Educational attainment and cardiovascular disease in the United States: A quasi-experimental instrumental variables analysis
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 regard...
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description | 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.
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.
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 |
doi_str_mv | 10.1371/journal.pmed.1002834 |
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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.
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 prevention guidelines for CVD.]]></description><identifier>ISSN: 1549-1676</identifier><identifier>ISSN: 1549-1277</identifier><identifier>EISSN: 1549-1676</identifier><identifier>DOI: 10.1371/journal.pmed.1002834</identifier><identifier>PMID: 31237869</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algorithms ; Bioindicators ; Biological markers ; Biology and Life Sciences ; Body mass ; Cardiovascular disease ; Cardiovascular diseases ; Cardiovascular Diseases - diagnosis ; Cardiovascular Diseases - epidemiology ; Causation ; Censuses ; Children ; Cholesterol ; Coronary artery disease ; Cross-Sectional Studies ; Decision making ; Demographic aspects ; Depression ; Diabetes ; Education ; Educational attainment ; Educational Status ; Employment ; Epidemiology ; Estimates ; Experiments ; Female ; Health aspects ; Health risks ; Health surveys ; Heart ; Heart diseases ; High density lipoprotein ; Humans ; Hypotheses ; Hypothesis testing ; Influence ; Literacy ; Male ; Medical research ; Medicine ; Medicine and Health Sciences ; Methods ; Mortality ; Non-Randomized Controlled Trials as Topic - methods ; Nutrition ; Nutrition Surveys - methods ; Obesity ; Population ; Population studies ; Public health ; Quasi-experimental methods ; Research methodology ; Risk Factors ; Smoking ; Social class ; Social Sciences ; Socioeconomic factors ; Socioeconomics ; Studies ; Triglycerides ; United States ; United States - epidemiology ; Waves</subject><ispartof>PLoS medicine, 2019-06, Vol.16 (6), p.e1002834</ispartof><rights>COPYRIGHT 2019 Public Library of Science</rights><rights>2019 Hamad et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2019 Hamad et al 2019 Hamad et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c764t-54e36cb977091d6e958f4a90688ee02250c5c053066468a19e93b629aa8dfae13</citedby><cites>FETCH-LOGICAL-c764t-54e36cb977091d6e958f4a90688ee02250c5c053066468a19e93b629aa8dfae13</cites><orcidid>0000-0002-7597-6513 ; 0000-0003-1185-045X ; 0000-0003-0730-4077 ; 0000-0003-3867-3174</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6592509/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6592509/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,725,778,782,862,883,2098,2917,23853,27911,27912,53778,53780,79355,79356</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31237869$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Rahimi, Kazem</contributor><creatorcontrib>Hamad, Rita</creatorcontrib><creatorcontrib>Nguyen, Thu T</creatorcontrib><creatorcontrib>Bhattacharya, Jay</creatorcontrib><creatorcontrib>Glymour, M Maria</creatorcontrib><creatorcontrib>Rehkopf, David H</creatorcontrib><title>Educational attainment and cardiovascular disease in the United States: A quasi-experimental instrumental variables analysis</title><title>PLoS medicine</title><addtitle>PLoS Med</addtitle><description><![CDATA[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.
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.
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 prevention guidelines for CVD.]]></description><subject>Algorithms</subject><subject>Bioindicators</subject><subject>Biological markers</subject><subject>Biology and Life Sciences</subject><subject>Body mass</subject><subject>Cardiovascular disease</subject><subject>Cardiovascular diseases</subject><subject>Cardiovascular Diseases - diagnosis</subject><subject>Cardiovascular Diseases - epidemiology</subject><subject>Causation</subject><subject>Censuses</subject><subject>Children</subject><subject>Cholesterol</subject><subject>Coronary artery disease</subject><subject>Cross-Sectional Studies</subject><subject>Decision making</subject><subject>Demographic aspects</subject><subject>Depression</subject><subject>Diabetes</subject><subject>Education</subject><subject>Educational attainment</subject><subject>Educational Status</subject><subject>Employment</subject><subject>Epidemiology</subject><subject>Estimates</subject><subject>Experiments</subject><subject>Female</subject><subject>Health aspects</subject><subject>Health risks</subject><subject>Health surveys</subject><subject>Heart</subject><subject>Heart diseases</subject><subject>High density lipoprotein</subject><subject>Humans</subject><subject>Hypotheses</subject><subject>Hypothesis testing</subject><subject>Influence</subject><subject>Literacy</subject><subject>Male</subject><subject>Medical