- Journal List
- HHS Author Manuscripts
- PMC11167453
As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsem*nt of, or agreement with, the contents by NLM or the National Institutes of Health.
Learn more: PMC Disclaimer | PMC Copyright Notice
AIDS. Author manuscript; available in PMC 2024 Jun 12.
Published in final edited form as:
AIDS. 2023 Feb 1; 37(2): 333–339.
Published online 2022 Oct 21. doi:10.1097/QAD.0000000000003413
PMCID: PMC11167453
NIHMSID: NIHMS1992114
PMID: 36541644
Gambo G. Aliyu,1 Jonathan G. Lawton,2 Andrew B. Mitchell,3 Alash’le G. Abimiku,3,4 Tapdiyel Jelpe,5 Orji Bassey,5 David J. Riedel,3 Mahesh Swaminathan,5 Joy Chih-Wei Chang,6 Joshua R. DeVos,6 Hetal Patel,6 Man E. Charurat,3 and Kristen A. Stafford2,3, on behalf on the NAIIS Study Group
Author information Copyright and License information PMC Disclaimer
The publisher's final edited version of this article is available at AIDS
Abstract
Background
HIV drug resistance (HIVDR) surveillance is an important tool to monitor threats to progress towards epidemic control. The characterization of HIVDR in Nigeria at the national level is needed to inform both clinical decisions and population-level HIV policy strategies. This study uses data obtained from the Nigeria HIV/AIDS Indicator and Impact Survey (NAIIS) to describe the prevalence and distribution of HIVDR in Nigeria.
Methods
NAIIS was a cross-sectional, population-based survey of households throughout Nigeria in 2018. NAIIS was designed to provide estimates of HIV prevalence and related health indicators from a nationally representative sample. The study population included participants aged 15–64 years who tested positive for HIV, had a viral load ≥1,000 copies/ml, and had available HIV drug-resistance genotypes. HIV isolates were genotyped to detect drug resistance mutations. Individual characteristics of study participants associated with HIVDR were identified using a weighted multivariable logistic regression model.
Results
Of 1355 respondents with available HIV genotypes, 293 (19%) had evidence of drug-resistant mutations (DRMs) that conferred resistance to ≥1 antiretroviral drugs. The majority of DRMs observed conferred resistance to NNRTIs (17.6%) and NRTIs (11.2%). HIVDR was associated with being ART experienced, longer duration on ART, and lower CD4 count, but not sociodemographic characteristics.
Conclusions
The population level DRM prevalence in Nigeria was consistent with what would be expected in a mature HIV treatment landscape. The continued roll out of dolutegravir anchored regimens should mitigate the impact of NNRTI resistance on population viral load suppression and progress towards epidemic control.
INTRODUCTION
Despite the expansion of antiretroviral therapy (ART) in Nigeria, the country has not achieved epidemic control [1]. Nigeria has one of the largest populations of people living with HIV in the world, with approximately 2.2 million cases and an HIV incidence rate exceeding all-cause mortality among people living with HIV [1,2]. Estimating the prevalence of HIV drug resistance (HIVDR) is important in countries like Nigeria with a large population in need of ART, limited availability of genotypic surveillance, and limited second- and third-line treatment options [3].
HIVDR patterns in Nigeria are important to monitor as the nation moves forward with efforts to widen ART access. When Sub-Saharan African countries were predominantly using NNRTI-based first-line ART, the prevalence of HIVDR was shown to exceed 80% among individuals failing initial regimens [4–6] and increase over time with continued virologic failure [7,8]. Levels of pretreatment and transmitted HIVDR are also rising throughout sub-Saharan Africa [9–11]. In Nigeria, prior studies have reported drug resistance mutations (DRMs) in 50–99% of people failing first-line ART [11–16]. Among Nigerians with recent HIV infections, HIVDR levels beyond 20% have been recorded [15]. The reported prevalence of DRMs associated with resistance to protease inhibitors (PIs) in Nigeria is especially concerning, as these mutations may reduce the efficacy of available second-line therapies [14,17,18].
Although prior assessments of HIVDR in clinical cohorts provide insight into the severity of this public health crisis in Nigeria, nationally representative estimates are needed to inform population-level treatment strategies. Identifying sociodemographic correlates of HIVDR among a representative sample of Nigeria will also be useful for targeting interventions such as viral load (VL) monitoring and HIV genotyping. In this study, we describe the prevalence and distribution of HIVDR among Nigerian adults with unsuppressed viral loads using data from the 2018 Nigeria HIV/AIDS Indicator and Impact Survey (NAIIS) [2,19].
