https://orcid.org/0000-0001-7137-0058
Figures
Abstract
Background
High viral load indicates poor treatment outcomes among people living with HIV (PLHIV) on antiretroviral therapy (ART). However, there is a dearth of evidence on specific factors associated with high viral load in resource-limited settings, including Tanzania.
Aim
The aim of this study is to identify factors contributing to high viral load among PLHIV aged 15 years and older on ART for at least six months in Tanga, Tanzania.
Methods
This is analytical cross-sectional study of 233 PLHIV attending the Care and Treatment Centre (CTC) in Tanga region from September to November 2023. A systematic sampling method was used to select participants for face-to-face interviews. A structured questionnaire was used to collect socio-demographic information while clinical data were collected from the patients’ records and CTC database. Descriptive analysis was used to estimate the prevalence of high viral load, while Pearson Chi-square tests compared categorical variables, and the logistic regression assessed determinants of high viral load.
Results
High viral load was prevalent among 35.2% [95% CI: 29.3%−41.6%] PLHIV attending CTC in Tanga region. Higher viral load was noted among younger adults (52.5%), those in sales/services (63.6%), professionals (54.5%), and unskilled workers (53.2%) compared to their counterparts. PLHIV with severe food insecurity were more likely to exhibit higher viral load compared to those from food secure households (AOR = 9.6; 95% CI: 2.6–35.2). Those consuming alcohol were 4.2 times more likely to have a higher viral load compared to non-drinkers (AOR = 4.2; 95% CI: 1.4–12.5). PLHIV aged 20–24 were 5.4 times more likely to exhibit elevated viral load levels compared to their older counterparts (AOR = 5.4; 95% CI: 1.8–15.7). Additionally, PLHIV in sales and services occupations were 6.1 times more likely to have a higher viral load compared to those in agriculture (AOR = 6.1; 95% CI: 1.1–35 and those facing high stigma were 4.3 times more likely to have a higher HVL than individuals with good social support and low stigma (AOR = 4.3, 95% CI: 1.0–18.7, p = 0.049).).
Conclusion
More than one in three adult PLHIV on ART in Tanga Tanzania had a high viral load. This burden highlights a steep climb to reach the last 95 target ahead of the deadline. Efforts should focus on young adults, those with households’ food insecurity, consuming alcohol, and with perceived stigma in Tanzania and areas with similar context.
Citation: Chande AI, Tesha NA, Sunguya B (2026) Factors associated with high viral load among HIV clients aged 15 years and older receiving treatment in Tanga city council, Tanzania: A facility-based cross-sectional study. PLoS One 21(5): e0348862. https://doi.org/10.1371/journal.pone.0348862
Editor: Hamufare Dumisani Mugauri, University of Zimbabwe Faculty of Medicine: University of Zimbabwe College of Health Sciences, ZIMBABWE
Received: April 18, 2025; Accepted: April 22, 2026; Published: May 7, 2026
Copyright: © 2026 Chande et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Data pertinent to this study are stored at the Muhimbili University of Health and Allied Sciences’ repository. The data is owned by Muhimbili University of Health and Allied Sciences Ethical Clearance Committee which has the legal and ethical mandate to share the data. It may be available to share upon request to the Director of Research and Publications, Muhimbili University of Health and Allied Science, P.0. Box 65001, email: drp@muhas.ac.tz.
Funding: The author(s) received no specific funding for this work.
Competing interests: This does not alter our adherence to PLOS ONE policies on sharing data and materials.
Abbreviation:: AIDS, Acquired immune deficiency syndrome; AOR, Adjusted odds ration; ART, Antiretroviral therapy; c-ART, Combined ART; CI, Confidence interval; CTCs, Care and Treatment Centre; HVL, HIV Viral Load; PCA, Principle Component Analysis; PEPFAR, U.S. President’s Emergency Plan for AIDS Relief; PLHIV, People living with HIV; SD, Standard deviation; TLD, Tenofovir + Lamivudine + Dolutegravir; UNAIDS, United Nations Programme on HIV/AIDS; VIF, Variance Inflation Factor; VLS, Viral Load Suppression.
