Combinations of interventions to achieve a national HIV incidence reduction goal

Gopalappa, Chaitra; Sansom, Stephanie L.; Farnham, Paul G.; Chen, Yao-Hsuan

doi:10.1097/qad.0000000000001653

Cited by 14 publications

(9 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For our analysis, we used an updated version of PATH 3.0, Progression and Transmission of HIV, an agent-based model created in NetLogo, 10 which has been used in previous analyses of HIV prevention interventions and costs. 6,9,11,12 The model was initialized with a population of 10,000 persons, representing a cross section of PWH in the United States in 2006. The initial population was weighted to represent PWH by age, sex, transmission risk group (men who have sex with men (MSM), persons who inject drugs (PWID)-male, PWID-female, PWID-MSM, heterosexual male, heterosexual female), and stage of the HIV care continuum (acutely infected, but not aware of infection; nonacutely infected and unaware; aware, but not in care; in care, but not virally suppressed; in care and virally suppressed).…”

Section: Methodsmentioning

confidence: 99%

Estimated Lifetime HIV–Related Medical Costs in the United States

et al. 2021

Self Cite

View full text Add to dashboard Cite

Background: Lifetime cost estimates are a useful tool in measuring the economic burden of HIV in the United States. Previous estimation methods need to be updated, given improving antiretroviral therapy regimens and updated costs. Methods:We used an updated version of the agent-based model progression and transmission of HIV (PATH) 3.0 to reflect current regimens and costs. We simulated a cohort of those infected in 2015 until the last person had died to track the lifetime costs for treatment of HIV, including HIV health care utilization costs (inpatient, outpatient, opportunistic infection prophylaxis, non-HIV medication, and emergency department), opportunistic infection treatment costs, and testing costs. We assumed a median per-person diagnosis delay of 3 years and a 3% base monthly probability of dropout from care for a base-case scenario. Additionally, we modeled a most favorable scenario (median diagnosis delay of 1 year and 1% base dropout rate) and a least favorable scenario (median diagnosis delay of 5 years and 5% base dropout rate).

show abstract

Section: Methodsmentioning

confidence: 99%

Estimated Lifetime HIV–Related Medical Costs in the United States

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…We modeled the effects of those interventions by altering the rates of each transition flow, holding all other flow rates constant, as well as by altering all flow rates together. Given their importance in previous models, 27 we examined rate changes in flow 2, linkage to care at diagnosis, in flow 9, loss of viral suppression after achieving it, and in all flows grouped together, by transmission group (MSM, HET, PWID). We examined changes in their base values of ±25%, 50%, and 75% in the scenario analyses.…”

Section: Hope Modelmentioning

confidence: 99%

Estimating the HIV Effective Reproduction Number in the United States and Evaluating HIV Elimination Strategies

Chen

Farnham

Hicks

et al. 2021

Journal of Public Health Management and Practice

Self Cite

View full text Add to dashboard Cite

Context:The reproduction number is a fundamental epidemiologic concept used to assess the potential spread of infectious diseases and whether they can be eliminated. Objective: We estimated the 2017 United States HIV effective reproduction number, R e , the average number of secondary infections from an infected person in a partially infected population. We analyzed the potential effects on R e of interventions aimed at improving patient flow rates along different stages of the HIV care continuum. We also examined these effects by individual transmission groups. Design: We used the HIV Optimization and Prevention Economics (HOPE) model, a compartmental model of disease progression and transmission, and the next-generation matrix method to estimate R e . We then projected the impact of changes in HIV continuum-of-care interventions on the continuum-of-care flow rates and the estimated R e in 2020. Setting: United States. Participants: The HOPE model simulated the sexually active US population and persons who inject drugs, aged 13 to 64 years, which was stratified into 195 subpopulations by transmission group, sex, race/ethnicity, age, male circumcision status, and HIV risk level. Main Outcome Measures:The estimated value of R e in 2017 and changes in R e in 2020 from interventions affecting the continuum-of-care flow rates. Results: Our estimated HIV R e in 2017 was 0.92 [0.82, 0.94] (base case [min, max across calibration sets]). Among the interventions considered, the most effective way to reduce R e substantially below 1.0 in 2020 was to maintain viral suppression among those receiving HIV treatment. The greatest impact on R e resulted from changing the flow rates for men who have sex with men (MSM). Conclusions: Our results suggest that current prevention and treatment efforts may not be sufficient to move the country toward HIV elimination. Reducing R e to substantially below 1.0 may be achieved by an ongoing focus on early diagnosis, linkage to care, and sustained viral suppression especially for MSM.