METHODS
Survey sampling and population
NAIIS was a cross-sectional survey of households throughout Nigeria conducted in 2018. The methodology of NAIIS has been previously described in detail [2,19]. Briefly, NAIIS was designed to provide estimates of HIV prevalence, HIV incidence, VL suppression, and related health indicators from a nationally representative sample of the Nigerian population. The eligible survey population included members of targeted households who were aged 0–64 years and slept in the household the night before. Written informed consent was obtained from all participants.
Field and laboratory testing
NAIIS survey personnel administered field-based testing for HIV infection and CD4 count, as previously described [2]. Blood specimens were obtained from participants who tested positive for HIV and sent to laboratories, where HIV status was confirmed and incidence testing was performed using the HIV-1 Lag-Avidity Assay, which categorized infections as either recent or long-term [20]. Serological evidence of ARV use was also evaluated in specimens from people living with HIV at the University of Cape Town, South Africa. The assay was configured to detect first- and second-line ART according to the Nigeria national treatment guidelines, including efavirenz (NNRTI), nevirapine (NNRTI), atazanavir (PI), and lopinavir (PI) [21,22].
HIV genotyping
Stored plasma samples with a detectable VL ≥ 1,000 copies/ml were HIV-1 genotyped at the Institute of Human Virology Nigeria’s laboratory in Abuja and at the Nigerian Institute of Medical Research, Lagos using published methods [23]. Quality checks, confirmation of genotype findings, and drug resistance classification was provided by the HIVDR laboratory and the International Laboratory Branch, Division of Global HIV and TB at CDC, Atlanta, Georgia, USA.
Statistical analysis
We conducted descriptive analyses on each covariate in the full sample and in subsets stratified by HIVDR status, noting missing or extreme values. We then determined crude associations between HIVDR status and each covariate of interest using bivariate logistic regression. We used multivariable logistic regression to determine the adjusted odds of HIVDR associated with select covariates. Independent variables were considered for the multivariable model if they were independently associated with HIVDR at a significance threshold of p<0.02. We started with a saturated model, then removed covariates with the highest p-value one-by-one until each of the remaining model terms met a significance threshold of p<0.05. Descriptive statistics, HIVDR prevalence estimates, and regression analyses included survey weights to account for non-response. Absolute numbers are reported with associated weighted prevalence estimates. Statistical analyses were generated using SAS software (Version 9.4, SAS Institute Inc., Cary, NC, USA).
Ethical considerations
Human subject review and approval for the NAIIS survey and secondary uses of the data were provided by the CDC Institutional Review Board (IRB), the University of Maryland Baltimore IRB, and the Nigerian National Health Research Ethics Committee (NHREC). Health facilities identified by the respondents were provided results for certain tests, including HIV status, HIV viral load, and CD4 count. Individuals who tested positive for HIV but were not on ART were offered linkage to care.
RESULTS
Demographic and virologic characteristics of the sample
We evaluated data from 1,355 adults with VL ≥1,000 copies/mL and successful viral genotypes. The sample was predominantly female (61%) and between the ages of 25 and 45 years (59%). Most were not aware of their HIV status (81%) nor on ART (86%). Of those who were on ART, 97% were taking an NNRTI and 3% were taking a PI. The majority of HIV infections were classified as long-term (98%). The most prevalent HIV subtypes overall were CRF02_AG (52.8%) and G (35.5%). Socio-demographic, clinical, and virological characteristics of the sample are summarized in Table 1. We observed missing covariate information for 6% of participants.
Table 1:
Sociodemographic, clinical, and virological characteristics of participants aged 15–64 with HIV and viral load ≥1,000 copies/ml, NAIIS 2018.
