Abstract
Background:
Food and water insecurity are associated with poor health outcomes that may be exacerbated by social marginalization and barriers to healthcare experienced by sexual- and gender-minorities (SGM) in resource-limited settings. We explored factors associated with food and water insecurity in SGM with HIV.
Setting:
A longitudinal study of 357 men who have sex with men (MSM), transgender women (TGW), and other gender-identifying people in Lagos, Nigeria.
Methods:
Laboratory testing, interviews, food and water assessments, and anthropometry were performed quarterly. Robust Poisson regression with generalized estimating equations was used to evaluate factors potentially associated with food and water insecurity.
Results:
From 2014–2018, 357 SGM with HIV completed either the food or water assessments. At baseline, participants identified as cisgender MSM 265(74.2%), TGW 63(17.7%), or as non-binary/other gender 29(8.1%). Food insecurity and water insecurity were reported by 63/344(18.3%) and 113/357(31.7%), respectively at any visit. Food and water insecurity each decreased with ongoing study participation. Food insecurity was associated with non-partnered relationship status, CD4 count <500 cells/mm3, and lack of access to piped water. Water insecurity was associated with age ≥25 years, living with a man, transactional sex, and food insecurity.
Conclusion:
Food and water insecurity were common among SGM in Nigeria and decreased with continued study participation, suggesting amenability to intervention when SGM are successfully engaged in care. Targeted interventions to support food and water security may improve HIV-related outcomes, such as CD4 count.
Keywords: Men who have sex with men, transgender women, other gender identity, food insecurity, water insecurity, HIV, Nigeria
INTRODUCTION
Food and water insecurity increase the risk of poor health outcomes in a range of conditions including tuberculosis[1] and depression[2]. Relationships may be bidirectional in nature[3]. Understanding the complex interactions between food and water insecurity, health outcomes, and their underpinning mechanisms is critical to devise appropriate interventions to improve health.
Previous observational studies of people living with HIV (PLWH) in mostly resource-rich settings suggested that food insecurity was associated with behaviors that increased HIV transmission risk[4, 5] and decreased access to HIV care[5–11]. Among PLWH receiving antiretroviral therapy (ART), food insecurity has been associated with decreased ART adherence, lower CD4 cell count, incomplete virologic suppression, and decreased survival[12, 13]. Similarly, water insecurity was associated with a range of negative outcomes in PLWH but is even less well-studied than food insecurity in resource-limited settings[14, 15]. Water insecurity can cause toxin exposure and psychosocial morbidity[14, 16, 17]. The use of non-piped tap water is a marker of poverty, linked to psychological distress that contributes to higher risk sexual behaviors including condomless sex[18]. Shared underlying risks predispose to food and water insecurity and HIV which in turn increases susceptibility to, and increases the severity of each [12].
Nigeria is home to approximately one-sixth of the total population of sub-Saharan Africa and about 70% of Nigerians live on less than US$1.25 per day. The proportion of Nigerians with severe food insecurity, as defined by the United Nations Food and Agriculture Organization, increased from 6.5% in 2014–2016 to 9.1% in 2017–2019[19]. In 2014, The World Health Organization estimated that 30% of Nigerians did not have access to “improved” sources of drinking water[20], such as piped water in a dwelling, plot, or yard[21].
A few small studies from Nigeria suggest that food insecurity is common and may be associated with poor outcomes among PLWH. The prevalence of household food insecurity was 71.7% in a cross sectional study of mostly women with HIV[22]. Food insecurity was significantly associated with skipping drugs, and exchanging sex for food[22, 23]. The relationship of food and water security with HIV has been under-studied among SGM in Nigeria, despite their disproportionate burden of HIV. Nigerian men who have sex with men (MSM) have an estimated HIV prevalence of 23%[24] which is between 4–10 times that seen among Nigerians overall. Nigerian MSM, transgender women (TGW), and other SGM who accessed care in community-based health centers providing SGM-friendly care had HIV prevalence between 44–66%[25].
Lagos is Nigeria’s most populous city, but scarce data on food security mostly exist at the household rather than individual level. In a 2007 survey of food insecurity in urban households in Lagos state, only 17% of female-headed households were food secure[26]. In 2019, it was estimated that only 33.8% of Lagos households were food secure[27]. Lagos is a major economic center where access to improved water technologies is much more common than in other parts of Nigeria and only about 2.8% of households relied on unimproved sources from ponds, streams, and rainwater in 2004–2005[28]. We hypothesized that marginalized SGM populations may be disproportionately represented in those lacking access to improved water[29] as previously demonstrated with food insecurity[30]. Structural and societal barriers prevent equitable access to healthcare for SGM people[31]; it is possible that similar barriers exist around access to food and water. In exploratory analyses we evaluated associations of food and water insecurity with body mass index (BMI), sexual behaviors, and HIV-related outcomes in SGM living with HIV in Lagos.
