Estimating the distribution of the window period for recent HIV infections: a comparison of statistical methods

PrEP Intervention in the Mitigation of HIV/AIDS Epidemics in China via a Data-Validated Age-Structured Model

Antiretroviral-based pre-exposure prophylaxis (PrEP) treatment offers a new opportunity for protecting humans against HIV and disrupting current HIV prevention systems. However, implementing this preventive measure has been difficult due to its high cost. In this paper, we propose an age-structured model that incorporates infection ages, HAART (highly active antiretroviral therapy), and PrEP intervention. We investigate the qualitative behavior of the model and find a threshold parameter (the basic reproduction number) that determines the asymptotic stability of equilibria. We validate the model and estimate the parameters by confronting the actual HIV/AIDS data from 2004 to 2018 in China using MCMC (Markov Chain Monte Carlo) method. Furthermore, we investigate the PrEP intervention strategy by using incremental cost-effectiveness and average cost-effectiveness. Our work suggests that PrEP intervention based on the infection characteristics of different age groups can be an effective strategy to eradicate HIV/AIDS epidemics in China.

bayroot: Bayesian sampling of HIV-1 integration dates by root-to-tip regression

The composition of the latent human immunodeficiency virus 1 (HIV-1) reservoir is shaped by when proviruses integrated into host genomes. These integration dates can be estimated by phylogenetic methods like root-to-tip (RTT) regression. However, RTT does not accommodate variation in the number of mutations over time, uncertainty in estimating the molecular clock, or the position of the root in the tree. To address these limitations, we implemented a Bayesian extension of RTT as an R package (bayroot), which enables the user to incorporate prior information about the time of infection and start of antiretroviral therapy. Taking an unrooted maximum likelihood tree as input, we use a Metropolis-Hastings algorithm to sample from the joint posterior distribution of three parameters (the rate of sequence evolution, i.e., molecular clock; the location of the root; and the time associated with the root). Next, we apply rejection sampling to this posterior sample of model parameters to simulate integration dates for HIV proviral sequences. To validate this method, we use the R package treeswithintrees (twt) to simulate time-scaled trees relating samples of actively and latently infected T cells from a single host. We find that bayroot yields significantly more accurate estimates of integration dates than conventional RTT under a range of model settings.

Incidence of HIV infection and associated factors among female sex workers in Côte d’Ivoire, results of the ANRS 12361 PrEP-CI study using recent infection assays

Background

This study aimed to estimate, using an HIV Recent Infection Testing Algorithm (RITA), the HIV incidence and its associated factors among female sex workers (FSW) in Côte d’Ivoire.

Methods

A cross-sectional study was conducted in 2016–2017 in Abidjan and San Pedro’s region among FSW aged ≥ 18 years. In addition, a sociodemographic questionnaire, HIV screening was carried out by two rapid tests. In the event of a positive result, a dried blood spot sample was taken to determine, using a RITA adapted to the Ivorian context, if it was a recent HIV infection.

Results

A total of 1000 FSW were surveyed with a median age of 25 years (interquartile range: 21–29 years). 39 (3.9%) tested positive for HIV. The incidence of HIV was estimated to be 2.3 per 100 person-years, with higher incidence rates among those 24 years old or less (3.0% vs. 1.9%), non-Ivorian FSW (3.2% vs. 1.9%) and those with the lowest education level (4.6% in FSW who never went to school vs. 2.6%). The incidence seemed to be associated with the sex work practice conditions: higher incidence among FSW whose usual price was less than 3.50$ (4.3% vs.1.0%), FSW who had a larger number of clients on the last day of work (6.1% in those with 7 clients or more vs. 1.8%), FSW who reported not always using condoms with their clients (8.5% vs. 1.5%) and FSW who reported agreeing to sex without a condom in exchange for a large sum of money (10.1% vs. 1.2%).

Conclusion

This study confirms that FSW remain highly exposed to HIV infection. Exposure to HIV is also clearly associated with certain sex-work factors and the material conditions of sex work. Efforts in the fight against HIV infection must be intensified to reduce new infections among FSW.

