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Published in final edited form as: J Subst Abuse Treat. 2021 Aug 8;134:108587. doi: 10.1016/j.jsat.2021.108587

Participation in methadone programs improves antiretroviral uptake and HIV suppression among people who inject drugs in Kenya

Loice W Mbogo c, Betsy Sambai c, Aliza Monroe-Wise b, Natasha T Ludwig-Barron a,b, Brandon L Guthrie a,b, David Bukusi e, Bhavna Chohan b,h, Paul Macharia g, Matt Dunbar a, Emily Juma d, William Sinkele f, Esther Gitau f, Ashley S Tseng a,b, Rose Bosire h, Sarah Masyuko b,g, Helgar Musyoki g, Tecla M Temu b, Joshua Herbeck a,d, Carey Farquhar a,b,d
PMCID: PMC11225265  NIHMSID: NIHMS2003181  PMID: 34391587

Abstract

Background:

HIV prevalence among people who inject drugs (PWID) in Kenya is estimated to be 18% compared to 4.5% in the general population. Studies from high-income countries have demonstrated that methadone use is associated with increased uptake of antiretroviral therapy (ART) and higher rates of viral suppression among PWID with HIV. However, it is unclear whether methadone use has the same effect among African PWID living with HIV.

Methods:

We performed a cross-sectional study to evaluate associations between methadone program participation and ART uptake and viral suppression (HIV RNA viral load <1000 copies/ml) among PWID with HIV in Kenya. Participants were recruited from needle and syringe programs and methadone clinics, interviewed on site, and samples were obtained and assayed for HIV viral loads. Univariate and multiple logistic regression were used to determine associations.

Results:

Among 679 participants, median age was 37 years, 48% were female, and 24% were in a methadone program. We observed higher proportions of ART use (96% vs. 87%, p=0.001) and HIV viral suppression (78% vs. 65%, p=0.012) among PWID on methadone compared to those not on methadone treatment. PWID who were not participating in a methadone program were 3-fold more likely to be off ART and approximately twice as likely to be viremic compared to those in methadone programs (adjusted odds ratio [aOR]=3.35, 95% confidence interval [CI]:1.35-8.35 and aOR=1.90, 95% CI:1.03-3.52, respectively).

Conclusions:

In this study, Kenyan PWID living with HIV participating in a methadone treatment program were more likely to be on ART and to have achieved viral suppression. Scale-up of methadone programs may have a positive impact on HIV epidemic control for this key population.

Keywords: Methadone, antiretroviral therapy, viral suppression, Kenya

Introduction

Globally, it is estimated that 15.6 million people who inject drugs (PWID) are living with HIV (Degenhardt et al., 2017) and an estimated 600,000 of these individuals are in sub-Saharan Africa (SSA) (Mathers et al., 2008). In SSA only a handful of countries operate harm reduction services through needle syringe programs (NSP) and incarceration of drug users is common; however, due to civil society advocacy, academic research and international donor support, awareness of NSP (Gen, 2016) and integrated methadone and HIV programs are slowly being scaled up (Guise et al., 2019).

Within the region, the HIV epidemic continues to be a major public health concern and provision of optimal care to PWID living with HIV is a significant challenge (Degenhardt et al., 2010; Vickerman et al., 2014). In particular, gaps exist in engagement in HIV care services, uptake of antiretroviral therapy (ART), and viral suppression among PWID (Azar et al., 2015; Li et al., 2018; Zhang et al., 2011). Achieving optimal adherence to ART and sustaining viral suppression are essential to reduce morbidity and mortality associated with HIV (Mocroft et al., 1998; Rodger et al., 2013).

Despite the availability of subsidized HIV care integrated with other essential clinical services, PWID face many barriers to accessing services, including incarceration, displacement, homelessness, untreated psychiatric illness, transportation costs and stigma, which may complicate engagement in HIV care and affect ART adherence (Treisman, 2001; Wood et al., 2005). Studies in high-income countries have shown engagement in methadone treatment may reduce drug use and improve ART adherence (Palepu et al., 2006), and in the United States (US), viral suppression was associated with methadone use (Michael, 2000). Similar studies done in Vietnam examined buprenorphine plus naloxone and referral for methadone maintenance therapy and showed reduced heroin use, increased receipt of ART and HIV viral suppression with these interventions (Korthuis et al., 2021), while a study in Taiwan showed high retention among PWID on methadone (Chao et al., 2020). Integrated methadone and HIV care programs can provide promising infrastructure for promoting ART uptake and adherence by reducing transportation costs associated with traveling to multiple clinic sites, establishing trust with healthcare providers who are trained on the specific needs of PWID, and offering non-judgmental, safe-spaces for PWID (Berg et al., 2011; Tran BX, 2012).

