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Res Allocation Model Nhpc09 Lasry
Res Allocation Model Nhpc09 Lasry
Res Allocation Model Nhpc09 Lasry
Res Allocation Model Nhpc09 Lasry
Res Allocation Model Nhpc09 Lasry
Res Allocation Model Nhpc09 Lasry
Res Allocation Model Nhpc09 Lasry
Res Allocation Model Nhpc09 Lasry
Res Allocation Model Nhpc09 Lasry
Res Allocation Model Nhpc09 Lasry
Res Allocation Model Nhpc09 Lasry
Res Allocation Model Nhpc09 Lasry
Res Allocation Model Nhpc09 Lasry
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Res Allocation Model Nhpc09 Lasry

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Slides from the HIV Prevention Resource Allocation Model session at the 2009 National HIV Prevention Conference in Atlanta. Primary presenter: Arielle Lasry, Division of HIV/AIDS Prevention, CDC

Slides from the HIV Prevention Resource Allocation Model session at the 2009 National HIV Prevention Conference in Atlanta. Primary presenter: Arielle Lasry, Division of HIV/AIDS Prevention, CDC

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  1. A model for allocating HIV prevention resources in the United States Arielle Lasry1, Stephanie Sansom1, Katherine Hicks2, Vladislav Uzunangelov2 1 Division of HIV/AIDS Prevention, National Center for HIV, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia (GA) 2 RTI International, Research Triangle Park, North Carolina (NC) National HIV Prevention Conference Atlanta, August 26, 2009 Disclaimer: The findings and conclusions in this study are those of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention. Presentation outline Background How the model works Model output Summary, limitations & next steps
  2. Presentation outline Background How the model works Model output Summary, limitations & next steps Background Generally, healthcare resource allocation is a process used to determine how to distribute resources among programs, populations or regions from a limited budget. The way health funds are allocated has an important influence on health outcomes.
  3. Background CDC’s Division of HIV/AIDS Prevention (DHAP) has total budget of approximately $650 Million. approximately $325 Million funds health departments and community based organizations for core HIV testing and prevention programs domestically. We continue to face considerable challenges. The overall number of new HIV infections per year has not declined for more than a decade. HIV resources are not unlimited. The resource allocation model evaluates how to allocate HIV prevention funds to further reduce new HIV infections given a budget of $325 Million. Modeling vs. the real world Models are a convenient representation of the real world. Models can help us project epidemic outcomes, better understand causal relationships and identify areas where prevention programs can have the most impact. Translation of model outcomes into the real word is difficult because models are a simplified representation of a complex reality. Some simplifying assumptions of the resource allocation model: Population subgroups are reachable and can be perfectly targeted. All other funding, including that from state and local government and the private sector, remains constant. Administrative costs of disbursing funds at multiple levels not considered.
  4. Presentation outline Background How the model works Model output Summary, limitations & next steps Resource allocation model Uses the best data and estimates available on HIV incidence, prevalence, prevention program costs and benefits, current spending, etc. Projects HIV infections for the United States as a whole given different allocation strategies. Based on the best currently available data, suggests hypothetical allocation to minimize incidence. Provides information that could be considered in future decision-making processes for resource allocation. One of many inputs and information sources – none should be used alone. Not intended to replace local decision-making.
  5. Populations considered Three transmission related risk groups High-risk Men who have Injection Drug heterosexuals sex w/men Users (HRH) (MSM) (IDU) Three race/ethnicity categories Black Hispanics Other races* * Mainly whites, + A/PI, AI/AN Two gender categories (M/F) We end up with 15 risk populations (2X3X3 - 3) Infection transmission Each of the 15 risk populations is modeled as 3 compartments. ti x e ti x e ti x e HIV+ HIV+ Susceptibles undiagnosed diagnosed yrt n e Infection from contact with HIV+ diagnosed or undiagnosed Screening & diagnosis The 15 risk populations interact (mix) with one another thereby generating new infections.
