Evidence and Policy Gaps on ART at 500 CD4, TasP and PrEP.

Why are we not scaling up the use of ART more aggressively?

“A frank dialogue on the future of HIV treatmentAll the way from the factory to the patient”

Pedro Cahn

Scenarios of ARV eligibility

Recommended Since 2003

CD4 ≤ 200

Recommended since 2010

CD4 ≤ 350 CD4 ≤ 350+

TB/HIVHBV/HIV

CD4 ≤ 500+

TB/HIVHBV/HIV

“Test and treat”

All HIV+

ART regardless of CD4 count for:• Serodiscordant couples• Pregnant women• Children < 5 years

Estimated millions of people eligible for ART in LMIC in 2012 11 m 15 m 17.6 m 28.6 m 33 m

1 2 3 4 5

Vision on treatment eligibility: Evolving scenarios

The promise of HIV treatment:1. Prevent HIV-related illness and disability

2. Avert AIDS-related deaths3. Prevent new HIV infections

Why test and offer ARVs for all cases irrespective to CD4+ count

Rational-based1. HIV continuingly destroys the immune cells

particularly CD4+ T cells2. HIV, microbial translocation, co-infection are

associated with chronic immune activation and inflammation that lead the association with the risk of non-AIDS-related death

3. More effective, more simple and more tolerable ART regimens are available

4. Treatment prevents further spread of HIV

Why should we start “early”

• Because it makes sense!• Several cohorts have shown better CD4 recovery and

survival with early ART • Starting with > 500 CD4 count, life expectancy is almost

the same as HIV (-) individuals• Reduces TB incidence• Prevents IRIS• Prevents further spreading of HIV• Limits viral reservoir during acute infection• Nobody as shown that starting earlier produces any harm

Expected impact of HIV treatment in survival of a 20 years old person living with HIV in a high income setting (different periods)

HIV treatment prevents HIV-related illness and disability, and AIDS-related death, thereby normalizing survival

Higher CD4 at ART Initiation Predicts Greater Long Term Likelihood of CD4 Normalization

F. Palella et al., CROI 2014, Abstract 560

Years after cART initiation to achieving CD4 > 750 by CD4 stratum at cART initiation: 1996-2012 (N=1,327)

Characteristics of Eligible Patients at HOPS by AI-CD4 (n = 1327)

Immunodeficiency at the Start of CombinationAntiretroviral Therapy in Low-, Middle-, and

High-Income Countries

The IeDEA and ART Cohort Collaborations, J Acquir Immune Defic Syndr. 2014 Jan 1;65(1):e8-16.

Trends of median CD4 cell counts at the start of cART (upper panel) and in proportions of men and women starting cART below 200 cells/mL (middle panel) or below 100 cells/mL (lower panel) in low-, middle-, and high-income countries, 2002– 2010. The shaded areas represent the 95% CI for each year. Results from mixed effects linear regression (model 3) are based on 379,865 patients; missing values imputed using multiple imputation.

Conclusions: Median CD4 cell counts at the start of cART increased 2000–2009 but remained below 200 cells/mL in LIC and MIC and below 300 cells/mL in HIC. Earlier start of cART will require substantial efforts and resources globally.

IeDEA Multinational Cohorts (2004)

IeDEA Multinational Cohorts (2013)

At the country-level, the HIV response is already having a dramatic impact on life expectancy

A clear correlation exists between HIV treatment and incidence

Source: Tanser et al. Science 2013;339:966-971

1.1% (0.8%-1.4%) reduction in HIV incidence, for each 1.0% increase in treatment coverage.

ART & HIV incidence: Hlabisa, South Africa

p=0.325p=0.003

p=0.013p=0.0001

Inci

denc

e ra

te ra

tio

1.0

0.8

0.6

0.4

0.2

0

ART coverage0% 30% 60%

Put a figure here that explores one particular outcome in a complicated table of results.

Presented at the World AIDS Conference,Melbourne, Australia, July 2014[abstract LBPE29]

Higher antiretroviral treatment coverage is associated with lower HIV infection rates: analysis of 51 low and middle-income countriesAndrew Hill1, Anton Pozniak2, Alice Raymond3, Katherine Heath3, and Nathan Ford4

1 Molecular and Clinical Pharmacology, University of Liverpool, UK, 2 St Stephens Centre, Chelsea and Westminster Hospital, London, UK, 3 Department of Public Health, Imperial College London, UK, 4 WHO, Geneva, Switzerland

LBPE29

IntroductionIn the HPTN 052 trial, antiretroviral treatment (ART) reduced

the risk of HIV transmission by 96% in sero-discordant

couples [1]. Increased ART coverage has also been

associated with lower rates of HIV transmission at the

community level [2].

