Educational attainment and cardiovascular disease in the United States: A quasi-experimental instrumental variables analysis

Abstract

Background There is ongoing debate about whether education or socioeconomic status (SES) should be inputs into cardiovascular disease (CVD) prediction algorithms and clinical risk adjustment models. It is also unclear whether intervening on education will affect CVD, in part because there is controversy regarding whether education is a determinant of CVD or merely correlated due to confounding or reverse causation. We took advantage of a natural experiment to estimate the population-level effects of educational attainment on CVD and related risk factors. Methods and findings We took advantage of variation in United States state-level compulsory schooling laws (CSLs), a natural experiment that was associated with geographic and temporal differences in the minimum number of years that children were required to attend school. We linked census data on educational attainment ( N = approximately 5.4 million) during childhood with outcomes in adulthood, using cohort data from the 1992–2012 waves of the Health and Retirement Study (HRS; N = 30,853) and serial cross-sectional data from 1971–2012 waves of the National Health and Nutrition Examination Survey (NHANES; N = 44,732). We examined self-reported CVD outcomes and related risk factors, as well as relevant serum biomarkers. Using instrumental variables (IV) analysis, we found that increased educational attainment was associated with reduced smoking (HRS β −0.036, 95%CI: −0.06, −0.02, p < 0.01; NHANES β −0.032, 95%CI: −0.05, −0.02, p < 0.01), depression (HRS β −0.049, 95%CI: −0.07, −0.03, p < 0.01), triglycerides (NHANES β −0.039, 95%CI: −0.06, −0.01, p < 0.01), and heart disease (HRS β −0.025, 95%CI: −0.04, −0.002, p = 0.01), and improvements in high-density lipoprotein (HDL) cholesterol (HRS β 1.50, 95%CI: 0.34, 2.49, p < 0.01; NHANES β 0.86, 95%CI: 0.32, 1.48, p < 0.01), but increased BMI (HRS β 0.20, 95%CI: 0.002, 0.40, p = 0.05; NHANES β 0.13, 95%CI: 0.01, 0.32, p = 0.05) and total cholesterol (HRS β 2.73, 95%CI: 0.09, 4.97, p = 0.03). While most findings were cross-validated across both data sets, they were not robust to the inclusion of state fixed effects. Limitations included residual confounding, use of self-reported outcomes for some analyses, and possibly limited generalizability to more recent cohorts. Conclusions This study provides rigorous population-level estimates of the association of educational attainment with CVD. These findings may guide future implementation of interventions to address the social determinants of CVD and strengthen the argument for including educational attainment in prediction algorithms and primary preventi...

Machine learning and algorithmic fairness in public and population health

“…Besides the addition of data to prediction models, a critical focus on the types of prediction task and how they are used in practice is also essential. For example, although the incorporation of patient socio-economics can improve risk assessment, and epidemiological evidence shows the relation of these to several important outcomes 42 , concerns regarding their use being utilized to justify lower standards of care for poor patients 43 have been voiced. Such important risks illustrate the importance of the development and use of prediction models that are closely linked to clinical and public health practices and priorities.…”

Educational level as a cause of type 2 diabetes mellitus: Caution from triangulation of observational and genetic evidence

“…Moreover, childhood socioeconomic position (SEP) was obtained and categorised into four groups according to father's main job when participants aged 14 years: high (i.e., managerial-, professional-, administrative occupations, or business owners); middle (i.e., trade-or services related occupations); low (manual or casual occupations, unemployed, sick and disabled); and miscellaneous (i.e., armed forces and retired). According to directed acyclic graphs (DAGs) adapted from Hamad et al [22] and Liang et al [14] only age, sex, and childhood SEP were considered confounders, whereas the rest were mediators (Figure S2).…”

Emerging trends and challenges of population-based cardiology

“…It hinders a comparison of the results obtained in different studies and attenuates evidence basis [54]. The questions regarding an introduction of the educational and socialeconomic features as risk factors in the algorithms for cardiovascular prediction and as adjustment variables in the models of clinical risk remain controversial [55].…”

“…Это затрудняет сравнение результатов, полученных в разных исследованиях, и ослабляет доказательную базу [54]. Спорными остаются вопросы, касающиеся того, целесообразно ли вносить образовательный и социально-экономический статус в качестве факторов риска в алгоритмы прогнозирования ССЗ и в качестве поправок в модели клинического риска [55].…”