In Kenya, key populations disproportionately contribute to the HIV epidemic and include female sex workers (FSW) and men who have sex with men (MSM) , all of which experience disproportionately high prevalence of HIV at 29.3% and 18.9% (Bhattacharjee et al., 2020; Musyoki et al., 2016; NASCOP, 2014). An estimated 18,000 PWID live in Kenya, primarily in Nairobi and coastal Kenya (NASCOP, 2019). Notably, HIV prevalence is approximately 18.3% among PWID (Kurth et al., 2015) compared to 4.5% in the general population in Kenya (National AIDS and STI Control Programme (NASCOP), Ministry of Health, 2018). NSP were introduced in 2013, providing harm reduction education and grassroots distribution of safe injection equipment through peer educators who are recovering PWID (NASCOP, 2013). In 2014, as a way to reduce opioid dependence and incident HIV infection in PWID communities, Kenya’s government introduced integrated methadone programs, which provide methadone and HIV care (Rhodes et al., 2015). Integration of HIV care services has long been recognized as essential to enhance service access for key populations, particularly for PWID (NACC, 2015; NASCOP, 2018). However, few studies have examined the relationship between methadone use and HIV outcomes including ART uptake and viral suppression in resource-limited settings, particularly in sub-Saharan Africa.

There is a need to understand how methadone service settings that provide care to PWID living with HIV can affect HIV treatment uptake and adherence in sub-Saharan Africa and other resource-limited settings (Guise et al., 2019; Rhodes et al., 2015). In this study, we evaluated the association between methadone program participation, ART use and viral suppression among PWID living with HIV in Kenya, comparing those enrolled in methadone clinics to those enrolled in NSP. We hypothesized that methadone use would be associated with increased ART uptake and viral suppression among PWID who are living with HIV in Kenya.

Methods

Study setting and participants

We analyzed baseline data from participants enrolled in a prospective cohort study in Kenya. The study was established to evaluate the effectiveness of assisted partner services (APS) in identifying, testing, and linking to care the sexual and injecting partners of HIV-positive PWID. APS is the practice through which health care providers facilitate the notification, testing and linkage to care of partners who are exposed to HIV or other sexually transmitted infections by an individual known to be positive, without revealing the identity of the person who may have exposed them (Monroe-Wise et al., 2021).

Study participants and procedures

The primary study recruited participants using convenience sampling through established needle and syringe programs and methadone clinics in the Nairobi and coastal region of Kenya. In Nairobi, participants were recruited from methadone sites at the Drug Rehabilitation Unit in Mathari Hospital and Ngara Health Center, and three NSP sites managed by a harm reduction organization, Support for Africa Addiction Prevention Treatment in Africa. At the coast region the participants were recruited from the methadone clinic at Malindi County Hospital and four NSP sites including the Reachout program in Mombasa, Muslim Education Welfare Association sites in Mtwapa and Kilifi, and the Omari Project in Malindi. In our analysis we included all participants enrolled in the parent study who had injected drugs at least once in the last year, were ≥18 years of age, and had a confirmed HIV diagnosis. We excluded newly HIV diagnosed participants from the analysis due to the short duration on ART.

The secondary analysis used data collected at baseline through a standardized questionnaire programmed through Open Data Kit software (Anderea Steiner, 2016), with iRespond (iris scan) software ensuring that each participant was assigned a unique identification number (Anne et al., 2021) to limit double-enrollments. Data collected included demographic information (e.g., age, sex, marital status, housing conditions, employment), current and previous drug use (e.g., types of drugs, drug administration practices, duration of injecting, alcohol use), sexual practices (e.g., types of partners, condom use) and HIV services (e.g., ART history and current use). All participants provided written informed consent and were compensated 400 Kenyan shillings ($4 USD) for their time and transportation. All study protocols and procedures were approved by the Ethics and Research Committee of Kenyatta National Hospital and the Institutional Review Board at University of Washington.

Laboratory analysis

Blood samples collected at baseline were processed for HIV RNA viral loads conducted using a real time polymerase chain reaction (RT-PCR) (Abbott). Viral suppression was defined as an HIV-1 RNA viral load of <1000 copies/ml as per the Kenya guidelines (NASCOP, 2018)

Statistical Analysis

Socio-demographic and behavioral characteristics were described using medians and interquartile ranges for continuous variables, and proportions for categorical variables. Differences in the distribution of these characteristics by methadone participation was assessed using chi-square tests for categorical variables and Mann Whitney U Test (Wilcoxon Rank Sum Test) for continuous variables. Univariate and multiple logistic regression models were used to determine whether enrollment in a methadone program was associated with ART use and HIV viral suppression controlling for region (Coast vs Nairobi), methamphetamine use, cocaine use and alcohol use. Analyses were conducted using Stata version 15.1 (College Station, TX).

Results

Among 679 PWID, 391 (58%) were from Nairobi and 163 (24%) were on methadone. Median age was 37 (IQR 32-42) years, slightly over 50% were males, median duration of drug injection was 4 years (IQR 2-9), and 41 (6%) were homeless. Methadone use was more than two times higher among coast participants when compared to Nairobi residents (67% vs 33%, p<0.001) and males were more likely to be taking methadone compared to females (29% vs 19%, p=0.003). Individuals on methadone had injected longer than those not on methadone (5 years (IQR 3-10) vs 4 years (IQR 2-8), p<0.001). Most individuals reported polysubstance use with nearly 90% using heroin, 51% using marijuana and 32% using alcohol in the previous month. Heroin use was more likely to be reported by those not on methadone compared to those on methadone (79% vs 21%, p<0.001). Those who had lived longer with HIV were also more likely to be on methadone (p=0.045) (Table 1).

