2Kyoto University School of Public Health, Department of Global Health and Socio-Epidemiology, Kyoto, Japan
3Faculty of Medicine, University of Goma, Goma, Democratic Republic of the Congo
4KwaZulu-Natal University, Dietetic and Human Nutrition, Pietermaritzburg, South Africa
5University of Kinshasa, School of Public Health, Department of Epidemiology and Biostatistics, Democratic Republic of Congo
6University of Bergen, Centre for International Health, Norway
Methodology: This was a cross-sectional survey carried out from February 1st to March 3rd, 2016. The prevalence of depressive symptoms and loss of appetite were estimated. A logistic regression model was used to identify the associations between under nutrition, loss of appetite and depressive symptoms, adjusted for other covariates.
Results: The proportion of participants with depressive symptoms was 21.3% (95% CI: 17.1 – 25.5). The prevalence of loss of appetite was 50.1% (95% CI: 45 – 55%). Depressive symptoms (AOR: 2.19; 95%CI: 1.27 – 3.79), smoking (AOR: 2.97; 95%CI: 1.03 – 8.58) and low socio-economic status (AOR: 1.74; 95%CI: 1.05 – 2.88) were associated with loss of appetite. Loss of appetite (AOR: 3.29; 95% CI: 1.92 – 5.64) and receiving efavirenz (AOR: 2.13; 95% CI: 1.24 – 3.66) were significantly associated with under nutrition.
Conclusion: The fact that about one-fifth hand half of the sample reported having respectively depressive symptoms and the lack of appetite demonstrates the magnitude of the problems. There is a need for longitudinal studies to elucidate the pathways linking depressive symptoms, appetite and under nutrition.
Keywords: Depressive Symptoms; Loss of Appetite; Under Nutrition; HIV; Antiretroviral
Malnutrition, and more specific under nutrition, is generally associated with increased mortality among HIV infected individuals initiating Anti Retroviral Therapy (ART) [5, 6]. The etiology of HIV-associated under nutrition is multi factorial. Indeed, weight loss is a major symptom of AIDS before starting ART several mechanisms need to be explored, such as poor appetite [7].
Depression is common among HIV patients [8] and remains important clinically because untreated or sub-optimally treated depression may result in worsened HIV-related outcomes [9, 10]. Depression is a critical element in HIV care as it has been associated with steeper declines in CD4 counts [11], greater risk of developing HIV-dementia [12], worse antiretroviral medication adherence [13], and more rapid progression to AIDS and death [11,12].
Although antiretroviral therapy (ART) can suppress human immune deficiency virus (HIV) replication and decrease morbidity and mortality in HIV-positive individuals, it has, however, numerous adverse drug reactions (ADR). Efavirenz (EFV) is one of the molecules included as a first-line drug in combination with other ART, in the DRC [14]. More than 50% of patients who take EFV have experienced neuropsychiatric ADR [15, 16] and depression following EFV therapy has been reported in 2% of patients [17].
Depression has been linked to decreased appetite, therefore there is a need to understand whether loss of appetite and depressive symptoms impact nutrition of HIV patients receiving ART in the DR Congo. A study among older people living in nursing homes showed that patients with depression ran twice the risk of having poor appetite compared to those not affected [18]. Appetite is a complex phenomenon that contributes to an individual's body weight. Loss of appetite may vary due to psychobiological changes and environmental factors [19]. The phenomenon becomes more complicated in cases of chronic illness. Because of the clinical importance of both depression and loss of appetite to HIV outcomes, and because of the elevated prevalence of both factors among HIV patients in conflict setting, there is a need for increased attention to issues of reverse causality and potential confounding.
The study particularly explored the impact of depressive symptoms and loss of appetite on the under nutrition among HIV patients on ART in Goma, a post conflict setting. We further hypothesize that depressive symptoms are related to under nutrition mainly in persons with loss of appetite among HIV under ARTMethodology
Participants were included in the study provided that they were at least 18 years old, on ART for at least 6 months, and had given written informed consent. Participants were interviewed during their routine visits to the treatment center from February 1st to March 3rd, 2016. The questionnaire was translated into French and Swahili and piloted in a sample of 20 respondents (not included in the final analysis). The interviews were conducted in French or Swahili, the most commonly used languages in Goma. The interviewers were provided guidance on questionnaire administration over two training sessions, which were organized by the research team. Participants were compensated for their time and transportation with an amount of 2 US dollars.
