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. Author manuscript; available in PMC: 2016 Jun 1.
Published in final edited form as: AIDS Care. 2015 Jan 6;27(6):706–715. doi: 10.1080/09540121.2014.996517

Gender differences in diet and nutrition among adults initiating anti-retroviral therapy in Dar es salaam, Tanzania

Ajibola I Abioye 1, Sheila Isanaka 2, Enju Liu 1, Ramadhani S Mwiru 3, Ramadhani A Noor 1,2, Donna Spiegelman 2,4,5, Ferdinand Mugusi 4, Wafaie Fawzi 1,2,5
PMCID: PMC4366319  NIHMSID: NIHMS648967  PMID: 25562355

Abstract

HIV infected males have poor treatment outcomes after initiation of antiretroviral therapy (ART) compared to HIV-infected women. Dietary factors might mediate the association between sex and disease progression. However, the gender difference in diet among HIV-infected individuals in sub-Saharan Africa is largely unknown. The objective of this study was to examine differences in dietary intake among HIV-infected men and women. We conducted a cross-sectional analysis of dietary questionnaire data from 2038 adults initiating ART in Dar es Salaam, Tanzania to assess whether nutrient adequacy differed by sex. We dichotomized participants’ nutrient intakes by whether recommended dietary allowances (RDAs) were met and estimated the relative risk of meeting RDAs in males using binomial regression models. We also estimated the mean difference in intake of foods and food groups by gender. We found poorer dietary practices among men compared to women. Males were less likely to meet the RDAs for micronutrients critical for slowing disease progression among HIV patients: niacin (RR=0.39, 95% confidence interval (CI): 0.27 – 0.55), riboflavin (RR=0.81, 95% CI: 0.73 - 0.91), vitamin C (RR=0.94, 95% CI: 0.89 - 1.00), and zinc (RR=0.06, 95% CI: 0.01 – 0.24). Intake of thiamine, pantothenate, vitamins B6, B12, and E did not vary by gender. Males were less likely to eat cereals (mean difference (servings per day) = −0.21, 95% CI: −0.44 to 0.001) and vegetables (mean difference= −0.47, 95% CI: −0.86 to −0.07) in their diet, but more likely to have meat (mean difference= 0.14, 95% CI: 0.06 to 0.21). We conclude that male HIV patients have poorer dietary practices than females, and this may contribute to faster progression of the disease in males.

Keywords: HIV, Acquired immunodeficiency syndrome, nutritional sciences, nutritional status, food policy

Introduction

The relationship of nutrition and infection with human immunodeficiency virus (HIV) is complex (WorldHealthOrganization., 2003). The burden of HIV disease is greatest in sub-Saharan Africa where malnutrition is endemic (Koethe & Heimburger, 2010). HIV infection compromises access to, appetite for and absorption of food, and predisposes to poor nutritional status, and nutrition-related illnesses (Carbonnel, et al., 1997; Tiyou, Belachew, Alemseged, & Biadgilign, 2012). Conversely, poor nutritional status significantly impairs the health status of HIV infected patients, leading to faster disease progression and higher risk of mortality (Koethe & Heimburger, 2010; Liu, et al., 2011).

Proper design and implementation of nutrition interventions among people living with HIV (PLHIV) require a clear understanding of the clinical and sociodemographic factors that may modify patients’ nutritional status, disease progression, or both (Hailemariam, Bune, & Ayele, 2013). HIV disease progression may differ by gender, and male HIV infected patients experience worse outcomes (Jarrin, et al., 2008). While women may be at greater risk of food insecurity due higher burden in accessing economic resources (Ivers & Cullen, 2011), dietary practices of men may be limited by poor knowledge of nutrition, inferior cooking skills and lower interest in healthy eating (Caperchione, et al., 2012; Le, et al., 2013; Wang, Worsley, & Hunter, 2012). Without an understanding of the differences in dietary intake, we may be unable to reliably determine whether nutritional interventions should be different for men and women and how.

The objective of this study was to examine differences in dietary intake among HIV-infected men and women initiating ART in Dar es Salaam, Tanzania.

Methods

Study Design and Population

This was a cross-sectional analysis of baseline data from a cohort of HIV-infected adults enrolled in a trial of high dose multivitamins (vitamins B complex, C and E) compared with standard amounts of the Recommended Dietary Allowance (RDA) conducted from 2006 to 2009 in Dar es Salaam, Tanzania (www.clinicaltrials.gov; NCT00383669). Participants were individuals aged ≥18years, who were initiating ART at enrolment. Pregnant and lactating women were excluded. The enrolment criteria and treatment guidelines have been previously described (Isanaka, et al., 2012; Sudfeld, et al.)

Measurement of dietary intake

Dietary intake was assessed using a semi-quantitative food frequency questionnaire (FFQ) developed by investigators in the research group and employed in previous studies (Lukmanji, Hertzmark, Spiegelman, & Fawzi, 2013) since 1995 and administered by trained health-workers. The questionnaire was comprised of 108 commonly consumed food items alone and 11 ingredients. Participants were asked if they had consumed the foods in the prior one month, and if so, how often, and the frequencies were converted to servings per day.