Table 1:

Socio-demographic characteristics stratified by methadone program participation

Total
N=679		 On Methadone
n=163		 Not on Methadone
n=516		 ap-value
Socio-demographics
Age years (Median, IQR) 37 (32-42) 36 (31-41) 37 (32-42) 0.158b
Sex
   Male 355 (52%) 102 (63%) 253 (49%) 0.003
Marital status
   Single 244 (36%) 52 (32%) 192 (37%) 0.033
   Married 170 (25%) 54 (33%) 116 (22%)
   Partnered 37 (5%) 12 (7%) 25 (5%)
   Divorced/separated 159 (23%) 30 (18%) 129 (25%)
   Widowed 69 (10%) 15 (9%) 54 (10%)
Living condition
   Person/friends/relatives/hotel/rental 603 (89%) 151 (93%) 452 (88%) 0.075
   Improvised shelter/open 76 (11%) 12 (7%) 64 (12%)
Region
   Coast 288 (42%) 110 (67%) 178 (35%) <0.001
   Nairobi 391 (58%) 53 (33%) 338 (65%)
Number of sexual partners in the last 3 months
   0 326 (48%) 59 (36%) 267 (52%) 0.003
   1 169 (25%) 49 (30%) 120 (23%)
   2-5 103 (15%) 35 (21%) 68 (13%)
   >5 81 (12%) 20 (12%) 61 (12%)
Years of HIV infection (Median (IQR)) 5 (2-10) 6 (2-10) 5 (2-10) 0.045 b
Years injecting (Median (IQR)) 4 (2-9) 5 (3-10) 4 (2-8) <0.001 b
Type of drug used in the past month
   Heroin 608 (89%) 128 (78%) 480 (93%) <0.001
   Methamphetamine/Speed 54 (8%) 16 (10%) 38 (7%) 0.313
   Cocaine 42 (6%) 4 (2%) 38 (7%) 0.023
   Marijuana 347 (51%) 97 (59%) 250 (48%) 0.014
Alcohol use
   Yes 216 (32%) 38 (23%) 178 (35%) 0.008
Number of times injected drugs in the past month (Median (IQR)) 60 (12-90) 30 (0-90) 60 (30-90) 0.048 b
ART use
   Yes 606 (89%) 157 (96%) 449 (87%) 0.001
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Abbreviations: IQR, Interquartile range

a

Pearson’s chi square test used to test association between each row variable and methadone participation

b

Mann Whitney U Test (Wilcoxon rank sum test) for difference of medians between those on methadone and those not on methadone.

Association between methadone program participation and ART uptake

We observed a greater proportion of ART use among PWID on methadone compared to those not on methadone (96% vs 87%, p=0.001). In the multivariable analysis, individuals who were not involved in a methadone program were 3.35 times more likely to report not taking ART (aOR=3.35, 95% CI: 1.35-8.35, p=0.009). Individuals were less likely to be off ART if they had had sex with an individual known to be HIV positive (aOR=0.41, 95% CI: 0.21-0.78, p=0.007), used condoms (aOR=0.42, 95% CI: 0.24-0.71, p=0.001), and were older (aOR=0.95, 95% CI: 0.92-0.99, p=0.011) (Table 2).

Table 2:

Factors associated with not currently taking ART

Adjusted OR (95%CI)** p-value
Methadone participation
   No 3.35 (1.35-8.35) 0.009
Age years (Median,IQR) 0.95 (0.92-0.99) 0.011
Sex
   Female 0.67 (0.29-1.53) 0.343
Marital status
   Married 1.51 (0.73-3.14) 0.409
   Partnered 1.18 (0.30-4.62)
   Divorced/separated 0.88 (0.41-1.85)
   Widowed 1.96 (0.82-4.69)
Living condition
   Improvised shelter/open 1.31 (0.59-2.92) 0.504
Region
   Coast 0.41 (0.21-0.79) 0.008
Number of sexual partners in the last 3 months
   1 1.39 (0.69-2.84) 0.509
   2-5 1.13 (0.46-2.82)
   6+ 2.14 (0.88-5.23)
Condom use
   Yes 0.42 (0.24-0.71) 0.001
Heterosexual
   Yes 1.92 (0.39-9.34) 0.418
Given money for sex
   Yes 0.62 (0.29-1.30) 0.204
Received money for sex
   Yes 0.82 (0.39-1.71) 0.601
Sex with known positive
   Yes 0.41 (0.21-0.78) 0.007
Years injecting (Median (IQR) 0.98 (0.93-1.03) 0.467
Methamphetamine/Speed
   Yes 0.40 (0.11-1.42) 0.158
Cocaine
   Yes 1.53 (0.58-4.02) 0.392
Alcohol use
   Yes 1.49 (0.81-2.71) 0.197
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**

Adjusted odds ratios from the multivariable model that included all the variables in the table.