Background
To combat the burden of HIV, the joint United Nations Programme on HIV/AIDS(UNAIDS) launched the “95-95-95” targets in 2015 [1]. The third 95 focuses on viral suppression, where only 53% had reached the agreed target globally [2]. Addressing the non-viral suppression can mitigate the burden of advanced HIV disease, prolong the lives of People Living with HIV (PHLIV), and therefore HIV transmissibility. Advanced HIV disease defined as clinical stages 3 or 4 is marked by severe symptoms like opportunistic infections and significant weight loss, alongside a CD4 count below 200 cells/mm³ [3]. Viral Load Suppression (VLS) is a crucial marker of treatment efficacy in HIV-positive patients and is considered achieved when the reading is less than 1,000 HIV RNA copies/mL of plasma. Despite the effective implementation of Antiretroviral Therapy (ART) initiatives in countries with high burden [4], individuals suffering from advanced HIV disease continue to present to hospitals, and as a result, medical admissions and deaths due to HIV-related infectious diseases remain high.
In Tanzania, the adult HIV prevalence rate is 4.4%, with higher rates in women (5.6%) than in men(3.0%) [5]. VLS among adults aged 15 years and older in Mainland Tanzania is 78.1% [5]. According to Tanzania’s ART recommendations, successful ART reduces HIV Viral Load (HVL), promotes immunological recovery, and hence increases CD4 cell count [6]. The Tanzania HVL testing guideline, recommends measuring CD4 counts every six months to monitor the immune response to ART [6]. HVL testing is also utilized alongside clinical and immunologic data to diagnose treatment failures more promptly, despite its limited capacity and availability [6,7].
Tanzania remains a priority country for HIV and acquired immune deficiency syndrome (AIDS) in sub-Saharan Africa, with approximately 1.7 million PLHIV as of 2019 year [8]. Njombe region in the Southern highlands of Tanzania reported the highest prevalence at 11.4%, while Tanga in the coastal areas had a moderate rate of 5.0% [8], although higher than the national average. The country has adopted the updated UNAIDS fast track goals to end the AIDS pandemic by 2030, aiming for 95% of PLHIV to know their status, 95% on treatment, and 95% of those on ART to be virally suppressed [9]. By 2022, 83% of HIV-positive individuals were aware of their status, 95% were on ART, and 92% of those on therapy had achieved VLS [9]. These rates of reaching these targets are not similar in all sub-population in the country. For example, the prevalence of VLS among adults aged 15 and older in Mainland Tanzania is only 78.1% [10]. This is below the 95% target for VLS by 2030 aimed at reducing morbidity, mortality, and the spread of HIV [6]. This study aimed to investigate factors influencing HVL among individuals aged 15 years and older receiving HIV treatment in Tanga City Council, Tanzania. This facility-based cross-sectional study aimed to provide insights into the determinants that impact VLS among PLHIV in the region.
Materials and methods
Study setting
This analytical cross-sectional study was conducted at four high-volume care and treatment clinics classified as Tier I in Tanga City Council. The facilities serve about 70% of the PLHIV population in the region (Source: City Medical Officer’s Office, Tanga). Data were collected from the 4th of September to 27th of November 2023, from Tanga Regional Referral Hospital, Ngamiani, Makorora, and Pongwe Health Centers. The total number of PLHIV across all these facilities was 11,374. These facilities serve half of the region’s HIV clients from the 98 of the facilities supported by the U.S. President’s Emergency Plan for AIDS Relief (PEPFAR), therefore offering a representative sample for the study. These facilities provided comprehensive HIV care and treatment services free of charge, including HIV testing, treatment, and VL monitoring, accessible to all individuals.
Study population
This study involved PLHIV aged 15 years and above, who had been on ART for at least six months, attending the four selected facilities. A systematic random sampling was used to recruit eligible participant who consented to participate in the study through written and verbal consent/assent. Patients with serious health complications or mental challenges were excluded from the study.
