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Localized economic modeling to ‘End the HIV epidemic in the US’: A case study across 6 cities

Full slide deck documenting the details of (1) Evidence synthesis; (2) Model calibration; (3) Establishing the status quo; (4) HIV interventions; (5) Core paper evaluating the cost-effectiveness of combination implementation of HIV interventions

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Localized economic modeling to ‘End the HIV epidemic in the US’: A case study across 6 cities

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  1. Localized economic modeling to ‘End the HIV epidemic in the US’ A case study across 6 cities Bohdan Nosyk, PhD Associate Professor, St. Paul’s Hospital CANFAR Chair in HIV/AIDS Research Faculty of Health Sciences, Simon Fraser University On behalf of the Health Economic Research Unit at the BC Centre for Excellence in HIV/AIDS NIH R01 DA041747. PI: Nosyk B; Co-I: Schackman BR, Gebo K, Metsch L, Feaster D, Kirk G, Golden M, Mehta S, Shoptaw S, Strathdee S, Dombrowski J, Montaner JSG, Del Rio C, Pandya A, Jalal H. Localized economic modeling to optimize public health strategies for HIV treatment and prevention. National Institutes on Drug Abuse.

  2. Sections This report is organized into the following sections: • Evidence Synthesis (slide 16) • Model Calibration and Validation (slide 344) • Defining the ‘status quo’ (slide 379) • Interventions (slide 399) • Localized combination implementation strategies to reach national ‘Ending the HIV Epidemic’ goals (slide 556)

  3. Background • Despite numerous successes in the fight against HIV/AIDS and a $20B annual investment in the US, progress is stalling • Evidence-based interventions available to Protect, Diagnose and Treat HIV/AIDS • Suboptimal implementation with wide disparities in access across regions, ethnic groups Source: www.hiv.gov

  4. Background • On February 5, 2019, the President of the United States announced the intention to end the HIV epidemic in the US by reducing new infections by 75% within 5 years and by 90% within 10 years • Department of Health and Human Services proposed to target 48 counties plus Washington, DC and San Juan, Puerto Rico and 7 Southern States • $291 million in new funding allocated in 2020

  5. Background • While investments are front-loaded, the benefits of HIV/AIDS treatment and prevention initiatives are accumulated over the long-term. • Given the need to project into the future with incomplete information, modeling is the only way to obtain an estimate of the full extent of costs and health benefits of HIV treatment and prevention interventions compared to other more-or less resource-intensive counterfactuals. • Health economic modeling can help us figure out what interventions work best for each epidemiological context.

  6. My frustrations with the current state of CEA • Non-standardized, largely unchecked methods for evidence synthesis • Some ‘black box’ elements of model development, calibration • Slow development process -> distance from prospective decision-making • Lacking connection to ‘real world’ application (public health interventions) • Needs of a given region, subpopulation • Possible/plausible scale of delivery, other aspects of implementation

  7. Focus on local HIV microepidemics: A case study for Kenya Same investment, greater health benefits for a localized vs. generalized HIV public health strategy Andersen et al., Lancet 2014; 384: 249-58.

  8. Background A primer on health economic evaluation • The comparative analysis of alternative courses of action in terms of both their costs and consequences. • Concerned with efficiency, not just effectiveness • Economic evaluation is about informing decisions on how to focus resources: Evidence-based decision-making • Our primary outcome: the incremental cost-effectiveness ratio (ICER):

  9. Background Orientation • Core principle: maximize population health • To do this, we use quality-adjusted life years (QALYs) in the denominator of our ICER. • The QALY a measure that captures improvements in both morbidity and mortality • Equity principle: QALYs gained across disease areas are of equal value • QALYs give priority to interventions that offer more time spent in good health.

