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Introduction The Case Surveillance and Vital Registration (CSAVR) model within Spectrum estimates HIV incidence trends from surveillance data on numbers of new HIV diagnoses and HIV‐related deaths. This article describes developments of the CSAVR tool to more flexibly model diagnosis rates over time, estimate incidence patterns by sex and age group and by key population group. Methods We modelled HIV diagnosis rate trends as a mixture of three factors, including temporal and opportunistic infection components. The tool was expanded to estimate incidence rate ratios by sex and age for countries with disaggregated reporting of new HIV diagnoses and AIDS deaths, and to account for information on key populations such as men who have sex with men (MSM), males who inject drugs (MWID), female sex workers (FSW) and females who inject drugs (FWID). We used a Bayesian framework to calibrate the tool in 71 high‐income or low‐HIV burden countries. Results Across countries, an estimated median 89% (interquartile range [IQR]: 78%–96%) of HIV‐positive adults knew their status in 2019. Mean CD4 counts at diagnosis were stable over time, with a median of 456 cells/μl (IQR: 391–508) across countries in 2019. In European countries reporting new HIV diagnoses among key populations, median estimated proportions of males that are MSM and MWID was 1.3% (IQR: 0.9%–2.0%) and 0.56% (IQR: 0.51%–0.64%), respectively. The median estimated proportions of females that are FSW and FWID were 0.36% (IQR: 0.27%–0.45%) and 0.14 (IQR: 0.13%–0.15%), respectively. HIV incidence per 100 person‐years increased among MSM, with median estimates reaching 0.43 (IQR: 0.29–1.73) in 2019, but remained stable in MWID, FSW and FWID, at around 0.12 (IQR: 0.04–1.9), 0.09 (IQR: 0.06–0.69) and 0.13% (IQR: 0.08%–0.91%) in 2019, respectively. Knowledge of HIV status among HIV‐positive adults gradually increased since the early 1990s to exceed 75% in more than 75% of countries in 2019 among each key population. Conclusions CSAVR offers an approach to using routine surveillance and vital registration data to estimate and project trends in both HIV incidence and knowledge of HIV status.

Background In line with the Joint United Nations Programme on HIV/AIDS (UNAIDS) 90-90-90 target, Norway aims for at least 90% of people living with HIV (PLHIV) to know their HIV-status. We produced current estimates of the number of PLHIV and undiagnosed population in Norway, overall and for six key subpopulations: Norwegian-born men who have sex with men (MSM), migrant MSM, Norwegian-born heterosexuals, migrant Sub-Saharan Africa (SSA)-born heterosexuals, migrant non-SSA-born heterosexuals and people who inject drugs. Methods We used the European Centre for Disease Prevention and Control (ECDC) HIV Modelling Tool on Norwegian HIV surveillance data through 2018 to estimate incidence, time from infection to diagnosis, PLHIV, and the number and proportion undiagnosed. As surveillance data on CD4 count at diagnosis were not collected in Norway, we ran two models; using default model CD4 assumptions, or a proxy for CD4 distribution based on Danish national surveillance data. We also generated alternative overall PLHIV estimates using the Spectrum AIDS Impact Model, to compare with those obtained from the ECDC tool. Results Estimates of the overall number of PLHIV in 2018 using different modelling approaches aligned at approximately 5000. In both ECDC models, the overall number undiagnosed decreased continuously from 2008. The proportion undiagnosed in 2018 was lower using default model CD4 assumptions (7.1% [95%CI: 5.3–8.9%]), than the Danish CD4 proxy (10.2% [8.3–12.1%]). This difference was driven by results for heterosexual migrants. Estimates for Norwegian-born MSM, migrant MSM and Norwegian-born heterosexuals were similar in both models. In these three subpopulations, incidence in 2018 was Conclusions Results allow cautious confidence in concluding that Norway has achieved the first UNAIDS 90–90-90 target, and clearly highlight the success of prevention strategies among MSM. Estimates for subpopulations strongly influenced by migration remain less clear, and future modelling should appropriately account for all-cause mortality and out-migration, and adjust for time of in-migration.

