Transcript Absolute Disparity Method - end+disparities Learning Exchange

end+disparities Learning
Exchange
Part IV: Calculator Assumptions
Quick Review
 This presentation is the fourth in a series of
presentations intended to familiarize you with
disparities calculation
 Part I: Disparity, a National Priority
 Part II: Subpopulations
 Part III: Calculating Disparity
 Part IV: YOU ARE HERE!
 Part V: Selecting QI Projects
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"All human beings are born free and equal in
dignity and rights."
- United Nations, Article 1 of the Universal
Declaration of Human Rights
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Learning Objectives
 Describe how was the calculator methodology
developed
 Understand the assumptions made within the
calculator tool
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Supreme Court of the United States
 Statisticians and scientists disagree on how to calculate
disparities – there are many methods
 The most uniform method for calculating and interpreting
disparities comes from SCOTUS
 SCOTUS has decades of history
dealing with disparities in case law
called Disparate Impact
 Housing
 Employment
 Jury Selection, etc.
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NQC Guide:
Qualifying Disparities in HIV Care
 Overviews
 What is a disparity?
 How do we qualify disparities statistically?
 How do we intersect probability and impact for disparity work?
 Mathematical backgrounds and calculation walkthroughs
 Absolute Disparity
 Rate Ratio
 Comparative Disparity
 Odds Ratio
 Absolute Impact
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NQC Guide: Qualifying Disparities in HIV Care
Instructional Slides
 Co-dissemination of Materials
 Disparities analysis is a challenging task that can cause confusion or
concern among users who are not adequately trained in how to use
the tools for QI
 NQC Guide + Instructional Slides + Calculator Tool = happy users
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Examples in this Presentation
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Youth (ages 13-24)
MSM of Color
African-American
& Latina Women
Transgender People
 Throughout this presentation, we’ll use examples
that involve hypothetical groups of people
But First Things First…
Terminology used in disparities statistics:
1. Significance
2. Power
3. Confidence
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Statistics for Disparities
 Power – ability of a statistical test to identify an effect
 We avoid discussing power in this tool, because it is not
completely scientific
 We simplify terminology and assumptions around power
to focus on whether or not results are useful
 The important thing to remember
is that UNDEFINED RESULT is
a product of low power among
other statistical assumptions
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Statistics for Disparities
 Confidence Intervals: the lower and upper bounds of
the range containing the true result with 95%
confidence
 If a confidence interval contains the value "1",
the calculated result is not significant and should
be ignored
 Narrower confidence intervals signify lower
standard error, wider intervals signify higher
standard error
 For our purposes, we are only interested in the
confidence intervals that fall within the range of
detectable disparities based on our assumptions
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Probability Methods
 Absolute Disparity Method
 The absolute difference in scores between two groups
 Relative Risk Method
 The ratio of the scores of two different groups divided into each other
 Comparative Disparity Method
 The relative risk minus 1 (method is used to highlight the work that has
yet to be done and supportively frames the values in terms of need
 Odds Ratio Method
 A measure of association between a status
(e.g., race) and an outcome (e.g., the number
of patients achieving or not achieving VLS)
based on odds.
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Probability Method Limitations
 Absolute Disparity Method
 Limitation: measured scores all > 50%*
 Relative Risk Method
 Limitation: measured scores all < 80%*
 Comparative Disparity Method
 Limitation: measured scores all < 80%*
 Odds Ratio Method
 Limitation: additional calculations required
and challenging to interpret (use as last resort)
* If these thresholds are not met, the method
does not have adequate power to detect disparity
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Why So Many Probability Methods?
 All statistical methods have limitations
 Completing all four probability methods increases the
likelihood we will detect true disparities
 At least one of the methods will always work (odds ratio),
regardless of the limitations of the other methods
 Applying all four methods in all cases allows for consistent
analysis
 The most “probable” disparity is the
group with the greatest number of
SIGNIFICANT results
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Example
 Let’s go back to our populations example, but let’s
just look at two groups for simplicity’s sake!
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Example
 Assume you have 100 people total and that 47 of
them are blue and 53 of them are yellow
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Example
 Now, let’s assume that some of the people are
retained in HIV care and some are not
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Example
 39 of the 47 blue people are retained in care (83%)
 48 of the 53 yellow people are retained in care (91%)
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Example
 Let’s walk through each of the probability
calculations to see if there is a disparity in retention
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“For to be free is not merely to cast off one’s chains, but to
live in a way that respects and enhances the freedom of
others.”
- Nelson Mandela
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Some Visual Cues to Help You
 Power Slider
 Reminds us of the power limitations of each method
50%
UNDEFINED RESULT
ABLE TO DETECT DISPARITIES
 Significance Slider
 Reminds us of the significance thresholds of each method
NO DISPARITY DETECTED
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∞
0.10
0.05
0
MAYBE
DISPARITY DETECTED
Absolute Disparity
 Does the Absolute Disparity Method qualify a disparity?
