by increasing energy requirements, reducing food intake, and adversely affecting nutrient absorption and metabolism inefficiencies due to cytokine activity and diarrhea [1, 3, 4]. Likewise, HIV leads to malnutrition, and on the other hand studies have shown that progression of the disease can be increased by a poor diet [5]. Malnutrition itself can induce immuno-depression [2] and modulates the immunological response to HIV infection, affecting the overall clinical outcome and worsen HIV-related immuno-depression [6].

The advent of a generalized HIV/AIDS epidemic in combination with drought and food crises exacerbated the famine across many parts of Africa [7]. HIV/AIDS and malnutrition are highly prevalent in many parts of the world, especially in sub-Saharan Africa [1, 4, 8]. An individual data based meta-analysis of Demographic Health Surveys (DHS) from 11 sub-Saharan countries (SSA) among HIV positive women has shown that prevalence estimates of HIV-related malnutrition, ranged from 0.6% in Lesotho to 16.9% in Burkina Faso. It has shown that an overall (pooled) prevalence of 10.3% in SSA and the women’s prevalence of 13.2% in Ethiopia [4].

Empirical evidences on malnutrition among People Living with HIV (PLHIV) have shown that socio demographic factors such as gender, employment, income, drinking water and sanitation were closely related determinants of nutritional status. Additionally, gastrointestinal complications, number of previous opportunistic infections and World Health Organization (WHO) clinical AIDS stage were reported to be risk factors for malnutrition among PLHIV [913].

HIV/AIDS and malnutrition combine to emasculate the immunity of many Ethiopians [14]. This has been witnessed by two collaborative case studies conducted by agencies of the United Nations. As the empirical evidences generated by United Nations’ Economic Commission of Africa (UNECA), United Nations’ Development Program (UNDP) and United Nations’ World Food Programme (WFP) on the impact of HIV/AIDS on household food security in rural Ethiopia have found that the households’ food security was seriously hit by HIV/AIDS [7]. The stark reality is opportunistic infections place PLWHA at a high risk of developing malnutrition [2]. HIV related debilitating infections, such as tuberculosis and diarrhea, have severe nutritional consequences that commonly precipitate appetite loss, weight loss and finally they lead to a wasting syndrome [3].

High rates of malnutrition in Ethiopia worsen the impact of HIV and pose significant challenges to HIV care and treatment programs [15]. A nutrition assessment carried out in 2007 at St. Peter’s hospital in Addis Ababa where the hospital have been offering Antiretroviral Treatment (ART) indicated that 35–40% of registered pre-ART clients had a BMI of less than 18.5kg/m2 (mild malnutrition) and 20% had a BMI of less than 17kg/m2 (moderate malnutrition) [16].

Taking this complex nature of the problem in to account this study was carried out to assess the prevalence of malnutrition and associated factors in PLWHA. It is our strong belief that the results of this study provide valuable information to strengthen HIV/AIDS continuum of care.

Methods

Study area

Dilla University referral Hospital is found in Dilla City administration which is located 360 kilometer far away from the capital city, Addis Ababa, in the south of Ethiopia. It is the public hospital which is an affiliate of Dilla University providing training for health sciences student in a range of disciplines. Additionally, the hospital provides higher level of clinical care for nearly a million of catchment area populations. Since 2005, the Hospital has been providing highly active antiretroviral therapy (HAART) for PLWHAs. During the study period (April, 2011 to March 2012), about a total of 3312 subjects had been enrolled in chronic HIV/AIDS care and 1585 patients were on HAART. According to the national guideline, ART shall be initiated for eligible patient. The eligibility of the patients is determined either if their CD4 cell count is < 200/mm3 or if they fulfill WHO clinical AIDS stage III or IV.

Study design

Cross-sectional study design was conducted including 520 PLWHAs who were 18 years or above. The sources population was a group PLWHAs who had been enrolled in Dilla University Referral Hospital in HAART. The study subjects were drawn by systematic sampling technique from ART registration data base from April 2011 to March 2012.

Data collection procedure and instrument

Socio-demographic details such as age, gender, residence, employment status, level of education, marital status were collected using interview administered questionnaire. Similar instrument was used for the collection of gastrointestinal symptoms’ data and side effect of ARVs in the past six months from each patient. Poverty status was assessed by index of socio economic status which was measured by summation of items of possession. In this study, it was measured by giving a score of “1” for possessing each of 22 items in the list [17]. The summed items were then classified into three categories. Respondents having item scores below a tertiles were categorized as study subjects in “absolute poverty”, respondents having item scores between the lower and upper tertiles as “relatively moderate”, and respondents having item scores above the upper tertiles as being “relatively better off” [18]. The height and weight of the patients were measured in light clothing and bare foots calibrated to 0.5cm and 0.5 kg, respectively. Height was measured while the patients were standing erect in a Frankfurt position and the weight was measured on a standing scale. Body mass index (BMI), was calculated as weight in kilograms divided by the square of height in meters (kg/m2). For the initial analysis, BMI was stratified into the WHO criteria: <17 (moderate to severe malnutrition), 17 to < 18.5 (mild malnutrition), >18.5 to 25 (normal nutrition) and >25 kg/m2 (overweight and obese) [4]. Blood samples were drawn from subjects as part of routine monthly ART follow up investigation to measure CD4 cell count. This study used the CD4 cell count to classify the patients into three categories according to WHO criteria; <200 cells/mm3 severe, 200–499 cells/mm3 as moderate and >500 cells/mm3 as mild. Patients’ medical chart was reviewed for extraction of AIDS’ clinical stage data and history of previous opportunistic infections (OIs) in the last 6 months. In addition, adherence to HAART was extracted from the medical chart of individual patients which was registered during their monthly spell of follow up. Similar to the previous opportunistic infection, adherence status is delimited to the last six months follow up time. However, self reported adherence measurement technique has been used by asking the patients about the number of times they have missed taking their pills each month and recorded. In this study, the mean adherence to HAART for each eligible record was operationally defined as “good adherent” if the average adherence was greater than 95% and “less-adherent” if it was ≤ 95%.