We calculated the consumption of food groups by summing the intake of food items in each food group, and computed nutrient intakes and total energy using the Tanzania Food Composition Tables (Lukmanji, et al., 2008). The Household Dietary Diversity Score (HDDS), which reflects the economic ability of a household to access a variety of foods (Kennedy, Ballard, & Dop, 2010), was calculated as a simple count of the food groups usually consumed in the prior month (table 3) based on the guidelines of the Food and Agricultural Organization (FAO). We restricted data to FFQs completed within 30 days of enrolment and excluded those with extreme total energy intake - <800 kcals or >5000 kcals.

Table 3.

Intake of food groups by gender (Diff = male – female)

Food group Mean intake (servings per day) Univariate Multivariate†
Female Male Mean difference (95% CI) p-value Mean difference p- value
Vegetables 3.63 3.25 −0.38 (−0.65 to −0.10) 0.01 −0.47 (−0.86 to −0.07) 0.02
Cereals 3.78 3.51 −0.27 (−0.43 to −0.11) 0.001 −0.21 (−0.44 to 0.001) 0.05
Sweet 1.44 1.31 −0.12 (−0.22 to −0.02) 0.01 −0.18 (−0.32 to −0.04) 0.01
Oil 1.62 1.58 −0.04 (−0.16 to 0.07) 0.48 −0.09 (−0.25 to 0.07) 0.28
Fish 0.92 0.91 −0.01 (−0.07 to 0.06) 0.88 0.01 (−0.08 to 0.10) 0.75
Dairy 0.29 0.28 −0.004 (−0.05 to 0.04) 0.87 −0.02 (−0.08 to 0.04) 0.56
Eggs 0.25 0.26 0.001 (−0.04 to 0.04) 0.96 0.02 (−0.04 to 0.07) 0.58
Pulses 1.24 1.31 0.07 (−0.04 to 0.18) 0.19 0.06 (−0.08 to 0.20) 0.38
Fruits 3.13 3.21 0.08 (−0.16 to 0.33) 0.5 0.05 (−0.27 to 0.38) 0.75
Tuber 1.39 1.48 0.09 (−0.04 to 0.21) 0.17 0.08 (−0.09 to 0.25) 0.34
Alcoholic beverage* 0.01 0.01 0.002 (−0.01 to 0.01) 0.65 −0.005 (−0.02 to 0.01) 0.5
Non-alcoholic beverage* 1.07 1.18 0.11 (0.02 to 0.19) 0.01 0.05 (−0.06 to 0.21) 0.35
Meat 0.55 0.7 0.14 (0.08 to 0.20) <.0001 0.14 (0.06 to 0.21) 0.001
Dietary diversity score 10.99 11.07 0.08 (−0.03 to 0.18) 0.17 0.06 (−0.08 to 0.20) 0.42
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1 We considered alcohol intake as a special sub-group of beverages, but in computing dietary diversity, total beverage intake was considered.

2 Multivariate estimates adjusted for demographic factors including age (<30, 30 – 50 and >50 years), educational attainment (primary/none, secondary, tertiary), occupation (business/professional, skilled formal, skilled informal, unskilled, unemployed), marital status (never married, not currently married and married/cohabiting), household assets (0 – 1, 2 – 3 and 4 - 5), household size (1 - 4, 5-6, More than 6), district (Ilala, Kinondoni and Temeke), and season of ART initiation [December – March (long rains), April – May (harvest), June – September (post-harvest) and October – November (short rain)]; social factors such as disclosure of status (yes, no), smoking history (yes, no), amount spent on food (US$1 or less, more than US$1), and social support (never, much less than desired, less than desired, as much as desired). The estimates were also adjusted for clinical factors including hemoglobin (Greater than 11mg/dl, 8.5 to 11mgdl, less than 8.5mg/dl), WHO clinical stage of HIV infection (1 - 4), body mass index (less than 18.5, 18.5 to 24.99, above 25 kg/m2), mid-upper arm circumference (less than or equal to 23cm if male or 22cm if female, above 23cm if male or 22cm if female), loss of appetite (yes, no), presence of mouth sores (yes, no) or oral thrush (yes, no) and history of tuberculosis (yes, no).

3 Dietary diversity score was a simple count of the food groups usually consumed in the last month: beverages (alcoholic and non-alcoholic), cereal, dairy, eggs, fish, meat, oil, pulses, sweet, tubers, vegetables and fruits based on the guidelines of the Food and Agricultural Organization (FAO)

4 Mean difference and confidence intervals were estimated from linear regression models using female gender as the reference. Difference estimates less than zero mean that intake was greater among females while estimates greater zero suggest that intake was greater among males.

