Secondary Logo

Journal Logo

Editorial

HIV estimates through 2018

data for decision-making

Mahy, Mary; Marsh, Kimberly; Sabin, Keith; Wanyeki, Ian; Daher, Juliana; Ghys, Peter D.

Author Information
doi: 10.1097/QAD.0000000000002321
  • Open

Abstract

Background

The HIV epidemic has affected populations around the world. Ministers of health, ministers of finance, donors and programme managers require high-quality data to determine how to respond and fund the HIV response [1]. Critical to those decisions is sound epidemiological data on new HIV infections and AIDS-related deaths. Global and country leaders have also set ambitious targets that, if met, will reduce the impact of the epidemic on future populations and end AIDS by 2030. Specific targets were set within the United Nations Political Declaration on HIV/AIDS in 2016 to reduce new HIV infections and AIDS-related deaths by 75% between 2010 and 2020 and by 95% between 2010 and 2030 [2]. Countries agreed to report on their progress towards the Sustainable Development Goals using HIV incidence [3].

To understand progress towards these targets and plan the response to the HIV epidemic, countries require measures of new HIV infections, people living with HIV (PLHIV), and AIDS-related deaths. UNAIDS and partners work with countries to improve capacity to measure these indicators. Efforts to develop surveillance systems include developing and producing guidelines on surveillance, moving countries towards sustainable routine data systems including case surveillance and using routine testing of pregnant women for surveillance, estimating key population size estimates, and considering how and when to implement surveys.

However, there are few reliable approaches to derive these indicators. Most measures of HIV incidence and mortality have biases or limited representativeness [4–6]. Cross sectional population-based surveys can be used to estimate HIV prevalence and incidence (and efforts are underway to incorporate mortality) in countries where HIV prevalence is sufficiently high to capture this relatively rare event with a reasonably large, and affordable, sample size. However, due to the high costs and human resources required for surveys, they are only conducted every 3–5 years, usually with significant donor contributions, making it difficult to regularly report on progress or trends over time. These surveys are also usually not sufficiently powered to provide credible estimates of HIV incidence by age, sex or at lower geographic levels [7]. Mortality can be captured through vital registration but the cause of death in these data are often flawed, especially for stigmatizing diseases such as HIV [8,9]. Other innovative efforts to measure mortality such as mortuary studies and postcensus surveys have been developed but are not conducted routinely [10,11].

In the absence of high-quality measures, models, informed by multiple data sources and well founded assumptions, can produce robust estimates of HIV incidence, prevalence and mortality in a country [12]. Even in middle-income and high-income countries with reliable case surveillance and vital registration systems modelled estimates are needed to know the number of PLHIV who have not been diagnosed and to quantify the delay between new infection and diagnosis [13].

UNAIDS and partners work with country teams to develop modelled HIV estimates using Spectrum software [14]. The country teams, including national epidemiologists, programme managers, UNAIDS country staff and other partners, ensure the country has ownership of the estimates and can use the modelling software for their own purposes of strategic planning and impact monitoring and for donor requests and reporting [15]. The estimates are used to measure success in operationalizing national HIV plans and adjusting those plans as needed.

Since 1998 UNAIDS has reported on the status of the HIV epidemic using modelled estimates in its flagship reports [1,16–18] and on its public database (aidsinfo.unaids.org). This article is the first journal article describing the full set of global and regional UNAIDS HIV estimates, although articles describing country specific estimates [19] and specific populations [20,21] have been published previously. The objective of this article is to provide an overview of the global and regional HIV epidemic and trends through 2018 and describe the use of those data for decision-making.

Methods

National HIV estimates are produced on an annual basis by country teams supported by UNAIDS staff and partners. In 2019, UNAIDS facilitated 12 regional workshops building the capacity of approximately 450 national officers, as well as United Nations and bilateral development partners’ strategic information staff. At the workshops participants develop expertise in the use of Spectrum to create rigorous, country-owned estimates. The country-developed Spectrum files are reviewed by UNAIDS headquarters and regional staff for quality assurance. Once completed the estimates are approved by authorities in countries before they are compiled for release in UNAIDS’ reports and publicly-accessible database. (The list of contributors to those national Spectrum files is available from UNAIDS upon request.) More information on the process of developing estimates is available elsewhere [22].

