# Research priorities to inform "Treat All" policy implementation for people living with HIV in sub-Saharan Africa: a consensus statement from the International epidemiology Databases to Evaluate AIDS (IeDEA)

M. Yotebieng, Ellen Brazier, D. Addison, A. D. Kimmel, M. Cornell, O. Keiser, A. M. Parcesepe, A. Onovo, K. E. Lancaster, B. Castelnuovo, et al.

<figure><img src="image_1.png" /></figure>

## ► To cite this version:

M. Yotebieng, Ellen Brazier, D. Addison, A. D. Kimmel, M. Cornell, et al. Research priorities to inform "Treat All" policy implementation for people living with HIV in sub-Saharan Africa: a consensus statement from the International epidemiology Databases to Evaluate AIDS (IeDEA). Journal of the International AIDS Society, In press, 22 (1), pp.e25218. 10.1002/jia2.25218. hal-02902893

HAL Id: hal-02902893

https://hal.science/hal-02902893v1

Submitted on 11 Dec 2024

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_2.png" /></figure>
REVIEW

Research priorities to inform “Treat All” policy implementation for people living with HIV in sub-Saharan Africa: a consensus statement from the International epidemiology Databases to Evaluate AIDS (IeDEA)

Marcel Yotebieng<fnref n="1" />,*, ID, Ellen Brazier²,³,* ID, Diane Addison²,³,* April D Kimmel⁴, Morna Cornell⁵, Olivia Keiser⁶, Angela M Parcesepe⁷ ID, Amobi Onovo⁷, Kathryn E Lancaster<fnref n="1" /> ID, Barbara Castelnuovo⁸, Pamela M Murnane⁹, Craig R Cohen¹⁰, Rachel C Vreeman¹¹ ID, Mary-Ann Davies¹² ID, Stephany N Duda¹³, Constantin T Yiannoutsos¹⁴, Rose S Bono⁴, Robert Agler<fnref n="1" />, Charlotte Bernard¹⁵, Jennifer L Syvertsen¹⁶, Jean d'Amour Sinayobye¹⁷, Radhika Wikramanayake²,³, Annette H Sohn¹⁸ ID, Per M von Groote¹⁹, Gilles Wandeler¹⁹ ID, Valeriane Leroy²⁰, Carolyn F Williams²¹, Kara Wools-Kaloustian²², Denis Nash²,³&* ID and for the IeDEA Treat All in sub-Saharan Africa Consensus Statement Working Group<fnref n="1" />

Corresponding author: Denis Nash, CUNY ISPH 55 West 125th Street, 6th Floor, New York, NY 10027, USA. Tel: +1 646-364-9618. (denis.nash@sph.cuny.edu)

**Abstract**

**Introduction:** "Treat All" – the treatment of all people with HIV, irrespective of disease stage or CD4 cell count – represents a paradigm shift in HIV care that has the potential to end AIDS as a public health threat. With accelerating implementation of Treat All in sub-Saharan Africa (SSA), there is a need for a focused agenda and research to identify and inform strategies for promoting timely uptake of HIV treatment, retention in care, and sustained viral suppression and addressing bottlenecks impeding implementation.

**Methods:** The Delphi approach was used to develop consensus around research priorities for Treat All implementation in SSA. Through an iterative process (June 2017 to March 2018), a set of research priorities was collectively formulated and refined by a technical working group and shared for review, deliberation and prioritization by more than 200 researchers, implementation experts, policy/decision-makers, and HIV community representatives in East, Central, Southern and West Africa.

**Results and discussion:** The process resulted in a list of nine research priorities for generating evidence to guide Treat All policies, implementation strategies and monitoring efforts. These priorities highlight the need for increased focus on adolescents, men, and those with mental health and substance use disorders – groups that remain underserved in SSA and for whom more effective testing, linkage and care strategies need to be identified. The priorities also reflect consensus on the need to: (1) generate accurate national and sub-national estimates of the size of key populations and describe those who remain underserved along the HIV-care continuum; (2) characterize the timeliness of HIV care and short- and long-term HIV care continuum outcomes, as well as factors influencing timely achievement of these outcomes; (3) estimate the incidence and prevalence of HIV-drug resistance and regimen switching; and (4) identify cost-effective and affordable service delivery models and strategies to optimize uptake and minimize gaps, disparities, and losses along the HIV-care continuum, particularly among underserved populations.

**Conclusions:** Reflecting consensus among a broad group of experts, researchers, policy- and decision-makers, PLWH, and other stakeholders, the resulting research priorities highlight important evidence gaps that are relevant for ministries of health, funders, normative bodies and research networks.

**Keywords:** Treat All; universal HIV treatment; 90-90-90 targets; sub-Saharan Africa; implementation science

<footnote>*These authors have contributed equally to the work.</footnote>

<footnote n="1">¹IeDEA Treat All in sub-Saharan Africa Consensus Statement Working Group members are listed in the Appendix.</footnote>
# 1 | INTRODUCTION

The World Health Organization's (WHO) "Treat All" guidance of September 2015, which recommended that all individuals be treated as soon as possible after HIV infection and diagnosis [1], was a true paradigm shift in HIV care and treatment [2]. Preventing illness and death among people living with HIV and averting new infections by reducing onward HIV transmission, Treat All is recognized as the primary strategy for achieving the Joint United Nations Programme on HIV/AIDS (UNAIDS) 90-90-90 targets [3–5] (Box 1). In view of its potential contribution to ending AIDS as a public health threat, Treat All is being adopted in countries around the globe.

By 2017, most countries in sub-Saharan Africa (SSA) had adopted some form of the Treat All recommendation (Figure 1) [2,6–8]. Nonetheless, new HIV infections and AIDS-related mortality remain higher in SSA than other world regions, and the majority of people living with undiagnosed and untreated HIV infection live in SSA [9]. To address high unmet need for HIV care and treatment and accelerate progress towards the UNAIDS 90-90-90 targets in SSA, policymakers and planners need evidence on effective strategies for promoting uptake of services and maximizing their impact on individual and population health outcomes, and on how best to address major bottlenecks impeding effective and efficient implementation of Treat All. To catalyze efforts to address these important evidence gaps, a global group of expert clinicians, researchers and programme specialists engaged in HIV research and service delivery in SSA undertook a multi-step process to identify a set of initial research priorities with the potential to inform and guide Treat All implementation. With the rollout of Treat All accelerating in SSA in 2017, the aim of this process was to ensure that evidence on the implementation and scale-up of Treat All policies is systematically gathered and examined to identify optimal programmatic strategies and to realize the individual and public health benefits of earlier HIV treatment more rapidly [10–12].

# 2 | METHODS

The Delphi method was used to develop and refine Treat All research priorities. The Delphi method is a flexible approach, widely used to reach consensus among experts in health research and other disciplines [13–18]. The Delphi method generally involves an iterative process of eliciting and aggregating opinions from a group of experts, with opportunities for participants to provide input during each round and to reassess and incorporate new insights and perspectives during subsequent rounds. A key feature of the Delphi method is that participants provide input independently during each round, resulting in a process that is not unduly influenced by any one individual or subset of participants [13,14].

The process for reaching consensus around Treat All research priorities involved six phases, carried out between June 2017 and March 2018 (Figure 2). This process was led by a group of researchers involved in the International epidemiology Databases to Evaluate AIDS (IeDEA) consortium, a global collaboration that consolidates, curates and analyses longitudinal data on care and treatment of PLWH (Box 2).

## 2.1 | Round 1

A core group of 25 IeDEA investigators were invited to participate in a Treat All Consensus Statement Working Group, based on their expertise leading HIV research in 10 population-specific and/or cross-cutting content areas (see Box 3). Subsequently, 15 working group members reviewed and summarized the literature [2] in their respective areas of expertise and proposed an initial list of 83 research priorities across the 10 content areas.

## 2.2 | Round 2

Via an online Qualtrics [19] survey, members of the working group were asked to anonymously rate (without ranking) the overall importance of each of the 83 proposed research

### Box 1. Key Terms and Definitions

**Treat All:** The Treat All approach recognizes that HIV infection should be treated as soon as possible after diagnosis because all patients, regardless of their stage of infection, benefit clinically from early treatment of HIV. In addition, because reduction in HIV viral load to undetectable levels eliminates the risk of onward transmission, the Treat All approach has the potential to provide the population health benefit of reducing HIV incidence. Treat All therefore is inclusive of efforts to diagnose and treat all persons with HIV as soon as possible after HIV infection.

**UNAIDS 90-90-90 Targets:** In 2014, the Joint United Nations Programme on HIV/AIDS and partners set targets to diagnose 90% of the people living with HIV (PLWH), provide treatment to 90% of those diagnosed with HIV, and achieve viral suppression among 90% of those on treatment by 2020 to help end the AIDS epidemic by 2030.

**Key and Underserved Populations:** We use the term Key Populations to refer to groups of people who are more likely to be exposed to HIV or to transmit it, and whose engagement is critical to a successful HIV response [65]. Depending on the epidemic context and setting, key populations may include infants/children, adolescents, younger adults, men, women, older adults, female sex workers (FSW), men who have sex with men (MSM), people who inject drugs (PWID), transgender (TG) individuals and migrant/mobile populations. "Underserved" refers to an unmet need for HIV care services, including both testing and treatment services.
<figure><img src="image_3.png" /><figcaption>Figure 1. Uptake of national 'Treat all' policies for adults and adolescents with HIV, July 2017. (Source: WHO)</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>Figure 2. Consensus development process.</figcaption></figure>

priorities, using a scale of 1 (least important) to 5 (most important). Respondents were also asked to provide feedback on whether each research priority was sufficiently specific and clear, and to suggest additional research priorities in response to perceived gaps. Sixteen out of 25 working group members (64%) participated in the survey, and 32 out of 83 research priorities were rated both "high" in importance (i.e. a "4" or "5" rating) and clear/specific by at least two-thirds of respondents. In addition, eight new research priorities were proposed, resulting in a list of 40 research priorities.

2.3 | Round 3

The revised list of 40 research priorities (in English and French) was distributed to the working group, as well as to a broader group of 203 researchers and stakeholders who were registered to attend a meeting on "Treat All" organized by the IeDEA consortium in Kigali, Rwanda. This meeting involved researchers and clinic staff involved in IeDEA in East, Central, Southern and West Africa, along with representatives from the Government of Rwanda, HIV policy and implementation
## Box 2. IeDEA

IeDEA is an international research consortium established in 2005 by the U.S. National Institute of Allergy and Infectious Diseases (NIAID) to provide a rich resource for globally diverse HIV/data (see www.iedea.org). The IeDEA Cohort Consortium collaborates to collect and harmonize clinical care data as a cost-effective means of generating large data sets to address high priority research questions and streamline HIV/AIDS research.

The Consortium includes 140 clinics (denoted by ⊙) in sub-Saharan Africa that have provided care to more than 1 million PLWH. Representing 23 SSA countries, IeDEA is poised to provide some of the first data on the uptake and outcomes of Treat All implementation.

<figure><img src="image_5.png" /></figure>

Location of IeDEA sites in SSA

Yellow: West Africa

Green: Central Africa

Orange: East Africa

Purple: Southern Africa

## Box 3. Population-specific and cross-cutting content areas of expertise

### Populations

- Infants and children

- Adolescents

- Adults

- Pregnant and postpartum women

- Key populations (sex workers, people who inject drugs, men who have sex with men, mobile populations)

### Cross-cutting content areas

- Metrics and monitoring

- Models of care/strategies

- Policy modelling

- Mental health

- Substance use

experts, donor representatives, other non-IeDEA affiliated researchers, and representatives from four advocacy organizations representing the HIV community. Via a second Qualtrics survey, all 203 meeting registrants, including members of the working group, were invited to review and anonymously rate the list of 40 research priorities, using the same five-point rating scale. Survey responses were received from 72/203 (36%) researchers from 14 countries. Mean ratings and the proportion of respondents rating each research priority a “5” (highest) in importance were calculated.