Data analysis

The data collected from the respondents was entered in to Epi info version 7 and imported to SPSS for windows-version 16. The data analysis ranged from the basic description of outcomes to the identification of statistically significant associations. First, the basic descriptive summaries of patients’ characteristics and outcome of interest was computed. Accordingly, simple frequencies, measure of central tendencies and measure of dispersions were scrutinized. Second, bivariate analysis and multiple logistic models used to show the relation between malnutrition and various associated factors. Finally, all explanatory variables that were significantly associated with the outcome variable in the bivariate analyses (P < 0.05) were entered in to stepwise logistic regression model to identify independent predictor of malnutrition. Confidence interval of 95% was used to see the precision of the study and the level of significance was taken at α <0.05.

Ethical clearance

The study protocol was reviewed and approved by Dilla University ethical clearance committee. Before data collection, an informed consent was obtained from respondents. Privacy and confidentiality were also maintained throughout the data collection, analysis, and manuscript preparation. Subjects with BMI < 18.5kg/m2 were examined by an ART clinician and they had been given a dietary counseling to address areas of their specific concern as it was revealed by the screening tool.

Results

Socio-demographic characteristics

A total of 520 patients were participated in the study. Majority of the patients were in the age group 30–39 years, with mean age of 33.9 (SD = ±8.13) years, and the majority (59%) were women. The greater part of the respondents were currently married (57.7%). About three forth (73.3%) of the study population were employed (Table 1).
Table 1

Socio-demographic characteristics of PLWHAs* in Dilla University Referral Hospital, Ethiopia 2011

 

Residential area

Total

Urban

Rural

 

Number

%

Number

%

Number

%

Gender

      

 Male

193

39.3

20

69.0

213

41.0

 Female

298

60.7

9

31.0

307

59.0

 Total

491

100.0

29

100.0

520

100.0

Age group (years)

      

 <30

160

32.6

6

20.7

166

31.9

 30-39

213

43.4

13

44.8

226

43.5

 40-49

92

18.7

6

20.7

98

18.8

 ≥ 50

26

5.3

4

13.8

30

5.8

 Total

491

100.0

29

100.0

520

100.0

Current marital status

      

 Married

276

56.2

24

82.8

300

57.7

 Single

24

4.9

0

0.0

24

4.6

 Divorced

50

10.2

0

0.0

50

9.6

 Widowed

131

26.7

5

17.2

136

26.2

 Separated

10

2.0

0

0.0

10

1.9

 Total

491

100.0

29

100.0

520

100.0

Educational level

      

 Not able to read & write

108

22.0

10

34.5

118

22.7

 Able to read and write

18

3.7

2

6.9

20

3.8

 Grade 1 − 4

99

20.2

3

10.3

102

19.6

 Grade 5 − 8

114

23.2

9

31.0

123

23.7

 Secondary school

112

24.8

5

17.2

127

24.4

 College/University

30

6.1

0

0.0

30

5.8

 Total

491

100.0

29

100.0

520

100.0

Ethnic group

      

 Amhara

204

41.6

3

10.3

207

39.9

 Oromo

89

18.2

10

34.5

99

19.1

 Tigray

6

1.2

0

0.0

6

1.2

 Gurage

67

13.7

0

0.0

67

12.9

 Gedeo

72

14.7

8

27.6

80

15.4

 Sidama

12

2.4

7

24.1

19

3.7

 Other

40

8.2

1

3.4

41

7.9

 Total

490

100.0

29

100.0

519

100.0

Religion

      

 Orthodox Christian

321

65.4

12

41.4

333

64.0

 Protestant

119

24.2

17

58.6

136

26.2

 Muslim

49

10.0

0

0.0

49

9.4

 Others

2

0.4

0

0.0

2

0.4

 Total

491

100.0

29

100.0

520

100.0

Poverty status

      

 Very Poor

143

29.5

13

44.8

156

30.4

 Moderate

125

25.8

12

41.4

137

26.7

 Relatively better off

217

42.7

4

13.8

221

43.0

 Total

485

100.0

29

100.0

514

100.0

Employment status

      