The UNAIDS Reference Group on Estimates, Modeling and Projections provides transparent guidance on the software ensuring the best science is used to estimate the epidemic, inform model assumptions, and use the latest statistical and mathematical modelling approaches. The group's meeting reports are available on the Reference Group website (www.epidem.org).

The methods used in the 2019 round of HIV estimates are described in other articles in this supplement. Summaries of the methodological developments made to the models over the course of the 15 years of development have been published biennially in peer-reviewed journals since 2004 [23–29]. In brief country teams used Spectrum software that was prepopulated with demographic data from the United Nations Population Division, World Population Prospects 2017 [30]. These underlying demographic data are updated as the United Nations Population Division releases new population estimates. Country teams can also modify the demographic data if they have evidence from a very recent census or survey that is not included in the World Population Prospects. In addition, country teams can create subnational Spectrum files (one file for each province), depending on the availability of subnational population, surveillance and programmatic data.

Trends in HIV incidence are calculated through different options depending on the data available. In most generalized epidemic countries, HIV prevalence data from nationally representative household surveys, and antenatal clinic attendees are used to estimate trends in HIV prevalence over time [31]. In concentrated epidemics surveillance data on HIV prevalence from key populations, integrated behavioural and biological surveys, and key population size estimates are used to determine prevalence trends over time. These prevalence trends are transformed into incidence based on country data on how many people are receiving antiretroviral therapy (ART) and region-specific assumptions about survival on and off ART [32]. In 13 Asian countries the AIDS Epidemic Model (AEM, formerly referred to as the Asian Epidemic Model) is used to estimate the incidence curve. The AEM model uses data on behaviours and transmission probabilities between different subpopulations to estimate an incidence curve [33]. In South Africa a country-specific model, Thembisa, is used to estimate the incidence trends incorporating data from antenatal clinic sentinel surveillance, household surveys, programme data and behavioural data [34]. Finally, in countries with good-quality case surveillance and AIDS mortality from vital registration data, the incidence curve is informed by a model using new diagnoses and assumptions about time from infection to diagnosis, cumulative case surveillance and AIDS deaths [35]. In 2019, 63 countries used the case surveillance modelling method or the European Center for Disease Control methods to determine incidence trends [36].

The incidence estimates generated with these different options are then used in the Spectrum AIDS Impact Module where they are distributed by age and sex based on household survey data, if available, or regional-specific and epidemic-specific assumptions for the remaining countries. Using the incidence patterns the population is then progressed over time from infection to treatment (or lack of treatment) to death. Additional programmatic data that are used in the model include the number of men, women and children receiving ART and the number of pregnant women receiving antiretroviral medicines to prevent vertical transmission. Spectrum estimates the number of child infections based on a child model that incorporates estimates of fertility among women living with HIV, antiretroviral regimens received by those women, breastfeeding duration and assumptions about transmission and survival of those children.

Indicators produced in Spectrum are available by 5-year age group and sex. In addition to incidence, mortality and prevalence the software package also produces estimates of orphan-hood due to HIV, mother-to-child transmission rate, births to women living with HIV, deaths due to all causes among PLHIV, estimates of CD4+ distribution among those not on ART, ART status at death, population on ART by age, among other indicators.

Estimates for 2019 were produced for the years 1970 through 2018. Each indicator is estimated with an uncertainty range that reflects the surveillance data used in the model as well as the uncertainty around the parameters used in Spectrum. Figure 1 shows the model structure of the AIDS Impact module.

Fig. 1
Fig. 1:
Model structure of AIDS impact module in Spectrum.

