Development of a Mathematical Model to Estimate the Cost-Effectiveness of HRSA's Ryan White HIV/AIDS Program

Agent based modelling of blood borne viruses: a scoping review

BACKGROUND

The models that historically have been used to model infectious disease outbreaks are equation-based and statistical models. However, these models do not capture the impact of individual and social factors that affect the spread of common blood-borne viruses (BBVs) such as human immunodeficiency virus (HIV), hepatitis C virus (HCV), and hepatitis B virus (HBV). Agent-based modelling (ABM) is an alternative modelling approach that is gaining popularity in public health and epidemiology. As the field expands, it is important to understand how ABMs have been applied. In this context, we completed a scoping review of research that has been done on the ABM of BBVs.

METHOD

The inclusion/exclusion criteria were drafted using the idea of Population, Concept, and Context (PCC). The Preferred Reporting Item for Systematic Reviews and Meta-Analysis, an extension to scoping review (PRISMA-ScR), was employed in retrieving ABM literature that studied BBVs. Three databases (Scopus, Pubmed, and Embase) were systematically searched for article retrieval. 200 articles were retrieved from all the databases, with 10 duplicates. After removing the duplicates, 190 papers were screened for inclusion. After analysing the remaining articles, 70 were excluded during the abstract screening phase, and 32 were excluded during the full-text decision. Eighty-eight were retained for the scoping review analysis. To analyse this corpus of 88 papers, we developed a five-level taxonomy that categorised each paper based first on disease type, then transmission mechanism, then modelled population, then geographic location and finally, model outcome.

RESULTS

The result of this analysis show significant gaps in the ABM of BBV literature, particularly in the modeling of social and individual factors influencing BBV transmission.

CONCLUSION

There is a need for more comprehensive models that address various outcomes across different populations, transmission and intervention mechanisms. Although ABMs are a valuable tool for studying BBVs, further research is needed to address existing gaps and improve our understanding of individual and social factors that influence the spread and control of BBVs. This research can inform researchers, modellers, epidemiologists, and public health practitioners of the ABM research areas that need to be explored to reduce the burden of BBVs globally.

Association of Racial Residential Segregation and Other Social Determinants of Health with HIV Late Presentation

Understanding social determinants of HIV late presentation with advanced disease (LPWA) beyond individual-level factors could help decrease LPWA and improve population-level HIV outcomes. This study aimed to examine county-level social determinants of health (SDOH) with HIV late presentation. We aggregated datasets for analysis by linking statewide HIV diagnosis data from the South Carolina (SC) Enhanced HIV/AIDS Reporting System and multiple social contextual datasets (e.g., the American Community Survey). All adult (18 years and older) people with HIV diagnosed from 2014 to 2019 in SC were included. Linear mixed models with forward selection were employed to explore the association of county-level SDOH with the county-level three-year moving average percentage of LPWA and average delay time from HIV infection to diagnosis. Around 30% of new HIV diagnoses were LPWA in SC, and the mean delay time for people with LPWA was approximately 13 years. Counties with more racial residential segregation had longer average delay time (Adjusted beta = 5.079, 95% CI: 0.268 ~ 9.889). Regarding other SDOH, the increased percentage of LPWA was associated with fewer Ryan White centers per 100,000 population (Adjusted beta = -0.006, 95% CI: -0.011~-0.001) and higher percentages of the population with less than a high school education (Adjusted beta = 0.008, 95% CI: 0 ~ 0.015). Reducing county-level disparities in LPWA requires multifaceted interventions addressing multiple dimensions of SDOH. Targeted interventions are needed for counties with more Black residential segregation, fewer Ryan White centers, and higher percentages of less than high school education.

The health equity implications of the Health Resources and Services Administration's Ryan White HIV/AIDS Program

OBJECTIVE

Investigate the role of the Ryan White HIV/AIDS Program (RWHAP) - which funds services for vulnerable and historically disadvantaged populations with HIV - in reducing health inequities among people with HIV over a 10-year horizon.

DESIGN

We use an agent-based microsimulation model to incorporate the complexity of the program and long-time horizon.

METHODS

We use a composite measure (the Theil index) to evaluate the health equity implications of the RWHAP for each of four subgroups (based on race and ethnicity, age, gender, and HIV transmission category) and two outcomes (probability of being in care and treatment and probability of being virally suppressed). We compare results with the RWHAP fully funded versus a counterfactual scenario, in which the medical and support services funded by the RWHAP are not available.

