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Modeling Emergency Preparedness in the Public Health System

Modeling Emergency Preparedness in the Public Health System. Maggie Potter, JD, MS PHASYS – MIDAS Graduate School of Public Health University of Pittsburgh. PHASYS-MIDAS TEAM. Shawn Brown – PSC Don Burke – GSPH Louise Comfort - GSPIA Josh Epstein - Brookings Sherrianne Gleason - GSPH

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Modeling Emergency Preparedness in the Public Health System

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  1. Modeling Emergency Preparedness in the Public Health System Maggie Potter, JD, MS PHASYS – MIDAS Graduate School of Public Health University of Pittsburgh

  2. PHASYS-MIDAS TEAM • Shawn Brown – PSC • Don Burke – GSPH • Louise Comfort - GSPIA • Josh Epstein - Brookings • Sherrianne Gleason - GSPH • John Grefenstette - GSPH • Chris Keane – GSPH • Tina Hershey - GSPH • Bruce Lee – Pitt-Med • Cho Cho Lin – Pitt-Med • Jon Parker – Brookings • Sandra Quinn – GSPH • Russ Schuh – Pitt-Med • Rob Skertich – GSPIA • Sam Stebbins – GSPH • Patt Sweeney - GSPH • Xiaozhi Zhou - GSPH

  3. Modeling Community Containment for Pandemic Influenza: A Letter Report Institute of Medicine, 2006 The committee recommends: • “a broader set of models to inform strategies and policies regarding pandemic influenza” • “a broader range of closure options in their analyses” • “observational or randomized studies … to evaluate the effectiveness of certain interventions, with results used in modeling

  4. The U.S. Public Health “System” • FEDERAL • U.S. Public Health Service without regulatory authority • STATE • 50 independent state & DC health departments • LOCAL • 3,140 counties (average 62 per state) • 2,794 local health departments • Some independent; some state-run

  5. Definitions:Public Health Systems Research on Pandemic Preparedness • Public Health Systems (PHS) • “promote health, provide health care delivery, and prevent disease and injury” (CDC, 2008) • Includes public health agencies, health & service organizations, professionals, businesses • The U.S. has many at state & local levels – and they differ • Pandemic preparedness • capacity for rapid and effective response • coordination within the PHS • implementing population-level mitigation strategies (i.e., vaccination; social distancing; anti-viral medications) • Research for PHS Pandemic Preparedness • How to invest for building and sustainability? • Based on what criteria and metrics?

  6. PHS Preparedness “Criteria & Metrics” DETERMINANTS “LEO” INDEX DATA OPERATIONAL OPERATIONAL ECONOMIC ECONOMIC LEGAL LEGAL INDICATORS

  7. Preparedness determinants: factors that optimize/inihibit PHS performance

  8. PHS modeling for pandemic preparedness policy implications, better evaluations, field study designs; feedback; hypotheses Expert opinion, table-top exercises MODELS DETERM- INANTS informing testing; validating Field observa-tions, after-action reports, evaluation DATA generating validating INDICA- TORS computer coding INDEX

  9. FromPreparedness Determinants to Preparedness Index DETERM- INANTS 1. Assess relevance (face validity) of data; parameterize qualitative data DATA 2. Conduct multivariate analyses among data sets; construct composite indicators from multiple data points INDICA- TORS 3. Compile indicators into location-specific index INDEX

  10. The SNS Score as Preparedness Index • States scored 0-100 on: 1-distribution plan 3-legal authority 2-community collaboration 4-exercising plan • Imperial model* in U.S. simulates pandemic influenza • SNS scaled to delay in vaccinations: whole days of delay = (100 – SNS)/10 • Vaccination priority for ages <24 years • Assume 75% efficacy after 7 days ________________ * Ferguson et al., Nature 442: 448 (2006)

  11. Influenza epidemic curves without vaccinations, with optimal vaccinations, and with state-specific differences in vaccination performance

  12. Agent-Based Model of Pandemic Mitigation: vaccine distribution, state by state

  13. PHS Modeling of Pandemic Mitigation Strategies • PHS not considered within the community context • Pandemics ≠ All Hazards • Iterations of modeling needed along with observational studies, validation, and scaling • “All models are wrong” – but very useful

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