research</subject><subject>Medicine</subject><subject>Medicine and Health Sciences</subject><subject>Methods</subject><subject>Mortality</subject><subject>Non-Randomized Controlled Trials as Topic - 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diagnosis</topic><topic>Cardiovascular Diseases - epidemiology</topic><topic>Causation</topic><topic>Censuses</topic><topic>Children</topic><topic>Cholesterol</topic><topic>Coronary artery disease</topic><topic>Cross-Sectional Studies</topic><topic>Decision making</topic><topic>Demographic aspects</topic><topic>Depression</topic><topic>Diabetes</topic><topic>Education</topic><topic>Educational attainment</topic><topic>Educational Status</topic><topic>Employment</topic><topic>Epidemiology</topic><topic>Estimates</topic><topic>Experiments</topic><topic>Female</topic><topic>Health aspects</topic><topic>Health risks</topic><topic>Health surveys</topic><topic>Heart</topic><topic>Heart diseases</topic><topic>High density lipoprotein</topic><topic>Humans</topic><topic>Hypotheses</topic><topic>Hypothesis testing</topic><topic>Influence</topic><topic>Literacy</topic><topic>Male</topic><topic>Medical research</topic><topic>Medicine</topic><topic>Medicine and Health Sciences</topic><topic>Methods</topic><topic>Mortality</topic><topic>Non-Randomized Controlled Trials as Topic - methods</topic><topic>Nutrition</topic><topic>Nutrition Surveys - methods</topic><topic>Obesity</topic><topic>Population</topic><topic>Population studies</topic><topic>Public health</topic><topic>Quasi-experimental methods</topic><topic>Research methodology</topic><topic>Risk Factors</topic><topic>Smoking</topic><topic>Social class</topic><topic>Social Sciences</topic><topic>Socioeconomic factors</topic><topic>Socioeconomics</topic><topic>Studies</topic><topic>Triglycerides</topic><topic>United States</topic><topic>United States - epidemiology</topic><topic>Waves</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hamad, Rita</creatorcontrib><creatorcontrib>Nguyen, Thu T</creatorcontrib><creatorcontrib>Bhattacharya, Jay</creatorcontrib><creatorcontrib>Glymour, M Maria</creatorcontrib><creatorcontrib>Rehkopf, David H</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Canada</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Neurosciences Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><collection>PLoS Medicine</collection><jtitle>PLoS medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hamad, Rita</au><au>Nguyen, Thu T</au><au>Bhattacharya, Jay</au><au>Glymour, M Maria</au><au>Rehkopf, David H</au><au>Rahimi, Kazem</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Educational attainment and cardiovascular disease in the United States: A quasi-experimental instrumental variables analysis</atitle><jtitle>PLoS medicine</jtitle><addtitle>PLoS Med</addtitle><date>2019-06-25</date><risdate>2019</risdate><volume>16</volume><issue>6</issue><spage>e1002834</spage><pages>e1002834-</pages><issn>1549-1676</issn><issn>1549-1277</issn><eissn>1549-1676</eissn><abstract><![CDATA[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.
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.
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 prevention guidelines for CVD.]]></abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>31237869</pmid><doi>10.1371/journal.pmed.1002834</doi><orcidid>https://orcid.org/0000-0002-7597-6513</orcidid><orcidid>https://orcid.org/0000-0003-1185-045X</orcidid><orcidid>https://orcid.org/0000-0003-0730-4077</orcidid><orcidid>https://orcid.org/0000-0003-3867-3174</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1549-1676 |
ispartof | PLoS medicine, 2019-06, Vol.16 (6), p.e1002834 |
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source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Public Library of Science (PLoS); PubMed Central |
subjects | Algorithms Bioindicators Biological markers Biology and Life Sciences Body mass Cardiovascular disease Cardiovascular diseases Cardiovascular Diseases - diagnosis Cardiovascular Diseases - epidemiology Causation Censuses Children Cholesterol Coronary artery disease Cross-Sectional Studies Decision making Demographic aspects Depression Diabetes Education Educational attainment Educational Status Employment Epidemiology Estimates Experiments Female Health aspects Health risks Health surveys Heart Heart diseases High density lipoprotein Humans Hypotheses Hypothesis testing Influence Literacy Male Medical research Medicine Medicine and Health Sciences Methods Mortality Non-Randomized Controlled Trials as Topic - methods Nutrition Nutrition Surveys - methods Obesity Population Population studies Public health Quasi-experimental methods Research methodology Risk Factors Smoking Social class Social Sciences Socioeconomic factors Socioeconomics Studies Triglycerides United States United States - epidemiology Waves |
title | Educational attainment and cardiovascular disease in the United States: A quasi-experimental instrumental variables analysis |
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