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end+disparities Learning Exchange Part IV: Calculator Assumptions
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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
"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
Throughout this presentation, we’ll use examples that involve hypothetical groups of people African-American & Latina Women MSM of Color Youth (ages 13-24) Transgender People
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But First Things First…
Terminology used in disparities statistics: Significance Power 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 Relative Risk 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 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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47% 53% of total of total Example
Assume you have 100 people total and that 47 of them are blue and 53 of them are yellow 47% of total 53% of total
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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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83% 91% retained retained Example
39 of the 47 blue people are retained in care (83%) 48 of the 53 yellow people are retained in care (91%) 83% retained 91% retained
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83% 91% retained retained Example
Let’s walk through each of the probability calculations to see if there is a disparity in retention 83% retained 91% retained
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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 Significance Slider Reminds us of the significance thresholds of each method 50% 0.05 0.10 ∞
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YES Absolute Disparity
Does the Absolute Disparity Method qualify a disparity? Are all scores > 50% 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 YES 50% 0% 5% 10% ∞
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NO Relative Risk Does Relative Risk Method Qualify Disparity?
Are all measured scores <80% Relative Risk is the ratio between measured scores In this method: A result greater than is a NO DISPARITY A ratio between 0.8 and 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 NO 80% 0.8 0.875 ∞
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Comparative Disparity
Does Comparative Disparity Method quality a disparity? Are measured scores <80% Comparative Disparity is the Relative Risk minus 1 In this method: Greater than is a NO DISPARITY Between and -.02 is a MAYBE DISPARITY Less than -0.2 is a YES DISPARITY (83% ÷ 91%) - 1 = Measured scores >80% so method has UNDEFINED RESULT NO 80% -∞ -0.2 -0.125 ∞
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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: 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 YES NO Blue People 39 8 Yellow People 48 5 0.67 ∞
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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 between red and blue ball durability
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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
FOCUS AVOID
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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 red balls 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 or
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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 Want to check out the NQC Disparities Calculator? Click this link and you’ll be taken there! THIS WILL BECOME AN ICON LINKED TO CALCULATOR Want to learn more about the NQC Guide on this topic? Click this link and you’ll be taken there! THIS WILL BECOME AN ICON LINKED TO GUIDE
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NationalQualityCenter.org Michael@NationalQualityCenter.org
NationalQualityCenter.org 36
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