### 2.4 | Round 4

At the Kigali meeting in November 2017, all 203 attendees (100%) actively participated in breakout sessions to further refine the research priorities. Meeting attendees self-selected into three groups, each focused on a different 90-90-90 target (i.e. diagnosis, treatment, or viral suppression). Each group reviewed the list of 40 research priorities and their ratings from Round 3 and deliberated to reach consensus on five to seven priorities most critical for attaining the group's respective 90-90-90 target. Groups were also encouraged to modify

the proposed priorities to reflect important topics that had not been identified a priori. This process resulted in a combined list of 16 research priorities.

### 2.5 | Round 5

In follow-up to the Kigali meeting, 21 members of the working group participated in two teleconference calls to review the research priorities recommended by the three breakout groups and to discuss areas of overlap and ways to combine research priorities. This step produced a final list of nine research priorities.

### 2.6 | Round 6

The last round of the Delphi process was designed to assess the degree of consensus around the final list of research priorities. In January 2018, individualized survey links for a third and final online Qualtrics survey (in both English and French) were sent to all Kigali meeting participants and working group members. As with previous rounds, respondents were invited to rate each research priority on a five-point scale in terms of its importance to Treat All implementation. Responses were received from 93/203 participants (46%), and mean ratings and the proportion rating each priority a “4” or “5” in importance were calculated.

### 2.7 | Human subjects

The protocol for this project was reviewed by the Institutional Review Board (IRB) at the City University of New York School of Public Health.

## 3 | RESULTS

### 3.1 | Rating of and consensus on research priorities

More than 200 individuals participated in one or more rounds of the research prioritization process, representing 12 countries in SSA (Burundi, Cameroon, Côte d'Ivoire, Democratic Republic of Congo, Kenya, Nigeria, Republic of the Congo, Rwanda, Senegal, South Africa, Tanzania and Uganda), as well
<table><caption>**Table 1. Treat All research priorities**</caption><thead><tr><th>Generating metrics, estimates, and evidence to guide Treat All policies, planning, monitoring and evaluation, and intervention development, with key metrics disaggregated by age, sex and population group</th><th>Mean rating<br>% rating "4" or "5"<br>in importance for Treat All</th></tr></thead><tbody><tr><td>1. Generate accurate national and sub-national estimates of the number and proportion of persons living with HIV who are undiagnosed</td><td>Mean: 4.4<br>Rating "5": 57.6%</td></tr><tr><td>2. Characterize and understand critical facilitators of and barriers to timely diagnosis, care linkage, antiretroviral therapy (ART) initiation, sustained care engagement, and ART adherence, particularly for key populations and underserved groups, including infants, adolescents and men</td><td>Mean: 4.7<br>Rating "5": 76.1%<br>Rating "4" or "5": 91.3%</td></tr><tr><td>3. Develop and validate country-specific policy models to support decision-making around Treat All implementation</td><td>Mean: 4.2<br>Rating "5": 41.8%<br>Rating "4" or "5": 78.0%</td></tr><tr><td>4. Develop and apply metrics that reflect the timeliness with which short-term and long-term HIV care continuum outcomes are achieved</td><td>Mean: 4.1<br>Rating "5": 44.1%<br>Rating "4" or "5": 77.4%</td></tr><tr><td>5. Estimate the incidence and prevalence of HIV drug resistance, as well as switching from second to third-line regimens at national and subnational levels</td><td>Mean: 4.2<br>Rating "5": 45.2%<br>Rating "4" or "5": 82.8%</td></tr><tr><td colspan="2"><strong>Intervention effectiveness trials and economic evaluations to improve the rollout of Treat All and its effect on the achievement of 90-90-90 goals</strong></td></tr><tr><td>6. Identify service delivery models and strategies to optimize uptake of HIV testing, including repeat testing and linkage to care for key and underserved populations</td><td>Mean: 4.3<br>Rating "5": 50.0%<br>Rating "4" or "5": 85.9%</td></tr><tr><td>7. Identify service delivery models and strategies to reduce the time from diagnosis to ART initiation for key and underserved populations</td><td>Mean: 4.3<br>Rating "5": 50.6%<br>Rating "4" or "5": 82.4%</td></tr><tr><td>8. Identify service delivery models and strategies to improve early and sustained viral suppression, early identification of drug resistance, and timely regimen switching</td><td>Mean: 4.5<br>Rating "5": 62.0%<br>Rating "4" or "5": 89.1%</td></tr><tr><td>9. Identify screening, diagnostic, and treatment interventions for mental health and substance use disorders that can be integrated into HIV care to improve timely diagnosis, ART initiation, retention in care and viral suppression</td><td>Mean: 4.1<br>Rating "5": 39.1%<br>Rating "4" or "5": 73.9%</td></tr></tbody></table>

as Europe (France and Switzerland) and North America (United States). While 123/203 (61%) of these individuals participated in at least one of the research priority rating surveys, the final list of nine research priorities was rated by 93 participants (response rate of 46%). Almost two-thirds (60%) of those who participated in the final rating of research priorities were based in SSA, 12% of respondents were based in Europe, and 28% were based in the United States. One-fifth of respondents completed the survey in French. Respondents' backgrounds and areas of work included clinical research and implementation science (71%), service provision (25%), programme management (16%), policy-making or advocacy (6%), and other (10%), with about one-fourth reporting multiple areas (e.g. research and service provision, programme management and research). Respondents reported a mean of 10 years of experience working in HIV/AIDS, across various disciplines, including adult HIV (54%), adolescents (35%), infants and children (29%), key populations (27%), maternal and child health/prevention of mother-to-child transmission (26%), metrics and monitoring (18%), mental health disorders

(12%), models of care (10%), substance use disorders (8%) and policy modelling (6%).

There was a high level of consensus across the final list of 9 research priorities, with 73.9% to 91.3% of respondents providing ratings of a "4" or "5" in importance (Table 1). In analyses stratified by respondent characteristics (e.g. identification as a researcher vs. non-researcher, engagement in local/national policy-making, residence in SSA, survey language), there were few differences in ratings, with the exception of Research Priority 1 (Generate accurate national and sub-national estimates of the number and proportion of persons living with HIV who are undiagnosed, disaggregated by age, sex, and population group) which was rated higher in importance by members of the working group than other respondents (4.7 vs. 4.3, *p* < 0.05; 100% vs. 81% rating as a "4" or "5").

## 3.2 | Research priorities

The final research priorities are presented in Table 1, with illustrative research questions related to each priority
<table><caption>Table 2. Illustrative research questions and possible methods to address them</caption><thead><tr><th>Research questions</th><th>Methods</th></tr></thead><tbody><tr><td><strong>Research Priority 1: Generate accurate national and sub-national estimates of the number and proportion of persons living with HIV who are undiagnosed</strong><ul><li>What is the prevalence of undiagnosed HIV, particularly for key and priority population groups (e.g. MSM, SW, PWID, infants, adolescent, pregnant women, men), and what is the size of key population groups (e.g. MSM, SW, PWID) at national and subnational levels?</li><li>How does the prevalence of undiagnosed HIV vary by sub-national geographic area?</li></ul></td><td><ul><li>Routine monitoring data; serosurveys, biobehavioural surveys; modelling.</li></ul></td></tr><tr><td><strong>Research Priority 2: Characterize and understand critical facilitators of and barriers to timely diagnosis, care linkage, ART initiation, and sustained care engagement and ART adherence, particularly for key populations and underserved groups, including infants, adolescents, and men</strong><ul><li>What factors (individual, cultural, and structural/systems) influence timely diagnosis of HIV (i.e. at higher CD4 counts) and timely linkage to HIV care? How does this vary by sociodemographics and for key and underserved populations (e.g. MSM, SW, PWID, infants, adolescents, men)?</li></ul></td><td><ul><li>Mixed methods approaches with PLWH, providers, and policy makers; implementation science/intervention studies; studies exploring new settings for HIV testing.</li></ul></td></tr><tr><td><strong>Research Priority 3: Develop and validate country-specific policy models to support decision-making around Treat All implementation</strong><ul><li>What are the country-specific health and economic outcomes, including cost-effectiveness and budget impact, associated with Treat All implementation?</li><li>How should interventions that address local implementation challenges (e.g. advanced HIV at entry to care; loss to follow-up; acquired and developed viral resistance) be efficiently prioritized?</li><li>What strategies can best engage local decision makers in mathematical model development and translation of model findings into policy?</li></ul></td><td><ul><li>Mathematical modelling; cost-effectiveness and other economic studies; stakeholder meetings; key informant interviews.</li></ul></td></tr><tr><td><strong>Research Priority 4: Develop and apply metrics that reflect the timeliness with which short-term and long-term HIV care continuum outcomes are achieved (i.e. early diagnosis, rapid linkage to care following diagnosis, rapid ART initiation following linkage, viral suppression within 4 weeks of ART initiation, and rapid achievement of sustained viral suppression)</strong><ul><li>What is the most appropriate care cascade metric for Treat All and what metrics should be used to monitor it? Is it possible to develop a metric of time from infection to ART initiation?</li><li>What is the optimal timing of ART initiation after diagnosis confirmation (e.g. immediately after diagnosis, after initial adherence counselling, etc.) for maximizing retention in care, adherence, and clinical outcomes, and how does this vary by population subgroup and co-morbidities (e.g. patients with TB co-infection, substance use and mental health disorders)?</li></ul></td><td><ul><li>RCT or cluster RCT in real world implementation setting (vs. research setting).</li></ul></td></tr><tr><td><strong>Research Priority 5: Estimate the incidence and prevalence of HIV drug resistance, as well as switch to second- and third-line regimens at national and subnational levels</strong><ul><li>What is the prevalence of acquired and developed HIV drug resistance, and how does this vary across national, subnational and patient populations?</li><li>What is the rate of switching to second- and third-line regimens, and how does this vary by setting and by patient characteristics?</li></ul></td><td><ul><li>Routine monitoring data; surveys; targeted studies at sentinel HIV care sites.</li></ul></td></tr></tbody></table>
<table><caption>**Table 2.** (Continued)</caption><thead><tr><th>Research questions</th><th>Methods</th></tr></thead><tbody><tr><td><strong>Research Priority 6: Identify service delivery models and strategies to optimize uptake of HIV testing, including repeat testing and linkage to care, for key and underserved populations</strong><ul><li>What testing strategies and settings (e.g. self-testing, home-, and community-testing, etc.) are effective in improving timely HIV diagnosis, for sociodemographic and other key subgroups (e.g. MSM, SW, PWID), and underserved populations (infants, adolescents, men, sexual partners of HIV-infected individuals)?</li><li>Which testing strategies are most preferred by client subgroups? Which can minimize stigma-related barriers to HIV testing?</li><li>What clinic and community-based strategies are effective in improving linkage to- and retention in care and sustained viral load suppression?</li></ul></td><td><ul><li>RCT/cluster RCT; hybrid trial design; mixed methods; discrete choice experiments.</li></ul></td></tr><tr><td><strong>Research Priority 7: Identify service delivery models and strategies to reduce the time from diagnosis to ART initiation for key and underserved populations</strong><ul><li>What clinic and community-based strategies are effective in linking patients to care, particularly for key and underserved populations (e.g. MSM, SW, PWID; men and adolescents)?</li><li>What clinic and community-based strategies are effective in ensuring timely initiation of ART, particularly for key and underserved populations?</li><li>What strategies are effective in addressing stigma-related barriers to HIV care?</li><li>Which service models are most preferred by client subgroups and care providers?</li><li>Are strategies, such as integrated care, task-shifting, and community- and home-based services an efficient use of scarce resources under Treat All?</li></ul></td><td><ul><li>Mixed methods; RCT/cluster RCT; hybrid trial design, cost-effectiveness and other economic studies; discrete choice experiments.</li></ul></td></tr><tr><td><strong>Research Priority 8: Identify service delivery models and strategies to improve early and sustained viral suppression, early identification of drug resistance, and timely regimen switching</strong><ul><li>What strategies are effective in ensuring early and sustained viral suppression, particularly for key populations and priority subgroups (e.g. MSM, SW, PWID; men, adolescents and infants)?</li><li>How can service integration strategies be used to support sustained viral suppression, particularly for key populations and priority subgroups?</li><li>What strategies are most effective in ensuring early identification of drug resistance, and timely regimen switching?</li></ul></td><td><ul><li>Mixed methods; RCT/cluster RCT; hybrid trial design; cost-effectiveness and other economic studies.</li></ul></td></tr><tr><td><strong>Research Priority 9: Identify screening, diagnostic and treatment interventions for mental health and substance use disorders that can be integrated into HIV care to improve timely diagnosis, ART initiation, retention and viral suppression</strong><ul><li>What is the feasibility and acceptability of integrating screening, diagnosis, and treatment (pharmacological and non-pharmacological) of mental health and substance use disorders (MH/SUD) and into HIV care delivered by lay healthcare workers?</li><li>What are effective strategies of integrating mental health and substance use disorders screening, diagnosis, and treatment into HIV care, particularly for improving timely diagnosis, ART initiation, retention and viral suppression.</li><li>How can effective models for screening, diagnosis, and treatment of MH/SUD within HIV clinic settings be scaled-up?</li><li>What are the health outcomes, economic costs, and cost-effectiveness of integrating MH/SUD screening/diagnosis and treatment within HIV clinic settings compared to current standard of care?</li></ul></td><td><ul><li>Mixed methods; RCT/cluster RCT; hybrid trial design, cost-effectiveness and other economic studies.</li></ul></td></tr></tbody></table>
presented in Table 2. Of the final list of nine research priorities, five priorities are focused on generating critical metrics, estimates and evidence needed to inform policies, planning, monitoring and evaluation related to Treat All implementation. Four priorities relate to the need to conduct focused effectiveness trials and economic evaluations to improve the rollout of Treat All. The research priorities reflect consensus around the need to more fully characterize the barriers faced by key populations and underserved groups along each step of the HIV care continuum and to identify programmatic strategies and tailored models of care that meet the preferences and needs of these populations. Finally, the research priorities highlight the need for enhanced metrics and data related to the timeliness of achieving short- and long-term outcomes along the HIV care continuum, with particular attention to drug resistance and regimen switching.