Serological diagnosis and prevalence of HIV-1 infection in Russian metropolitan areas

BACKGROUND

HIV infection is a major health problem in Russia. We aimed to assess HIV prevalence in different population groups and to compare the characteristics of 4th generation immunoassays from Abbott, Bio-Rad, Vector-Best, Diagnostic Systems, and Medical Biological Unit.

METHODS

The study included 4452 individuals from the general population (GP), 391 subjects at high risk of HIV infection (HR) and 699 with potentially interfering conditions. HIV positivity was confirmed by immunoblot and by HIV RNA, seroconversion and virus diversity panels were also used. HIV avidity was employed to assess recent infections.

RESULTS

The prevalence in GP was 0.40%, higher in males (0.62%) and in people aged < 40 years (0.58%). Patients attending dermo-venereal centers and drug users had a high prevalence (34.1 and 58.8%). Recent infections were diagnosed in 20% of GP and in 4.2% of HR. Assay sensitivity was 100% except for one false negative (99,54%, MBU). Specificity was 99.58-99.89% overall, but as low as 93.26% on HR (Vector-Best). Small differences on early seroconversion were recorded. Only the Abbott assay detected all samples on the viral diversity panel.

CONCLUSION

HIV infection rate in the high-risk groups suggests that awareness and screening campaigns should be enhanced. Fourth generation assays are adequate but performance differences must be considered.

Evaluation of the Performance of Three Biomarker Assays for Recent HIV Infection Using a Well-Characterized HIV-1 Subtype C Incidence Cohort

Biomarkers for detecting early HIV infection and estimating HIV incidence should minimize false-recent rates (FRRs) while maximizing mean duration of recent infection (MDRI). We compared HIV subtypes B, E and D (BED) capture enzyme immunoassay (BED), Sedia limiting antigen (LAg) avidity enzyme immunoassay, and Bio-Rad avidity incidence (BRAI) assays using samples from Zimbabwean postpartum women infected with clade C HIV. We calculated MDRIs using 590 samples from 351 seroconverting postpartum women, and FRRs using samples from 2,825 women known to be HIV positive for >12 months. Antibody kinetics were more predictable with LAg and had higher precision compared with BED or BRAI. BRAI also exhibited more variability, and avidity reversal in some cases. For BED, LAg, and BRAI, used alone or with viral load, MDRI values in days were: BED-188 and 170 at normalized optical density (ODn) 0.8; LAg-104 and 100 at ODn cutoff 1.5; BRAI-135 and 134 at avidity index cutoff 30%. Corresponding FRRs were: BRAI 1.1% and 1.0% and LAg 0.57% and 0.35%: these were 3.8-10.9 times lower than BED values of 4.8% and 3.8%. BRAI and LAg have significantly lower FRRs and MDRIs than in published studies, and much lower than BED and could be used to estimate incidence in perinatal women and to measure population-level HIV incidence in HIV control operations in Africa.

Cross-sectional HIV incidence estimation in an evolving epidemic

The cross-sectional approach to HIV incidence estimation overcomes some of the challenges with longitudinal cohort studies and has been successfully applied in many settings around the world. However, the cross-sectional approach does rely on an initial training data set to develop and calibrate the statistical methods to be used in cross-sectional surveys. The problem addressed in this paper is that the initial training data set may, over time, not reflect the current target population of interest because of evolution of the epidemic. For example, the mismatch between the target population and the initial data set could occur because of increasing use of anti-retroviral therapy among HIV-infected persons throughout the world. We developed methods to adjust the initial training data set with the goal that the adjusted data sets better reflect the target population. These adjustment procedures could help avoid the time and expense of collecting a completely new training data set from the current target population. We report the results of a simulation study to evaluate the procedures. We applied the methods to a dataset of HIV subtype B infection. The adjustment procedures could be applicable in situations other than cross-sectional incidence estimation where complex statistical analyses are to be conducted using an initial data set but those results may not be directly transportable to a new target population of interest. The approach we have proposed could offer a practical and cost-effective way to apply cross-sectional incidence methods to new target populations as the epidemic evolves.