Socioeconomic and clinical assessments

At initiation of ART, research nurses administered questionnaires to collect socio-demographic data and conducted anthropometric measurements (table 1). We categorized the amount of money spent on food as ≤1250 Tanzanian shillings per day (USD 1.02, using the exchange rate at study start in 2006 (OANDACorporation, 2013)). Body mass index (BMI) was calculated as weight in kilograms divided by square of height in meters, and categorized as underweight if <18.5, normal weight if 18.5–24.99 and overweight and obese if ≥25kg/m2(WHO, 2006). Study physicians performed a complete medical examination and assessed HIV disease stage in accordance with WHO guidelines (WHO, 2004).

Table 1.

Clinical, social, demographic and clinical characteristics (n=2038)1

Characteristics Range No. Female Percent Male Percent
Age (yrs) <30 308 18.9 6.9
30-<40 yrs 987 51.2 42.4
40-<50yrs 551 23.3 35.0
50+ 192 6.6 15.6
Education completed Primary or none 1017 62.1 60.0
Secondary 546 34.2 30.8
Tertiary 89 3.7 9.2
Marital status Never married 240 14.6 11.3
Not currently married 645 41.7 22.9
Married or cohabiting 903 43.7 65.8
business/professional 210 10.8 18.7
Occupation skilled formal 174 3.7 28.0
skilled informal 179 5.7 24.2
Unskilled 574 41.3 23.2
Unemployed 461 38.6 5.9
Household assets1 0-1 99 7.0 3.5
2-3 945 57.1 55.3
4-5 627 36 41.2
History of smoking No 1346 97.6 79.8
Yes 121 2.5 20.2
Amount spent on food Household size2 More than 1 USD (>1250 379 20.9 28.7
1USD or less (≤1250 Tsh) 1244 79.1 71.3
Four persons or fewer 1074 63.4 60.2
Five or six persons 371 21.0 22.9
Seven or more people 277 15.6 17.0
Disclosure of HIV status No disclosure 160 8.7 8.7
Disclosed status 1637 91.3 91.3
Never 280 14.6 17.4
Social support Much less than desired 335 18.6 17.8
Less than desired 477 26.8 25.7
As much as desired 724 40.0 39.1
Body mass index (kg/m2) Less than 18.5 518 24.8 27.6
18.5 to 24.99 1193 56.7 62.9
25 and above 316 18.5 9.5
Hemoglobin (mg/dl) > 11 696 25.4 57.5
8.5 -≤ 11 832 48.7 30.8
< 8.5 414 25.9 11.6
CD4 cell count Less than 200 1557 79.3 79.8
200 to 349.99 374 19.1 18.4
350 and above 32 1.5 1.8
Clinical stage of HIV Disease 1 104 6.0 4.2
2 351 19.9 16.1
3 1207 63.5 63.5
4 232 10.5 16.2
Loss of appetite at enrollment No 1632 89.8 90.2
Yes 180 10.2 9.8
Presence of oral sores at enrollment Present 107 5.7 4.9
Absent 1856 94.3 95.1
History of tuberculosis Present 430 20.3 28.3
Absent 1454 79.7 71.7
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1

Household assets were computed from a list of assets that included a sofa, television, radio, refrigerator and fan

2

Household size was the number of adults and children with whom the participant lived.

Ethics

Ethical approval for the parent trial was obtained from the institutional review boards of the Harvard School of Public Health, Muhimbili University of Health and Allied Sciences, and the Tanzanian National Institute for Medical Research. Informed consent was obtained from participants at enrolment. All participants received standard care as per the Ministry of Health, Tanzania care and treatment guidelines.

Data analysis

We dichotomized the intake of each nutrient by whether recommended levels were consumed (nutrient adequacy) based on the Institute of Medicine's guidelines. In addition to the thresholds presented in table 2, 30g/d of fiber intake was regarded as adequate for males >50yrs and 21g/d for females >50yrs, 14g/d of PUFA for males >50yrs and 11g/d for females >50yrs, 1.7g/d of vitamin B6 for males >50yrs and 1.5g/d for females >50yrs, 20μg/d of vitamin D for males and females >70yrs, 1.3g/d of sodium for males and females aged 51 – 70yrs and 1.2g/d for those older than 70yrs, 400mg/d of magnesium for males aged 19 – 30yrs and 310mg/d for females aged 19 – 30yrs, 1200mg/d of calcium for males and females >50yrs and 18mg/d of iron for females aged 18 – 50yrs (IOM, 2010).

Table 2.

Association of gender and intake of recommended dietary allowance (RDA) of nutrients at baseline (ref=female)