  10. Background The health production function • The maximum output that can be produced out of a given combination of inputs. • The set of points corresponding to combination implementation strategies providing the greatest health benefits at different investment levels. ICER: (CostsC – CostsB) / (QALYsC – QALYsB) • Incremental Cost Effectiveness Ratios (ICERs) for combinations of strategies lying along the health production function are compared to the next-most resource intensive strategy Total benefits Total costs

  11. Localized economic modeling in 6 US cities

  12. Our focal cities: Home to 24.1% of the US population of people living with HIV/AIDS

  13. Background Objectives • Aim 1: Execute a comprehensive synthesis of available data sources on: • (i) The spatiotemporal course of the HIV and drug use epidemics in each of the demonstration cities; • (ii) Health resource use of PLHIV • (iii) Evidence on costs, effectiveness and implementation of HIV treatment and prevention interventions • Aim 2: Calibrate a published and validated dynamic HIV transmission model to define an optimal set of HIV care interventions in each setting. • Aim 3: Establish and validate a semi-automated report for each demonstration city, developed iteratively in accordance with direct responses from site-specific knowledge users in a qualitative survey over the two ‘pilot’ annual reports.

  14. Our Scientific Advisory Committee • Czarina N Behrends, PhD, Department of Healthcare Policy and Research, Weill Cornell Medical College • Carlos Del Rio, MD, Hubert Department of Global Health, Emory Center for AIDS Research, Rollins School of Public Health, Emory University • Julia C Dombrowski, MD, primary with Department of Medicine, Division of Allergy & Infectious Disease, adjunct in Epidemiology, University of Washington • Daniel J Feaster, PhD,Center for Family Studies, Department of Epidemiology and Public Health, Leonard M. Miller School of Medicine, University of Miami • Kelly A Gebo, MD, Bloomberg School of Public Health, Johns Hopkins University • Matthew Golden, MD, primary with Department of Medicine, Division of Allergy & Infectious Disease, adjunct in Epidemiology, University of Washington • Hawre Jalal, MD, PhD, Health Policy and Management, Graduate School of Public Health, University of Pittsburgh • Gregory Kirk, PhD, Bloomberg School of Public Health, Johns Hopkins University • Brandon DL Marshall, PhD, Department of Epidemiology, Brown School of Public Health, Rhode Island • Shruti H Mehta, PhD, Bloomberg School of Public Health, Johns Hopkins University • Zachary Meisel, MD, University of Pennsylvania, Leonard Davis Institute of Health Economics • Lisa Metsch, PhD, Department of Sociomedical Sciences, Mailman School of Public Health, Columbia University • Ankur Pandya, PhD, Department of Health Policy and Management, Harvard T.H. Chan School of Public Health • Bruce R Schackman, PhD, Department of Healthcare Policy and Research, Weill Cornell Medical College • Steven Shoptaw, PhD, Centre for HIV Identification, Prevention and Treatment Services, School of Medicine, University of California Los Angeles • Steffanie A Strathdee, PhD, School of Medicine, University of California San Diego

  15. Publications • Scientific Case (Panagiotoglou et al, AIDS Behav. 2018;22(9):3071-3082) • Evidence Synthesis (Krebs et al, PLoS One. 2019;14(5):e0217559) • Medical Care Costs (Enns et al, AIDS. 2019;33(9):1491-1500) • Disease progression, ART persistence (Wang et al, Lancet HIV. 2019;6(8):e531-e539) • Model Calibration and Validation (Zang et al, Accepted, Medical Decision Making) • Defining the ‘status quo’ comparator (Nosyk et al, In press, Clin Infect Dis. 2019) • Defining the evidence-based interventions (Krebs et al, Second review) • What will it take to ‘End the HIV Epidemic’ in the US? (Nosyk et al., under review)

  16. 1. Evidence Synthesis • Scientific Case (Panagiotoglou et al, AIDS Behav. 2018;22(9):3071-3082) • Evidence Synthesis (Krebs et al, PLoS One. 2019;14(5):e0217559) • Medical Care Costs (Enns et al, AIDS. 2019;33(9):1491-1500) • Disease progression, ART persistence (Wang et al, Lancet HIV. 2019;6(8):e531-e539) • Model Calibration and Validation (Zang et al, Accepted, Medical Decision Making) • Defining the ‘status quo’ comparator (Nosyk et al, In press, Clin Infect Dis. 2019) • Defining the evidence-based interventions (Krebs et al, Second review) • What will it take to ‘End the HIV Epidemic’ in the US? (Nosyk et al., under review)

