Reconstruction and future trends of the AIDS epidemic in the United States

Missed Opportunities: Characteristics of People Who Received a Concurrent HIV/AIDS Diagnosis in New York State From 2016 to 2021

BACKGROUND

Late HIV diagnosis is associated with a wide range of negative outcomes. The aim of this study was to identify the characteristics of individuals who received a concurrent diagnosis (CDX) in New York State (NYS) so that more effective interventions can be developed to encourage earlier testing among these populations.

METHODS

The NYS HIV registry was used to identify people who received a CDX from 2016 to 2021. A CDX was a diagnosis that met the criteria for a stage 3 HIV infection within 30 days of the initial HIV diagnosis. Sex at birth, race/ethnicity, transmission risk group, age at diagnosis, region of residence at diagnosis, urbanicity of zip code of diagnosis, and type of diagnosing facility were used as covariates. Bivariate and multivariate risk ratios were calculated to quantify associations between CDX and covariates.

RESULTS

There were 14,866 people newly diagnosed with HIV in NYS from 2016 to 2021, of which 19.0% had a CDX. Those with female sex at birth, history of injection drug use, or history of male-to-male sexual contact/history of injection drug use risk were less likely to have a CDX. Increased age, Asian race/ethnicity, residence outside of New York City, and diagnosis at inpatient facilities or emergency rooms were associated with an increased likelihood of a CDX.

CONCLUSION

Populations with the highest proportions of CDX were ones that made up a small percentage of all new HIV diagnoses and may not be benefiting as much from current HIV prevention efforts. There are complex interactions between many factors including geographic and social characteristics that may lead to delayed diagnostic testing.

Statistical Deconvolution for Inference of Infection Time Series

Accurate measurement of daily infection incidence is crucial to epidemic response. However, delays in symptom onset, testing, and reporting obscure the dynamics of transmission, necessitating methods to remove the effects of stochastic delays from observed data. Existing estimators can be sensitive to model misspecification and censored observations; many analysts have instead used methods that exhibit strong bias. We develop an estimator with a regularization scheme to cope with stochastic delays, which we term the robust incidence deconvolution estimator. We compare the method to existing estimators in a simulation study, measuring accuracy in a variety of experimental conditions. We then use the method to study COVID-19 records in the United States, highlighting its stability in the face of misspecification and right censoring. To implement the robust incidence deconvolution estimator, we release incidental, a ready-to-use R implementation of our estimator that can aid ongoing efforts to monitor the COVID-19 pandemic.

Effect of Adjustment for Case Misclassification and Infection Date Uncertainty on Estimates of COVID-19 Effective Reproduction Number

BACKGROUND

Surveillance data captured during the COVID-19 pandemic may not be optimal to inform a public health response, because it is biased by imperfect test accuracy, differential access to testing, and uncertainty in date of infection.

METHODS

We downloaded COVID-19 time-series surveillance data from the Colorado Department of Public Health & Environment by report and illness onset dates for 9 March 2020 to 30 September 2020. We used existing Bayesian methods to first adjust for misclassification in testing and surveillance, followed by deconvolution of date of infection. We propagated forward uncertainty from each step corresponding to 10,000 posterior time-series of doubly adjusted epidemic curves. The effective reproduction number (Rt), a parameter of principal interest in tracking the pandemic, gauged the impact of the adjustment on inference.

RESULTS

Observed period prevalence was 1.3%; median of the posterior of true (adjusted) prevalence was 1.7% (95% credible interval [CrI]: 1.4%, 1.8%). Sensitivity of surveillance declined over the course of the epidemic from a median of 88.8% (95% CrI: 86.3%, 89.8%) to a median of 60.8% (95% CrI: 60.1%, 62.6%). The mean (minimum, maximum) values of Rt were higher and more variable by report date, 1.12 (0.77, 4.13), compared to those following adjustment, 1.05 (0.89, 1.73). The epidemic curve by report date tended to overestimate Rt early on and be more susceptible to fluctuations in data.

CONCLUSION

Adjusting for epidemic curves based on surveillance data is necessary if estimates of missed cases and the effective reproduction number play a role in management of the COVID-19 pandemic.

Estimating age-specific COVID-19 fatality risk and time to death by comparing population diagnosis and death patterns: Australian data

Background

Mortality is a key component of the natural history of COVID-19 infection. Surveillance data on COVID-19 deaths and case diagnoses are widely available in the public domain, but they are not used to model time to death because they typically do not link diagnosis and death at an individual level. This paper demonstrates that by comparing the unlinked patterns of new diagnoses and deaths over age and time, age-specific mortality and time to death may be estimated using a statistical method called deconvolution.

Methods

Age-specific data were analysed on 816 deaths among 6235 cases over age 50 years in Victoria, Australia, from the period January through December 2020. Deconvolution was applied assuming logistic dependence of case fatality risk (CFR) on age and a gamma time to death distribution. Non-parametric deconvolution analyses stratified into separate age groups were used to assess the model assumptions.

Results

It was found that age-specific CFR rose from 2.9% at age 65 years (95% CI:2.2 – 3.5) to 40.0% at age 95 years (CI: 36.6 – 43.6). The estimated mean time between diagnosis and death was 18.1 days (CI: 16.9 – 19.3) and showed no evidence of varying by age (heterogeneity P = 0.97). The estimated 90% percentile of time to death was 33.3 days (CI: 30.4 – 36.3; heterogeneity P = 0.85). The final age-specific model provided a good fit to the observed age-stratified mortality patterns.

Conclusions

Deconvolution was demonstrated to be a powerful analysis method that could be applied to extensive data sources worldwide. Such analyses can inform transmission dynamics models and CFR assessment in emerging outbreaks. Based on these Australian data it is concluded that death from COVID-19 occurs within three weeks of diagnosis on average but takes five weeks in 10% of fatal cases. Fatality risk is negligible in the young but rises above 40% in the elderly, while time to death does not seem to vary by age.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-021-01314-w.

AIDS and COVID: A tale of two pandemics and the role of statisticians

The world has experienced three global pandemics over the last half-century: HIV/AIDS, H1N1, and COVID-19. HIV/AIDS and COVID-19 are still with us and have wrought extensive havoc worldwide. There are many differences between these two infections and their global impacts, but one thing they have in common is the mobilization of scientific resources to both understand the infection and develop ways to combat it. As was the case with HIV, statisticians have been in the forefront of scientists working to understand transmission dynamics and the natural history of infection, determine prognostic factors for severe disease, and develop optimal study designs to assess therapeutics and vaccines.