 Employed

359

73.1

22

75.9

381

73.3

 Un employed

132

26.9

7

24.1

139

26.7

 Total

491

100.0

29

100.0

520

100.0

Source of water

      

 Protected

491

100.0

14

48.3

505

97.1

 Un protected

0

0.0

15

51.7

15

2.9

 Total

491

100.0

29

100.0

29

100.0

* People Living With HIV/AIDS

Age- and gender-specific prevalence proportions of Malnutrition

The mean BMI was 19.5 (SD = ±2.52) for male and 17 (SD = ±2.84) for female. Over all prevalence of malnutrition was 12.3% (95% CI 9.5–15.0). While 7.0% (95% C I 3.60–10.40) of the males had malnutrition, the proportion among females was 16.0% (95% CI 12.89–20.10). The prevalence of malnutrition was increased when the age of the study subjects was increased. For instance, in the age group of less than 30 year-olds, the prevalence was 9.0% (95% CI 4.65–13.35); among the 30–39 year-old it was 13.3% (95% CI 8.87 –17.73); amid 40–49 years old it was 14.3% (95% CI 7.47–21.23); and in the age group of 50 years of age and older it was 16.7% (95% CI 3.35–30.05).

Table 2 shows BMI distribution across different socio-demographic and clinical characteristics of study subjects. Nearly nine percent and 3.5% of the patients have mild & moderate to severe malnutrition, respectively. Among the study subjects; who were in WHO clinical stage four and those patients with two & more previous opportunistic infections (OIs) had higher proportion BMI score less than 17kg/m2. In the same way, those patients who had manifested gastrointestinal symptoms and those patients who had poor adherence to HAART has shown a BMI score less than 17kg/m2.
Table 2

Socio-demographic and clinical characteristics of study subjects grouped by BMI** in PLWHA* in Dilla University Referral Hospital, Ethiopia 2011

Variables

BMI

> 25 kg/m2

18.5 − 25 kg/m2

17 − 18.4 kg/m2

< 17 kg/m2

Total

No(%)

No(%)

No(%)

No(%)

No(%)

Gender

     

 Male

17 (40.5)

181 (43.7)

10 (21.7)

5 (27.8)

213 (41.0)

 Female

25 (59.5)

233 (56.3)

36 (78.3)

13(72.2)

307 (59.0)

 Total

42 (100.0)

414 (100.0)

46 (100.0)

18 (100.0)

520 (100.0)

Age group (years)

     

 < 30

14 (33.3)

137 (33.1)

11 (23.9)

4 (22.2)

166 (31.9)

 30 − 39

20 (47.6)

176 (42.5)

19 (41.3)

11 (61.1)

226 (43.5)

 40 − 49

6 (14.3)

78 (18.8)

11 (23.9)

3 (16.7)

98 (18.8)

 50+

2 (4.8)

23 (5.6)

5 (10.9)

0 (0.0)

30 (5.8)

 Total

42 (100.0)

414 (100.0)

46 (100.0)

18 (100.0)

520 (100.0)

Marital status

     

 Married

22 (52.4)

244 (58.9)

27 (58.7)

7 (38.9)

300 (57.7)

 Single

4 (9.5)

19 (4.6)

1 (2.2)

0 (0.0)

24 (4.6)

 Divorced

2 (4.8)

45 (10.9)

2 (4.3)

1 (5.6)

50 (9.6)

 Widowed

13 (31.0)

97 (23.4)

16 (34.8)

10 (55.6)

136 (26.2)

 Separated

1 (2.4)

9 (2.2)

0 (0.0)

0 (0.0)

10 (1.9)

 Total

42 (100.0)

414 (100.0)

46 (100.0)

18 (100.0)

520 (100.0)

Educational level

     

 Not able to read & write

3 (7.1)

92 (22.2)

16 (34.8)

7 (38.9)

118 (22.7)

 Able to read & write

2 (4.8)

16 (3.9)

2 (4.3)

0 (0.0)

20 (3.8)

 Grade 1 − 4

7 (16.7)

88 (21.3)

7 (15.2)

0 (0.0)

102 (19.6)

 Grade 5 − 8

14 (33.3)

91 (22.0)

12 (26.1)

6 (33.3)

123 (23.7)

 Secondary school (9–12)

13 (31.0)

101 (24.4)

9 (19.6)

4 (22.2)

127 (24.4)

 College/University

3 (7.1)

26 (6.3)

0 (0.0)

1 (5.6)

30 (5.8)

 Total

42 (100.0)

414 (100.0)

46 (100.0)

18 (100.0)

520 (100.0)

Residential area

     

 Urban

42 (100.0)

390 (94.2)

41 (89.1)

18 (100.0)

491 (94.4)

 Rural

0 (0.0)

24 (5.8)

5 (10.9)

0 (0.0)

29 (5.6)

 Total

42 (100.0)

414 (100.0)

46 (100.0)

18 (100.0)

520 (100.0)

Employment status

     

 Employed

37 (88.1)

313 (75.6)

20 (43.5)

11 (61.1)

