+Preconception Health and Racial/Ethnic Disparities in Birth Weight: A National Study

Kelly L. Strutz, MPHLiana J. Richardson, PhD, MPH

Jon M. Hussey, PhD, MPHUniversity of North Carolina at Chapel Hill

3rd National Summit on Preconception Health and Health CareJune 13, 2011

+Introduction:

Evidence regarding the relationship between preconception health and birth outcomes is limited.

Even less is known about the contribution of preconception health to racial/ethnic disparities in birth outcomes.

This represents a critical gap in the knowledge base needed to inform MCH policy and practice.

+Study Questions:

How does preconception health impact birth weight?

Does preconception health contribute to racial/ethnic disparities in birth weight?

Does preconception health impact all racial/ethnic groups equally?

+Data and Analytic Sample: National Longitudinal Study of Adolescent Health (Add

Health), a nationally representative probability sample of US adolescents in the 1994-95 school year

Respondent interviews Wave I (1994-95): Adolescents in 7th-12th grades Wave III (2001-02): Young Adults aged 18-26 Wave IV (2007-08): Adults aged 24-32

Analytic sample 3046 singleton births conceived after Wave III and reported at Wave IV to non-Hispanic White, non-Hispanic Black, Mexican-

Origin Latina, and Asian/Pacific Islander mothers

+Measures and Analysis:

Infant birth weight Reported by respondentAdjusted for preterm birth

Preconception Health Indicators Cigarette smoking, alcohol

consumption, physical activity, and obesity status

Measured prospectively at Waves I and III

+Measures and Analysis: Confounders

Respondent’s age and parity at birth, nativity, and early life SES

Mediators Respondent’s prenatal smoking and alcohol

consumption, and trimester began prenatal care Statistical Analysis

Univariate and bivariate analyses Linear regression Accounted for complex survey design

+Results: Mean Birth Weight (g)

Series12200

2400

2600

2800

3000

3200

3400

WhiteBlackLatinaAsian

+Results: Any Smoking

+Results: Heavy Drinking

+Results: Overweight/Obese

+Results: Physically Active

+Results: Contribution to Disparities

Model 1: Race/Ethnicity

Model 2: R/E + Smoking

Model 3: R/E + Smoking + Prenatal

Latina -- -- --BlackAsianAny Smoking

Wave I OnlyWave III Only -- --

Both Waves

+Results: Contribution to Disparities

Model 1: Race/Ethnicity

Model 3: R/E + Obesity

Model 4: R/E + Obesity + Prenatal

Latina -- -- --BlackAsian -- --Overwt/Obese

Wave I Only --Wave III Only

Both Waves

+Results: Contribution to Disparities

Model 1: Race/Ethnicity

Model 6: R/E + Activity

Model 7: R/E + Activity + Prenatal

Latina -- -- --BlackAsianPhysically Active

Wave I Only -- --Wave III Only

Both Waves

+Effects by Racial/Ethnic Group:Racial/Ethnic Group Effects

White•Overweight/Obese Wave III Only ↑•Physically Active Either or Both Waves ↑

Black •Any Smoking Wave I Only ↓•Overweight/Obese Both Waves ↑

Latina •Heavy Drinking Both Waves ↓

Asian•Any Smoking Wave III Only ↓ •Overweight/Obese Wave III Only ↑ •Heavy Drinking Wave I Only ↓

+Discussion:Findings

Overall associations between birth weight and preconception health indicators

Varied by racial/ethnic groupEffects of adjusting for preconception

factors on Black/White and Asian/White disparities in birth weight

+Discussion: Strengths

Prospective measures of preconception health and subsequent birth weight

Diverse national cohort Limitations

Adequacy of available data and measures Public Health Significance

Utility of applying a life course perspective Further research is needed to identify modifiable

preconception factors

+Acknowledgments Alexis Dennis and Chirayath Suchindran This work was supported by the Eunice Kennedy Shriver National

Institute of Child Health & Human Development (grant numbers R01-HD057073, R01-HD058535, and T32-HD052468-02)

This research used data from Add Health, a program project directed by Kathleen Mullan Harris and designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill, and funded by grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), with cooperative funding from 23 other federal agencies and foundations. Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Information on how to obtain the Add Health data files is available on the Add Health website (http://www.cpc.unc.edu/addhealth). No direct support was received from grant P01-HD31921 for this analysis.

+Contact

Kelly StrutzUniversity of North Carolina at Chapel HillCarolina Population CenterCB# 8120, University Square123 West Franklin StreetChapel Hill, NC 27516-2524strutz@email.unc.edu