Case, KK

Ghys, PD

Gregson, S

Hallett, TB

Porter, K

AIDS

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Development and future directions for the JointUnited Nations Programme on HIV/AIDS estimatesKelsey K. Casea, Timothy B. Halletta, Simon Gregsona,Kholoud Porterb and Peter D. GhyscAIDS 2014, 28 (Suppl 4):S411–S414IntroductionEvery year, the Joint United Nations Programme onHIV/AIDS (UNAIDS) produces estimates that chart themagnitude of the worldwide HIV epidemic, its impactand the progress taken to curb its spread and reduce itsimpact. The UNAIDS Reference Group on Estimates,Modelling and Projections [1] exists to review anddevelop the methods supported by UNAIDS to generatenational estimates of HIV and related indicators.UNAIDS and its Reference Group routinely producespecial editions of scientific journals to document themethods used to produce the UNAIDS estimates and toprovide updates on the directions the methods areexpected to take in the future [2–6].The work of the Reference Group is two-fold – tocontinually calibrate, update and validate the methods,while simultaneously extending the methods to takeadvantage of new data sources, and to adapt approaches soas to maximise their usefulness to country programmeplanning and in evaluation. The articles in thissupplement fall into both of these categories. Twoarticles document recent improvements to the estimationand projection models that are used, and anothercompares the UNAIDS estimates with other sets ofpublished estimates and data. Several other articles showhow the methods have expanded to accommodate theneed to better understand data requirements for youngchildren and older people; how estimates of key indicatorsare produced at finer spatial scales; and finally, howmethods can and will synthesise newly available data intoestimates. We address each of these topics in turn below.Together, these updates, modifications and newmethods aim to provide more accurate knowledgeaDepartment of Infectious Disease Epidemiology, School of PublicClinical Trials Unit, University College London, London, UK, andGeneva, Switzerland.Correspondence to Kelsey K. Case, Department of Infectious DiseasUK, London, UK.E-mail: k.case@imperial.ac.ukReceived: 9 September 2013; revised: 9 September 2014; accepteDOI:10.1097/QAD.0000000000000487ISSN 0269-9370 Q 2014 Wolters Kluwer Health | Lippincott Williams &of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0provided it is properly cited. The work cannot be changed in any way oand better understanding of HIVepidemics which shouldinform and assist programme planners and decisionmakers, as well as scientists, researchers and the widerpublic.Developments in the estimation toolsThe Estimation and Projection Package (EPP) and theAIDS Impact Module (AIM) in Spectrum were used by158 countries to produce the 2013 estimates of HIV andHIV-related indicators [7]. EPP and Spectrum are thecore set of tools used by UNAIDS and are continuallyunder development. Brown et al. [8] describe the threemodels that can be fit to prevalence data in EPP– r-spline,r-trend and EPP classic. This new repertoire of modelsshould enable many types of epidemic trajectory to becaptured and uncertainty in the shape of the epidemiccurve represented where data are sparse. Stover et al. [9]describe, among other changes, how the AIM modulenow includes a procedure whereby, in large epidemics,the total death rate is made consistent with publishedprojections through modification of backgroundmortality rates where necessary. Another importantupdate is in the methods used for estimating the numberof orphans, which now include the changing effects ofantiretroviral therapy (ART) and prevention of mother-to-child transmission (PMTCT) programmes over time asguidelines have changed [9]. Lastly, Stover et al. [9]describe how ART need based on the new WHOguidelines (reflecting recommendations for ARTinitiation regardless of CD4þ cell count for serodiscor-dant couples, HIV-positive pregnant women and allHIV-positive children under 5 years, among others) canbe reflected in the model.Health, Faculty of Medicine, Imperial College London, bMRCcDivision of Strategic Information and Monitoring, UNAIDS,e Epidemiology, Imperial College London, London, W2 1PG,d: 9 September 2014.Wilkins. This is an open-access article distributed under the termsLicense, where it is permissible to download and share the workr used commercially. S411S412 AIDS 2014, Vol 28 (Suppl 4)In this category, the Reference Group had also been con-cerned that models were not fully incorporating infor-mationontheexcessdeathratesexperiencedbypeoplewhoinject drugs (PWID). In response to these concerns,Mathers andDegenhardt [10]updatedaprevious systematicreview [11] to examine non-AIDS mortality in PWID.Theyexamine differential effects of non-AIDS mortality inPWID by region, income status, sex, HIV status andwhether opioid substitution therapy is used. Non-AIDSmortality in HIV-positive PWID (at 2.51 per 100 