Extreme Makeover -- Data Edition:  Outside the Box

Extreme Makeover -- Data Edition: Outside the Box PowerPoint PPT Presentation


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Multilevel Modeling: A Real Life Example. True or False statement??Before I had children, I didn't have any gray hairs. NONE. Now I have a lot."MK (speaking to his children) . Multilevel Modeling ? Real Life Example. TRUE!But what's wrong with the statement?. Linear and Logistic Regression ?

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Extreme Makeover -- Data Edition: Outside the Box

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1. Extreme Makeover -- Data Edition: Outside the Box

2. Multilevel Modeling: A Real Life Example True or False statement? “Before I had children, I didn’t have any gray hairs. NONE. Now I have a lot.” MK (speaking to his children) I would substitute “age increment” for “the age of the children” in the first bullet.I would substitute “age increment” for “the age of the children” in the first bullet.

3. Multilevel Modeling – Real Life Example TRUE! But what’s wrong with the statement? I would substitute “age increment” for “the age of the children” in the first bullet.I would substitute “age increment” for “the age of the children” in the first bullet.

4. Linear and Logistic Regression – Review linear model review: logistic model review:

5. First Model Y = ß0 + ß1X1 + e Where Y = # of gray hairs on MK’s head ß1X1 = The presence of children (the exposure of interest) I would substitute “age increment” for “the age of the children” in the first bullet.I would substitute “age increment” for “the age of the children” in the first bullet.

6. Second Model Y = ß0 + ß1X1 + ß2X2 + ß3X3 + ß4X4 + e Where Y = # of gray hairs on MK’s head ß1X1 = The presence of children (the exposure of interest) ß2X2 = The number of children (the covariate or a variable that needs to be controlled for) ß3X3 = His age (a covariate) ß4X4 = Marital status (a covariate) I would substitute “age increment” for “the age of the children” in the first bullet.I would substitute “age increment” for “the age of the children” in the first bullet.

7. BUT… … notice that all those factors are individual-level factors (age, number of children, marital status). What if there are broader factors influencing the number of gray hairs between 1989 and 2007?

8. Multilevel Modeling: Definition and Synonyms Multilevel modeling: a method that allows researchers to investigate the effect of group or place characteristics on individual outcomes while accounting for non-independence of observations synonyms: different models: multilevel models - fixed effects contextual models - random effects hierarchical analysis - generalized estimating equations

9. Why Use Multilevel Models? outcomes may be clustered by some unit of aggregation (contextual unit) individuals within contexts may be similar in ways that are unmeasured to take into account clustering / non-independence of observations to partition the observed variability into within-context and between- context variables

10. Why Context Matters empirically, individual outcomes can’t be explained exclusively by individual-level exposures persistent contextual effects are observed in all (?) outcomes across populations exposures are structured; distributions are differential

11. Beyond Individual Determinants of Health

12. Beyond Individual Determinants of Health: Multilevel Analyses

13. Different from Ecological Analyses

14. Area and Health Area—states, counties, cities, neighborhoods--acts as a source of adverse or protective exposures and factors impacting health, such as: Policies Economic well-being—jobs, unemployment, economic development Stressors—physical, economic, segregation, “social disorganization” social support social capital/social cohesion toxins proximity to (competition for) resources (goods, services, transportation, employment opportunities, recreation)

15. Other Considerations Unit of analysis: states, counties, zip codes, census tracts, census block groups, etc. Should “neighborhood” be defined using to geographic boundaries? If so, which one? Data sources—going beyond census Characteristics to examine, need rationale for each indicator Modeling approaches

16. Sources of Data

17. Direct Mechanisms Community Social Environment Social relationships? transmit information Neighborhood cohesion?social control Shared cultural norms and values Civic participation? demand services Access to education and employment Community Service Grocery stores Recreational opportunities Health care facilities Retail stores Physical Environment Toxicants Noise Poor housing

18. Indirect Mechanisms Chronic Stress Selye, 1956 “General Adaptive Response” Alarm, resistance, exhaustion. Repeated cycles lead to cumulative damage to organism. McEwen & Stellar, 1993—Allostatic Load The cost of maintaining stability through change Mental Health Negative Emotions Depression Anger/hostility

19. Neighborhood Effects: Evidence Community context consistently has a “modest association” with numerous health outcomes. 25 studies reviewed Developed countries Individual-level attributes controlled for 23/25 had significant neighborhood effects Reviewed in Pickett & Pearl, J. Epidemiol Community Health, 2001; 55

20. What We Know About Neighborhoods and Child Well-Being Protective Affluence Resources Social capital Cohesion Collective efficacy Urban renewal? Political activity? Risks Poverty Concentrated deprivation Unemployment Residential mobility Incivilities (physical & social Urban renewal?

21. Are We Studying the Right Factors for Policy & Program Purposes? Protective Affluence Social capital Cohesion Collective efficacy Safe public spaces Services & resources Political activity & support Risks Concentrated deprivation Unemployment Residential mobility Incivilities Poor availability of services (health & social) Norms (health and other) Areas for play/social interaction Segregation Poor housing quality

22. Neighborhoods & Child Well-Being: Modeling

23. Neighborhoods & Child Well-Being: Modeling

24. Adding Group-Level Variables problem: making cross-level inferences [drawing inferences regarding factors associated with variability in outcome at one level based on data collected at another level] e.g., making individual inferences based on group-level associations

25. When are Observations Not Independent? when data are collected by cluster / aggregating unit children within schools patients within hospitals drug users within neighborhoods cholesterol levels within a patient why care about clustered data? two children / observations within one school are probably more alike than two children / observations drawn from different schools does knowing one outcome inform your understanding about another outcome?

26. Back to the Example… Potential aggregate factors to consider: In 2000, MK moved to a neighborhood with a very high percentage of lawyers; In 2003, the Red Sox lost in the 7th game of a playoff series to the Yankees, blowing a three run lead in the 8th inning. (MK threw pillow at the tv, and was properly chastised for that).

27. Final Outcome of Real-Life Example (Assume Two-Level Outcome)

28. Acknowledgments Lynne Messer, PhD, University of North Carolina Pat O’Campo, PhD, University of Toronto Jennifer Culhane, PhD, Drexel University

29. Contact Information Michael Kogan, Ph.D. HRSA/MCHB Director, Office of Data and Program Development 5600 Fishers Lane, Room 18-41 Rockville, MD 20857 301-443-3145 [email protected]

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