Health Status Adjustment to Initial Barrier-Free Demand Estimate

1. Health Status Adjustment toInitial Barrier-Free Demand Estimate

2. Starting Point for Health Status Adjustment • The basic Barrier Free calculation produces an Age/Gender adjusted estimate of primary care demand for a population, assuming that population is of ‘average’ health status • Adjustment made using the US average for self assessed health status (% reporting “fair” or “poor” health) as a benchmark • Question asked of respondents in MEPS survey • Same question asked in BRFSS

3. Conceptual Outline for Designation Component Integration Quantify Need/Demand (Visits for Benchmark, Age Gender Adjusted, Average Health Status) Assess Health Outcome Deficits/Disparities (Areas/Populations with persistently and significantly negative health indicators ) Quantify Supply (Visit capacity for appropriate primary care providers ) Adjust for Population Health Status (Increase if below avg. health status, decrease if above ) Assess Other Indicators of Med.Underservice (Nature/Indicators TBD) Scale(s) of Provider Adequacy/Shortage (Combined measure of Supply vs Demand ) Scale(s) of Medical Underservice (Assessed separately or Integrated into an index) or or Set Threshold(s) for HPSA Designation Set Threshold(s) for MUA/P Designation HPSA MUA/P

4. Purpose of Health Status Adjustment of Demand • Goal: To incorporate an adjustment factor which modifies estimated Barrier Free demand to reflect the degree to which a population’s health status is above or below the national average. • Initial analysis of MEPS showed that health status is a driver of demand independent of age, gender, and barriers to care • Measure based on a broad-based assessment of health status that can be equated to the health status parameter used in MEPS

5. Sensitivity Analysis of Utilization by % Fair-Poor Health • National average for % Fair-Poor Health is approximately 14% overall • Barrier free population weighted to this value • Proportion of population reporting Fair-Poor health weighted Up/Down from average with resulting primary care utilization examined • Results converted into a scale of multipliers representing the ratio of utilization at Actual/Average % Fair-Poor Health • Multiplier can be used to vary estimated demand derived based on average health status

6. Sensitivity Analysis Results

7. Assessing Health Status • Direct: Actual % Fair-Poor Health • Derived from Behavioral Risk Factor (BRFSS) Survey • 2002-2008 age adjusted rates reported in RWJF County Health Rankings • Available for 2,711 of 3,141 counties (86%) • Represents 98% of US population • Some states oversample for more detailed local data • For Sub-Populations • Overall % Fair-Poor Health multiplier can be calculated if attribute is available in MEPS or BRFSS

8. Assessing Health Status • Indirect: Health Status measure correlated with Fair-Poor health status • Select a broad-based measure that is more readily available at a local level / narrower time frame • Standardized Mortality Ratio (SMR) • Actual Deaths / Expected deaths (national age*gender rates) • 5-Year rate can be calculated for every county (WONDER 2002-6) • May be available/calculable for smaller geographic units and some sub-populations at the state level • Requires age/gender distribution of pop. and total deaths for the group • Correlate with Fair-Poor Health Status to apply to demand estimation • Relationship between self-rated health and mortality validated in literature as a ‘method to identify vulnerable persons with the greatest health needs’ • DeSalvo KB, et.al. Mortality predication with a single general self-rated health question. A Meta-Analysis. Journal of General Internal Medicine. 2006. 21:267-75

9. Relationship of SMR to Estimate Health Status • Robust positive relationship to % Fair-Poor Health (age adj.) • Significant: p-value of <0.0001, Correlation Coefficient = 0.6 • Beta Coefficient: 0.0179 • For every 5% increase in fair/poor health status, the SMR increases by approximately 0.09 (.0179 * 5)

10. Table of Demand Multipliers w/ SMR • Crosswalks SMR to % Fair-Poor Health based on ‘best fit’ relationship • Ties SMR to variation in Barrier Free use

11. Example of Application to Demand • Sample County, US • Direct Barrier Free Demand Visits = 45,000 • % Fair/Poor Health = 25% • Health Status Multiplier = 1.07 (+7%) • Adjusted Barrier Free demand (45,000*1.07 = 48,150) OR (if % Fair/Poor not known) • SMR = 1.20 • Equates to Fair-Poor Health = 25% • Sample Low Income Population • Low Income Pop Fair-Poor Health = 24% (from MEPS, age adj.) • Health Status Multiplier = approx. 6% • All low income designations would get same % boost unless better local data or SMR available to support different adjustment

12. Potential Alternatives to SMR as Proxy • Age Adjusted Mortality Rate • Need all deaths reported by age • Life Expectancy • Requires valid age-specific death rates for local population • Years of Potential Life Lost (YPLL) • Other BRFSS based measures • Unhealthy Days • Combination Measures • Health Adjusted Life Expectancy (HALE) • Years of Healthy Life • Disability Adjusted Life Years (DALY) • Multivariate analysis of social factors as drivers of SMR or Health Status