Primary independent variables: Depressive symptoms and loss of appetite
The Simplified Nutritional Appetite Questionnaire (SNAQ) was used to assess the loss of appetite and loss of weight within six months [23]. A SNAQ score of ≤ 14 indicates poor appetite and significant risk of at least 5% weight loss within six months. This score had good internal reliability with a Cronbach alpha of 0.80.
To measure depressive symptoms, we used the 10-item Hopkins Symptom Checklist for depression (HSCL-10) [24]. HSCL is a prime candidate as a valid clinical instrument for identifying depression in the primary health care [25], and in research [26, 27]. It has been used in the similar context in Uganda [28].
In the present study, respondents were asked whether they were bothered by each symptom on a 4-point scale from "not at all" (0) to "extremely" (3). The HSCL-10 score was calculated by dividing the total score by 10 (number of items). A score equal or above 1.6 was considered as indicative of the presence of depressive symptoms in the present study [24]. The internal consistency of this measure was good in this sample, with a Cronbach's alpha of 0.84.
Adherence to medication was based on patient self-reports. Patients were asked to quantify the number of days during which they failed to take their medicines over the previous 7 days. Selfreported non- adherence was defined as taking < 95% of the prescribed drugs over this period of time [29].
Food insecurity was measured by the Household Food Insecurity Access Scale (HFIAS) developed by the USAID-funded Food And Nutrition Technical Assistance (FANTA) project [30]. The HFIAS is a validated instrument and has been shown to distinguish food insecure from food secure households across different cultural contexts. It is a set of nine questions designed to reflect universal domains of the experience of food insecurity including 1) anxiety and uncertainty about the household food supply, 2) insufficient quality (includes variety and preferences of the type of food), and 3) insufficient food intake and its physical consequences. We calculated results in a categorical format including 1) food secure, 2) mildly food insecure 3) moderately food insecure, and 4) severely food insecure, which we dichotomized into food insecure versus food secure. The Cronbach's alpha was 0.89, demonstrating a good internal consistency of the scale in our sample.
The household index was constructed based on the method proposed by Filmer and Prichett [31]. The method relies on principal component analysis to create an index from a set of household assets (radio, tape recorder, television set, bicycle, hand torch, horse or donkey cart), housing conditions (roof material, number of rooms, wall type, windows, availability and type of latrine), and ownership of domestic animals. The study participants were ranked according to the wealth index score, divided into quintiles, from the lowest (first quintile) to the highest (fifth quintile). Higher quintiles indicate higher socioeconomic status. In the analysis we presented SES in three levels: low (lowest and lower quintile), middle (middle quintile) and High (Higher and highest quintile).
Bivariate analyses were performed using Chi-square tests for categorical variables and the t-test for age and BMI. The bivariate correlation coefficient was calculated. Factors associated with loss of appetite and under nutrition by bivariate analysis with p value ≤0.10 were entered into a respective logistic regression model to obtain Adjusted Odds Ratios (AOR) and their 95% confidence intervals (95% CI). An interaction term was introduced between loss of appetite and smoking in the under nutrition model to assess the presence of effect modification. The Breslow-day test was used for assessing the interaction effect. Significance level was set at p < 0.05.
Ethical review
This study was conducted in accordance with the Declaration of Helsinki and all procedures involving human subjects/ patients were approved by the ethics committee for research at the Kinshasa University, School of Public Health (No App: ESP/ CE/128/15 of 22.12.2015). Written and verbal informed consent were obtained from all patients. Verbal consent was witnessed and formally recorded. Subjects were also explained their right to withdraw at any point of the study.
Loss of appetite This study showed that 182 of the patients (50.1%: 95% CI: 45 – 55) had a poor appetite, i.e., SNAQ scores < 14, with a risk of developing future weight loss. We found that depression (2.17; 95%CI: 1.11 – 4.22), smoking (2.97; 95%CI: 1.03 – 8.58) and low socio-economic status (1.77; 95%CI: 1.07 – 2.92) were associated with loss of appetite Table 3.
Factors associated with under nutrition
The overall prevalence of under nutrition (BMI <18.5 kg/ m2) was 26.2% (95% CI: 21.7% - 30.7%). The logistic regression showed that loss of appetite (AOR: 3.29; 95% CI: 1.92 – 5.64) and receiving EFV (AOR: 2.13; 95% CI: 1.24 – 3.66) were significantly associated with under nutrition Table 4.