The aims of this study were (1) to investigate the relationship

between the percentage of HIV infected individuals on ART

and both HIV incidence and HIV-related death in a multi-

country analysis; (2) To compare ART coverage rates

between low, middle and high-income countries.

MethodsThis analysis was based upon UNAIDS standardised country-

level estimates for 2012: number of people with HIV-infection,

receiving antiretroviral treatment, new HIV infections and HIV-

related deaths per year [3]. There were 36 African countries

included, plus 15 non-African low and middle-income

countries with at least 50,000 HIV-infected individuals . Data

from high-income countries were extracted from national

reports (United Kingdom [6], Netherlands [9], Australia [11]),

conference proceedings (France [8]), and peer-reviewed

articles (Denmark [7], British Columbia [10] and United States

[12]). Weighted least squares and linear regression methods

were used to investigate the association between the

percentage of HIV-infected individuals receiving ART and

both the numbers of new infections and AIDS-related deaths

(as percentages of the national HIV epidemic).

The relevance of GDP [4] and PEPFAR status [5] was

assessed using nested models and likelihood ratio testing.

The number of preventable HIV cases and deaths were

calculated using the relevant regression coefficients of the

fitted weighted least squares model.

ResultsART coverage varied widely across different countries (Figure

1). In the 51 low and middle income countries, the mean

percentage of HIV-infected individuals receiving antiretroviral

treatment was 30% (range 0.6% in Madagascar to 62% in

Botswana).

The mean percentage of new HIV infections was 6.1% (range

2.0% in Thailand to 12.5% in Indonesia). There was a highly

significant association between higher ART coverage and

lower percentage incidence (Figure 2, p<0.00001). Higher

ART coverage was also significantly correlated with lower

annual AIDS-related deaths (Figure 2, p<0.00001). These

results were consistent in separate analyses of African and

non-African countries.

Weighted least squares regression analysis showed that each

10% increase in ART coverage was associated with a 1.15%

reduction in new HIV infections (Figure 2), and a 1.13%

reduction in HIV-related deaths (Figure 3).

According to these analyses, if all 51 low and middle income

countries had had the same ART coverage as Botswana

(62%), 1,243,647 of the 1,901,800 total HIV infections in 2012

(65%) could have been prevented. Under the same

conditions, 998,732 of the total 1,427,200 deaths from HIV in

2012 in these 51 countries (70%) could have been avoided.

Data from the 51 countries included in the analysis, plus

seven high-income countries, are presented in Table 1.

Countries were ranked according to the percentage of HIV-

infected individuals receiving ART. Levels of ART coverage

among high income countries ranged widely, from the United

Kingdom (67%), to the United States (33%).

Figure 2: HIV incidence (as a percentage of the total number of HIV-infected people) versus ART coverage in 51 countries, weighted by epidemic size (2012 data).

Figure 3: AIDS-related death rates (as a percentage of the total number of HIV-infected people) versus ART coverage in 51 countries, weighted by epidemic size (2012 data).

Figure 1. The percentage of all HIV-infected people taking antiretrovirals, by country

