Is educational attainment associated with young adult cardiometabolic health?

Abstract

Educational disparities in health and mortality are well-documented and evidence suggests that they may be widening. Yet, there is much unknown about when educational disparities begin to emerge and for whom. This paper investigates the association between educational attainment and cardiometabolic health in young adults with critical attention paid to differences across racial/ethnic and sex subgroups. We focus on cardiometabolic health in young adulthood as it is particularly relevant for understanding current population health trends. We used data from the National Longitudinal Study of Adolescent to Adult Health (Add Health) when participants were aged 12–19 years (Wave I) and aged 24–32 years (Wave IV). Using a series of logistic regression models, we first estimated the association between education and five markers of cardiometabolic health (high-risk blood pressure, high-risk waist circumference, diabetes/pre-diabetes, hyperlipidemia, and high-risk inflammation). We then examined the extent to which this association was explained by adolescent health and both adolescent and young adult socioeconomic status (SES) (including parental education, participant educational attainment, household income, and employment status). Finally, we investigated whether the association between educational attainment and cardiometabolic health differed by race/ethnicity and sex. We found evidence of an association between educational attainment and cardiometabolic health that persisted net of adolescent health, adolescent SES, and young adult SES. We also found some evidence of modest differences in this association by race/ethnicity and sex. Our findings suggest that even as early as young adulthood there are disparities in cardiometabolic health by educational attainment, which may lead to even larger disparities in late life health.

Explainable machine learning framework for predicting long-term cardiovascular disease risk among adolescents

“…For instance, adolescents’ physical activity is obtained from the seven survey questions where individuals are asked to report how many times they are engaged in a specific activity in the last week. The responses from these questions are aggregated to create the adolescents’ total physical activity variable 33 . For sedentary behavior, the total screen time is captured based on the following questions—“How many hours a week do you watch television?”, “How many hours a week do you watch videos?” and “How many hours a week do you play video or computer games?” Participants’ responses are summed to obtain the total number of hours of screen-time per week 35 .…”

“…Adolescent depression is self-reported and measured based on 15 questions from the CES-D questionnaire. The responses to these 15 questions are summed to create a depressive score that ranged from 0 to 45, with 45 indicating higher depression 32 , 37 , 44 . The Add Health survey items associated with adolescent predictors (Table 1 ) are provided in the Supplementary Information.…”

“…Since Add Health survey questions are not specifically targeted toward CVD risk factors, many survey items are not pertinent to this research. Therefore, we selected relevant questions based on expert opinion (e.g., endocrinologist or cardiologist) and prior research findings [31][32][33][34][35][36][37][38][39][40][41][42][43][44][45] . Survey items from Waves I and II that reflected the following factors are selected as input variables for ML model development-sociodemographic, socioeconomic, lifestyle and health risk, stressful life events, positive well-being, and depression.…”

“…For instance, adolescents' physical activity is obtained from the seven survey questions where individuals are asked to report how many times they are engaged in a specific activity in the last week. The responses from these questions are aggregated to create the adolescents' total physical activity variable 33 . For sedentary behavior, the total screen time is captured based on the following questions-"How many hours a week do you watch television?…”

Improving cardiac care delivery using predictive and prescriptive analytics

“…Since Add Health survey questions are not specifically targeted towards CVD risk factors, there are many survey items that are not pertinent to this research. Therefore, we selected relevant questions based on expert opinion (e.g., endocrinologist or cardiologist) and prior research findings [158,159,[168][169][170][171][172][173][160][161][162][163][164][165][166][167]. Survey items from Waves I and II that reflected the following factors are selected as input variables for ML model development sociodemographic, socioeconomic, lifestyle and health risk, stressful life events, positive well-being, and depression.…”