Estimates teams from 151 countries developed their own estimates while a further 19 country estimates were produced by UNAIDS using data abstracted from publicly available sources. Since 2015, 20 countries have created subnational estimates, allowing more granular understanding of the epidemic in those countries [37]. In 2019, 20 sub-Saharan African countries disaggregated their national estimates to the district-level (the second lower subnational level) for even finer planning. Countries used different methods to disaggregate the estimates including the HIVE model, the distribution of prevalence among women attending antenatal clinics, and small area estimates [38,39].

Lists of the countries included in each of the UNAIDS regions are available at www.unaids.org.

Results

Globally there were an estimated 37.9 million (32.7–44.0 million) PLHIV in 2018. This represents an increase from 24.9 million (21.5–28.9 million) in 2000 and 31.7 million (27.3–36.8 million) in 2010. This increase is due in part to the success of treatment programmes increasing survival among PLHIV. Over 54% of PLHIV reside in Eastern and Southern Africa and a further 15% reside in Asia and the Pacific. Over 50% of PLHIV reside in eight countries (Fig. 2). Just over half (52%) of PLHIV are women. An estimated 1.7 million (1.3–2.2 million) children under age 15 were living with HIV in 2018. Almost two-thirds (63%) of children living with HIV are in sub-Saharan Africa. This number of children living with HIV is declining over time as fewer children are becoming infected due to successful prevention of mother-to-child transmission programmes and older children living with HIV are aging into the adult age group. (Country level data by age and sex are available on UNAIDS website at aidsinfo.unaids.org.)

Fig. 2
Fig. 2:
Distribution of people living with HIV in the 10 highest burden countries, 2018.

In the 2016 Political Declaration on HIV/AIDS countries committed to a 75% reduction in new HIV infections and a 75% reduction in AIDS-related deaths between 2010 and 2020 [2]. To be on track to reach the 75% decline regions and countries should have reached 60% decline by 2018 (assuming a linear decline).

An estimated 1.7 million (1.4–2.3 million) people were newly infected with HIV in 2018 down from 2.1 million (1.6–2.7 million) new infections in 2010. New HIV infections declined by 16% globally between 2010 and 2018, and no region was on track to reach the 75% reduction. In three regions (Eastern Europe and Central Asia, the Middle East and North Africa and Latin America) estimated new HIV infections increased between 2010 and 2018 (Table 1). Among countries the declines in new infections in Cambodia, Rwanda and Viet Nam were over 60% between 2010 and 2018 among all age groups. Globally, new HIV infections among women ages 15 years and older declined by 17% compared with 9% among men ages 15 years and older. While new infections among children decreased by 41%.

Table 1
Table 1:
New HIV infections, AIDS-related deaths and people living with HIV, globally and by region 2000, 2010 and 2018 and progress towards targets of reducing new HIV infections and AIDS-related deaths by 75% by 2020.

AIDS deaths have seen sharper declines with a 33% decline since 2010 globally reflecting the scale-up of antiretroviral therapy. The greatest declines in AIDS deaths occurred in Eastern and Southern Africa with a 44% decline since 2010 while AIDS deaths in Western and Central Africa only declined by 29% over the same period (Table 1). Burundi, Democratic Republic of the Congo, Dominican Republic and Portugal showed declines that put them on track to reach the 2020 target for AIDS deaths. An estimated 770 000 (570 000–1.1 million) people died of AIDS-related deaths in 2018. Declines in AIDS-related deaths varied by sex. Globally, among adult women 15 years and above AIDS deaths declined by 40% compared with 21% among men of the same age group. AIDS deaths among children declined by 51% globally, some of this decline is due to children aging into the adult cohort.

