Conducting Multilevel Modeling for Informing Maternal and Child Health Outcomes

1. Conducting Multilevel Modeling for Informing Maternal and Child Health Outcomes Jessica Griffin Burke, PhD, MHS Assistant Professor Department of Behavioral and Community Health Sciences University of Pittsburgh Graduate School of Public Health Jay S. Kaufman, PhD Associate Professor Department of Epidemiology University of North Carolina School of Public Health

2. MODE-PTD (Multi-level modeling of Disparities and the Environment in Preterm Delivery) • Funded by the MCHB, HRSA • On-going, multi-site collaborative of university researchers and state MCH officials • Maryland • Patricia O’Campo, Jessica Burke • North Carolina • Barbara Laria, Jay Kaufman, Lynne Messer, Paul • Pennsylvania • Jennifer Culhane, Irma Elo • Michigan • Claudia Holzman, Janet Eyster

3. Training Session • Part I: Background - incorporating neighborhood context into the study of health outcomes • Part II: Methods - interpretation of multilevel and contextual analysis

4. Part I: Background incorporating neighborhood context into the study of health outcomes

5. outline • history of ecological analysis • how neighborhoods are defined • how to characterize neighborhoods and sources of data • theories • evidence • the case of preterm birth • challenges

6. John Snow (1813-1858)public health epidemiologist • 1854 On the Mode of Communication of Cholera - Broad Street Pump Outbreak

7. “The City”, 1925Park & Burgess Theory: Via competition urban areas become divided into niches or “natural areas” where residents share common social characteristics I Loop II Transition zone III Working men’s homes IV Residential zone V Commuter zone

8. social science research base map of Chicago1923-1924 Ernest Burgess

9. Hull-House maps and papers, 1895Nina Brown • undertook extensive primary data collection in Chicago neighborhoods • recorded nationality, wages, and employment history through door to door surveys • did not attempt to explain the distribution of poverty • intent to prompt humanitarian response- public policy

10. Hull-House maps and papers, 1895

11. declining interest in neighborhoods • during the mid-1970s interest in the connection between spatial location, social position and health began to wane. • both socioeconomic and health outcomes were conceptualized as individual-level processes constrained only by family circumstances – “status attainment model” Douglas S. Massey, Contemporary Sociology, Vol. 27, No6 (Nov., 1998), 570-572

12. why the study of neighborhoods lost its appeal? • wariness of ecological data- “ecological fallacy” • advances in statistical methods kept researchers busy with complex modeling of individual-level data – rise of social surveys with detailed information on individuals, families and households but little, if any information on neighborhood. • methodological, conceptual and political individualism dominated since 1950’s

13. back to the future:the rediscovery of neighborhood context • by the mid-1980’s more and more complex statistical models were developed to “push around a fixed amount of variance in ever more trivial ways” with very few gains • key event that brought back ecology forcefully was the publication of William Julius Wilson’s , The Truly Disadvantaged. Douglas S. Massey, Contemporary Sociology, Vol. 27, No6 (Nov., 1998), 570-572.

14. “The truly disadvantaged”William Julius Wilson • argued that black urban poverty was perpetuated not only through individual and family-level mechanisms but also through structural mechanisms functioning at the neighborhood-level

15. the reemergence of neighborhood Since the 1990’s “the new public health” • calls to examine the upstream causes of disease • calls to simultaneously examine both the distal and proximal causes of disease

16. Trends in Neighborhood research:articles with “neighborhood” in the title Social Science numbers from Sampson et al., 2002

17. Individual and Neighborhood Effects Neighborhood A Neighborhood B

18. Individual and Neighborhood Effects Individual Attribute Protection Neighborhood A Neighborhood B

19. Individual and Neighborhood Effects Neighborhood Characteristic Protection Individual Attribute Protection Neighborhood B Neighborhood A

20. what is a neighborhood? • geographic boundary around a residential setting. Census boundaries are often used as proxy measures for residential neighborhoods • neighborhoods are usually thought of as smaller than communities. Communities can also be defined by unit of identity, i.e., church, school, work, social circle

21. neighborhood definitions • administrative units • blocks • block groups - counties • census tracts - states • political units - wards - city council districts • service districts - health districts - fire battalion boundaries - police districts - school boundaries - recreation districts - postal zip codes • self-defined neighborhood boundaries - shared environment - natural boundaries

22. Wake neighborhoods • block groups – 263 • census tracts – 105 • zip codes – 44 in Raleigh • neighborhood groups • police jurisdictions • voting districts

23. how do you measure neighborhood? administrative units in Wake County, NC within same census tract? within same block group?

24. characterizing neighborhoods What types of data can we use? • compositional effects • properties of individuals • a.k.a., derived variables; aggregation of individual-level variables • contextual effects • properties of places • a.k.a., integral variables; no individual-level analogs • direct measures: • measure of the built environment like “walkability”

25. sources of neighborhood-level data • administrative and public health data at the address level • census data at the block, block group, and census tract level • proprietary data • primary data collection

26. Licensing and Inspection Medicaid Enrollment Shelter Episodes Birth Records Lead Test Results Fetal Death Records Immunization Registry Death Records Geocode School Data Match/Merge Hospital Discharge Data Reportable Diseases Utility Cut-off Rate DHS Records City Health Center & FQHC Encounters Cancer Registry Crime Data Medical Claims Data Tax Delinquency Air Quality Census and Property Data Business Registry