HIV avidity index performance using a modified fourth-generation immunoassay to detect recent HIV infections

BACKGROUND

Detecting recent HIV infections is important to evaluate incidence and monitor epidemic trends. We aimed to evaluate the diagnostic performance and accuracy of the avidity index (AI) for discriminating for recent HIV infections.

METHODS

We collected serum samples from HIV-1 positive individuals: A) with known date of infection (midpoint in time between last HIV-negative and first HIV-positive test); B) infected for >1 year. Samples were divided into two aliquots: one diluted with phosphate buffered saline (PBS) and the other with 1 M guanidine. Both aliquots were assayed by the Architect HIV Ag/Ab Combo 4th generation assay (Abbott). We compared AI found in recent (RI=<6 months from seroconversion) and established (EI) infections. The diagnostic accuracy was evaluated by receiver operating characteristic (ROC) curve analysis. The proportion of samples misclassified as recent (FRR) was calculated.

RESULTS

In total, 647 samples were collected: 455 in group A (51.6% RI and 48.4% EI) and 192 in group B. Among these, sixteen samples were from elite controllers, 294 from treated patients, 328 from patients infected with non-B subtypes. Samples before antiretroviral initiation showed a mean AI significantly lower among RI compared to EI (0.66+0.28 vs. 1.00±0.12; p<0.000). The FRR was 0% using a cut-off of ≤0.70. An extremely low FRR was observed among elite controllers, samples with low VL or CD4. HIV subtype had no impact on AI misclassifications. All individuals in group A reached the AI threshold of 0.80 within 24 months after seroconversion.

CONCLUSIONS

The AI is an accurate serological marker for discriminating recent from established HIV infections and meets WHO requirements for HIV incidence assays.

Cross-Sectional HIV Incidence Surveillance: A Benchmarking of Approaches for Estimating the 'Mean Duration of Recent Infection'

The application of biomarkers for 'recent' infection in cross-sectional HIV incidence surveillance requires the estimation of critical biomarker characteristics. Various approaches have been employed for using longitudinal data to estimate the Mean Duration of Recent Infection (MDRI) - the average time in the 'recent' state. In this systematic benchmarking of MDRI estimation approaches, a simulation platform was used to measure accuracy and precision of over twenty approaches, in thirty scenarios capturing various study designs, subject behaviors and test dynamics that may be encountered in practice. Results highlight that assuming a single continuous sojourn in the 'recent' state can produce substantial bias. Simple interpolation provides useful MDRI estimates provided subjects are tested at regular intervals. Regression performs the best - while 'random effects' describe the subject-clustering in the data, regression models without random effects proved easy to implement, stable, and of similar accuracy in scenarios considered; robustness to parametric assumptions was improved by regressing 'recent'/'non-recent' classifications rather than continuous biomarker readings. All approaches were vulnerable to incorrect assumptions about subjects' (unobserved) infection times. Results provided show the relationships between MDRI estimation performance and the number of subjects, inter-visit intervals, missed visits, loss to follow-up, and aspects of biomarker signal and noise.

Cross-Sectional HIV Incidence Surveillance: A Benchmarking of Approaches for Estimating the 'Mean Duration of Recent Infection'

The application of biomarkers for 'recent' infection in cross-sectional HIV incidence surveillance requires the estimation of critical biomarker characteristics. Various approaches have been employed for using longitudinal data to estimate the Mean Duration of Recent Infection (MDRI) - the average time in the 'recent' state. In this systematic benchmarking of MDRI estimation approaches, a simulation platform was used to measure accuracy and precision of over twenty approaches, in thirty scenarios capturing various study designs, subject behaviors and test dynamics that may be encountered in practice. Results highlight that assuming a single continuous sojourn in the 'recent' state can produce substantial bias. Simple interpolation provides useful MDRI estimates provided subjects are tested at regular intervals. Regression performs the best - while 'random effects' describe the subject-clustering in the data, regression models without random effects proved easy to implement, stable, and of similar accuracy in scenarios considered; robustness to parametric assumptions was improved by regressing 'recent'/'non-recent' classifications rather than continuous biomarker readings. All approaches were vulnerable to incorrect assumptions about subjects' (unobserved) infection times. Results provided show the relationships between MDRI estimation performance and the number of subjects, inter-visit intervals, missed visits, loss to follow-up, and aspects of biomarker signal and noise.

