Extreme Makeover -- Data Edition: Extreme Makeover -- Data Edition: Outside the BoxOutside 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

Multilevel Modeling: Multilevel Modeling: A Real Life ExampleA Real Life Example

True or False statement?True or False statement?

““Before I had children, I didn’t have any Before I had children, I didn’t have any gray hairs. NONE. Now I have a lot.”gray hairs. NONE. Now I have a lot.”– MK (speaking to his children) MK (speaking to his children)

Multilevel Modeling – Real Life Multilevel Modeling – Real Life ExampleExample

TRUE!TRUE!

But what’s wrong with the statement?But what’s wrong with the statement?

Linear and Logistic Regression – Linear and Logistic Regression – ReviewReview

linear model review:linear model review:

logistic model review:logistic model review:

Y = β0 + β1X1 + β2X2…+ εβ0 = 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 exposureβ2X2 = beta associated with first covariate

First ModelFirst Model

Y = β0 + β1X1 + Y = β0 + β1X1 + εε

Where Y = # of gray hairs on MK’s headWhere Y = # of gray hairs on MK’s head

β1X1 = The presence of children (the β1X1 = The presence of children (the exposure of interest)exposure of interest)

Second ModelSecond ModelY = β0 + β1X1 + β2X2 + β3X3 + β4X4 + Y = β0 + β1X1 + β2X2 + β3X3 + β4X4 + εε

Where Y = # of gray hairs on MK’s headWhere Y = # of gray hairs on MK’s head

β1X1 = The presence of children (the β1X1 = The presence of children (the exposure of interest)exposure of interest)

β2X2 = The number of children (the β2X2 = The number of children (the covariate or a variable that needs to be covariate or a variable that needs to be controlled for)controlled for)

β3X3 = His age (a covariate)β3X3 = His age (a covariate)

β4X4 = Marital status (a covariate)β4X4 = Marital status (a covariate)

BUT…BUT…

… … notice that all those factors are notice that all those factors are individual-level factors (age, number of individual-level factors (age, number of children, marital status).children, marital status).

What if there are broader factors What if there are broader factors influencing the number of gray hairs influencing the number of gray hairs between 1989 and 2007?between 1989 and 2007?

Multilevel Modeling:Multilevel Modeling:Definition and SynonymsDefinition and Synonyms

Multilevel modeling: a method that allows Multilevel modeling: a method that allows researchers to investigate the effect of group or researchers to investigate the effect of group or place characteristics on individual outcomes place characteristics on individual outcomes while accounting for non-independence of while accounting for non-independence of observationsobservations

synonyms:synonyms: different models:different models:– multilevel modelsmultilevel models - fixed effects- fixed effects– contextual modelscontextual models - random effects- random effects– hierarchical analysishierarchical analysis - generalized estimating equations- generalized estimating equations

Why Use Multilevel Models?Why Use Multilevel Models?

outcomes may be clustered by some unit outcomes may be clustered by some unit of aggregation (contextual unit)of aggregation (contextual unit)

individuals within contexts may be similar individuals within contexts may be similar in ways that are unmeasuredin ways that are unmeasured

to take into account clustering / non-to take into account clustering / non-independence of observationsindependence of observations

to partition the observed variability into to partition the observed variability into within-context and between- context within-context and between- context variablesvariables

Why Context MattersWhy Context Matters

empirically, individual outcomes can’t be empirically, individual outcomes can’t be explained exclusively by individual-level explained exclusively by individual-level exposuresexposures

persistent contextual effects are persistent contextual effects are observed in all (?) outcomes across observed in all (?) outcomes across populationspopulations

exposures are structured; distributions exposures are structured; distributions are differentialare differential

Beyond Individual Determinants Beyond Individual Determinants of Healthof Health

Health Status

Demographics, health behaviors, socioeconomic position, support, etc

Ind

ivid

ual-

level

Health Status

Demographics, health behaviors, socioeconomic position, support, etc

Ind

ivid

ual-

level

Neighborhoods/counties/area

Workplaces

Health care setting

Etc

Con

texts

Beyond Individual Determinants Beyond Individual Determinants of Health: Multilevel Analysesof Health: Multilevel Analyses