Reference group for methadone participation is (Yes), sex (male), marital status (single), living condition (person/friends/relatives/hotel/rental), region (Nairobi), Number of sexual partners in the last 3 months (0), condom use (No), heterosexual (No), given money for sex (No), received money for sex (No), sex with known positive (No), methamphetamine use (No), cocaine use (No) and alcohol use (No).

Association between methadone program participation and viral suppression

Viral load results were available for a subset of 400 (59%) participants of whom 109 (27%) were on methadone. Our analysis showed a higher proportion of PWID on methadone (78%) were virally suppressed compared to those not on methadone (65%, p=0.011). In the multivariable analysis, individuals not involved in a methadone program were 1.90 times more likely to be viremic compared to those in a methadone program (aOR=1.90, 95% CI: 1.03-3.52, p=0.040). Those who had injected for more years were less likely to be viremic (aOR=0.95, 95% CI:0.91-0.99, p=0.049) and older age was associated with lower odds of viremia (aOR=0.97, 95% CI;0.94-0.99, p=0.046) (Table 3).

Table 3:

Factors associated with HIV viremia

Adjusted OR (95%CI)** p-value
Methadone participation
   No 1.90 (1.03-3.52) 0.040
Age in years (Median, IQR) 0.97 (0.94-0.99) 0.046
Sex
   Female 0.83 (0.39-1.71) 0.610
Marital status
   Married 0.86 (0.45-1.65) 0.292
   Partnered 3.33 (0.97-11.40)
   Divorced/separated 1.00 (0.54-1.87)
   Widowed 1.26 (0.58-2.75)
Living condition
   Improvised shelter/open 1.11 (0.53-2.32) 0.775
Region
   Coast 0.90 (0.48-1.70) 0.749
Number of sexual partners in the last 3 months
   1 1.54 (0.85-2.81) 0.133
   2-5 1.18 (0.56-2.52)
   6+ 2.43 (1.09-5.41)
Condom use
   Yes 0.79 (0.49-1.28) 0.352
Heterosexual
   Yes 6.58 (1.25-34.42) 0.026
Given money for sex
   Yes 0.84 (0.46-1.57) 0.595
Received money for sex
   Yes 0.98 (0.52-1.83) 0.945
Sex with known positive
   Yes 0.64 (0.38-1.06) 0.086
Years injecting 0.95 (0.91-0.99) 0.049
Methamphetamine/Speed
   Yes 0.94 (0.40-2.22) 0.900
Cocaine
   Yes 0.82 (0.32-2.10) 0.679
Alcohol use
   Yes 1.09 (0.65-1.86) 0.733
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**

Adjusted odds ratios from the multivariable model that included all the variables in the table.

Reference group for methadone participation is (Yes), sex (male), marital status (single), living condition (person/friends/relatives/hotel/rental), region (Nairobi), Number of sexual partners in the last 3 months (0), condom use (No), heterosexual (No), given money for sex (No), received money for sex (No), sex with known positive (No), methamphetamine use (No), cocaine use (No) and alcohol use (No).

Discussion

This finding suggest that methadone use was associated with both ART uptake and achieving HIV viral suppression among PWID in Kenya. These findings show differential uptake of methadone in Kenya and suggest that scaling up of methadone use could improve uptake of HIV care and retention and the achievement of viral suppression in this population.

In SSA there is a dearth of evidence about the impact of interventions related to methadone use on HIV care outcomes, and only five countries in Africa now have harm reduction programs: Kenya, Mauritius, Senegal, South Africa and Tanzania (Guise et al., 2019). Tanzania established that integration of HIV and TB services in methadone programs is of benefit to both the patient and the healthcare provider (Bruce et al., 2014) and results in increased ART initiation (Hassan et al., 2019). In high income countries such as the US, studies have demonstrated a complex interplay between optimal ART adherence, current injection-related practices, and viral suppression (Shrestha Roman, 2019). In Canada, studies have found that engagement in a methadone maintenance program was associated with adherence to ART and achieved HIV-1 RNA suppression (Palepu et al., 2006). In Greece, expedited ART linkages for PWID identified by respondent-driven sampling surveys led to an apparent decline in HIV incidence (Sypsa et al., 2017) and other high income countries have shown improvement of ART uptake and viral suppression associated with methadone use. (Hogg et al., 2011; Miller et al., 2018).