In 2019, two countries made substantial changes to their estimates based on newly available evidence. These changes had important impacts on the regional estimates. China created Spectrum estimates for each of their 34 regions in 2018. The estimated number of PLHIV in the country was about 500 000 higher than what had previously been estimated by UNAIDS based on publicly available information [40]. In 2018, Nigeria conducted a statistically well powered household survey to estimate prevalence in each of the 36 states and the capital territory. The survey estimated HIV prevalence was considerably lower than previously published survey data. When the new survey data were included and the previous surveys were excluded from Spectrum the estimated number of PLHIV in Nigeria was adjusted from 3.1 million [2.1 - 4.4 million] to 1.9 million [1.4 - 2.6 million] [41]. Changes in the PLHIV in China and Nigeria affect both estimates of programme achievement such as antiretroviral therapy coverage and declines in incidence and AIDS-related mortality. The adjustments to the estimates for both China and Nigeria were based on new information, resulting in a revised full set of historical HIV estimates.

Discussion

The HIV estimates developed in 2019 suggest that, based on progress through 2018, no region or country had reached the 2020 targets of a 75% reduction in new HIV infections or AIDS-related deaths from 2010 estimates. In addition, no region had reached a 60% decline by 2018, the decrease required to be ‘on track’ to reach a 75% decline by 2020. Given the available evidence on the effectiveness of HIV prevention and treatment, the lack of global and regional progress is alarming. Especially troubling is that more than 30 years into the epidemic three of eight regions have increasing HIV incidence and two have increasing mortality.

Coverage of antiretroviral therapy scaled-up quickly between 2005 and 2018, however the year on year increases in recent years are stagnating [42]. The impact of this stagnation will slow the decline in AIDS deaths as well as new HIV infections. The estimated number of PLHIV continues to increase reflecting in part the success of reaching more people with antiretroviral therapy and the subsequent reductions in AIDS-related deaths.

Country-specific progress within regions provides more specific data on successes and where more effort is needed. Three countries were on track to reach the decline in new HIV infections and four countries were on track to reach the reduction in AIDS-related deaths by 2018. These countries reached a 60% decline in new infections or AIDS-related deaths respectively.

In addition to the UNAIDS HIV estimates, the Institute for Health Metrics and Evaluation, based at the University of Washington, USA, has also published estimates of regional and global HIV incidence and mortality [43]. These estimates have used publicly available UNAIDS Spectrum files from previous rounds. The Spectrum files are modified to fit within an envelope of total deaths as determined by the Global Burden of Disease estimates. The results are largely consistent with the UNAIDS estimates from previous rounds but do not reflect the latest surveillance or antiretroviral therapy data. They also fail to capture the most recent modifications implemented by the country teams that produce, analyze and fully understand the data going into the models.

As a result of this effort, 170 countries (with populations of at least 250 000) have the capacity to produce, or access, robust estimates and measures of progress towards the Political Declaration on HIV targets. Programme managers and policy makers in these countries use this information, in addition to other programmatic data, to plan and respond to their HIV epidemic. Many countries further use the Spectrum model to determine interventions that are the most effective and efficient to meet national targets [44,45]. PEPFAR's priority countries use subnational estimates of PLHIV derived from Spectrum to decide on geographical focus and to inform programme targets [15].

At global and regional level the UNAIDS HIV estimates are used to make critical decisions about the HIV response. Reporting on the slow decline in HIV incidence spawned the UNAIDS Prevention Gap report and the corresponding Global Prevention Coalition and multiple country prevention plans to refocus their HIV response to prevention [17,45,46]. Also, the estimates have been used as evidence for the US Government to continue funding PEPFAR, the largest donor in the HIV response [47,48]. The Global Fund to fight AIDS, Tuberculosis and Malaria relies on the UNAIDS estimates to determine eligibility for funding and determine impact [49].

Recent population-based surveys have started estimating national HIV incidence [50] providing a useful comparison with the model-derived incidence. UNAIDS estimates are consistent with national HIV incidence collected through these cross-sectional surveys. If the survey incidence data are available at the same subnational level as the model, those data can be included in the curve fitting process in Spectrum. In seven of eleven countries with survey-derived incidence, the information was not available at the required subnational level to allow for direct inclusion in Spectrum. In those countries the aggregated national estimate of incidence from Spectrum was within the confidence interval of the survey (Fig. 3).

