1. Ibrahim Kamara, MS, MPH, Sc.D Torrance Brown, MPH June 2011

2.  Background  Purpose  Current HPP Capabilities  Data Collection Approach  Analytic Approach  Results of Selected Snapshots  Methodological Issues  Recommendations  Next Steps

3.  Understand the status of the HPP data assessment and its response capabilities  Obtain a better understanding of the HPP data collection, analytic approaches, and results of selected snapshots  Understand some of the obvious methodological challenges of the data as it relates to response  Obtain a better understanding of the next steps

4. The goal of the Hospital Preparedness Program (HPP) is to ensure awardees use cooperative agreement funds to maintain, refine, and to the extent enhance the capacities and capabilities of their healthcare entities for exercising and improving all- hazards preparedness plans, including pandemic influenza. Purpose Background  Mandatory Formula Awards State/Territory Departments of Public Health and Directly funded metro areas (LA, Chicago, NYC, & DC); 62 Awardees  Authorizing Legislation Established in 2002 under the Public Health Security & Bioterrorism Preparedness & response Act of 2002 Pandemic and All-Hazards Preparedness Act (PAHPA) signed in December 2006.  Program Foci Capacity 2002-2006 Capabilities 2007-Present

5.  HPP Overarching Requirements  National Incident Management System (NIMS)  Education and Preparedness Training  Exercises, Evaluation, and Corrective Actions  Needs of At-Risk Populations  Level 1 Sub-Capabilities  Interoperable Communication Systems  Tracking of Bed Availability (HAvBED)  ESAR-VHP  Fatality Management  Partnership/Coalition Development  Level 2 Sub-Capabilities  Alternate Care Sites  Mobile Medical Assets  Pharmaceutical Caches  Personal Protective Equipment  Decontamination

7.  Data is analyzed using SAS, Excel, and Geographic Information System (GIS) mapping for graphical depiction  Descriptive Statistics  Continuous data~ weighted and un-weighted means or median, std. errors or Confidence interval (CI)  Discreet data ~ proportions, comparative ratios, percentages and CI to profile proxy indices as “snapshots” for situational awareness  Inferential statistics from varied multiple regression analysis

17.  Subjectivity  Measurement indicators are determined from a listing of indicators used in other models determined by expert panel process  Bias  Differences between awardees’ response on their capabilities  Inconsistent interpretation of the indices/measures among awardees  Lack of specificity on capabilities definitions  Weighting  Lack of weighting techniques for prioritization of indicators  Mathematical nomenclatures/combinations  Lack of consistent units in summing indicators relating to item being measured  Indicators are unit less and mathematics or arithmetic combinations can be odd  The values sometimes do not represent anything outside of the context in which the situation is being compared  Selection of Indicators  Not based on tested theoretical concepts, and an understanding of the relationship of the indicators, and statistical relationships  Data Sources  Source of the data used to create indicators that describe the measure were defined through the availability of existing datasets, rather than data that represents vulnerability

18.  Data should be from objective sources and reasonable accessible  Indicators should be standardized and normalized to make community comparison  Indicators should be thoroughly validated with scientific reasoning or concepts  There should be a consensus agreement on the type, scope, and appropriateness of measures and indicators  The indicators and measures should be applicable to field experiences within an institutional framework  The conceptual framework should require collaborative process to determine measures’ importance  Qualitative techniques such as surveys or case studies should be conducted to assess the reliability of data.

19. Goal Candidate Capabilities Measurement Approach Formative and Summative (Process & Performance Outcomes) 1.How well-prepared are healthcare coalitions to respond to mass casualty and catastrophic events, and then return to normal daily operations? 2.How well prepared are health care organizations to respond to daily stressors and events that challenge resources and then return to normal operations as a result of participating in a healthcare coalition? Questions Healthcare Coalitions/Health Care Organizations Candidate Measures Pilot Testing, Primary Data Collection, Grant Alignment Coordination, and External Data Sources In Development (Interview Questionnaire for Healthcare Coalitions, Evidenced Based Measures, Existing Relevant HPP Measures, and Existing Grant Alignment Outcomes Unit of Analysis