Methadone programs have been scaled up in 8 out of the 47 counties within Kenya to curb addiction since 2014. Needle and syringe programs, which are key components of harm reduction initiatives, play an important role in endorsing individuals who are ready to be initiated in methadone treatment. They do this with the support and coordination of the Kenya National AIDS and STI Control Programme (NASCOP), the Ministry of Health and with international funding support (Guise et al., 2019; Salleh et al., 2021) . While rehabilitation programs are often associated with failing to prevent relapse, methadone engenders hope for a more profound recovery and reintegration into society. However, limitations such as lack of resources, stigma, and discrimination have been identified as barriers to accessing methadone treatment (Rhodes et al., 2015; Wolfe et al., 2010). Previous studies have also identified barriers to integrating HIV care into methadone programs at the policy, institutional, provider and client levels (Li et al., 2018). Individual who had injected drugs for a short period of time were more likely to be viremic. Consistent with previous literature, younger and ongoing drug use was associated with poorer ART uptake (Arnsten H et al., 2002). Programmatic data show that PWID on Kenya’s coast are more likely to be enrolled in an NSP program than those in Nairobi; furthermore, NSP participation has been linked to methadone use. As a result, the pipeline from NSP to methadone remains better utilized in Kenya’s coast than in Nairobi (Ndimbii et al., 2015; Rhodes et al., 2015).

Our study had several limitations. This was a cross sectional study, and therefore could not evaluate the sustainability of methadone uptake in this population. We limited enrollment in this study to people who had injected drugs within the previous year, introducing potential selection bias and limiting the generalizability to others who may not have recently injected drugs. Finally, ART use was self-reported, which may have introduced recall or desirability bias. Nevertheless, our study is among the first to show significant associations between methadone use and important HIV care outcomes among PWID in a low-resource setting.

Methadone treatment improves ART uptake in a variety of settings and populations (Sasha Uhlmann, 2011; Weber et al., 2009). Improving the engagement of people living with HIV who inject drugs in methadone programs will reduce morbidity and mortality caused by low viral suppression and nonadherence to ART (Altice et al., 2010; Mayer et al., 2013; Miller et al., 2018). In turn, this will decrease ongoing HIV transmission both within the PWID community and between PWID and the general population. In our study, out of 679 only 24% were taking methadone, an indication of the need to enhance the acceptability of, feasibility of and access to integrated methadone and HIV care (Guise et al., 2019; Saleem et al., 2016).

Acknowledgements

We would like to thank participants, clinical officers, health advisors, and peer educators for their contributions to data collection. We would like to acknowledge the needle and syringe and methadone programs that worked with us (Support for Africa Addiction Prevention Treatment in Africa, Muslim Education Welfare Association, The Omari Project, Reachout, Ngara Health center, Mathari Referral Hospital drug rehabilitation unit) for their support. We would also like to recognize the National AIDS and STI and Control Program and Kenyatta National Hospital for their collaboration.

Funding Statement

The first author and collaborating authors are supported through the SHARP Study, which is National Institutes of Health (NIDA) funded study (R01DA043409; co-PIs: Farquhar & Herbeck). The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Conflicts of Interest

The authors declare that they have no conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome. We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that this study was conducted with the ethical approval of the Institutional Review Board at the University of Washington and the Ethical Review Committee at Kenyatta National Hospital/University of Nairobi.

Footnotes

CREDIT AUTHORSHIP CONTRIBUTION STATEMENT

Loice Mbogo: conceptualization, formal analysis, writing-original draft. Betsy Sambai: formal analysis. Aliza Monroe-Wise: writing – review & editing. Natasha Ludwig-Barron: writing -review & editing. Brandon L. Guthrie: formal analysis, data curation. David Bukusi: writing – review & editing. Bhavna Chohan: data curation, writing – review & editing. Paul Macharia: software programming. Matt Dunbar: software programming. Emily Juma: project administration. William Sinkele: project administration, writing – review & editing. Esther Gitau: project administration, resources. Ashley Tseng: writing – review & editing. Rose Bosire: writing – review & editing. Sarah Masyuko: writing – review & editing, Helgar Musyoki: writing – review & editing. Tecla Temu: writing – review & editing. Joshua Herbeck: funding acquisition, writing – review & editing. Carey Farquhar: funding acquisition, conceputalization, formal analysis, writing – review & editing.