Health Status Rates

NeighborhoodsCounty/State

Con

texts

Different from Ecological Different from Ecological AnalysesAnalyses

Area and HealthArea and Health

Area—states, counties, cities, neighborhoods--Area—states, counties, cities, neighborhoods--acts as a source of adverse or protective acts as a source of adverse or protective exposures and factors impacting health, such as:exposures and factors impacting health, such as:– PoliciesPolicies– Economic well-being—jobs, unemployment, economic Economic well-being—jobs, unemployment, economic

developmentdevelopment– Stressors—physical, economic, segregation, “social Stressors—physical, economic, segregation, “social

disorganization” disorganization” – social supportsocial support– social capital/social cohesionsocial capital/social cohesion– toxinstoxins– proximity to (competition for) resources (goods, services, proximity to (competition for) resources (goods, services,

transportation, employment opportunities, recreation)transportation, employment opportunities, recreation)

Other ConsiderationsOther Considerations Unit of analysis: states, counties, zip codes, Unit of analysis: states, counties, zip codes,

census tracts, census block groups, etc. census tracts, census block groups, etc. – Should “neighborhood” be defined using to Should “neighborhood” be defined using to

geographic boundaries? If so, which one?geographic boundaries? If so, which one?

Data sources—going beyond censusData sources—going beyond census

Characteristics to examine, need rationale for Characteristics to examine, need rationale for each indicatoreach indicator

Modeling approachesModeling approaches

Sources of DataSources of Data

Direct MechanismsDirect Mechanisms Community Social EnvironmentCommunity Social Environment

Social relationshipsSocial relationships transmit information transmit information Neighborhood cohesionNeighborhood cohesionsocial controlsocial control Shared cultural norms and valuesShared cultural norms and values Civic participationCivic participation demand services demand services Access to education and employmentAccess to education and employment

Community ServiceCommunity Service Grocery storesGrocery stores Recreational opportunitiesRecreational opportunities Health care facilitiesHealth care facilities Retail storesRetail stores

Physical EnvironmentPhysical Environment ToxicantsToxicants NoiseNoise Poor housingPoor housing

Indirect MechanismsIndirect Mechanisms

Chronic StressChronic StressSelye, 1956 “General Adaptive Response”Selye, 1956 “General Adaptive Response”

Alarm, resistance, exhaustion.Alarm, resistance, exhaustion. Repeated cycles lead to cumulative damage to Repeated cycles lead to cumulative damage to

organism.organism.

McEwen & Stellar, 1993—Allostatic LoadMcEwen & Stellar, 1993—Allostatic Load The cost of maintaining stability through changeThe cost of maintaining stability through change

Mental HealthMental Health Negative EmotionsNegative Emotions DepressionDepression Anger/hostilityAnger/hostility

Neighborhood Effects: EvidenceNeighborhood Effects: Evidence

Community context consistently has aCommunity context consistently has a““modest association” with numerous healthmodest association” with numerous healthoutcomes.outcomes.

25 studies reviewed25 studies reviewedDeveloped countriesDeveloped countriesIndividual-level attributes controlled forIndividual-level attributes controlled for23/25 had significant neighborhood 23/25 had significant neighborhood

effectseffects

Reviewed in Pickett & Pearl,Reviewed in Pickett & Pearl, J. Epidemiol Community Health J. Epidemiol Community Health, 2001; 55, 2001; 55

What We Know About What We Know About Neighborhoods and Child Neighborhoods and Child

Well-BeingWell-Being ProtectiveProtective

– AffluenceAffluence– ResourcesResources– Social capitalSocial capital– CohesionCohesion– Collective efficacyCollective efficacy– Urban renewal?Urban renewal?– Political activity?Political activity?

RisksRisks– PovertyPoverty– Concentrated Concentrated

deprivationdeprivation– UnemploymentUnemployment– Residential mobilityResidential mobility– Incivilities (physical Incivilities (physical

& social & social – Urban renewal? Urban renewal?

Are We Studying the Right Are We Studying the Right Factors for Policy & Program Factors for Policy & Program

Purposes?Purposes?

