Developing Methods for Monitoring Global HIV Epidemic Trends that have Informed the Worldwide Response to the Pandemic
Submitting Institution
Imperial College LondonUnit of Assessment
Public Health, Health Services and Primary CareSummary Impact Type
PoliticalResearch Subject Area(s)
Medical and Health Sciences: Public Health and Health Services
Summary of the impact
The Joint United Nations Programme on HIV/AIDS (UNAIDS) and World Health
Organisation (WHO) regularly report estimates for the prevalence of HIV
and associated metrics for almost every country in the world. These
statistics are essential for tracking the scale and the impact of HIV
epidemic and are used routinely in the policy decisions and funding
allocation decisions of national governments and international donors and
therefore have a major impact on international public health. The methods
underlying those estimates were originally developed, and continue to be
refined and updated, by an international group of researchers at Imperial
College London.
Underpinning research
Key Imperial College London researchers:
Professor Geoff Garnett, Professor of Microparasite Epidemiology
(2002-2012)
Professor Simon Gregson, Professor in Demography and Behavioural Science
(2001-present)
Professor Nicholas Grassly, Professor of Infectious Disease and Vaccine
Epidemiology (2001-present)
Dr Peter White, Senior Lecturer (2002-present)
Professor Timothy Hallett, Professor of Global Health (2004-present)
In 2002, the data that were available to generate international estimates
for HIV consisted of measurements of HIV prevalence among small samples of
pregnant women attending a non-random selection of antenatal-clinics in
each country. Methods developed by Professor Grassly and Professor Garnett
provided a way to integrate those sparse data points within a unified
theory for epidemiological dynamics so that reliable estimates of the
time-course of HIV prevalence could be generated (1). In 2005, Professor
Grassly and colleagues created a method to estimate the number of orphans
generated by the HIV epidemic (a key impact of HIV) (2), and, in 2003, the
group also invented a decision-making tool, the `Modes of Transmission'
model (3), and showed that it could be used to analyse surveillance data
and identify priority groups for HIV prevention interventions. These
estimates were then used in extended models to investigate the impact that
expanded responses to the HIV epidemic can generate (4).
The group has since led the continuous evaluation and development of the
estimation methods. The methods have been adapted to produce estimates of
mother-to-child transmission rates and the numbers in need of
antiretroviral therapy. Additional methods developed by the group
demonstrated that changes in sexual behaviour had led to a decline in the
HIV epidemic in Zimbabwe, at that time the largest epidemic for which such
a change had occurred. Methods developed in 2008 by Professor Hallett and
Professor Gregson allow for the sex-ratio and age-pattern of new HIV
infections to be estimated reliably from available data (5), allowing for
substantially refined estimates of demographic impact of HIV and
highlighting the burden of HIV among women. New analyses and data
collection by Professor's Hallett and Gregson in 2010, also demonstrated
that the way in which UN agencies track progress toward Millennium
Development Goal 5 (child mortality) could lead to significant
biases (6), which subsequently led to the official monitoring methods
being revised.
This body of work has had a clear and lasting impact on policy and
funding in the field of global health at the highest decision-making
levels.
References to the research
(1) Artzrouni, M., Brown,
T., Feeney,
G., Garnett,
G., Ghys,
P., Grassly, N., Schneider,
D., Stanecki,
K., Stover,
J., Schwartlander,
B., Walker,
N., Way,
P., Yan, P., Zaba,
B., Zlotnik,
H., Timaeus,
I., Walker,
N. (2002). UNAIDS Reference Group on Estimates Modelling and
Projections. Improved methods and assumptions for estimation of the
HIV/AIDS epidemic and its impact: Recommendations of the UNAIDS Reference
Group on Estimates, Modelling and Projections. AIDS. 16(9), W1 -
14. DOI
Times cited: 99 (as at 4th Novemebr 2013 from ISI Web of
Science). Journal Impact Factor 6.4
(3) Pisani, E., Garnett, G.P., Grassly, N.C., Brown, T., Stover, J.,
Hankins, C., Walker, N., & Ghys, P. (2003). Back to basics in HIV
prevention: focus on exposure. BMJ, 326, 1384-1387. DOI.
Times cited: 75 (as at 4th November 2013 from ISI Web of
Science). Journal Impact Factor: 17.21
(4) Stover, J., Walker, N., Garnett, G.P., Salomon, J.A., Stanecki, K.A.,
Ghys, P.D., Grassly, N.C., Anderson, R.M., Schwartlander, B. (2002) Can we
reverse the HIV/AIDS pandemic with an expanded response? Lancet,
360, 73-77. DOI.