Daily Forecasting of New Cases for Regional Epidemics of Coronavirus Disease 2019 with Bayesian Uncertainty Quantification

To increase situational awareness and support evidence-based policy-making, we formulated a mathematical model for COVID-19 transmission within a regional population. This compartmental model accounts for quarantine, self-isolation, social distancing, a non-exponentially distributed incubation period, asymptomatic individuals, and mild and severe forms of symptomatic disease. Using Bayesian inference, we have been calibrating region-specific models daily for consistency with new reports of confirmed cases from the 15 most populous metropolitan statistical areas in the United States and quantifying uncertainty in parameter estimates and predictions of future case reports. This online learning approach allows for early identification of new trends despite considerable variability in case reporting.

ARTICLE SUMMARY LINE

We report models for regional COVID-19 epidemics and use of Bayesian inference to quantify uncertainty in daily predictions of expected reporting of new cases, enabling identification of new trends in surveillance data.

Development and dissemination of infectious disease dynamic transmission models during the COVID-19 pandemic: what can we learn from other pathogens and how can we move forward?

The current COVID-19 pandemic has resulted in the unprecedented development and integration of infectious disease dynamic transmission models into policy making and public health practice. Models offer a systematic way to investigate transmission dynamics and produce short-term and long-term predictions that explicitly integrate assumptions about biological, behavioural, and epidemiological processes that affect disease transmission, burden, and surveillance. Models have been valuable tools during the COVID-19 pandemic and other infectious disease outbreaks, able to generate possible trajectories of disease burden, evaluate the effectiveness of intervention strategies, and estimate key transmission variables. Particularly given the rapid pace of model development, evaluation, and integration with decision making in emergency situations, it is necessary to understand the benefits and pitfalls of transmission models. We review and highlight key aspects of the history of infectious disease dynamic models, the role of rigorous testing and evaluation, the integration with data, and the successful application of models to guide public health. Rather than being an expansive history of infectious disease models, this Review focuses on how the integration of modelling can continue to be advanced through policy and practice in appropriate and conscientious ways to support the current pandemic response.

Modeling methods for estimating HIV incidence: a mathematical review

Estimating HIV incidence is crucial for monitoring the epidemiology of this infection, planning screening and intervention campaigns, and evaluating the effectiveness of control measures. However, owing to the long and variable period from HIV infection to the development of AIDS and the introduction of highly active antiretroviral therapy, accurate incidence estimation remains a major challenge. Numerous estimation methods have been proposed in epidemiological modeling studies, and here we review commonly-used methods for estimation of HIV incidence. We review the essential data required for estimation along with the advantages and disadvantages, mathematical structures and likelihood derivations of these methods. The methods include the classical back-calculation method, the method based on CD4+ T-cell depletion, the use of HIV case reporting data, the use of cohort study data, the use of serial or cross-sectional prevalence data, and biomarker approach. By outlining the mechanistic features of each method, we provide guidance for planning incidence estimation efforts, which may depend on national or regional factors as well as the availability of epidemiological or laboratory datasets.

HIITE: HIV-1 incidence and infection time estimator

Motivation

Around 2.1 million new HIV-1 infections were reported in 2015, alerting that the HIV-1 epidemic remains a significant global health challenge. Precise incidence assessment strengthens epidemic monitoring efforts and guides strategy optimization for prevention programs. Estimating the onset time of HIV-1 infection can facilitate optimal clinical management and identify key populations largely responsible for epidemic spread and thereby infer HIV-1 transmission chains. Our goal is to develop a genomic assay estimating the incidence and infection time in a single cross-sectional survey setting.

Results

We created a web-based platform, HIV-1 incidence and infection time estimator (HIITE), which processes envelope gene sequences using hierarchical clustering algorithms and informs the stage of infection, along with time since infection for incident cases. HIITE's performance was evaluated using 585 incident and 305 chronic specimens' envelope gene sequences collected from global cohorts including HIV-1 vaccine trial participants. HIITE precisely identified chronically infected individuals as being chronic with an error less than 1% and correctly classified 94% of recently infected individuals as being incident. Using a mixed-effect model, an incident specimen's time since infection was estimated from its single lineage diversity, showing 14% prediction error for time since infection. HIITE is the first algorithm to inform two key metrics from a single time point sequence sample. HIITE has the capacity for assessing not only population-level epidemic spread but also individual-level transmission events from a single survey, advancing HIV prevention and intervention programs.

Availability and implementation

Web-based HIITE and source code of HIITE are available at http://www.hayounlee.org/software.html.

Supplementary information

Supplementary data are available at Bioinformatics online.

Diversity, stigma, and social integration among older adults with HIV

The population of people with HIV is aging globally as access to anti-retroviral therapy becomes more widely available. The diversity of older population with HIV has an impact on their experiences of stigma. HIV stigma may be enacted or felt. Enacted stigma is the prejudice, discrimination, and mistreatment that individuals and societies use to sanction people with HIV. Felt stigma refers to the internalized feelings of shame, guilt, and fear that arise from enacted stigma. Nondisclosure is rooted in the fear of negative consequences of revealing one's HIV status, such as losing a job, or being rejected by one's social network. Stigma may also affect social integration through self-protective withdrawal to avoid anticipated stigma. In addition to facing HIV stigma, people with HIV may possess multiple discredited identities due to their race, ethnicity, gender identity, etc., which is described as intersectionality. Older age represents an additional intersectional identity that affects people with HIV through the experience of ageism. Stigma and discrimination from HIV or any discredited identity are linked to poorer physical and mental health outcomes. Given the pervasiveness of stigma, it is not surprising that many older adults with HIV are socially isolated and report greater self-perceived stigma compared to those who are more socially integrated. While there is evidence that HIV stigma has declined compared to previous eras, more research is needed on HIV stigma among older adults in low- and middle-income countries to design policies and programs to combat HIV stigma globally.