References

  1. Altice FL, Kamarulzaman A, Soriano VV, Schechter M, & Friedland GH (2010). Treatment of medical, psychiatric, and substance-use comorbidities in people infected with HIV who use drugs. The Lancet, 376(9738), 367–387. 10.1016/S0140-6736(10)60829-X [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Anderea Steiner LF (2016). Managing research and surveillance projects in real-time with a novel open-source eManagement tool designed for under-resourced countries. PubMed, 23, 916–923. 10.1093/jamia/ocv185 [DOI] [PubMed] [Google Scholar]
  3. Anne N, Dunbar MD, Abuna F, Simpson P, Macharia P, Betz B, Cherutich P, Bukusi D, Carey F, States U, Hospital N, States U, States U, Hospital N, States U, & States U (2021). Feasibility and acceptability of an iris biometric system for unique patient identification in routine HIV services in Kenya. 1–16. 10.1016/j.ijmedinf.2019.104006.Feasibility [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Arnsten H,J, Demas, Grant, Gourevitch, Farzadegan, Howard, & Schoenbaum. (2002). Impact of active drug use on antiretroviral therapy adherence and viral suppression in HIV-infected drug users. Journal of General Internal Medicine, 17(5), 377–381. http://www.embase.com/search/results?subaction=viewrecord&from=export&id=L34626108%5Cnhttp://dx.doi.org/10.1046/j.1525-1497.2002.10644.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Azar P, Wood E, Nguyen P, Luma M, Montaner J, Kerr T, & Milloy MJ (2015). Drug use patterns associated with risk of non-adherence to antiretroviral therapy among HIV-positive illicit drug users in a Canadian setting: A longitudinal analysis. BMC Infectious Diseases, 15(1), 1–6. 10.1186/s12879-015-0913-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Berg KM, Litwin A, Li X, Heo M, & Arnsten JH (2011). Directly observed antiretroviral therapy improves adherence and viral load in drug users attending methadone maintenance clinics: A randomized controlled trial. Drug and Alcohol Dependence, 113(2–3), 192–199. 10.1016/j.drugalcdep.2010.07.025 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Bhattacharjee P, Isac S, Musyoki H, Emmanuel F, Olango K, Kuria S, Ongaro MK, Walimbwa J, Musimbi J, Mugambi M, Kaosa S, Kioko J, Njraini M, Melon M, Onyoni J, Bartilol K, Becker M, Lorway R, Pickles M, … Mishra S (2020). HIV prevalence, testing and treatment among men who have sex with men through engagement in virtual sexual networks in Kenya: a cross-sectional bio-behavioural study. Journal of the International AIDS Society, 23(S2), 72–81. 10.1002/jia2.25516 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Bruce RD, Lambdin B, Chang O, Masao F, Mbwambo J, Mteza I, Nyandindi C, Zamudio-Haas S, Buma D, Dunbar MS, & Kilonzo G (2014). Lessons from Tanzania on the integration of HIV and tuberculosis treatments into methadone assisted treatment. International Journal of Drug Policy, 25(1), 22–25. 10.1016/j.drugpo.2013.09.005 [DOI] [PubMed] [Google Scholar]
  9. Chao E, Hung CC, Lin CP, Ku YCJ, Ain QU, Metzger DS, & Lee TSH (2020). Adherence among HIV-positive injection drug users undergoing methadone treatment in Taiwan. BMC Psychiatry, 20(1), 1–8. 10.1186/s12888-020-02764-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Degenhardt L, Mathers B, Vickerman P, Rhodes T, Latkin C, & Hickman M (2010). Prevention of HIV infection for people who inject drugs: Why individual, structural, and combination approaches are needed. The Lancet, 376(9737), 285–301. 10.1016/S0140-6736(10)60742-8 [DOI] [PubMed] [Google Scholar]
  11. Degenhardt L, Peacock A, Colledge S, Leung J, Grebely J, Vickerman P, Stone J, Cunningham EB, Trickey A, Dumchev K, Lynskey M, Griffiths P, Mattick RP, Hickman M, & Larney S (2017). Global prevalence of injecting drug use and sociodemographic characteristics and prevalence of HIV, HBV, and HCV in people who inject drugs: a multistage systematic review. The Lancet Global Health, 5(12), e1192–e1207. 10.1016/S2214-109X(17)30375-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Gen S. (2016). The Global State of Harm Reduction (Vol. 31). [Google Scholar]