Nutrient adequacy RDA % meeting Univariate Multivariate1
Female Males Female Males RR3 (95% CI) p-value RR p-value
Carbohydrate (g/d)2 130 130 100 100
Carbohydrate (%) 45 – 65 45 – 65 52.2 51.2 0.98 (0.90 – 1.08) 0.68 0.93 (0.82 – 1.05) 0.24
Protein (g/d) 46 56 61.2 25.8 0.42 (0.37 - 0.48) <.0001 0.44 (0.37 - 0.52) <.0001
Protein (% calories) 10 – 35 10 – 35 44 52.9 1.20 (1.09 - 1.32) 0.0001 1.12 (0.98 - 1.28) 0.1
Total fat (% calories)2 20 – 35 20 – 35 100 100
PUFA (g/d) 17 12 2 0.17 0.08 (0.01 - 0.61) 0.01
Fiber (g/d) 25 38 53.8 4.8 0.09 (0.06 - 0.13) <.0001 0.14 (0.10 - 0.20) <.0001
Vitamin A (μg/d) 700 900 39.8 39.5 0.99 ( 0.89 - 1.11) 0.72 0.96 (0.82 - 1.13) 0.64
Thiamin (mg/d) 1.1 1.2 76.7 71.6 0.93 (0.88 - 0.99) 0.02 0.98 (0.93 - 1.03) 0.46
Riboflavin (mg/d) 1.1 1.3 69.9 53.1 0.75 (0.70 - 0.82) <.0001 0.82 (0.74 - 0.92) 0.0006
Niacin (mg/d) 14 16 22.8 10.2 0.45 (0.35 - 0.57) <.0001 0.39 (0.27 - 0.55) <.0001
Vitamin B6 (mg/d) 1.3 1.3 73.2 77.2 1.06 (1.00 - 1.11) 0.05 1.03 (0.98 - 1.09) 0.22
Vitamin B12 (μg/d) 2.4 2.4 46.5 49.9 1.07 (0.97 - 1.18) 0.15 1.05 (0.92 - 1.20) 0.48
Pantothenate (mg/d) 5 5 22.2 23.8 1.07 (0.90 - 1.27) 0.44 0.97 (0.77 - 1.24) 0.83
Vitamin C (mg/d) 75 90 83.5 77.3 0.93 (0.88 - 0.97) 0.001 0.95 (0.90 - 1.01) 0.08
Vitamin D (μg/d) 15 15 0.4 0.9 2.57 (0.79 - 8.40) 0.12 2.91 (1.21 - 7.00) 0.02
Vitamin E (mg/d) 15 15 0.5 0.6 1.23 (0.36 - 4.17) 0.74 2.04 (0.29 - 14.46) 0.48
Folic acid (μg/d) 400 400 21.7 24.4 1.12 (0.95 - 1.33) 0.18 1.08 (0.85 - 1.38) 0.53
Sodium (g/d) 1.5 1.5 99.9 99.8 1.00 (0.99 - 1.00) 0.95 1.00 (0.89 - 1.13) 0.97
Potassium(μg/d) 4.7 4.7 99.9 100
Manganese (mg/d) 2.3 2.3 98.4 94.1 0.96 (0.94 - 0.98) <.0001 0.97 (0.91 - 1.04) 0.42
Magnesium(mg/d) 320 420 28.1 19.4 0.69 (0.58 - 0.83) <.0001 1.05 (0.83 - 1.32) 0.7
Copper (mg/d) 900 900 0 0
Calcium (mg/d) 1000 1000 0.6 0.3 0.54 (0.11 - 2.52) 0.43 0.32 (0.06 - 1.76) 0.19
Iron (mg/d) 18 8 5.7 82.7 14.49 (11.58 - 18.12) <.0001 14.29 (11.42 - 17.89) <0.0001
Zinc 8 11 7.9 0.3 0.35 (0.30 - 0.42) <.0001 0.05 (0.01 - 0.20) <0.0001
Phosphorus 700 700 91.5 91.4 1.00 (0.97 - 1.03) 0.91 0.98 (0.95 - 1.01) 0.12
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1

Multivariate estimates adjusted for age (<30, 30 – 50 and >50 years), educational attainment (primary/none, secondary, tertiary), occupation (business/professional, skilled formal, skilled informal, unskilled, unemployed), marital status (never married, not currently married and married/cohabiting), household assets (0 – 1, 2 – 3 and 4 – 5), district (Ilala, Kinondoni and Temeke), disclosure of status (yes, no), smoking history (yes, no), social support (never, much less than desired, less than desired, as much as desired), hemoglobin (Greater than 11mg/dl, 8.5 to llmgdl, less than 8.5mg/dl), WHO clinical stage of HIV infection (1 - 4), amount spent on food (1250 Tsh or less, more than 1250 Tsh), total energy intake (kcals) and household size (1 - 4, 5-6, More than 6), body mass index (less than 18.5, 18.5 to 24.99, above 25 kg/m2), mid-upper arm circumference (less than or equal to 23cm if male or 22cm if female, above 23cm if male or 22cm if female) and season of ART initiation [December – March (long rains), April – May (harvest), June – September (post-harvest) and October – November (short rain)], loss of appetite (yes, no), presence of mouth sores (yes, no) or oral thrush (yes, no) and history of tuberculosis (yes, no).

2

Intake of recommended levels of carbohydrates, potassium and copper did not vary. The percent meeting the recommended range of intake of total fat as a percent of calories did not also vary. Relative risks were not computed.

3

Relative risks (RR) and confidence intervals were estimated from negative binomial regression, using female gender as the reference. RR above 1 implies that men were more likely to meet the recommended levels of intake for the relevant nutrient compared to women. RR below 1 implies that women were more likely to meet the recommended levels of intake for the relevant nutrient compared to men.