**3.2.1 | Research Priority 1: Generate accurate national and sub-national estimates of the number and proportion of persons living with HIV who are undiagnosed**

*   **Context:** To achieve the "first 90" target, timely estimates of the number and proportion of persons with undiagnosed HIV infection are critical for countries to ensure that HIV testing programmes are targeted appropriately and efficiently [20,21]. One recent study that analysed population-based Demographic and Health Survey (DHS) data from 16 SSA countries estimated that only 54% of people living with HIV (range across countries 26% to 84%) were aware of their status, contributing to delays in care enrollment and ART initiation [22]. Men, adolescents, those with lower education levels, and the poorest individuals are less likely to be aware of their status, resulting in late initiation of treatment, as well as lower and later attainment of viral suppression [22]. Infants less than 18 months of age are also at risk of delays in care enrollment and treatment initiation because of challenges in early infant diagnosis testing, especially in resource-limited settings [23,24]. Estimating the "first 90" for key populations (e.g. men who have sex with men [MSM], sex workers [SW], people who inject drugs [PWID], etc.) is difficult in contexts with unknown population size estimates [20,21].

*   **Research approaches:** Population-based studies, such as demographic and health surveys that evaluate the implementation of testing services, frequent (annual) targeted HIV sero-prevalence surveys in sub-national geographic areas, and biobehavioural surveys [20], may be necessary for monitoring this population-level metric. Surveys should report estimates disaggregated by sex and age, with finer age disaggregations (e.g. two-year age ranges) used for children and adolescents [25]. Studies leveraging health service utilization data, including antenatal care and other sentinel surveillance-based methods, can also provide information about specific populations. Systematically characterizing individuals diagnosed and enrolling in HIV care with advanced disease can also provide insights about which populations are not being reached with existing testing and surveillance strategies.

**3.2.2 | Research Priority 2: Characterize and understand critical facilitators of and barriers to timely diagnosis, care linkage, ART initiation, and sustained care engagement and ART adherence, particularly for key populations and underserved groups, including infants, adolescents and men**

*   **Context:** Global data suggest that the timeliness of ART initiation, as measured by the level of immunodeficiency at the start of ART, is highly suboptimal in relation to WHO guidelines, particularly in SSA, where a recent analysis of data from 767,000 patients in 21 countries showed that median CD4 counts at ART initiation remained below 300 cells/mm³ in 2015 [26]. Infants, adolescents, and men, in particular, are more likely to initiate treatment late and to not be retained in care [24,25,27,28].

*   **Research approaches:** Important factors for further study include quality of care; policy and administrative requirements; costs of services, including user fees; HIV-related stigma; integrated screening and treatment of other health conditions (i.e. non-communicable diseases); and community- and home-based services (e.g. home-based self-testing, provision of multi-month medication supplies, etc.). Mixed methods approaches should be used to better understand these barriers and their relative contribution to delays in diagnosis, linkage, ART initiation, and viral suppression, as well as to losses along the care continuum, particularly for key populations. Additionally, mixed methods research, such as discrete choice experiments [29,30], should be used to identify preferences (i.e. facilitators) that could improve timely uptake of testing, HIV care, and sustained care engagement. The magnitude and type of HIV-related stigma, and its impact must be measured along the care continuum.

**3.2.3 | Research Priority 3: Develop and validate country-specific policy models to support decision-making around Treat All implementation**

*   **Context:** The use of mathematical modelling techniques, coupled with detailed, individual-level observational data, can inform Treat All policy questions, including the efficiency, prioritization and affordability of HIV-related interventions [31]. A strong modelling literature confirms that earlier ART initiation reduces morbidity and mortality, is cost-effective compared to deferred ART initiation [32–39], and prevents new HIV infections [32,40], which may reduce population-level economic costs [31]. However, additional work is needed to develop and validate mathematical models that better reflect local clinical context (e.g. epidemiologic and clinical care data, economic costs), integrate local health system constraints (e.g. workforce capacity, antiretroviral stockouts, etc.), and areas of potential health system improvement (e.g. integrated care). Addressing these gaps will facilitate policy-relevant modelling projections that inform cost-effectiveness of individual interventions, the affordability of these interventions, and allocative efficiency across interventions when resources are constrained [31,41,42].

*   **Research approaches:** The development of country-specific mathematical models can provide projections of health and economic outcomes related to Treat All, including budget
impact, which is essential information for programme planning and decision-making. A key component of this effort is incorporating realistic model assumptions and inputs that reflect local treatment and care patterns. These include real-world challenges and health system constraints surrounding late diagnosis, linkage to care, ART initiation, de novo development of viral resistance, sustained viral suppression, and reaching key and underserved populations, all of which can inform contextually relevant analyses on the cost-effectiveness, affordability and prioritization of alternative interventions to inform local Treat All implementation. Evaluating approaches for engaging decision-makers in mathematical modelling studies can facilitate translation of study findings into policy and practice. Validation of modelling approaches and the use of observational data sources for deriving appropriate model parameter estimates remain important research areas.

**3.2.4 | Research Priority 4: Develop and apply metrics that reflect the timeliness with which short-term and long-term HIV care continuum outcomes are achieved (i.e. early diagnosis, rapid linkage to care following diagnosis, rapid ART initiation following linkage, viral suppression within four weeks of ART initiation, and rapid achievement of sustained viral suppression)**

*   **Context:** A priority for Treat All approaches is minimizing the time between HIV infection and sustained viral suppression. Shortening this period maximizes individual clinical benefit and reduces the risk of onward transmission, ultimately reducing both new infections and HIV-related morbidity and mortality. Although the 90-90-90 targets and HIV care continuum delineate important milestones, current metrics do not reflect the timeliness with which these key outcomes are achieved [43]. In addition, in some settings, routine HIV viral load monitoring is infrequent, and pre-ART CD4 count monitoring is declining in frequency with Treat All implementation [44,45], which limits opportunities to evaluate individual and public health impacts of HIV programming. Four key metrics for assessing the timeliness of continuum milestones include: (1) median CD4 count at diagnosis [46,47], care enrollment [48,49], and ART initiation [26,45,50,51]; (2) time between diagnosis, enrollment [52–54]; (3) time between enrollment and ART initiation [49]; and (4) time to first HIV viral suppression and sustained HIV viral suppression.

*   **Research approaches:** Metrics for this research priority could be generated from routinely collected, patient-level, programmatic data. Where CD4 count and viral load data are not readily available for a large enough proportion of clinics and patients, it may be possible to produce estimates from a systematic sample of sites. Additionally, if such data are non-existent because of unavailability of testing or a lack of systematic monitoring, sentinel sites could be established to do systematic CD4 count monitoring immediately prior to ART initiation and viral load monitoring following ART initiation in accordance with national protocols. Such metrics should be disaggregated by age, sex and population group (i.e. key and underserved populations, as well as pregnant and breastfeeding women) [55]. Implementers and researchers should also consider disaggregating cascades by period of diagnosis or enrollment, so that short-term outcomes of newly diagnosed

persons and new enrollees can be differentiated from patients already enrolled in HIV care.

**3.2.5 | Research Priority 5: Estimate the incidence and prevalence of HIV drug resistance, as well as switch to second- and third-line regimens at national and subnational levels**

*   **Context:** Emerging HIV drug resistance, including transmitted resistance to NNRTI-based first-line ART regimens, is a growing clinical and public health concern [56,57]. As a result, many countries are rolling out dolutegravir-based ART as first-line therapy [58]. While implementation of Treat All dictates that healthy patients with good immunological status and no clinical signs of disease initiate treatment, these patients may have suboptimal treatment adherence and lower rates of retention, raising risks for development of drug resistance and subsequent transmission of resistant virus [59]. A recent review [57] and several modelling studies have raised concerns about the potential for an increasing rate of drug resistance associated with Treat All strategies [56,60,61]. Routine viral load monitoring and assessment of treatment adherence are therefore essential for detecting virologic failure early and for limiting the development of drug resistance [45]. Equally important are data on the impact of patient "churn" (i.e. recurring patient disengagement and re-engagement in care) on viral suppression, disease progression, the emergence of viral resistance, and the durability of ART – particularly for the first-line regimens that form the backbone of care in SSA. Such data are limited, particularly in settings where routine pre-ART CD4 count monitoring has been discontinued because it is not required for treatment initiation.

*   **Research approaches:** Patient tracing studies should consider including an array of evaluations, including care status, viral load, CD4 count and genotyping, to estimate the true frequency of these outcomes and to assess the effect of disengagement from care on viral resistance. As loss to care and patient churn vary by patient characteristics, disaggregating results by sex, age and other key demographics (e.g. pregnancy status) will be important for understanding the dynamics of treatment failure and drug resistance. In addition, the impact of reduced treatment adherence or interruption of ART on the development of drug resistance should be assessed and compared among different treatment regimens. Investigations should also explore early signs of non-adherence (e.g. dose timing measures and drug level measurements) among patients who are traced after being lost from original clinic of enrolment. Models designed to capture trends and drivers of drug resistance development are important for predicting outcomes and assessing the effectiveness of different treatment and monitoring strategies [57].

**3.2.6 | Research Priority 6: Identify service delivery models and strategies to optimize uptake of HIV testing, including repeat testing and linkage to care, for key and underserved populations**

*   **Context:** Stigma and discrimination remain important barriers to HIV testing [62–64], and about half of all people
living with HIV do not know their status [22]. Accordingly, closing the testing gap via differentiated service models, tailored approaches for populations at risk, and stigma reduction strategies is central to Treat All implementation [65]. For example, men are a particularly important group for tailored testing strategies, as they are less likely to be tested for HIV until they become ill [66–70]. A number of models show promise for optimizing uptake of HIV testing and screening, including home- and community-based testing, index partner testing, the integration of HIV testing into multi-disease community-level health campaigns, the use of lay cadres to expand testing and linkage to care, and self-testing [71–78].

