Current Work

For this project, we will further extend the previously calibrated/validated simulation model of HIV among MSM in Baltimore to incorporate explicit representation of rectal gonorrhea/chlamydia (NG/CT), modeled as a single entity, in parallel to the existing model of HIV transmission. NG/CT contribute to the increased risk and racial disparities in HIV among MSM[1]. We will explicitly and separately model transmission of NG/CT through the Baltimore MSM population, accounting for the fact that the selected STI will co-localize with HIV and will indeed increase the transmission risk of HIV in individuals harboring the NG/CT. We will calibrate the model to the estimated incidence and prevalence of NG/CT in Baltimore’s MSM population, including co-localization with HIV and the fact that, for many individuals, HIV infection is preceded/foreshadowed by infection with NG/CT (thereby making infection with NG/CT a potential target for PrEP initiation). We will use the model to demonstrate how NG/CT affects the dynamics of HIV among Baltimore’s MSM population. We will then project the potential impact of better STI care – including offering PrEP to individuals diagnosed with NG/CT – on HIV transmission in the city.

In this analysis, we will apply the methodology for adjusting CDC Medical Monitoring Project-based HIV care continuum estimates, developed earlier using data from the HIV Outpatient Survey, to the more robust NA-ACCORD clinical cohort data. This manuscript will examine the 5-year period from 2009-2014 to provide adjusted and more-representative estimate of the national HIV care continuum and compare these results to results from HIV NHSS laboratory surveillance.

The primary goal of this project is to use stochastic network modeling to estimate the proportion of HIV infections among MSM that are caused by prevalent STIs. HIV transmission in a dynamic sexual network of MSM will be modeled in the context of five STIs: urethral and rectal chlamydia, urethral and rectal gonorrhea, and syphilis. The proportion of HIV infections occurring among men with prevalent STI infection will be considered the population attributable fraction. Due to the uncertainty in estimates of transmission probabilities, we will conduct sensitivity analyses across a range of plausible values for increased susceptibility to HIV acquisition due to STI infection. 

This study will investigate the implications of race-specific PrEP uptake and adherence on HIV incidence and prevalence among white and black MSM in the United States. Model scenarios will explore the level of PrEP utilization needed to close the gap in HIV incidence between these two groups.

For this project, we will further extend the previously calibrated/validated simulation model of HIV among MSM in Baltimore to incorporate explicit representation of rectal gonorrhea/chlamydia (NG/CT), modeled as a single entity, in parallel to the existing model of HIV transmission. NG/CT contribute to the increased risk and racial disparities in HIV among MSM[1]. We will explicitly and separately model transmission of NG/CT through the Baltimore MSM population, accounting for the fact that the selected STI will co-localize with HIV and will indeed increase the transmission risk of HIV in individuals harboring the NG/CT. We will calibrate the model to the estimated incidence and prevalence of NG/CT in Baltimore’s MSM population, including co-localization with HIV and the fact that, for many individuals, HIV infection is preceded/foreshadowed by infection with NG/CT (thereby making infection with NG/CT a potential target for PrEP initiation). We will use the model to demonstrate how NG/CT affects the dynamics of HIV among Baltimore’s MSM population. We will then project the potential impact of better STI care – including offering PrEP to individuals diagnosed with NG/CT – on HIV transmission in the city.

The primary goal of this project is to use stochastic network modeling to estimate the proportion of HIV infections among MSM that are caused by prevalent STIs. HIV transmission in a dynamic sexual network of MSM will be modeled in the context of five STIs: urethral and rectal chlamydia, urethral and rectal gonorrhea, and syphilis. The proportion of HIV infections occurring among men with prevalent STI infection will be considered the population attributable fraction. Due to the uncertainty in estimates of transmission probabilities, we will conduct sensitivity analyses across a range of plausible values for increased susceptibility to HIV acquisition due to STI infection. 

This study will examine the role of expedited partner therapy (EPT) for STI prevention among MSM by extending our HIV/STI modeling platform to include this new STI control technique for site-specific gonorrhea, chlamydia, and syphilis. EPT is not currently recommended for MSM by CDC and thus traditional epidemiological studies are not We propose an analysis to use this platform to test how EPT could be used alone and alongside other STI prevention interventions to reduce disease incidence. 
In the proposed models, EPT will be simulated as the immediate provision of antibiotic medication to the current sexual partners of those MSM who are routinely diagnosed and treated for those STIs in clinical settings. Parameters in these models will include the coverage fraction for EPT (the proportion of MSM with EPT-indicated partners who accept it) and medication uptake among those partners. Sensitivity analyses for these models could explore how EPT targeted at STI-infected MSM with particular partnership configurations could maximize the epidemiological impact (infections averted) and efficiency (number needed to treat) of that intervention. With collaboration from DSTDP colleagues at CDC, these outcomes could be directly translated into a cost-effectiveness analysis comparing standard clinic-based testing and treatment of STIs versus EPT. The paper may also include an online web-based modeling tool that will allow STI program officials at local jurisdictions to explore the potential impact, efficiency, and cost-effectiveness of EPT to be integrated within their STI prevention programs. This will be based on the design and content of the web tool for our Year 2 paper [http://prism.shinyapps.io/cdc-prep-guidelines].