Sample size and sampling
To have a representative sample of PLHIV across the four facilities, the study used a sample size formula for finite populations. The total population of PLHIV in the study area was 11,374, with a reported prevalence of unsuppressed HVL of 6.5% in Tanga [10]. For this study, unsuppressed HVL was defined as a plasma HIV RNA level of ≥1000 copies/mL, in line with WHO guidelines [11]. The sample size was calculated using a 95% confidence level (Z = 1.96), a margin of error of 5%(ε = 0.05), and the known population size [12]. The minimum sample size was 73 for each facility. To account for a 5% non-response rate, the final sample size per facility was adjusted to 77, resulting in a total of 308 PLHIV to be recruited across the four facilities. A systematic sampling approach was utilized to select participants, ensuring representation of PLHIV aged 15 years and older on ART for at least six months. Each clinic day, the CTC appointment register was used to identify eligible clients arriving for services. Based on the expected daily attendance and the required sample size, every second eligible client was approached for participation. When a selected client declined participation, or was ineligible, or was not available after the clinical visit, the next eligible client was invited to participate. A total of 243 out of the estimated sample size of 308 participants were recruited, resulting in a response rate of approximately 78.9%. Data of 233(95.9%) participants with HVL tests conducted were therefore analysed further.
Data collection procedures
Primary and secondary data sources were used in this study. Primary data were collected using a pretested, interviewer-administered Swahili translated questionnaire, adapted from previous studies [13,14]. Secondary data were retrieved retrospectively from patients’ records. This included viral load results, history of opportunistic infections, duration on ART, ART regimen, and CTC2 database entries. After being informed about the study objectives, participants who provided written consent were interviewed in Swahili at their Care and Treatment Clinics. Research assistants received training on data collection procedures, participant rights, and rapport building. The questionnaire was pretested at Duga and Tumaini Health Centres to ensure clarity, validity, and reliability. The Content Validity Index (CVI) was 0.86, demonstrating good agreement among experts on the relevance and comprehensiveness of the items. Internal consistency of the multi-item scales was also confirmed, with Cronbach’s alpha values ranging from 0.78 to 0.85, indicating acceptable reliability.
Study variables
The dependent variable was HVL, defined as a viral load ≥1000 copies/mL based on routine viral load monitoring records documented in the CTC2 database and facility laboratory reports. HVL was categorized as “yes” (coded as 1) for participants with viral load ≥1000 copies/mL and “no” (coded as 0) for those with viral load <1000 copies/mL, independent variables included socio-economic and demographic factors such as age, gender, education, occupation, marital status, smoking status, alcohol consumption [14], stigma (measured through the Abridge Scale) [15], household food insecurity, socio-economic factors (wealth index generated through principle component analysis – PCA)14; and clinical data (WHO HIV clinical stage, c-ART type, duration, and presence of opportunistic infections such as Tuberculosis, Cryptococcal infection, and toxoplasmosis. Stigma was measured using the Abridged HIV Stigma Scale, a validated multi-item instrument assessing perceived HIV-related stigma. Responses were scored and summed to generate a total stigma score, which was then categorized into low and high stigma based on predefined thresholds. Food insecurity was assessed using the Household Food Insecurity Access Scale (HFIAS).
Data analysis
This study employed both descriptive and inferential analyses. Descriptive statistics summarized data using frequencies/counts and mean ± standard deviation (SD) for continuous variables, while percentages were used for categorical variables. The proportion of HVL (>1000 copies/mL) was assessed at a 95% confidence interval and tabulated against participants’ sociodemographic and clinical characteristics using a Pearson Chi-square test. For inferential analyses, multivariate logistic regression estimated the association between independent variables and HVL. Initially, univariate analysis explored unadjusted associations. In the adjusted logistic regression model, a forward stepwise method was used to fit the predictors of HVL where one independent variable was added into a model at a time. The variables which showed multicollinearity (Variance Inflation Factor (VIF)>4) and those with p > 0.2 in the unadjusted model were excluded. Probability > 0.2 were excluded to avoid overfitting the model with variables which might not be significant [16]. However, important sociodemographic and clinical characteristics variables of interest were retained regardless of statistically insignificant in the unadjusted model except those with multicollinearity. Statistical significance was set at p < 0.05. Data analysis was conducted using Stata/MP 14.2 (StataCorp LLC).