RESULTS

The model indicates the RWHAP will improve health equity across all demographic subgroups and outcomes over a 10-year horizon. In Year 10, the Theil index for race and ethnicity is 99% lower for both outcomes under the RWHAP compared to the non-RWHAP scenario; 71-93% lower across HIV transmission categories; 31-44% lower for age; and 73-75% lower for gender.

CONCLUSION

Given the large number of people served by the RWHAP and our findings on its impact on equity, the RWHAP represents an important vehicle for achieving the health equity goals of the National HIV/AIDS Strategy (2022-2025) and the Ending the HIV Epidemic Initiative goal of reducing new infections by 90% by 2030.

Leveraging social networks for identification of people with HIV who are virally unsuppressed

OBJECTIVES

This study investigates primary peer-referral engagement (PRE) strategies to assess which strategy results in engaging higher numbers of people with HIV (PWH) who are virally unsuppressed.

DESIGN

We develop a modeling study that simulates an HIV epidemic (transmission, disease progression, and viral evolution) over 6 years using an agent-based model followed by simulating PRE strategies. We investigate two PRE strategies where referrals are based on social network strategies (SNS) or sexual partner contact tracing (SPCT).

METHODS

We parameterize, calibrate, and validate our study using data from Chicago on Black sexual minority men to assess these strategies for a population with high incidence and prevalence of HIV. For each strategy, we calculate the number of PWH recruited who are undiagnosed or out-of-care (OoC) and the number of direct or indirect transmissions.

RESULTS

SNS and SPCT identified 256.5 [95% confidence interval (CI) 234-279] and 15 (95% CI 7-27) PWH, respectively. Of these, SNS identified 159 (95% CI 142-177) PWH OoC and 32 (95% CI 21-43) PWH undiagnosed compared with 9 (95% CI 3-18) and 2 (95% CI 0-5) for SPCT. SNS identified 15.5 (95% CI 6-25) and 7.5 (95% CI 2-11) indirect and direct transmission pairs, whereas SPCT identified 6 (95% CI 0-8) and 5 (95% CI 0-8), respectively.

CONCLUSION

With no testing constraints, SNS is the more effective strategy to identify undiagnosed and OoC PWH. Neither strategy is successful at identifying sufficient indirect or direct transmission pairs to investigate transmission networks.

Estimating contact network properties by integrating multiple data sources associated with infectious diseases

To effectively mitigate the spread of communicable diseases, it is necessary to understand the interactions that enable disease transmission among individuals in a population; we refer to the set of these interactions as a contact network. The structure of the contact network can have profound effects on both the spread of infectious diseases and the effectiveness of control programs. Therefore, understanding the contact network permits more efficient use of resources. Measuring the structure of the network, however, is a challenging problem. We present a Bayesian approach to integrate multiple data sources associated with the transmission of infectious diseases to more precisely and accurately estimate important properties of the contact network. An important aspect of the approach is the use of the congruence class models for networks. We conduct simulation studies modeling pathogens resembling SARS-CoV-2 and HIV to assess the method; subsequently, we apply our approach to HIV data from the University of California San Diego Primary Infection Resource Consortium. Based on simulation studies, we demonstrate that the integration of epidemiological and viral genetic data with risk behavior survey data can lead to large decreases in mean squared error (MSE) in contact network estimates compared to estimates based strictly on risk behavior information. This decrease in MSE is present even in settings where the risk behavior surveys contain measurement error. Through these simulations, we also highlight certain settings where the approach does not improve MSE.

Assessing the reliability of the CD4 depletion model in the presence of Ending the HIV Epidemic initiatives

BACKGROUND

Accurate estimates of HIV incidence are necessary to monitor progress towards Ending the HIV Epidemic (EHE) initiative targets (90% decline by 2030). U.S. incidence estimates are derived from a CD4 depletion model (CD4 model). We performed simulation-based analyses to investigate the ability of this model to estimate HIV incidence when implementing EHE interventions that have the potential to shorten the duration between HIV infection and diagnosis (diagnosis delay).

METHODS

Our simulation study evaluates the impact of three parameters on the accuracy of incidence estimates derived from the CD4 model: rate of HIV incidence decline, length of diagnosis delay, and sensitivity of using CD4 + cell counts to identify new infections (recency error). We model HIV incidence and diagnoses after the implementation of a theoretical prevention intervention and compare HIV incidence estimates derived from the CD4 model to simulated incidence.