Recalibration of the limiting antigen avidity EIA to determine mean duration of recent infection in divergent HIV-1 subtypes

Background

Mean duration of recent infection (MDRI) and misclassification of long-term HIV-1 infections, as proportion false recent (PFR), are critical parameters for laboratory-based assays for estimating HIV-1 incidence. Recent review of the data by us and others indicated that MDRI of LAg-Avidity EIA estimated previously required recalibration. We present here results of recalibration efforts using >250 seroconversion panels and multiple statistical methods to ensure accuracy and consensus.

Methods

A total of 2737 longitudinal specimens collected from 259 seroconverting individuals infected with diverse HIV-1 subtypes were tested with the LAg-Avidity EIA as previously described. Data were analyzed for determination of MDRI at ODn cutoffs of 1.0 to 2.0 using 7 statistical approaches and sub-analyzed by HIV-1 subtypes. In addition, 3740 specimens from individuals with infection >1 year, including 488 from patients with AIDS, were tested for PFR at varying cutoffs.

Results

Using different statistical methods, MDRI values ranged from 88–94 days at cutoff ODn = 1.0 to 177–183 days at ODn = 2.0. The MDRI values were similar by different methods suggesting coherence of different approaches. Testing for misclassification among long-term infections indicated that overall PFRs were 0.6% to 2.5% at increasing cutoffs of 1.0 to 2.0, respectively. Balancing the need for a longer MDRI and smaller PFR (<2.0%) suggests that a cutoff ODn = 1.5, corresponding to an MDRI of 130 days should be used for cross-sectional application. The MDRI varied among subtypes from 109 days (subtype A&D) to 152 days (subtype C).

Conclusions

Based on the new data and revised analysis, we recommend an ODn cutoff = 1.5 to classify recent and long-term infections, corresponding to an MDRI of 130 days (118–142). Determination of revised parameters for estimation of HIV-1 incidence should facilitate application of the LAg-Avidity EIA for worldwide use.

Mixture models for calibrating the BED for HIV incidence testing

A number of antibody biomarkers have been developed to distinguish between recent and established Human Immunodeficiency Virus (HIV) infection and used for HIV incidence estimation from cross-sectional specimens. In general, a cut-off value is specified, and estimates of the following parameters are needed: (i) the mean time interval .w/ between seroconversion and reaching that cut-off; (ii) the probability of correctly identifying individuals who became infected in the last w years (sensitivity); and (iii) the probability of correctly identifying individuals who have been infected for more than w years (specificity). We develop two statistical methods to study the distribution of a biomarker and derive a formula for estimating HIV incidence from a cross-sectional survey. Both methods allow handling interval censored data and basically consist of using a generalized mixture model to model the growth of the biomarker as a function of time since infection. The first uses data from all followed-up individuals and allows incidence estimation in the cohort, whereas the second only uses data from seroconverters. We illustrate our methods using repeated measures of the IgG capture BED enzyme immunoassay. Estimates of calibration parameters, that is, mean window period, mean recency period, sensitivity, and specificities obtained from both models are comparable. The formula derived for incidence estimation gives the maximum likelihood estimate of incidence which, for a given window period, depends only on sensitivity and specificity. The optimal choice of the window period is discussed. Numerical simulations suggest that data from seroconverters can provide reasonable estimates of the calibration parameters.