Ethical considerations
Ethical clearance was granted by Muhimbili University of Health and Allied Sciences (DA.282/298/01.C/1878). Permission was obtained from the City Medical Officer and healthcare officials. Written and verbal consent was obtained from participants with an assent for those below 18 years of age. Participants were informed of their rights, including withdrawal at any time without affecting care they receive. Unique identity numbers were used and no personal identifiers were collected. Throughout data collection and analysis we maintained confidentiality. Data was stored on a password-protected computer.
Results
Sociodemographic and clinical characteristics of study participants
Majority of participants were aged above 24 years 172(73.8%), the mean age was 32.4 years (±11.4 SD), with females making up 144(61.8%). 134(68.4%) had pre and primary education. Majority of participant were on Combined ART (c-ART) of Tenofovir + Lamivudine + Dolutegravir (TLD) regimen 220 (94.4%) with no participant on HIV clinical stage 4. High HVL was noted among 82(35.2%) [95% CI; 29.3%−41.6%] of the participants with HVL tests results available (Table 1).
Viral load levels by sociodemographic characteristics of the study participants
High HVL prevalence was noted among 32(52.5%) young adults, 14(63.6%) participants from sales and services industry, 6(54.5%) professionals, and 25(53.2%) unskilled labor categories. Additionally, high HVL was prevalent among 59(52.2%) of participants who consume alcohol and 70(39.5%) participants with high level of stigma. Moreover, high HVL was prevalent among 33(82.5%) participants with severe household food insecurity (Table 2).
Viral load by clinical characteristics of the study participants
Participants on the TLD c-ART regimen had a lower proportion of high HVL 73(33.2%) compared to those on other regimens 9(69.2%, p = 0.008). Those on c-ART for 13–24 months had a higher proportion of high HVL rate of 29(74.4%) than those on c-ART for over 25 months (26.5%, p = 0.001). Proper storage of c-ART significantly reduced high HVL 60(29.0%, p = 0.001). Additionally, individuals in WHO HIV clinical stage three had a higher HVL proportion of 10(76.9%) compared to those in stage one 50(33.6%) and two 22(31.0%, p = 0.005). Table 3
Predictors of high viral load among study participants
Several variables met the inclusion criteria for the regression model, having p-values less than 0.2, indicating their potential association with high viral load. These variables included age groups (p = 0.001), alcohol consumption (p = 0.001), stigma status (p = 0.015), food insecurity severity (p = 0.001) and c-ART storage in the container (p = 0.001).
Young adults aged 20–24 were 5.4 times more likely to have a higher HVL than those aged 25 and older (AOR = 5.4, 95% CI: 1.8–15.7, p = 0.002). PLHIV working in sales and services were 6.1 times more likely to have a higher HVL compared to those in agriculture, (AOR = 6.1, 95% CI: 1.1–35.4, p = 0.044). Alcohol consumption increased the likelihood of a higher HVL by 4.2 times compared to non-drinkers (AOR = 4.2, 95% CI: 1.4–12.5, p = 0.011), while those facing high stigma were 4.3 times more likely to have a higher HVL than individuals with good social support and low stigma (AOR = 4.3, 95% CI: 1.0–18.7, p = 0.049). PLHIV experiencing severe food insecurity were 9.6 times more likely to have a higher HVL than those with mild to moderate food insecurity (AOR = 9.6, 95% CI: 2.6–35.2, p = 0.001). Furthermore, improper storage of c-ART outside its original container increased the risk of a higher HVL by 6.7 times compared to those who properly stored their medication (AOR = 6.7, 95% CI: 1.2–38.1, p = 0.033). (Table 4)
Discussion
This study found a high HVL prevalence of 35.2% [95% CI; 29.3%−41.6%] among PLHIV aged 15 and older on c-ART for at least 6 months. Higher proportions were found in young adults, varied with occupations, individuals experiencing stigma, those from severe food insecurity households, from households with low wealth index, traveling long distances to health facilities, those on non-TLD regimens, those on c-ART for 12–24 months, and with advanced WHO HIV stages. Similar studies in Rwanda and Cameroon reported a prevalence of 29% and 20.6% [13,17], particularly among young adults and those on c-ART for 12–24 months.