show abstract

“…Action is required to increase quality evaluations of anti-human trafficking programs; to ensure that programs are targeted, implemented, and delivered effectively; and to improve the knowledge concerning the impact of programs (Davy, 2016). Indeed, having modeled the spread of disease using dynamic epidemic models researchers have been able to successfully determine the potential impact of various disease intervention strategies, prioritize prevention needs and develop resource allocation plans for treatment and eradication (Aleman et al, 2011;Büyüktahtakın et al, 2018;Eaton et al, 2015;Gopalappa et al, 2012Gopalappa et al, , 2017Zaric & Brandeau, 2001). Utilizing such frameworks and methodologies from epidemiology and adapting them to the context of labor trafficking have the potential to improve the efficacy and impact of anti-trafficking measures.…”

Section: Allocation Of Resources and Provision Of Informationmentioning

confidence: 99%

An Epidemiological Perspective on Labor Trafficking

Gallo¹,

Konrad

Thinyane³

2020

Journal of Human Trafficking

View full text Add to dashboard Cite

Ending all forms of labor trafficking by 2030 has been prioritized within the United Nations Sustainable Development Goals (Target 8.7), yet progress in combatting trafficking has been limited. Human trafficking is recognized as a global public health concern because of its widespread negative impacts on individual and population level health. This has led researchers, policymakers, and NGOs to frequently claim that trafficking has reached "epidemic proportions". If such is the case, then using tools from public health and epidemiology can help to generate novel insights in the way we conceptualize, research, and address the problem of trafficking. Using a methodological framework known as the Epidemiologic Problem Oriented Approach (EPOA), we analyze the various components of labor trafficking identification and response, drawing contextualized comparisons with infectious disease epidemiology to characterize labor trafficking as a "disease". This crossdisciplinary approach provides a well-defined conceptual organization of the components that play a role in understanding trafficking dynamics. Epidemiology is highly relevant in the study of trafficking and contributes to the growing research interest surrounding the intersection of public health and human trafficking. KEYWORDS Labor trafficking; public health; epidemiology There are no jobs in Sein's hometown in Myanmar, so he made the hard decision to look for work in Thailand. Sein has been working as a fisherman now for five years. Though the hours are long, and the conditions are harsh, he doesn't mind as he is able to provide for his family. Compared to his previous boss Ohnmar, Cho is a saint, and doesn't take it out on the fishers when the nets don't come up full. Ohnmar was brutal-on one trip the nets kept coming up empty and the crew had to stay out at sea a further 3 days than what they had planned. He didn't let them rest until the hold was full, and because the extended trip was unplanned, they ran out of drinking water. When they returned to port, Ohnmar refused to give extra time off in lieu of the extended time at sea, and only gave Sein a fraction of the salary that he had been promised. Ohnmar had said that this would teach the fishers for not working hard enough. If the fishers complained, Ohnmar would think of another punishment for disrespecting himoften keeping them onboard the boat while it was in port. Sein had often considered leaving, but needed the money that Ohnmar owed him. Ohnmar hadn't always been like this-they had grown up in the same village, and Ohnmar had been the one who initially told Sein about his work in Thailand. Three years after Sein joined him on the boat, Ohnmar had been promoted to the position of Chiu-the right-hand man of the captain. It seemed like Ohnmar wanted to prove he was right for the job and deserving of the increased authority (and pay) that the promotion included. Cho was different. He pushed Sein and the crew to work very long hours, but always recorded their days at sea accurately. They often ran trips ...

show abstract

Combinations of interventions to achieve a national HIV incidence reduction goal

Cited by 14 publications

References 8 publications

Estimated Lifetime HIV–Related Medical Costs in the United States

Estimated Lifetime HIV–Related Medical Costs in the United States

Estimating the HIV Effective Reproduction Number in the United States and Evaluating HIV Elimination Strategies

An Epidemiological Perspective on Labor Trafficking

Contact Info

Product

Resources

About