Joint modeling of longitudinal and survival data with missing and left-censored time-varying covariates

We propose a joint model for longitudinal and survival data with time-varying covariates subject to detection limits and intermittent missingness at random. The model is motivated by data from the Multicenter AIDS Cohort Study (MACS), in which HIV+ subjects have viral load and CD4 cell count measured at repeated visits along with survival data. We model the longitudinal component using a normal linear mixed model, modeling the trajectory of CD4 cell count by regressing on viral load, and other covariates. The viral load data are subject to both left censoring because of detection limits (17%) and intermittent missingness (27%). The survival component of the joint model is a Cox model with time-dependent covariates for death because of AIDS. The longitudinal and survival models are linked using the trajectory function of the linear mixed model. A Bayesian analysis is conducted on the MACS data using the proposed joint model. The proposed method is shown to improve the precision of estimates when compared with alternative methods.

Predicting time to threshold for initiating antiretroviral treatment to evaluate cost of treatment as prevention of human immunodeficiency virus

The goal of this paper is to predict the additional amount of antiretroviral treatment that would be required to implement a policy of treating all HIV-infected people at time of detection of infection rather than at the time that their CD4 T-lymphocyte counts are observed to be below a threshold-the current standard of care. We describe a sampling-based inverse prediction method for predicting time from HIV infection to attainment of the CD4 threshold and apply it to a set of treatment-naive HIV-infected subjects in a village in Botswana who participated in a household survey that collected cross-sectional CD4 counts. The inferential target of interest is the population-level mean time to reaching CD4-based treatment threshold in this group of subjects. To address the challenges arising from the fact that these subject's dates of HIV infection are unknown, we make use of data from an auxiliary cohort study of subjects enrolled shortly after HIV infection in which CD4 counts were measured over time. We use a multiple imputation framework to combine across the different sources of data, and discuss how the methods compensate for the length-biased sampling inherent in cross-sectional screening procedures, such as household surveys. We comment on how the results bear upon analyses of costs of implementation of treatment-for-prevention use of antiretroviral drugs in HIV prevention interventions.

A serial risk score approach to disease classification that accounts for accuracy and cost

The performance of diagnostic tests for disease classification is often measured by accuracy (e.g., sensitivity or specificity); however, costs of the diagnostic test are a concern as well. Combinations of multiple diagnostic tests may improve accuracy, but incur additional costs. Here, we consider serial testing approaches that maintain accuracy while controlling costs of the diagnostic tests. We present a serial risk score classification approach. The basic idea is to sequentially test with additional diagnostic tests just until persons are classified. In this way, it is not necessary to test all persons with all tests. The methods are studied in simulations and compared with logistic regression. We applied the methods to data from HIV cohort studies to identify HIV infected individuals who are recently infected (<1 year) by testing with assays for multiple biomarkers. We find that the serial risk score classification approach can maintain accuracy while achieving a reduction in cost compared to testing all individuals with all assays.

HIV-1 coreceptor usage in paired plasma RNA and proviral DNA from patients with acute and chronic infection never treated with antiretroviral therapy

Although an independent evolution of viral quasispecies in different body sites might determine a differential compartmentalization of viral variants, the aim of this paper was to establish whether sequences from peripheral blood mononuclear cells (PBMCs) and plasma provide different or complementary information on HIV tropism in patients with acute or chronic infection. Tropism was predicted using genotypic testing combined with geno2pheno (coreceptor) analysis at a 10% false positive rate in paired RNA and DNA samples from 75 antiretroviral-naïve patients (divided on the basis of avidity index into patients with a recent or long-lasting infection). A high prevalence of R5 HIV strains (97%) was observed in both compartments (plasma and PBMCs) in patients infected recently. By contrast, patients with a long-lasting infection showed a quite different situation in the two compartments, revealing more (46%) X4/DM in PBMCs than patients infected recently (3%) (P = 0.008). As- a knowledge of viral strains in different biological compartments might be crucial to establish a therapeutic protocol, it could be extremely useful to detect not only viral strains in plasma, but also viruses hidden or archived in different cell compartments to better understand disease evolution and treatment efficacy in patients infected with HIV.