METHODS
Study Population
TRUST/RV368 was a prospective cohort study that enrolled SGM participants in Abuja and Lagos, Nigeria, as described elsewhere[25]. Respondent-driven sampling (RDS) was used for recruitment, which consisted of several first-wave participants each provided with three referral coupons to distribute to other potential participants in their social network. Each new enrollee was provided with another three coupons to distribute. RDS is an efficient means of accessing populations that might otherwise be underrepresented in studies[32]. To be eligible, participants had to be adult (≥16 years in Abuja; ≥18 years in Lagos), be assigned male sex at birth, report anal sex with a male partner in the 12 months before enrollment, and present a valid RDS referral coupon. Participants completed enrollment evaluations at two visits approximately two weeks apart, then returned to the clinic every three months thereafter. Reimbursement was provided for each study visit (Nigerian Naira (NGN) 2000–3400 [about US$6–11]) and for each referral resulting in an enrollment (NGN 1500 [about US$5]). Food and water insecurity were only assessed from participants at the Lagos site who were living with HIV, so these analyses were restricted to those participants. The study opened to accrual at the Lagos site in April 2014 and the last follow-up visits were completed in May 2018.
Study Setting
TRUST/RV368 worked in partnership with existing community health centers that were supported by the President’s Emergency Plan for AIDS Relief (PEPFAR) and operated by non-governmental organizations to provide SGM-focused comprehensive HIV treatment and prevention services. Standard of care services at these centers included syndromic sexually transmitted infection (STI) management, and quarterly HIV testing. TRUST/RV368 provided additional peer support, free condoms and condom-compatible lubricants, and counseling by staff trained in SGM health on the prevention of HIV and other STIs. Trusted community leaders and peer navigators helped to foster continuing study participation[40]. Participants with HIV were provided ART via an on-site pharmacy without consideration of CD4 count. Adherence counseling occurred at each visit. Participants had access to educational interventions regarding the importance of nutrition, clean drinking water, hand washing, food preparation, and diarrhea management. Study participants could also be referred to support groups, food by prescription, nutrition counseling, or a local food bank. Bottled drinking water could also be supplied by the study.
Ethical Considerations
Prior to enrollment, all participants provided written informed consent. The study was conducted in accordance with the ethical standards of the responsible committee on human experimentation and with the Helsinki declaration. The study protocol was approved by Health Research Ethics Committees or Institutional Review Boards at the Nigerian Federal Capital Territory and Nigerian Ministry of Defense, Abuja, Nigeria; University of Maryland, Baltimore, MD; Johns Hopkins University, Baltimore, MD; and Walter Reed Army Institute of Research, Silver Spring, MD.
Demographics, sexual behaviors, and food and water insecurity
Demographic details and sexual behaviors were assessed by structured interview at each visit. The contents of the structured interview varied by visit. For example, self-reported gender identity, sexual orientation, education level, marital status, and internet use were recorded only at the enrollment visit. Self-reported occupation and engagement in transactional sex—defined as any exchange of sex for goods or money—were recorded at enrollment, 3 months, 9 months, and 15 months.
For participants at the Lagos site, counselors conducted safe food and water assessments for PLWH every 3 months, providing education and referrals to additional support services as needed. Participants who entered the study without HIV completed these assessments only after seroconversion. Food security was assessed using excerpted questions from the Household Food Insecurity Access Scale (HFIAS) and was defined as a positive response to any of the following questions about the past four weeks: ‘Did you or any other household member have to eat fewer meals in a day because there was not enough food?’ or ‘did you worry that your household would not have enough food?’ and a negative response to ‘do you have access to additional food supply when in need?’ Water insecurity focused on access to an improved source of water and was defined as lack of access to piped tap water. The food and water questions were asked every 3 months so responses could change at each study visit.
Laboratory and anthropometric measures
HIV testing used a parallel algorithm of two point-of-care tests with a third tie-breaker test as previously described[33]. At each visit, PLWH underwent CD4 quantification using a BD FACSCount (Becton Dickinson, Franklin Lakes, NJ). Plasma HIV RNA was quantified using nucleic acid amplification with the COBAS® AmpliPrep/COBAS® TaqMan® HIV-1 Test, v2.0 (Roche Molecular Diagnostics, Pleasanton, CA).
Height was measured to the nearest centimeter using a wall-mounted stadiometer (Sino Healthcare Group Ltd, Hong Kong), participants removed shoes and any items on their head. At each study visit, weight was measured to the nearest kilogram (kg) using a digital scale (Camry, Kowloon, Hong Kong) with participants wearing light clothing and no shoes. BMI was calculated as the participant’s weight in kg divided by the square of their height in meters (m). Underweight, normal, overweight, and obese were defined as BMI <18.5, 18.5–24.9, 25–29.9, ≥30.0 kg/m2, respectively.