Fig. 3
Fig. 3:
Adult (15–49 years) HIV incidence, UNAIDS estimates and surveys, 2015–2018.Countries noted with an asterisk have included the survey incidence value in the model. The year of the survey is provided in parentheses. The UNAIDS estimate is for the survey year. Survey results are based on a recent infection algorithm including limiting antigen avidity assay, viral load, and antiretroviral medicines. The recent Ethiopia PHIA survey is not included because the survey did not include rural areas. Sources for the survey data are: Mozambique: https://www.dhsprogram.com/pubs/pdf/AIS12/AIS12_SE.pdf Cameroon, Tanzania, Uganda, Malawi, Namibia, Zimbabwe, Zambia, Eswatini, Lesotho: https://phia.icap.columbia.edu/ South Africa: https://serve.mg.co.za/content/documents/2018/07/17/7M1RBtUShKFJbN3NL1Wr_HSRC_HIV_Survey_Summary_2018.pdf.

Only a few countries have conducted household surveys with the statistical power to measure HIV prevalence among children. Prevalence among children is not an input to Spectrum but can be used to validate the modelled estimates. An analysis of the 2018 UNAIDS estimates found prevalence among children 0–14 years was within the confidence intervals of the household surveys in five of six countries [51]. The 2019 estimates, using an improved methodology, remain within the survey confidence intervals for ten of eleven countries with available data (data not shown).

A number of limitations exist within the estimates; four important limitations are mentioned here.

First, the reliance on programme data leaves the estimates vulnerable to weak data systems, which potentially bias results. For example, estimating AIDS deaths requires an accurate number of people receiving antiretroviral therapy. These data can be over counted if clinics are not able to identify and deduplicate individuals recorded to be on treatment at multiple clinics. In the past 2 years countries, with support from US Government, WHO, UNICEF, Global Fund to fight AIDS, Tuberculosis and Malaria, and UNAIDS, have made considerable effort to improve the recording of the number of people on antiretroviral therapy. Trends in recent new infections rely on prevalence data from routine antenatal clinic testing. If those data are biased because women with known positive HIV status are not captured when calculating prevalence, or women found to be negative at initial antenatal care visit are retested later in the pregnancy, the derived incidence trends might be biased. While some limitations of the models are reflected in the uncertainty bounds the measurement biases and the uncertainty caused by these biases are not easily quantified and are thus not included [52].

Second, in concentrated epidemics the surveillance systems for key hard-to-reach populations are particularly challenging and the surveillance data are often not comparable over time due to changing survey and sampling methods [53,54]. The sizes of key populations, a critical input to the Spectrum model for concentrated epidemics, are difficult to estimates accurately which can lead to important under or over estimation of HIV epidemics in concentrated epidemics [55].

Third, although HIV prevalence among children appears to be reasonably robust in generalized epidemics, estimating the paediatric HIV epidemic in concentrated epidemics remains a challenge because no robust measures exist of fertility among key populations living with HIV. Other limitations in all epidemics include potentially weak assumptions about AIDS mortality among children not receiving ART due to the lack of evidence, which, appropriately, will not be available in the future.

Finally, additional research is needed to improve the assumptions about time from seroconversion to diagnosis when using case surveillance to estimate incidence.

The UNAIDS 2019 estimates provide evidence that the world is off track from reaching established targets for reductions in new HIV infections and AIDS-related deaths globally and in all regions. The UNAIDS estimates provide countries with the ability to measure progress towards the 2016 Political Declaration goals and the Sustainable Development Goal target 3.3.1. The estimates continue to be a cornerstone for Global Fund impact measurement and for demonstrating the benefits of the US Government's Emergency Plan for AIDS Relief. The process of developing UNAIDS estimates builds capacity in countries to understand epidemics and to refine and focus services to PLHIV to reduce new HIV infections and AIDS-related deaths.