The Joint United Nations Programme on HIV/AIDS (UNAIDS) publishes estimates of the HIV epidemic every year [1]. For 2016, estimates are available for 160 countries representing 98% of the global population. These estimates are produced by countries with guidance from UNAIDS. The methods used in this process continue to evolve over time under the stewardship of the UNAIDS Reference Group on Estimates, Modelling and Projections [2]. In 2014, the WHO convened the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) Working Group with the aim to define and promote good practice in reporting global health estimates [3]. The GATHER Statement is the outcome produced by this group. It defines a list of reporting requirements to allow for the accurate interpretation, and facilitate the appropriate use, of global health estimates [4]. UNAIDS fully endorses and supports the GATHER Statement. The current special supplement, which details the methods used to produce the 2016 UNAIDS estimates, further supports the routine publication of data sources and methods used as part of an open and transparent process. It provides updates of the evolving understanding of the data on which the estimates are based, the methods used to derive the estimates, justification of changes in these methods, and the sources of new data available to inform these modifications. It follows a series of such collections [5–10] which have documented and described the evolving methods used to produce the UNAIDS Global AIDS estimates since 2004.

Background: Joint United Nations Programme on HIV/AIDS (UNAIDS) and Murray et al. have both produced sets of estimates for worldwide HIV incidence, prevalence and mortality. Understanding differences in these estimates can strengthen the interpretation of each. Methods: We describe differences in the two sets of estimates. Where possible, we have drawn on additional published data to which estimates can be compared. Findings: UNAIDS estimates that there were 6 million more people living with HIV (PLHIV) in 2013 (35 million) compared with the Murray et al. estimates (29 million). Murray et al. estimate that new infections and AIDS deaths have declined more gradually than does UNAIDS. Just under one third of the difference in PLHIV is in Africa, where Murray et al. have relied more on estimates of adult mortality trends than on data on survival times. Another third of the difference is in North America, Europe, Central Asia and Australasia. Here Murray et al. estimates of new infections are substantially lower than the number of new HIV/AIDS diagnoses reported by countries, whereas published UNAIDS estimate tend to be greater. The remaining differences are in Latin America and Asia where the data upon which the UNAIDS methods currently rely are more sparse, whereas the mortality data leveraged by Murray et al. may be stronger. In this region, however, anomalies appear to exist between the both sets of estimates and other data. Interpretation: Both estimates indicate that approximately 30 million PLHIV and that antiretroviral therapy has driven large reductions in mortality. Both estimates are useful but show instructive discrepancies with additional data sources. We find little evidence to suggest that either set of estimates can be considered systematically more accurate. Further work should seek to build estimates on as wide a base of data as possible.

Every year, the Joint United Nations Programme on HIV/AIDS (UNAIDS) produces estimates that chart the magnitude of the worldwide HIV epidemic, its impact and the progress taken to curb its spread and reduce its impact. The UNAIDS Reference Group on Estimates, Modelling and Projections [1] exists to review and develop the methods supported by UNAIDS to generate national estimates of HIV and related indicators. UNAIDS and its Reference Group routinely produce special editions of scientific journals to document the methods used to produce the UNAIDS estimates and to provide updates on the directions the methods are expected to take in the future [2–6]. The work of the Reference Group is two-fold – to continually calibrate, update and validate the methods, while simultaneously extending the methods to take advantage of new data sources, and to adapt approaches so as to maximise their usefulness to country programme planning and in evaluation. The articles in this supplement fall into both of these categories. Two articles document recent improvements to the estimation and projection models that are used, and another compares the UNAIDS estimates with other sets of published estimates and data. Several other articles show how the methods have expanded to accommodate the need to better understand data requirements for young children and older people; how estimates of key indicators are produced at finer spatial scales; and finally, how methods can and will synthesise newly available data into estimates. We address each of these topics in turn below. Together, these updates, modifications and new methods aim to provide more accurate knowledge and better understanding of HIV epidemics which should inform and assist programme planners and decision makers, as well as scientists, researchers and the wider public.

Background Truck drivers have unique health needs, and by virtue of their continuous travel, experience difficulty in accessing healthcare. Currently, planning for effective care is hindered by lack of knowledge about their health needs and about the impact of on-going programmes on this population’s health outcomes. We reviewed healthcare programmes implemented for sub-Saharan African truck drivers, assessed the evaluation methods, and examined impact on health outcomes. Methods We searched scientific and institutional databases, and online search engines to include all publications describing a healthcare programme in sub-Saharan Africa where the main clients were truck drivers. We consulted experts and organisations working with mobile populations to identify unpublished reports. Forest plots of impact and outcome indicators with unadjusted risk ratios and 95% confidence intervals were created to map the impact of these programmes. We performed a subgroup analysis by type of indicator using a random-effects model to assess between-study heterogeneity. We conducted a sensitivity analysis to examine both the summary effect estimate chosen (risk difference vs. risk ratio) and model to summarise results (fixed vs. random effects). Results Thirty-seven publications describing 22 healthcare programmes across 30 countries were included from 5,599 unique records. All programmes had an HIV-prevention focus with only three expanding their services to cover conditions other primary healthcare services. Twelve programmes were evaluated and most evaluations assessed changes in input, output, and outcome indicators. Absence of comparison groups, preventing attribution of the effect observed to the programme and lack of biologically confirmed outcomes were the main limitations. Four programmes estimated a quantitative change in HIV prevalence or reported STI incidence, with mixed results, and one provided anecdotal evidence of changes in AIDS-related mortality and social norms. Most programmes showed positive changes in risk behaviours, knowledge, and attitudes. Our conclusions were robust in sensitivity analyses. Conclusion Diverse healthcare programmes tailored to the needs of truck drivers implemented in 30 sub-Saharan African countries have shown potential benefits. However, information gaps about availability of services and their effects impede further planning and implementation of effective healthcare programmes for truck drivers.