Recent infection testing algorithm (RITA) applied to new HIV diagnoses in England, Wales and Northern Ireland, 2009 to 2011

In 2009, Public Health England (PHE) introduced the routine application of a recent infection testing algorithm (RITA) to new HIV diagnoses, where a positive RITA result indicates likely acquisition of infection in the previous six months. Laboratories submit serum specimens to PHE for testing using the HIV 1/2gO AxSYM assay modified for the determination of HIV antibody avidity. Results are classified according to avidity index and data on CD₄ count, antiretroviral treatment and the presence of an AIDS-defining illness. Between 2009 and 2011, 38.4% (6,966/18,134) of new HIV diagnoses in England, Wales and Northern Ireland were tested. Demographic characteristics of those tested were similar to all persons with diagnosed HIV. Overall, recent infection was 14.7% (1,022/6,966) and higher among men who have sex with men (MSM) (22.3%, 720/3,223) compared with heterosexual men and women (7.8%, 247/3,164). Higher proportions were among persons aged 15-24 years compared with those ≥50 years (MSM 31.2% (139/445) vs 13.6% (42/308); heterosexual men and women 17.3% (43/249) vs 6.2% (31/501)). Among heterosexual men and women, black Africans were least likely to have recent infection compared with whites (4.8%, 90/1,892 vs 13.3%, 97/728; adjusted odds ratio: 0.6; 95% CI: 0.4-0.9). Our results indicate evidence of ongoing HIV transmission during the study period, particularly among MSM.

Guidance and practice on frequency of HIV and sexually transmitted infection testing in men who have sex with men - what is the European situation?

Men who have sex with men (MSM) are at particular risk for HIV/sexually transmitted infections (STI). To investigate the European guidance used for MSM STI and HIV screening, risk level profiling and how this translated to practice, we conducted a questionnaire survey of leading physicians in the European branch of the International Union against Sexually Transmitted Infections (IUSTI). We identified that most European countries have limited guidance on screening intervals for MSM. Where risk profiling is advised, it is often left to clinicians to weight different behaviours and decide on screening frequency. Our results suggest that European MSM STI and HIV testing guidelines be developed with clear and specific recommendations around screening intervals and risk profiling. These guidelines will be particularly helpful due to rapidly evolving models of sexual healthcare, and the emergence of new providers who may benefit from guidelines that require less interpretation.

Estimation of HIV incidence using multiple biomarkers

The incidence of human immunodeficiency virus (HIV) is the rate at which new HIV infections occur in populations. The development of accurate, practical, and cost-effective approaches to estimation of HIV incidence is a priority among researchers in HIV surveillance because of limitations with existing methods. In this paper, we develop methods for estimating HIV incidence rates using multiple biomarkers in biological samples collected from a cross-sectional survey. An advantage of the method is that it does not require longitudinal follow-up of individuals. We use assays for BED, avidity, viral load, and CD4 cell count data from clade B samples collected in several US epidemiologic cohorts between 1987 and 2010. Considering issues of accuracy, cost, and implementation, we identify optimal multiassay algorithms for estimating incidence. We find that the multiple-biomarker approach to cross-sectional HIV incidence estimation corrects the significant deficiencies of currently available approaches and is a potentially powerful and practical tool for HIV surveillance.

How should we best estimate the mean recency duration for the BED method?