| Variable | Total N=1,355 n (weighted %) | No HIVDR N=1,062 n (weighted %) | HIVDR N=293 n (weighted %) | uOR (95% CI) | aOR (95% CI) | |
|---|---|---|---|---|---|---|
| Age (years) | ||||||
| 15–24 | 196 (14.7) | 156 (15.0) | 40 (13.3) | Reference | ||
| 25–34 | 422 (30.2) | 338 (30.2) | 84 (30.3) | 1.3 (0.8 – 2.2) | ||
| 35–44 | 398 (28.6) | 303 (28.1) | 95 (30.7) | 1.4 (0.8 – 2.4) | ||
| 45–54 | 237 (18.3) | 185 (18.5) | 52 (17.3) | 1.1 (0.6 – 2.0) | ||
| 55–64 | 102 (8.3) | 80 (8.2) | 22 (8.4) | 1.4 (0.6 – 3.2) | ||
| Sex | ||||||
| Male | 445 (39.2) | 358 (40.6) | 87 (33.4) | Reference | ||
| Female | 910 (60.8) | 704 (59.4) | 206 (66.6) | 1.3 (0.9 – 1.8) | ||
| Highest Education Level | ||||||
| Not completed primary | 196 (13.5) | 156 (13.9) | 40 (11.8) | Reference | ||
| Primary | 380 (27.1) | 307 (27.7) | 73 (24.5) | 1.2 (0.7 – 2.1) | ||
| Secondary | 583 (43.4) | 461 (43.5) | 122 (43.0) | 1.3 (0.7 – 2.2) | ||
| Tertiary | 160 (12.2) | 117 (11.9) | 43 (13.3) | 1.5 (0.8 – 2.8) | ||
| Other | 35 (3.6) | 21 (3.0) | 14 (6.2) | 3.3 (1.4 – 7.7) | ||
| Missing | 1 (0.2) | 0 (0.0) | 1 (1.1) | |||
| Marital Status | ||||||
| Never married | 296 (23.4) | 228 (23.5) | 68 (22.7) | Reference | ||
| Married or living together | 761 (56.2) | 609 (56.7) | 152 (54.3) | 1.1 (0.7 – 1.7) | ||
| Divorced or separated | 128 (8.7) | 102 (8.9) | 26 (8.1) | 1.1 (0.6 – 2.0) | ||
| Widowed | 165 (11.3) | 119 (10.6) | 46 (14.4) | 1.6 (1.0 – 2.8) | ||
| Missing | ||||||
| Number of Sexual Partners (Last 12 Months) | ||||||
| None | 448 (32.5) | 320 (30.0) | 128 (43.0) | 3.5 (2.0– 6.3) | 2.8 (1.5 – 5.2) | |
| One | 724 (51.4) | 583 (51.9) | 141 (49.3) | 2.3 (1.3 – 4.1) | 1.9 (1.0 – 3.4) | |
| Two or more | 173 (15.4) | 151 (17.4) | 22 (6.8) | Reference | Reference | |
| Missing | ||||||
| Geopolitical Zone | ||||||
| Northwest | 84 (10.0) | 62 (9.2) | 22 (13.2) | 2.4 (1.0 – 6.1) | ||
| Northeast | 186 (10.3) | 121 (9.2) | 65 (14.8) | 2.6 (1.2 – 5.7) | ||
| Northcentral | 207 (10.7) | 150 (9.8) | 57 (14.9) | 2.4 (1.1 – 5.3) | ||
| Southeast | 268 (17.0) | 215 (16.7) | 53 (18.5) | 1.7 (0.8 – 3.7) | ||
| South-south | 439 (34.4) | 361 (36.0) | 78 (27.5) | 1.3 (0.6 – 2.7) | ||
| Southwest | 171 (17.6) | 153 (19.1) | 18 (11.1) | Reference | ||
| Knowledge of HIV Status1 | ||||||
| Aware | 255 (16.7) | 84 (7.4) | 171 (56.3) | 17.3 (11.3 – 26.2) | ||
| Unaware | 1075 (81.4) | 957 (90.6) | 118 (42.1) | Reference | ||
| Missing | 25 (1.9) | 21 (2.0) | 4 (1.6) | |||
| CD4 Count (cells/mm3) | ||||||
| Below 100 | 81 (5.8) | 41 (3.3) | 40 (16.4) | 7.8 (4.1 – 14.7) | 3.4 (1.5 – 7.9) | |
| 100–199 | 148 (11.0) | 105 (10.0) | 43 (15.1) | 2.7 (1.5 – 4.6) | 1.8 (1.0 – 3.3) | |
| 200–349 | 324 (23.2) | 254 (23.0) | 70 (24.0) | 1.6 (1.0 – 2.5) | 1.6 (0.9 – 2.7) | |
| 350–500 | 296 (22.1) | 240 (23.4) | 56 (16.6) | 1.0 (0.6 – 1.6) | 1.1 (0.6 – 1.8) | |
| Above 500 | 484 (36.3) | 407 (38.8) | 77 (25.9) | Reference | Reference | |
| Missing | 22 (1.6) | 15 (1.5) | 7 (1.9) | |||
| Viral Load (copies/ml) | 150,426 | 144,741 | 174,582 | 1.0 (1.0 – 1.0) | ||
| Recency of Infection2 | Recent Infection | 37 (3.1) | 30 (3.3) | 7 (2.3) | N/A | |
| Long-term Infection | 1,318 (96.9) | 1,032 (96.7) | 286 (97.7) | |||