person-years) was found to be 1.5 times higher than among HIV-negative PWID (at 1.66 per 100 person-years). Althoughadditional differences appear to exist, more representativedata are required, particularly in low-income and middle-income countries, to improve model estimates.A new focus on young children and olderadultsOriginally, estimates were published only for adults 15–49years. This focus has expanded over time in parallel withthe epidemic ageing into older age groups and with cohortsof children infected. Maternal and child health is a majorglobal priority, and HIV plays a significant role, particularlyin sub-Saharan Africa. Understanding trends in paediatricinfections and theneed for ART in this group is critical, notonly for health systems and programme management, butalso for planning future drug procurement and develop-ment. Penazzato et al. [12] explore different plausiblescenarios to estimate the potential future need forpaediatric ART. They find that, even under the mostoptimistic prevention scenarios, by 2020 there will benearly 2 million children living with HIV, signifying theimportance for age-appropriate drug regimens.It is becoming increasingly important to better understandthe size of the older population living with HIV. In sub-Saharan Africa, where the burden of infection is greatest,surveillance in older populations has been limited. Mahyet al. [13] present estimated trends in prevalence over timeshowing that HIV prevalence among those aged over 50years has increased considerably over time. However, therestill appear to be some discrepancies between nationalsurveys and estimates, which underline the need forexpanded surveillance in older populations to improve theprogrammatic response and to better understand the healthneeds of this population.Estimates that are responsive toprogramme managers needsCountries require programme-relevant information atsub-national levels to support local planning, decisionmaking and resource allocation. With the tools availableat present, it is possible to produce estimates at finergeographic levels. For example, Mahy et al. [14] describethe process of producing state-level estimates in Nigeriausing EPP and Spectrum and the challenges that resultfrom using the more limited data available at this level andissues in model selection for each state. In the future,hierarchical and spatial models may allow researchers tobetter address these limitations.The spatial distribution of people living with HIV in sub-Saharan Africa, at a much finer resolution, is describedby Larmarange and Bendaud [15] using an approachthat combines geolocation information from nationalpopulation-based surveys with estimates of prevalencefrom EPP/Spectrum. This approach represents an initialphase of method development to represent spatialvariation in order to better understand heterogeneityacross national epidemics and has already had an impact inhow programme managers are able to understand theepidemics they face.New methods have also been added that allow theincidence of tuberculosis disease among HIV-positivepersons to be estimated. Tuberculosis and HIV coinfec-tion is a Millennium Development Goal target indicatorfor countries in sub-Saharan Africa but, until now,estimates have only been made at the regional level andhave not necessarily been integrated with the dataavailable on HIV epidemics. Here, Pretorius et al. [16]detail the methods used to estimate tuberculosis–HIVincidence and mortality at the country level allowing forcoherence between existing tuberculosis incidence andmortality estimates [17], while leveraging other HIVestimates, such as HIV prevalence and the CD4þ cellcount distribution of the HIV-infected population. Thispart of the model will expand in the future to incorporateadditional tuberculosis data to improve these estimates.Leveraging more data sources tostrengthen estimatesA key aim for ongoing method development is toincorporate all available data sources to strengthenestimates. To date, estimates coordinated by UNAIDShave largely relied on surveillance data (particularly datafrom women attending antenatal care (ANC) and fromtargeted surveys among key populations) and nationalsurvey data (e.g. household-based Demographic andHealth Surveys) to inform both the overall trends and thelevel of HIV prevalence. In concentrated epidemics,many countries do not have all the data required(including nationally representative data for key popu-lations and size estimates), but do have additional datasources such as detailed case report and vital registrationdata which may be used to better inform estimates.Stover et al. [9] describe a new approach used inArgentina, Mexico and Brazil for their 2013 estimateswhereby incidence trends, based on fits to prevalencedata, are selected to better match programme estimates ofDevelopment and future directions for the UNAIDS estimates Case et al. S413new HIV diagnoses, people living with HIV and/ordeaths due to HIV/AIDS. Another