Extreme Makeover -- Data Edition: Outside the Box

1. Extreme Makeover -- Data Edition: Outside the Box Presentation at the CityMatch Conference, August 2007 Michael Kogan, Ph.D. U.S. Department of Health and Human Services (DHHS) Health Resources and Services Administration (HRSA) Maternal and Child Health Bureau (MCHB) Director, Office of Data and Program Development

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)

3. Multilevel Modeling – Real Life Example • TRUE! • But what’s wrong with the statement?

4. Linear and Logistic Regression – Review • linear model review: • logistic model review: Y = β0 + β1X1 + β2X2…+ ε β0 = intercept β1X1 = beta associated with exposure β2X2 = beta associated with first covariate + … ε = error term ln [P(X) / (1-P((X))] = α + β1X1 + β2X2… α = constant β1X1 = beta associated with exposure β2X2 = beta associated with first covariate

5. First Model Y = β0 + β1X1 + ε Where Y = # of gray hairs on MK’s head β1X1 = The presence of children (the exposure of interest)

6. Second Model Y = β0 + β1X1 + β2X2 + β3X3 + β4X4 + ε Where Y = # of gray hairs on MK’s head β1X1 = The presence of children (the exposure of interest) β2X2 = The number of children (the covariate or a variable that needs to be controlled for) β3X3 = His age (a covariate) β4X4 = Marital status (a covariate)

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 Demographics, health behaviors, socioeconomic position, support, etc Individual-level Health Status

12. Beyond Individual Determinants of Health: Multilevel Analyses Neighborhoods/counties/area Health care setting Workplaces Etc Contexts Demographics, health behaviors, socioeconomic position, support, etc Individual-level Health Status

13. Different from Ecological Analyses County/State Neighborhoods Contexts Health Status Rates

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

21. 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 Are We Studying the Right Factors for Policy & Program Purposes?

22. Neighborhoods & Child Well-Being: Modeling N. Affluence/ poverty Parent/ family factors Child outcomes N. Social capital/ cohesion N. Mobility

23. Neighborhoods & Child Well-Being: Modeling N. Affluence/ poverty Parent/ family factors Child outcomes N. Social capital/ cohesion N. Mobility

24. Adding Group-Level Variables Yij = β0 + β1ijX1 + β2ijX2…+ jGj + εij • 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 Yij = outcome for individual i in context j β1ijX1 = beta associated with exposure for individual i in context j βj Gj = observed community variable εij = error term

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 mkogan@hrsa.gov