| Viral Subtype3 | CRF02_AG | 719 (52.8) | 578 (54.3) | 141 (46.1) | Reference | |
| G | 469 (35.5) | 353 (34.0) | 116 (41.7) | 1.5 (1.0 – 2.2) | ||
| A | 76 (5.5) | 59 (5.5) | 17 (5.8) | |||
| CRF06_cpx | 56 (4.1) | 42 (3.9) | 14 (5.0) | |||
| CRF09_cpx | 6 (0.4) | 5 (0.4) | 1 (0.2) | |||
| CRF11_cpx | 6 (0.4) | 6 (0.5) | 0 (0.0) | |||
| C | 5 (0.2) | 5 (0.3) | 0 (0.0) | |||
| Other A, G or AG-related recombinants | 5 (0.3) | 4 (0.3) | 1 (0.3) | |||
| B | 4 (0.2) | 3 (0.2) | 1 (0.3) | |||
| D | 3 (0.2) | 2 (0.1) | 1 (0.3) | |||
| F2 | 3 (0.1) | 3 (0.2) | 0 (0.0) | |||
| K | 1 (0.1) | 1 (0.1) | 0 (0.0) | |||
| CRF18_cpx | 1 (0.1) | 1 (0.2) | 0 (0.0) | |||
| ART Status4 | ||||||
| On ART | 205 (13.2) | 49 (4.2) | 156 (51.3) | 24.9 (15.6 – 39.9) | 14.9 (8.4 – 26.6) | |
| Not on ART | 1,107 (86.0) | 1,008 (94.8) | 137 (48.7) | Reference | Reference | |
| Missing | 5 (0.8) | 5 (1.0) | 0 (0) | |||
| NNRTI Detection* | ||||||
| On NNRTI | 197 (96.8) | 47 (94.8) | 150 (97.5) | 23.7 (14.5 – 38.7) | ||
| Not on NNRTI | 8 (3.2) | 2 (5.2) | 6 (2.5) | Reference | ||
| PI Detection* | ||||||
| On PI | 8 (3.2) | 2 (5.2) | 6 (2.5) | 3.4 (0.5 – 234) | ||
| Not on PI | 197 (96.8) | 47 (94.8) | 150 (97.5) | Reference | ||
| Duration on ART5* | ||||||
| Not on ART | 96 (50.1) | 30 (68.7) | 66 (43.6) | Reference | Reference | |
| < 12 months | 20 (9.2) | 7 (9.3) | 11 (6.1) | 51.1 (22.4 – 117.0) | 4.3 (1.5 – 11.8) | |
| 12–23 months | 4 (2.0) | 1 (1.0) | 3 (2.4) | 41.3 (4.1 – 419.1) | 3.1 (0.4 – 22.9) | |
| ≥ 24 months | 70 (32.4) | 9 (18.2) | 63 (40.6) | 6.3 (2.4 – 16.9) | 0.6 (0.2 – 1.6) | |
| Missing | 15 (6.2) | 2 (2.7) | 13 (7.4) | |||
Open in a separate window
Variable frequencies and weighted column percentages are displayed unless otherwise noted. Odds ratios denote crude (uOR) and adjusted (aOR) associations between HIVDR and each covariate. Only complete-case observations with long-term infections (N=1,235) were included in logistic regression analyses. Statistically significant odds ratios are bolded.
1Combining self-report and ARV biomarker.
2Assessed via Limiting Antigen-Avidity enzyme immunoassays [19].
3Only majority HIV subtypes G and CRF02_AG were compared in regression analyses.
4Detection of efavirenz, lopinavir, atazanavir, and nevirapine in blood specimens at the time of sample collection [21].
5Self-reported.
*ART-related statistics were calculated among participants with serological evidence of ARVs at the time of sample collection.
HIVDR mutations
Among 1,355 HIV genotypes, 293 (19%) had evidence of DRMs to ≥1 ART class. We estimated a drug resistance prevalence (including pretreatment and acquired drug resistance) of 19.2% (286 of 1,318 long-term infections) and a transmitted drug resistance prevalence of 13.8% (7 of 37 recent infections) (Table 1).
The majority of observed DRMs were associated with resistance to NNRTIs (17.6%) and NRTIs (11.2%). K103N was the predominant NNRTI-associated DRM (11.3%) and M184V was the most common NRTI-associated mutation (9.2%) (Table 2). Among participants with NNRTI resistant infections, 53.4% were taking an NNRTI at the time of sample collection (data not shown). PI DRMs were detected in just 1% of respondents. The most frequent PI-associated DRM was M46I (0.5%) (Table 2).