  13. Guise A, Ndimbii J, Igonya EK, Owiti F, Strathdee SA, & Rhodes T (2019). Integrated and differentiated methadone and HIV care for people who use drugs: A qualitative study in Kenya with implications for implementation science. Health Policy and Planning, 34(2), 110–119. 10.1093/heapol/czz002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Hassan S, Cooke A, Saleem H, Mushi D, Mbwambo J, & Lambdin BH (2019). Evaluating the integrated methadone and anti-retroviral therapy strategy in tanzania using the RE-AIM framework. International Journal of Environmental Research and Public Health, 16(5). 10.3390/ijerph16050728 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Hogg RS, Montaner JSG, & Wood E (2011). Antiretroviral Therapy among drug injection. 105(5), 907–913. 10.1111/j.1360-0443.2010.02905.x.METHADONE [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Korthuis PT, King C, Cook RR, Khuyen TT, Kunkel LE, Bart G, Nguyen T, Thuy DT, Bielavitz S, Nguyen DB, Tam NTM, & Giang LM (2021). HIV clinic-based buprenorphine plus naloxone versus referral for methadone maintenance therapy for treatment of opioid use disorder in HIV clinics in Vietnam (BRAVO): an open-label, randomised, non-inferiority trial. The Lancet HIV, 8(2), e67–e76. 10.1016/S2352-3018(20)30302-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Kurth AE, Cleland CM, Des Jarlais DC, Musyoki H, Lizcano JA, Chhun N, & Cherutich P (2015). HIV prevalence, estimated incidence, and risk behaviors among people who inject drugs in Kenya. Journal of Acquired Immune Deficiency Syndromes, 70(4), 420–427. 10.1097/QAI.0000000000000769 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Li L, Lin C, Lee S, Tuan LA, Feng N, Tuan NA, Behavior H, & Angeles L (2018). HHS Public Access. 28(12), 1247–1254. 10.1177/0956462417696431.Antiretroviral [DOI] [Google Scholar]
  19. Mathers BM, Degenhardt L, Phillips B, Wiessing L, Hickman M, Strathdee SA, Wodak A, Panda S, Tyndall M, Toufik A, & Mattick RP (2008). Global epidemiology of injecting drug use and HIV among people who inject drugs: a systematic review. The Lancet, 372(9651), 1733–1745. 10.1016/S0140-6736(08)61311-2 [DOI] [PubMed] [Google Scholar]
  20. Mayer KH, Meyer JP, Althoff AL, & Altice FL (2013). Optimizing care for HIV-Infected people who use drugs: Evidence-based approaches to overcoming healthcare disparities. Clinical Infectious Diseases, 57(9), 1309–1317. 10.1093/cid/cit427 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Michael SD (2000). Adherence to Antiretroviral Therapy Among HIV-Infected Methadone Patients: Effect of Ongoing Illicit Drug Use. Published Online. 10.1081/ADA-100100600 [DOI] [PubMed] [Google Scholar]
  22. Miller WC, Hoffman IF, Hanscom BS, Ha TV, Dumchev K, Djoerban Z, Rose SM, Latkin CA, Metzger DS, Lancaster KE, Go VF, Dvoriak S, Mollan KR, Reifeis SA, Piwowar-Manning EM, Richardson P, Hudgens MG, Hamilton EL, Sugarman J, … Burns DN (2018). A scalable, integrated intervention to engage people who inject drugs in HIV care and medication-assisted treatment (HPTN 074): a randomised, controlled phase 3 feasibility and efficacy study. The Lancet, 392(10149), 747–759. 10.1016/S0140-6736(18)31487-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Mocroft A, Vella S, Benfield TL, Chiesi A, Miller V, Gargalianos P, Monforte AA, Yust I, & Bruun JN (1998). Changing patterns of mortality across Europe in patients infected with HIV-1. 352, 1725–1730. [DOI] [PubMed] [Google Scholar]
  24. Musyoki H, Muraguri N, Okal J, & Kim AA (2016). Prevalence of HIV, STIs, and risk behaviors among FSWs in Nairobi Kenya. AIDS Behav, 19(Suppl 1). 10.1007/s10461-014-0919-4.Prevalence [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. NACC. (2015). Kenya AIDS Strategic Framework (KASF) 2015. 37–45. http://nacc.or.ke/wp-content/uploads/2015/09/KASF_Final.pdf
  26. NASCOP. (2013). National Guidelines for the comprehensive management of the Health Risk and consequences of the Drug use,Nairobi. [Google Scholar]
  27. NASCOP. (2014). National Guidelines for HIV/ STI Programming with Key populations. [Google Scholar]
  28. NASCOP. (2018). Guidelines on use of Antiretroviral Drugs for testing and preventing HIV infection in Kenya. [Google Scholar]
  29. NASCOP. (2019). Key Population Mapping and Size Estimation in Selected Counties in Kenya: Phase 1. April. [Google Scholar]
  30. National AIDS and STI Control Programme(NASCOP), Ministry of Health, K. (2018). KENPHIA. [Google Scholar]
  31. Ndimbii JN, Guise A, Ayon S, Kalama M, McLean S, & Rhodes T (2015). Implementing needle and syringe programmes in Kenya: Changes, opportunities and challenges in HIV prevention. African Journal of Drug and Alcohol Studies, 14(2), 95–103. [Google Scholar]
  32. Palepu A, Tyndall MW, Joy R, Kerr T, Wood E, Press N, Hogg RS, & Montaner JSG (2006). Antiretroviral adherence and HIV treatment outcomes among HIV/HCV co-infected injection drug users: The role of methadone maintenance therapy. Drug and Alcohol Dependence, 84(2), 188–194. 10.1016/j.drugalcdep.2006.02.003 [DOI] [PubMed] [Google Scholar]
  33. Rhodes T, Guise A, Ndimbii J, Strathdee S, Ngugi E, Platt L, Kurth A, Cleland C, & Vickerman P (2015). Is the promise of methadone Kenya’s solution to managing HIV and addiction? A mixed-method mathematical modelling and qualitative study. BMJ Open, 5(3). 10.1136/bmjopen-2014-007198 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Rodger AJ, Lodwick R, Schechter M, Deeks S, Amin J, Gilson R, Paredes R, Bakowska E, Engsig FN, & Phillips A (2013). Mortality in well controlled HIV in the continuous antiretroviral therapy arms of the SMART and ESPRIT trials compared with the general population. November 2012. 10.1097/QAD.0b013e32835cae9c [DOI] [PubMed] [Google Scholar]