This study found that severe food insecurity is associated with elevated VL among PLHIV. Alongside c-ART, a balanced diet is essential for improving health and nutritional status. These findings align with previous research linking food insecurity to higher VL, highlighting the importance of ensuring food security and access to essential nutrients for vulnerable populations [18]. Hunger can lead to medication discontinuation and reduced care attendance, negatively affecting adherence and contributing to psychological disorders.
Participants with high stigma levels in this study were more likely to have high HVL, likely due to poor communication with healthcare providers and reduced treatment adherence. Stigma may also worsen mental health issues like depression [19], contributing to treatment fatigue. A study in Dar es Salaam and other contexts found similar effects of stigma on VLS [20,21],. suggesting that addressing stigma and psychological issues could reduce HVL and prevent treatment failure.
Alcohol consumption, especially binge drinking, can impair judgment regarding medication adherence and increase engagement in high-risk sexual behaviours, leading to poor adherence and new HIV infections [22], which may result in persistently high HVL. This study’s findings align with research from Rwanda, showing significantly higher odds of high HVL among alcohol consumers [13]. Similarly, previous studies have found a strong association between alcohol use and increased odds of high HVL among PLHIV on c-ART [23]. Providing intensive counselling among PLHIV on alcohol cessation or moderation can help reduce HVL and the risk of treatment failure.
This study found that young adults had significantly higher odds of high viral loads compared to older participants, likely due to their relatively lower capacity to mobilize resources for ongoing care. Factors such as cognitive and physical development, mental health, substance use, and family support can impact treatment adherence among adolescents and young adults [13]. The Tanzania HIV Impact Survey (2022/23) indicated that younger adults were less likely to achieve VLS than their adult counterparts. These findings align with studies in Botswana [24] and the U.S. [25], which also showed that younger adults struggle more with treatment adherence and achieving viral load suppression compared to older adults.
Studies shows occupation significantly impacts medication adherence and VLS among PLHIV [7,26]. The study found that participants working in sales, skilled manual, and unskilled manual occupations were more likely to have elevated HVL. This may be due to tight work schedules and concerns about job security, which can limit their ability to attend clinic appointments and adhere consistently to ART.
Studies have reported conflicting results whereby a study in Ghana did not found a link between employment and HVL [27], while a study in Nigeria suggested unemployment correlates with better VLS [28]. Furthermore, a meta-analysis study indicated that employed individuals generally adhere better to treatment [29]. These conflicting results highlight the complexity of the relationship between employment status and VLS among PLHIV. Nonetheless, it is essential to consider occupation as a potential influencing factor when initiating c-ART for people living with HIV.
Strength and limitation
This study has several strengths, including its focus on PLHIV in Tanga, Tanzania, the use of probability sampling, and standardized data collection tools, which enhance the relevance and reliability of the findings. The study also provides important insights into the influence of stigma, food insecurity, and substance use on viral load suppression among PLHIV. However, several limitations should be acknowledged. The cross-sectional design limits the ability to establish causal relationships between variables. The study relied on self-reported information, which may be subject to recall and social desirability bias. Viral load measurements were based on a single time point, which may not fully capture longitudinal treatment outcomes. The response rate was 78.9%, with non-participation due to absent clients, ineligibility, or refusal, which may introduce selection bias. Some adjusted odds ratios had wide confidence intervals, likely due to small cell counts or limited sample sizes, reducing estimate precision. Finally, the measurement of economic status may not have fully captured participants’ socioeconomic conditions.