Table 2:
HIV-1 drug-resistance mutations among participants aged 15–64 with HIV and viral load ≥1,000 copies/ml, NAIIS 2018
| NRTI resistance mutations (N=1,355) | NNRTI resistance mutations (N=1,355) | PI resistance mutations (N=1,355) | |||
|---|---|---|---|---|---|
| Mutation | n (weighted %) | Mutation | n (weighted %) | Mutation | n (weighted %) |
| Any NRTI | 181 (11.2) | Any NNRTI | 274 (17.6) | Any PI | 18 (1.0) |
| M41L | 38 (2.1) | L100I | 8 (0.5) | L24I | 5 (0.2) |
| K65R | 25 (1.3) | K101E | 22 (1.1) | M46I | 10 (0.5) |
| D67N | 19 (1.4) | K101P | 4 (0.3) | M46L | 4 (0.3) |
| D67G | 9 (0.4) | K103N | 178 (11.3) | I50V | 2 (0.1) |
| T69D | 4 (0.2) | K103S | 8 (0.6) | I50L | 2 (0.0) |
| K70R | 27 (1.6) | V106M | 2 (0.2) | F53L | 1 (0.1) |
| K70E | 7 (0.3) | V106A | 13 (1.0) | I54V | 5 (0.2) |
| L74V | 1 (0.1) | Y181C | 50 (2.9) | I54L | 1 (0.0) |
| L74I | 10 (0.6) | Y181V | 4 (0.2) | G73S | 1 (0.1) |
| V75M | 6 (0.3) | Y188L | 9 (0.5) | L76V | 3 (0.2) |
| F77L | 2 (0.1) | Y188H | 2 (0.0) | V82A | 3 (0.1) |
| Y115F | 8 (0.6) | Y188C | 3 (0.1) | V82F | 1 (0.0) |
| M184V | 152 (9.2) | G190A | 53 (3.0) | V82M | 4 (0.1) |
| M184I | 13 (0.8) | G190S | 3 (0.1) | N83D | 1 (0.0) |
| L210W | 8 (0.6) | G190E | 1 (0.0) | I84V | 2 (0.1) |
| T215Y | 26 (1.4) | P225H | 28 (2.3) | L90M | 1 (0.1) |
| T215F | 21 (1.3) | M230L | 5 (0.3) | ||
| T215I | 3 (0.1) | ||||
| T215S | 2 (0.1) | ||||
| T215C | 1 (0.1) | ||||
| T215E | 2 (0.2) | ||||
| K219Q | 14 (0.6) | ||||
| K219E | 16 (1.2) | ||||
| K219N | 5 (0.3) | ||||
| K219R | 2 (0.1) | ||||
Open in a separate window
Frequency and weighted prevalence of specific HIV-1 drug resistance mutations among NAIIS participants aged 15–64 with viral load ≥ 1000 copies/ml.
Factors associated with HIV drug resistance
We examined individual factors associated with HIVDR in long-term infections. In unadjusted analyses, 51% of respondents with HIVDR had serological evidence of ARVs versus 4% among those without HIVDR (p<0.01). CD4 count was <100 cells/mm3 for 16% of those with HIVDR compared to 3% of those without. Viral subtype G was more likely to have ≥1 DRM than subtype CRF02_AG (p=0.04). HIVDR was also associated with NNRTI use (p<0.001), ART duration (p<0.001), knowledge of HIV status (p<0.001), and geopolitical zone (p=0.01) analyses (Table 1).
After multivariable adjustment, the odds of HIVDR were significantly higher for participants with CD4 counts <100 cells/mm3 (aOR: 3.44, 95% CI: 1.51 – 7.85) and between 100 to 200 cells/mm3 (aOR: 1.84, 95% CI: 1.02 – 3.34) compared to those with CD4 counts >500 cells/mm3. Being on ART (aOR: 14.9, 95% CI: 8.4 – 26.6) and taking ARVs for less than a year (aOR: 4.27, 95% CI: 1.54 – 11.8) were also associated with HIVDR after adjustment (aOR: 4.27, 95% CI: 1.54 – 11.8) (Table 1).
DISCUSSION
Nearly one in five virally unsuppressed Nigerian adults had a drug resistant HIV infection. However, our data indicate lower HIVDR rates compared to earlier studies, which showed that up to 99% of ART-experienced people failing first-line therapy had HIV DRMs [11–16]. Prior studies were smaller and were not population-based, which may explain their higher HIVDR prevalence estimates. We also observed HIVDR in 14% of recent infections, which roughly coincides with a recent report of a 20.5% prevalence of TDR in men who have sex with men and transgender women [15].