method which doesnot rely on prevalence data and size estimates of keypopulations but instead utilises only case report data toestimate incidence is described by Vesga et al. [18] andapplied in Colombia. For the same reason of aiming toleverage additional data to check projections, the newversion of EPP includes a procedure for comparingestimates, with ad hoc data on AIDS case reports andsmall local surveys, which have been assembled into adatabase [8]. These approaches are intended to bringhistorical estimates of new HIV infections and AIDSdeaths in better alignment with other national data. It isexpected that use of these approaches will increase in thefuture as the methods are further developed and refined,and additional data sources are integrated.Brown et al. [8] and Marsh et al. [19] describe hownational population-based surveys are now directlyincluded in the fitting process in EPP, which allowssurvey trends to inform the fitting and allows for moreappropriate representation of uncertainty. Marsh et al.[19] additionally analyse prevalence in national surveyscompared with ANC prevalence to derive updatedcalibrations for ANC data in countries without a nationalpopulation-based survey. In order to further examinehow these two data sources (ANC data and householdsurveys) should be considered together, Eaton et al. [20]compare prevalence trends among pregnant women withprevalence trends among all women in the generalpopulation. The authors illustrate that, in sub-SaharanAfrica, over time the age distribution of HIV prevalencehas shifted towards older women, who are less likely tobecome pregnant and be captured in ANC surveillance,so that observed ANC prevalence trends could haveexaggerated the extent to which HIV prevalence inthe general population has declined. This would haveimportant implications for use of ANC data to informpopulation-level trends. Age-structured modelling fra-meworks that explicitly account for fertility schedulesmay be less susceptible to these issues and are a significantavenue for further investigation.Incidence assays are an additional potential source of datawhich may strengthen estimates by providing cross-sectional measures of incidence. While some countrieshave already used incidence assays, more countries areplanning for their inclusion in future national population-based surveys. Bao et al. [21] present methods thatincorporate incidence assays, or combinations of assays,into the fitting process in EPP. The authors illustrate howthe effect of including assays is largely dependent on thesample size and the calibrating parameters of the assay (thefalse recent rate and the mean duration of recentinfection). Data from assays used in a single survey withsmall sample sizes will have only a modest effect oninforming estimates and results may be biased if thecalibrating parameters are not accurate. However,the added value of these assays is expected to increasein the future with multiple measures of incidence andimproved calibrating parameters.The use of different data sources and methods forestimation can lead to different results. Hallett et al. [22]compare estimates of HIV incidence, prevalence andmortality from the Global Burden of Disease (GBD)Study 2013 [23] with UNAIDS estimates, and triangu-lates these estimates with other data where available. Theauthors illustrate how HIV incidence estimates from theGBD Study 2013 are substantially lower than HIV casereport data in countries with strong surveillance systemsincluding the United States, the United Kingdom,Australia and France among others. The mortality trendsin the Murray et al. [23] study suggest a much shallowerreduction in the era of ART than UNAIDS has estimated.Population-based cohorts, however, offer the opportu-nity to examine real trends in mortality, albeit in selectedsmall locations. Reniers et al. [24] in this supplementpresent trends in mortality among people living with HIVfrom population-based cohort studies in East andSouthern Africa which illustrate declines in mortalityrates of more than 50% in the ARTera compared with thepre-ART era.The work of bringing more data sources to bear on theestimates is ongoing and is intended to strengthen futuresets of estimates produced by UNAIDS. Additional datasources that will become integrated in the coming roundsof methods revisions may also include data from HIVtesting of pregnant women in PMTCT programmes. TheReference Group also calls for further data collectionespecially related to ART: confirmed numbers of newinitiators, numbers currently in care, age distributionsand CD4þ cell counts at ART initiation, among otherfactors.In conclusion, UNAIDS supports and empowerscountries to develop their own estimates, simultaneouslyharnessing local knowledge and understanding ofepidemics and fostering the routine application of theestimates in programme planning. The studies in thiscollection emphasise the dynamic nature of the methodsthat are used to produce the UNAIDS