CountryNumber with HIV infection

Number on ART

% on ART

Number of new

infections

Number of deaths

United Kingdom [6] 98,400 65,928 67.0 3,800 500Botswana 340,000 212,083 62.4 12,000 5,700Denmark [7] 6,500 4,029 62.0 300 100France [8] 149,900 89,940 60.0 7,000 1,000Netherlands [9] 25,000 14,817 59.0 900 200Rwanda 210,000 114,978 54.8 7,800 5,600Thailand 440,000 239,090 54.3 8,800 21,000Namibia 220,000 116,687 53.0 10,000 5,000Brazil 595,000 313,175 52.6 34,000 15,000Argentina 98,000 50,725 51.8 4,200 3,700British Columbia [10] 11,700 5,975 51.1 380 105Mexico 170,000 83,800 49.3 9,300 -Cambodia 110,000 48,913 44.5 6,000 2,700Zambia 1,100,000 480,882 43.7 56,000 30,000Swaziland 210,000 87,534 41.7 12,000 5,500Zimbabwe 1,400,000 565,675 40.4 69,000 39,000Venezuela 110,000 43,032 39.1 6,600 3,800Ethiopia 760,000 288,137 37.9 20,000 47,000Kenya 1,600,000 604,027 37.8 98,000 57,000Burkina Faso 110,000 40,925 37.2 5,800 5,500Malawi 1,100,000 405,131 36.8 66,000 46,000Peru 76,000 27,502 36.2 4,600 4,100Benin 72,000 26,035 36.2 4,100 3,100Gabon 41,000 14,646 35.7 1,000 2,300South Africa 6,100,000 2,150,881 35.3 370,000 240,000Australia [11] 33,000 11,523 35.0 1,100 200Dominican Republic 64,000 22,221 34.7 5,900 1,900Senegal 43,000 14,692 34.2 2,000 1,900Burundi 89,000 29,121 32.7 4,600 4,800United States [12] 1,148,200 375,461 32.7 47,500 16,000Uganda 1,500,000 438,542 29.2 140,000 63,000Ghana 240,000 69,870 29.1 8,000 12,000Tanzania 1,500,000 432,293 28.8 83,000 80,000Haiti 150,000 43,229 28.8 8,500 7,500India 2,100,000 604,987 28.8 130,000 140,000Mali 100,000 28,751 28.8 4,200 4,900Vietnam 260,000 72,689 28.0 13,000 12,000Myanmar 200,000 53,709 26.9 7,100 12,000Colombia 150,000 39,397 26.3 9,000 6,500Lesotho 360,000 92,747 25.8 26,000 15,000Niger 46,000 11,810 25.7 1,100 3,400Congo 74,000 17,234 23.3 4,700 5,200Cote d’ivoire 450,000 104,750 23.3 30,000 31,000Guinea 120,000 26,666 22.2 10,000 5,100Equatorial Guinea 31,000 6,512 21.0 2,600 1,400Cameroun 600,000 122,283 20.4 45,000 35,000Chad 210,000 40,856 19.5 16,000 14,000Mozambique 1,600,000 309,851 19.4 120,000 77,000Ukraine 230,000 40,350 17.5 11,000 18,000Angola 250,000 42,607 17.0 28,000 13,000Guinea-bisseau 41,000 6,101 14.9 3,600 2,300Nigeria 3,400,000 491,021 14.4 260,000 240,000Sierra Leone 58,000 8,259 14.2 3,100 3,300DR Congo 480,000 64,219 13.4 34,000 32,000Indonesia 610,000 31,655 5.2 76,000 27,000Somalia 29,000 1,450 5.0 3,300 2,500South Sudan 150,000 4,929 3.3 15,000 13,000Madagascar 59,000 371 0.6 3,900 6,200

Table 1. Number of people taking antiretroviral treatment, new HIV infections and HIV-related deaths (2012 data) for 51 low/middle income and 7 high income countries

ConclusionsFor the 51 low and middle income countries in this analysis, the mean percentage of HIV-infected people

receiving antiretroviral treatment was 30% (range 0.6% in Madagascar to 62% in Botswana).

Countries with higher ART coverage had significantly lower rates of new HIV infection and lower HIV-

related death rates.

According to this analysis, 65% of the new HIV infections and 70% of the HIV-related deaths in 2012 could

have been prevented, if all 51 countries had antiretroviral coverage rates as high as Botswana (62%).

Some low and middle-income countries have rates of antiretroviral treatment coverage higher than certain

high-income countries – for example the United States (33% coverage, ranked 30th out of 58 countries).

These results provide a compelling argument for continuing to improve antiretroviral treatment coverage

worldwide

HIV treatment, the most effective biomedical intervention for the prevention of HIV transmission

103 centers; 16,597 patients, 12,069 on HAART. Median FU time. 5.43 years;

Median time on HAART: 4.42 years

Crude incidence of all cause mortalitydecreased with longer exposure to cART.

Rates of non-AIDS deaths remained constant

NNT for success

• Universal access and TasP: Modelling shows that if 72% of the population has undetectable VL, AIDS could be no longer a public health threat

• PreP: ???? • Treatment is efficacious (it works) and effective• PreP is efficacious (in some trials) but not

effective*, so far. (Might be considered for special populations)

* It requires test and periodic monitoring of target populations, adherence in healthy individuals, etcNNT: Numbers needed to treat

Key challenges

1. Societal: Stigma, discrimination, criminalization2. Diversity of facility costs3. Gaps in the treatment cascade4. Delivery challenges5. Financing6. Addressing the epidemic in adolescents, children

and other left behind populations7. To control a epidemic without a vaccine, nor a

cure

Why are we behind our needs?

• Insufficient funding• Low testing rate, missed opportunities• Stigma, discrimination, criminalization• Retention in care, adherence• Less community mobilization• Logistical issues (Procurement, stockouts)• Lack of political will (“I invest, the benefits will came

when I am no longer in office”)• Mixed messagges from the scientific community

(“Waiting for the results of RCTs”)

The way forward

• Set aspirational new targets• Promote a renewed alliance including

international organizations, funders, countries, academia, pharma

• Increase countries’ ownership• Integrate HIV with NCD care• Generate new activism• Each day we miss, thousands of lives are lost