We used binomial regression models to estimate relative risks and their 95% confidence intervals of meeting RDAs in males versus females (Spiegelman & Hertzmark, 2005). We estimated the mean intake of each nutrient (in servings per day), and mean difference with 95% confidence interval using linear regression models. From linear models that specified the nutrient intake as the dependent variable and frequently consumed foods (median serving per day above zero) as the independent variables, we selected foods that were significant predictors of between person variability (p<0.05) in intake of vitamins B, C, E and zinc, micronutrients which have been shown to be associated with HIV progression and mortality (Baum, Lai, Sales, Page, & Campa, 2010; Fawzi, et al., 2004; Kupka & Fawzi, 2002) (Baum, et al., 2010). We estimated mean differences by gender in intake of the foods with 95% confidence intervals using linear regression models. The selected foods contributed to more than 70% in the variation in intake of the micronutrients.

In multivariate analyses, we adjusted for potential confounders known to be associated with nutrition and/or HIV disease severity or progression (Carbonnel, et al., 1997; Lukmanji, et al., 2013; Weiser, et al., 2011) or identified in regression models (p<0.2) to be significantly related to the intake of adequate amounts of vitamins B, C, E and zinc. All multivariate estimates were adjusted for age (≤30, 31 – 40, 41 – 50 and >50 years), educational attainment (primary/none, secondary, tertiary), occupation (business/professional, skilled formal, skilled informal, unskilled, unemployed), marital status (never married, not currently married and married/cohabiting), household assets (0 – 1, 2 – 3 and 4 – 5), district (Ilala, Kinondoni and Temeke), disclosure of HIV status (yes, no), smoking history (yes, no), social support (never, much less than desired, less than desired, as much as desired), hemoglobin (>11mg/dl, 8.5 – 11mgdl, <8.5mg/dl), WHO clinical stage of HIV infection (1 - 4), amount spent on food (≤1250Tsh1250 Tsh), household size (small if 1 - 4, medium if 5-6, large if >6 persons), BMI (<18.5, 18.5 – 24.99, ≥25 kg/m2), mid-upper arm circumference (≤23cm if male or ≤22cm if female, >23cm if male or >22cm if female), season of ART initiation [December – March (long rains), April – May (harvest), June – September (post-harvest) and October – November (short rains)], total energy intake (calories) and clinical characteristics, including loss of appetite (yes, no), presence of mouth sores (yes, no), oral thrush (yes, no) and history of tuberculosis (yes, no). Adjustment for total energy intake was done using the nutrient residual method (Willett, Howe, & Kushi, 1997). Covariates with missing data were retained in the analysis using the missing indicator method(Miettinen, 1985). P-values were two-sided and significance was set at < 0.05. All statistical analyses conducted with SAS v 9.2 (SAS Institute Inc).

Results

This study included 2038 HIV-infected adults with male to female ratio of 1:2 (648 males, 1390 females). Mean age (±SD) was 38.1 years (±8.5). Men and women were similar with regard to most clinical and socio-demographic factors (Table 1). Thirty-seven percent of women were unemployed compared to only 6% of the men. Anemia was severe (hemoglobin<8.5mg/dl) in 26% of the females compared to only 12% of the men. A greater proportion of the men were at clinical stage four of HIV disease at enrolment: 16% compared to 11% women.

The mean (±SD) total energy intake was 2555 kcal (±828). There were no significant differences in the mean intake in males (2537 kcal ±807) and females (2563 kcal ±838). On average, total energy intake was contributed by carbohydrates (71.4% ±14.5), protein (9.9% ±1.6) and total fat (18.7% ±14.5). Males had a slightly greater intake of proteins (percent difference=0.24, 95% confidence interval (CI): 0.03 – 0.44) as a percentage of total calories intake. No significant gender differences were observed in the contribution of carbohydrates (percent difference=0.99, 95% CI: −0.91 – 2.89) and total fat (percent difference=−1.23, 95% CI:−3.13 – 0.68) to total calorie intake.

Males were less likely to meet the RDA for the B vitamins including niacin (RR=0.39, 95% CI: 0.27 – 0.55), riboflavin (RR=0.82, 95% CI: 0.74 - 0.92), zinc (RR=0.05, 95% CI: 0.01 –0.20), protein (RR=0.44, 95% CI: 0.37 – 0.53) and dietary fiber (RR=0.14, 95% CI: 0.10 – 0.20). Males were more likely to meet the RDA for iron intake (RR=14.29, 95% CI: 11.42 – 17.89) and vitamin D (RR=2.91, 95% CI: 1.21 – 7.00). Intake of thiamine, pantothenate, vitamins B6, B12, C and E did not vary by gender (table 2).