The HIV Genomic Incidence Assay Meets False Recency Rate and Mean Duration of Recency Infection Performance Standards

HIV incidence is a primary metric for epidemic surveillance and prevention efficacy assessment. HIV incidence assay performance is evaluated via false recency rate (FRR) and mean duration of recent infection (MDRI). We conducted a meta-analysis of 438 incident and 305 chronic specimens' HIV envelope genes from a diverse global cohort. The genome similarity index (GSI) accurately characterized infection stage across diverse host and viral factors. All except one chronic specimen had GSIs below 0.67, yielding a FRR of 0.33 [0-0.98] %. We modeled the incidence assay biomarker dynamics with a logistic link function assuming individual variabilities in a Beta distribution. The GSI probability density function peaked close to 1 in early infection and 0 around two years post infection, yielding MDRI of 420 [361, 467] days. We tested the assay by newly sequencing 744 envelope genes from 59 specimens of 21 subjects who followed from HIV negative status. Both standardized residuals and Anderson-Darling tests showed that the test dataset was statistically consistent with the model biomarker dynamics. This is the first reported incidence assay meeting the optimal FRR and MDRI performance standards. Signatures of HIV gene diversification can allow precise cross-sectional surveillance with a desirable temporal range of incidence detection.

HIV Prevention, Cost-Utility Analysis, and Race/Ethnicity: Methodological Considerations and Recommendations

In this methodological article, the author reviews 1) the disproportionate impact that HIV/AIDS is having on communities of color in the United States, 2) what is known about the cost-effectiveness of HIV prevention interventions for racial/ethnic minority communities (including the methods used in these studies), and 3) the relative lack of methodological guidance in the field for conducting economic evaluation studies specifically for communities of color. The author finds that race/ethnicity affects cost-utility analyses in several heretofore unrecognized ways. In this article, methodological techniques to address these concerns are proposed. In particular, the author recommends cost-utility analytic strategies that maximize comparability among studies and avoid the introduction of methodological discrimination.

Human T-cell Lymphotropic Viruses: Review and Prospects for Antiviral Therapy

The human T-cell lymphotropic viruses types I and II (HTLV-I, II) pose challenges to researchers and clinicians who seek to unveil mechanisms of viral transformation and pathogenesis. HTLV-I infection in humans is associated with a wide array of primary and secondary diseases ranging from mild immunosuppression to adult T-cell leukaemia/lymphoma and HTLV-associated myelopathy/tropical spastic paraparesis (HAM/TSP), a neurological degenerative syndrome. As retroviruses, HTLV-I and II share similar replicative cycles with human immunodeficiency virus (HIV), the causative agent of acquired immunodeficiency syndrome. However, in contrast to HIV-I which destroys CD4+ T cells, HTLV-I and II can preferentially transform a CD4+ T-cell subset to an unrestricted growth state.

HTLV-I and II, along with simian T-lymphotropic virus (STLV) and bovine leukaemia virus (BLV), form a phylogenetic group which is distinct from ungulate, non-human primate and human lentiviruses such as visna, simian immunodeficiency virus (SIV), and human immunodeficiency viruses types 1 and 2. The proviral genome of HTLV-I is flanked at the 5′ and 3′ ends by long terminal repeats (LTR) and is further subdivided into structural gag and env genes, a pro gene encoding an aspartyl protease, a pol gene which encodes reverse transcriptase and endonuclease, and the regulatory gene elements tax and rex. Regions within the LTR contain recognition sites for cellular proteins and the tax gene product that collectively promote viral expression. Tax-mediated activation of cellular genes involved in growth and differentiation is suspected to play a dominant role in the leukaemogenic process associated with HTLV-I infection. Differential rex-regulated splicing of viral message gives rise to transcripts encoding the polyprotein precursor gag-pro-pol (unspliced), envelope (single spliced), or tax/rex (doubly spliced). The 100nm HTLV virion contains an electron-dense core surrounding a divalent-single stranded DNA genome. This core is in turn enclosed by concentric shells of matrix protein and an outer lipid bilayer, the latter acquired as the virus buds from the surface of the infected cell. Envelope glycoproteins associated with the outside of this lipid bilayer can interact with viral receptors on cells and mediate virus entry. Antiviral strategies have been directed at inhibiting viral entry into cells (sulphated and non-sulphated polysaccharides, vaccines), blocking of viral replication (AZT, suramin), intracellular immunization (transdominant repression of rex), and elimination of virus infected cells (IL-2 receptor-directed toxins). Serological screening of the blood supply and curtailing breast feeding of children by HTLV-I + mothers have likely had a major impact in preventing HTLV-I infection.

Unrecognized "AIDS" in Monkeys, 1969-1980: Explanations and Implications

AIDS was recognized in humans in 1981 and a simian form was described in the years 1983 to 1985. However, beginning in the late 1960s, outbreaks of opportunistic infections of AIDS were seen in monkeys in the United States. This apparent syndrome went unrecognized at the time. We have assembled those early cases in monkeys and offer reasons why they did not result in earlier recognition of simian or human AIDS, including weaknesses in understanding disease mechanisms, absence of evidence of human retroviruses, and a climate of opinion that devalued investigation of infectious disease and immunologic origins of disease. The "epistemological obstacle" explains important elements of this history in that misconceptions blocked understanding of the dependent relationship among viral infection, immunodeficiency, and opportunistic diseases. Had clearer understanding of the evidence from monkeys allowed human AIDS to be recognized earlier, life-saving prevention and treatment interventions might have been implemented sooner.

Mechanistic Models of Infectious Disease and Their Impact on Public Health

From the 1930s through the 1940s, Lowell Reed and Wade Hampton Frost used mathematical models and mechanical epidemic simulators as research tools and to teach epidemic theory to students at the Johns Hopkins Bloomberg School of Public Health (then the School of Hygiene and Public Health). Since that time, modeling has become an integral part of epidemiology and public health. Models have been used for explanatory and inferential purposes, as well as in planning and implementing public health responses. In this article, we review a selection of developments in the history of modeling of infectious disease dynamics over the past 100 years. We also identify trends in model development and use and speculate as to the future use of models in infectious disease dynamics.