Times cited: 100 (as at 4th November 2013 on ISI Web of
Science). Journal Impact Factor: 39.06
(5) Hallett, T.B., Zaba, B., Todd, J., Lopman, B., Mwita, W., Biraro, S.,
Gregson, S., Boerma, J.T.; ALPHA Network. (2008). Estimating incidence
from prevalence in generalised HIV epidemics: methods and validation. PLoS
Med, 5 (4), 611 - 622. DOI.
Times cited: 38 (as at 4th Novemebr 2013 from ISI Web of
Science). Journal Impact Factor: 15.25
(6) Hallett, T.B., Gregson, S., Kurwa, F., Garnett, G.P., Dube, S.,
Chawira, G., Mason, P.R., Nyamukapa, C.A. (2010). Measuring and correcting
biased child mortality statistics in countries with generalized epidemics
of HIV infection. Bull. World Health Organ., 88 (10), 761 -768. DOI. Times cited: 5
(as at 4th November 2013 from ISI Web of Science). Journal
Impact Factor: 5.25
Key funding:
• UNAIDS (2001-2003; £49,104), Principal Investigator (PI) G. Garnett,
Estimating the present and future impact of HIV — the establishment of a
reference group.
• UNAIDS (2003-2010; £576,742), PI G. Garnett, Secretariat for Global
Reference Group on HIV.
• UNAIDS (2009-2012; £341,318), PI G. Garnett, Providing Academic
Leadership through a Secretariat for the UNAIDS Reference Group on
Estimates Modelling and Projections.
Details of the impact
Impacts include: health and welfare, public policy and services,
international development Main beneficiaries include: UNAIDS,
International Government bodies, international donors
The methods developed at Imperial are used by UNAIDS, the UN agency with
responsibility to lead the international response to AIDS (www.unaids.org),
to generate AIDS statistics for almost every country in the world (http://www.unaids.org/en/dataanalysis/).
These statistics are used by countries and international donors (e.g. the
Global Fund to Fight AIDS, Tuberculosis and Malaria) to track the epidemic
and determine funding and policy decisions for HIV prevention and
treatment interventions. These estimates have been crucial in describing
the scale and the nature of the HIV epidemic. Without this basic
information, effective and sustained action against the epidemic would not
have been possible. Furthermore, it would not be possible to detect
reductions in HIV prevalence that may be ascribed to programmes, which are
expected to be vital in strengthening the response to epidemics. Without
these estimates, neither the numbers in need of treatment nor the
potential impact of interventions would have been known, both of which
have been crucial in allowing major international and bilateral donors to
donate billions of dollars to tackling HIV.
The routine use and impact of these statistics are typified by citations
by President George W Bush as he announced the President's Plan for AIDS
Relief (PEPFAR, which continued between 2008 and 2013) and more recently
by President Barack Obama and Senator Hilary Clinton in public addresses
in 2009, 2011 and 2012. In his keynote speech in 2009, President Obama is
quoted as saying: "Globally, there are over 33 million people living with
HIV. While millions have died from this disease, the death rate is slowly
declining due, in part, to our Nation's global effort through the
President's Emergency Plan for AIDS Relief (PEPFAR) program." [1]. In a
keynote speech (2012), Hilary Clinton cited the UNAIDS estimates of HIV
impact: "Just last week, UNAIDS announced that, over the past decade, the
rate of new HIV infections has dropped by more than half in 25
low-and-middle-income countries, most of them in Sub-Saharan Africa. Just
listen to these numbers: In Zimbabwe, a 50% reduction; in Namibia, a 68%
reduction; and in Malawi, a 73% in the rate of new infections. So as we
continue to drive down the number of new infections and drive up the
number of people on treatment, eventually we will be able to treat more
people than become infected every year. That will be the tipping point. We
will then get ahead of the pandemic, and an AIDS-free generation will be
in our sight. Now, we don't know how long it will take to do this
everywhere..." [2]. The only source for the statistics is the UNAIDS
reports that are developed using the Imperial methods.
