EDITORIAL COMMENT
Putting the burden of HIV in context Simon Gregsona, Timothy B. Halletta, John Stoverb and Peter D. Ghysc
AIDS 2013, 27:2161–2162
United Nations Programme on HIV/AIDS (UNAIDS) produces regular HIV estimates, globally and by country, including estimates of numbers of AIDS deaths [1]. These estimates are essential to establish the magnitude of the HIV epidemic and to track progress in the response. The methods used in these estimates [2] have been developed and refined over many years with guidance from an independent group of international experts in HIV epidemiology and demography (UNAIDS Reference Group on Estimates, Modelling and Projections; http:// www.epidem.org). These methods are applied incountry using purpose-built software, incorporating data from national surveys and key populations, as well as from regular surveys of pregnant women attending antenatal clinics, and from treatment and other programmes. The estimates are produced by groups of local experts, a process which harnesses local knowledge and secures national buy-in, and has helped to define the AIDS response at national and international levels. Whilst this approach has great strengths, it is important that the UNAIDS estimates are continuously improved by incorporating new data sources and refining the methods, and through triangulation with independent estimates. To establish priorities for allocating scarce resources, it is helpful to put the burden of HIV in context through comparison with estimates for other leading causes of morbidity and mortality. In this issue of AIDS, Ortblad et al. [3] from the Institute for Health Metrics and Evaluation (IHME) present estimates of HIV mortality and disability-adjusted life years (DALYs) from the 2010 Global Burden of
Disease (GBD) study and compare these with linked estimates for other leading causes of morbidity and mortality. The findings endorse the UNAIDS estimates in showing an enormous rise in the burden of HIV from 1990 to 2005/2006, followed by a reduction as investments in interventions, including antiretroviral therapy, were increased. By comparing the burden of HIV disease with those from other causes, the authors provide valuable new insights; for example, by highlighting countries where HIV is the dominant threat to health and the emerging shift in the relative burden of AIDS deaths globally towards countries where HIV is a less dominant – and possibly a less prioritized – health problem. Whilst the broad findings show a similar story, there are important differences between the two estimation procedures both in approach and in estimates of AIDS deaths. In contrast to the UNAIDS philosophy, the IHME approach essentially entails top-down algorithmic synthesis. The authors suggest that involvement of countries experiencing HIV epidemics undermines the integrity of the UNAIDS estimates. However, their concerns about possible political interference could apply equally to many of the data sources (e.g. national censuses, surveys and vital registration systems) on all-cause mortality used in the GBD exercise. In UNAIDS’ experience, country ownership comes with better access to relevant data (including unpublished surveillance and programme data) and virtually all countries show ownership of the resulting estimates, enabling data-based programmatic actions.
a
Department of Infectious Disease Epidemiology, Imperial College London School of Public Health, London, UK, bFutures Institute, Glastonbury, Connecticutt, USA, and cJoint United Nations Programme on HIV/AIDS (UNAIDS), Geneva, Switzerland. Correspondence to Simon Gregson, Department of Infectious Disease Epidemiology, Imperial College London School of Public Health, London, UK. Tel: +44 207 594 3279; fax: +44 207 402 3927; e-mail: [email protected] Received: 20 May 2013; accepted: 22 May 2013. DOI:10.1097/QAD.0b013e3283638641
ISSN 0269-9370 Q 2013 Wolters Kluwer Health | Lippincott Williams & Wilkins
2161
Copyright © Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
2162
AIDS
2013, Vol 27 No 13