estimates. Furtherchanges can be expected in the coming years as new datasources become available, additional opportunities tovalidate the estimates present themselves, and the needs ofprogramme managers continue to evolve.AcknowledgementsThe HIV Modelling Consortium is funded by a grantfrom the Bill and Melinda Gates Foundation.Imperial College London currently holds the secretariatfor the UNAIDS Reference Group on Estimates,S414 AIDS 2014, Vol 28 (Suppl 4)Modelling and Projections which is funded by a grantfrom UNAIDS.Conflicts of interestThere are no conflicts of interestReferences1. UNAIDS Reference Group on Estimates, Modelling and Projec-tions. London: Imperial College London. www.epidem.org.2. Methods and tools for HIV/AIDS estimates and projections. SexTransm Infect 2004; 80 (Suppl 1):i1–i38.3. Improved methods and tools for HIV/AIDS estimates andprojections. Sex Transm Infect 2006; 82 (Suppl 3):i1–i91.4. Improved data, methods and tools for the 2007 HIV and AIDSestimates and projections. Sex Transm Infect 2008; 84 (Suppl1):i1–i96.5. The 2009 HIV and AIDS estimates and projections: methods,tools and analyses. Sex Transm Infect 2010; 86 (Suppl 2):i1–i99.6. UNAIDS Report 2012. Sex Transm Infect 2012; 88 (Suppl 2):i1–i101.7. UNAIDS. The Gap Report. Geneva: Joint United Nations Pro-gramme on HIV/AIDS; 2014. http://www.unaids.org/en/resources/documents/2014/name,97466,en.asp. [Accessed 28 August2014]8. Brown T, Bao L, Eaton JW, Hogan DR, Mahy M, Marsh K, et al.Improvements in prevalence trend fitting and incidence esti-mation in EPP 2013. AIDS 2014; 28 (Suppl 4):S415–S425.9. Stover J, Andreev K, Slaymaker E, Gopalappa C, Sabin K,Velasquez C, et al. Updates to the Spectrum model to estimatekey HIV indicators for adults and children. AIDS 2014; 28(Suppl 4):S427–S434.10. Mathers BM, Degenhardt L. Examining non-AIDS mortalityamong people who inject drugs. AIDS 2014; 28 (Suppl 4):S435–S444.11. Degenhardt L, Hall W, Warner-Smith M. Using cohort studiesto estimate mortality among injecting drug users that is notattributable to AIDS. Sex Transm Infect 2006; 82 (Suppl 3):iii56–iii63.12. Penazzato M, Bendaud V, Nelson L, Stover J, Mahy M. Estimat-ing future trends in paediatric HIV. AIDS 2014; 28 (Suppl 4):S445–S451.13. Mahy M, Autenrieth CS, Stanecki K, Wynd S. Increasing trends inHIV prevalence among people aged 50 years and older: evidencefrom estimates and survey data. AIDS 2014; 28 (Suppl 4): S453–S459.14. Mahy M, Nzima M, Ogungbemi MK, Ogbang DA, Chinenye M,Stover J. Redefining the HIV epidemic in Nigeria: from nationalto state level. AIDS 2014; 28 (Suppl 4):S461–S467.15. Larmarange J, Bendaud V. HIV estimates at second subnationallevel from national population-based surveys. AIDS 2014; 28(Suppl 4):S469–S476.16. Pretorius C, Glazioiu P, Dodd PJ, White R, Houben R. Usingthe TIME model in Spectrum to estimate tuberculosis–HIVincidence and mortality. AIDS 2014; 28 (Suppl 4):S477–S487.17. WHO. Global tuberculosis report 2013. Geneva: World HealthOrganization; 2013. http://www.who.int/tb/publications/global_report/en/. [Accessed 28 August 2014].18. Vesga JF, Cori A, van Sighem A, Hallett TB. Estimating HIVincidence from case-report data: method and an application inColombia. AIDS 2014; 28 (Suppl 4):S489–S496.19. Marsh K, Mahy M, Salomon JA, Hogan DR. Assessing andadjusting for differences between HIV prevalence estimatesderived from national population-based surveys and antenatalcare surveillance, with applications for Spectrum 2013. AIDS2014; 28 (Suppl 4):S497–S505.20. Eaton JW, Rehle TM, Jooste S, Nkambule R, Kim AA, Mahy M,Hallett TB. Recent HIV prevalence trends among pregnantwomen and all women in sub-Saharan Africa: implicationsfor HIV estimates. AIDS 2014; 28 (Suppl 4):S507–S514.21. Bao L, Ye J, Hallett TB. Incorporating incidence informationwithin the UNAIDS Estimation and Projection Package frame-work: a study based on simulated incidence assay data. AIDS2014; 28 (Suppl 4):S515–S522.22. Hallett TB, Zaba B, Stover J, Brown T, Slaymaker E, Gregson S,et al. Embracing different approaches to estimating HIV inci-dence, prevalence and mortality. AIDS 2014; 28 (Suppl 4):S523–S532.23. Murray CJ, Ortblad KF, Guinovart C, Lim SS, Wolock TM,Roberts DA, et al. Global, regional, and national incidenceand mortality for HIV, tuberculosis, and malaria during 1990-2013: a systematic analysis for the Global Burden of DiseaseStudy 2013. Lancet 2014; 384:1005–1070.24. Reniers G, Slaymaker E, Nakiyingi-Miiro J, Nyamukapa C,Crampin AC, Herbst K, et al. Mortality trends in the era ofantiretroviral therapy: evidence from the Network for Analys-ing Longitudinal Population based HIV/AIDS data on Africa(ALPHA). AIDS 2014; 28 (Suppl 4):S533–S542.

Development and future directions for the Joint United Nations Programme on HIV/AIDS estimates

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Development and future directions for the Joint United Nations Programme on HIV/AIDS estimates

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