Statistical Analysis
Among participants who provided data, baseline comparisons between groups who did and did not report food and water security were made using Pearson χ2 test, and exact χ2 test, as appropriate. Unadjusted and adjusted Poisson regression models with generalized estimating equations and robust error variance were used to estimate relative risks (RRs) and 95% confidence intervals (CIs) for factors potentially associated with (1) food insecurity and (2) water insecurity, based on prior knowledge from literature review [34]. Age, last month’s income (in Nigeria, a per-person monthly ‘living wage’ has been estimated at NGN 43,201[35]), CD4 count, ART use, HIV RNA, BMI, and food and water insecurity status were time-updated variables in the models. Other variables were considered static and ascertained at enrollment. CD4 count and HIV RNA data, if missing, were imputed using the closest value within 183 days for CD4 and 120 days for HIV RNA. For participants who answered food insecurity but not water insecurity questions, the water insecurity question was coded as missing/unknown, and vice versa. Data that were not collected at a given visit were carried forward from the last assessment or coded as unknown if no prior assessment was available. BMI was calculated using collected height and weight measures at each visit. When height and weight were missing they were imputed from the most proximal visit where they were recorded. If height and/or weight were still missing after applying these imputations, previously calculated BMI was used if available. A 2-sided type-1 error of 5% was considered statistically significant for all analyses. Analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC).
Model selection
The HPGENSELECT procedure[36] (SAS 9.4) was used to perform the model selection for longitudinal data using backward elimination. The backward method started with a full model that included all candidate variables, which allowed the joint predictive ability of variables to be assessed. Variables were then removed one-by-one from the full model until all remaining variables passed the significance inclusion criterion. This method removes the least important variables early on and leaves only the most important variables in the model. A high p-value of 0.25 was set as an inclusion criterion to allow non-significant but potentially influential variables to be kept in the model; this approach allows for the assessment of variables that may be of clinical relevance, but which may not rise to statistical significance[37, 38].
RESULTS
Between April 2014 and September 2016, 672 participants were enrolled in TRUST/RV368 in Lagos, including 370 who were living with HIV when first tested in the cohort and 35 who subsequently seroconverted during follow-up; three additional participants living with HIV transferred from the Abuja site over the course of the study. Of the 408 total participants with HIV seen at the Lagos site, 357 (87.5%) completed either the food or the water assessments, including 265 (74.2%) cisgender MSM, 63 (17.7%) TGW, and 29 (8.1%) identified as non-binary or other gender identity. The median (interquartile range [IQR]) person-years of observation, from the visit when HIV was first diagnosed, was 1.52 (0.23, 1.76). Overall, 344 (84.3%) participants generated 1473 visits with food security data available; and 357 (87.5%) participants generated 1653 visits with water security data. Missing data were accounted for by 181 visits from 132 participants who provided only water data, and in 1 visit from 1 participant who provided only food data. Participants who completed the food assessments were older than those who did not, median age (IQR) 23(21, 27) years and 21(20, 25) years, respectively (p=0.007). There were no other significant differences by baseline characteristics (Supplemental Table 1).
At the first visit with an assessment available, food insecurity was reported by 63/344 (18.3%), and lack of access to piped water was reported by 113/357 (31.7%) of respondents respectively (Table 1). Participants who reported food insecurity were more likely to be unemployed, have a lower CD4 count, report transactional sex with men, and lack access to piped water. Those reporting water insecurity were more likely to be younger and report food insecurity.