RESULTS

Theoretical interventions that shortened the diagnosis delay (10-50%) result in overestimation of HIV incidence by the CD4 model (10-92%) in the first year and by more than 10% for the first 6 years after implementation of the intervention. Changes in the rate of HIV incidence decline and the presence of recency error had minimal impact on the accuracy of incidence estimates derived from the CD4 model.

CONCLUSION

In the setting of EHE interventions to identify persons with HIV earlier during infection, the CD4 model overestimates HIV incidence. Alternative methods to estimate incidence based on objective measures of incidence are needed to assess and monitor EHE interventions.

Economic benefits of the United States’ AIDS drug assistance Program: A systematic review of cost analyses to guide research and policy priorities

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The AIDS Drug Assistance Program (ADAP) is a $2.4 billion a year program.

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Little formal economic analysis of the ADAP in the literature.

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The few economic analyses of ADAP use 2008 or older data.

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ADAP programs’ healthcare delivery have changed substantially since 2008.

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Updated person-centered cost effectiveness models assessing ADAP are needed.

As part of the Ryan White HIV/AIDs Program, the federally-funded, state-administered AIDS Drug Assistance Program (ADAP) provides prescription drug medications, including antiretroviral therapy, for people with HIV (PWH) who are uninsured/underinsured and have a low income. ADAP expenditures are ∼$2.4 billion annually, but there is a dearth of formal economic analysis supporting the societal perspective. We conducted a systematic review of economic analyses of the United States’ AIDS Drug Assistance Program to establish future research priorities based on gaps in knowledge. We searched six electronic databases for articles published before January 2022 that met inclusion criteria. We used the 2022 Consolidated Health Economic Evaluation Reporting Standards to assess the quality of reporting of the economic evaluations. We extracted data into categories to assess gaps and needs for future economic evaluation. Seven studies met inclusion criteria. Two used the same modeling approaches but were published with slightly different outcomes. The few economic analyses that focused solely on ADAP were conducted using 2008 or older data. The most recent study modeled the net cost per quality-adjusted life-year (QALY) secondary to reducing new HIV cases among those virally suppressed, but did not include the economic or health benefits for PWH. ADAP programs’ delivery of antiretroviral therapy has shifted from primarily direct provision to subsidizing insurance plans. None of the models take these shifts into account. Updated person-centered cost effectiveness models assessing ADAP are needed on a national and state-by-state level to guide policy decisions and coverage determinations.

Cost-Effectiveness of HRSA's Ryan White HIV/AIDS Program?

BACKGROUND

With an annual budget of more than $2 billion, the Health Resources and Services Administration's Ryan White HIV/AIDS Program (RWHAP) is the third largest source of public funding for HIV care and treatment in the United States, yet little analysis has been done to quantify the long-term public health and economic impacts of the federal program.

METHODS

Using an agent-based, stochastic model, we estimated health care costs and outcomes over a 50-year period in the presence of the RWHAP relative to those expected to prevail if the comprehensive and integrated system of medical and support services funded by the RWHAP were not available. We made a conservative assumption that, in the absence of the RWHAP, only uninsured clients would lose access to these medical and support services.

RESULTS

The model predicts that the proportion of people with HIV who are virally suppressed would be 25.2 percentage points higher in the presence of the RWHAP (82.6 percent versus 57.4 percent without the RWHAP). The number of new HIV infections would be 18 percent (190,197) lower, the number of deaths among people with HIV would be 31 percent (267,886) lower, the number of quality-adjusted life years would be 2.7 percent (5.6 million) higher, and the cumulative health care costs would be 25 percent ($165 billion) higher in the presence of the RWHAP relative to the counterfactual. Based on these results, the RWHAP has an incremental cost-effectiveness ratio of $29,573 per quality-adjusted life year gained compared with the non-RWHAP scenario. Sensitivity analysis indicates that the probability of transmitting HIV via male-to-male sexual contact and the cost of antiretroviral medications have the largest effect on the cost-effectiveness of the program.

CONCLUSIONS

The RWHAP would be considered very cost-effective when using standard guidelines of less than the per capita gross domestic product of the United States. The results suggest that the RWHAP plays a critical and cost-effective role in the United States' public health response to the HIV epidemic.