The IHME estimate for global HIV deaths in 2010 (1.5 million, 95% uncertainty interval 1.3–1.6 million) is 19% lower than the UNAIDS estimate (1.8m, 1.6–2.0 million) [1]. Whilst the uncertainty intervals overlap, the UNAIDS Reference Group has expressed reservations about the accuracy of the IHME estimates [4]. The difference is substantial and results largely from an adjustment made by IHME whereby the HIV mortality estimates (initially taken largely from the UNAIDS estimates) are ‘squeezed’ to fit within an envelope of all-cause mortality estimated from demographic data [3]. Two major assumptions are implicit in this adjustment: first, that the all-cause mortality estimates are robust; and second, that accurate estimates of causespecific mortality can be obtained by rescaling the numbers of deaths for each disease by the same proportion in each uncertainty draw. The authors claim that the first assumption is justified because demographic measurement of mortality has a longer history than descriptive HIV epidemiology and assert that more datasets are available to estimate all-cause mortality in countries most affected by HIV. However, in estimating AIDS mortality, what matters is the availability of datasets over the course of HIV epidemics – rather than over all time. Also, the IHME estimates of all-cause mortality are substantially lower than those produced by both the WHO and United Nations Development Programme [5]. Elsewhere, they acknowledge that leading demographers have expressed serious misgivings about bias in the sibling survival method that is central to their approach [5]. If the true level of overall mortality is higher than estimated by IHME, there would be less need for a squeeze on causespecific rates and the results would be in closer agreement. Regarding the second assumption, IHME’s estimates for malaria and tuberculosis (TB) deaths [6] exceed WHO estimates [7,8] by more than 700 000. Had WHO’s estimates of malaria and TB deaths been used in the IHME framework, this would again have resulted in less squeezing of HIV deaths. Furthermore, the IHME pattern of HIV deaths by age is significantly younger in most countries than the UNAIDS pattern which is based on national prevalence surveys. This leads to significant reductions in AIDS mortality in key age groups. Use of the UNAIDS pattern would result in smaller adjustments. A minor point requiring clarification is that IHME present estimates for several countries for which UNAIDS does not produce estimates of HIV mortality due to difficulties in calculating robust estimates. These are generally countries where HIV prevalence is low and data are sparse; for example, Iraq, Taiwan and the Seychelles. These points should not overshadow the main conclusion about the rise in the burden of HIV disease relative to
other causes and the subsequent impact of interventions. Comparison of estimates of DALYs for HIV with numbers for other diseases is certainly helpful as a guide to establishing priorities for allocating resources. However, in applying this information, it should not be forgotten that enormous investments in HIV control were required to achieve the recent reduction in DALYs and must be sustained just to keep HIVat current levels. In emerging and re-emerging epidemics (e.g. Uganda where HIV incidence has risen after a decline), funding decisions based purely on DALYs will come too late to prevent large numbers of new infections. UNAIDS’ estimates of AIDS deaths combined with estimates of people living with HIV, people eligible for treatment and people newly infected with HIV and their likely mode of transmission [9] may provide a more complete basis for important investment decisions to respond to the HIV epidemic.
Acknowledgements Conflicts of interest S.G. and T.B.H. run the secretariat for the UNAIDS Reference Group on National Estimates, Models and Projections. J.S. is also a regular member of this group and works with PDG and others in the development of the methods used in the UNAIDS estimates. There are no other conflicts of interest.
References 1. UNAIDS. Global report: UNAIDS report on the global AIDS epidemic 2012. Geneva: UNAIDS; 2012. 2. Stover J, Brown T, Marston M. Updates to the Spectrum/Estimation and Projections Package (EPP) model to estimate HIV trends for adults and children. Sex Transm Infect 2012; 88:i11–i16. 3. Ortblad KF, Lozano R, Murray CJL. The burden of HIV: insights from the Global Burden of Disease Study 2010. AIDS 2013; 27:2003–2017. 4. UNAIDS Reference Group on Estimates MaP. Mortality from HIV in the Global Burden of Disease study. Lancet 2013; 381:991. 5. Wang H, Dwyer-Lindgren L, Lofgren KT, Rajaratnam JK, Marcus JR, Levin-Rector A, et al. Age-specific and sex-specific mortality in 187 countries, 1970–2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 2013; 380:2071– 2094. 6. Lozano R, Naghavi M, Foreman K, Lim S, Shibuya K, Aboyans V, et al. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 2013; 380:2095– 2128. 7. Lynch M, Korenromp EL, Eisele TP, Newby H, Steketee RW, Kachur SP, et al. New global estimates of malaria deaths. Lancet 2012; 380:559. 8. World Health Organisation. Global tuberculosis report 2012. Geneva: World Health Organisation; 2012. 9. Gouws E, Cuch P. Transmission. obotICoEHIbMo. Focusing the HIV response through estimating the major modes of HIV transmission: a multicountry analysis. Sex Transm Infect 2012; 88:i76–i85.