For each country, UNAIDS also recommends application of the `Modes of
Transmission' model (research reference 4) as part of a UNAIDS-GAMET
(Global AIDS Monitoring and Evaluation Team) supported synthesis process,
a component of the UNAIDS `know your epidemic, know your response'
strategy, and the World Bank's Epidemic, Response and Policy Syntheses
[3]. Over 30 countries have completed, or are currently conducting,
analyses with the model [4, 5]. The results from these applications have
been used for the design of prevention programmes, for resource allocation
and prioritisation and to inform the development of national strategic
planning for HIV. In many cases, the findings have led to realignment of
funding, sometimes increasing the focus of intervention in key populations
that had been previously neglected. For example, in Morocco strategic
information has been used to optimize the allocation of resources [6]. The
distribution of the people newly infected with HIV according to the
Imperial-developed model was compared with recent spending patterns to
focus future prevention planning. As a result, the 2012-2016 National
Strategic Plan for Morocco proposed to allocate 63% of AIDS resources
towards prevention among key populations at higher risk, up from about 25%
according to the 2008 spending assessment, which the model indicated would
generate a far greater health impact in that setting.
Estimates of Mother-To-Child Transmission events and numbers in need of
Antiretroviral Therapy in every country in the world are used by WHO to
track progress toward their goals of eliminating mother-to-child
transmission and universal access to antiretroviral therapy [7]. The UN
methods of estimating child mortality were updated to correct for the bias
effects that our research demonstrated, and these estimates are now used
to measure progress against a key Millennium Development Goal [8]. Without
a demonstration of that effect, it is possible that child mortality could
have been substantially under-estimated which could have resulted in an
inappropriate reduction in focus on child mortality in post-2015 targets.
Sources to corroborate the impact
[1] In his keynote speech, President Obama, cites the UNAIDS statistics,
which are taken from the UNAIDS Global Reports November 25th, 2009
(paragraph 3): http://www.whitehouse.gov/the-press-office/presidential-proclamation-world-aids-day
Archived on
26th November 2013.
[2] In her keynote speech in November 29th 2012, the Secretary
Clinton cites the UNAIDS estimates of HIV impact: http://www.state.gov/secretary/rm/2012/11/201198.htm
(refer to paragraph 12). Archived
on 26th November 2013.
[3] UNAIDS recommends the use of the `Modes of Transmission model' is
setting a country's priorities for spending: Modelling the expected
Short-term Distribution of new HIV Infections by Modes of transmission.
https://www.unaids.org/en/media/unaids/contentassets/documents/document/2012/guidelines/JC2427_ModelingNewHIVInfectionsbyModesofTransmission_en.pdf
(refer to p.2). Archived
on 4th November 2013.
[4] The estimates produced by UNAIDS/WHO are based on methods and
parameters that are informed by the UNAIDS Reference Group on HIV/AIDS
Estimates (2010) Global Report http://www.unaids.org/globalreport/documents/20101123_GlobalReport_full_en.pdf
(refer to page 8). Archived
on 4th November 2013.
[5] UNAIDS. New HIV infections by mode of transmission in West Africa: A
multi-country analysis. Dakar, Senegal: UNAIDS Regional Support Team for
West and Central Africa; 2010.
http://www.unaids.org/en/media/unaids/contentassets/documents/countryreport/2010/201003_MOT_West_Africa_en.pdf.
Archived
on 4th November 2013.
[6] HIV modes of transmission in Morocco and reallocation of resources:
http://www.unaids.org/en/media/unaids/contentassets/documents/epidemiology/2012/gr2012/20121120_UNAIDS_Global_Report_2012_en.pdf
(see page 68). Archived
on 4th November 2013.
[7] Tracking Numbers on Antiretroviral Therapy. A key report provides the
statistics:
UNAIDS/WHO. Monitoring the Declaration of Commitment on HIV/AIDS:
Guidelines on construction of core indicators: 2010 Reporting. Geneva,
WHO.
http://data.unaids.org/pub/manual/2009/jc1676_core_indicators_2009_en.pdf
Archived
on 4th November 2013.
[8] UN methods for estimating Child Mortality Updated. A paper describing
the updates to the methods (Walker, N., Hill, K., Zhao, F. (2012). Child
Mortality Estimation: Methods Used to Adjust for Bias due to AIDS in
Estimating Trends in Under-Five Mortality. PLoS Med 9(8):
e1001298. DOI.
Provides a direct citation to our article 6 on page 2 as providing the
evidence for the need to change in the UN methods and guidance on the
correction. The following quotes are taken from that paper: "To our
knowledge, only one analysis of the magnitude of bias in direct child
mortality estimates due to AIDS mortality has been carried out using
real data rather than simulations. Hallett et al. [8]
used data from a prospective open cohort in Manicaland, Zimbabwe, to
measure the bias introduced by deaths of HIV-positive mothers.... Based
on the findings of Hallett et al.,
UN IGME [United Nations Inter-agency Group for Child Mortality
Estimation] recently implemented an adjustment approach for use in
countries where prevalence of HIV/AIDS has reached 5% or above in the
adult population (ages 15-49 years)."