* **Research approaches:** Cluster randomized controlled trials (RCTs), rigorous programme evaluations, mixed methods studies on optimal timing of and barriers to repeat testing, and discrete choice experiments on preferences related to testing location (home or community based) and modalities (e.g. integration of HIV testing into other health services) will aid in identifying effective strategies that improve early diagnosis and linkage to care, particularly for underserved groups.

### 3.2.7 | Research Priority 7: Identify service delivery models and strategies to reduce the time from diagnosis to ART initiation for key and underserved populations

* **Context:** With the rollout of Treat All, the public health approaches that have been effective in the rapid scale-up of treatment to date may not be sufficient for reaching the 90-90-90 targets and achieving epidemic control [79,80]. For the general population and key population groups (e.g. MSM, SW, PWID, those with mental health or other substance use disorders, etc.), stigma remains a barrier to HIV care, contributing to delays in ART initiation [63,81,82]. Men, particularly, are not adequately served by traditional approaches, as they remain less likely than women to start ART [27,83–86] and they have more advanced disease than women when they start ART [27,87,88]. Wide-scale implementation of Option B+ may further increase gender disparities in access to ART, as has been reported in Malawi [89] and Mozambique [90]. Differentiated models of community- and facility-based care hold promise for reducing the burden of clinic visits for both clients and providers, while supporting ART initiation, adherence and retention in care and improving health system cost efficiencies [79,91–93].

* **Research approaches:** Rigorous programme monitoring and evaluation, cohort studies, step-wedge trials, and other implementation science approaches can be used to generate evidence on the effects of differentiated care models and integrated service delivery on patient outcome measures across diverse epidemic contexts and populations. Mixed methods studies would be useful to identify specific populations who remain underserved by conventional service delivery models and the role of HIV-related stigma in limiting access for these groups. Data are also needed on the cost and efficiency of various models of HIV care delivery for priority and key populations.

### 3.2.8 | Research Priority 8: Identify service delivery models and strategies to improve early and sustained viral suppression, early identification of drug resistance, and timely regimen switching

* **Context:** Differentiated care strategies are essential for meeting the needs of underserved groups and key populations who do not access routine services and/or require additional support to achieve optimal HIV outcomes [75,91,94–97]. It is estimated that about 15% of patients on first-line ART do not achieve viral suppression within 12 months [98,99], with children and adolescents more likely to have elevated viral loads [100–102]. Moreover, even when patients fail to achieve viral suppression on first-line ART, rates of regimen switching are lower than expected, and loss to care rates are high [103,104]. Monitoring viral load and resistance is critical for ascertaining patients' status and the impact of treatment programmes.

* **Research approaches:** Additional research is needed on barriers to viral suppression and regimen switching in response to regimen failure/toxicity. Research is also needed to identify differentiated care models, enhanced adherence counselling, enhanced patient monitoring or continuous quality improvement techniques that can address the “failure cascade” [103] and improve retention and viral suppression rates, particularly for children, adolescents, and underserved populations. Mathematical modelling studies can demonstrate the importance and cost-effectiveness of routine viral monitoring and resistance monitoring [57]. In addition, the validation of algorithms for predicting treatment failure can inform selective testing strategies for settings with limited resources/capacity for routine viral load monitoring. Treat All implementers and researchers can benefit from effectiveness evaluations for current strategies to ensure timely viral load monitoring and switching to second-line ART regimens for those with detectable viral load. Qualitative research to explore reasons why providers do not switch patients to second-line regimens after first-line therapy failure are also important.

### 3.2.9 | Research Priority 9: Identify screening, diagnostic, and treatment interventions for mental health and substance use disorders that can be integrated into HIV care to improve timely diagnosis, ART initiation, retention and viral suppression

* **Context:** Mental health disorders (e.g. depression, post-traumatic stress disorder) are highly prevalent comorbidities in PLWH, globally, with rates that exceed those in the general population [105–107]. Substance use disorders (alcohol, injection drugs and non-injection drugs) among PLWH are also a growing concern in SSA [108–114]. While mental health and substance use disorders are associated with suboptimal HIV treatment outcomes, including late ART initiation, poor ART adherence, lack of viral suppression and increased AIDS-related mortality [115–125], the coverage of screening, diagnosis and treatment services for these disorders is extremely limited in SSA [117,126,127]. Key constraints include workforce shortages, limited training on mental health and substance use disorders, the lack of
validated and culturally appropriate screening and diagnos-
tic tools, as well as the lack of proven treatment interven-
tions that can be integrated into HIV care and delivered by
non-specialists in contexts facing mental health and sub-
stance use workforce challenges [128-131].

* Research approaches: The magnitude of mental health and substance use disorders merits further study, along with the effects of integrated treatment for these disorders on HIV outcomes under Treat All [105,108]. In addition, implementation science approaches should be used to assess the delivery, efficiency, and effects of existing intervention models in SSA settings in order to identify scalable, cost-effective mental health and substance use interventions to improve HIV outcomes. Such initiatives should prioritize task-shifting modalities that can be integrated into HIV clinics.

4 | DISCUSSION

With engagement and input from a diverse group of over 200 experts and stakeholders, our process yielded a set of research priorities that an overwhelming majority of the group agreed were important for the successful implementation of Treat All policies in SSA. The priorities highlighted by this process are broadly aligned with those identified by funding agencies, such as the National Institutes of Health [132] and The Global Fund [133], as well as the recent Lancet Commission on strengthening the HIV response [134].

A persistent programmatic challenge reflected in these research priorities is early diagnosis and linkage to care. Recent data demonstrate that PLWH in the SSA region continue to initiate ART late [26], indicating that many PLWH live for years before achieving first viral suppression. Late ART initiation has persisted [26,27] and is preventing more rapid declines in HIV mortality and incidence in the region. Importantly, men, those experiencing multiple dimensions of stigma, and other underserved populations are being left behind as HIV treatment expands in SSA [63,66–70,81,135]. New age and sex-disaggregated metrics and targeted strategies for earlier diagnosis (i.e. at higher CD4 counts) and linkage to care are needed. Although more real-world data on timely ART uptake under Treat All implementation are needed, early evidence on ‘Treat All’ in SSA and evidence from previous HIV treatment guideline expansions suggest that if people are eligible for treatment when they link to care, they will start ART rapidly with early retention in care and viral suppression following ART initiation [2,49].

Once treatment is initiated, there is a need for better met-
rics and monitoring related to sustained viral suppression,
treatment failure and regimen switching (i.e. second- and
third-line regimens). There is particular concern under Treat
All implementation that persons who are not experiencing any
clinical signs or symptoms may be at higher risk of disengage-
ment from care and poor treatment outcomes. Information on
these outcomes – disaggregated by age, sex and disease stage
– should be used to guide the development and deployment
of differentiated care strategies to maximize sustained viral
suppression and minimize the development of viral resistance.

Another major challenge is achieving optimal outcomes for
key and underserved populations and those with mental

health [105] and substance use disorders [108], making this a
critical area for investigation. Country-specific models and
modelling studies can help support these efforts by character-
izing the potential public health benefits to be gained through
optimal implementation of Treat All [31].

In pursuing these research priorities, it is critical to utilize
rigorous study designs (e.g. comparison groups whenever fea-
sible/possible) and to specify implementation approaches,
intervention components, and programme outcomes in order
to support the replication and adoption of effective strategies.
Ministries of health and donors can leverage programmatic
implementation opportunities that can support advancement
of the implementation science agenda around these priorities.
Through early and effective engagement of decision-makers,
researchers and implementers can ensure that their findings
are relevant and will be translated into policy, programmes
and services that ensure that the individual and population
health benefits of Treat All are realized sooner rather than
later.

5 | STRENGTHS AND LIMITATIONS

The use of a Delphi approach in formulating and refining a list of research priorities to inform Treat All implementation leveraged the expertise of more than 200 researchers and partners who work across no fewer than 23 SSA countries, as well as the multi-disciplinary perspectives of an extended network of implementation experts, researchers, policy-/decision-makers, advocates and other stakeholders. The process also facilitated the participation of researchers from both English- and French-speaking contexts, with 20% of those participating in the final round being French-speakers. Despite the diverse backgrounds of participants, there was a high degree of consensus in ratings of the research priorities across groups.

The Delphi approach provides a means of engaging diverse participants in a research prioritization process, however, sustaining participation across rounds is a known challenge [136,137]. In this initiative, participation varied considerably across rounds, with more than 200 participants involved in breakout sessions at the November 2017 meeting in Kigali to identify five to seven priorities most critical for each of the 90-90-90 targets from a list of 40 proposed priorities. While participation in online surveys in other rounds was lower, the overall number of participants increased with each survey round.

The Delphi method also allows for independent and decen-
tralized input from a diverse group of participants [14].
Nonetheless, the outcomes of the process are strongly shaped
by those who are most engaged. In this undertaking, the initial
working group was predominantly composed of IeDEA
researchers with backgrounds in clinical and epidemiological
research, rather than social science. For example, IeDEA
research primarily focuses on outcomes of patients already
diagnosed and enrolled in HIV care. While research related to
HIV testing strategies, community care, technological innova-
tion, and safer and more effective drugs are recognized as
vitally important, the backgrounds of participants in this process
resulted in a more emphasis on questions related to the second
and third 90-90-90 targets. Thus, there may be important
research priorities for some settings that are not reflected here.
# 6 | CONCLUSIONS

While priorities for specific countries and contexts inevitably will differ, the priorities generated through this modified Delphi process reflect the consensus of a broad group of individuals actively engaged in addressing HIV throughout SSA. As Treat All gains momentum in the region, these research priorities highlight critical areas of inquiry with potential relevance for ministries of health, funders, normative bodies, and other research networks as they develop research agendas, programme strategies, and funding priorities to accelerate progress in meeting the 90-90-90 goals.

## COMPETING INTEREST

Diane Addison, Rose S. Bono, Ellen Brazier, Stephany N. Duda, April D. Kimmel, Pamela N. Murnane, Denis Nash, Kara Wools-Kaloustian, and Marcel Yotebieng report grants/funding from the U.S. National Institutes of Health (NIH) during the conduct of this work, including the NIH leDEA funding; outside of this work, Kara Wools-Kaloustian also reports grants from the Centers for Disease Control (CDC), and the CDC Foundation. There are no potential conflicts of interests for any of the manuscript authors.