BED estimates of HIV incidence from cross-sectional surveys are obtained by restricting, to fixed time T, the period over which incidence is estimated. The appropriate mean recency duration () then refers to the time where BED optical density (OD) is less than a pre-set cut-off C, given the patient has been HIV positive for at most time T. Five methods, tested using data for postpartum women in Zimbabwe, provided similar estimates of for C = 0.8: i) The ratio (r/s) of the number of BED-recent infections to all seroconversions over T = 365 days: 192 days [95% CI 168–216]. ii) Linear mixed modeling (LMM): 191 days [95% CI 174–208]. iii) Non-linear mixed modeling (NLMM): 196 days [95% CrI 188–204]. iv) Survival analysis (SA): 192 days [95% CI 168–216]. Graphical analysis: 193 days. NLMM estimates of - based on a biologically more appropriate functional relationship than LMM – resulted in best fits to OD data, the smallest variance in estimates of , and best correspondence between BED and follow-up estimates of HIV incidence, for the same subjects over the same time period. SA and NLMM produced very similar estimates of but the coefficient of variation of the former was >3 times as high. The r/s method requires uniformly distributed seroconversion events but is useful if data are available only from a single follow-up. The graphical method produces the most variable results, involves unsound methodology and should not be used to provide estimates of . False-recent rates increased as a quadratic function of C: for incidence estimation C should thus be chosen as small as possible, consistent with an adequate resultant number of recent cases, and accurate estimation of . Inaccuracies in the estimation of should not now provide an impediment to incidence estimation.

HIV incidence estimate combining HIV/AIDS surveillance, testing history information and HIV test to identify recent infections in Lazio, Italy

BACKGROUND

The application of serological methods in HIV/AIDS routine surveillance systems to identify persons with recently acquired HIV infection has been proposed as a tool which may provide an accurate description of the current transmission patterns of HIV. Using the information about recent infection it is possible to estimate HIV incidence, according to the model proposed by Karon et al. in 2008, that accounts for the effect of testing practices on the number of persons detected as recently infected.

METHODS

We used data from HIV/AIDS surveillance in the period 2004-2008 to identify newly diagnosed persons. These were classified with recent/non-recent infection on the basis of an avidity index result, or laboratory evidence of recently acquired infection (i.e., previous documented negative HIV test within 6 months; or presence of HIV RNA or p24 antigen with simultaneous negative/indeterminate HIV antibody test). Multiple imputation was used to impute missing information. The incidence estimate was obtained as the number of persons detected as recently infected divided by the estimated probability of detection. Estimates were stratified by calendar year, transmission category, gender and nationality.

RESULTS

During the period considered 3,633 new HIV diagnoses were reported to the regional surveillance system. Applying the model, we estimated that in 2004-2008 there were 5,465 new infections (95%CI: 4,538-6,461); stratifying by transmission category, the estimated number of infections was 2,599 among heterosexual contacts, 2,208 among men-who-have-sex-with-men, and 763 among injecting-drug-users. In 2008 there were 952 (625-1,229) new HIV infections (incidence of 19.9 per 100,000 person-years). In 2008, for men-who-have-sex-with-men (691 per 100,000 person-years) and injecting drug users (577 per 100,000 person-years) the incidence remained comparatively high with respect to the general population, although a decreasing pattern during 2004-2008 was observed for injecting-drug-users.

CONCLUSIONS

These estimates suggest that the transmission of HIV infection in Lazio remains frequent and men-who-have-sex-with men and injecting-drug-users are still greatly affected although the majority of new infections occurs among heterosexual individuals.

Bayesian evidence synthesis for a transmission dynamic model for HIV among men who have sex with men

Understanding infectious disease dynamics and the effect on prevalence and incidence is crucial for public health policies. Disease incidence and prevalence are typically not observed directly and increasingly are estimated through the synthesis of indirect information from multiple data sources. We demonstrate how an evidence synthesis approach to the estimation of human immunodeficiency virus (HIV) prevalence in England and Wales can be extended to infer the underlying HIV incidence. Diverse time series of data can be used to obtain yearly “snapshots” (with associated uncertainty) of the proportion of the population in 4 compartments: not at risk, susceptible, HIV positive but undiagnosed, and diagnosed HIV positive. A multistate model for the infection and diagnosis processes is then formulated by expressing the changes in these proportions by a system of differential equations. By parameterizing incidence in terms of prevalence and contact rates, HIV transmission is further modeled. Use of additional data or prior information on demographics, risk behavior change and contact parameters allows simultaneous estimation of the transition rates, compartment prevalences, contact rates, and transmission probabilities.