Table 1.
Characteristics of MSM, TGW, and other gender minority participants living with HIV in Lagos, Nigeria, by results of first available food and water assessments
| Food insecurity (N=344) |
Water insecurity (N=357) |
||||||
|---|---|---|---|---|---|---|---|
| Characteristics | Category | Yes (N=63) |
No (N=281) |
P-value | Yes (N=113) |
No (N=244) |
P-value |
| Age | <25 years | 39 (61.9) | 170 (60.5) | 0.887* | 60 (53.1) | 158 (64.8) | 0.047* |
| ≥25 years | 24 (38.1) | 111 (39.5) | 53 (46.9) | 86 (35.2) | |||
| Gender Identity | Cisgender man | 46 (73.0) | 208 (74.0) | 0.666 | 81 (71.7) | 184 (75.4) | 0.715 |
| Transgender woman | 13 (20.6) | 48 (17.1) | 21 (18.6) | 42 (17.2) | |||
| Non-binary/Other | 4 (6.3) | 25 (8.9) | 11 (9.7) | 18 (7.4) | |||
| Sexual Orientation | Gay/Homosexual | 39 (61.9) | 143 (50.9) | 0.203 | 64 (56.6) | 125 (51.2) | 0.715 |
| Bisexual | 24 (38.1) | 136 (48.4) | 48 (42.5) | 117 (48.0) | |||
| Other/Missing/Unknown | 0 (0.0) | 2 (0.7) | 1 (0.9) | 2 (0.8) | |||
| Education | Junior Secondary or Less | 0 (0.0) | 9 (3.2) | 0.285 | 3 (2.7) | 7 (2.9) | 0.790 |
| Senior Secondary | 46 (73.0) | 188 (66.9) | 80 (70.8) | 163 (66.8) | |||
| Higher than Senior Secondary | 17 (27.0) | 84 (29.9) | 30 (26.5) | 74 (30.3) | |||
| Marital status | Single/Never Married | 53 (84.1) | 257 (91.5) | 0.068 | 97 (85.8) | 222 (91.0) | 0.408 |
| Married/Living with a Woman | 3 (4.8) | 5 (1.8) | 3 (2.7) | 6 (2.5) | |||
| Living with a Man | 2 (3.2) | 12 (4.3) | 8 (7.1) | 8 (3.3) | |||
| Divorced/Separated/Widowed/Other | 5 (7.9) | 7 (2.5) | 5 (4.4) | 8 (3.3) | |||
| Occupation | Employed/student | 42 (66.7) | 229 (81.5) | 0.004 | 87 (77.0) | 191 (78.3) | 0.367 |
| Unemployed | 21 (33.3) | 44 (15.7) | 26 (23.0) | 49 (20.1) | |||
| Missing/Unknown | 0 (0.0) | 8 (2.8) | 0 (0.0) | 4 (1.6) | |||
| Last month income (NGN) | No income | 7 (11.1) | 42 (14.9) | 0.409 | 16 (14.2) | 40 (16.4) | 0.274 |
| 1–43,201 | 45 (71.4) | 198 (70.5) | 82 (72.6) | 171 (70.1) | |||
| 43201–100000 | 7 (11.1) | 34 (12.1) | 9 (8.0) | 28 (11.5) | |||
| >100000 | 4 (6.3) | 7 (2.5) | 6 (5.3) | 5 (2.0) | |||
| Internet use | Three times per week or less | 16 (25.4) | 61 (21.7) | 0.744 | 28 (24.8) | 52 (21.3) | 0.452 |
| Almost Every Day | 47 (74.6) | 218 (77.6) | 85 (75.2) | 190 (77.9) | |||
| Missing/Unknown | 0 (0.0) | 2 (0.7) | 0 (0.0) | 2 (0.8) | |||
| CD4 cell count cells/mm3 | Under 500 | 50 (79.4) | 158 (56.2) | 0.003 | 72 (63.7) | 145 (59.4) | 0.653 |
| ≥500 | 13 (20.6) | 115 (40.9) | 39 (34.5) | 92 (37.7) | |||
| Missing/Unknown | 0 (0.0) | 8 (2.8) | 2 (1.8) | 7 (2.9) | |||
| ART and HIV viral load | on ART, HIV RNA <50 cpm | 6 (9.5) | 51 (18.1) | 0.222 | 8 (7.1) | 35 (14.3) | 0.098 |
| on ART, HIV RNA ≥50 cpm | 9 (14.3) | 49 (17.4) | 14 (12.4) | 36 (14.8) | |||
| not on ART | 48 (76.2) | 178 (63.3) | 91 (80.5) | 170 (69.7) | |||
| Missing/Unknown | 0 (0.0) | 3 (1.1) | 0 (0.0) | 3 (1.2) | |||
| Transactional sex with men | No | 20 (31.7) | 120 (42.7) | 0.055 | 36 (31.9) | 101 (41.4) | 0.079 |
| Yes | 40 (63.5) | 133 (47.3) | 72 (63.7) | 125 (51.2) | |||
| Missing/Unknown | 3 (4.8) | 28 (10.0) | 5 (4.4) | 18 (7.4) | |||
| Lack of access to piped tap water | No | 30 (47.6) | 205 (73.0) | <0.001* | NA | NA | NA |
| Yes | 33 (52.4) | 76 (27.0) | NA | NA | |||
| Food insecurity | No | NA | NA | NA | 61 (54.0) | 174 (71.3) | <0.001 |
| Yes | NA | NA | 29 (25.7) | 24 (9.8) | |||
| Missing/Unknown | NA | NA | 23 (20.4) | 46 (18.9) | |||
| Body mass index kg/m2 | <25 | 58 (92.1) | 241 (85.8) | 0.218* | 95 (84.1) | 217 (88.9) | 0.307 |
| ≥25 | 5 (7.9) | 40 (14.2) | 18 (15.9) | 26 (10.7) | |||
| Missing/Unknown | 0 (0.0) | 0 (0.0) | 0 (0.0) | 1 (0.4) | |||
Note: P-values via Exact Chi-square or *Fisher’s Exact test.
Bold P-value <0.05. NGN Nigerian Naira. cpm copies per ml.
Factors associated with food and water insecurity
After adjusting for other factors, participants were less likely to report food insecurity after the enrollment study visit and if they had BMI ≥25 kg/m2 but more likely to report food insecurity if they had a non-partnered relationship status, had a CD4 count <500, and lacked access to piped water (Table 2). Unemployment, lack of ART use, and transactional sex with men had significant associations with food insecurity in unadjusted models but were not robust to adjustment.
Table 2.