Acknowledgements

The authors acknowledge the work of each of the country teams that develop the HIV estimates and the UNAIDS country and regional advisers who support the process of developing the estimates.

Conflicts of interest

There are no conflicts of interest.

References

1. UNAIDS. Miles to go: closing gaps, breaking barriers, righting injustices. Geneva: Joint United Nations Programme on HIV/AIDS; 2018.
2. United Nations. Political declaration on HIV and AIDS: on the fast-track to accelerate the fight against HIV and to end the AIDS epidemic by 2030. New York: United Nations; 2016.
3. United Nations. Sustainable development goals. New York: United Nations; 2015.
4. Rehle T, Johnson L, Hallett T, Mahy M, Kim A, Odido H, et al. A comparison of South African national HIV incidence estimates: a critical appraisal of different methods. PLoS One 2015; 10:e0133255.
5. Kim AA, Hallett T, Stover J, Gouws E, Musinguzi J, Mureithi PK, et al. Estimating HIV incidence among adults in Kenya and Uganda: a systematic comparison of multiple methods. PLoS One 2011; 6:e17535.
6. UNAIDS/WHO Surveillance working, group. Guidelines for HIV mortality measurement. Geneva: World Health Organization; 2014.
7. UNAIDS/WHO Surveillance working group. Monitoring HIV impact using population-based surveys. Geneva, Switzerland: Joint United Nations Programme on HIV/AIDS (UNAIDS); 2015.
8. Fazito E, Cuchi P, Fat DM, Ghys PD, Pereira MG, Vasconcelos AM, et al. Identifying and quantifying misclassified and under-reported AIDS deaths in Brazil: a retrospective analysis from 1985 to 2009. Sex Transm Infect 2012; 88: (Suppl 2:i): 86–94.
9. Yudkin PL, Burger EH, Bradshaw D, Groenewald P, Ward AM, Volmink J. Deaths caused by HIV disease under-reported in South Africa. AIDS 2009; 23:1600–1602.
10. Nyagah LM, Young PW, Kim AA, Wamicwe J, Kimani M, Waruiru W, et al. HIV-related deaths in Nairobi, Kenya: results from a HIV mortuary surveillance study, 2015. J Acquir Immune Defic Syndr 2019; 81:18–23.
11. Mazive E. Mozambique: post census mortality survey. Bangkok, Thailand: Second Global HIV Surveillance Meeting 2009; 2009.
12. Garnett GP, Cousens S, Hallett TB, Steketee R, Walker N. Mathematical models in the evaluation of health programmes. Lancet 2011; 378:515–525.
13. Pharris A, Quinten C, Noori T, Amato-Gauci AJ, van Sighem A, Surveillance EHA, et al. Estimating HIV incidence and number of undiagnosed individuals living with HIV in the European Union/European Economic Area, 2015. Euro Surveill 2016; 21: pii=30417.
14. Avenir Health. Spectrum software, AIDS Impact Module. 2019; Glastonbury, CT, USA: Avenir Health, https://avenirhealth.org/software-spectrum.phphttps://avenirhealth.org/software-spectrum.php. [Accessed 20 July 2019].
15. U.S. President's Emergency Plan for AIDS Relief (PEPFAR). PEPFAR 2019 country operational plan guidance for all PEPFAR countries. Washington, DC: PEPFAR; 2019.
16. UNAIDS. The Gap report. Geneva: Joint United Nations Programme on HIV/AIDS; 2014.
17. UNAIDS. The prevention Gap report. Geneva: Switzerland; 2016.
18. UNAIDS. Report on the global HIV/AIDS epidemic. Geneva: UNAIDS and WHO; 1998.
19. Duncan J, Beckford Jarrett S, Harvey K. Using estimation and projection package and spectrum for Jamaica's national HIV estimates and targets. Sex Transm Infect 2010; 86:ii43–ii47.