Conclusion and recommendation
The high prevalence of elevated viral load among PLHIV on c-ART for six months is a concern, with 1 in 3 at risk of treatment failure. Key predictors include sociodemographic factors, food insecurity, stigma, alcohol use, and employment. Targeted interventions like food availability support and stigma reduction are vital.
References
- 1. Jamieson D, Kellerman SE. The 90 90 90 strategy to end the HIV Pandemic by 2030: Can the supply chain handle it?. J Int AIDS Soc. 2016;19(1):20917. pmid:27370169
- 2.
UNAIDS data 2018. https://www.unaids.org/en/resources/documents/2018/unaids-data-2018 2023 March 22.
- 3.
Migone C, Ford N. Packaged interventions for people presenting with advanced HIV disease: A systematic review. World Health Organization. 2017. https://www.ncbi.nlm.nih.gov/books/NBK475971/
- 4. Matoga MM, Rosenberg NE, Stanley CC, LaCourse S, Munthali CK, Nsona DP, et al. Inpatient mortality rates during an era of increased access to HIV testing and ART: A prospective observational study in Lilongwe, Malawi. PLoS One. 2018;13(2):e0191944. pmid:29415015
- 5.
Tanzania summary sheet 2022-2023. PHIA Project. https://phia.icap.columbia.edu/tanzania-summary-sheet-2022-3/ 2024 July 30.
- 6.
NACP-National AIDS Control Programme. National hiv viral load testing guideline to support hiv and aids prevention, care and treatment. NACP-National AIDS Control Programme. https://nacp.go.tz/download/national-hiv-viral-load-testing-guideline-to-support-hiv-and-aids-prevention-care-and-treatment/ 2023 March 22.
- 7. Tun NN, Smithuis F, Tun NL, Min M, Hlaing MMM, Olmen J van, et al. Near point-of-care HIV viral load testing: Cascade after high viral load in suburban Yangon, Myanmar. PLoS One. 2023;18(4):e0272673. pmid:37053171
- 8.
National Bureau of Statistics. 2022 Population and Housing Census - Preliminary Report in Swahili Language. https://www.nbs.go.tz/index.php/en/census-surveys/population-and-housing-census/802-matokeo-ya-mwanzo-ya-sensa-ya-watu-na-makazi-ya-mwaka-2022
- 9.
UNAIDS. UNAIDS report on the global AIDS epidemic shows that 2020 targets will not be met because of deeply unequal success; COVID-19 risks blowing HIV progress way off course. https://www.unaids.org/en/resources/presscentre/pressreleaseandstatementarchive/2020/july/20200706_global-aids-report
- 10.
The Tanzania HIV Impact Survey. https://www.nbs.go.tz/statistics/topic/the-tanzania-hiv-impact-survey 2026 March 10.
- 11. Ochodo EA, Olwanda EE, Deeks JJ, Mallett S. Point-of-care viral load tests to detect high HIV viral load in people living with HIV/AIDS attending health facilities. Cochrane Database Syst Rev. 2022;3(3):CD013208. pmid:35266555
- 12.
Asad N. Calculating Sample Size. 2021. https://doi.org/10.13140/RG.2.2.31110.91208
- 13. Nyirabatsinda E, Nsanzabera C, Mochama M, Kubahoniyesu T. Viral Load Suppression and Associated Factors among Young Adults Living with HIV at Muhima District Hospital. CJMS. 2023;3(9):9–25.
- 14.