GEE modeling the risk of food insecurity among MSM, TGW, and other gender minority participants living with HIV in Lagos, Nigeria
| Characteristics | Category | Relative Risk | P-value | Adjusted Relative Risk | P-value |
|---|---|---|---|---|---|
| Visit | 0 month | Reference | |||
| 3 month | 0.66(0.43–1.01) | 0.056 | 0.76(0.48–1.18) | 0.224 | |
| 6 month | 0.57(0.36–0.89) * | 0.013 | 0.68(0.42–1.09) | 0.111 | |
| 9 month | 0.35(0.20–0.61) * | <0.001 | 0.47(0.27–0.82) * | 0.008 | |
| 12 month | 0.35(0.21–0.59) * | <0.001 | 0.45(0.26–0.77) * | 0.003 | |
| 15 month | 0.39(0.23–0.67) * | <0.001 | 0.56(0.33–0.97) * | 0.039 | |
| 18 month | 0.34(0.20–0.57) * | <0.001 | 0.45(0.26–0.77) * | 0.004 | |
| Age# | <25 years | Reference | |||
| ≥25 years | 1.06(0.76–1.47) | 0.733 | |||
| Gender Identity | Cisgender man/Missing/Unknown | Reference | |||
| Transgender woman | 0.80(0.53–1.21) | 0.294 | |||
| Non-binary/Other | 1.00(0.54–1.83) | 0.996 | |||
| Sexual Orientation | Gay/Homosexual/Other/Missing/Unknown | Reference | |||
| Bisexual | 0.85(0.61–1.17) | 0.318 | |||
| Education | Senior Secondary or Less/Missing/Unknown | Reference | |||
| Higher than Senior Secondary | 1.07(0.75–1.53) | 0.695 | |||
| Marital status | Single/Never Married/Missing/Unknown | Reference | |||
| Married/Living with a Woman | 1.60(0.64–4.02) | 0.319 | 2.03(0.88–4.70) | 0.097 | |
| Living with a Man | 0.75(0.31–1.79) | 0.518 | 0.69(0.32–1.47) | 0.336 | |
| Divorced/Separated/Widowed/Other | 1.52(0.82–2.81) | 0.188 | 1.71(1.07–2.74) * | 0.024 | |
| Occupation | Employed/student/Missing/Unknown | Reference | |||
| Unemployed | 1.97(1.34–2.92) * | <0.001 | 1.43(0.98–2.08) | 0.060 | |
| Last month income# (NGN)† | No income/Unknown | Reference | |||
| 1–43,201 | 0.99(0.65–1.51) | 0.959 | |||
| 43201–100000 | 0.90(0.53–1.52) | 0.687 | |||
| >100000 | 0.83(0.32–2.17) | 0.708 | |||
| Internet use | Three times per week or less/Missing/Unknown | Reference | |||
| Almost Every Day | 0.84(0.57–1.22) | 0.355 | |||
| CD4 cell count# cells/mm3 | ≥500 | Reference | |||
| Under 500 | 1.71(1.22–2.40) * | 0.002 | 1.57(1.12–2.19) * | 0.008 | |
| Missing/Unknown | 1.55(0.40–6.00) | 0.524 | 1.21(0.26–5.57) | 0.811 | |
| ART# and HIV viral load# | on ART, HIV RNA <50 cpm | Reference | |||
| on ART, HIV RNA ≥50 cpm | 1.40(0.92–2.12) | 0.112 | |||
| not on ART | 2.50(1.68–3.71) * | <0.001 | |||
| Missing/Unknown | 0.60(0.08–4.50) | 0.620 | |||
| Transactional sex with men# | No | Reference | |||
| Yes | 1.54(1.12–2.10) * | 0.007 | 1.12(0.83–1.52) | 0.460 | |
| Missing/Unknown | 1.39(0.74–2.61) | 0.302 | 1.75(0.90–3.42) | 0.100 | |
| Lack of access to piped tap water# | No/Missing/Unknown | Reference | |||
| Yes | 2.27(1.68–3.08) * | <0.001 | 1.86(1.39–2.48) * | <0.001 | |
| Body mass index# kg/m2 | <25/Missing/Unknown | Reference | |||
| ≥25 | 0.62(0.38–1.01) | 0.054 | 0.60(0.37–0.98) * | 0.042 |
Note:
P-value < 0.05.
Time-updated variables. NGN Nigerian Naira.
NGN 43,201 cited as a ‘living wage’ in Nigeria. cpm copies per ml.
Similarly, after adjusting for other factors, water insecurity was less common at post-enrollment visits (Table 3). Participants with water insecurity were more likely to be aged 25 years or more, be living with a man, report transactional sex with men, report concomitant food insecurity, and have a BMI of ≥25 kg/m2. In unadjusted analyses, participants with water insecurity were less likely to be employed, taking ART, or be virally suppressed, but these associations were not robust to adjustment.
Table 3.