  17. 1. Evidence Synthesis Background • This section describes our process for synthesizing the best available evidence to populate model parameter estimates. • Due to data limitations, uncertainty, and the goal to replicate key epidemiological trends for each city, we calibrated our model to match a number of different external targets for total PLHIV, new yearly diagnosis, and all-cause deaths among PLHIV1. • Full calibration process and results described in Section 2. Model Calibration and Validation (Slide 329). 1. Zang X, Krebs E, Min J, Pandya A, Marshall B, Schackman B, et al. Development and calibration of a dynamic HIV transmission model for 6 US cities. Medical Decision Making. 2019; Accepted. Krebs et al. PLoS One 2019; 14(5):e0217559.

  18. 1. Evidence Synthesis Background • Objective: To provide a comprehensive description of a novel evidence synthesis process to populate and calibrate a dynamic, compartmental HIV transmission model for six US cities. • We executed a mixed-method strategy to identify parameters in 6 categories: • Population estimates • Probability of HIV transmission • Treatment and HIV disease progression • HIV prevention programs • Costs • Health utility weights • We ranked the quality of each parameter using context- and domain-specific criteria. • To inform the 1,667 parameters needed for our model, we synthesized evidence from: • 57 peer-reviewed publications; • 23 public health and surveillance reports; • And we executed primary analyses using 11 data sets. Krebs et al. PLoS One 2019; 14(5):e0217559.

  19. 1. Evidence Synthesis Model Parameters by Category Krebs et al. PLoS One 2019; 14(5):e0217559.

  20. 1. Evidence Synthesis Our Model, At a Glance • For each city, the population aged 15-64 was stratified as  • Disease progression accounted for acute infection and CD4 strata gender race/ethnicity risk behavior type X X X Black PWID MSM White Latino Hetero female male MWID low high yes no yes no sexual risk level PrEP Opioid agonist treatment 42 strata s X X 19 states Krebs et al. PLoS One 2019; 14(5):e0217559.

  21. 1. Evidence Synthesis Summary of model parameters and evidence quality ranking Krebs et al. PLoS One 2019; 14(5):e0217559.

  22. 1. Evidence Synthesis Summary of model parameters and evidence quality ranking Common model parameters across cities • Common parameters selected based on estimates that were unlikely to vary across cities (probability of HIV transmission per sexual encounter, health-related quality of life estimates for each health state), or lacked data granularity at the city level (parameters for individuals who are out of treatment, OAT entry/exit rates). • 150 common parameters;150/1667 (8.9%) of total parameters. • 85/1,075 (8%) 1. Initial population estimates and population dynamics • 23/201 (11%) 2. Parameters used to calculate the probability of HIV transmission • 6/312 (2%) 3. Screening, diagnosis, treatment and HIV disease progression • 6/23 (26%) 4. HIV prevention programs • 0/26 (0%) 5. Costs of medical care • 30/30 (100%) 6. Health utility weights • 39% were best- or moderate-quality. Krebs et al. PLoS One 2019; 14(5):e0217559.

  23. 1. Evidence Synthesis Initial population estimates Model states populated by initial population estimates gender race/ethnicity risk behavior type X X male female White Latino Black MSM PWID MWID Hetero low high yes no yes no sexual risk level PrEP Opioid agonist treatment 42 strata s X X • 42 population subgroups divided across 19 health states • 798total possible model strata • Only heterosexual and MSM/MWID stratified by high/low sexual risk • Only PWID/MWID stratified by on/off opioid agonist treatment Krebs et al. PLoS One 2019; 14(5):e0217559.