27. theories of how neighborhoods influence health • Social contagion or social diffusion theory • imitating behavior / peer pressure, close proximity has spillover effect • Socialization • internalization of social norms and learning the boundaries of acceptable behavior • Social comparison • processes governed by levels of relative deprivation and status organizing processes • Collective efficacy = social cohesion + informal social control • Institutionalization • behavioral regularities through structured and semi-structured organizations and actors, including employers, schools, enforcement agencies, and other social institutions • Community resources • Psychosocial stress pathways • fear of crime and violence influences behavior change

28. neighborhood effects: evidence • Neighborhood 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

29. neighborhood effects associated with… • all cause mortality • chronic disease among adults • self-rated health • long-term disability • cardiovascular symptoms or disease • respiratory function • health behaviors (i.e.,-smoking, sexual practices) • domestic violence • low birth weight and preterm birth

30. neighborhood context and MCH outcomes • To review recently published (1999-2004) multilevel research addressing neighborhood context and maternal and child health outcomes • 31 selected and reviewed in detail and neighborhood characteristics classified into like categories • Maternal and child health topics ranged across the life course from infant birth weight to parenting practices • Majority of studies were from the United States, a few from Canada (6%), UK (10%) and the Netherlands (13%) Rajaratnam JK, Burke JG, O’Campo P. (2006). Maternal and child health and neighborhood context: The selection and construction of area-level variables. Health and Place.

31. Categories of Neighborhood Characteristics (% of the 31 studies)* • Income/Wealth (100%) • Employment (74%) • Family Structure (55%) • Population Composition (52%) • Housing (39%) • Mobility (39%) • Education (26%) • Occupation (16%) • Social Resources (36%) • Violence and Crime (26%) • Deviant Behavior (13%) • Physical Conditions (19%)

32. challenges • what is the theory linking contextual variable and outcome of interest? • what is the context? • Geographic? Work, Religious affiliations, Clubs, Social Networks ? • what contextual-level variables should be measured? • what is the spatial range of a contextual variable? • what statistical techniques should be employed? • limitations of a cross-sectional design-when to measure context? • what individual-level characteristics need to be controlled for?

33. Part II: interpretation of multilevel and contextual analysis

34. overview • why context matters • multilevel models - synonyms • what are we trying to explain? • issues specific to nested data • different types of multilevel models • interpreting the different types of multilevel models • fallacies

35. why context matters • empirically, individual outcomes can’t be explained exclusively by individual-level exposures • persistent contextual effects are observed in outcomes across populations • exposures are structured; distributions are differential

36. methodological vs. conceptual issues • recent methodological advancements have improved the estimation of neighborhood effects through fixed and random effects modeling; however, this often relegates the neighborhood to a covariate, or the black box of influences on health • conceptual issues, or mechanisms through which neighborhoods influence health are still needed

37. definition and synonyms • ml 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 • panel data, repeated measures designs use ml methods as well

38. 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

39. 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?

40. why partition variance? • ml models allow you to decompose the total variance in individual-level outcomes into within group and between group components • empirically useful • conceptually important (non-individualistic) • allows for different types of policy or interventions to change population values / distributions

41. 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 β1 = beta associated with exposure β2 = beta associated with first covariate + …

42. model assumptions • baseline outcome means (mean values when exposure = 0) differs only due to variability between subjects • individuals, and their errors are, independent and identically distributed • all non-specified variables (e.g., area-level variables; those confounders you did not measure) assumed = 0

43. 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

44. maybe group variables just interact with individual variables? • interacting group- and individual-level variables will get you close to the right answer • problem: error structure is multilevel, but errors only specified at the individual-level • individuals within contexts are correlated with each other • errors not independent and identically distributed ln P(Xij) / [1-P(Xij)] = αi + β1ijX1ij + β2ijX2ij + β3jGj + β2ijX2*β3jGj

45. what’s the problem with a multilevel error structure? • standard methods produce unbiased point estimates • your betas or ORs will be ~correct • standard errors too small • confidence intervals will be wrong (too precise) • unless you can demonstrate there are no correlations between the following: • individual-level predictors • group-level predictors • unobserved characteristics

46. how to tell if you need multilevel models • reality 1: anytime you have data collected from some aggregate unit / clusters, you will have to use ml models • reality 2: calculating an intraclass correlation coefficient will quantify your clustering (in absence of running a ml model) • reality 3: even if your ‘clustered data’ aren’t empirically clustered, article and grant reviewers may demand it

47. intraclass correlation coefficient • estimates the degree of clustering by unit of aggregation • icc = between cluster variance / total variance* • icc = 0 : people within the same context no more similar to each other than they are to any random person elsewhere • icc > 0 : people in same context more similar to each other than to people in other contexts *total variance = between cluster + within cluster variances

48. modeling clustered data • Two main approaches: • population average models with robust variance estimators • marginal models that account for cross level correlation across all units of aggregation • not conditional on being in a certain cluster; does not model clustering directly • provides robust tests, corrected standard errors, corrects for heteroskedasticity* • ml models • random effects models (unit-specific models that condition on specific units of aggregation for inference) • fixed effects models (area-level coefficients held constant across units of aggregation) • mixed models (models that combine some fixed and random effects; not going into any more detail about mixed models in this lecture) * heteroskedasticity results from errors not having constant variance

49. population average models • Yij = preterm birth (1) versus term birth (0) for woman i in tract j • Xij = low (1) or high (0) ses for woman i in tract j • no locations specified, just averaged over all tracts • allows you to compare ‘average low’ versus ‘average high’ ses women Pr (Y ij=1 | Xij) = f (Xij) note: no group / cluster mean

50. population average models • pros • model response change as function of covariates ‘averaged’ over group to group heterogeneity • economists, sociologists like these models • cons • do not explicitly account for heterogeneity across higher-level units / contexts • do not allow examination of group to group variation