NNRTI-resistant infections were the most common type of inhibitor-specific HIVDR in our sample, driven primarily by the K103N mutation. NRTI resistance was also common, led by the M184V mutation. The predominance of these two mutations is consistent with several previous reports in Nigeria [11,14,15], whereas other studies noted a stronger predominance of Y181C [13,16,18]. Although the prevalence of NNRTI DRMs in our sample supports the move to dolutegravir-based combinations as first-line ART in Nigeria, resistance to NRTIs may still threaten initial treatment regimens. DRMs conferring resistance to PIs were rare in our sample (1%). PI usage was uncommon in NAIIS, which may suggest a weak selective pressure for PI resistance. Earlier studies report varying levels of PI resistance in Nigeria, possibly due to significant heterogeneity in sample sizes and ART experience [14,17,18]. Two recent studies by Crowell et al. did not detect any PI-associated DRMs [11,15].
Despite low ART uptake overall, more than half of the people with HIVDR were taking ARVs at the time of sample collection. These people were considered to be failing treatment. Our observation is in agreement with canonical knowledge of HIVDR, as the most common scenarios that give rise to DRMs are poor adherence to treatment and maintenance of a failing ART regimen [7,9,10,24,25].
We acknowledge certain limitations in this study. It is challenging to accurately estimate acquired and transmitted drug resistance based on infection recency testing alone, since we cannot not definitively determine if long-term drug resistant infections were developed due to ART exposure or transmitted from another individual. Similarly, our ability to differentiate pre-treatment HIVDR was limited by the absence of complete ART history data due to non-disclosure of HIV status to survey staff. Serological evidence of ARV metabolites was assessed at the time of sampling for the NNRTI and PI inhibitors that comprised the most common first- and second-line treatment options in Nigeria. Respondents were not tested for presence of resistance to integrase inhibitors such as dolutegravir, as it was just beginning scale up in Nigeria. Future surveillance efforts should prioritize this gap, as a high prevalence of the integrase inhibitor-associated DRM L71I in Nigeria was recently reported prior to treatment [26].
Conclusion
Population level DRM prevalence in Nigeria was consistent with what would be expected in a mature HIV treatment landscape. The continued rollout of dolutegravir-based regimens may mitigate the impact of NNRTI resistance on population-level VL suppression. Routine care visits including VL monitoring for people living with HIV surveillance is recommended to preserve the efficacy of NRTIs and PIs.
Funding Statement
This project is supported by the President’s Emergency Plan for AIDS Relief (PEPFAR) through the Centers for Disease Control and Prevention (CDC) under the cooperative agreement #U2GGH002108 to the University of Maryland, Baltimore and by the Global Funds to Fight AIDS, Tuberculosis, and Malaria through the National Agency for the Control of AIDS, Nigeria, under the contract # NGA-H-NACA to the University of Maryland, Baltimore.
Disclaimer
The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the funding agencies.
Footnotes
The NAIIS Study Group is constituted as follows:
The NAIIS Study Group includes Principal Investigators: Isaac Adewole (Federal Ministry of Health), Sani Aliyu (National Agency for the Control of AIDS), Osagie Ehanire (Federal Ministry of Health), Mahesh Swaminathan (CDC Nigeria), Megan Bronson (CDC Atlanta), Manhattan Charurat (University of Maryland, Baltimore); Co-Investigators: Evelyn Ngige, Sunday Aboje, Charles Nzelu, Emanuel Meribole, Chike Ihekweazu, Chukuma Anyaike, Kayode Ogungbemi, Mukhtar Muhammad, Gregory Ashefor, Ibrahim Dalhatu, Ibrahim Jahun, Victor Sebastian, Ahmed Mukhtar, Tapdiyel Jelpe, Orji Bassey, McPaul Okoye, Aminu Yakubu, Matthias Alagi, Stacie Greby, Bharat Parekh, Hetal Patel, Andrew Voetsch, Daniel B. Williams, Kristin Brown, Stephen McCracken, Anne McIntyre, Nibretie Workneh, Bryan Morris, Rex Gadama Mpazanje, Wondimagegnehu Alemu, Erasmus Morah, Gatien Ekanmian, Gambo Aliyu, Alash’le Abimiku, Bola Gobir, Mercy Niyang, Isiramen Olajide, Baffa Ibrahim, Stephen Ohakanu, Ryan Leo, Geoffrey Greenwell, Adedayo Adeyemi, Bamgboye Afolabi, Ekanem Ekanem, Mustapha Jamda, Annie Chen, Otse Ogorry, Aminu Suleiman, Kolapo Usman, Ojor R. Ayemoba, Adebobola Bashorun; Collaborating Institutions: Federal Ministry of Health (FMOH), National Agency for the Control of AIDS (NACA), National Population Commission (NPopC), National Bureau of Statistics (NBS), the U.S. Centers for Disease Control and Prevention (CDC) Nigeria, CDC Atlanta, The Global Funds to Fight AIDS, Tuberculosis, and Malaria, University of Maryland Baltimore (UMB), ICF International, African Field Epidemiology Network, University of Washington, the Joint United Nations Programme on HIV and AIDS (UNAIDS), the World Health Organization (WHO), and the United Nations Children’s Fund (UNICEF).