The modes of transmission model has been widely used to help decision-makers target measures for preventing human immunodeficiency virus (HIV) infection. The model estimates the number of new HIV infections that will be acquired over the ensuing year by individuals in identified risk groups in a given population using data on the size of the groups, the aggregate risk behaviour in each group, the current prevalence of HIV infection among the sexual or injecting drug partners of individuals in each group, and the probability of HIV transmission associated with different risk behaviours. The strength of the model is its simplicity, which enables data from a variety of sources to be synthesized, resulting in better characterization of HIV epidemics in some settings. However, concerns have been raised about the assumptions underlying the model structure, about limitations in the data available for deriving input parameters and about interpretation and communication of the model results. The aim of this review was to improve the use of the model by reassessing its paradigm, structure and data requirements. We identified key questions to be asked when conducting an analysis and when interpreting the model results and make recommendations for strengthening the model’s application in the future.

Gabriela Gomez and colleagues systematically review cost-effectiveness modeling studies of pre-exposure prophylaxis (PrEP) for preventing HIV transmission and identify the main considerations to address when considering the introduction of PrEP to HIV prevention programs., Background Cost-effectiveness studies inform resource allocation, strategy, and policy development. However, due to their complexity, dependence on assumptions made, and inherent uncertainty, synthesising, and generalising the results can be difficult. We assess cost-effectiveness models evaluating expected health gains and costs of HIV pre-exposure prophylaxis (PrEP) interventions. Methods and Findings We conducted a systematic review comparing epidemiological and economic assumptions of cost-effectiveness studies using various modelling approaches. The following databases were searched (until January 2013): PubMed/Medline, ISI Web of Knowledge, Centre for Reviews and Dissemination databases, EconLIT, and region-specific databases. We included modelling studies reporting both cost and expected impact of a PrEP roll-out. We explored five issues: prioritisation strategies, adherence, behaviour change, toxicity, and resistance. Of 961 studies retrieved, 13 were included. Studies modelled populations (heterosexual couples, men who have sex with men, people who inject drugs) in generalised and concentrated epidemics from Southern Africa (including South Africa), Ukraine, USA, and Peru. PrEP was found to have the potential to be a cost-effective addition to HIV prevention programmes in specific settings. The extent of the impact of PrEP depended upon assumptions made concerning cost, epidemic context, programme coverage, prioritisation strategies, and individual-level adherence. Delivery of PrEP to key populations at highest risk of HIV exposure appears the most cost-effective strategy. Limitations of this review include the partial geographical coverage, our inability to perform a meta-analysis, and the paucity of information available exploring trade-offs between early treatment and PrEP. Conclusions Our review identifies the main considerations to address in assessing cost-effectiveness analyses of a PrEP intervention—cost, epidemic context, individual adherence level, PrEP programme coverage, and prioritisation strategy. Cost-effectiveness studies indicating where resources can be applied for greatest impact are essential to guide resource allocation decisions; however, the results of such analyses must be considered within the context of the underlying assumptions made. Please see later in the article for the Editors' Summary, Editors' Summary Background Every year approximately 2.5 million people are infected with HIV, the virus that causes AIDS. Behavioral strategies like condom use and reduction of sexual partners have been the hallmarks of HIV prevention efforts. However, biological prevention measures have also recently been shown to be effective. These include male circumcision, treatment as prevention (treating HIV-infected people with antiretroviral drugs to reduce transmission), and pre-exposure prophylaxis (PrEP), where people not infected with HIV take antiretroviral drugs to reduce the probability of transmission. Strategies such as PrEP may be viable prevention measure for couples in long-term relationships where one partner is HIV-positive and the other is HIV-negative (HIV serodiscordant couples) or groups at higher risk of HIV infection, such as men who have sex with men, and injection drug users. Why Was This Study Done? The findings from recent clinical trials that demonstrate PrEP can reduce HIV transmission have led to important policy discussions and in the US, Southern Africa, and the UK new clinical guidelines have been developed on the use of PrEP for the prevention of HIV infection. For those countries that are considering whether to introduce PrEP into HIV prevention programs, national policy and decision makers need to determine potential costs and health outcomes. Cost-effectiveness models—mathematical models that simulate cost and health effects of different interventions—can help