Show Me the Data! Getting Ready for San Francisco

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Show Me the Data! Getting Ready for San
Franciscos Next HIV Prevention Plan

• HIV Prevention Planning Council
• August 9, 2007

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Committee Members

• William Bland, Chad Campbell, Thomas Ganger,
  David Gonzalez, Isela Gonzalez, Jen Hecht, John
  Newmeyer, Tei Okamoto, Perry Rhodes III, Frank
  Strona, Rakli Wilburn, Tracey Packer, Eileen
  Loughran, John Melichar

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Our Goals for Today

• Update Council members on our progress toward
  developing a priority setting model for the next
  HIV Prevention Plan.
• Get full Councils feedback on the direction in
  which were headed.
• We still have a few questions/concerns, and we
  want your thoughts!

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SMTDs Job this Year

• Tackle how the HPPC sets priorities for funding.
  
• Tasks include
• Review HPPCs model for prioritizing populations
  for HIV prevention based on most up-to-date
  epidemiologic data.
• Review guidelines for allocation of funds, based
  on most up-to-date epidemiologic data.
• Identify priority issues and next steps for the
  Plan.

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What is Priority Setting?

• Priority setting uses scientific or epidemiologic
  data to identify populations at high risk, with
  high or increasing HIV incidence.
• The Centers for Disease Control and Prevention
  (CDC) doesnt require community planning groups
  to address resource allocation, but SFDPH asks us
  to.
• Purpose is to stay one step ahead of the
  epidemic, while allowing model to be flexible
  throughout the duration of the Plan.

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What is the Priority Setting Model?

• Ultimately, the PS Model guides DPH in its
  funding decisions, ensuring that resource
  allocation matches epidemiology.
• Summarized in table format, which in 2004
  included
• Behavioral Risk Populations (BRPs)
• Prioritized Subpopulations
• Prioritized Cofactors
• Resource Allocation Tiers
• Recommended Funding Percentages

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History of Priority Setting in SF

• 1995 Many population groupings
• No criteria for funding priorities
• 1997 12 BRPs created and ranked
• Focus on behavior was new
• Existing data didnt fit within BRP categories
• Did not address subpopulations

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History of Priority Setting in SF

• 2001 12 BRPs were collapsed into 8 BRPs
• Subpopulations included in model for the first
  time
• BRPs grouped into 3 tiers for funding allocation
• 2004 (currently in use) Still 8 BRPs
• Cofactors added to model for the first time
• BRPs grouped into 4 funding tiers

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Our Work So Far

• Reviewed 2006 consensus data
• Reviewed 2004 Priority Setting Model
• Many long discussions about strengths and
  limitations of 2004 model, and various ways to
  address concerns

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Limitations of the 2004 Current Model

• HIV risk is not just a behavior
• No language to effectively reflect transgender
  population
• Resources shifting based on new consensus numbers
  
• Does decrease in incidence justification for
  decrease in money?
• How can we ensure that resource allocation
  results in strong programs?

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Our Principles

• The priority setting model should be
• Inclusive
• Up to date
• Reflect todays epidemic
• Dynamic
• In addition, we should regularly process and
  include emerging data in the priority setting
  model.

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A Primary Discussion Point

• What is the primary mode of HIV transmission
  among IDU populations in San Francisco?
• Sexual risk behaviors vs. IDU risk behaviors
• One study points to sexual risk behaviors as the
  primary mode of HIV transmission among IDU
  populations.

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Our Priority Setting Model (so far)

• Some things youll notice that are different from
  the 2004 model
• 4 BRPs instead of 8 BRPs
• Narrative descriptions of high-risk behaviors for
  transmission of HIV within each BRP
• Injection drug use listed as a prioritized
  cofactor
• HIV persons added as a priority subpopulation
• Addresses populations that are important but for
  which we have limited risk behavior data and no
  seroprevalence data

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Strengths of the Proposed Priority Setting Model
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4 BRPs

• Strengths
• Takes into account scientific evidence of how HIV
  transmission is happening.
• This model will encourage needle exchange and
  other programs to include sexual risk behavior
  harm reduction and prevention with their IDU
  clients.
• Implementation of this model would be more
  user-friendly for providers.

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Narrative Descriptions of High-Risk Behaviors

• Strengths
• Conveys more information and context than is
  possible with one or two words.
• Emphasizes the behavior part of behavioral risk
  populations.
• Emphasizes the importance of both sexual risk
  behaviors and IDU behaviors in HIV transmission.

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Injection Drug Use as a Cofactor

• Strengths
• As with the descriptions of high-risk behaviors,
  continues to emphasize the importance of IDU
  regardless of how BRPs are written.
• Addressing IDU behaviors is a critical part of
  HIV prevention.
• HIV prevention programs that target IDU
  populations in San Francisco are extremely
  important only because of their hard work and
  success are we able to consider a 4 BRP model as
  an option.

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HIV Persons as a Subpopulation

• Strengths
• Includes HIV persons in San Franciscos priority
  setting model for the first time.
• Reaching HIV people is an important prevention
  strategy.

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Additional Populations of Concern

• Strengths
• Takes into account populations for which we have
  no seroprevalence data and only limited risk
  behavior data.
• Allows the model to stay one step ahead of the
  epidemic.
• Emphasizes areas where more data is needed.

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Some of Our Concerns

• BRPs within each tier
• Language in the narrative descriptions
• Addressing populations with no seroprevalence
  data and only limited risk behavior data
• Implementation of the proposed model

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Next Steps

• Finish defining subpopulations and cofactors
• Develop resource allocation tiers and recommended
  funding percentages
• Develop a list of program considerations to
  further guide resource allocation
• Identify priority issues for consideration by
  next years Plan committee

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Your Feedback

• Any questions?
• Wed like your thoughts on some of the
  questions/concerns that were still grappling
  with
• And, wed like your feedback overall on our
  proposed model