Social Determinants of Health and Adolescent Pregnancy: An Analysis From the National Longitudinal Study of Adolescent to Adult Health.

PURPOSE: Although rates of adolescent pregnancy are at an all-time low in the United States, racial/ethnic and geographic disparities persist. This research used National Longitudinal Study of Adolescent to Adult Health (Add Health) data to analyze empirical relationships between social determinants of health (SDoH) and adolescent pregnancy. Examining relationships between the SDoH and adolescent pregnancy provides support for funding priorities and interventions that expand on the current focus on individual- and interpersonal-level factors. METHODS: On the basis of the Healthy People 2020 Social Determinants of Health Framework, the identification of proxy measures for SDoH within the Add Health study allowed for an analysis of relationships to adolescent pregnancy (N = 9,204). Logistic regression examined associations between adolescent pregnancy and each measure of SDoH. RESULTS: Results indicated that 6 of 17 measures of SDoH had an empirical relationship with adolescent pregnancy. Measures negatively associated with adolescent pregnancy included the following: feeling close to others at school, receipt of high school diploma, enrollment in higher education, participation in volunteering or community service, reporting litter or trash in the neighborhood environment as a big problem, and living in a two-parent home. CONCLUSIONS: Findings from this study support the need for increased research and intervention focus in SDoH related to areas of education and social and community context. Results of this study provide information for the allocation of resources to best address SDoH that show a link with adolescent pregnancy. Areas of future research can further explore the areas in which SDoH show a relationship with adolescent pregnancy.

How Do Propensity Score Methods Measure Up in the Presence of Measurement Error? A Monte Carlo Study.

Considering that the absence of measurement error in research is a rare phenomenon and its effects can be dramatic, we examine the impact of measurement error on propensity score (PS) analysis used to minimize selection bias in behavioral and social observational studies. A Monte Carlo study was conducted to explore the effects of measurement error on the treatment effect and balance estimates in PS analysis across seven different PS conditioning methods. In general, the results indicate that even low levels of measurement error in the covariates lead to substantial bias in estimates of treatment effects and concomitant reduction in confidence interval coverage across all methods of conditioning on the PS.