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Educational Success and Adult Health: Findings from the Chicago Longitudinal Study

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Abstract

Growing evidence indicates that education is associated with health, yet we lack knowledge about the specific educational experiences influencing health trajectories. This study examines the role school factors play in the emergence of poor young adult health outcomes for a low-income, minority sample. The following research questions are addressed. First, what are the education-based predictors of daily tobacco smoking, frequent substance use, depression, and no health insurance coverage? Second, do later-occurring school factors explain the association between earlier school measures and the outcomes and, if so, what pathways account for this mediation effect? Data were derived from the Chicago Longitudinal Study, an investigation of a cohort of 1,539 individuals, born around 1980, who attended kindergarten programs in the Chicago Public Schools. Participants were followed prospectively from early childhood through age 24, and study measures were created from various data sources and multiple assessment waves. Findings from probit hierarchical regressions with controls for early sociodemographic covariates indicated that elementary school socioemotional classroom adjustment and high school completion were significantly and negatively associated with all four study outcomes. Participation in the Chicago Child Parent Center preschool program predicted lower rates of both daily tobacco smoking and no health insurance coverage (p < .05). Middle school reading achievement was inversely related to depression (p < .01), while middle school frustration tolerance was inversely associated with daily tobacco smoking and frequent drug use (p < .05). Also, negatively linked to frequent drug use was a high school measure of students’ expectation to attend college (p < .01). In nearly all cases, later-occurring school factors fully mediated significant associations between earlier ones and the outcomes. Patterns of mediation were explored along with implications of results.

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Notes

  1. The outcomes modeled in this study were meant to represent the domains of behavioral health, mental health and health care access, respectively. The authors did not analyze measures such as health status and chronic health problems given so few CLS respondents indicated they were in fair to poor health or had chronic health issues, resulting in highly skewed variables with little variance. Instead, we explored health-related outcomes more commonly experienced in young adulthood with demonstrated links to ultimate health concerns (e.g., Lleras-Muney, 2005). We also considered outcomes such as family problems, anxiety, and involvement in crime as potential dependent measures but rejected these based on issues of measurement or conceptual incongruity with health.

  2. Outcomes examined in this current study are relevant young adult indicators of poor health trajectories for two reasons. First, as mentioned above, they presage health concerns that often do not appear until middle to late adulthood. Second, they represent distinct, albeit related, domains of functionality. For instance, while the association between depression and substance use is well accepted (e.g., Brady & Sinha, 2005), the correspondence is not extremely high, the causal direction varies, and the relation is nuanced (see Green & Ritter, 2000; Murphy et al, 2003). Likewise, depression symptoms or depression itself appear to heighten the risk for addictive use of tobacco; however, it does not act as a primary predictor of smoking initiation (e.g., Leff et al., 2003). Additionally, the majority of adult smokers do not have depression or significant depressive symptomology (Murphy et al., 2003). The outcomes examined in this current study, therefore, describe different although overlapping subgroups of young adults who are vulnerable to poor health by virtue of their current and distinct health risks. Indeed, our measures of tobacco/substance use, depression, and no health insurance coverage share at most 4% variance.

  3. Two of the five Chicago Effective Schools Project sites randomly chosen for the comparison group offered CPC school-age but not CPC preschool programming.

  4. We did not aggregate years of CPC program participation in this study, from 0-6, for several reasons. First, the preschool program and school-age program are, as discussed in the text, structurally different. Also, previous CPC studies highlight the relative potency and uniqueness of the preschool program given the high quality, tailored services it delivers and the sensitive period of development it targets. Although the preschool and school-age program variables are relatively highly correlated, there is variability in program participation, which is amplified when the school-age participation measure is treated as a continuous variable.

  5. Total refers to the adult survey sample, N = 1,142. We do not show missing data percentages based on the insurance sample due to space limitations and our primary reliance on the adult survey sample; i.e., three of four outcomes modeled in this study use this sample.

  6. To calculate this estimated value, we identified the available test score most proximal in time to 8th grade and added to it the overall sample’s average incremental change in test score from that grade to the 8th grade. To arrive at an estimated 8th grade score from a known 9th grade score, the average score change was subtracted from the known value; i.e., the 9th grade score. This procedure is based on the scoring structure of the reading subtests, which “have equal-interval scale points and index continuous development over the grade year.” (Reynolds, 2000, p. 73).

  7. We considered the acting out subscale of the T-CRS over frustration tolerance, but the latter was more gender neutral and consistent with other predictor measures in directionality. School mobility was also considered for inclusion in the middle/high school block of predictors but was rejected because it implied family circumstance rather than student-school interface.

  8. Not shown are parameter estimates associated with covariates, all of which were interpretable and stable. For instance, the risk index consistently generated a positive, statistically significant marginal effect on our indicators of poor adult health-related outcomes.

  9. This result is not robust to alternative model specifications. For instance, when environmental risks are characterized as single-item covariates in comprehensive predictor models, CPC preschool does not exert a statistically significant main effect on daily tobacco use (see Reynolds et al., 2007).

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Authors and Affiliations

  1. University of Wisconsin-Milwaukee, Enderis 1057, P.O. Box 786, Milwaukee, WI, 53201, USA

    James Topitzes & Joshua P. Mersky

  2. Waisman Center, University of Wisconsin-Madison, Madison, WI, USA

    Olga Godes & Sudi Ceglarek

  3. Institute of Child Development, University of Minnesota-Twin Cities, Twin Cities, MN, USA

    Arthur J. Reynolds

Authors
  1. James Topitzes
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  2. Olga Godes
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  3. Joshua P. Mersky
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  4. Sudi Ceglarek
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  5. Arthur J. Reynolds
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Corresponding author

Correspondence to James Topitzes.

Additional information

Chicago Longitudinal Study grants from the National Institutes of Health (No. R01HD034294) and the Doris Duke Charitable Foundation (No. 20030035) supported the research reported herein. An Advanced Graduate Student Grant from the Interdisciplinary Training Program in Educational Sciences at the University of Wisconsin-Madison along with a Category A Research Grant from the Institute on Race and Ethnicity of the University of Wisconsin System provided additional funding for the completion of this study. Geetika Tiwari contributed to several study analyses.

Appendix A: Flowchart of study sites and participants in the Chicago longitudinal study for the adult survey sample

Appendix A: Flowchart of study sites and participants in the Chicago longitudinal study for the adult survey sample

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Topitzes, J., Godes, O., Mersky, J.P. et al. Educational Success and Adult Health: Findings from the Chicago Longitudinal Study. Prev Sci 10, 175–195 (2009). https://doi.org/10.1007/s11121-009-0121-5

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