HIV infection is a major public health problem in Sub-Saharan Africa, a region with 70.8% of the total global HIV burden.1 Of the estimated 3.3 million children worldwide under the age of 15 years living with HIV in 2012, 2.9 million were living in Sub-Saharan Africa.1 The Sub-Saharan country of Botswana has a high prevalence of HIV infection, with 23% of adults aged 15–49 years infected. Similarly in 2012, Botswana had an estimated 11,000 children under the age of 15 years living with HIV and had 120,000 children who were orphans because of HIV.1 HIV infection has major implications for child rearing, family socioeconomic status, and the allocation of resources for meeting basic needs such as food, medical care, housing, and schooling.2 Food insecurity, defined as the lack of physical or economic access (through socially acceptable means) to sufficient food to meet people's dietary needs for a productive and healthy life,3 is also prevalent in Botswana where an estimated 27.9% were food insecure in 2010–2012.4 Food insecurity is an important predictor of several adverse child health outcomes including undernutrition/nutrient deficiencies, developmental delay, and poor overall health.5 Several pathways connect household food insecurity (HFI) to HIV disease progress.6 Food insecurity may directly lead to lack of an adequate dietary intake and subsequent macro- and micronutrient deficiencies that lead to an impaired immune system and further reductions to CD4%.6 A patient's continued disease progression, in turn, worsens HFI by redirecting income, assets, and time from employment or food procurement to caregiving.2,7 Moreover, food insecurity itself may adversely influence antiretroviral (ARV) medication adherence or absorption, further contributing to disease progression.6
Studies among low-income HIV+ adults in developed settings8–13 reported associations between higher food insecurity and lower CD4 counts or higher HIV-viral loads.6,14,15 A similar study among adolescents and young adults reported an inverse association between food insecurity and CD4 counts among HIV+ patients at Texas Children's Hospital in Houston.16 Building on these previous reports, studies are necessary examining the relationship between food insecurity and CD4% among young children, who may be especially vulnerable to the effects of food insecurity because of their dependence on caregivers for food and their unique nutritional needs. Moreover, pediatric studies are necessary in Sub-Saharan Africa, where food insecurity and HIV are highly prevalent. Thus, to fill these gaps, we conducted this study and hypothesized that food insecurity was inversely associated with CD4% among young HIV+ children treated at the Botswana-Baylor Children's Clinical Center of Excellence (COE) in Gaborone, Botswana.
This study was cross-sectional in design. The COE is a partnership between the Government of Botswana and the Baylor International Pediatric AIDS Initiative.17 The COE is the largest pediatric ARV therapy clinic in Botswana18 with over 2100 children on ARVs. All study materials including consent forms and questionnaires were available in Setswana and English. Research investigators and staff members at the COE were fluent in both Setswana and English. A convenience sample of participants were recruited from October 2011 to February 2013 during routine HIV+ clinic visits at the COE by the second author on 2–3 days of the week. Eligible children were 2–6 years of age, diagnosed with perinatally acquired HIV infection, and active patients of the COE (defined as having a clinical encounter within the past 6 months). Eligibility for the study was not affected by the date of the clinic visit or availability of study personnel. Only 1 child per household was enrolled. There were 137 eligible patients for this study from the clinic, based on a review of medical records. The Botswana Ministry of Health and the Institutional Review Board of Baylor College of Medicine approved this study.