We estimated gender differences in intake (in servings per day) of 12 food groups (Table 3). The mean intake of vegetables (mean difference= −0.47, 95% CI: −0.86 to −0.07), cereals (mean difference= −0.21, 95% CI: −0.44 to 0.001) and sweets (mean difference= −0.18, 95% CI: - 0.32 to −0.04) were significantly lower among males than females. Intake of meat was greater among males than females (mean difference= 0.14, 95% CI: 0.06 to 0.21). There were no significant differences in intake of fruits, oils, fish, dairy, eggs and pulses, and the household dietary diversity score (HDDS).We explored gender differences in intake of 34 individual foods identified as significant contributors of between person variability in intake of vitamins B, C, E and zinc (Table 4). Intake of maize flour porridge (mean difference= −0.24, 95% CI: −0.3 to -0.17), green pepper as an ingredient (mean difference= −0.16, 95% CI: −0.24 to −0.08), pumpkin leaves (mean difference= −0.07, 95% CI: −0.11 to −0.02), and bitter tomato (mean difference= −0.05, 95% CI: −0.08 to −0.01) were less in males than females. Intake of ripe banana (mean difference= 0.01, 95% CI: 0.04 to 0.16), beef (mean difference= 0.09, 95% CI: 0.05 to 0.13), and groundnuts (mean difference= 0.07, 95% CI: 0.04 to 0.10) were greater in males. Intake of the other foods did not vary by gender.

Table 4.

Gender differences in the intake of 34 frequently eaten foods and ingredients that contribute to variation in intake of vitamins B, C, E and zinc (Diff = male – female)

Food group Foods/meals Mean intake (servings per day) Univariate Multivariate
Female Male Mean difference p-value Mean difference p-value
Vegetables Pumpkin leaves 0.23 0.17 −0.07 (−0.11 to −0.02) 0.001 −0.04 (−0.1 to 0.02) 0.23
Bitter tomato mixed 0.24 0.2 −0.05 (−0.08 to −0.01) 0.01 −0.07 (−0.11 to 0.02) 0.003
Cowpea leaves 0.15 0.13 −0.02 (−0.05 to 0.01) 0.13 −0.002 (−0.04 to 0.04) 0.93
Chinese cabbage 0.17 0.16 −0.02 (−0.05 to 0.01) 0.24 −0.02 (−0.06 to 0.02) 0.39
Spinach 0.56 0.58 −0.01 (−0.07 to 0.05) 0.71 −0.05 (−0.12 to 0.03) 0.22
Green pepper (ingredient) 0.81 0.66 −0.16 (−0.24 to −0.08) <.0001 −0.18 (--0.29 to −0.07) 0.001
Green pepper salad 0.19 0.18 −0.01 (−0.05 to 0.02) 0.53 −0.02 (−0.08 to 0.03) 0.35
Okra mixed 0.22 0.21 −0.01 (−0.04 to 0.03) 0.71 0.01 (−0.03 to 0.05) 0.72
Cassava leaves 0.13 0.13 0.005 (−0.02 to 0.03) 0.68 −0.002 (−0.03 to 0.02) 0.86
Cabbage 0.11 0.12 0.01 (−0.01 to 0.03) 0.31 0.01 (−0.01 to 0.03) 0.47
Cucumber 0.23 0.24 0.01 (−0.03 to 0.05) 0.66 0.02 (−0.04 to 0.08) 0.53
Cereals Uji (porridge) 0.64 0.46 −0.24 (−0.3 to −0.17) <.0001 −0.2 (−0.29 to −0.11) <.0001
Rice-not pilau 0.66 0.64 −0.03 (−0.08 to 0.02) 0.22 −0.04 (−0.11 to 0.03) 0.28
Stiff porridge (maize ugali) 0.95 0.94 −0.03 (−0.08 to 0.03) 0.35 −0.02 (−0.1 to 0.05) 0.52
Rice -mixed 0.13 0.13 −0.003 (0.02 to 0.84) 0.84 0.01 (−0.02 to 0.05) 0.47
Fruits Avocado 0.18 0.17 −0.01 (−0.05 to 0.02) 0.43 −0.01 (−0.06 to 0.04) 0.65
Orange fruit 0.46 0.5 0.01 (−0.05 to 0.07) 0.73 −0.01 (−0.09 to 0.08) 0.87
Mango 0.27 0.29 0.01 (−0.03 to 0.06) 0.62 0.03 (−0.03 to 0.09) 0.39
Watermelon 0.16 0.17 0.02 (−0.01 to 0.04) 0.25 0.03 (−0.01 to 0.06) 0.16
Papaya 0.25 0.27 0.03 (−0.01 to 0.07) 0.21 0.02 (−0.03 to 0.07) 0.47
Plantain mixed 0.17 0.2 0.03 (0.001 to 0.06) 0.05 0.03 (−0.02 to 0.08) 0.21
Ripe banana 0.46 0.57 0.1 (0.04 to 0.16) 0.001 0.08 (−0.005 to 0.16) 0.06
Tuber Sweet potato alone 0.16 0.15 −0.01 (−0.04 to 0.02) 0.5 −0.02 (−0.06 to 0.01) 0.22
Irish potato chips 0.2 0.25 0.02 (−0.01 to 0.05) 0.2 0.04 (0.001 to 0.08) 0.04
Cassava 0.22 0.26 0.04 (0.01 to 0.07) 0.02 0.02 (−0.02 to 0.07) 0.33
Fish Sardines 0.24 0.23 −0.02 (−0.05 to 0.01) 0.21 −0.03 (−0.07 to 0.01) 0.14
Fresh fish 0.16 0.15 −0.01 (−0.04 to 0.03) 0.61 −0.01 (−0.03 to 0.03) 0.67
Fried fish 0.37 0.41 0.02 (−0.02 to 0.06) 0.31 0.04 (−0.01 to 0.09) 0.11
Meat Chicken 0.13 0.15 0.03 (0.005 to 0.05) 0.02 0.03 (−0.001 to 0.06) 0.06
Beef 0.31 0.41 0.09 (0.05 to 0.13) <.0001 0.09 (0.04 to 0.14) 0.0004
Oils Vegetable oil (ingredient) 1.34 1.3 −0.04 (−0.13 to 0.05) 0.37 −0.04 (−0.16 to −0.09) 0.55
Dairy Cow milk 0.29 0.28 −0.004 (−0.05 to 0.04) 0.85 −0.02 (−0.08 to 0.04) 0.54
Eggs Eggs 0.25 0.25 0.0002 (−0.04 to 0.04) 0.99 0.02 (−0.04 to 0.07) 0.58
Pulses Groundnuts alone 0.16 0.23 0.07 (0.04 to 0.10) <.0001 0.07 (0.03 to 0.11) 0.001
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1 Multivariate estimates adjusted for age (<30, 30 – 50 and >50 years), educational attainment (primary/none, secondary, tertiary), occupation (business/professional, skilled formal, skilled informal, unskilled, unemployed), marital status (never married, not currently married and married/cohabiting), household assets (0 – 1, 2 – 3 and 4 – 5), district (Ilala, Kinondoni and Temeke), disclosure of status (yes, no), smoking history (yes, no), social support (never, much less than desired, less than desired, as much as desired), hemoglobin (Greater than 11mg/dl, 8.5 to 11mgdl, less than 8.5mg/dl), WHO clinical stage of HIV infection (1 - 4), amount spent on food (1250 Tsh or less, more than 1250 Tsh), and household size (1 - 4, 5-6, More than 6), body mass index (less than 18.5, 18.5 to 24.99, above 25 kg/m2), mid-upper arm circumference () and season of ART initiation [December – March (long rains), April – May (harvest), June – September (post-harvest) and October – November (short rain)], loss of appetite (yes, no), presence of mouth sores (yes, no) or oral thrush (yes, no) and history of tuberculosis (yes, no)