A Generalized Entropy Measure of Within-Host Viral Diversity for Identifying Recent HIV-1 Infections

There is a need for incidence assays that accurately estimate HIV incidence based on cross-sectional specimens. Viral diversity-based assays have shown promises but are not particularly accurate. We hypothesize that certain viral genetic regions are more predictive of recent infection than others and aim to improve assay accuracy by using classification algorithms that focus on highly informative regions (HIRs).We analyzed HIV gag sequences from a cohort in Botswana. Forty-two subjects newly infected by HIV-1 Subtype C were followed through 500 days post-seroconversion. Using sliding window analysis, we screened for genetic regions within gag that best differentiate recent versus chronic infections. We used both nonparametric and parametric approaches to evaluate the discriminatory abilities of sequence regions. Segmented Shannon Entropy measures of HIRs were aggregated to develop generalized entropy measures to improve prediction of recency. Using logistic regression as the basis for our classification algorithm, we evaluated the predictive power of these novel biomarkers and compared them with recently reported viral diversity measures using area under the curve (AUC) analysis.Change of diversity over time varied across different sequence regions within gag. We identified the top 50% of the most informative regions by both nonparametric and parametric approaches. In both cases, HIRs were in more variable regions of gag and less likely in the p24 coding region. Entropy measures based on HIRs outperformed previously reported viral-diversity-based biomarkers. These methods are better suited for population-level estimation of HIV recency.The patterns of diversification of certain regions within the gag gene are more predictive of recency of infection than others. We expect this result to apply in other HIV genetic regions as well. Focusing on these informative regions, our generalized entropy measure of viral diversity demonstrates the potential for improving accuracy when identifying recent HIV-1 infections.

Statistical estimations for Plasmodium vivax malaria in South Korea

OBJECTIVE

To calculate the numbers of weekly infections and prevalence of malaria, and to predict future trend of malaria incidences in South Korea.

METHODS

Weekly incidences of malaria for 13 years from the period 2001-2013 in South Korea were analyzed. The back-calculation equations were used with incubation period distributions. The maximum likelihood estimation for Poisson model was also used. The confidence intervals of the estimates were obtained by a bootstrap method. A regression model for time series of malaria incidences over 13 years was fitted by the non-linear least squares method, and used to predict futuretrend.

RESULTS

The estimated infection curve is narrower and more concentrated in the summer than in the incidence distribution. Infection started around the 19th week and was over around the 41st week. The maximum weekly infection 110 was obtained at the 29th week. The prevalence at the first week was around 496 persons, the minimum number was 366 at 22nd week, and the maximum prevalence was 648 at 34th week. Prevalence drops in late spring with people that falling ill and had had long incubation periods and rose in the summer with new infections. Our future forecast based on the regression model was that an increase at year 2014 compared to 2013 may reach a peak (at maximum about 70 weekly cases) at year 2015, with a decreasing trend after then.

CONCLUSIONS

This work shows that back-calculation methods could work well in estimating the infection rates and the prevalence of malaria. The obtained results can be useful in establishing an efficient preventive program for malaria infection. The method presented here can be used in other countries where incidence data and incubation period are available.

[Quality of life in elderly patients living with HIV/AIDS]

This study's objective was to evaluate quality of life in older adults living with HIV/AIDS and the associations with socio-demographic, economic, and clinical characteristics, using a cross-sectional design. Data were collected on demographics, disease history, and economic status according to the Brazilian Economic Classification Criterion and Targeted Quality of Life Instrument (HAT-QoL). A total of 201 elderly participated, with the following characteristics: males (63.7%), single or divorced (51.3%), economic classes C, D, or E (61.7%), and low schooling (53.7%). Nearly two-thirds (130, or 64.7%) had been infected by the sexual route. The following were significantly correlated with two or up to seven domains in the HAT-QoL: age, individual and family income, and time since diagnosis. Quality of life in elderly living with HIV/AIDS showed the lowest scores in the domains related to concern over disclosure of their diagnosis, sexual activity, and financial concerns. Age, income, and time since diagnosis were the variables that most accounted for altered quality of life.

Developing high-throughput HIV incidence assay with pyrosequencing platform

Human immunodeficiency virus (HIV) incidence is an important measure for monitoring the epidemic and evaluating the efficacy of intervention and prevention trials. This study developed a high-throughput, single-measure incidence assay by implementing a pyrosequencing platform. We devised a signal-masking bioinformatics pipeline, which yielded a process error rate of 5.8 × 10(-4) per base. The pipeline was then applied to analyze 18,434 envelope gene segments (HXB2 7212 to 7601) obtained from 12 incident and 24 chronic patients who had documented HIV-negative and/or -positive tests. The pyrosequencing data were cross-checked by using the single-genome-amplification (SGA) method to independently obtain 302 sequences from 13 patients. Using two genomic biomarkers that probe for the presence of similar sequences, the pyrosequencing platform correctly classified all 12 incident subjects (100% sensitivity) and 23 of 24 chronic subjects (96% specificity). One misclassified subject's chronic infection was correctly classified by conducting the same analysis with SGA data. The biomarkers were statistically associated across the two platforms, suggesting the assay's reproducibility and robustness. Sampling simulations showed that the biomarkers were tolerant of sequencing errors and template resampling, two factors most likely to affect the accuracy of pyrosequencing results. We observed comparable biomarker scores between AIDS and non-AIDS chronic patients (multivariate analysis of variance [MANOVA], P = 0.12), indicating that the stage of HIV disease itself does not affect the classification scheme. The high-throughput genomic HIV incidence marks a significant step toward determining incidence from a single measure in cross-sectional surveys.

IMPORTANCE

Annual HIV incidence, the number of newly infected individuals within a year, is the key measure of monitoring the epidemic's rise and decline. Developing reliable assays differentiating recent from chronic infections has been a long-standing quest in the HIV community. Over the past 15 years, these assays have traditionally measured various HIV-specific antibodies, but recent technological advancements have expanded the diversity of proposed accurate, user-friendly, and financially viable tools. Here we designed a high-throughput genomic HIV incidence assay based on the signature imprinted in the HIV gene sequence population. By combining next-generation sequencing techniques with bioinformatics analysis, we demonstrated that genomic fingerprints are capable of distinguishing recently infected patients from chronically infected patients with high precision. Our high-throughput platform is expected to allow us to process many patients' samples from a single experiment, permitting the assay to be cost-effective for routine surveillance.

Is back-projection methodology still relevant for estimating HIV incidence from national surveillance data?

Accurate estimates of HIV incidence are crucial to understand the extent of transmission of the infection, evaluate intervention strategies and effectively plan new public health control measures. HIV/AIDS surveillance systems in numerous industrialised countries record the number of known new HIV and/or AIDS diagnoses, which are often used as a surrogate marker for HIV incidence. HIV/AIDS diagnosis data have been used to reconstruct historical HIV incidence trends using modified back-projection methods. Estimates of HIV incidence are most robust when reliable data on the number of incident infections, a subset of all diagnoses, is widely available, and surveillance systems should prioritise the collection of these data. Back-projection alone provides reliable estimates of HIV incidence in the past, but is not useful when estimating current or future HIV incidence. However, back-projection methodology should be used in conjunction with other corroborative methods to estimate current HIV incidence, and methods to combine the various techniques should be investigated.