Copyright © Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
Putting the burden of HIV in context Simon Gregsona, Timothy B. Halletta, John Stoverb and Peter D. Ghysc
AIDS 2013, 27:2161–2162
United Nations Programme on HIV/AIDS (UNAIDS) produces regular HIV estimates, globally and by country, including estimates of numbers of AIDS deaths [1]. These estimates are essential to establish the magnitude of the HIV epidemic and to track progress in the response. The methods used in these estimates [2] have been developed and refined over many years with guidance from an independent group of international experts in HIV epidemiology and demography (UNAIDS Reference Group on Estimates, Modelling and Projections; http:// www.epidem.org). These methods are applied incountry using purpose-built software, incorporating data from national surveys and key populations, as well as from regular surveys of pregnant women attending antenatal clinics, and from treatment and other programmes. The estimates are produced by groups of local experts, a process which harnesses local knowledge and secures national buy-in, and has helped to define the AIDS response at national and international levels. Whilst this approach has great strengths, it is important that the UNAIDS estimates are continuously improved by incorporating new data sources and refining the methods, and through triangulation with independent estimates. To establish priorities for allocating scarce resources, it is helpful to put the burden of HIV in context through comparison with estimates for other leading causes of morbidity and mortality. In this issue of AIDS, Ortblad et al. [3] from the Institute for Health Metrics and Evaluation (IHME) present estimates of HIV mortality and disability-adjusted life years (DALYs) from the 2010 Global Burden of
Disease (GBD) study and compare these with linked estimates for other leading causes of morbidity and mortality. The findings endorse the UNAIDS estimates in showing an enormous rise in the burden of HIV from 1990 to 2005/2006, followed by a reduction as investments in interventions, including antiretroviral therapy, were increased. By comparing the burden of HIV disease with those from other causes, the authors provide valuable new insights; for example, by highlighting countries where HIV is the dominant threat to health and the emerging shift in the relative burden of AIDS deaths globally towards countries where HIV is a less dominant – and possibly a less prioritized – health problem. Whilst the broad findings show a similar story, there are important differences between the two estimation procedures both in approach and in estimates of AIDS deaths. In contrast to the UNAIDS philosophy, the IHME approach essentially entails top-down algorithmic synthesis. The authors suggest that involvement of countries experiencing HIV epidemics undermines the integrity of the UNAIDS estimates. However, their concerns about possible political interference could apply equally to many of the data sources (e.g. national censuses, surveys and vital registration systems) on all-cause mortality used in the GBD exercise. In UNAIDS’ experience, country ownership comes with better access to relevant data (including unpublished surveillance and programme data) and virtually all countries show ownership of the resulting estimates, enabling data-based programmatic actions.
a
Department of Infectious Disease Epidemiology, Imperial College London School of Public Health, London, UK, bFutures Institute, Glastonbury, Connecticutt, USA, and cJoint United Nations Programme on HIV/AIDS (UNAIDS), Geneva, Switzerland. Correspondence to Simon Gregson, Department of Infectious Disease Epidemiology, Imperial College London School of Public Health, London, UK. Tel: +44 207 594 3279; fax: +44 207 402 3927; e-mail: [email protected] Received: 20 May 2013; accepted: 22 May 2013. DOI:10.1097/QAD.0b013e3283638641
ISSN 0269-9370 Q 2013 Wolters Kluwer Health | Lippincott Williams & Wilkins
2161
Copyright © Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
2162
AIDS
2013, Vol 27 No 13