2 Mean difference and confidence intervals were estimated from linear regression models using female gender as the reference. Difference estimates less than zero mean that intake was greater among females while estimates greater zero suggest that intake was greater among males.

Discussion

This study evaluated baseline dietary intake among adults initiating antiretroviral therapy, and found females to be more likely to meet the recommended intake levels of many nutrients, including micronutrients important in protecting against HIV-related morbidity and mortality. There were gender differences in the intake of foods and food groups: while females were more likely to eat vegetables, cereals and sweets in their diet, males were more likely to have meat and tubers.

Evidence from well-designed clinical studies suggest that, in settings of equitable treatment access, among adults undergoing antiretroviral therapy, HIV disease progression and mortality is worse among men than women (Jarrin, et al., 2008). This survival difference may be attributed to genetic and environmental factors, including differential exposure to hormones, co-infections, illicit drugs, and effectiveness of health interventions (Jarrin, et al., 2008). Our results point to a potential role for nutrition in explaining the differential response to antiretroviral therapy among men and women, although a prospective analysis may better characterize the relationship, if any, of dietary intake and disease progression. From our results, among adults with the same total energy intake, men would be 2 – 96% less likely to meet the recommended dietary intake levels for vitamins B, C, and zinc.

Gender differences in diet may be driven by energy needs for daily living. Among older Caucasians and African-American adults, total daily energy expenditure was found to be 16% lower in females compared to males, a result of lower energy expenditure at rest and during physical exertion (Carpenter, et al., 1998). Evidence from systematic reviews suggests that resting energy expenditure (REE) may be higher among HIV-infected patients than in healthy adults, and may be greater in the presence of secondary infections (Batterham, 2005). Further, secondary infections are more common among men (Batterham, 2005). HIV-infected men may therefore have greater energy requirements in excess of sex differences seen in the general population due to higher energy requirements.

Women's financial decisions suggest that they give a higher priority to dietary quality (Ruel, Minot, & Smith, 2005). In an analysis of household expenditure survey data from 10 African countries (including Tanzania), female-headed households were found to spend more on fruits and vegetables than male-headed households, after controlling for income, household size and location (urban versus rural) (Ruel, et al., 2005). Women generally had greater influence in the household if resources are pooled, decisions taken jointly, or if they were better educated or older at the time of marriage, or if the difference in the age of the woman and her husband was small, and this often reflected in the family's nutrition (Ruel, et al., 2005; Smith, 2003).