Parents/guardians completed a household demographic questionnaire that assessed the following covariates: child age, gender, and orphan status (both parents alive or 1/both parents deceased); parent education; household income and location (village/rural or city); and food assistance in the past 4 weeks. Additionally, household wealth was estimated using a 10-item asset-based scale previously reported to be a valid indicator of wealth in a cohort study of HIV/STD prevention in Zimbabwe and related to risk of new HIV infection.19 This wealth scale assessed “fixed” assets (eg, running water and housing structure), and “sellable” assets (eg, radio and car/truck).19 The summed scores (0–10 range) were split into tertiles.19
Household food security, the main independent variable, was assessed using the Household Food Insecurity Assessment Survey (HFIAS) developed for cross-cultural use.20 The HFIAS was designed to measure predictable behavioral responses related to food insecurity, rather than actual household food supply or economic status. HFIAS was also developed to provide a unified measurement of food insecurity for widespread international use among developing countries. For example, in Burkina Faso, HFIAS had good reliability during multiple assessments (Cronbach's alpha = 0.81–0.85).21 On multiple assessments for validity, food insecurity was negatively associated with economic status (coefficient = −0.224 to −0.438, P < 0.05), dietary energy intake (coefficient = −0.185 to −0.235, P < 0.05), and body mass index (−0.186 to −0.238, P < 0.05).21 In another study in Burkina Faso, HFIAS was inversely associated with diet quality.22 Likewise, HFIAS had acceptable reliability and validity compared with household wealth in Tanzania.23 In Ethiopia, HFIAS was inversely associated with income and dietary diversity while positively associated with receiving wheat as food support among AIDS caregivers.24 In Bangladesh, HFIAS items had high reliability (Cronbach's alpha = 0.885)25 and were significantly correlated with food share of total household expenditures (−0.24 to −0.41, P < 0.01).25 For this study, key informant interviews (n = 4) conducted with COE staff and feasibility testing at the COE (n = 6 families not included in the analytic sample) were undertaken to culturally adapt the HFIAS for the local population.20 For this study, HFIAS measured food security over the past 30 days, providing a continuous summed score from 0 to 27. Internal validity was acceptable (Cronbach's alpha = 0.93).
The following clinical data were obtained from the patients' medical records: (1) current ARV therapy as a covariate (number of ARVs), (2) length of time on the newest ARV as a covariate (days on newest ARV), and (3) CD4%, the main dependent variable, determined by using a BD FACSCalibur flow cytometer (Becton Dickson, Franklin Lakes, NJ) at the Botswana Harvard Reference Laboratory, Princess Marina Hospital, Gaborone, Botswana. CD4% was chosen as the main dependent variable, rather than absolute CD4 counts, because absolute CD4 counts may vary within an individual young HIV+ child more than CD4%.26 Thus, the World Health Organization recommended measuring CD4% for surveillance of immune status in younger children with HIV rather than CD4 counts.26 The mean length of time from the clinic visit to collection of CD4% laboratories was 87 days. Research staff obtained duplicate measures of participants' height using a portable stadiometer (Seca model 213; Seca North America West, Chino, CA) and weight using a digital scale (Tanita model BWB-800S; Tanita Corporation of America, Inc, Arlington Heights, IL). Participants' BMI z-scores were calculated using standardized growth charts27 and included as a covariate because CD4 counts and food security have been associated with adiposity.5,6 For descriptive purposes, we also calculated participants' weight for height and height for age z-scores specific for this sample.
We calculated descriptive statistics, mean ± (standard deviation), median and interquartile range, or number (percentage), for the sample. We used linear regression for bivariate comparisons between each of the independent variables separately and CD4%. We used multiple linear regression with the dependent variable of CD4%, food insecurity score as the main independent variable, and controlled for covariates above. Participants with missing variables were dropped (n = 5). Differences between included and excluded participants were examined using a t test for continuous variables and Pearson χ2/Fisher exact tests for categorical variables. We used SAS 9.2 (SAS Institute Inc., Cary, NC) to conduct all analyses, and a significance level of P < 0.05 was chosen.
A total of 83 of 137 eligible participants were recruited and enrolled in the study (recruitment rate of 60.6%). Five participants had missing data and were excluded from the remaining analyses. Included and excluded participants did not differ by age, gender, parent highest education, household characteristics (income, wealth score, or location), orphan status, BMI z-score, food security category, ARV status, or CD4%.
For the analytic sample (n = 78; Table 1), children's mean age was 3.9 ± 1.3 years, 42.3% were female, 66.7% of parents/guardians had a junior secondary school education or less (≤eighth or ninth grade), 92.3% had household annual incomes of <3000 Pula (ie, less than approximately 344 US dollars), 28.2% received food assistance in the past 4 weeks, 43.6% resided in a city/town, 15.4% were orphans, and 100% were on 3 ARVs (26.9% on a 2-tablet regimen and 73.1% on a 3-tablet regimen). The average number of days for the newest ARV medication was 1058 ± 623 days. The prevalence of each of the 3 levels of food insecurity was as follows: 16.7% mild, 21.8% moderate, and 38.5% severe. Mean BMI z-score was −0.6 ± 1.4 and mean CD4% was 32.8% ± 9.4%.