381 (73.3)

 Unemployed

5 (11.9)

101 (24.4)

26 (56.5)

7 (38.9)

139 (26.7)

 Total

42 (100.0)

414 (100.0)

46 (100.0)

18 (100.0)

520 (100.0)

Poverty status

     

 Very Poor

7 (16.7)

121 (29.7)

22 (47.8)

6 (33.3)

156 (30.3)

 Moderate

11 (26.2)

114 (27.9)

6 (13.0)

6 (33.3)

137 (26.7)

 Relatively better off

24 (57.1)

173 (42.4)

18 (39.1)

6 (33.3)

221 (43.0)

 Total

42 (100.0)

408 (100.0)

46 (100.0)

18 (100.0)

514 (100.0)

Side effect of HAART

 Yes

4 (9.5)

30 (7.2)

3 (6.5)

3 (16.7)

40 (7.7)

 No

38 (90.5)

384 (92.8)

43 (93.5)

15 (83.3)

480 (92.3)

 Total

42 (100.0)

414 (100.0)

46 (100.0)

18 (100.0)

520 (100.0)

WHO clinical AIDS staging

 Stage One

3 (7.1)

46 (11.1)

4 (8.7)

0 (0.0)

53 (10.2)

 Stage Two

13 (31.0)

91 (22.0)

10 (21.7)

1 (5.6)

115 (22.1)

 Stage Three

23 (54.8)

252 (60.9)

19 (41.3)

14 (77.8)

308 (59.2)

 Stage Four

3 (7.1)

25 (6.0)

13 (28.3)

3 (16.7)

44 (8.5)

 Total

42 (100.0)

414 (100.0)

46 (100.0)

18 (100.0)

520 (100.0)

Adherence to HAART in past 6 month

 Good adherence

38 (90.5)

394 (95.6)

39 (84.8)

16 (88.9)

487 (94.0)

 Poor adherence

4 (9.5)

18 (4.4)

7 (15.2)

2 (11.1)

31 (6.0)

 Total

42 (100.0)

412 (100.0)

46 (100.0)

18 (3.5)

518 (100.0)

CD4 cell count

     

 Mild

14 (33.3)

109 (26.5)

13 (28.3)

5 (27.8)

141 (27.2)

 Moderate

22 (52.4)

243 (59.0)

19 (41.3)

10 (55.6)

294 (56.8)

 Severe

6 (14.3)

60 (14.6)

14 (30.4)

3 (16.7)

83 (16.0)

 Total

42 (100.0)

412 (100.0)

46 (100.0)

18 (3.5)

518 (100.0)

Gastrointestinal symptoms

 Yes

8 (19.0)

85 (20.5)

20 (43.5)

10 (55.6)

123 (23.7)

 No

34 (81.0)

329 (79.5)

26 (56.5)

8 (44.4)

397 (76.3)

 Total

42 (100.0)

414 (100.0)

46 (100.0)

18 (100.0)

520 (100.0)

Number of previous Opportunistic Infections episode

 None

24 (57.1)

308 (74.6)

11 (23.9)

6 (33.3)

349 (67.2)

 1

13 (31.0)

70 (16.9)

13 (28.3)

4 (22.2)

100 (19.3)

 2+

5 (11.9)

35 (8.5)

22 (47.8)

8 (44.4)

70 (13.5)

 Total

42 (100.0)

413 (100.0)

46 (100.0)

18 (100.0)

519 (100.0)

* People Living With HIV/AIDS

** Body Mass Index

Socio-demographic factor associated with malnutrition

Tables 3 and 4 shows Univariate and multiple variable analyses of different independent variables associated with malnutrition respectively. In a Bivariate analysis women found to be more likely to develop malnutrition than men (COR = 2.50, 95% CI 1.37 − 4.60), however after controlling for all variables the association was no longer statistically significant (Table 4).
Table 3

Bivariate association of different variables with Malnutrition in PLWHA* in Dilla University Referral Hospital, Ethiopia 2011

Variable

Normal nutritional status

Under nutrition

COR*** (95% CI****)

Gender

No (%)

No (%)

 

 Male

198 (92.96)

15 (7.0)

1

 Female

258 (84.04)

49 (16.0)

2.51 (1.37 − 4.60)§

 Total

456 (87.69)

64 (12.3)

-

Age group (years)

   

 < 30

151 (90.96)

15 (9.0)

0.65 (0.34 − 1.25)

 30 − 39

196 (86.73)

30 (13.3)

1

 40 − 49

84 (85.71)

14 (14.3)

1.18 (0.55 − 2.16)

 50+

25 (83.33)

5 (16.7)

1.31 (0.47 − 3.68)

 Total

456 (87.69)

64 (12.3)

 

Marital status

   

 Married

266 (88.67)

34 (11.3)

1

 Single

23 (95.83)

1 (4.2)

0.34 (0.05 − 2.60)

 Divorced

47 (94)

3 (6.0)

0.51 (0.15 − 1.69)

 Widowed

110 (80.88)

26 (19.1)

1.85 (1.06 − 3.23) §

 Separated

10 (100)

