This thesis has investigated the relationship between food insecurity and adherence to antiretroviral therapy, a relationship that is increasingly recognized and examined in different settings. Below the main conclusions from the study are summarized.
Although high adherence rates are found, this study indicates that adherence is negatively influenced by food insecurity for the most vulnerable members of the society, people suffering from severe food insecurity. No indications are found that people suffering from a
9 Mpande High School in KwaMpande, The Msunduzi Rural
10 Zane Mchunu, Project Manager at Pietermaritzburg Community Chest, phone call on the 6th of march 2017.
30
milder degree of food insecurity will be less likely to achieve a high level of adherence to their medication. This study therefore identifies the most vulnerable parts of the society as a key population where food insecurity will influence adherence to ART. Indications are also given that food supplement programs can increase adherence for PLHIV in this category, even though significance is lost when controlling for other variables. This would mean that food supplement programs such as nutritional support from clinics and feeding schemes in schools have a positive effect on adherence to ART. However, this would have to be studied further to draw conclusions on the topic. Even though adherence rates are lower among people suffering from severe food insecurity, it should be mentioned that this was not found to be an absolute hindrance to adherence. However, assisting the economically vulnerable by reducing severe food insecurity could positively affect adherence and help ensure the
attainment of the UNAIDS 90-90-90 goals.
The study has several limitations. The fact that the independent variable is measured at a household level instead of an individual level is problematic. The study assumption was that the reported level of food insecurity in the household was also representative for the
individual interviewed in that household. However, food might be split unequally between members of the family. For example, in a family where someone has HIV and is in need of food, that individual might be prioritized when food split between family members. If that would be the case in many households, then the effect of food insecurity on adherence might be underestimated in this study. If this was the case, it could also be an explanation as to why significant results are only found for PLHIV in the most vulnerable families. This remains only speculation. A further issue with the measure of food insecurity is that it measures self-perceived hunger. Different people in the same situation might answer differently even though they are in comparable situations, which could also affect the data. No available data on how long respondents had been on ART limited the analysis and working with self-reported data, there is the risk of respondents reporting higher levels of adherence than is the case. Homeless people and people living in shelters was not included in the study since the study was
conducted by random sampling of houses in the study area. This causes a selection bias where the most marginalised members of the community are not included. Finally, using cross-sectional data also limits our possibilities to draw conclusions about causality.
As mentioned in the paragraph above, there are reasons to analyse the reportedly high levels of adherence in this study with caution. However, it is not the first time high levels of
adherence are reported in this area. Previous studies on adherence to ART in KwaZulu Natal
31
by both Ncama et al. (2008) and Peltzer et al. (2010) found adherence levels above 80%.
Since then, one of the improvements in ART is that treatment regimens have become simpler through the introduction of single dose regimens. As difficult treatment regimens have been found to be an important hindrance to adherence (Orrell et al. 2003, Mills et al. 2006a), this could be one explanation for the high adherence rates observed in this study. Further research in the uMgungundlovu District would be interesting to try to understand the key determinants for achieving high rates of adherence.
Food security remains not only relevant for adherence but also contributes to better health outcomes. Even though further research might be necessary to verify the relationship found in this study, the fact that virologic suppression is aided by proper nutrition (Kalichman et al.
2015) provides further evidence of the importance of food security among PLHIV.
32
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Appendix
A1
Data collection
The data used in this essay comes from a big cohort study as explained in the data section. In this cohort, 10 236 individuals aged 15-49 years were randomly selected through a process described by Kharsany et al. (2015). People were eligible if they were in the right age-span, provided written informed consent in English or Zulu and were willing to take part in all the study procedures, which included clinical testing for both HIV and other sexually transmitted diseases and tuberculosis when indicated. Parental consent was also needed if the participant was < 18 years of age.
The study staff consisted of trained nurses with training in how to collect the data. Interviews were performed in the participants’ homes and upon arrival to a randomly selected household appropriate introductions were made and the head of the household/designee identified. A household composition form was then completed in order to establish the age, gender, and basic socio-demographic profile of all household members. Here questions on food insecurity were asked, which therefore reflects food insecurity on a household level. Study staff
collected the data through a handheld personal digital assistant (PDA) which, upon
completion of the composition form, randomly selected one individual who met the eligibility criteria to be asked if they were willing to participate in the study. There was in other words a two-step randomization process, where first the household and then the individual where randomly selected. In cases where the selected individual chose not to participate, the PDA chose a new randomly selected person from the household. Upon a second refusal, the household was replaced.