The IHME estimate for global HIV deaths in 2010 (1.5 million, 95% uncertainty interval 1.3–1.6 million) is 19% lower than the UNAIDS estimate (1.8m, 1.6–2.0 million) [1]. Whilst the uncertainty intervals overlap, the UNAIDS Reference Group has expressed reservations about the accuracy of the IHME estimates [4]. The difference is substantial and results largely from an adjustment made by IHME whereby the HIV mortality estimates (initially taken largely from the UNAIDS estimates) are ‘squeezed’ to fit within an envelope of all-cause mortality estimated from demographic data [3]. Two major assumptions are implicit in this adjustment: first, that the all-cause mortality estimates are robust; and second, that accurate estimates of causespecific mortality can be obtained by rescaling the numbers of deaths for each disease by the same proportion in each uncertainty draw. The authors claim that the first assumption is justified because demographic measurement of mortality has a longer history than descriptive HIV epidemiology and assert that more datasets are available to estimate all-cause mortality in countries most affected by HIV. However, in estimating AIDS mortality, what matters is the availability of datasets over the course of HIV epidemics – rather than over all time. Also, the IHME estimates of all-cause mortality are substantially lower than those produced by both the WHO and United Nations Development Programme [5]. Elsewhere, they acknowledge that leading demographers have expressed serious misgivings about bias in the sibling survival method that is central to their approach [5]. If the true level of overall mortality is higher than estimated by IHME, there would be less need for a squeeze on causespecific rates and the results would be in closer agreement. Regarding the second assumption, IHME’s estimates for malaria and tuberculosis (TB) deaths [6] exceed WHO estimates [7,8] by more than 700 000. Had WHO’s estimates of malaria and TB deaths been used in the IHME framework, this would again have resulted in less squeezing of HIV deaths. Furthermore, the IHME pattern of HIV deaths by age is significantly younger in most countries than the UNAIDS pattern which is based on national prevalence surveys. This leads to significant reductions in AIDS mortality in key age groups. Use of the UNAIDS pattern would result in smaller adjustments. A minor point requiring clarification is that IHME present estimates for several countries for which UNAIDS does not produce estimates of HIV mortality due to difficulties in calculating robust estimates. These are generally countries where HIV prevalence is low and data are sparse; for example, Iraq, Taiwan and the Seychelles. These points should not overshadow the main conclusion about the rise in the burden of HIV disease relative to
other causes and the subsequent impact of interventions. Comparison of estimates of DALYs for HIV with numbers for other diseases is certainly helpful as a guide to establishing priorities for allocating resources. However, in applying this information, it should not be forgotten that enormous investments in HIV control were required to achieve the recent reduction in DALYs and must be sustained just to keep HIVat current levels. In emerging and re-emerging epidemics (e.g. Uganda where HIV incidence has risen after a decline), funding decisions based purely on DALYs will come too late to prevent large numbers of new infections. UNAIDS’ estimates of AIDS deaths combined with estimates of people living with HIV, people eligible for treatment and people newly infected with HIV and their likely mode of transmission [9] may provide a more complete basis for important investment decisions to respond to the HIV epidemic.
Acknowledgements Conflicts of interest S.G. and T.B.H. run the secretariat for the UNAIDS Reference Group on National Estimates, Models and Projections. J.S. is also a regular member of this group and works with PDG and others in the development of the methods used in the UNAIDS estimates. There are no other conflicts of interest.
References 1. UNAIDS. Global report: UNAIDS report on the global AIDS epidemic 2012. Geneva: UNAIDS; 2012. 2. Stover J, Brown T, Marston M. Updates to the Spectrum/Estimation and Projections Package (EPP) model to estimate HIV trends for adults and children. Sex Transm Infect 2012; 88:i11–i16. 3. Ortblad KF, Lozano R, Murray CJL. The burden of HIV: insights from the Global Burden of Disease Study 2010. AIDS 2013; 27:2003–2017. 4. UNAIDS Reference Group on Estimates MaP. Mortality from HIV in the Global Burden of Disease study. Lancet 2013; 381:991. 5. Wang H, Dwyer-Lindgren L, Lofgren KT, Rajaratnam JK, Marcus JR, Levin-Rector A, et al. Age-specific and sex-specific mortality in 187 countries, 1970–2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 2013; 380:2071– 2094. 6. Lozano R, Naghavi M, Foreman K, Lim S, Shibuya K, Aboyans V, et al. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 2013; 380:2095– 2128. 7. Lynch M, Korenromp EL, Eisele TP, Newby H, Steketee RW, Kachur SP, et al. New global estimates of malaria deaths. Lancet 2012; 380:559. 8. World Health Organisation. Global tuberculosis report 2012. Geneva: World Health Organisation; 2012. 9. Gouws E, Cuch P. Transmission. obotICoEHIbMo. Focusing the HIV response through estimating the major modes of HIV transmission: a multicountry analysis. Sex Transm Infect 2012; 88:i76–i85.
Copyright © Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