0 (0)

 Total

456 (87.69)

64 (12.3)

 

Educational level

   

 Not able to Read & write

95 (80.51)

23 (19.5)

3.50 (1.35 − 8.02) §

 Able to read & Write

18 (90)

2 (10.0)

1.50 (0.29 − 7.85)

 Grade 1 − 4

95 (93.14)

7 (6.9)

1

 Grade 5 − 8

105 (85.37)

18 (14.6)

2.30 (0.93 − 5.82)

 Secondary School (9–12)

114 (89.76)

13 (10.2)

1.60 (0.59 − 4.04)

 College or University

29 (96.67)

1 (3.3)

0.50 (0.55 − 3.96)

 Total

456 (87.69)

64 (12.3)

 

Residential area

 Urban

432 (87.98)

59 (12.0)

1

 Rural

24 (82.76)

5 (17.2)

1.50 (0.56 − 4.15)

 Total

456 (87.69)

64 (12.3)

 

Employment status

   

 Employed

350 (91.86)

31 (8.1)

1

 Unemployed

106 (76.26)

33 (23.7)

3.50 (2.16 − 6.01) §

 Total

456 (87.69)

64 (12.3)

Poverty status

   

 Very Poor

128 (82.05)

28 (17.9)

1.80 (1.01 − 3.24) §

 Moderate

125 (91.24)

12 (8.8)

0.80 (0.38 − 1.63)

 Relatively better off

197 (89.14)

24 (10.9)

1

 Total

450 (87.55)

64 (12.5)

 

Side effect of HAART

 Yes

34 (85)

6 (15.0)

1

 No

422 (87.92)

58 (12.1)

1.28 (0.52 − 3.19)

 Total

456 (87.69)

64(12.3)

 

WHO clinical AIDS staging

 Stage One

49 (92.45)

4 (7.5)

1

 Stage Two

104 (90.43)

11 (9.6)

1.30 (0.39 − 4.28)

 Stage Three

275 (89.29)

33 (10.7)

1.50 (0.51 − 4.33)

 Stage Four

28 (63.64)

16 (36.4)

7.01 (2.13 − 23.01) §

 Total

456 (87.69)

64 (12.3)

 

Adherence to HAART in past 6 month

 Good adherence

432 (88.71)

55 (11.3)

1

 Poor adherence

22 (70.97)

9 (29.0)

3.20 (1.41 − 7.33) §

 Total

454 (87.64)

64 (12.4)

 

CD4 cell count

   

 Mild

123 (87.23)

18 (12.8)

1

 Moderate

265 (90.14)

29 (9.9)

0.80 (0.40 − 1.41)

 Severe

66 (79.52)

17 (20.5)

1.80 (0.85 − 3.61)

 Total

454 (87.64)

  

Gastrointestinal symptoms

 Yes

93 (75.61)

30 (24.4)

3.40 (2.01 − 5.92) §

 No

363 (91.44)

34 (8.6)

1

 Total

456 (87.69)

64 (12.3)

 

Number of previous OIs

 None

332 (95.13)

17 (4.9)

1

 1

83 (83)

17 (17.0)

4.00 (1.96 − 8.17) §

 2+

40 (57.14)

30 (42.9)

4.70 (7.43 − 11.91) §

 Total

455 (87.67)

64 (12.3)

 

§P-Value < 0.05.

* People Living With HIV/AIDS.

*** Crude Odds Ratio.

**** Confidence interval.

Table 4

Adjusted association of different variables with malnutrition in PLWHA* in Dilla University Referral Hospital, Ethiopia 2011

Variable

Normal nutritional status

Under nutrition

Adjusted Odds Ratio (AOR) for all variables (95% CI)

Gender

   

 Male

198 (92.96)

15 (7.0)

1

 Female

258 (84.04)

49 (16.0)

1.30 (0.53 − 2.94)

Marital status

   

 Married

266 (88.67)

34 (11.3)

1

 Single

23 (95.83)

1 (4.2)

0.40 (0.04 − 3.98)

 Divorced

47 (94)

3 (6.0)

0.80 (0.18 − 3.10)

 Widowed

110 (80.88)

26 (19.1)

2.00 (0.85 − 4.33)

 Separated

10 (100)

0 (0)

Educational level

   

 Not able to read & write

95 (80.51)

23 (19.5)

1.70 (0.57 − 5.12)

 Able to read & write

18 (90)

2 (10.0)

0.30 (0.03 − 2.11)

 Grade 1 − 4

95 (93.14)

7 (6.9)

1

 Grade 5 − 8

105 (85.37)

18 (14.6)

1.91 (0.62 − 5.88)

 Secondary school (9–12)

114 (89.76)

13 (10.2)

1.20 (0.39 − 3.88)

 College/University

29 (96.67)

1 (3.3)

0.10 (0.01 − 0.96) §

Employment status

   

 Employed

350 (91.86)

31 (8.1)

1

 Unemployed

106 (76.26)

33 (23.7)

3.60 (1.63 − 7.76) §

Poverty status

   

 Very Poor

128 (82.05)