## AUTHORS' AFFILIATIONS

¹The Ohio State University, Columbus, OH, USA; ²Institute for Implementation Science in Population Health, City University of New York, New York, NY, USA; ³Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York, New York, NY, USA; ⁴Department of Health Behavior and Policy, Virginia Commonwealth University School of Medicine, Richmond, VA, USA; ⁵Centre for Infectious Disease Epidemiology & Research, School of Public Health & Family Medicine, University of Cape Town, Cape Town, South Africa; ⁶Institute of Global Health, University of Geneva, Geneva, Switzerland; ⁷University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; ⁸Infectious Diseases Institute, Makerere University, Kampala, Uganda; ⁹Center for AIDS Prevention Studies, Department of Medicine, University of California San Francisco, San Francisco, CA, USA; ¹⁰Department of Obstetrics, Gynecology & Reproductive Sciences, Bixby Center for Global Reproductive Health, University of California San Francisco, San Francisco, CA, USA; ¹¹Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA; ¹²School of Public Health and Family Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa; ¹³Vanderbilt University School of Medicine, Nashville, TN, USA; ¹⁴Fairbanks School of Public Health, Indianapolis, IN, USA; ¹⁵Inserm, Centre INSERM U1219-Epidémiologie-Biostatistique, School of Public Health (ISPED), University of Bordeaux, Bordeaux, France; ¹⁶Department of Anthropology, University of California at Riverside, Riverside, CA, USA; ¹⁷Rwanda Military Hospital, Kigali, Rwanda; ¹⁸TREAT Asia, amfAR - The Foundation for AIDS Research, Bangkok, Thailand; ¹⁹Institute of Social and Preventive Medicine (ISPM), University of Bern, Bern, Switzerland; ²⁰Inserm (French Institute of Health and Medical Research), UMR 1027 Université Toulouse 3, Toulouse, France; ²¹Epidemiology Branch, Division of AIDS at National Institute of Allergy and Infectious Diseases (NIAID), National Institute of Health (NIH), Rockville, MD, USA; ²²Indiana University School of Medicine, Indianapolis, IN, USA

## AUTHORS' CONTRIBUTIONS

Marcel Yotebieng and Denis Nash conceived of the project, and Ellen Brazier coordinated all rounds of the Delphi process. Diane Addison, Ellen Brazier, Barbara Castelnuovo, Craig R. Cohen, Morna Cornell, Stephany N. Duda, Olivia Keiser, April D. Kimmel, Kathryn E. Lancaster, Valeriane Leroy, Pamela M. Murnane, Denis Nash, Amobi Onovo, Angela M. Parcesepe, Jean d'Amour Sinayobye, Annette H. Sohn, Per M. von Groote, Rachel C. Vreeman, Gilles Wandeler, Radhika Wikramanayake, Carolyn F. Williams, Kara Wools-Kaloustian, Constantin Yiannoutsos, and Marcel Yotebieng participated in calls and meetings of the Treat All Consensus Statement Working Group to define and direct the consensus development process. Robert Agler, Charlotte Bernard, Rose S. Bono, Ellen Brazier, Barbara Castelnuovo, Craig R. Cohen, Morna Cornell, Mary-Ann Davies, Stephany N. Duda, Olivia Keiser, April D. Kimmel, Kathryn E. Lancaster, Pamela M. Murnane, Denis Nash, Amobi Onovo, Angela M. Parcesepe, Jennifer L. Syvertsen, Rachel C. Vreeman, Per M. von Groote, Radhika Wikramanayake, Kara Wools-Kaloustian, Constantin Yiannoutsos, and Marcel Yotebieng

conducted literature reviews, proposed initial lists of research priorities, and wrote/co-wrote sections of the background document for the 2017 meeting in Kigali, Rwanda on Treat All implementation in sub-Saharan Africa. Denis Nash, Ellen Brazier, and Marcel Yotebieng drafted the consensus statement manuscript, drawing on the background document prepared for the Kigali meeting. Diane Addison, Ellen Brazier, Barbara Castelnuovo, Craig R. Cohen, Morna Cornell, Stephany N. Duda, Olivia Keiser, April D. Kimmel, Kathryn E. Lancaster, Valeriane Leroy, Pamela M. Murnane, Denis Nash, Amobi Onovo, Annette H. Sohn, Rachel C. Vreeman, Per M. von Groote, Gilles Wandeler, Carolyn F. Williams, Kara Wools-Kaloustian, and Marcel Yotebieng critically reviewed drafts and contributed substantive content to the consensus statement manuscript.

## ACKNOWLEDGEMENTS

The authors thank Honorable Dr. Diane Gashumba, Rwanda Minister of Health; Colonel Dr. Jean Paul Bitega, Commandant, Rwanda Military Hospital; and Dr. Jean d'Amour Sinayobye, Athanase Munyaneza, Gallican Kubwimana, Benjamin Muhoza, Gad Murenzi of Central Africa-leDEA/Rwanda Military Hospital for hosting the Africa regional leDEA meeting on "Treat All" in Kigali, Rwanda, 5 to 6 November 2017. In addition, the authors would like to recognize the following speakers and discussants whose insights and research contributions shaped deliberations on the research priorities: Robert Agler, The Ohio State University; Eran Bendavid, Stanford University; Paula Braitstein, University of Toronto & Moi University; Morna Cornell, School of Public Health & Family Medicine, University of Cape Town; Mary-Ann Davies, School of Public Health & Family Medicine, University of Cape Town; Anastase Dzudie, Douala General Hospital & Clinical Research Education Networking and Consultancy (CRENC); Nathan Ford, World Health Organization; Charles Holmes, Johns Hopkins University; Antoine Jaquet, Université de Bordeaux, ISPED, INSERM 1219; Heidi Jones, CUNY Graduate School of Public Health and Health Policy; Elizabeth Kelvin, CUNY Graduate School of Public Health and Health Policy; Dr. Andre Mbayiha, CDC-Rwanda; Lt. Col. Dr. Pacifique Mugenzi, Rwanda Military Hospital; Dr. Ribakare Muhayimpundu, Rwanda Biomedical Center; Denis Nash, Institute for Implementation Science in Population Health, CUNY SPH; Lucy Wanjiko Njenga, Positive Young Women Voices; Dr. Julien Nyombayire, Rwanda Zambia HIV Research Group, Project San Francisco; Aggrey Semeere, Infectious Disease Institute, Makerere University; Dr. Placidie Umugwaneza, Rwanda Biomedical Center; S. Wakefield, HIV Vaccine Trials Network; Gilles Wandeler, Institute of Social and Preventive Medicine (ISPM), University of Bern; and Marcel Yotebieng, The Ohio State University. The authors also thank all the individuals who participated in the Treat All research priority rating exercises: Adebola Adimeji, Albert Einstein College of Medicine; Semeere Aggrey, Infectious Disease Institute, Makerere University, Uganda; Rogers Ajeh Awoh, Clinical Research Education Networking and Consultancy (CRENC), Cameroon; Jacquelyne Alesi, Uganda Network of YPLHIV; Keri Altoff, Johns Hopkins University, Bloomberg School of Public Health; Amah Madeleine Amorissani Folquet, leDEA West Africa; Dzudie Anastase, Douala General Hospital, Douala/CRENC, Cameroon; Kathryn Anastos, Albert Einstein College of Medicine; Annie Isabelle Izimukwiye, Rwanda Military Hospital; Samuel Ayaya, Moi University; Eran Bendavid, Stanford University; Jean Pierre Bideri, Rwanda Military Hospital; Mwebesa Bosco Bwana, Mbarara University of Science & Technology; Paula Braitstein, University of Toronto & Moi University; Helene Bukuru, Centre Hospitalo-Universitaire de Kamenge (CHUK); Elizabeth Bukusi, Kenya Medical Research Institute (KEMRI); Lydia Businge, Rwanda Military Hospital; Helen Byakwaga, Mbarara University of Science & Technology; Francois Dabis, Bordeaux University - Institute of Public Health; Merlin Diafouka, Centre de Traitement Ambulatoire de Brizzaville; Geraldina Dominguez, National Cancer Institute, NIH; Leslie Enane, The Ryan White Center for Pediatric Infectious Disease and Global Health, Indiana University School of Medicine; Aimee Freeman, Johns Hopkins University, Bloomberg School of Public Health; Nathan Ford, World Health Organization; Norbert Fuhngwa, CRENC, Cameroon; Patrick Gateretse, Centre National de Reference en Matiere de VIH/SIDA (CNR); Suzanne Goodrich, Indiana University School of Medicine; Charles Holmes, Georgetown/Johns Hopkins University; Jules Onesphore Igirimbabazi, Bethsaida Health Centre; Marie Grace Ingabire, Rwanda Military Hospital; Victoria Iyun, University of Cape Town; Antoine Jaquet, Université de Bordeaux, ISPED, INSERM 1219; Julie Jesson, Inserm U1027, University of Toulouse; Heidi Jones, CUNY Graduate School of Public Health and Health Policy; Faustin Kanyabwisha, RMH/Einstein Research Consortium; Jean Bosco Karangwa, Rwanda Military Hospital; Claudine Karigire, Rwanda Military Hospital; Pacifique Kayitare, Rwanda Military Hospital; Elizabeth Kelvin, CUNY Graduate School of Public Health and Health Policy; Ernestine Kendowo, Clinical Research Education Networking and Consultancy (CRENC), Cameroon; Agnes Kiragga, Infectious Disease Institute, Uganda; Gallican Kubwimana, Rwanda Military Hospital; Tom LaSalvia, End AIDS Coalition;
Patricia Lelo Vangu Matondo, Kalembelembé Paediatric Hospital; Leon Ruvu-
gabigwi, Rwanda Military Hospital; Lukas Fenner, Institute of Social and Preven-
tive Medicine (ISPM), University of Bern; Adolphe Mafoua, Centre de
Trajetoire Ambulatoire - Pointe Noire; Emmanuel Manirakiza, Gisenyi Hospital;
Risase Scholastique Manyundo, HPRC; Jean Paul Mivumbi, Rwanda Military
Hospital; Chris Usani Moki, University of Nairobi, School of Medicine; Innocent
Muhire, Rwanda Military Hospital; Jeanine Munezero, Centre National de Refer-
ence en Matière de VIH/SIDA (CNR), Burundi; Jerome Munyaneza, Busanza
Health Center; Athanase Munyaneza, Rwanda/Einstein Research Consortium/
Rwanda Military Hospital; Anthere Murangwa, Rwanda Military Hospital; Gad
Murenzi, CA-leDEA, Rwanda Military Hospital; Emmanuel Musabeyezu, King
Faisal & Glamerc Polyclinic; Francoise Musabyimana, Rwanda Military Hospital;
Jacqueline Musaninyange, WE-ACTx For HOPE; Janviere Mutamuliza, Rwanda
Military Hospital; Thierry Nahimana, Centre Hospitalo-Universitaire de
Kamenge (CHUK); Fred Nalugoda, Rakai Health Sciences Program; Fred Nalu-
goda Kakaire, Rakai Health Sciences Program; Halifa Ndayisabye, Rwanda Mili-
ary Hospital; Jules Nduumhire, Masaka Health Center; Denis Nsame Nforniwe,
Regional Hospital Limbe, Ministry of Public Health - Cameroon; Marc Lionel
Ngamani, Clinical Research Education Networking and Consultancy (CRENC);
Eric Ngassam, Jamot Hospital; Pelagie Nimbona, ANSS-Burundi; Theodore Niy-
ongabo, CHUK; Lucy Njenga, Positive Young Women Voices; Hovaire Nsabi-
mana, Butare University Teaching Hospital; Bonifice Nsentiyumva, Rwanda
Military Hospital; Dominique Mahambou Nsonde, Centre de Traitement Ambu-
latoire de Brazzaville; Sister Marie Goretti Nyirabahutu, Masaka Health Center;
Julienne Nyirarukundo, Rwanda Military Hospital; Edith Ogalo, AMPATH-USAID,
Eldoret, Kenya; Kelli O'Laughlin, Harvard Medical School; Gabriela Patten, Cen-
tre for Infectious Disease Epidemiology and Research, University of Cape Town;
Eric Walter Pefura-Yone, Yaoundé Jamot Hospital and Faculty of Medicine and
Biomedical Sciences, University of Yaoundé: Eliane Rohner, Institute of Social
and Preventive Medicine (ISPM), University of Bern; Jonathan Ross, Albert Ein-
stein College of Medicine/Montefiore Medical Center; Mbu Eyongetah Tabe-
nyang, Bamenda Regional Hospital, Cameroon; Katayoun Taghavi, Institute of
Social and Preventive Medicine (ISPM), University of Bern; Edmond Tchassem,
Regional Hospital Bamenda Ministère de Santé, Cameroon; Boris Kevin
Tchounga, PACCI Research center/leDEA West Africa; Kien-Atsu Tsi, CRENC,
Cameroon; Patrick Tuyisenge, Rwanda Military Hospital/Albert Einstein College
of Medecine; Liliane Tuyisenge, Gikondo Health center; Christella Twizere, Cen-
tre National de Reference en Matière de VIH/SIDA (CNR), Burundi; Esperance
Umumararungu, Rwanda Military Hospital; Mark Urassa, Tanzania National Insi-
tute for Medical Reserach, Mwanza Centre; Providence Uwineza, Busanza
Health Center; S. Wakefield, HIV Centre Trials Network - USNIH; Landry Wen-
zi, Ecole de Santé Publique de Kinshasa et Hôpital Pédiatrique de Kalembe
Lemb; A. Elizabeth Zaniewski, Institute of Social and Preventive Medicine
(ISPM), University of Bern.