REFERENCES
1. UNAIDS Data 2021. Geneva, Switzerland: Joint United Nations Programme on HIV/AIDS (UNAIDS); 2021. https://www.unaids.org/sites/default/files/media_asset/JC3032_AIDS_Data_book_2021_En.pdf (accessed 1 Oct2022).
2. Nigeria HIV/AIDS Indicator and Impact Survey (NAIIS) 2018: Technical Report. Abuja, Nigeria: Federal Ministry of Health, Nigeria; 2019. [Google Scholar]
3. Sigaloff KCE, Hamers RL, Wallis CL, Kityo C, Siwale M, Ive P, et al. Unnecessary Antiretroviral Treatment Switches and Accumulation of HIV Resistance Mutations; Two Arguments for Viral Load Monitoring in Africa. JAIDS J Acquir Immune Defic Syndr2011; 58:23–31. [PubMed] [Google Scholar]
4. Ayitewala A, Kyeyune F, Ainembabazi P, Nabulime E, Kato CD, Nankya I. Comparison of HIV drug resistance profiles across HIV-1 subtypes A and D for patients receiving a tenofovir-based and zidovudine-based first line regimens in Uganda. AIDS Res Ther2020; 17:2. [PMC free article] [PubMed] [Google Scholar]
5. Miti S, Handema R, Mulenga L, Mwansa JK, Abrams E, Frimpong C, et al. Prevalence and characteristics of HIV drug resistance among antiretroviral treatment (ART) experienced adolescents and young adults living with HIV in Ndola, Zambia. PLOS ONE2020; 15:e0236156. [PMC free article] [PubMed] [Google Scholar]
6. Muri L, Gamell A, Ntamatungiro AJ, Glass TR, Luwanda LB, Battegay M, et al. Development of HIV drug resistance and therapeutic failure in children and adolescents in rural Tanzania: an emerging public health concern. AIDS2017; 31:61–70. [PMC free article] [PubMed] [Google Scholar]
7. Boender TS, Kityo CM, Boerma RS, Hamers RL, Ondoa P, Wellington M, et al. Accumulation of HIV-1 drug resistance after continued virological failure on first-line ART in adults and children in sub-Saharan Africa. J Antimicrob Chemother2016; 71:2918–2927. [PubMed] [Google Scholar]
8. Boender TS, Sigaloff KCE, McMahon JH, Kiertiburanakul S, Jordan MR, Barcarolo J, et al. Long-term Virological Outcomes of First-Line Antiretroviral Therapy for HIV-1 in Low- and Middle-Income Countries: A Systematic Review and Meta-analysis. Clin Infect Dis Off Publ Infect Dis Soc Am2015; 61:1453–1461. [PMC free article] [PubMed] [Google Scholar]
9. Gupta RK, Gregson J, Parkin N, Haile-Selassie H, Tanuri A, Andrade Forero L, et al. HIV-1 drug resistance before initiation or re-initiation of first-line antiretroviral therapy in low-income and middle-income countries: a systematic review and meta-regression analysis. Lancet Infect Dis2018; 18:346–355. [PMC free article] [PubMed] [Google Scholar]
10. Hamers RL, Schuurman R, Sigaloff KC, Wallis CL, Kityo C, Siwale M, et al. Effect of pretreatment HIV-1 drug resistance on immunological, virological, and drug-resistance outcomes of first-line antiretroviral treatment in sub-Saharan Africa: a multicentre cohort study. Lancet Infect Dis2012; 12:307–317. [PubMed] [Google Scholar]
11. Crowell TA, Danboise B, Parikh A, Esber A, Dear N, Coakley P, et al. Pretreatment and Acquired Antiretroviral Drug Resistance Among Persons Living With HIV in Four African Countries. Clin Infect Dis Published Online First: 12
12. El Bouzidi K, Datir RP, Kwaghe V, Roy S, Frampton D, Breuer J, et al. Deep sequencing of HIV-1 reveals extensive subtype variation and drug resistance after failure of first-line antiretroviral regimens in Nigeria. J Antimicrob Chemother2021; 77:474–482. [PMC free article] [PubMed] [Google Scholar]
13. Chaplin B, Eisen G, Idoko J, Onwujekwe D, Idigbe E, Adewole I, et al. Impact of HIV Type 1 Subtype on Drug Resistance Mutations in Nigerian Patients Failing First-Line Therapy. AIDS Res Hum Retroviruses2011; 27:71–80. [PMC free article] [PubMed] [Google Scholar]