 Are all scores > 50%
50%
UNDEFINED RESULT
ABLE TO DETECT DISPARITIES
 Absolute Disparity is the difference between measured scores
 In this method:
― A difference 0% to 5% is a NO DISPARITY
― A difference 5% to 10% is a MAYBE DISPARITY
― A difference more than 10% is a YES DISPARITY
 91% - 83% = 8%
 8% falls between 5% and 10% so a MAYBE DISPARITY
0%
5%
NO DISPARITY DETECTED
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∞
10%
MAYBE
DISPARITY DETECTED
Relative Risk
 Does Relative Risk Method Qualify Disparity?
 Are all measured scores <80%
ABLE TO DETECT DISPARITIES
80%
UNDEFINED RESULT
 Relative Risk is the ratio between measured scores
 In this method:
― A result greater than 0.875 is a NO DISPARITY
― A ratio between 0.8 and 0.875 is a MAYBE DISPARITY
― A ratio less than 0.8 is a YES DISPARITY
 83% ÷ 91% = 0.91
 Measured scores >80% so method has UNDEFINED RESULT
0.8
0
DISPARITY DETECTED
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∞
0.875
MAYBE
NO DISPARITY DETECTED
Comparative Disparity
 Does Comparative Disparity Method quality a disparity?
 Are measured scores <80%
80%
ABLE TO DETECT DISPARITIES
UNDEFINED RESULT
 Comparative Disparity is the Relative Risk minus 1
 In this method:
― Greater than -0.125 is a NO DISPARITY
― Between -0.125 and -.02 is a MAYBE DISPARITY
― Less than -0.2 is a YES DISPARITY
 (83% ÷ 91%) - 1 = - 0.09
 Measured scores >80% so method has UNDEFINED RESULT
-∞
DISPARITY DETECTED
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-0.125
-0.2
MAYBE
∞
NO DISPARITY DETECTED
Odds Ratio
 Odds ratios should be used as a last resort, because they can be
challenging to interpret based on the math behind them
 Odds ratios are produced through cross-multiplication
 (39 * 5) ÷ (48 * 8) = 0.53
 In this method:
YES
NO
Blue People
39
8
Yellow People
48
5
― Greater than 0.67 is a NO DISPARITY
― Less than 0.67 is a YES DISPARITY
 0.53 is less than 0.67so is a YES DISPARITY
0.67
0
DISPARITY DETECTED
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∞
NO DISPARITY DETECTED
Let’s Review Probability Method Findings
 Absolute Disparity – MAYBE DISPARITY
 Relative Risk – UNDEFINED RESULT
 Comparative Disparity – UNDEFINED RESULT
 Odds Ratio – YES DISPARITY
 Based on the findings – there is very likely a real
disparity for retention in HIV care among these
groups
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The Calculator Does All Four Every Time!
 All statistical methods have limitations
 Completing all four probability methods increases the
likelihood we will detect true disparities
 At least one of the methods will always work (odds ratio),
regardless of the limitations of the other methods
 Applying all four methods in all cases allows for consistent
analysis
 The most “probable” disparity is the
group with the greatest number of
YES DISPARITY results
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Determining Priorities for Disparities
Medium
Low
IMPACT
High
FOCUS
AVOID
Low
Medium
PROBABILITY
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High
Impact Method
 Absolute Impact
 Measures the number of lives that are affected if the
performance is raised to match average performance
 Multiply the absolute difference by the size of the
population being assessed
 Limitation: be aware whether the population
being assessed is doing better or worse than
the average
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Absolute Impact Example
 Absolute impact is the absolute disparity multiplied by the
population size of focus (blue people in this case since their
measured performance is lower).
 Absolute Disparity is the difference between measured scores
― 91% - 83% = 8%
― 47 * 0.08 = 4 blue people’s lives
* If blue people were retained in HIV care as well as
yellow people are retained in HIV care, we would expect
an additional 4 blue people of the original 47 to be
retained in HIV care
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Don’t Forget! The Tool Does This All For You!
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Question & Answer
Additional disparities calculation and QI resources are available www.NationalQualityCenter.org
or email [email protected]
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Ending disparities will end the HIV epidemic.
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Next Steps
 This presentation is the fourth in a series of
presentations intended to familiarize you with
disparities calculation
 Part I: Disparity, a National Priority
 Part II: Subpopulations
 Part III: Calculating Disparity
 Part IV: YOU ARE HERE!
 Part V: Selecting QI Projects
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Next Steps
Check out the NQC Disparities Calculator and NQC Guide!
 Want to check out the NQC Disparities Calculator?
Click this link and you’ll be taken there!
 Want to learn more about the NQC Guide on this
topic? Click this link and you’ll be taken there!
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212-417-4730
NationalQualityCenter.org
[email protected]