Data Weighting Committee Report. For Discussion May 18-20, 2011 By Negotiated Rulemaking Committee on HPSAs and MUAs. Where we are and where we have come from. Variables have been proposed and considered
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For Discussion May 18-20, 2011
By Negotiated Rulemaking Committee on HPSAs and MUAs
Access to Care Barriers --
Ability to Pay
Other measures that have been proposed:
(combined into “index”)
“Factor analysis” – considering those proposed to reflect underlying “underservice” concept
THREE “factors” (clusters of variables) identified
(examine the results – request Eric present JSI analysis)
(Option1) THREE-STEP PROCESS:
Determine P2P and Health Status/Barriers/Ability to Pay Levels for population in a Rational Service Area.
Step 1. EXTREME SHORTAGE: If P2P (reflecting age/gender structure of population) shows extreme shortage (<1:2 x Y where Y is the either national norm or selected threshold for “appropriate” population per provider), designate as HPSA.
Step 2. POTENTIAL SHORTAGE: If (a) P2P is between 1:Y and 1:2Y, consider first for total population, for geo HPSA (then, if not qualifying, consider population HPSA):
b. Health status (SDI 4 variable index and direct measures – SMR and others)
c. Ability to pay (low income and/or uninsured and/or not employed 18-64
d. Access barriers (select two or more(?):
Step 3. NO GEOGRAPHIC SHORTAGE: If not geo HPSA, consider population HPSA
(Option 1) Create an index (index of medical underservice) using scores for each of the 4 factors, each ranging from 0 (best) to 100 (worst), for rational service area (same definition as for HPSAs).
Sum of the scores may be weighted as follows:
(TENTATIVE – to be refined with analysis of factors considered to be representing “underservice,” review of literature, and discussion of values – policy decision to be made)
For rational service area – evaluate MUA and if area does not qualify, consider MUP options.
(a) Health Status(SEIH and direct measure(s) (use SMR & one or more of LBW or IMR or diabetes prevalence or Ambulatory care sensitive conditions (ACSCs) or ?; most other candidate measures are not as generally available for all geographies so some “places” would be unable to use them, and they won’t be available for testing )
(b) Access barriersincluding Race (non-white), and one or more of the following: linguistic isolation or LEP, portion of the population with geographic barriers or excessive travel time to care, and disability prevalence.
(c) Ability to pay: uninsured &/or low income percent (<200% of poverty – or use just the percent between 100 and 200% of poverty threshold)
(d) Population to provider ratio– pop will be weighted to reflect age and sex composition of the RSA (using MEPS – some of us recommended and thought it had been agreed by large committee that we’d use the age/sex use rates of people without impeded care which is about 10% higher than impeded) – the age/sex adjustment may not be possible for special pops MUPs because we don’t always know their age/sex distribution.
Could be based on:
The barriers and ability to pay components can be used in one of several ways:
(a) to discount the apparent capacity (P2P) reflecting that the apparent capacity isn’t as available as it appears or
(b) to add to the score for the health status/need dimension reflecting that there are even greater needs to be met than the health status component suggests.
(Deemed “sufficient” even if health status of population is poor, and access and economic barriers are present. Could be national average recalculated annually, or a preferred ratio based on evidence, which may also take into account different models of service provision over time.)
INDEX – relies on factor analysis – variables that in combination show one DIMENSION of interest.
Score associated with index factor #1? (could be 0 – 50 points)
MENU – relies on analysis of variables that are not necessarily related to each other, but are direct or indirect measures of a factor -- such as direct measures of health status: SMR, diabetes prevalence, or low birth weight. Pick for example SMR and one OR the other – depending on which is more reflective of your area’s health status.
…or such as “barriers to care”
Examples: Race + one: Limited English Proficiency, Travel time excessive, or Ethnicity
Could be based on
Graphic Reflecting Continuum of “Barriers and Health Status” (Y Axis) and P2P (X Axis) (Ability to pay factor may be included with factors on either axis or be a third dimension)
Y axis: represents components of “need” – measured by risk factors (SEIH) of poverty, single parent household, educational attainment < high school, and not being employed, which may limit current use as well being predictive of worse health status which is indicative of need for services, as well as direct measures of health status indicative of need for services (and likely higher use than average); additional barriers to access and additional measures of potential higher than average need could be “added” into this dimension.
(that is, don’t be overly restrictive).
Data Weighting Sub-Committee Report
Counties: 10% of counties in each decile regardless of population?
PCSAs? 10% in each decile regardless of population?
Or weight by population.
Or use natural breaks in the distribution – use the top 10% of the range for the top decile etc. (extremes can be truncated), more appropriate for rarer or skewed characteristics
Babitz, Scanlon, Hawkins, Holloway, Owens, Diaz, Camacho, Phillips, Taylor, Jordan, Lee, Proser, Rarig (chair)
Hofer, Abraham, Moscovice paper; JSI summary of variables status; graphic presented at April meeting (Holloway), Weighting Considerations.xls (Rarig, Holloway criteria), socio economic indicators of health (SEIH) and direct measures of health status and barriers.