6/21/2017 1 1 A review of modern methods of estimating the size of health disparities May 24, 2017 Emil Coman 1 Helen Wu 2 1 UConn Health Disparities Institute , 2 UConn Health Modern Modeling conference, May 22-24, 2017 Health Disparities (HD): It’s just about comparing two groups Goals 1. Simplify and reposition common analytic methods 2. Compare methods to estimate HDs 3. Suggest cross-pollinations 4. Encourage disparities investigations Modern Modeling conference, May 22-24, 2017 2
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Health Disparities (HD): It’s just about comparing two groups
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6/21/2017
1
1
A review of modern methods
of estimating the size of health disparities
May 24, 2017
Emil Coman1
Helen Wu2
1 UConn Health Disparities Institute, 2 UConn Health
Lu, M. C., & Halfon, N. (2003). Racial and ethnic disparities in birth outcomes: a life-course perspective. Maternal & Child Health
Journal, 7(1), 13-30. Modern Modeling conference, May 22-24, 2017
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One intuitive model of Health Disparities
HD
Modern Modeling conference, May 22-24, 2017
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Another intuitive model of HDs
Tony Iton “Framework for Health Equity”, referenced in the text: Tackling Health Inequities Through Public Health Practice: Theory to Action. Edited by Richard
Hofrichter and Rajiv Bhatia. Based on a project by the National Association of County and City Health Officials. Oxford University Press, 2010 (page 380).
Iton, A., & Shrimali, B. P. (2016). Power, Politics, and Health: A New Public Health Practice Targeting the Root Causes of Health Equity. Maternal and Child
Health Journal, 20(8), 1753-1758. doi:10.1007/s10995-016-1980-6
Modern Modeling conference, May 22-24, 2017 6
A testable model of HDs
Caveats:
1. Causality: can race ‘cause’ health?
2. Compare the comparable
3. Control for ‘covariates’
AnxietyBlack
vs. WhiteHD
VanderWeele TJ, & Hernán MA. (2012). Causal effects and natural laws: towards a conceptualization of causal counterfactuals for non-manipulable exposures
with application to the effects of race and sex. In Berzuini C, Dawid P, & Bernardinelli L (Eds.), Causality: Statistical Perspectives and Applications (pp. 101–
113). West Sussex, UK John Wiley & Sons.
VanderWeele, T. J., & Robinson, W. R. (2014). On the causal interpretation of race in regressions adjusting for confounding and mediating variables.
Epidemiology, 25(4), 473-484.
Others
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Modeling options for HDs
1. Independent samples t-test
2. Anova
3. Regression
4. Instrumental Variable regression
5. SEM
6. Matching methods
7. +
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Modeling options for HDs
1. Independent samples T-
test is a 2-group model
Coman, E. N., Suggs, L. S., Iordache, E., Coman, M. A., & Fifield, J. (2015). A Review of Graphical Approaches to Common Statistical Analyses. The
Omnipresence of Latent Variables in Statistics International Journal of Clinical Biostatistics and Biometrics, 1(1), 1-9.
Coman, E. N., Suggs, L. S., Iordache, E., Coman, M. A., & Fifield, J. (2015). A Review of Graphical Approaches to Common Statistical Analyses. The
Omnipresence of Latent Variables in Statistics International Journal of Clinical Biostatistics and Biometrics, 1(1), 1-9.
1. Independent samples
T-test is a 2-group modelttest y, by(binary)pwmean y , over(binary) effects
2. Anova – similar for 2 groupsanova y binary
Allows for covariates thoughanova y binary c.x1 c.x2
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Modeling options for HDs
3. Regression
reg y binary x1 x2
But: ? Can race cause health?
Binary
X2
Y
X1
VanderWeele TJ, & Hernán MA. (2012). Causal effects and natural laws: towards a conceptualization of causal counterfactuals for non-manipulable exposures
with application to the effects of race and sex. In Berzuini C, Dawid P, & Bernardinelli L (Eds.), Causality: Statistical Perspectives and Applications (pp. 101–
113). West Sussex, UK John Wiley & Sons.
VanderWeele, T. J., & Robinson, W. R. (2014). On the causal interpretation of race in regressions adjusting for confounding and mediating variables.