The evolving epidemiology of HIV/AIDS

Following its recognition in 1981, the HIV/AIDS epidemic has evolved to become the greatest challenge in global health, with some 34 million persons living with HIV worldwide. Early epidemiologic studies identified the major transmission routes of the virus before it was discovered, and enabled the implementation of prevention strategies. Although the first identified cases were in MSM in the United States and western Europe, the greatest impact of the epidemic has been in sub-Saharan Africa, where most of the transmission occurs between heterosexuals. Nine countries in southern Africa account for less than 2% of the world's population but now they represent about one third of global HIV infections. Where broadly implemented, HIV screening of donated blood and antiretroviral treatment (ART) of pregnant women have been highly effective in preventing transfusion-associated and perinatally acquired HIV, respectively. Access to sterile equipment has also been a successful intervention for injection drug users. Prevention of sexual transmission has been more difficult. Perhaps the greatest challenge in terms of prevention has been in the global community of MSM in which HIV remains endemic at high prevalence. The most promising interventions are male circumcision for prevention of female-to-male transmission and use of ART to reduce infectiousness, but the extent to which these interventions can be brought to scale will determine their population-level impact.

Mathematical modelling and evaluation of the different routes of transmission of lumpy skin disease virus

Lumpy skin disease (LSD) is a severe viral disease of cattle. Circumstantial evidence suggests that the virus is transmitted mechanically by blood-feeding arthropods. We compared the importance of transmission via direct and indirect contact in field conditions by using mathematical tools. We analyzed a dataset collected during the LSD outbreak in 2006 in a large dairy herd, which included ten separated cattle groups. Outbreak dynamics and risk factors for LSD were assessed by a transmission model. Transmission by three contact modes was modelled; indirect contact between the groups within a herd, direct contact or contact via common drinking water within the groups and transmission by contact during milking procedure. Indirect transmission was the only parameter that could solely explain the entire outbreak dynamics and was estimated to have an overall effect that was over 5 times larger than all other possible routes of transmission, combined. The R0 value induced by indirect transmission per the presence of an infectious cow for 1 day in the herd was 15.7, while the R0 induced by direct transmission was 0.36. Sensitivity analysis showed that this result is robust to a wide range of assumptions regarding mean and standard deviation of incubation period and regarding the existence of sub-clinically infected cattle. These results indicate that LSD virus spread within the affected herd could hardly be attributed to direct contact between cattle or contact through the milking procedure. It is therefore concluded that transmission mostly occurs by indirect contact, probably by flying, blood-sucking insects. This has important implications for control of LSD.

Designing a genome-based HIV incidence assay with high sensitivity and specificity

OBJECTIVE

Considerable inaccuracy in estimates of HIV incidence has been a serious obstacle to the development of efficient HIV/AIDS prevention and interventions. Accurately distinguishing recent or incident infections from chronic infections enables one to monitor epidemics and evaluate the impact of HIV prevention/intervention trials. However, serological testing has not been able to realize these promises due to a number of critical limitations. Our study is to design a novel scheme of identifying incident infections in a highly accurate manner, based on the characteristics of HIV gene diversification within an infected individual.

METHODS

We perform a comprehensive meta-analysis on 5596 full envelope HIV genes generated by single genome amplification-direct sequencing from 182 incident and 43 chronic cases. We devise a binary classification test based on the tail characteristics of the Hamming distance distribution of sequences.

RESULTS

We identify a clear signature of incident infections, the presence of closely related strains in the sampled HIV envelope gene sequences in each HIV-infected patient, in both single-variant and multivariant transmissions. The sequence similarity used as a biomarker is found to have high specificity and sensitivity, greater than 95%, and is robust to viral and host-specific factors such as the clade of the viral strain, viral load, and the length and location of sequences in the HIV envelope gene.

CONCLUSION

Because of rapid and continuing improvements in sequencing technology and cost, sequence-based incidence assays hold great promise as a means of quantifying HIV incidence from a single blood test.

Use of a rapid HIV testing algorithm to improve linkage to care

BACKGROUND

Awaiting definitive diagnosis before scheduling healthcare visits complicates HIV screening and referral. Clients screened by rapid tests as initially reactive often fail to return to receive definitive test results, are not linked to care and enter care late or not at all.

OBJECTIVES

To evaluate statewide, (1) the accuracy of a single-visit, two test HIV rapid testing algorithm (RTA) and (2) its effect on referral to care for positive clients.

STUDY DESIGN

A two-test RTA was implemented at 24 sites in New Jersey beginning in December 2008. All clients with a reactive rapid HIV test were offered a second rapid HIV test, and RTA results were compared with Western blot (WB). Referral to care occurred based upon two sequential positive rapid tests.

RESULTS

The RTA program has screened 51,413 individuals obtaining 426 reactive rapid test results; 394 (92.5%) were reactive by a second rapid test, 32 (7.5%) had a negative second rapid test. Twenty-eight individuals refused WB testing. Of 369 RTA-positive individuals who have WB results, 368 (99.5%) were confirmed positive. Of RTA-positive clients, 290 (73.6%), including 25 (6.6%) who refused Western blot, were immediately referred for care including one individual with a false-positive RTA.

CONCLUSIONS

The RTA reduced false positive results by 6.2% and agreed with WB results 99.5% of the time. Improved referral to care compared to traditional rapid HIV screening occurs when immediate referral is based on RTA verification of a preliminary positive rapid test. WB confirmation is not essential for effective screening and contributes to difficulties linking individuals to care.

A nonparametric estimation of the infection curve

Predicting the future course of an epidemic depends on being able to estimate the current numbers of infected individuals. However, while back-projection techniques allow reliable estimation of the numbers of infected individuals in the more distant past, they are less reliable in the recent past. We propose two new nonparametric methods to estimate the unobserved numbers of infected individuals in the recent past in an epidemic. The proposed methods are noniterative, easily computed and asymptotically normal with simple variance formulas. Simulations show that the proposed methods are much more robust and accurate than the existing back projection method, especially for the recent past, which is our primary interest. We apply the proposed methods to the 2003 Severe Acute Respiratory Syndorme (SARS) epidemic in Hong Kong.

Do people who develop AIDS within 12 months of HIV diagnosis delay HIV testing?