14. Odaibo GN, Okonkwo P, Adewole IF, Olaleye DO. Pattern of HIV-1 Drug Resistance among Adults on ART in Nigeria. World J AIDS2013; 03:327–334. [Google Scholar]
15. Crowell TA, Kijak GH, Sanders-Buell E, O’Sullivan AM, Kokogho A, Parker ZF, et al. Transmitted, pre-treatment and acquired antiretroviral drug resistance among men who have sex with men and transgender women living with HIV in Nigeria. Antivir Ther2019; 24:595–601. [PMC free article] [PubMed] [Google Scholar]
16. Etiebet M-AA, Shepherd J, Nowak RG, Charurat M, Chang H, Ajayi S, et al. Tenofovir-based regimens associated with less drug resistance in HIV-1-infected Nigerians failing first-line antiretroviral therapy. AIDS Lond Engl2013; 27:553–561. [PMC free article] [PubMed] [Google Scholar]
17. Rawizza HE, Chaplin B, Meloni ST, Darin KM, Olaitan O, Scarsi KK, et al. Accumulation of Protease Mutations among Patients Failing Second-Line Antiretroviral Therapy and Response to Salvage Therapy in Nigeria. PLOS ONE2013; 8:e73582. [PMC free article] [PubMed] [Google Scholar]
18. Udeze AO, Olaleye DO, Odaibo GN. Polymorphisms and drug resistance analysis of HIV-1 isolates from patients on first line antiretroviral therapy (ART) in South-eastern Nigeria. PLoS ONE2020; 15. doi: 10.1371/journal.pone.0231031 [PMC free article] [PubMed] [CrossRef] [Google Scholar]
19. Jahun I, Greby SM, Adesina T, Agbakwuru C, Dalhatu I, Yakubu A, et al. Lessons From Rapid Field Implementation of an HIV Population-Based Survey in Nigeria, 2018. JAIDS J Acquir Immune Defic Syndr2021; 87:S36. [PubMed] [Google Scholar]
20. Duong YT, Qiu M, De AK, Jackson K, Dobbs T, Kim AA, et al. Detection of recent HIV-1 infection using a new limiting-antigen avidity assay: potential for HIV-1 incidence estimates and avidity maturation studies. PloS One2012; 7:e33328. [PMC free article] [PubMed] [Google Scholar]
21. National Guidelines for HIV Prevention Treatment and Care. In: NAaSC Programme. Abuja: Federal Ministry of Health, Nigeria; 2016. [Google Scholar]
22. Koal T, Burhenne H, Römling R, Svoboda M, Resch K, Kaever V. Quantification of antiretroviral drugs in dried blood spot samples by means of liquid chromatography/tandem mass spectrometry. Rapid Commun Mass Spectrom2005; 19:2995–3001. [PubMed] [Google Scholar]
23. Rousseau CM, Birditt BA, McKay AR, Stoddard JN, Lee TC, McLaughlin S, et al. Large-scale amplification, cloning and sequencing of near full-length HIV-1 subtype C genomes. J Virol Methods2006; 136:118–125. [PubMed] [Google Scholar]
24. Gupta RK, Jordan MR, Sultan BJ, Hill A, Davis DH, Gregson J, et al. Global trends in antiretroviral resistance in treatment-naive individuals with HIV after rollout of antiretroviral treatment in resource-limited settings: a global collaborative study and meta-regression analysis. The Lancet2012; 380:1250–1258. [PMC free article] [PubMed] [Google Scholar]
25. Ekong E, Ndembi N, Okonkwo P, Dakum P, Idoko J, Banigbe B, et al. Epidemiologic and viral predictors of antiretroviral drug resistance among persons living with HIV in a large treatment program in Nigeria. AIDS Res Ther2020; 17:7. [PMC free article] [PubMed] [Google Scholar]
26. El Bouzidi K, Kemp SA, Datir RP, Murtala-Ibrahim F, Aliyu A, Kwaghe V, et al. High prevalence of integrase mutation L74I in West African HIV-1 subtypes prior to integrase inhibitor treatment. J Antimicrob Chemother2020; 75:1575–1579. [PMC free article] [PubMed] [Google Scholar]