28 (17.9)

0.75 (0.31 − 1.76)

 Moderate

125 (91.24)

12 (8.8)

0.40 (0.14 − 0.95) §

 Relatively better off

197 (89.14)

24 (10.9)

1

WHO clinical AIDS staging

   

 Stage One

49 (92.45)

4 (7.5)

1

 Stage Two

104 (90.43)

11 (9.6)

2.60 (0.53 − 12.55)

 Stage Three

275 (89.29)

33 (10.7)

2.10 (0.51 − 9.01)

 Stage Four

28 (63.64)

16 (36.4)

12.90 (2.49 − 15.25) §

 Good adherence

432 (88.71)

55 (11.3)

1

 Poor adherence

22 (70.97)

9 (29.0)

1.40 (0.41 − 4.65)

Gastrointestinal symptoms

   

 Yes

93 (75.61)

30 (24.4)

5.30 (2.56 − 10.78) §

 No

363 (91.44)

34 (8.6)

1

Number of previous OIs

   

 None

332 (95.13)

17 (4.9)

1

 1

83 (83)

17 (17.0)

3.10 (2.06 − 5.46) §

 2+

40 (57.14)

30 (42.9)

4.50 (3.38 − 10.57) §

* People living with HIV/AIDS.

§ P-value < 0.05.

With regard to marital status, a greater number of malnourished subjects were found in the group of the widowed (19.1%) followed by the married ones (11.3%). Being a widow (COR = 1.60, 95% CI1.06– 3.2) was significantly associated with malnutrition, but this association was not maintained after adjusting for all independent variables (AOR = 2.0, 95% CI, 0.85 − 4.33).

Concerning literacy variables, it has been found that only illiterate educational status were a risk factor for malnutrition, (COR = 3.50, 95% CI 1.35–8.0). Nonetheless, after controlling for all other important variables the association was no longer significant (AOR = 1.70, 95% CI 0.57 − 5.12). With reference to employment status, the proportion of malnutrition was higher (23.7%) in unemployed group compared to those employed (8.1%), this difference was statistically significant as well (COR = 3.50, 95% CI 2.16–6.01) for unemployed as compared to their counterpart. Moreover, the association remained statistically significant after controlling for other variables (AOR = 3.60, 95% CI 1.63 − 7.76). In the same way, moderately poor economic status was found to be protective factor of malnutrition (AOR = 0.40, 95% CI 0.14– 0.95).

Clinical factors associated with malnutrition

WHO clinical stage four was found to have a statistically significant association with malnutrition (COR = 7.0, 95% CI 2.13 − 23.01). Independent of all other variables, the result was remained an important risk factor for malnutrition (AOR = 12.90, 95% CI 2.49 − 15.25). Those who had poor adherence to HAART in the past six month had a higher risk of developing malnutrition and was statistically significant (COR = 3.2, 95% CI 1.41 − 7.33) although controlling for all other independent variables nullified the association (AOR = 1.40, 95% CI 0.41 − 4.65).

In spite of the fact that the proportion of malnutrition was higher; (20.5%) among those with severe CD4 cell count and (12.8%) among those in the mild and (9.9%) among those in moderate CD4 cell count category, the association was not statistically significant. The bivariate analysis has revealed the crude odds ratios of (COR = 1.80, 95% CI 0.85 − 3.61) and (COR = 0.80, 95% CI 0.40 − 1.41) for severe and moderate CD4 cell count, respectively.

Number of previous opportunistic infections (OIs) showed a significant association with malnutrition after fully adjusting it for all variables. Being having one diagnosis of previous OI had a higher risk for developing malnutrition (AOR = 3.10, 95% CI 2.06–5.46) and having two or more diagnoses of OIs further increases the likelihood (AOR = 4.50, 95% CI 3.38–10.57) of malnutrition as compared to those with no previous diagnosis of OIs in the past 6 month. Likewise, independent of all other variables gastrointestinal symptoms (GIS) had significant association with malnutrition. Those patients with one or more GIS had a higher risk of developing malnutrition (AOR = 5.30, 95% CI 2.56 − 10.78) as compared to those with no GIS.

Discussion

Meta-analysis from 11 sub-Saharan African countries indicated that the prevalence of malnutrition in Ethiopia among HIV-infected women was 13.2% [4]. It is a bit lower than the prevalence proportion of women’s malnutrition in this study (16%), confirming malnutrition is an important concern in the management of HIV- infected patients. Malnutrition (under nutrition) is more common in developing countries, where patients are often not diagnosed or do not commence ART until they have advanced disease. Ominously, the HIV epidemic itself may be contributing to food insecurity at a population level [9]. On the other hand, in comparison to other studies, the overall prevalence of malnutrition in this study is lower than the finding from Botswana [2] which was 28.5% and 77% in Iranian HIV-infected persons [19].

The mean BMI was 19.5kg/m2 for male which is almost similar with the general Ethiopian male population of mean BMI of 19kg/m2, but the mean BMI of 17 for female in this study is lower than the general Ethiopian women population of mean BMI 20 [20]. This might be due to the fact that HIV is common in women than the men. It is straightforward to appreciate the proportion of malnutrition in the majorly affected segment of the population.