Bivariate and multiple regression models for CD4% are presented in Table 2. The multiple regression model showed a significant main effect for food insecurity as measured by the continuous HFIAS score [beta = −0.6, 95% confidence interval (CI): −1.0 to −0.1], child gender (beta = 5.6, 95% CI: 1.3 to 9.8), and wealth score tertile (beta = 5.7, 95% CI: 0.3 to 11.2). For every 1-unit increase in food insecurity score, the CD4% decreased by 0.6% units. Compared with males, females had a CD4% that was 5.6% units higher. Compared with children from households in the highest tertile for wealth score, those from the middle tertile had CD4% that was 5.7% units higher. The multiple regression model accounted for 34% of the variability in CD4%.
This study reports the high prevalence of HFI (76.9%) among an outpatient sample of HIV+ children in Sub-Saharan Africa, where both HIV and food insecurity are highly prevalent. This prevalence was comparable to other African countries where the HFIAS has been administered to more general populations (not exclusively HIV+) to measure HFI, that is, 83.8% in Ethiopia,24 79.3% in Tanzania,23 77%–88% in Burkina Faso,28 and 61.8% in Nigeria.29 This prevalence of food insecurity was higher than the prevalence in the United States and Canada using a similar US Department of Agriculture HFI survey for samples of HIV+ adults who were of low income and/or homeless, that is, 48%–63% food insecure,8–11 and higher than the prevalence among a sample of HIV+ adolescents and young adults treated at Texas Children's Hospital in Houston, that is, 37.1% food insecure.16
Food insecurity was inversely associated with CD4%, adjusting for covariates, among HIV+ pediatric patients in a Sub-Saharan African setting, the region with the highest prevalence of HIV in the world. For every 1-unit increase in HFIAS score, there was a 0.6% unit decrease in CD4%. This inverse association replicates the only other report to include pediatric patients on the association of food insecurity and CD4 counts among HIV+ adolescents and young adults in the United States.16 This inverse association between food insecurity and CD4% was also consistent with multiple previous studies among low-income HIV+ adult populations in the United States and Canada.8–13 Altogether, there is a growing body of observational studies (1) that indicate food insecurity as a potential negative influence on HIV clinical outcomes and (2) that the HFIAS instrument may be a useful screening method to identify food insecure HIV+ individuals and target them for food supplementation. Further studies, including experimental trials, to confirm and better quantify the relationship between food insecurity and CD4% or counts among HIV+ patients, are warranted.
This study has several limitations. The study was cross-sectional and directionality cannot be assessed. Generalizability is limited because of the recruitment rate (60.6%) from 1 center in Gaborone, Botswana. The small sample size may lead to a type 2 error. We did not collect biomarkers of undernutrition, which may help characterize the mechanisms underlying the relationship between food insecurity and CD4%. We did not assess ARV therapy adherence or total length of time on any ARV therapy, although validated instruments to assess these variables in Sub-Saharan Africa are lacking and beyond the scope of this study. This study has several strengths, we (1) uniquely examined food insecurity and CD4% among a young HIV+ pediatric sample in a Sub-Saharan Africa setting, (2) used the HFIAS, a well validated and a preferred instrument to assess HFI, and (3) obtained CD4% from the medical record.
HFI was highly prevalent among an outpatient sample of young HIV+ children in Gaborone, Botswana. Our results suggest that addressing food insecurity may help improve outpatient HIV clinical outcomes, such as CD4% among young HIV+ patients in similar Sub-Saharan urban settings. Although studies are limited among HIV+ children,30 ready-to-use fortified spreads, fortified blended foods, and take-home rations seem promising.31,32 Further studies including experimental trials aimed at reducing food insecurity are necessary to confirm this speculation. Regardless, screening for and addressing food insecurity among HIV+ children in Sub-Saharan Africa seems warranted given its association with multiple other adverse health outcomes.
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