OBJECTIVE

Individuals diagnosed with AIDS within 12 months of HIV diagnosis have been considered "late testers." Prevalence estimates of late testers have been made using HIV/AIDS surveillance data, and high rates of late testing have been reported. However, studies evaluating this definition have not been conducted. We measured the degree of misclassification of delayed testing based on this surveillance definition of late testing.

METHODS

We used dates of negative HIV tests among people who met this definition of late testing in San Francisco from 2007 to 2008 to reclassify people as "verified non-late testers" if there was a negative HIV test within five years of HIV diagnosis, as "verified late testers" if there were no prior tests or if negative tests were recorded five or more years prior to diagnosis, or as "late-tester status not verified." We measured misclassification of late-tester status and the prevalence of late testing using the different definitions of late testing.

RESULTS

Of the 270 people who developed AIDS within 12 months of HIV diagnosis, we found that 89 (33.0%) were verified late testers, 131 (48.5%) were verified non-late testers, and 50 (18.5%) were unverifiable. Using the surveillance definition (individuals who develop AIDS within 12 months of HIV diagnosis), the prevalence of late testing was 26.3%, whereas it was 9.0% when restricted to individuals verified as late testers.

CONCLUSION

Defining people who develop AIDS within 12 months of HIV diagnosis without taking into consideration the dates of prior negative HIV tests leads to substantial misclassification of late testing.

Modelling the past, current and future HCV burden in France: detailed analysis and perspectives

Two national HCV projections have been published in France which assumed that a part of observed hepatocellular carcinoma (HCC) deaths is a consequence of HCV epidemic. They applied the back-calculation method, in combination with a Markov model, to reconstruct the past history of HCV infection and then to predict HCV-related mortality. A preliminary model was first developed in the absence of effective therapy. It allowed testing many assumptions to model HCV natural history that were compatible with observed incidence of HCV-related HCC deaths. This model was then updated to take into account the availability of treatment and more recent epidemiological data. These two models are described in detail and results are discussed with a view to addressing the models' limitations. The models offered a useful tool to assess public health policy scenarios in planning healthcare responses to the HCV epidemic.

Estimation of HIV incidence in the United States

CONTEXT

Incidence of human immunodeficiency virus (HIV) in the United States has not been directly measured. New assays that differentiate recent vs long-standing HIV infections allow improved estimation of HIV incidence.

OBJECTIVE

To estimate HIV incidence in the United States.

DESIGN, SETTING, AND PATIENTS

Remnant diagnostic serum specimens from patients 13 years or older and newly diagnosed with HIV during 2006 in 22 states were tested with the BED HIV-1 capture enzyme immunoassay to classify infections as recent or long-standing. Information on HIV cases was reported to the Centers for Disease Control and Prevention through June 2007. Incidence of HIV in the 22 states during 2006 was estimated using a statistical approach with adjustment for testing frequency and extrapolated to the United States. Results were corroborated with back-calculation of HIV incidence for 1977-2006 based on HIV diagnoses from 40 states and AIDS incidence from 50 states and the District of Columbia.

MAIN OUTCOME MEASURE

Estimated HIV incidence.

RESULTS

An estimated 39,400 persons were diagnosed with HIV in 2006 in the 22 states. Of 6864 diagnostic specimens tested using the BED assay, 2133 (31%) were classified as recent infections. Based on extrapolations from these data, the estimated number of new infections for the United States in 2006 was 56,300 (95% confidence interval [CI], 48,200-64,500); the estimated incidence rate was 22.8 per 100,000 population (95% CI, 19.5-26.1). Forty-five percent of infections were among black individuals and 53% among men who have sex with men. The back-calculation (n = 1.230 million HIV/AIDS cases reported by the end of 2006) yielded an estimate of 55,400 (95% CI, 50,000-60,800) new infections per year for 2003-2006 and indicated that HIV incidence increased in the mid-1990s, then slightly declined after 1999 and has been stable thereafter.

CONCLUSIONS

This study provides the first direct estimates of HIV incidence in the United States using laboratory technologies previously implemented only in clinic-based settings. New HIV infections in the United States remain concentrated among men who have sex with men and among black individuals.

The economics of sexuality: the effect of HIV/AIDS on homosexual behavior in the United States

In this paper, I test a simple microeconomic theory of sexuality. I apply the theory to make predictions about the effect of AIDS on sexuality, since AIDS dramatically altered the cost of sexual activities. Using a nationally representative dataset on sexuality in the United States, I estimate the effect of AIDS on male and female homosexual behavior. To do so, I postulate that people who have a relative with AIDS, on average, have more knowledge, awareness, and fear of AIDS. Empirically, this variable is uncorrelated with a number of individual background characteristics. I present evidence that AIDS causes some men to shift from homosexual to heterosexual behavior, whereas AIDS causes some women to shift from heterosexual to homosexual behavior. Thus, sexual behavior may respond to incentives. I consider alternative hypotheses, including biological theories of sexual orientation and stigma-related survey bias, and argue that they are unlikely to explain the results.

When did HIV incidence peak in Harare, Zimbabwe? Back-calculation from mortality statistics

HIV prevalence has recently begun to decline in Zimbabwe, a result of both high levels of AIDS mortality and a reduction in incident infections. An important component in understanding the dynamics in HIV prevalence is knowledge of past trends in incidence, such as when incidence peaked and at what level. However, empirical measurements of incidence over an extended time period are not available from Zimbabwe or elsewhere in sub-Saharan Africa. Using mortality data, we use a back-calculation technique to reconstruct historic trends in incidence. From AIDS mortality data, extracted from death registration in Harare, together with an estimate of survival post-infection, HIV incidence trends were reconstructed that would give rise to the observed patterns of AIDS mortality. Models were fitted assuming three parametric forms of the incidence curve and under nine different assumptions regarding combinations of trends in non-AIDS mortality and patterns of survival post-infection with HIV. HIV prevalence was forward-projected from the fitted incidence and mortality curves. Models that constrained the incidence pattern to a cubic spline function were flexible and produced well-fitting, realistic patterns of incidence. In models assuming constant levels of non-AIDS mortality, annual incidence peaked between 4 and 5% between 1988 and 1990. Under other assumptions the peak level ranged from 3 to 8% per annum. However, scenarios assuming increasing levels of non-AIDS mortality resulted in implausibly low estimates of peak prevalence (11%), whereas models with decreasing underlying crude mortality could be consistent with the prevalence and mortality data. HIV incidence is most likely to have peaked in Harare between 1988 and 1990, which may have preceded the peak elsewhere in Zimbabwe. This finding, considered alongside the timing and location of HIV prevention activities, will give insight into the decline of HIV prevalence in Zimbabwe.