The higher risk of developing malnutrition in unemployed subjects found in this study is agreed with other study [4] where unemployment promotes poverty, which in turn limits the ability of individual to expend money for food consumption. The less likelihood of developing malnutrition among respondents in the moderate economic status implies improved income level insures food security at household level. As it is confirmed by findings from previous study in Ethiopia, food insecurity is a significant problem for PLWHAs with low household income [21]. The implication is improving household income and creating employment opportunities for PLWHAs might be among the tenets of comprehensive continuum of care.

Independent of all other variables, WHO clinical stage four has significant effect on the likelihood of malnutrition development. Malnutrition is usually encountered at the advanced phase or end of the HIV infection course [18]. An anthropometric measurement like BMI is lower in symptomatic patients classified by WHO stages [22]. Similarly, study from Uganda showed HIV positive persons in WHO clinical stage four often characterized by sever wasting (chronic fever, chronic diarrhea and weight loss greater than 10% from base line), and food aid to PLHIV delayed HIV disease progression [23]. Further research with longitudinal design recommended seeing the effect of malnutrition on HIV infection progression since nutritional status could modulate the immunological responses to HIV infection over time [24].

Consistent with other findings [2, 25, 26] this study has proven the statistical significance of the association between gastrointestinal symptoms (GIS) and malnutrition among PLHIV. As it has been discussed elsewhere in this article, HIV infection affects nutritional status by reducing dietary intake & nutrient absorption. It affects the nutritional status by increasing nutrient absorption as a result of the increased demand or utilization of protein, excretion of protein and other micronutrients [8, 22, 23]. Similar to this study and references cited elsewhere have shown that patients with GIS like chronic diarrhea, vomiting and loss of appetite found to be significantly threatening the nutritional status of PLWHAs [1, 22].

This study did not assess the effect of each opportunistic infection on the nutritional status of the study subjects. Nevertheless, it has been learnt that the number of previous opportunistic infections were independent risk factors of malnutrition. This finding is well supplemented by similar other studies conducted by international agencies and researchers [2, 9, 11]. HIV-induced immune impairment and heightened subsequent risk of opportunistic infection can worsen nutritional status [8]. This necessitates the importance of managing patients with opportunistic infection promptly.

One of the strengths of this study was the use of large sample size and assessment of some important clinical factors associated with malnutrition. To avoid recall biases, medical charts and ART data base were triangulated with the primary data collected by structured interview administered questionnaire. However, the cross-sectional nature of the study limits the investigation to the level of the association between determinants and outcomes of interest (malnutrition). Hence, it’s impossible to get information about causal relationship in the majority of associated factors. Likewise, further comparative study, between HIV-infected and non-HIV infected persons that could explore more risk factors for malnutrition is recommended.

Conclusion

The results of this study provide data on the characteristics of nutritional status of HIV-positive patients and important associated factors. To mention few, employment status, poverty status, WHO clinical staging, number of previous opportunistic infections and gastrointestinal symptoms were among the imperatives. It has been learnt that malnutrition & its problems in HIV patients are complex & interwoven; no single recipe exists as solution either. Hence, there is a prompt need to integrate nutritional care in comprehensive continuum of HIV care. As well, it’s needless to argue about improving household income through creating employment opportunities and engaging the needy unfortunates in amenable income generating activities could possibly alleviate these predicaments.

Declarations

Acknowledgments

This research was supported by the joint NORAD/UNICEF fund to Dilla University HIV/AIDS Prevention & Control Office.

Authors’ Affiliations

(1)
Department of Health Officer, College of Health Science & referral Hospital, Dilla University
(2)
Epidemiology Department, Julius Center for Health Sciences & Primary Care, University Medical Center Utrecht