Upon consent from a participant, he/she was assigned a unique study number. This was linked to a questionnaire with structured questions that were read out to the participant by the nurse and answers were filled in directly in the PDA, reducing risks of error by being programmed to go to the right follow-up questions and having set answer options. Interviews were
conducted as private as possible, away from other family members. Questions covered a range of topics, including demographic, psycho-social and behavioural variables as well as HIV status variables. Upon completion of the questionnaire and taking of clinical samples a small
38
token in the form of a hat and a pair of gloves. Was given to the participant. Apart from that, no payment was made for participation.
To further strengthen the reliability of the results, a Quality Control team did random checks to see that data collected was correct and answers were also audited throughout the period of data collection and interview location and length of visit automatically registered by the PDA.
39 A2
Case Adherence Index questionnaire
A1. How often do you feel that you have difficulty taking your HIV medications on time? By
‘on time’ we mean no more than two hours before or two hours after the time your doctor told you to take it.
4 Never 3 Rarely
2 Most of the time 1 All of the time
A2. On average, how many days per week would you say that you missed at least one dose of your HIV medications?
1 Everyday 2 4–6 days/week 3 2–3 days/week 4 Once a week
5 Less than once a week 6 Never
A3. When was the last time you missed at least one dose of you HIV medications?
1 Within the past week 2 1–2 weeks ago 3 3–4 weeks ago
4 Between 1 and 3 months ago 5 More than 3 months ago 6 Never
The CASE Index score is calculated by adding up the points received on each of the three questions above. A score between one and four is possible on the first question and a score between one and six on the following questions. Index range: 3-16
Classification:
A score >10 = good adherence A score ≤10 = poor adherence
40 A3
General Household Survey Questions on Food Security
A1. Did your household run out of money to buy food during the past year?
A2. Has it happened 5 or more days in the past 30 days? (Skipped if A1 ≠ yes)
A3. Did you cut the size of meals during the past year because there was not enough food in the house?
A4. Has it happened 5 or more days in the past 30 days? (Skipped if A3 ≠ yes)
A5. Did you skip any meals during the past year because there was not enough food in the house?
41
A6. Has it happened 5 or more days in the past 30 days? (Skipped if A5 ≠ yes)
A7. Did you eat a smaller variety of foods during the past year than you would have like to, because there was not enough food in the house?
A8. Has it happened 5 or more days in the past 30 days? (Skipped if A7 ≠ yes)
Food Adequacy Access Index is calculated by giving one point for every affirmative answer. Range: 0-8.
People who did not respond to the questions were excluded from analysis.
Categorization 1:
0-1 Adequate access to food 2-6 Inadequate access to food
7-8 Severely inadequate access to food
Categorization 2:
0-1 Food secure
2-7 Food insecure
8 Severely food insecure
42 A4
Shortened version of CES-D used in the study
Instruction: We would like you to describe ways that you may have felt or behaved during the last week.
A1. I felt depressed.
A2. My sleep was restless.
A3. I had crying spells.
A4. I felt lonely.
43 A5. I could not get going.
Answers gave a shortened version of the Center for Epidemiologic Studies Depression Scale. Each question gave a score between 0-3. 0, for the answer “Rarely” and 3 for the answer “All of the time”.
Range: 0-15.
Regression with depression scale cut-off level of 4
This shortened version of the CES-D was developed based on the findings of Shrout and Yager (1989). In a large study, they found that a shortened 5-item version of the CES-D scale was nearly as sensitive and specific as the full 20-item scale, suggesting that such a scale could be used to save resources. In the analyses in this thesis, a cut-off level is not used. Shrout and Yager suggests that the original cut-off of 16 can be multiplied by n/20 when using a shortened scale (n= number of questions in shortened version). For a robustness check, the multivariate regression analysis is run with this cut-off level. This does not change the significance level for any of the results except the depression variable itself that is now significant at the p<0.05 level with an odds ratio of 0.58. Including a depression with this cut-off only slightly changes odds ratios the confidence intervals, while significance remains. This is displayed in the shortened regression output in table 7 below.
TABLE 7.MULTIVARIATE REGRESSION ANALYSIS
Adjusted model:
Variable
Probability of being adherent to ART O.R. [95%CI] P-value
Food insecure 0.98 [0.52-1.84] 0.944
Severely food insecure 0.40 [0.18-0.87] 0.020**
cons 134.76 [11.27-1610.75] 0.000***
Prob > F 0.000***
In this regression analysis control variables include all control variables included in the multivariate analysis displayed in table 6, only changing the variable depression. Results are significant at the same
In this regression analysis control variables include all control variables included in the multivariate analysis displayed in table 6, only changing the variable depression. Results are significant at the same