GEE modeling the risk of water insecurity among MSM, TGW, and other gender minority participants living with HIV in Lagos, Nigeria
| Characteristics | Category | Relative Risk | P-value | Adjusted Relative Risk | P-value |
|---|---|---|---|---|---|
| Visit | 0 month | Reference | |||
| 3 month | 0.90(0.72–1.11) | 0.318 | 1.00(0.80–1.26) | 0.975 | |
| 6 month | 0.62(0.47–0.81) * | <0.001 | 0.69(0.52–0.93) * | 0.013 | |
| 9 month | 0.45(0.33–0.63) * | <0.001 | 0.52(0.37–0.74) * | <0.001 | |
| 12 month | 0.47(0.35–0.64) * | <0.001 | 0.53(0.37–0.74) * | <0.001 | |
| 15 month | 0.35(0.24–0.51) * | <0.001 | 0.40(0.27–0.60) * | <0.001 | |
| 18 month | 0.19(0.12–0.31) * | <0.001 | 0.21(0.13–0.36) * | <0.001 | |
| Age# | <25 years | Reference | |||
| ≥25 years | 1.27(1.00–1.63) | 0.054 | 1.34(1.05–1.71) * | 0.017 | |
| Gender Identity | Cisgender man/Missing/Unknown | Reference | |||
| Transgender woman | 1.25(0.91–1.72) | 0.165 | 1.25(0.93–1.69) | 0.142 | |
| Non-binary/Other | 1.00(0.63–1.58) | 0.998 | 0.98(0.63–1.55) | 0.944 | |
| Sexual Orientation | Gay/Homosexual/Other/Missing/Unknown | Reference | |||
| Bisexual | 0.99(0.77–1.28) | 0.928 | |||
| Education | Senior Secondary or Less/Missing/Unknown | Reference | |||
| Higher than Senior Secondary | 0.99(0.74–1.31) | 0.925 | 0.90(0.70–1.18) | 0.455 | |
| Marital status | Single/Never Married/Missing/Unknown | Reference | |||
| Married/Living with a Woman | 1.22(0.64–2.34) | 0.542 | 1.06(0.58–1.91) | 0.857 | |
| Living with a Man | 1.63(1.11–2.40) * | 0.012 | 1.66(1.13–2.43) * | 0.009 | |
| Divorced/Separated/Widowed/Other | 0.79(0.44–1.39) | 0.411 | 0.67(0.39–1.16) | 0.152 | |
| Occupation | Employed/student/Missing/Unknown | Reference | |||
| Unemployed | 1.42(1.07–1.89) * | 0.015 | 1.19(0.91–1.56) | 0.202 | |
| Last month income# (NGN)† | No income/Unknown | Reference | |||
| 1–43,201 NGN | 0.95(0.73–1.23) | 0.695 | |||
| 43201–100000 NGN | 0.96(0.68–1.36) | 0.823 | |||
| >100000 NGN | 1.04(0.65–1.68) | 0.869 | |||
| Internet use | Three times per week or less/Missing/Unknown | Reference | |||
| Almost Every Day | 0.89(0.67–1.19) | 0.435 | |||
| CD4 cell count# cells/mm3 | ≥500 | Reference | |||
| Under 500 | 1.22(0.99–1.52) | 0.066 | |||
| Missing/Unknown | 0.94(0.34–2.57) | 0.904 | |||
| ART# and HIV viral load# | on ART, HIV RNA <50 cpm | Reference | |||
| on ART, HIV RNA ≥50 cpm | 1.33(1.01–1.74) * | 0.042 | |||
| not on ART | 2.06(1.61–2.63) * | <0.001 | |||
| Missing/Unknown | 0.52(0.12–2.27) | 0.382 | |||
| Transactional sex with men# | No | Reference | |||
| Yes | 1.70(1.36–2.11) * | <0.001 | 1.31(1.05–1.63) * | 0.016 | |
| Missing/Unknown | 0.80(0.49–1.29) | 0.353 | 1.19(0.72–1.98) | 0.492 | |
| Food insecurity# | No | Reference | |||
| Yes | 1.82(1.43–2.31) * | <0.001 | 1.51(1.20–1.90) * | <0.001 | |
| Missing/Unknown | 1.29(0.98–1.70) | 0.069 | 1.03(0.80–1.33) | 0.816 | |
| Body mass index# kg/m2 | <25/Missing/Unknown | Reference | |||
| ≥25 | 1.27(0.94–1.70) | 0.117 | 1.35(1.02–1.79) * | 0.034 |
Note:
Bold P-value < 0.05.
Time-updated variables. NGN Nigerian Naira.
NGN 43,201 cited as a ‘living wage’ in Nigeria. cpm copies per ml.