ProtectiveProtective– AffluenceAffluence– Social capitalSocial capital– CohesionCohesion– Collective efficacyCollective efficacy– Safe public spacesSafe public spaces– Services & Services &

resourcesresources– Political activity & Political activity &

supportsupport

RisksRisks– Concentrated deprivationConcentrated deprivation– UnemploymentUnemployment– Residential mobilityResidential mobility– Incivilities Incivilities – Poor availability of services Poor availability of services

(health & social)(health & social)– Norms (health and other)Norms (health and other)– Areas for play/social interactionAreas for play/social interaction– SegregationSegregation– Poor housing qualityPoor housing quality

Neighborhoods & Child Well-Neighborhoods & Child Well-Being: ModelingBeing: Modeling

Child outcomes

N. Affluence/poverty

N. Social capital/cohesion

N. Mobility

Parent/familyfactors

Neighborhoods & Child Well-Neighborhoods & Child Well-Being: ModelingBeing: Modeling

Child outcomes

N. Affluence/poverty

N. Social capital/cohesion

N. Mobility

Parent/familyfactors

Adding Group-Level VariablesAdding Group-Level Variables

problemproblem: making cross-level inferences : making cross-level inferences [drawing inferences regarding factors [drawing inferences regarding factors associated with variability in outcome at one associated with variability in outcome at one level based on data collected at another level]level based on data collected at another level]

e.g., making individual inferences based on e.g., making individual inferences based on group-level associationsgroup-level associations

Yij = β0 + β1ijX1 + β2ijX2…+ jGj + εij

Yij = outcome for individual i in context j β1ijX1 = beta associated with exposure for individual i in context j

βj Gj = observed community variable ε ij = error term

When are Observations Not When are Observations Not IndependentIndependent??

when data are collected by cluster / when data are collected by cluster / aggregating unitaggregating unit– children within schoolschildren within schools– patients within hospitalspatients within hospitals– drug users within neighborhoodsdrug users within neighborhoods– cholesterol levels within a patientcholesterol levels within a patient

why care about clustered data?why care about clustered data?– two children / observations within one school are two children / observations within one school are

probably more alike than two children / observations probably more alike than two children / observations drawn from different schoolsdrawn from different schools

– does knowing one outcome inform your does knowing one outcome inform your understanding about another outcome?understanding about another outcome?

Back to the Example…Back to the Example…

Potential aggregate factors to consider:Potential aggregate factors to consider:

In 2000, MK moved to a neighborhood with In 2000, MK moved to a neighborhood with a very high percentage of lawyers;a very high percentage of lawyers;

In 2003, the Red Sox lost in the 7In 2003, the Red Sox lost in the 7thth game of game of a playoff series to the Yankees, blowing a a playoff series to the Yankees, blowing a three run lead in the 8three run lead in the 8thth inning. (MK inning. (MK threw pillow at the tv, and was properly threw pillow at the tv, and was properly chastised for that).chastised for that).

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

Adjusted Odds RatioAdjusted Odds Ratio

Presence of ChildrenPresence of Children 1.15 (1.0, 1.3)1.15 (1.0, 1.3)

Number of ChildrenNumber of Children 1.06 (.80, 1.4)1.06 (.80, 1.4)

AgeAge 1.79 (1.5, 2.1)1.79 (1.5, 2.1)

Marital StatusMarital Status 1.22 (1.1, 1.4)1.22 (1.1, 1.4)

% of Lawyers in % of Lawyers in Neighborhood Neighborhood

1.47 (1.3, 1.6)1.47 (1.3, 1.6)

Red Sox Loss to Yankees Red Sox Loss to Yankees in 2003 Playoffsin 2003 Playoffs

1.75 (1.4, 2.3)1.75 (1.4, 2.3)

AcknowledgmentsAcknowledgmentsLynne Messer, PhD, University of North Lynne Messer, PhD, University of North

CarolinaCarolinaPat O’Campo, PhD, University of TorontoPat O’Campo, PhD, University of TorontoJennifer Culhane, PhD, Drexel UniversityJennifer Culhane, PhD, Drexel University

Contact InformationContact Information

Michael Kogan, Ph.D.Michael Kogan, Ph.D.HRSA/MCHBHRSA/MCHBDirector, Office of Data and Program Director, Office of Data and Program

DevelopmentDevelopment5600 Fishers Lane, Room 18-415600 Fishers Lane, Room 18-41Rockville, MD 20857Rockville, MD 20857301-443-3145301-443-3145mkogan@hrsa.govmkogan@hrsa.gov