20. Autenrieth CS, Beck EJ, Stelzle D, Mallouris C, Mahy M, Ghys P. Global and regional trends of people living with HIV aged 50 and over: estimates and projections for 2000–2020. PLoS One 2018; 13:e0207005.
21. Mahy M, Penazzato M, Ciaranello A, Mofenson L, Yianoutsos CT, Davies MA, et al. Improving estimates of children living with HIV from the spectrum AIDS impact model. AIDS 2017; 31: (Suppl 1): S13–S22.
22. Mahy M, Brown T, Stover J, Walker N, Stanecki K, Kirungi W, et al. Producing HIV estimates: from global advocacy to country planning and impact measurement. Glob Health Action 2017; 10:1291169.
23. Ghys PD, Brown T, Grassly NC, Garnett G, Stanecki KA, Stover J, et al. The UNAIDS estimation and projection package: a software package to estimate and project national HIV epidemics. Sex Transm Infect 2004; 80: (Suppl 1:i): 5–9.
24. Ghys PD, Walker N, Garnett GP. Improving analysis of the size and dynamics of AIDS epidemics. Sex Transm Infect 2006; 82: (Suppl 3:iii): 1–2.
25. Ghys PD, Walker N, McFarland W, Miller R, Garnett GP. Improved data, methods and tools for the 2007 HIV and AIDS estimates and projections. Sex Transm Infect 2008; 84: (Suppl 1:i): 1–4.
26. Ghys PD, Garnett GP. The 2009 HIV and AIDS estimates and projections: methods, tools and analyses. Sex Trans Infect 2010; 86:ii1–ii2.
27. Stanecki K, Garnett GP, Ghys PD. Developments in the field of HIV estimates: methods, parameters and trends. Sex Transm Infect 2012; 88: (Suppl 2:i): 1–2.
28. Case KK, Hallett TB, Gregson S, Porter K, Ghys PD. Development and future directions for the Joint United Nations Programme on HIV/AIDS estimates. AIDS 2014; 28: (Suppl 4): S411–S414.
29. Case KK, Gregson S, Mahy M, Ghys PD, Hallett TB. Editorial: methodological developments in the Joint United Nations Programme on HIV/AIDS estimates. AIDS 2017; 31: (Suppl 1): S1–S4.
30. UN Population Division. World population prospects 2017. New York: UN Population Division; 2017.
31. Eaton JW, et al. The estimation and projection package age-structured model (EPP-ASM). AIDS 2019; 33: (Suppl 3): S235–S244.
32. Stover J, Glaubius R, Mofenson L, Dugdale CM, Davies MA, Patten M, et al. Updates to the Spectrum/AIM model for estimating key HIV indicators at national and sub-national levels. AIDS 2019; 33: (Suppl 3): S227–S234.
33. Brown T, Peerapatanapokin W. The Asian Epidemic Model: a process model for exploring HIV policy and programme alternatives in Asia. Sex Transm Infect 2004; 80: (Suppl 1:i): 19–24.
34. Johnson LF, Chiu C, Myer L, Davies MA, Dorrington RE, Bekker LG, et al. Prospects for HIV control in South Africa: a model-based analysis. Glob Health Action 2016; 9:30314.
35. Mahiane G, Marsh K, Glaubius R, Eaton JW. Estimating and Projecting the number of new HIV diagnoses and incidence in Spectrum's Case Surveillance and Vital Registration tool. AIDS 2019; 33: (Suppl 3): S245–S253.
36. van Sighem A, Nakagawa F, De Angelis D, Quinten C, Bezemer D, de Coul EO, et al. Estimating HIV incidence, time to diagnosis, and the undiagnosed HIV epidemic using routine surveillance data. Epidemiology 2015; 26:653–660.
37. Mahy M, Nzima M, Ogungbemi MK, Ogbang DA, Morka MC, Stover J. Redefining the HIV epidemic in Nigeria: from national to state level. AIDS 2014; 28: (Suppl 4): S461–S467.
38. Gutreuter S, Igumbor E, Wabiri N, Desai M, Durand L. Improving estimates of district HIV prevalence and burden in South Africa using small area estimation techniques. PLoS One 2019; 14:e0212445.