FUNDING

Research reported in this publication was supported by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health under Award Numbers 13AI134393, U01AI096299 (leDEA Central Africa) and U01AI069924 (leDEA Southern Africa); the National Institute of Allergy and Infectious Diseases, the Eunice Kennedy Shriver National Institute of Child Health and Human Development [www.nichd.nih.gov], the National Cancer Institute [www.cancer.gov], and the National Institute of Mental Health [www.nimh.nih.gov] under Award Number U01AI069919 (leDEA West Africa); and the National Institute of Allergy and Infectious Diseases, Eunice Kennedy Shriver National Institute of Child Health and Human Development, the National Institute on Drug Abuse [www.drugabuse.gov], the National Cancer Institute, and the National Institute of Mental Health under Award Number U01AI069911 (leDEA East Africa); The End AIDS Coalition (Thomas LaSalvia); the NIH Office of AIDS Research (OAR); The Einstein-Rockefeller-CUNY Center for AIDS Research (CFAR) grant (P30 AI124414); The HIV Center for Clinical and Behavioral Studies grant (P30 MH043520); and the Institute for Implementation Science in Population Health, City University of New York.

REFERENCES

1. World Health Organization. Guideline on when to start antiretroviral therapy and on pre-exposure prophylaxis for HIV. Geneva: World Health Organization; 2015.

2. Nash D, Yotebieng M, Sohn AH. Treating all people living with HIV in sub-Saharan Africa: a new era calling for new approaches. J Virus Era d. 2018;4 (Suppl 2):1-4.

3. UNAIDS. 90-90-90: on the right track towards the global target. Geneva: Switzerland; 2016.

4. Agyepong IA, Sewankambo N, Binagwaho A, Coll-Seck AM, Corrah T, Ezek A, et al. The path to longer and healthier lives for all Africans by 2030: the Lancet Commission on the future of health in sub-Saharan Africa. Lancet. 2018;390 (10114):2803-59.

5. Vos T, Abajobir AA, Abate KH, Abbafati C, Abbas KM, Abd-Allah F, et al. Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries: a systematic analysis for the Global Burden of Disease Study 2016. Lancet. 2017;390(10100):1211-59.

6. WHO (World Health Organization). Progress report 2016: prevent HIV, test and treat all, Geneva, Switzerland: World Health Organization; 2016.

7. WHO (World Health Organization). Treat all: policy adoption and implementation status in countries: fact sheet. Geneva, Switzerland: World Health Organization, Department of HIV/AIDS; 2017.

8. Ford N, Ball A, Baggaley R, Vitoria M, Low-Beer D, Penazzato M, et al. The WHO public health approach to HIV treatment and care: looking back and looking ahead. Lancet Infect Dis. 2018;18(3):e76-86.

9. UNAIDS. UNAIDS data, 2018. Geneva, Switzerland: UNAIDS; 2018 [cited 2018 Oct 25]. Available from: http://www.unaids.org/sites/default/files/media_a set/unaids-data-2018_en.pdf

10. INSIGHT START Study Group, Lundgren JD, Babiker AG, Gordin F, Emery S, Grund B, Sharma S, et al. Initiation of antiretroviral therapy in early asymptomatic HIV infection. N Engl J Med. 2015;373(9):795-807.

11. Danel C, Moh R, Gabillard D, Badje A, Le Carrou J, Ouassa T, et al. A trial of early antiretrovirals and isoniazid preventive therapy in Africa. N Engl J Med. 2015;373(9):808-22.

12. Grinsztejn B, Hosseinipour MC, Ribaudo HJ, Swindells S, Eron J, Chen YQ, et al. Effects of early versus delayed initiation of antiretroviral treatment on clinical outcomes of HIV-1 infection: results from the phase 3 HPTN 052 randomised controlled trial. Lancet Infect Dis. 2014;14(4):281-90.

13. Diamond IR, Grant RC, Feldman BM, Pencharz PB, Ling SC, Moore AM, et al. Defining consensus: a systematic review recommends methodologic criteria for reporting of Delphi studies. J Clin Epidemiol. 2014;67(4):401-9.

14. Jorn AF. Using the Delphi expert consensus method in mental health research. Aust N Z J Psychiatry. 2015;49(10):887-97.

15. Schneider P, Evaniew N, Rendon JS, McKay P, Randall RL, Turcotte R, et al. Moving forward through consensus: protocol for a modified Delphi approach to determine the top research priorities in the field of orthopaedic oncology. BMJ Open. 2016;6(5):e011780.

16. Forsman AK, Wahlbeck K, Aarø LE, Alonso J, Barry MM, Brunn M, et al. Research priorities for public mental health in Europe: recommendations of the ROAMER project. Eur J Public Health. 2015;25(2):249-54.

17. Rushton AB, Fawkes CA, Carnes D, Moore AP. A modified Delphi consensus study to identify UK osteopathic profession research priorities. Man Ther. 2014;19(5):445-52.

18. Jones J, Hunter D. Consensus methods for medical and health services research. BMJ. 1995;311(7001):376-80.

19. Qualtrics. Qualtrics Provo. Utah, USA: Qualtrics; 2018 [cited 2018 Oct 1].
Available from: https://www.qualtrics.com

20. Hakim AJ, MacDonald V, Hladik W, Zhao J, Burnett J, Sabin K, et al. Gaps and opportunities: measuring the key population cascade through surveys and services to guide the HIV response. J Int AIDS Soc. 2018;5 Suppl 5:e25119.

21. Edwards JK, Hileman S, Donastorg Y, Zadrozny S, Baral S, Hargreaves JR, et al. Estimating sizes of key populations at the national level: considerations for study design and analysis. Epidemiology. 2018;29(6):795-803.

22. Staveteig S, Croft TN, Kampa KT, Head SK. Reaching the 'first 90': gaps in coverage of HIV testing among people living with HIV in 16 African countries. PLoS One. 2017;12(10):e0186316.

23. Ciaranello AL, Park J-E, Ramirez-Avila L, Freedberg KA, Walensky RP, Leroy V. Early infant HIV-1 diagnosis programs in resource-limited settings: opportunities for improved outcomes and more cost-effective interventions. BMC Med. 2011;9(1):59.

24. Desmonde S, Tanser F, Vreeman R, Takassi E, Edmonds A, Lumbiganon P, et al. Access to antiretroviral therapy in HIV-infected children aged 0-19 years in the International Epidemiology Databases to Evaluate AIDS (leDEA) Global Cohort Consortium, 2004-2015: a prospective cohort study. PLoS Med. 2018;15(5):e1002565.

25. Enane L, Davies M, Leroy V. Traversing the cascade: urgent research priorities for implementing the "treat all" strategy for children and adolescents living with HIV in sub-Saharan Africa. J Virus Era d. 2018;4(Suppl 2):40-46.

26. The leDEA and COHERE Cohort Collaborations. Global trends in CD4 cell count at the start of antiretroviral therapy: collaborative study of treatment programs. Clin Infect Dis. 2018;66(6):893-903.
27. Giles ML, Achhra AC, Abraham AG, Haas AD, Gill MJ, Lee MP, et al. Sex-based differences in antiretroviral therapy initiation, switching and treatment interruptions: global overview from the International Epidemiologic Databases to Evaluate AIDS (IeDEA). J Int AIDS Soc. 2018;21(6):e25149.

28. Saito S, Chung H, Mahy M, Radin AK, Jonnalagadda S, Hakim A, et al. Pedi-atriic HIV treatment gaps in 7 East and Southern African Countries: examination of modeled, survey, and routine program data. J Acquir Immune Defic Syndr. 2018;78:S134–41.

29. Strauss M, George G, Lansdell E, Mantell JE, Govender K, Romo M, et al. HIV testing preferences among long distance truck drivers in Kenya: a discrete choice experiment. AIDS Care. 2018;30(1):72–80.

30. Zanolini A, Sikombe K, Sikazwe I, Eshun-Wilson I, Somwe P, Bolton Moore C, et al. Understanding preferences for HIV care and treatment in Zambia: evidence from a discrete choice experiment among patients who have been lost to follow-up. PLoS Med. 2018;15(8):e1002636.

31. Kimmel AD, Bono RS, Keiser O, Sinayobye JD, Estill J, Mujwara D, et al. Mathematical modelling to inform 'treat all' implementation in sub-Saharan Africa: a scoping review. J Virus Erad. 2018;4(Suppl 2):47–54.

32. Walensky RP, Ross EL, Kumarasamy N, Wood R, Noubary F, Paltiel AD, et al. Cost-effectiveness of HIV treatment as prevention in serodiscordant couples. N Engl J Med. 2013;369(18):1715–25.

33. Eaton JW, Menzies NA, Stover J, Cambiano V, Chindelevitch L, Cori A, et al. Health benefits, costs, and cost-effectiveness of earlier eligibility for adult antiretroviral therapy and expanded treatment coverage: a combined analysis of 12 mathematical models. Lancet Glob Health. 2013;2(1):23–34.

34. Walensky RP, Wolf LL, Wood R, Fofana MO, Freedberg KA, Martinson NA, et al. When to start antiretroviral therapy in resource-limited settings. Ann Intern Med. 2009;151(3):157–66.

35. Loubiere S, Meiners C, Sloan C, Freedberg KA, Yazdanpanah Y. Economic evaluation of ART in resource-limited countries. Curr Opin HIV AIDS. 2010;5(3):225–31.

36. Kimmel AD, Charles M, Deschamps M-M, Severe P, Edwards AM, Johnson WD, et al. Lives saved by expanding HIV treatment availability in resource-limited settings: the example of Haiti. J Acquir Immune Defic Syndr. 2013;63(2):e40–8.

37. Sempa J, Ssennono M, Kuznik A, Lamorde M, Sowinski S, Semeere A, et al. Cost-effectiveness of early initiation of first-line combination antiretroviral therapy in Uganda. BMC Public Health. 2012;12(1):736.

38. Koenig SP, Bang H, Severe P, Jean Juste MA, Ambroise A, Edwards A, et al. Cost-effectiveness of early versus standard antiretroviral therapy in hiv-infected adults in Haiti. PLoS Med. 2011;8(9):e1001095.

39. Hontelez JA, Chang AY, Ogbuoji O, de Vlas SJ, Barnighausen T, Atun R. Changing HIV treatment eligibility under health system constraints in sub-Saharan Africa: investment needs, population health gains, and cost-effectiveness. AIDS. 2016;30(15):2341–50.

40. Eaton JW, Johnson LF, Salomon JA, Barnighausen T, Bendavid E, Bershteyn A, et al. HIV treatment as prevention: systematic comparison of mathematical models of the potential impact of antiretroviral therapy on HIV incidence in South Africa. PLoS Med. 2012;9(7):e1001245.

41. Kimmel AD, Nash D. HIV prevention resources: time to move toward affordability. Lancet HIV. 2017;4(5):e191–3.

42. Mikkelsen E, Hontelez JA, Jansen MP, Barnighausen T, Hauck K, Johansson KA, et al. Evidence for scaling up HIV treatment in sub-Saharan Africa: a call for incorporating health system constraints. PLoS Med. 2017;14(2):e1002240.

43. Perlman DC, Jordan AE, Nash D. Conceptualizing care continua: lessons from HIV, hepatitis C virus, tuberculosis and implications for the development of improved care and prevention continua. Front Public Health. 2016;4:296.

44. Osler M, Hilderbrand K, Goemaere E, Ford N, Smith M, Meintjes G, et al. The continuing burden of advanced HIV disease over 10 years of increasing antiretroviral therapy coverage in South Africa. Clin Infect Dis. 2018;66 Suppl 2:S118–25.