inform such decisions. However, the cost-effectiveness estimates that could provide guidance for PrEP programs are dependent on, and limited by, the assumptions included in the models, which can make their findings difficult to generalize. A systematic comparison of published cost-effectiveness models of HIV PrEP interventions would be useful for policy makers who are considering introducing PrEP intervention programs. What Did the Researchers Do and Find? The researchers performed a systematic review to identify published cost-effectiveness models that evaluated the health gains and costs of HIV PrEP interventions. Systematic reviews attempt to identify, appraise, and synthesize all the empirical evidence that meets pre-specified eligibility criteria to answer a given research question by using explicit methods aimed at minimizing bias. By searching databases the authors identified 13 published studies that evaluated the impact of PrEP in different populations (heterosexual couples, men who have sex with men, and injection drug users) in different geographic settings, which included Southern Africa, Ukraine, US, and Peru. The authors identified seven studies that assessed the introduction of PrEP into generalized HIV epidemics in Southern Africa. These studies suggest that PrEP may be a cost effective intervention to prevent heterosexual transmission. However, the authors note that funding PrEP while other cost-effective HIV prevention methods are underfunded in this setting may have high opportunity costs. The authors identified five studies where PrEP was introduced for concentrated epidemics among men who have sex with men (four studies in the US and one in Peru). These studies suggest that PrEP may have a substantial impact on the HIV epidemic but may not be affordable at current drug prices. The authors also identified a single study that modeled the introduction of PrEP for people who inject drugs in the Ukraine, which found PrEP not to be cost effective. In all settings the price of antiretroviral drugs was found to be a limiting factor in terms of affordability of PrEP programs. Behavioral changes and adherence to PrEP were estimated to have potentially significant impacts on program effectiveness but the emergence of drug resistance or PrEP-related toxicity did not significantly affect cost-effectiveness estimates. Several PrEP prioritization strategies were explored in included studies and delivering PrEP to populations at highest risk of HIV exposure was shown to improve cost-effectiveness estimates. However, the extra costs of identifying and engaging with high-risk populations were not taken into consideration. The authors note that the geographic coverage of identified studies was limited and that the findings are very dependent on the setting which limits generalizability. What Do these Findings Mean? These findings suggest that PrEP could be a cost-effective tool to reduce new HIV infections in some settings. However, the cost-effectiveness of PrEP is dependent upon cost, the epidemic context, program coverage and prioritization strategies, participants' adherence to the drug regimen, and PrEP efficacy estimates. These findings will aid decision makers quantify and compare the reductions in HIV incidence that could be achieved by implementing a PrEP program. Additional Information Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001401. The US National Institute of Allergy and Infectious Diseases has information on HIV/AIDS aidsmap provides basic information about HIV/AIDS, summaries of recent research findings on HIV care and treatment, and has a section on PrEP Information is available from Avert, an international AIDS charity, on many aspects of HIV/AIDS, including HIV prevention AVAC Global Advocacy for HIV Prevention provides information on HIV prevention, including PrEP The US Centers for Disease Control and Prevention also has information on PrEP The World Health Organization has a page on its WHO-CHOICE criteria for cost-effectiveness

As the global HIV/AIDS pandemic nears the end of its third decade, the challenges of efficient mobilisation of funds and management of resources are increasingly prominent. The aids2031 project modelled long-term funding needs for HIV/AIDS in developing countries with a range of scenarios and substantial variation in costs: ranging from US$397 to $722 billion globally between 2009 and 2031, depending on policy choices adopted by governments and donors. We examine what these figures mean for individual developing countries, and estimate the proportion of HIV/AIDS funding that they and donors will provide. Scenarios for expanded HIV/AIDS prevention, treatment, and mitigation were analysed for 15 representative countries. We suggest that countries will move in increasingly divergent directions over the next 20 years; middle-income countries with a low burden of HIV/AIDS will gradually be able to take on the modest costs of their HIV/AIDS response, whereas low-income countries with a high burden of disease will remain reliant upon external support for their rapidly expanding costs. A small but important group of middle-income countries with a high prevalence of HIV/AIDS (eg, South Africa) form a third category, in which rapid scale-up in the short term, matched by outside funds, could be phased down within 10 years assuming strategic investments are made for prevention and efficiency gains are made in treatment.