# Presentation: 149125

## Contextual Analysis: Applying the Statistical Concepts to Real Data

- Seminar III
- MODE-PTD Project
- July 2007
- Patricia O’Campo Ph.D.
- St. Michael’s Hospital &
- Univeristy of Toronto

## Project Areas 

- Four University – Government health departments partnerships; 8 study sites total (5 suburban, 3 urban)
- Baltimore City, MD
- Baltimore County, MD
- Prince Georges County, MD
- Montgomery County, MD
- 16 pooled cities, MI
- Durham County, NC
- Wake County, NC
- Philadelphia, PA

## Overview

- How was an index of neighborhood deprivation developed?
- Is the Neighborhood Deprivation Index associated with Preterm Birth in a single site?
- Is the Neighborhood Deprivation Index associated with Preterm Birth across multiple sites?
- What are some resources available for those wanting to learn more about multilevel modeling?

## Neighborhood characteristics

- Existing domains in the literature
- Poverty/income
- Racial/ethnic composition
- Education
- Employment
- Occupation
- Housing/crowding
- Residential stability

- Limitations:
- Use of theory rare in selecting area factors
- Inconsistent operationalization of neighborhood domains
- Poverty alone is not a good proxy for other neighborhood factors

## Data source & Specification of “neighborhood”

- Neighborhood=Census tract
- Data come from 2000 Census
- Census tracts are small (~4000 residents), relatively permanent statistical subdivisions of counties, designed to be fairly homogeneous united with respect to socio-demographic characteristics and living conditions

## Construction of neighborhood deprivation index (NDI)

- Identified 20 census variables in 7 domains used consistently to approximate neighborhood-level environments for inclusion in deprivation index
- One education variable
- Two employment variables
- Five housing variables
- Four occupation variables
- Five poverty variables
- One racial composition variable
- Two residential stability variables

## Neighborhood index: Data reduction 

- Census tract data from the eight study sites were used in principal components analyses (PCA) to create the index
- PCA is a data reduction technique that transforms several correlated variables into a smaller set of uncorrelated variables or components
- The first component explains as much of the overall variance as possible

## Neighborhood index: Data reduction 

- Across study areas, loadings on first principal component ranged from -0.041 to 0.295, with a mean loading of 0.211
- Item loadings (>.25) were used to retain 8 items in the index
- Final item loadings were used to weight each variable’s contribution to the summary score for each census tract
- The index was standardized to have a mean of 0 and a standard deviation of 1.

## Neighborhood deprivation index

- The neighborhood deprivation index accounted for 67% of the total variance
- Consistency of loading for each item across sites suggests that variables function similarly across geography, despite heterogeneity in demographics and economic status

## Principal Components Analysis

- 8 variables were retained for the neighborhood deprivation
- % males in managerial or professional occup
- % crowded housing
- % HH in poverty
- % female headed HH
- % HH on public assistance
- % HH earnings <$30k
- % < HS educ
- % unemployed

## Socio-demographic variability across geographic areas

## Distribution of deprivation scores across eight study sites

## Individual-level Data from Vital Records (1999-2001)

| Individual-level variables | Categories |
| --- | --- |
|      Preterm birth | <=37 weeks (preterm)*
>=38 weeks |
|      Maternal Age  | < 20
20-24 
25-29 
30-34
35+ years of age |
|      Maternal Race | Non-Hispanic Black
Non-Hispanic White |
|      Maternal Education |   < 20 years &  < High school
   > 20 years & < High school
   High school or equiv
   > High school |

- *preterm infants had to weigh less than 3888 grams

## Baltimore City: Preterm Birth %

## Baltimore City Demographics: Age

## Baltimore City Demographics: Education

## Neighborhood Deprivation Index (NDI)

- 	NH White
-   (174 Census tracts)
**Median -0.267**
- 25%  	-0.629
- 75%	0.202
- Min	-1.34
- Max	2.59

- 	NH Black
-    (191 Census tracts)
**Median 0.742**
- 25%  	0.082
- 75%	1.191
- Min	-1.27
- Max	2.59

## Methods: Data Analysis 

- Race-stratified models
- 1. Multi-level (random intercept) logistic regression models with NDI as only predictor
- 2. Multi-level (random intercept) logistic regression models with NDI plus the potential confounders maternal age and education
- Statistical methods were presented in detail in the previous DataSpeak session on “Methods for Understanding and Interpreting Multilevel Analysis” (June 07)

