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Pre-Impact Test of Low-Income MUP and Pop-HPSA

Pre-Impact Test of Low-Income MUP and Pop-HPSA. Assessment of Population-Specific Scoring of Barriers and ATP. Methods. % Non-White – using the % non-white amongst the poor as a proxy (ACS) % Hispanic – using the % Hispanic amongst the poor as a proxy (ACS)

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Pre-Impact Test of Low-Income MUP and Pop-HPSA

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  1. Pre-Impact Test of Low-Income MUP and Pop-HPSA Assessment of Population-Specific Scoring of Barriers and ATP

  2. Methods • % Non-White – using the % non-white amongst the poor as a proxy (ACS) • % Hispanic – using the % Hispanic amongst the poor as a proxy (ACS) • % Uninsured – Using ACS ratio of uninsurance for those <200% FPL to uninsurance for those <400% FPL within each state • ATP scored at 100 (all Low Income are Low Income) • P2P, Health Status, and other Barriers remain at the overall community level • Scales and thresholds remain unchanged

  3. Influence of Barriers and ATP • MUP: Barriers and ATP weight = 60 of 100 points • Designation threshold = 54.3 • Pop-HPSA: ATP = 50 of 100 points in mid range • Designation threshold = maximum of 80 with most of the range below that (down to 0)

  4. Results on Low Income MUP Pre-Index

  5. Results on HPSA-MidRange Pre-Index

  6. Low Income pre-Scoring Results MUP: • 93% of Universal RSAs designated before any adjustment for low-income health status or access • 90% of total pop, 94% of low income pop nationally • Likely that most or all remaining would qualify once P2P and other factors were determined for the low income pop • 15% of areas designated will designate on demographics only, regardless of P2P/Health Status factors • 87% of areas not qualifying as an MUA would qualify via this MUP method Pop HPSA: • 69% of areas in mid range designate (vs 28% without change) • Areas in/out due to high/low P2P alone are unchanged due to no P2P update • Many more areas will fall into mid- or P2P-Only designation range once low income P2P is assessed

  7. Considerations • Scales still fit the range of values • Different factors/variables would get ‘full scoring’ for different populations • Dropping factors/variables would leave a ‘gap’ in the scoring – would need to reweight remaining factors • Reweighting could reduce impact but not eliminate underlying scoring issues • Adjusting threshold would lead to a different threshold value for every population group • No data to make threshold assessment • Current methods score ‘risk’ at the community level only for MUP, and not for Pop.HPSA

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