The prevalence of depressive symptoms estimated in this study is almost similar to what has been reported previously using the same tool [22, 32], though lower than reported in other HIV primary care sites that reported prevalences above 20% [33, 34]. The scales for screening depressive symptoms used in these studies did not exclude somatic measures, and probably may have therefore overestimated the prevalence of depressive symptoms. There were an overlap between symptoms of depression and symptoms of HIV infection [35, 36] in these estimates.
While depression may lead to poor ART uptake, it is also
Characteristics |
Overall* |
Gender |
p |
|
male female |
||||
Age, mean ± SD(years) |
43.36 ± 10.68 |
48.44 ± 10.11 |
41.81 ± 10.37 |
< 0.001 |
Age** - > median - ≤ median |
170(46.1) 199(53.9) |
58(67.4) 28(32.6) |
112(39.6) 171(60.4) |
< 0.001 |
Education level attained - None/ primary - Secondary or higher |
309(84.0) 59(16.0) |
57(66.3) 29(33.7) |
252(89.4) 30(10.6) |
< 0.001 |
Marital status - Widow(ed) - Other |
120(32.6) 248(67.4) |
6(7.1) 79(92.9) |
169(59.7) 114(40.3) |
< 0.001
|
Household size - > 6 members - ≤ 6 members |
175(47.9) 190(52.1) |
36(41.9) 50(58.1) |
154(55.2) 125(44.8) |
0.030 |
Disclosure ART with others
- No - Yes |
177(49.9) 178(50.1) |
58(69.0) 26(31.0) |
119(43.9) 152(56.1) |
< 0.001 |
Alcohol - Yes - No |
135(36.6) 234(63.4) |
37(43.0) 49(57.0) |
98(34.6) 185(65.4) |
0.157 |
Smoking - Yes - No |
20(5.4) 349(94.6) |
16(18.6) 70(81.4) |
4(1.4) 279(98.6) |
< 0.001 |
SES*** - Low - Middle - Higher |
175(47.4) 64(17.3) 130(35.2) |
32(37.2) 13(15.1) 41(47.7) |
143(50.5) 51(18.0) 89(31.4) |
0.021 |
Residence - At Goma - Outside Goma |
346(94.3) 21(5.7) |
82(95.3) 4(4.7) |
264(94.0) 17(6.0) |
0.625 |
Duration on ART - 5 years and + - 1- 4.9 years - < 1 year |
152(41.9) 136(37.5) 75(20.7) |
36(41.9) 33(38.4) 17(19.8) |
116(41.9) 103(37.2) 58(20.9) |
0.966 |
CD4 (cells/μl) - < 200 - ≥ 200 |
39(15.4) 215(84.6) |
6(10.5) 51(89.5) |
33(16.8) 164(83.2) |
0.251 |
Adherence to ART - Yes - No |
303(83.0) 62(17.0) |
73(84.9) 13(15.1) |
230(82.4) 49(17.6) |
0.597 |
ART Regimen - including EFV - without EFV |
105(28.5) 264(71.5) |
22(25.6) 64(74.4) |
83(29.3) 200(70.7) |
0.500 |
Food insecurity - Yes - No |
350(94.9) 19(5.1) |
80(93.0) 6(7.0) |
270(95.4) 13(4.6) |
0.381 |
Characteristics* |
Depression |
p |
|
Yes |
No |
||
Age, mean ± SD(years) |
42.24 ± 10.82 |
43.71 ± 10.65 |
0.284 |
Age** - > median - ≤ median |
32(18.8) 46(23.4) |
138(81.2) 151(76.6) |
0.291 |
Gender - Male - Female |
15(17.4) 63(22.4) |
71(82.6) 218(77.6) |
0.323 |
Education level attained - None/ primary - Secondary or higher |
70(22.7) 7(12.7) |
238(77.3) 51(87.9) |
0.068 |
Marital status - Widowed - Others |
30(25.0) 48(19.5) |
90(75.0) 198(80.5) |
0.229 |
Household size - > 6 members - ≤ 6 members |
36(20.7) 41(21.7) |
138(79.3) 148(78.3) |
0.815 |
Residence - At Goma - Outside Goma |
74(21.4) 4(20.0) |
271(78.6) 16(80.0) |
0.878 |
Alcohol - Yes - No |
28(20.7) 50(21.6) |
107(79.3) 182(78.4) |
0.855 |
Smoking - Yes - No |
5(25.0) 73(21.0) |
15(75.0) 274(79.0) |
0.674 |
Disclosure ART with others - No - Yes |
42(23.6) 34(19.4) |
136(76.4) 141(80.6) |
0.341 |
SES*** - Low - Middle - Higher |
42(24.1) 16(25.0) 20(15.5) |
132(75.9) 48(75.0) 109(84.5) |
0.139 |
Duration on ART - 5 years and + - 1-4.9 years - < 1 year |
32(21.1) 30(22.2) 15(20.0) |
120(78.9) 105(77.8) 60(80.0) |
0.685 |
Food insecurity - Yes - No |
78(22.4) 0(0.0) |
299(85.9) 19(100.0) |
0.020 |
CD4 (cells/μl) - < 200 - ≥ 200 |
15(38.5) 52(24.3) |
24(61.5) 162(75.7) |
0.065 |
Adherence to ART - Yes - No |
63(20.9) 13(21.0) |
238(79.1) 49(79.0) |
0.995 |
Regimen - Including EFV - Without EFV |
27(25.7) 51(19.5) |
78(74.3) 211(80.5) |
0.186
|
overall |
78(21.3) |
289(78.7) |
|