  24. 1. Evidence Synthesis Initial population estimates HIV negative population • We derived baseline 15-64 population estimates from census data for all six cities • Census estimates were stratified by male/female and black/Hispanic/white & other • MSM1 and PWID2 numbers were derived from estimates of prevalence in the general population, stratified by race/ethnicity and gender. 1. Grey JA, Bernstein KT, Sullivan PS, Purcell DW, Chesson HW, et al. (2016) Estimating the Population Sizes of Men Who Have Sex With Men in US States and Counties Using Data From the American Community Survey. JMIR Public Health and Surveillance 2: e14. 2. Tempalski B, Pouget E, Cleland C, Brady J, Cooper H, et al. (2013) Trends in the population prevalence of people who inject drugs in US metropolitan areas 1992-2007. PLoS One 8: e64789. Krebs et al. PLoS One 2019; 14(5):e0217559.

  25. 1. Evidence Synthesis Initial population estimates HIV negative population • Due to greater uncertainty in estimates, MWID numbers triangulated from prevalence of PWID among MSM, and MSM among PWID1,2. • Baseline, MSM and PWID population estimates were high-moderate quality for all cities, while MWID population estimates were low quality due to lack of direct estimates. 1. Centers for Disease Control and Prevention (2016) HIV Infection Risk, Prevention, and Testing Behaviors among Men Who Have Sex With Men—National HIV Behavioral Surveillance, 20 U.S. Cities, 2014. HIV Surveillance Special Report 15. 2. Centers for Disease Control and Prevention (2014) HIV Infection and Risk, Prevention, and Testing Behaviors Among Injecting Drug Users — National HIV Behavioral Surveillance System, 20 U.S. Cities, 2009. Krebs et al. PLoS One 2019; 14(5):e0217559.

  26. 1. Evidence Synthesis Initial population estimates HIV negative population – Atlanta (total population) Krebs et al. PLoS One 2019; 14(5):e0217559.

  27. 1. Evidence Synthesis Initial population estimates HIV negative population – Baltimore (total population) Krebs et al. PLoS One 2019; 14(5):e0217559.

  28. 1. Evidence Synthesis Initial population estimates HIV negative population – Los Angeles (total population) Krebs et al. PLoS One 2019; 14(5):e0217559.

  29. 1. Evidence Synthesis Initial population estimates HIV negative population – Miami (total population) Krebs et al. PLoS One 2019; 14(5):e0217559.

  30. 1. Evidence Synthesis Initial population estimates HIV negative population – New York City (total population) Krebs et al. PLoS One 2019; 14(5):e0217559.

  31. 1. Evidence Synthesis Initial population estimates HIV negative population – Seattle (total population) Krebs et al. PLoS One 2019; 14(5):e0217559.

  32. 1. Evidence Synthesis Initial population estimates HIV negative population – Total PWID prevalence by race/ethnicity across cities Krebs et al. PLoS One 2019; 14(5):e0217559.

  33. 1. Evidence Synthesis Initial population estimates HIV negative population – Relative PWID proportion by gender across cities Krebs et al. PLoS One 2019; 14(5):e0217559.

  34. 1. Evidence Synthesis Initial population estimates HIV negative population – Total MSM prevalence across cities (males aged 15-64) Krebs et al. PLoS One 2019; 14(5):e0217559.

  35. 1. Evidence Synthesis Initial population estimates HIV negative population – Relative MWID prevalence among MSM and PWID Krebs et al. PLoS One 2019; 14(5):e0217559.