39. Stevens O. Next generation tools for subnational HIV strategic information in sub-Saharan Africa. Report and recommendations from a meeting of the UNAIDS Reference Group on Estimates, Modelling, and Projections, Glastonbury, USA - 8-10th May 2019 (http://epidem.org/sites/default/files/reports/Geospatial_report_FINAL_0.pdf).
40. Xinhua. HIV/AIDS rate at low level in China: health authority. Beijing: xinhuanet.com; 2018.
41. National Agency for the Control of AIDS. NAIIS national summary sheet. Abuja: NACA; 2019.
42. Marsh K, et al. Global, regional and country-level 90–90–90 estimates for 2018: monitoring progress towards the 2020 target. AIDS 2019; 33: (Suppl 3): S213–S226.
43. Murray CJ, Ortblad KF, Guinovart C, Lim SS, Wolock TM, Roberts DA, et al. Global, regional, and national incidence and mortality for HIV, tuberculosis, and malaria during 1990–2013: a systematic analysis for the Global Burden of Disease Study. Lancet 2014; 384:1005–1070.
44. Korenromp EL, Gobet B, Fazito E, Lara J, Bollinger L, Stover J. Impact and cost of the HIV/AIDS national strategic plan for mozambique, 2015–2019 – projections with the spectrum/goals model. PLoS One 2015; 10:e0142908.
45. Kenya Ministry of Health. Kenya HIV prevention revolution road map. Nairobi: NACA; 2014.
46. Dehne KL, Dallabetta G, Wilson D, Garnett GP, Laga M, Benomar E, et al. HIV prevention 2020: a framework for delivery and a call for action. Lancet HIV 2016; 3:e323–e332.
47. Heaton LM, Bouey PD, Fu J, Stover J, Fowler TB, Lyerla R, et al. Estimating the impact of the US President's Emergency Plan for AIDS Relief on HIV treatment and prevention programmes in Africa. Sex Transm Infect 2015; 91:615–620.
48. Office of the United States Global AIDS Coordinator, Health Diplomacy; The U.S. President's Emergency Plan for AIDS Relief. 2018 Annual report to congress. Washington, DC: PEPFAR; 2018.
49. Global Fund to fight AIDS Tuberculosis and Malaria. The global fund eligibility policy Geneva. Geneva: GFATM; 2018.
50. ICAP at Columbia University. https://phia.icap.columbia.edu/. 2019. [Accessed 20 July 2019]
51. Saito S, Chung H, Mahy M, Radin AK, Jonnalagadda S, Hakim A, et al. Pediatric 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: (Suppl 2): S134–S141.
52. Stover J, Johnson P, Zaba B, Zwahlen M, Dabis F, Ekpini RE. The Spectrum projection package: improvements in estimating mortality, ART needs, PMTCT impact and uncertainty bounds. Sex Transm Infect 2008; 84: (Suppl 1): i24–i30.
53. Rao A, Schwartz S, Sabin K, Wheeler T, Zhao J, Hargreaves J, et al. HIV-related data among key populations to inform evidence-based responses: protocol of a systematic review. Syst Rev 2018; 7:220.
54. Garcia Calleja JM, Jacobson J, Garg R, Thuy N, Stengaard A, Alonso M, et al. Has the quality of serosurveillance in low- and middle-income countries improved since the last HIV estimates round in 2007? Status and trends through 2009. Sex Transm Infect 2010; 86:ii35–ii42.
55. Sabin K, Zhao J, Garcia Calleja JM, Sheng Y, Arias Garcia S, Reinisch A, et al. Availability and quality of size estimations of female sex workers, men who have sex with men, people who inject drugs and transgender women in low- and middle-income countries. PLoS One 2016; 11:e0155150.
Keywords:

AIDS deaths; estimates; HIV; HIV incidence; mathematical models

Copyright © 2019 Wolters Kluwer Health, Inc.