Modeling Disease Progression with Longitudinal Markers

In this paper we propose a Bayesian natural history model for disease progression based on the joint modeling of longitudinal biomarker levels, age at clinical detection of disease and disease status at diagnosis. We establish a link between the longitudinal responses and the natural history of the disease by using an underlying latent disease process which describes the onset of the disease and models the transition to an advanced stage of the disease as dependent on the biomarker levels. We apply our model to the data from the Baltimore Longitudinal Study of Aging on prostate specific antigen (PSA) to investigate the natural history of prostate cancer.

Use of rapid behavioral assessments to determine the prevalence of HIV risk behaviors in high-risk populations

Rapid HIV Behavioral Assessment (RHBA) is a method for collecting much-needed information about sexual, drug-use, and HIV testing behaviors from people at high risk for HIV infection in areas with low-to-moderate HIV prevalence. During 2004, RHBAs were conducted in seven small to moderate-sized cities in the United States during Gay Pride events. Anonymous 10-minute interviews were administered to eligible attendees using handheld computers. Depending on the city, between 47% and 97% of individuals approached agreed to hear more about the survey. Enrollment rates exceeded 90% in every location. RHBAs conducted during 2004 were well received by the gay and public health communities. They were simple to organize and administer, flexible, and cost-efficient, suggesting that this approach holds promise for expansion to additional high-risk groups and geographic locations. RHBAs can provide state and local health departments with demographic and behavioral data that can be used to design, target, and evaluate local HIV prevention programs.

Estimating the future health burden of chronic hepatitis C and human immunodeficiency virus infections in the United States

The aim of this work was to estimate the future disease burden of hepatitis C virus (HCV) and human immunodeficiency virus (HIV) infections in the United States until the year 2030. Two back-calculation models of the HIV and the HCV epidemic were developed. They were based on US epidemiological data regarding prevalence, age and gender of incident cases, AIDS, hepatocellular carcinoma (HCC) mortality and general population mortality from the Centers for Disease Control and WHO. Based on the HCV back-calculation model, HCV incidence peaked in 1984 at 350,000 new infections and then fell to about 77,000 in 1998. Based on the HIV back-calculation model, HIV incidence reached its maximum in 1989 at 142,000 new infections and then declined to 79,000 in 1998. Mortality related to HCV (death from liver failure or HCC) rose from about 3,700 in 1998 and is expected to peak at about 13,000 in 2030. Predicted HCV mortality would fall only if increased access to or more effective antiviral therapy occurs. For comparison, observed HIV-related mortality was 14,400 in 1998 and projected to be 4,200 for 2030. With the availability of effective highly active antiretroviral therapy for HIV infection, mortality from HIV appears to have declined substantially, whereas HCV-related deaths as a result of pre-1999 infections will likely continue to increase over the next 25 years.

HIV incidence and CDC's HIV prevention budget: an exploratory correlational analysis

BACKGROUND

The central evaluative question about a national HIV prevention program is whether that program affects HIV incidence. Numerous factors may influence incidence, including public investment in HIV prevention. Few studies, however, have examined the relationship between public investment and the HIV epidemic in the United States.

METHODS

This 2006 exploratory analysis examined the period from 1978 through 2006 using a quantitative, lagged, correlational analysis to capture the relationship between national HIV incidence and Centers for Disease Control and Prevention's HIV prevention budget in the United States over time.

RESULTS

The analyses suggest that early HIV incidence rose in advance of the nation's HIV prevention investment until the mid-1980s (1-year lag correlation, r=0.972, df=2, p <0.05). From that point on, it appears that the nation's investment in HIV prevention became a strong correlate of HIV incidence (1-year lag correlation, r=-0.905, df=18, p <0.05).

CONCLUSIONS

This exploratory study provides correlational evidence of a relationship between U.S. HIV incidence and the federal HIV prevention budget over time, and calls for further analysis of the role of funding and other factors that may influence the direction of a nation's HIV epidemic.

Conflicting messages: how criminal HIV disclosure laws undermine public health efforts to control the spread of HIV

Twenty-three U.S. states currently have laws that make it a crime for persons who have HIV to engage in various sexual behaviors without, in most cases, disclosing their HIV-positive status to prospective sex partners. As structural interventions aimed at reducing new HIV infections, the laws ideally should complement the HIV prevention efforts of public health professionals. Unfortunately, they do not. This article demonstrates how HIV disclosure laws disregard or discount the effectiveness of universal precautions and safer sex, criminalize activities that are central to harm reduction efforts, and offer, as an implicit alternative to risk reduction and safer sex, a disclosure-based HIV transmission prevention strategy that undermines public health efforts. The article also describes how criminal HIV disclosure laws may work against the efforts of public health leaders to reduce stigmatizing attitudes toward persons living with HIV.

WHAT WORKS, AND WHAT REMAINS TO BE DONE, IN HIV PREVENTION IN THE UNITED STATES

Since the beginning of the HIV epidemic in the United States, HIV prevention programs have prevented hundreds of thousands of HIV infections, and the investment in these programs has actually been cost-saving to society in terms of medical costs averted. A substantial body of evidence exists (including randomized controlled trials and careful meta-analyses) which demonstrates that various HIV prevention services are effective; an increasingly large body of data also demonstrates the cost-effectiveness of these interventions. However, the efforts to utilize these interventions in a comprehensive HIV prevention program are hampered by insufficient funding, imperfect targeting strategies, and a problematic policy environment that creates barriers to the use of some of these life-saving interventions. Progress toward reducing new HIV infections will likely be as much a function of improvements in funding and policies as it will in the development of new tools for HIV prevention.

Estimating the Distribution of a Renewal Process from Times at which Events from an Independent Process Are Detected

The analysis of length-biased data has been mostly limited to the interarrival interval of a renewal process covering a specific time point. Motivated by a surveillance problem, we consider a more general situation where this time point is random and related to a specific event, for example, status change or onset of a disease. We also consider the problem when additional information is available on whether the event intervals (interarrival intervals covering the random event) end within or after a random time period (which we call a window period) following the random event. Under the assumptions that the occurrence rate of the random event is low and the renewal process is independent of the random event, we provide formulae for the estimation of the distribution of interarrival times based on the observed event intervals. Procedures for testing the required assumptions are also furnished. We apply our results to human immunodeficiency virus (HIV) test data from public test sites in Seattle, Washington, where the random event is HIV infection and the window period is from the onset of HIV infection to the time at which a less sensitive HIV test becomes positive. Results show that the estimator of the intertest interval length distribution from event intervals ending within the window period is less biased than the estimator from all event intervals; the latter estimator is affected by right truncation. Finally, we discuss possible applications to estimating HIV incidence and analyzing length-biased samples with right or left truncated data.