  6. Intervention types Behavioral Testing interventions Intervention to reduce Targeted testing to Risk risk among susceptibles identify positives populations and the infected unaware of their status Testing in general healthcare settings to General e.g. Social marketing identify positives population unaware of their status Compares the outcome of these interventions in terms of estimated HIV infections prevented when targeted to the general population and to risk populations defined by race/ethnicity, gender, and risk group. How the model works 1. Epidemic model: Simulates the epidemic outcome given a defined allocation. Defined as dynamic compartmental model and written S− U+ D+ out as a system of difference equations noitacollA weN oiranecs snoitcefni 2. Optimization engine: Generates different allocation scenarios, which feed into the epidemic model and stops when best outcome is reached. Yes No Aims to minimize the total number Improve? Stop of new infections over 5 years, by deciding how much to allocate to the interventions considered.
  7. Summary of data used 1. Population data 4. Intervention costs and Total size of risk population outcomes Number of positives Cost of testing by target group % unaware Level of background testing Cost of behavioral interventions 2. Rates of movement in and by target group out of each risk population Effect and duration of behavioral Entry into susceptible intervention by target group Exit rate from susceptible and 5. Constraints undiagnosed+ Maximum reachability (%) by Exit rate from diagnosed + intervention category by risk (death and disease) population 3. Transmission (optional) Minimum or Maximum Mixing % investment by intervention, Incidence by subpopulation target group and/or risk Effective contact rate for population diagnosed and undiagnosed Budget Validation and quality assurance Validation of input data Internal vetting and sign-off by subject matter experts within DHAP. External review committees provided written reviews and participated in a series of conference calls. Their feedback was incorporated into our data estimates. Validation of model structure Modeling experts (outside CDC) provided written review of model and participated in conference call. Comments were used to update the model. Quality assurance Several measures taken including sensitivity analysis. Model demonstrated stability and robustness.
  8. Presentation outline Background How the model works Model output Summary, limitations & next steps Allocations by intervention type $350 $300 Testing Testing (Risk pop) (Risk pop) $250 Testing $200 (Gen pop) $150 Behavioral Behavioral Intervention $100 intervention (Risk pop) (Risk pop) $50 Behavioral intvn. (Gen pop) $- Baseline Model
  9. Allocations to behavioral interventions by serostatus $250 $200 Untargeted $150 HIV+ Diagnosed HIV+ $100 Diagnosed Susceptibles & HIV+ $50 Undiagnosed Susceptibles & HIV+ Undiagnosed $- Baseline Model Allocations to behavioral interventions by race/ethnicity $250 $200 Others Untargeted $150 Others $100 Hispanic Hispanic $50 Black Black $- Baseline Model *Others: Whites, APIs, American Indians and Alaska Natives
  10. Allocations to behavioral interventions by risk group $250 $200 HRH Untargeted IDU $150 HRH $100 IDU MSM $50 MSM $- Baseline Model Allocations to testing by race/ethnicity $180 $160 $140 $120 Untargeted $100 $80 Others Others $60 Hispanic $40 Hispanic $20 Black Black $- Baseline Model *Others: Whites, APIs, American Indians and Alaska Natives
  11. Allocations to testing by risk group $180 $160 $140 $120 Untargeted $100 HRH $80 IDU $60 HRH $40 MSM $20 IDU MSM $- Baseline Model Presentation outline Background How the model works Model output Summary, limitations & next steps
  12. Select model output Directs resources for testing and behavioral interventions to those at greatest risk (not general population). Increases allocation to behavioral interventions for diagnosed positives. Increases allocation to testing for MSMs and IDUs. More than doubles total allocation to MSMs. More than doubles total allocation to IDUs. Increases allocation to behavioral interventions for Blacks. Limitations Budget only includes DHAP extramural funds for testing and behavioral programs, not all HIV prevention funds. Accounts for current levels of non-CDC funded screening and behavioral intervention efforts. Assumes non-CDC funding levels are constant. Data Data are often uncertain. Data updates required as new evidence emerges. Assumes that resources can be “perfectly” targeted. Considers prevention strategies that are currently federally funded (i.e. no needle exchange or biomedical strategies). Does not account for regional/geographical differences.
  13. Next steps Continuous model refinements Data updates Broaden scope of interventions Explore how model could be adapted for regional/local planning uses. Consider how the model might be integrated into DHAP’s priority setting process. Resource allocation model - Technical briefing September 14th, 2009 from 1:00-2:00PM ET Resource allocation model - Program briefing September 15th, 2009 from 1:30-2:30PM ET Thank you Questions?

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