  35. Saleem HT, Mushi D, Hassan S, Douglas Bruce R, Cooke A, Mbwambo J, & Lambdin BH (2016). “Can’t you initiate me here?”: Challenges to timely initiation on antiretroviral therapy among methadone clients in Dar es Salaam, Tanzania. International Journal of Drug Policy, 30, 59–65. 10.1016/j.drugpo.2015.12.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Salleh NM, Voon P, Karamouzian M, Milloy M-J, & Richardson L (2021). Methadone maintenance therapy service components linked to improvements in HIV care cascade outcomes: A systematic review of trials and observational studies. Drug and Alcohol Dependence, 218, 108342. 10.1016/j.drugalcdep.2020.108342 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Uhlmann Sasha. (2011). Methadone Maintenace therapy promotes initiation of antiretroviral therapy among injection drug users. PMC. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2857602/ [DOI] [PMC free article] [PubMed]
  38. Shrestha Roman MMC (2019). Viral suppression among HIV-infected methadone-maintained patient:The role of ongoing injection drug use and adherence to antiretroviral therapy (ART). PMC, 85(10), 88–93. 10.1016/j.addbeh.2018.05.031 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Sypsa V, Psichogiou M, Paraskevis D, Nikolopoulos G, Tsiara C, Paraskeva D, Micha K, Malliori M, Pharris A, Wiessing L, Donoghoe M, Friedman S, Jarlais D. Des, Daikos G, & Hatzakis A (2017). Rapid Decline in HIV Incidence among Persons Who Inject Drugs during a Fast-Track Combination Prevention Program after an HIV Outbreak in Athens. Journal of Infectious Diseases, 215(10), 1496–1505. 10.1093/infdis/jix100 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Tran BX. (2012). Cost effectiveness of intergrating methadone maintenance and antiretroviral treatment for HIV drugs users in Vietnam’s injection-driven HIV epidemics. Drugs and alcohol dependence. DOI, 125 (3), 260–266. https://pubmed.ncbi.nlm.nih.gov/22436971/ [DOI] [PubMed] [Google Scholar]
  41. Treisman GJ (2001). Psychiatric Issues in the Management of Patients With HIV Infectione. JAMA, 286(22), 2857–2864. https://jamanetwork.com/journals/jama/article-abstract/194450 [DOI] [PubMed] [Google Scholar]
  42. Vickerman P, Platt L, Jolley E, Rhodes T, Kazatchkine MD, & Latypov A (2014). Controlling HIV among people who inject drugs in Eastern Europe and Central Asia: Insights from modelling. International Journal of Drug Policy, 25(6), 1163–1173. 10.1016/j.drugpo.2014.09.013 [DOI] [PubMed] [Google Scholar]
  43. Weber R, Huber M, Rickenbach M, Furrer H, Elzi L, Hirschel B, Cavassini M, Bernasconi E, Schmid P, Ledergerber B, Battegay M, Bernasconi E, Böni J, Bucher HC, Bürgisser P, Calmy A, Cattacin S, Cavassini M, Dubs R, … Yerly S (2009). Uptake of and virological response to antiretroviral therapy among HIV-infected former and current injecting drug users and persons in an opiate substitution treatment programme: The Swiss HIV Cohort Study. HIV Medicine, 10(7), 407–416. 10.1111/j.1468-1293.2009.00701.x [DOI] [PubMed] [Google Scholar]
  44. Wise AM, Mbogo L, Guthrie B, Bukusi D, Sambai B, Chohan B, Scott J, Cherutich P, Musyoki H, Bosire R, Dunbar M, Macharia P, Masyuko S, Sinkele B, Wilkinson E, Oliveira T. De, Ludwig N, Herbeck J, & Farquhar C (2021). mediated HIV assisted partner services to identify and link to care positive and HCV- - positive people who inject drugs : a cohort study protocol. 1–10. 10.1136/bmjopen-2020-041083 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Wolfe D, Carrieri MP, & Shepard D (2010). Treatment and care for injecting drug users with HIV infection: A review of barriers and ways forward. The Lancet, 376(9738), 355–366. 10.1016/S0140-6736(10)60832-X [DOI] [PubMed] [Google Scholar]
  46. Wood E, Hogg RS, Yip B, Dong WWY, Wynhoven B, Mo T, Brumme CJ, Montaner JSG, & Harrigan PR (2005). Rates of antiretroviral resistance among HIV-infected patients with and without a history of injection drug use. Aids, 19(11), 1189–1195. 10.1097/01.aids.0000176219.48484.f1 [DOI] [PubMed] [Google Scholar]
  47. Zhang F, Dou Z, Ma Y, Zhang Y, Zhao Y, Zhao D, Zhou S, Bulterys M, Zhu H, & Chen RY (2011). Effect of earlier initiation of antiretroviral treatment and increased treatment coverage on HIV-related mortality in China: A national observational cohort study. The Lancet Infectious Diseases, 11(7), 516–524. 10.1016/S1473-3099(11)70097-4 [DOI] [PubMed] [Google Scholar]

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