45. Castelnuovo B, Reynolds SJ. Optimizing treatment monitoring in resource-limited settings in the era of routine viral load monitoring. Curr Trop Med Rep. 2017;4(1):1–5.

46. Kujawski SA, Lamb MR, Lahuerta M, McNairy ML, Ahoua L, Abacassamo F, et al. Advanced human immunodeficiency virus disease at diagnosis in mozambique and Swaziland. Open Forum Infect Dis. 2017;4(3):ofx156.

47. Bor J, Ahmed S, Fox MP, Rosen S, Meyer-Rath G, Katz IT, et al. Effect of eliminating CD4-count thresholds on HIV treatment initiation in South Africa: an empirical modeling study. PLoS One. 2017;12(6):e0178249.

48. Nash D, Tymeijczyk O, Gadisa T, Kulkarni SG, Hoffman S, Yigzaw M, et al. Factors associated with initiation of antiretroviral therapy in the advanced stages of HIV infection in six Ethiopian HIV clinics, 2012 to 2013. J Int AIDS Soc. 2016;19(1):20637.

49. Tymeijczyk O, Brazier E, Yiannoutsos C, Wools-Kaloustian K, Althoff K, Crab-tree-Ramírez B, et al. HIV treatment eligibility expansion and timely antiretroviral treatment initiation following enrollment in HIV care: a metaregression analysis of programmatic data from 22 countries. PLoS Med. 2018;15(3):e1002534.

50. Ford N, Meintjes G, Pozniak A, Bygrave H, Hill A, Peter T, et al. The future role of CD4 cell count for monitoring antiretroviral therapy. Lancet Infect Dis. 2015;15(2):241–7.

51. Ford N, Meintjes G, Vitoria M, Greene G, Chiller T. The evolving role of CD4 cell counts in HIV care. Curr Opin HIV AIDS. 2017;12(2):123–8.

52. Elul B, Lamb MR, Lahuerta M, Abacassamo F, Ahoua L, Kujawski SA, et al. A combination intervention strategy to improve linkage to and retention in HIV care following diagnosis in Mozambique: a cluster-randomized study. PLoS Med. 2017;14(11):e1002433.

53. Sanga ES, Lerebo W, Mushi AK, Clowes P, Olomi W, Maboko L, et al. Linkage into care among newly diagnosed HIV-positive individuals tested through outreach and facility-based HIV testing models in Mbeya, Tanzania: a prospective mixed-method cohort study. BMJ Open. 2017;7(4):e013733.

54. MacKellar DA, Williams D, Storer N, Okello V, Azih C, Drummond J, et al. Enrollment in HIV care two years after HIV diagnosis in the kingdom of Swaziland: an evaluation of a National Program of New Linkage Procedures. PLoS One. 2016;11(2):e0150086.

55. Abuogi L, Humphrey J, Mpody C. Achieving UNAIDS 90-90-90 targets for pregnant and postpartum women in sub-Saharan Africa: progress, gaps, and research needs. J Virus Erad. 2018;4(Suppl 2):33–39.

56. WHO (World Health Organization). HIV drug resistance report 2017. Geneva: World Health Organization; 2017.

57. de Waal R, Lessels R, Hauser R, Kouyos R, Davies MA, Egger M, et al. HIV drug resistance in sub-Saharan Africa: public health questions and the potential role of real-world data and mathematical modelling. J Virus Erad. 2018;4(Suppl 2):55–58.

58. World Health Organization. WHO HIV policy adoption and implementation status in countries: fact sheet. Geneva: World Health Organization; 2018.

59. Forhan SE, Modi S, Houston JC, Broyles LN. Moving toward test and start: learning from the experience of universal antiretroviral therapy programs for HIV-infected pregnant/breastfeeding women. AIDS. 2017;31(10):1489–93.

60. Sood N, Wagner Z, Jaycocks A, Drabo E, Vardavas R. Test-and-treat in Los Angeles: a mathematical model of the effects of test-and-treat for the population of men who have sex with men in Los Angeles County. Clin Infect Dis. 2013;56(12):1789–96.

61. Cambiano V, Bertagnolio S, Jordan MR, Pillay D, Perriëns JH, Venter F, et al. Predicted levels of HIV drug resistance: potential impact of expanding diagnosis, retention, and eligibility criteria for antiretroviral therapy initiation. AIDS. 2014;28:S15–23.

62. Musheke M, Ntalasha H, Gari S, McKenzie O, Bond V, Martin-Hilber A, et al. A systematic review of qualitative findings on factors enabling and determining uptake of HIV testing in Sub-Saharan Africa. BMC Public Health. 2013;13:220.

63. Merten S, Ntalasha H, Musheke M. Non-uptake of HIV testing in children at risk in two urban and rural settings in Zambia: a mixed-methods study. PLoS One. 2016;11(6):e0155510.

64. Nyblade L, Reddy A, Mbote D, Kraemer J, Stockton M, Kemunto C, et al. The relationship between health worker stigma and uptake of HIV counseling and testing and utilization of non-HIV health services: the experience of male and female sex workers in Kenya. AIDS Care. 2017;29(11):1364–72.

65. UNAIDS. On the fast-track to end AIDS: 2016-2021 strategy. Geneva: UNAIDS; 2017.

66. Venkatesh KK, Madiba P, De Bruyn G, Lurie MN, Coates TJ, Gray GE. Who gets tested for HIV in a South African urban township? Implications for test and treat and gender-based prevention interventions. J Acquir Immune Defic Syndr. 2011;56(2):151–65.

67. Huerga H, Van Cutsem G, Ben Farhat J, Reid M, Bouhenia M, Maman D, et al. Who needs to be targeted for HIV testing and treatment in Kwazulu-Natal? Results from a population-based survey. J Acquir Immune Defic Syndr. 2016;73(4):411–8.

68. Dovel K, Yeatman S, Watkins S, Poulin M. Men's heightened risk of AIDS-related death: the legacy of gendered HIV testing and treatment strategies. AIDS. 2015;29(10):1123–5.

69. Floyd S, Ayles H, Schaap A, Shanaube K, MacLeod D, Phiri M, et al. Towards 90-90: findings after two years of the HPTN 071 (PopART) cluster-randomized trial of a universal testing-and-treatment intervention in Zambia. PLoS One. 2018;13(8):e0197904.

70. Camlin CS, Ssemondo E, Chamie G, El Ayadi AM, Kwarisiima D, Sang N, et al. Men "missing" from population-based HIV testing: insights from qualitative research. AIDS Care. 2016;28 Suppl 3:67–73.
71. Asliimwe S, Ross JM, Arinaitwe A, Tumusiime O, Turyamureeba B, Roberts DA, et al. Expanding HIV testing and linkage to care in southwestern Uganda with community health extension workers. J Int AIDS Soc. 2017;20 Suppl 4:21633.

72. Hayes R, Floyd S, Schaap A, Shanaube K, Bock P, Sabapathy K, et al. A universal testing and treatment intervention to improve HIV control: one-year results from intervention communities in Zambia in the HPTN 071 (PopART) cluster-randomised trial. PLoS Med. 2017;14(5):e1002292.

73. Bock P, Phiri C, Piwowar-Manning E, Kosloff B, Mandla N, Young A, et al. Understanding low sensitivity of community-based HIV rapid testing: experiences from the HPTN 071 (PopART) trial in Zambia and South Africa. J Int AIDS Soc. 2017;20 Suppl 6:21780.

74. Sharma M, Ying R, Tarr G, Barnabas R. Systematic review and meta-analysis of community and facility-based HIV testing to address linkage to care gaps in sub-Saharan Africa. Nature. 2015;528(7580):S77-85.

75. Macdonald V, Verster A, Baggaley R. A call for differentiated approaches to delivering HIV services to key populations. J Int AIDS Soc. 2017;20 Suppl 4:21658.

76. Francois V. Index partner testing and targeted case finding in northern Haiti. Paris, France: International AIDS Society (IAS); 2017.

77. Ndonko FT. Sex, test and treat: implementing an incentivized community-driven intervention to promote the uptake of HIV testing services among clients of sex workers. Paris, France: International AIDS Society; 2017.

78. Indravudh PP, Choko AT, Corbett EL. Scaling up HIV self-testing in sub-Saharan Africa: a review of technology, policy and evidence. Curr Opin Infect Dis. 2018;31(1):14-24.

79. El-Sadr WM, Harripersaud K, Rabkin M. Reaching global HIV/AIDS goals: what got us here, won't get us there. PLoS Med. 2017;14(11):e1002421.

80. Ehrenkranz PD, Calleja JMG, El-Sadr W, Fakoya AO, Ford N, Grimsrud A, et al. A pragmatic approach to monitor and evaluate implementation and impact of differentiated ART delivery for global and national stakeholders. J Int AIDS Soc. 2018;21(3):e25080.

81. Erku DA, Mekuria AB, Gebresillassie BM. Perceived HIV stigma as a barrier to sustained art adherence in North West ethiopia: a cohort study. Value Health. 2016;19(3):A219.

82. Treves-Kagan S, Steward WT, Ntswane L, Haller R, Gilvydis JM, Gulati H, et al. Why increasing availability of ART is not enough: a rapid, community-based study on how HIV-related stigma impacts engagement to care in rural South Africa. BMC Public Health. 2016;16:87.

83. Auld AF, Shiraishi RW, Mbofana F, Couto A, Fetogang EB, El-Halabi S, et al. Lower levels of antiretroviral therapy enrollment among men with HIV compared with women – 12 Countries, 2002-2013. MMWR Morb Mortal Wkly Rep. 2015;64(46):1281-6.

84. Cornell M, Schomaker M, Garone DB, Giddy J, Hoffmann CJ, Lessells R, et al. Gender differences in survival among adult patients starting antiretroviral therapy in South Africa: a multicentre cohort study. PLoS Med. 2012;9(9):e1001304.

85. UNAIDS. Fact sheet. 2017 [cited 2018 Oct 1]. Available from: http://www.unaids.org/en/resources/fact-sheet

86. Petersen M, Balzer L, Kwarsiima D, Sang N, Chamie G, Ayieko J, et al. Association of implementation of a universal testing and treatment intervention with HIV diagnosis, receipt of antiretroviral therapy, and viral suppression in East Africa. JAMA. 2017;317(21):2196-206.

87. Hawkins C, Chalamilla G, Okuma J, Spiegelman D, Hertzmark E, Aris E, et al. Gender differences in antiretroviral treatment outcomes among HIV-infected adults in Dar es Salaam, Tanzania. AIDS. 2011;201125(9):1189-97.

88. Maman D, Pujades-Rodriguez M, Subtil F, Pinoges L, McGuire M, Ecochard R, et al. Gender differences in immune reconstitution: a multicentre cohort analysis in Sub-Saharan Africa. PLoS One. 2012;7(2):e31078.

89. Dovel K, Yeatman S, van Oosterhout JJ, Chan A, Mantengeni A, Landes M, et al. Trends in ART initiation among men and non-pregnant/non-breastfeeding women before and after option B+ in Southern Malawi. PLoS One. 2016;11(12):e0165025.

90. Auld AF, Shiraishi RW, Couto A, Mbofana F, Colborn K, Alfredo C, et al. A decade of antiretroviral therapy scale-up in mozambique: evaluation of outcome trends and new models of service delivery among more than 300,000 patients enrolled during 2004-2013. J Acquir Immune Defic Syndr. 2016;73(2):e11-22.

91. Grimsrud A, Barnabas RV, Ehrenkranz P, Ford N. Evidence for scale up: the differentiated care research agenda. J Int AIDS Soc. 2017;20 Suppl 4:22024.

92. Barker C, Dutta A, Klein K. Can differentiated care models solve the crisis in HIV treatment financing? Analysis of prospects for 38 countries in sub-Saharan Africa. J Int AIDS Soc. 2017;20 Suppl 4:21648.