MSM in the United States are disproportionately affected by the STI epidemic. In previous work, we have demonstrated the efficacy of HIV preexposure prophylaxis (PrEP) for STI prevention thanks to the recommended STI screening process for PrEP users. Men who are eligible for PrEP engage in certain risk behaviors for both HIV and STI transmission, making them good candidates for targeted STI testing. There is a particular need for modeling the potential impact of various targeting and intervention programs on STI incidence in this key population. We extend our existing agent-based mathematical model of rectal and urogenital HIV and STD transmission to include syphilis with an aim to evaluate the effect of the current STD screening recommendations referenced in the CDC STI treatment guidelines and other potential targeting mechanisms on STI incidence for MSM.
Although these recommendations have been present for a long period of time, uptake of STI testing in this population has been suboptimal. Parameters in these models will include the coverage fraction for STI testing (the proportion of MSM with EPT-indicated partners who accept it) and adherence to recommended testing intervals. Sensitivity analyses for these models could explore the length of the recommended screening intervals for all sexually active and high-risk MSM, respectively, as well as potential definitions of “high-risk” that could maximize the epidemiological impact (infections averted) and efficiency (number needed to treat) of that intervention. With collaboration with DSTDP colleagues at CDC, we also aim to conduct an economic evaluation of these guidelines, appraising the economic impact of different recommended testing interval lengths.

This work will complete a systematic review and meta-analysis of published literature to produce prevalence estimates of risk behaviors among young MSM (aged 13-18) in the United States. It will produce meta-analytic prevalence estimates for each of the outcomes of interest.

This modeling study will extend the work of our initial modeling efforts for adolescent MSM, which have been investigating the role of PrEP scale-up on HIV incidence in this group, by examining racial disparities in HIV incidence. Given the existing racial disparities in MSM, as well as racial differences in age of sexual debut, PrEP access and adherence, the scale-up of PrEP is expected to have differential effects on HIV incidence among Black versus non-Black adolescent MSM. Our goals are to estimate the expected impact of PrEP on Black and White ASMM separately, and to estimate the level of uptake that would be needed among Black ASMM to eliminate existing race disparities.

This modeling study will extend the work of our initial modeling efforts for adolescent MSM, which have been investigating the role of PrEP scale-up on HIV incidence in this group, by examining age-specific outcomes. The current adolescent model tracks 13 to 18 year olds explicitly, but does not simulate their disease dynamics during adulthood, when the downstream impact of prevention interventions could be still accrued. We will expand the adolescent model to be able to capture extended impact of these interventions on adolescents as they advance as far as age 40. Considering this extended model will allow us to consider how lifetime incidence of HIV is impacted by engaging adolescents with PrEP, as well as identify the number needed to treat in the combined population.

Four states - California, Florida, New York, and Texas - are responsible for half of incident tuberculosis (TB) in the United States. These states, however, differ from one another in their demographic make up (e.g., the size of the foreign-born and their origin) as well their recent trends of TB dynamics (e.g., the rates of declines in the TB incidence among US- and foreign-born in the last decade). These factors likely play important roles in driving local TB dynamics and may result in meaningful differences in TB dynamics in these four states. This in turn can have important implications for state-level TB control. 

Our primary goal is to estimate the potential impact (and key determinants of impact) for selected TB control interventions in four states– California, New York, Texas and Florida. We propose to use our modeling framework to study the potential impact of specific TB interventions, namely (1) improved contact investigation, (2) expanded treatment of latent TB infection, and (3) enhanced screening and treatment of immigrants (as they enter) and other high-risk groups. For each intervention (individually and in combination), we will estimate the impact in terms of potential reduction in TB incidence over the coming five years, relative to a baseline in which current trends are continued. While we will not perform a full economic costing of each intervention, we will estimate the number of key resources required (e.g., drug doses, number of physician visits, number of hospitalizations, number of contacts visited), which could be combined with unit cost data to provide a rough estimate of cost-effectiveness. The impact of specific interventions can vary considerably between states; our model will be able to identify such differences, and help inform optimal interventions at the state-level. Furthermore, given that we have an individual-based modeling framework, it is possible for us to estimate the impact of interventions (e.g., preventive therapy, contact investigation) that are individually focused, and to evaluate how that impact might differ from one state to the next. 

Substantial geographic heterogeneity in the distribution of TB risk factors contribute substantially to observed differences in the incidence and prevalence of TB in the US by state.  Such heterogeneity can have direct implications for TB interventions. In particular, impact and effectiveness of interventions estimated at the national level may not translate appropriately to state-level requirements. A quantitative understanding of geographic heterogeneity of TB-risk factors at a more granular level can therefore complement a targeted intervention approach. The primary research goal is to quantify the role of TB risk factors (including: Region of birth, HIV, diabetes, incarceration, and homeless status). This will require estimating the size of each subpopulation by state, the within-group incidence, and the overall contribution of the risk factor on state-level TB incidence. Additionally, we will calculate the population attributable fractions and total cases that could be averted based on the best performing state among California, Texas, Florida, or New York by subpopulation in order to illustrate and quantify a range of best-case scenarios.