How was the French BSE epidemic underestimated?

In a previous study, we showed that estimates of the BSE epidemic in France were censored by cattle mortality and by a lack of diagnosis. Indeed, we estimated that 51 300 cattle were infected by the BSE agent between 1987 and 1997, whereas only 103 clinical BSE cases were detected by the passive surveillance system up to June 2000. The question thus arises as to the part played by each form of censorship in this underestimation. Here, using an updated cattle survival distribution, we estimated that 44 800 cattle were infected by the BSE agent between 1987 and 1997, and that 7100 of them showed clinical signs of BSE up to June 2000, showing the low efficiency of the surveillance system. Moreover, between 2087 and 5980 'infectious' cattle, with clinical or preclinical BSE, entered the human food chain before July 1996, the date of the ban on specified bovine offal.

Estimation of Annual HIV Transmission Rates in the United States, 1978???2000

The HIV/AIDS epidemic in the United States generally has been characterized by AIDS case incidence and AIDS-associated death rates; in a more limited fashion, the epidemic has also been described by AIDS prevalence; population-specific HIV prevalence; and HIV incidence statistics. However, HIV transmission rate information (i.e., the rate of transmission from persons living with HIV to HIV-seronegative persons) has received relatively little attention. The purpose of the present paper is to estimate the annual HIV transmission rate (from HIV-seropositive to HIV-seronegative persons) in the United States for the time period 1978-2000 and to discuss the practical utility of the findings. Using as input annual AIDS-associated deaths and HIV incidence (both variables, especially the latter, contain some element of uncertainty), the model described here finds that HIV transmission rates have dropped dramatically in the United States since the beginning of the epidemic and stayed approximately between 4.00-4.34% during the 1990s. This implies a programmatic success in that for more than roughly 95% of persons living with HIV in any given year, no HIV transmission occurs. Research is urgently needed to fully understand the circumstances that allow the remaining instances of HIV transmission to take place; moreover, serostatus-appropriate HIV-related services are needed to disrupt these remaining instances of transmission.

Extending backcalculation to analyse BSE data

We review the origins of backcalculation (or back projection) methods developed for the analysis of AIDS (acquired immunodeficiency syndrome) incidence data. These techniques have been used extensively for >15 years to deconvolute clinical case incidence, given knowledge of the incubation period distribution, to obtain estimates of past HIV (human immunodeficiency virus) infection incidence and short-term predictions of future AIDS incidence. Adaptations required for the analysis of bovine spongiform encephalopathy (BSE) incidence included: stratification of BSE incidence by age as well as birth cohort; allowance for incomplete survival between infection and the onset of clinical signs of disease; and decomposition of the age- and time-related infection incidence into a time-dependent feed risk component and an age-dependent exposure/susceptibility function. The most recent methodological developments focus on the incorporation of data from clinically unaffected cattle screened using recently developed tests for preclinical BSE infection. Backcalculation-based predictions of future BSE incidence obtained since 1996 are examined. Finally, future directions of epidemiological analysis of BSE epidemics are discussed taking into account ongoing developments in the science of BSE and possible changes in BSE-related policies.

Economic implications of failure to reduce incident HIV infections by 50% by 2005 in the United States

BACKGROUND

The Centers for Disease Control and Prevention have set a national goal of reducing new HIV infections by 50% by 2005 in the United States. There are no available published estimates of the economic consequences of failure to meet this national goal, however.

OBJECTIVES

The purpose of this article is to calculate the potential net economic implications of a failure to meet the national HIV prevention goal of reducing new HIV infections by 50% by 2005.

METHODS

Standard methods of cost-effectiveness analysis were used to determine 1) the excess number of HIV infections incurred if the goal is not met and 2) the excess net medical costs (without the cost of an expanded HIV prevention program in the United States) incurred if the goal is not achieved.

RESULTS

Base case results indicate that if the goal is not met, 130,000 excess HIV infections will occur between the present and 2010 and that the excess net medical costs incurred will total over $18 billion during the same time frame. Sensitivity analyses indicate that although changes in some parameter values do alter the quantitative results, none alter the basic qualitative finding that even dramatically expanded HIV prevention efforts that reduce new HIV infections by 50% are actually cost saving to society.

CONCLUSIONS

The human and fiscal stakes of meeting the CDC's national HIV prevention goal of reducing new infections by 50% by 2005 are sufficiently large to make the achievement of this goal an urgent public health priority.

The predictability of the epidemic of variant Creutzfeldt-Jakob disease by back-calculation methods

We present a back-calculation analysis of the variant Creutzfeldt-Jakob (vCJD) epidemic in the UK to estimate the number of infected individuals and to explore the likely future incidence of the disease. The main features of the model are that the hazard of infection was assumed proportional to the incidence of BSE in the UK with allowance for precautionary control measures taken in 1988 and in 1996, and that the incubation period distribution of vCJD follows an offset generalized F distribution. Our results indicate that current the numbers of cases with onset up to 31 December 2000 data are broadly compatible with numbers of primary infections ranging from a few hundred to several million. However, if a very large number of persons were infected, the model suggests that the mean incubation period is likely to be well beyond the human lifespan, resulting in a disease epidemic of much smaller size (maximum several thousand). A sensitivity analysis indicates that our results are sensitive to the underreporting of vCJD cases before 1996. Finally, we show that, in the absence of a reliable test for asymptomatic infection, uncertainty in estimates of the total number of infections is likely to remain for at least several years, even if the number of clinical cases remains low.

Estimating the effectiveness and efficiency of US HIV prevention efforts using scenario and cost-effectiveness analysis

Scenario and cost-effectiveness analyses were used to estimate the effectiveness and efficiency of HIV prevention activities in the USA (1978-2000). Under four conservative scenarios on the course of HIV epidemic that might have occurred had there been no prevention efforts. we estimate that prevention activities averted 204,000-1,585,000 infections at a cost of between 49,700 US dollars and 6400 US dollars per infection prevented (less than the medical costs of treating a case of HIV disease).