Sociological evidence from Western settings suggests that dietary choices may be related to body image perception. In those cultures, women are expected to be light and thin while men are expected to be large and muscular, and women frequently seek to be underweight by avoiding energy dense foods and increasing intake of fruits and vegetables (Fagerli & Wandel,1999; McElhone, Kearney, Giachetti, Zunft, & Martínez, 1999). Energy-dense foods, more likely to lead to weight gain and preferred by men, are thus referred to as masculine, while vegetables and fruits are thought to be feminine (Fagerli & Wandel, 1999). Women's dietary preferences are more greatly influenced by social norms and this ‘gender identity’ for the foods than men (Kiefer, Rathmanner, & Kunze, 2005; McElhone, et al., 1999). It is unclear if similar patterns may underlie the associations observed in our population. Among very ill patients, however, the need to meet the energy needs for resting metabolism and moderate exertion may be more relevant than social pressures to maintain weight.

Previous studies, conducted in Northeastern USA(Kim, Spiegelman, Rimm, & Gorbach, 2001) & North India(Wig, Bhatt, Sakhuja, Srivastava, & Agarwal, 2008) reporting on gender differences in dietary adequacy among people living with HIV found a higher risk of inadequacy for macronutrients and micronutrients among women. More women, however, met the recommended intake levels for dietary fiber, fat, and protein than men in our study. The deviation of our results from previous studies may be attributable to remarkably distinct cultural and socioeconomic features of our setting compared to theirs. In Kim et al, patients who were unable to converse in English language were excluded. Men included in the final cohort were better educated than the women and the authors attributed gender differences in their results, in part, to the differential educational attainment and health consciousness (Kim, et al., 2001). Wig et al attributed their findings to gender differences in per capita income. Our study was conducted in a large African city in a setting of relative food security. Food is usually available in Dar es Salaam, at fairly affordable rates all year round (Jacobi, Amend, & Kiango, 2000). Household dietary diversity scores were high in our population and did not differ by gender. There were no significant gender differences in literacy and educational attainment among patients in our study.

At the time of enrolment to the study, patients would have known their HIV status and may have modified their diet on account of their improved health consciousness or the experience of oral and gastrointestinal symptoms. We did not have information on dates of diagnosis and were unable to explore the influence of time since diagnosis on dietary practices. It is arguable that the resulting potential misclassification is likely to have been random, and unrelated to gender. We however restricted our analysis to FFQs filled within 30days from commencement of anti-retroviral therapy to exclude the potential change in diet that may result from commencing anti-retroviral therapy. In spite of the smaller sample size that resulted, we still had sufficient power to estimate the presence of differences in adequacy of the intake of the micronutrients considered. Although the clinical stage of participants who were excluded was more likely to be stage 4 compared to those who were included, there were no differences in the socio-demographic characteristics and in the proportions attaining adequacy of intake of the individual nutrients between excluded and included participants.

Our findings may be limited by our use of average portion sizes in estimating nutrient intake, rather than sex-specific portion sizes. The possible effect is an underestimation of the nutrient intake among males. In the absence of sufficient data on sex-specific portion sizes from our population and similar settings, we considered sensitivity analysis to evaluate the effect of higher portion sizes among males on nutrient intake estimations to be arbitrary. Further, there is some evidence from previous research that social approval bias may lead to misclassification in estimating portion size and total energy intake, with males overestimating and females underestimating their actual dietary intake (Hebert, et al., 1997). Calorie-adjusted results remained consistent with unadjusted results, suggesting any error in the estimation of total energy intake may not have been substantial or the effects may have cancelled out. This requires further evaluation.

Conclusion

Male patients living with HIV/AIDS may be less likely to meet the recommended dietary allowance for micronutrients that may play a role in slowing down HIV disease progression and preventing adverse outcomes. It is unclear if this difference in nutrient intake may explain the differential survival among males and females. More research would be required to better characterize the influence of portion size of intake on the observed differences, and to examine the associations of these nutrients on HIV disease progression and ART outcomes.

Nutritional interventions, usually considered for HIV-infected women, may be important for HIV infected men as well.

Acknowledgement

We thank the study participants and field teams, including physicians, nurses, supervisors, laboratory staff, and administrative staff who made the study possible and the Muhimbili National Hospital, Muhimbili University of Health and Allied Sciences (MUHAS), Dar es Salaam City and the Municipal Medical Offices of Health, and the Ministry of Health and Social Welfare for their institutional support and guidance.

Funding: The parent trial (Clinical trials.gov identifier: NCT00383669) was sponsored by the National Institute of Health (NIH). This particular analysis received no specific grant from any funding agency in the public, commercial or not-for-profit sectors. The National Institute of Health had no role in the design, analysis or writing of this article.

Footnotes

Authorship: The authors’ responsibilities were as follows—AIA, SI, EL, RSM, RAN, FMM, and WWF: designed the research; FMM and WWF: conducted the research; AIA, SI and EL: analyzed data; AIA: wrote the manuscript; AIA and WWF: had primary responsibility for the final content of the manuscript; and all authors: contributed to and approved the final manuscript.

Conflicts of Interest: None of the authors had a conflict of interest

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