## Neighborhood deprivation odds ratios comparing the 1st to the 5th quintile

- Odds ratios

## Percent preterm among non-Hispanic White and non-Hispanic Black women by site

## Percent age 30-34 by race and site

## Percent with more than high school or equivalent education by race and site

## Neighborhood Deprivation Index Values by Site

## Neighborhood deprivation odds ratios for the outcome preterm birth

|  | White-unadj | White-adj | Black-unadj | Black-adj |
| --- | --- | --- | --- | --- |
| Philadelphia | 1.68*
(1.45, 1.92)  | 1.52* 
(1.28, 1.75) | 1.40* 
(1.27, 1.53)  | 1.26* 
(1.13, 1.39)  |
| Wake | 1.82* 
(1.48, 2.16)  | 1.52* 
(1.15, 1.88)  | 1.33* 
(1.02, 1.64)  | 1.20 
(0.89, 1.51)  |
| Durham | 2.18* 
(1.69, 2.68)  | 1.68*
(1.14, 2.23)  | 1.48* 
(1.24, 1.71)  | 1.20 
(0.97, 1.43)  |
| 16 Cities | 1.64* 
(1.51, 1.77)  | 1.48* 
(1.32, 1.63)  | 1.14*
(1.06, 1.22)  | 1.05 
(0.95, 1.16)  |
| PG Cnty | 2.07* 
(1.29, 2.85)  | 1.68 
(0.90, 2.46)  | 1.20 
(0.97, 1.43)  | 1.14 
(0.90, 1.37)  |
| Montgomery Cnty | 1.82* 
(1.38, 2.26)  | 1.56* 
(1.09, 2.02)  | 0.86 
(0.36, 1.35)  | 0.88 
(0.38, 1.37)  |
| Baltimore County,  | 3.14* 
(2.72, 3.56)  | 2.24* 
(1.77, 2.71)  | 1.37 
(0.74, 1.99)  | 1.40 
(0.78, 2.03)  |
| Baltimore City, MD  | 2.79* 
(2.30, 3.08)  | 1.87*  
(1.42, 2.31)  | 1.37*
(1.24, 1.50)  | 1.23* 
(1.08, 1.39)  |

- Adjusted models include maternal age and maternal education

## Neighborhood-level deprivation and preterm birth: similarity across sites

- The Cochran’s Q statistic for homogeneity was calculated across sites to test the null hypothesis that the neighborhood deprivation betas are similar across sites
- Cochran’s Q was not significant for both non-Hispanic White women (p=0.83) and non-Hispanic Black women (p=0.50).

## Neighborhood-level deprivation and preterm birth: similarity across sites

- Because the hypothesis of homogeneity was not rejected, we calculated a summary statistic representing the effect of neighborhood deprivation and preterm birth across sites.
- The summary effect for non-Hispanic White women was **1.57** (95% CI: 1.41 to 1.74) and for non-Hispanic Black women it was **1.15** (95% CI: 1.08 to 1.23).

## Are the associations similar across geographic area? 

- Are the associations similar across geographic area?

## Summary of deprivation and preterm birth in multiple sites

- Despite the diversity of geographic settings, there was homogeneity in the effect of neighborhood deprivation on preterm birth across sites for both non-Hispanic Blacks and non-Hispanic Whites.
- The weaker effect for non-Hispanic Blacks versus Whites was explored but could not be explained with the data we had.

## Additional project analyses

- Examine separate components of the deprivation index
- Examine other exposures such as smoking and maternal age
- Examine other outcomes such as small for gestational age

## Selected examples

- Extensive resource listing on the Dataspeak website
- Books
- Luke DA.  Multilevel Modeling: Quantitative Applications in the Social Sciences. July 2004 Sage Publications Inc.
- Raudenbush, S. W., and A. S. Bryk.. Heirarchical linear models: Applications and data analysis methods. 2002 Thousand Oaks, CA: Sage.

## Selected examples

- Websites for General Information & software
- Centre for Multilevel Modeling, University of Bristol: _[http://](http://www.mlwin.com/)__[www.mlwin.com](http://www.mlwin.com/)__[/](http://www.mlwin.com/)_
- For SAS users: _[http://](http://gseweb.harvard.edu/~faculty/singer/)__[gseweb.harvard.edu](http://gseweb.harvard.edu/~faculty/singer/)__[/~faculty/singer/](http://gseweb.harvard.edu/~faculty/singer/)_

## Selected examples

- Websites for General Information & software (cont’d)
- Muthen and Muthen, MPLUS _[http://](http://www.statmodel.com/)__[www.statmodel.com](http://www.statmodel.com/)__[/](http://www.statmodel.com/)_
- For SPSS users: http://www.unc.edu/~painter/SPSSMixed/SPSSMixedModels.PDF

## Contact Information

- Patricia O’Campo, PhD
- 	St. Michael’s Hospital & University of Toronto
- E-mail:
-     pat.ocampo@utoronto.ca