A sensitivity analysis including only participants who completed all seven study visits did not show any significant associations with food insecurity (N=53) (Supplemental Table 2). In those who completed all visits and completed water insecurity assessments (N=100) there were significant decreases in water insecurity at 15, and 18 months after enrollment (aRR 0.29 [0.14–0.59], p<0.001), and a negative association with being unpartnered (aRR 0.34 [0.12–0.94], p=0.038). The positive association with transactional sex was not robust to adjustment. Water insecurity was associated with food insecurity (aRR 1.49 [1.01–2.20] p=0.045, and a BMI ≥25 kg/m2 (aRR 1.72 [1.10–2.70], p=0.017); (Supplemental Table 3).
DISCUSSION
Food and water insecurity were common among SGM living with HIV in Lagos. More than one in six participants reported experiencing food insecurity, and water insecurity was even more common, reported by more than 30% of the study population. This is much more than the 10.5% of Lagos households that reported water insecurity in 2021[39], suggesting added vulnerability of our study population of SGM living with HIV as compared to the general Lagos population. In addition, those with food insecurity had a greater risk of water insecurity and vice versa[40]. Social and structural factors[41] including economic, organizational and political inequities probably increased vulnerability to food and water insecurity among SGM participants. Overlapping stigmas[42] including living with HIV, sex work, and sexual minority status[43] likely contributed to inequality of access to food and improved water through limited housing and employment opportunities. Gender and sexual minorities in sub-Saharan Africa have a high baseline burden of stigma-associated stressors[43] without the addition of food and water insecurity. However, there is likely an interplay between the two where increasing poverty and food and water insecurity is mediated through mental health stressors and workplace exclusion and discrimination. Though we did not directly measure the influence of syndemics, our findings suggest overlapping stigmas associated with HIV, sexual minority partnerships, transactional sex, and food and water insecurity.
Some studies have implicated persistent food insecurity as more predictive of depression than living with HIV[44]. Other studies have demonstrated complex interactions; water insecurity may predispose to food insecurity and that both, with HIV, increase the likelihood of depressive symptoms[14]. Food and water insecurity, and other psychological stressors[45] may increase sexual risk behaviors that indirectly lead to poor sexual health and undesirable HIV-associated outcomes.
Food and water insecurity decreased with ongoing study participation, illustrating that engagement in healthcare services sensitive to the needs of SGM leads to advantageous outcomes. This is in keeping with previous work by our group, which demonstrated that ongoing care engagement at a trusted community clinic resulted in improvements over time in other health-related outcome measures such as increased HIV knowledge, condom use, and condom compatible lubricant use[46, 47]. Unmeasured biases may have contributed to these outcomes and alternative explanations are possible. For example, study participants who had the most food and water security could be more likely to remain in the study, however several observed factors associated with water insecurity were robust to sensitivity analysis limited to participants who were followed through the end of the study. The primary focus of the TRUST/RV368 cohort was to evaluate risk behaviors associated with HIV and other STIs, not food or water insecurity. However, even in this non-nutrition-focused study an unexpectedly high proportion reported food and water insecurity. Interventions to identify those at risk and address deficiencies are critical and could make a large difference to SGM people experiencing these resource limitations regardless of the focus of the healthcare interactions. Testing and implementation of coordinated care packages—addressing one or more of the vulnerabilities identified in this study related to transactional sex, CD4 count, and/or BMI—are worthy of exploration.
There are scarce data on the prevalence of food insecurity in PLWH in resource-limited settings[12]. Of 67,038 individuals in HIV care programs in western Kenya, 33.5% of enrollees reported being food insecure[48]. In Uganda, a survey of 144 households, including primarily women with HIV, found that 59% had low dietary diversity and 44% were accessing food aid[49]. Households living with HIV managed food insecurity by consistently reducing household meal portion sizes and by selling household assets[49]. These food insecurity prevalence estimates are higher than those found in Lagos in SGM participants. However, differences in methodology and populations surveyed do not allow direct comparison. Inequalities in access to improved water between study participants and the vast majority of Lagos’ population were highlighted; likely partly explained by stigma and limited material resources experienced by minority populations.
As with other studies primarily of cisgender women in Africa[12, 22, 23], these data support the existence of associations between food and water insecurity and sex work. People may engage in transactional sex because they need food and water. Engagement in transactional sex[50] is associated with numerous adverse HIV-related and other health outcomes[51]. Addressing underlying access issues to food and water could improve HIV- and other health-related outcomes by reducing one of the drivers for sex work.
Poverty is strongly associated with poor nutrition, typically hunger and undernutrition, though overnutrition has also been described in different low-income populations globally[52, 53]. In a South African study, the poorest people saw a 50% increase in the prevalence of being overweight between 1992 and 2005 compared with only 7% in the richest[54]. Food and water vulnerability in minority PLWH in Lagos were associated with some markers of poverty e.g. transactional sex but not others such as income. Poverty and structural barriers to care, less access to ART, lower adherence to ART resulting in poorer health outcomes may result in unstable food and water access. Alternatively, reverse causality may predispose those with precarious access to not take ART because of the need to take medication with food or clean water[22].