References

  1. The US President’s Emergency Plan for AIDS Relief: Report on Food and Nutrition for People Living with HIV/AIDS. USA. 2006Google Scholar
  2. Nnyepi MS: The risk of developing malnutrition in people living with HIV/AIDS: Observations from six support groups in Botswana. S Afr J Clin Nutr. 2009, 22 (2): 89-93.Google Scholar
  3. World Health Organization: Nutrition requirements for people living with HIV/AIDS: Report of a technical consultation. 2003, Geneva: WHOGoogle Scholar
  4. Olalekan AU: Prevalence and pattern of HIV-related malnutrition among women in sub- Saharan Africa: a meta-analysis of demographic health surveys. BMC Public Health. 2008, 8: 226-10.1186/1471-2458-8-226.View ArticleGoogle Scholar
  5. Stephen D: The role of nutrition in living with HIV/AIDS. 2008, WORLD AIDS DAY, Accessed at http://www.nutraingredients.com/Research/The-role-of-nutrition-in-living-with-HIV-AIDS Google Scholar
  6. Elizabeth B, Stuart G, Mabel N: Integrating Nutrition Security with Treatment of People Living with HIV: Lessons being Learned in Kenya. 2006Google Scholar
  7. Scott D, Tafesse G, Bruce F: RENEWAL Ethiopia Background Paper: HIV/AIDS, Food and Nutrition Security. 2006, International Food Policy Research Institute (IFPRI), Accessed at http://www.ifpri.org/publication/renewal-ethiopia-background-paper?print Google Scholar
  8. Paton NI, Sangeetha S, Earnest A, Bellamy R: The impact of malnutrition on survival and the CD4 count response in HIV-infected patients starting antiretroviral therapy. HIV Med. 2006, 7: 323-330. 10.1111/j.1468-1293.2006.00383.x.View ArticlePubMedGoogle Scholar
  9. Agatha CO, Mary KW, Grace M, Rose K: Body Composition and CD4 Cell Count of HIV Sero-Positive Adults Attending Out-Patient Clinic in Chulaimbo Sub-District Hospital. Kenya. Pak. J. Nutr. 2011, 10 (6): 582-588.View ArticleGoogle Scholar
  10. Tsehaye B: Changes in body composition and dietary patterns among HIV positive adults on first line antiretroviral treatment at the AIDS support organization (TASO) Mulago Kampala. Asm University. mak.ac.ug 2010. 2010Google Scholar
  11. Hong R, Hong R: Economic inequality and undernutrition in women: multilevel analysis of individual, household, and community levels in Cambodia. Food Nutr Bull. 2007, 281 (1): 56-66.Google Scholar
  12. Fotso JC, Kuate-Defo B: Socioeconomic inequalities in early childhood malnutrition and morbidity: Modification of the household-level effects by the community SES. Health Place. 2005, 11 (3): 205-225. 10.1016/j.healthplace.2004.06.004.View ArticlePubMedGoogle Scholar
  13. Living with HIV: Lessons being learned in Kenya. 2006Google Scholar
  14. The federal ministry of Ethiopia: National guidelines for HIV/AIDS and nutrition in Ethiopia. 2008Google Scholar
  15. FANTA publications, Ethiopia Nutrition and HIV Tools: 2008, Food and nutrition. The federal democratic of Ethiopia, Ministry of health National Nutrition and HIV/AIDS Implementation Reference Manual, Ministry of health September 2008.
  16. Assistance. Retrieved from http://www.fantaproject.org
  17. Krieger N, Williams DR, Moss NE: Measuring social class in US public health research: concepts, methodologies, and guidelines. Annu Rev Public Health. 1997, 18: 341-378. 10.1146/annurev.publhealth.18.1.341.View ArticlePubMedGoogle Scholar
  18. Deyessa N, Berhane Y, Alem A, Hogberg U, Kullgren G: Depression among women in rural Ethiopia as related to socioeconomic factors: A community-based study on women in reproductive age groups. Scand J Public Health. 2008, 36 (6): 589-597. 10.1177/1403494808086976.View ArticlePubMedGoogle Scholar
  19. Khalili H, Soudbakhsh A, Hajiabdolbaghi M, et al: Nutritional status and serum zinc and selenium levels in Iranian HIV infected individuals. BMC Infect Dis. 2008, 8: 165-10.1186/1471-2334-8-165.View ArticlePubMedPubMed CentralGoogle Scholar
  20. Central Statistical Agency (CSA) [Ethiopia] and ORC Macro: 2011, Maryland, USA: CSA and ORC Macro, Ethiopia Demographic and Health Survey 2011, Addis Ababa, Ethiopia and Calverton.
  21. Tiyou A, Belachew T, Alemseged F, Biadgilign S: Food insecurity and associated factors among HIV-infected individuals receiving highly active antiretroviral therapy in Jimma zone Southwest Ethiopia. Nutr J. 2012, 11: 51-10.1186/1475-2891-11-51.View ArticlePubMedPubMed CentralGoogle Scholar
  22. Ellen GP, Elizabeth AP: HIV/AIDS and nutrition: A review of literature and recommendations for nutritional care and support in sub-Saharan Africa. 2000Google Scholar
  23. Rawat R, Kadiyala S, McNamara EP: The impact of food assistance on weight gain and disease progression among HIV-infected individuals accessing AIDS care and treatment services in Uganda. BMC Public Health. 2010, 10: 316-10.1186/1471-2458-10-316.View ArticlePubMedPubMed CentralGoogle Scholar
  24. Somarriba G, Neri D, Schaefer N, Miller LT: The effect of aging, nutrition, and exercise during HIV infection. HIV/AIDS - Research and Palliative Care. 2010, 2: 191-201.Google Scholar
  25. Colecraft E: HIV/AIDS: nutritional implications and impact on human development. Proc Nutr Soc. 2008, 67: 109-113. 10.1017/S0029665108006095.View ArticlePubMedGoogle Scholar
  26. Niyongabo T, Bouchaud O, Henzel D, et al: Nutritional status of HIV-seropositive subjects in an AIDS clinic in Paris. Eur J Clin Nutr. 1997, 51: 637-640. 10.1038/sj.ejcn.1600461.View ArticlePubMedGoogle Scholar

Copyright

© Hailemariam et al.; licensee BioMed Central Ltd. 2013

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.