Half of the patients reported a loss of appetite, which puts them at risk of weight loss. The prevalence of loss of appetite in this study was similar to which reported among patients with kidney disease [38], but, lower than what has been observed in patients with other chronic conditions, for example cancer (88%) [39]. Symptoms of depression, smoking and low socio economic status were associated with loss of appetite. Symptoms of depression are suitable for interventions. These findings correspond with other studies that have demonstrated that depressive symptoms constitute a barrier for nutrition intake [40, 41]. As depression can be a barrier for performing adequate nutritional self-care activities, health care professionals could routinely screen for and treat depressive symptoms among HIV patients under ART [42]. Smoking was significantly associated with loss of appetite in multivariate analysis. Smoking is known to reduce taste perception and appetite [43, 44]. Moreover, smoking may increase the feeling of fullness when consuming a hot evening meal, thereby lowering energy intake [45]. In addition, smoking is known to increase resting energy expenditure, which could lead to higher energy needs [46]. Those metabolic effects of smoking could explain the loss of appetite levels and put the smokers at risk of weight loss. However, some drugs may also suppress the appetite or cause side effects that put you off food, such as nausea, vomiting, indigestion or altered taste but we did not assess this aspect in this study.
Under nutrition was associated with loss of appetite and regimen containing EFV. Previous research showed that loss of appetite is an independent factor associated with under nutrition [47, 48]. A poor appetite can cause a lower nutritional intake [49] and thereby increases the risk of under nutrition [50]. As loss of appetite is associated with under nutrition and worsened prognosis [51, 52], these findings need to be addressed in the care of HIV patients on ART. In particular, Screening for poor appetite could potentially decrease underweight in HIV clinics.
We also found that people who received EFV reported to be more undernourished. But we did not find association between EFV and depression, while previous studies reported associations between EFV and depressives symptoms [15, 17]. EFV is one of the molecules of choice as the first-line drug, in combination with other ART for treatment of HIV infection according to DR Congo policy [14]. Researchers, policy makers and health professionals should find solutions helping HIV patients to better benefit from EFV treatment taking into consideration possible depressive symptoms among patients.
The link between BMI and depressive symptoms is inconsistent in the literature. The failure to show statistical association between depressive symptoms and under nutrition may be due to the relatively small sample size. However, the causality between the state of nutrition and the appearance of symptoms of depressions is ambiguous because we do not have explicit evidence whether the undernourishment actually stands behind the depression, or whether perhaps the state of nutrition leads to depressive disorders [53].
In order to prevent under nutrition, particularly in HIV infected patients, health care professionals should routinely assess and discuss appetite in HIV infected care. Discussions about appetite and dietary habits with patients and family members may serve for individualized nutrition advice and interventions.
Concerning appetite, we used SNAQ scale for assessing appetite, which even if have shown good measurement properties [23]. This scale has not previously been validated in DR Congo. However, this resulted in difficulties comparing the finding with other studies. Appetite is not a variable likely to generate more socially desirable responding. However, response bias related to respondent cannot be excluded. Another limitation was the small number of assessments to determine nutritional status, as BMI cannot, alone, fully assess nutritional status.
Even if we have used a multivariate technique, there can be two causes of residual confounding in this study. Firstly, there were probably the additional confounding factors that were not considered, because data on these factors was not collected. Secondly, there could be residual confounding in the noncompliance regression analysis because age was simply classified as "≤ 44 years" or "> 44 years". There could occur during data collection the errors in the classification of subjects with respect to confounding variables. Some people in Africa does not know exactly their age. These aspects can introduce a misclassification
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