  36. 1. Evidence Synthesis Initial population estimates and population dynamics HIV diagnosed/infected population • We used city-specific surveillance data to directly estimate initial numbers of diagnosed PLHIV, stratified by race/ethnicity, gender, risk group1-6and CD4 cell count in 2011. • For PLHIV who were infected but undiagnosed, we used state-level estimates for the proportion of HIV-positive individuals who were aware of their status7,8with CD4 stratification derived from literature estimates9. 1. County of Los Angeles (2011) 2010 Annual HIV Surveillance Report. Los Angeles: Public Health LA County. 2. Florida Department of Health in Miami-Dade County (2015) HIV Surveillance. 3. New York City Department of Health and Mental Hygiene (2011) New York City HIV/AIDS Annual Surveillance Statistics 2010. New York City: New York City Department of Health and Mental Hygiene. 4. Public Health - Seattle and King County (2018) HIV/AIDS annual and quarterly reports. 5. Center for HIV Surveillance EaE, Department of Health and Mental Hygiene, Baltimore, MD., (2015) Baltimore City Annual HIV Epidemiological Profile 2013. 6. Georgia Department of Public Health (2012) HIV/AIDS Epidemiology Program HIV Care Continuum Surveillance Report. 7. Centers for Disease Control and Prevention (2015) Prevalence of Diagnosed and Undiagnosed HIV infection - United States, 2008-2012. Morbidity and mortality weekly report (MMWR) 64: 657-662. 8. Song R, Hall HI, Green TA, Szwarcwald CL, Pantazis N (2017) Using CD4 Data to Estimate HIV Incidence, Prevalence, and Percent of Undiagnosed Infections in the United States. J Acquir Immune DeficSyndr 74: 3-9. 9. Long E, Mandalia R, Mandalia S, Alistar S, Beck E, et al. (2014) Expanded HIV testing in low-prevalence, high-income countries: A cost-effectiveness analysis for the United Kingdom. PLoS One 9: e95735. Krebs et al. PLoS One 2019; 14(5):e0217559.

  37. 1. Evidence Synthesis Initial population estimates and population dynamics HIV diagnosed male population (2011) – Atlanta (total 15-64) Krebs et al. PLoS One 2019; 14(5):e0217559.

  38. 1. Evidence Synthesis Initial population estimates and population dynamics HIV diagnosed female population (2011) – Atlanta (total 15-64) Krebs et al. PLoS One 2019; 14(5):e0217559.

  39. 1. Evidence Synthesis Initial population estimates and population dynamics HIV diagnosed male population (2011) – Baltimore (total 15-64) Krebs et al. PLoS One 2019; 14(5):e0217559.

  40. 1. Evidence Synthesis Initial population estimates and population dynamics HIV diagnosed female population (2011) – Baltimore (total 15-64) Krebs et al. PLoS One 2019; 14(5):e0217559.

  41. 1. Evidence Synthesis Initial population estimates and population dynamics HIV diagnosed male population (2011) – Los Angeles (total 15-64) Krebs et al. PLoS One 2019; 14(5):e0217559.

  42. 1. Evidence Synthesis Initial population estimates and population dynamics HIV diagnosed female population (2011) – Los Angeles (total 15-64) Krebs et al. PLoS One 2019; 14(5):e0217559.

  43. 1. Evidence Synthesis Initial population estimates and population dynamics HIV diagnosed male population (2011) – Miami (total 15-64) Krebs et al. PLoS One 2019; 14(5):e0217559.

  44. 1. Evidence Synthesis Initial population estimates and population dynamics HIV diagnosed female population (2011) – Miami (total 15-64) Krebs et al. PLoS One 2019; 14(5):e0217559.

  45. 1. Evidence Synthesis Initial population estimates and population dynamics HIV diagnosed male population (2011) – New York City (total 15-64) Krebs et al. PLoS One 2019; 14(5):e0217559.

  46. 1. Evidence Synthesis Initial population estimates and population dynamics HIV diagnosed female population (2011) – New York City (total 15-64) Krebs et al. PLoS One 2019; 14(5):e0217559.

  47. 1. Evidence Synthesis Initial population estimates and population dynamics HIV diagnosed male population (2011) – Seattle (total 15-64) Krebs et al. PLoS One 2019; 14(5):e0217559.

  48. 1. Evidence Synthesis Initial population estimates and population dynamics HIV diagnosed female population (2011) – Seattle (total 15-64) Krebs et al. PLoS One 2019; 14(5):e0217559.

  49. 1. Evidence Synthesis Initial population estimates and population dynamics HIV diagnosed/infected population – Atlanta (percentage HIV aware) Krebs et al. PLoS One 2019; 14(5):e0217559.

  50. 1. Evidence Synthesis Initial population estimates and population dynamics HIV diagnosed/infected population – Baltimore (percentage HIV aware) Krebs et al. PLoS One 2019; 14(5):e0217559.

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