Food insecurity was predictably associated with lower BMI, but was also associated with being unpartnered in adjusted analyses; one hypothesis is that divorced/widowed people may have financial responsibility for children, and extended family and be more likely to divert resources from themselves to others. Water insecurity was associated with living with a male partner and transactional sex in adjusted analyses. Food but not water insecurity was associated with lower BMI even though food and water insecurity were very strongly associated. More data are required to better understand the relationship between household structure, stigma, food and water vulnerability, and body composition.
The strengths of the study included its longitudinal design, inclusion of highly-marginalized and understudied SGM populations in Nigeria, and collaboration with trusted community organizations. However, the study had limitations; because food and water insecurity assessments were not administered to participants without HIV, the impact of HIV status on food and water security could not be assessed. No nutritional intake data, such as food frequency questionnaires[55] or detailed body composition data were available to better understand associations with BMI. Food security assessment was partial and was excerpted from a larger, validated tool. However, water assessment was not assessed using a validated measure. Methodologically robust, internationally validated tools are now available, allowing measurement of water security in different domains[56]. The food insecurity sensitivity analysis did not show the same associations seen in the whole sample, which may represent a true lack of association or be the result of small sample size. The trends in the sample overall may have been significant with a larger sample size. Also, as older persons were more likely to provide food and water insecurity data, it is possible that the protective effect of study visits over time on food and water insecurity may be from an enrichment of those who were already more secure. A high proportion of participants in the water insecurity dataset were missing food security data, which may have introduced selection bias and contributed to inconsistent associations of these outcomes with relationship status and BMI. The exploratory nature of these analyses limited our ability to define the relationships between exposure and outcomes of interest, but still produced a useful addition to the limited available data on food and water security amongst African SGM.
CONCLUSIONS
The presented data provide evidence that ongoing engagement in favorable clinical environments improved markers of food and water insecurity in sexual and gender minority PLWH in Lagos, Nigeria. This was likely facilitated by general health and nutrition advice and access to healthcare information. While food and water insecurity were closely interlinked, with many similarities in risk factors, they were not synonymous and each requires specific investigation in minority populations in Africa. Associations between less food and water access, lower ART usage, lower CD4 count, and higher HIV RNA presents an ideal opportunity for intervention. Nutritionally-focused screening could identify vulnerabilities that are amenable to intervention. Early identification of those with unreliable access to food and safe water, less likely to be on ART, would allow enhanced ART support. Further work is required to examine relationships between food and water insecurity, HIV, and other health outcomes using hypothesis-driven approaches such as sex work as a predictor of food and water insecurity. Access to safe, clean water and nutrition have been a mainstay of public health interventions in resource-limited settings and are readily scalable in marginalized populations living with HIV.
Supplementary Material
Acknowledgements
The TRUST/RV368 Study Group includes Principal Investigators: Manhattan Charurat (IHV, University of Maryland, Baltimore, MD, USA), Julie Ake (MHRP, Walter Reed Army Institute of Research, Silver Spring, MD, USA); Co-Investigators: Aka Abayomi, Sylvia Adebajo, Stefan Baral, Trevor Crowell, Charlotte Gaydos, Afoke Kokogho, Jennifer Malia, Olumide Makanjuola, Nelson Michael, Nicaise Ndembi, Rebecca Nowak, Oluwasolape Olawore, Zahra Parker, Sheila Peel, Habib Ramadhani, Merlin Robb, Cristina Rodriguez-Hart, Eric Sanders-Buell, Elizabeth Shoyemi, Sodsai Tovanabutra, Sandhya Vasan; Institutions: Institute of Human Virology at the University of Maryland School of Medicine (IHV-UMB), Johns Hopkins Bloomberg School of Public Health (JHSPH), Johns Hopkins University School of Medicine (JHUSOM), U.S. Military HIV Research Program (MHRP), Walter Reed Army Institute of Research (WRAIR), Henry M. Jackson Foundation for the Advancement of Military Medicine (HJF), Henry M. Jackson Foundation Medical Research International (HJFMRI), Institute of Human Virology Nigeria (IHVN), International Centre for Advocacy for the Right to Health (ICARH), The Initiative for Equal Rights (TIERS), Population Council Nigeria.
Conflicts of interests and Source of Funding
The authors declare that they have no conflict of interests.
This work was supported by a cooperative agreement between the Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., and the U.S. Department of Defense [W81XWH-11-2-0174, W81XWH-18-2-0040]; the National Institutes of Health [R01 MH099001, R01 AI120913, R01 MH110358]; Fogarty Epidemiology Research Training for Public Health Impact in Nigeria program [D43TW010051]; and the President’s Emergency Plan for AIDS Relief through a cooperative agreement between the Department of Health and Human Services/Centers for Disease Control and Prevention, Global AIDS Program, and the Institute for Human Virology-Nigeria [NU2GGH002099].
Footnotes
Disclaimer
The views expressed are those of the authors and should not be construed to represent the positions of the U.S. Army or the Department of Defense or the Department of Health and Human Services. The investigators have adhered to the policies for protection of human subjects as prescribed in AR-70
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