Tanzania Demographic and Health Survey and Malaria Indicator Survey 2022 - Key Indicators Report (English). Accessed June 16, 2023. https://dhsprogram.com/publications/publication-PR144-Preliminary-Reports-Key-Indicators-Reports.cfm
- 15. King M, Dinos S, Shaw J, Watson R, Stevens S, Passetti F, et al. The Stigma Scale: development of a standardised measure of the stigma of mental illness. Br J Psychiatry. 2007;190:248–54. pmid:17329746
- 16. Mickey RM, Greenland S. The impact of confounder selection criteria on effect estimation. Am J Epidemiol. 1989;129(1):125–37. pmid:2910056
- 17. Fokam J, Sosso SM, Yagai B, Billong SC, Djubgang Mbadie RE, Kamgaing Simo R, et al. Viral suppression in adults, adolescents and children receiving antiretroviral therapy in Cameroon: adolescents at high risk of virological failure in the era of “test and treat”. AIDS Res Ther. 2019;16(1):36. pmid:31744517
- 18. Chohan BH, Ronen K, Khasimwa B, Matemo D, Osborn L, Unger JA, et al. Food insecurity, drug resistance and non-disclosure are associated with virologic non-suppression among HIV pregnant women on antiretroviral treatment. PLoS One. 2021;16(8):e0256249. pmid:34407133
- 19. Kemp CG, Lipira L, Huh D, Nevin PE, Turan JM, Simoni JM, et al. HIV stigma and viral load among African-American women receiving treatment for HIV. AIDS. 2019;33(9):1511–9. pmid:31259767
- 20. Kilapilo MS, Mosha IH, Bwire GM, Sambayi GL, Sangeda RZ, Killewo J. Factors Associated with Viral Load Suppression and Indicators of Stigma among People Living with HIV in Dar es Salaam Tertiary Hospitals, Tanzania. Microbiology Research. 2023;14(2):704–13.
- 21. Hargreaves JR, Pliakas T, Hoddinott G, Mainga T, Mubekapi-Musadaidzwa C, Donnell D, et al. HIV Stigma and Viral Suppression Among People Living With HIV in the Context of Universal Test and Treat: Analysis of Data From the HPTN 071 (PopART) Trial in Zambia and South Africa. J Acquir Immune Defic Syndr. 2020;85(5):561–70. pmid:32991336
- 22. Jiao K, Wei R, Lin Y, Li C, Wang L, Ma W. Alcohol consumption and utilization of HIV prevention services among men who have casual sex with women in China. AIDS Care. 2023;35(4):564–71. pmid:36369927
- 23. Cachay ER, Moges TS, Qin H, Bamford L, Grelotti DJ, Mathews WC. Effects of drug and hazardous alcohol use on having a detectable HIV viral load: An adherence mediation analysis. Addict Behav Rep. 2023;17:100486. pmid:36950716
- 24. Novitsky V, Gaolathe T, Mmalane M, Moyo S, Chakalisa U, Yankinda EK, et al. Lack of Virological Suppression Among Young HIV-Positive Adults in Botswana. J Acquir Immune Defic Syndr. 2018;78(5):557–65. pmid:29771781
- 25. Nance RM, Delaney JAC, Simoni JM, Wilson IB, Mayer KH, Whitney BM, et al. HIV Viral Suppression Trends Over Time Among HIV-Infected Patients Receiving Care in the United States, 1997 to 2015: A Cohort Study. Ann Intern Med. 2018;169(6):376–84. pmid:30140916
- 26. Peltzer K, Pengpid S. Socioeconomic factors in adherence to HIV therapy in low- and middle-income countries. Journal of Health, Population and Nutrition. 2013;31(2).
- 27. Lokpo SY, Ofori-Attah PJ, Ameke LS, Obirikorang C, Orish VN, Kpene GE, et al. Viral Suppression and Its Associated Factors in HIV Patients on Highly Active Antiretroviral Therapy (HAART): A Retrospective Study in the Ho Municipality, Ghana. AIDS Research and Treatment. 2020;2020:1–7.
- 28. Abdullahi SB, Ibrahim OR, Okeji AB. Viral suppression among HIV-positive patients on antiretroviral therapy in northwestern Nigeria: an eleven-year review of tertiary care centre records, January 2009–December 2019. BMC Infectious Diseases. 2021;21(1):1031.
- 29. Nachega JB, Uthman OA, Peltzer K, Richardson LA, Mills EJ, Amekudzi K, et al. Association between antiretroviral therapy adherence and employment status: systematic review and meta-analysis. Bull World Health Organ. 2015;93(1):29–41. pmid:25558105