93. Labhardt ND, Ringera I, Lejone TI, Klimkait T, Muhairwe J, Amstutz A, et al. Effect of offering same-day art vs usual health facility referral during home-based HIV testing on linkage to care and viral suppression among adults with HIV in Lesotho: the cascade randomized clinical trial. JAMA. 2018;319 (11):1103-12.

94. Reidy WJ, Rabkin M, Syowai M, Schaaf A, El-Sadr WM. Patient-level and program-level monitoring and evaluation of differentiated service delivery for HIV: a pragmatic and parsimonious approach is needed. AIDS. 2018;32(3):399-401.

95. Kwarsiima D, Kamya MR, Owaraganise A, Mwangwa F, Byonanebye DM, Ayieko J, et al. High rates of viral suppression in adults and children with CD4 + counts using a streamlined ART delivery model in the SEARCH trial in rural Uganda and Kenya. J Int AIDS Soc. 2017;20 Suppl 4:21673.

96. Tsondal PR, Wilkinson LS, Grimsrud A, Mdlalo PT, Ullauri A, Boulle A. High rates of retention and viral suppression in the scale-up of antiretroviral therapy adherence clubs in Cape Town, South Africa. J Int AIDS Soc. 2017;20 Suppl 4:21649.

97. Mutasa-Apollo T, Ford N, Wiens M, Socias ME, Negussie E, Wu P, et al. Effect of frequency of clinic visits and medication pick-up on antiretroviral treatment outcomes: a systematic literature review and meta-analysis. J Int AIDS Soc. 2017;20 Suppl 4:21647.

98. 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. 2015;61(9):1453-61.

99. McMahon JH, Elliott JH, Bertagnolio S, Kubiak R, Jordan MR. Viral suppression after 12 months of antiretroviral therapy in low- and middle-income countries: a systematic review. Bull World Health Organ. 2013;91(5):377-85E.

100. Jobanputra K, Parker LA, Azih C, Okello V, Maphalala G, Kershberger B, et al. Factors associated with virological failure and suppression after enhanced adherence counselling, in children, adolescents and adults on antiretroviral therapy for HIV in Swaziland. PLoS One. 2015;10(2):e0116144.

101. Woolfs-Kaloustian K, Marete I, Ayaya S, Sohn AH, Van Nguyen L, Li S, et al. Time to first-line art failure and time to second-line ART switch in the IeDEA pediatric cohort. J Acquir Immune Defic Syndr. 2018;78(2):221-30.

102. Salou M, Dagnra AY, Butel C, Vidal N, Serrano L, Takassi E, et al. High rates of virological failure and drug resistance in perinatally HIV-1-infected children and adolescents receiving lifelong antiretroviral therapy in routine clinics in Togo. J Int AIDS Soc. 2016;19(1):20683.

103. Labhardt ND, Ringera I, Lejone TI, Cheleboi M, Wagner S, Muhairwe J, et al. When patients fail UNAIDS' last 90 – the “failure cascade” beyond 90-90-90 in rural Lesotho, Southern Africa: a prospective cohort study. J Int AIDS Soc. 2017;20(1):21803.

104. Jobanputra K, Parker LA, Azih C, Okello V, Maphalala G, Jouquet G, et al. Impact and programmatic implications of routine viral load monitoring in Swaziland. J Acquir Immune Defic Syndr. 2014;67(1):45-51.

105. Parcesepe AM, Bernard C, Agler R, Ross J, Yotebieng Y, Bass J, et al. Mental health and HIV: research priorities related to the implementation and scale up of 'treat all' in sub-Saharan Africa. J Virus Erad. 2018;4(Suppl 2):16-25.

106. Narayan KM, Miotti PG, Anand NP, Kline LM, Harmston C, Gulakowski R III, et al. HIV and noncommunicable disease comorbidities in the era of antiretroviral therapy: a vital agenda for research in low- and middle-income country settings. J Acquir Immune Defic Syndr. 2014;67 Suppl 1:S2-7.

107. Chibandla D, Benjamin L, Weiss HA, Abas M. Mental, neurological, and substance use disorders in people living with HIV/AIDS in low- and middle-income countries. J Acquir Immune Defic Syndr. 2014;67 Suppl 1:S54-67.

108. Lancaster K, Hetrick A, Jaquet A, Adedimeji A, Atwoli L, Colby DJ, et al. Substance use and universal access to HIV testing and treatment in sub-Saharan Africa: implications and research priorities. J Virus Erad. 2018;4(Suppl 2):26-32.

109. Kasirye R. Diffusion of evidence-based interventions in HIV and substance use programs: flaws and lessons from the Sub-Saharan African region. Subst Use Misuse. 2015;50(8-9):1110-6.

110. El-Bassel N, Shaw SA, Dasgupta A, Strathdee SA. Drug use as a driver of HIV Risks: re-emerging and emerging issues. Curr Opin HIV AIDS. 2014;9 (2):150-5.

111. Mathers BM, Degenhardt L, Phillips B, Wiessling L, Hickman M, Strathdee SA, et al. Global epidemiology of injecting drug use and HIV among people who inject drugs: a systematic review. Lancet. 2008;372(9651):1733-45.

112. Raguin G, Lepretre A, Ba I, Ndoye I, Toufik A, Brucker G, et al. Drug use and HIV in West Africa: a neglected epidemic. Trop Med Int Health. 2011;16 (9):1131-3.

113. Rhodes T, Abdool R. Drug harms and drug policies in Sub-Saharan Africa: implementation science and HIV epidemics. Int J Drug Policy. 2016;30:1-6.

114. Haldane V, Cervero-Liceras F, Chuah FLH, Ong SE, Murphy G, Sigfrid L, et al. Integrating HIV and substance use services: a systematic review. J Int AIDS Soc. 2017;20(1):21585.
115. Mayston R, Kinyanda E, Chishinga N, Prince M, Patel V. Mental disorder and the outcome of HIV/AIDS in low-income and middle-income countries: a systematic review. AIDS. 2012;26 Suppl 2:S117-35.

116. Azar P, Wood E, Nguyen P, Luma M, Montaner J, Kerr T, et al. 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 Infect Dis. 2015;15(1):193.

117. Hahn JA, Woolf-King SE, Muyindike W. Adding fuel to the fire: alcohol's effect on the HIV epidemic in Sub-Saharan Africa. Curr HIV/AIDS Rep. 2011;8 (3):172-80.

118. Azar MM, Springer SA, Meyer JP, Altice FL. A systematic review of the impact of alcohol use disorders on HIV treatment outcomes, adherence to antiretroviral therapy and health care utilization. Drug Alcohol Depend. 2010;112(3):178-93.

119. Todd JV, Cole SR, Pence BW, Lesko CR, Bacchetti P, Cohen MH, et al. Effects of antiretroviral therapy and depressive symptoms on all-cause mortality among HIV-infected women. Am J Epidemiol. 2017;185(10):869-78.

120. Memiah P, Shumba C, Etienne-Mesubi M, Agbor S, Hossain MB, Komba P, et al. The effect of depressive symptoms and CD4 count on adherence to highly active antiretroviral therapy in sub-Saharan Africa. J Int Assoc Provid AIDS Care. 2014;13(4):346-52.

121. Mills JC, Pence BW, Todd JV, Bengtson AM, Breger TL, Edmonds A, et al. Cumulative burden of depression and all-cause mortality in women living with human immunodeficiency virus. Clin Infec Dis. 2018;67:1575-81.

122. Lancaster KE, Lungu T, Mmodzi P, Hosseinipour MC, Chadwick K, Powers KA, et al. The association between substance use and sub-optimal HIV treatment engagement among HIV-infected female sex workers in Lilongwe, Malawi. AIDS Care. 2017;29(2):197-203.

123. Pandrea I, Happel KI, Amedee AM, Bagby GJ, Nelson S. Alcohol's role in HIV transmission and disease progression. Alcohol Res Health. 2010;33(3):203-18.

124. Weber R, Huber M, Battegay M, Stähelin C, Castro Batanjer E, Calmy A, et al. Influence of noninjecting and injecting drug use on mortality, retention in the cohort, and antiretroviral therapy, in participants in the Swiss HIV Cohort Study. HIV Med. 2015;16(3):137-51.

125. Altice FL, Kamarulzaman A, Soriano VV, Schechter M, Friedland GH. Treatment of medical, psychiatric, and substance-use comorbidities in people infected with HIV who use drugs. Lancet. 2010;376(9738):367-87.

126. Mathers BM, Degenhardt L, Ali H, Wiessing L, Hickman M, Mattick RP, et al. HIV prevention, treatment, and care services for people who inject drugs: a systematic review of global, regional, and national coverage. Lancet. 2010;375 (9719):1014-28.

127. Parcesepe AM, Mugglin C, Nalugoda F, Bernard C, Yunihastuti E, Althoff K, et al. Screening and management of mental health and substance use disorders in HIV treatment settings in low- and middle-income countries within the global leDEA consortium. J Int AIDS Soc. 2018;21(3):e25101.

128. Akena D, Stein DJ, Joska J. Does screening HIV-positive individuals in Uganda for major depressive disorder improve case detection rates and antidepressant prescription? AIDS Behav. 2013;17(8):2802-7.

129. Abas M, Ali GC, Nakimuli-Mpungu E, Chibanda D. Depression in people living with HIV in sub-Saharan Africa: time to act. Trop Med Int Health. 2014;19 (12):1392-6.

130. Durvasula R, Miller TR. Substance abuse treatment in persons with HIV/AIDS: challenges in managing triple diagnosis. Behav Med. 2014;40(2):43-52.

131. Petersen I, Marais D, Abdulmalik J, Ahuja S, Alem A, Chisholm D, et al. Strengthening mental health system governance in six low- and middle-income countries in Africa and South Asia: challenges, needs and potential strategies. Health Policy Plan. 2017;32(5):699-709.

132. NIH (National Institutes of Health). Funding opportunity title: targeted implementation science to achieve 90/90/90 goals for HIV/AIDS prevention and treatment. 2017 [cited 2017 Nov 29]. Available from: https://grants.nih.gov/grants/guide/pa-files/PA-18-279.html

133. The Global Fund. The global fund strategy 2017-2022: investing to end epidemics. 2017.

134. Bekker L-G, Alleyne G, Baral S, Cepeda J, Daskalakis D, Dowdy D, et al. Advancing global health and strengthening the HIV response in the era of the Sustainable Development Goals: the International AIDS Society-Lancet Commission. Lancet. 2018;392(10144):312-58.

135. Peitzmeier SM, Grosso A, Bowes A, Ceesay N, Baral SD. Associations of stigma with negative health outcomes for people living with HIV in the Gambia: implications for key populations. J Acquir Immune Defic Syndr. 2015;68:S146-53.

136. Trevelyan EG, Robinson PN. Delphi methodology in health research: how to do it? Eur J Integr Med. 2015;7(4):423-8.

137. Keeney S, Hasson F, McKenna HP. A critical review of the Delphi technique as a research methodology for nursing. Int J Nurs Stud. 2001;38(2):195-200.

# APPENDIX

## TREAT ALL CONSENSUS STATEMENT
WORKING GROUP

Diane Addison, Keri Althoff, Ellen Brazier, Barbara Castelnuovo, Craig R. Cohen, Morna Cornell, Mary-Ann Davies, Geraldina Dominguez, Stephany N. Duda, Aimee Freeman, Antoine Jaquet, Olivia Keiser, April D. Kimmel, Kathryn E. Lancaster, Valeriane Leroy, Janne Markus, Rosemary McKaig, Pamela M. Murnane, Denis Nash (co-Chair), Dominique Nsonde, Amobi Onovo, Angela M. Parcesepe, Jean d'Amour Sinayobye, Annette H. Sohn, Per M, von Groote, Rachel C. Vreeman, Gilles Wandeler, Radhika Wikramanayake, Carolyn F. Williams, Kara Wools-Kaloustian, Constantin Yiannoutsos, Marcel Yotebieng (co-Chair).