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      The causal effects of education on adult health, mortality and income: evidence from Mendelian randomization and the raising of the school leaving age

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          Abstract

          Background

          On average, educated people are healthier, wealthier and have higher life expectancy than those with less education. Numerous studies have attempted to determine whether education causes differences in later health outcomes or whether another factor ultimately causes differences in education and subsequent outcomes. Previous studies have used a range of natural experiments to provide causal evidence. Here we compare two natural experiments: a policy reform, raising the school leaving age in the UK in 1972; and Mendelian randomization.

          Methods

          We used data from 334 974 participants of the UK Biobank, sampled between 2006 and 2010. We estimated the effect of an additional year of education on 25 outcomes, including mortality, measures of morbidity and health, ageing and income, using multivariable adjustment, the policy reform and Mendelian randomization. We used a range of sensitivity analyses and specification tests to assess the plausibility of each method’s assumptions.

          Results

          The three different estimates of the effects of educational attainment were largely consistent in direction for diabetes, stroke and heart attack, mortality, smoking, income, grip strength, height, body mass index (BMI), intelligence, alcohol consumption and sedentary behaviour. However, there was evidence that education reduced rates of moderate exercise and increased alcohol consumption. Our sensitivity analyses suggest that confounding by genotypic or phenotypic confounders or specific forms of pleiotropy are unlikely to explain our results.

          Conclusions

          Previous studies have suggested that the differences in outcomes associated with education may be due to confounding. However, the two independent sources of exogenous variation we exploit largely imply consistent causal effects of education on outcomes later in life.

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          Most cited references32

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          ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease?*

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            Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals

            Here we conducted a large-scale genetic association analysis of educational attainment in a sample of approximately 1.1 million individuals and identify 1,271 independent genome-wide-significant SNPs. For the SNPs taken together, we found evidence of heterogeneous effects across environments. The SNPs implicate genes involved in brain-development processes and neuron-to-neuron communication. In a separate analysis of the X chromosome, we identify 10 independent genome-wide-significant SNPs and estimate a SNP heritability of around 0.3% in both men and women, consistent with partial dosage compensation. A joint (multi-phenotype) analysis of educational attainment and three related cognitive phenotypes generates polygenic scores that explain 11-13% of the variance in educational attainment and 7-10% of the variance in cognitive performance. This prediction accuracy substantially increases the utility of polygenic scores as tools in research.
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              Triangulation in aetiological epidemiology

              Abstract Triangulation is the practice of obtaining more reliable answers to research questions through integrating results from several different approaches, where each approach has different key sources of potential bias that are unrelated to each other. With respect to causal questions in aetiological epidemiology, if the results of different approaches all point to the same conclusion, this strengthens confidence in the finding. This is particularly the case when the key sources of bias of some of the approaches would predict that findings would point in opposite directions if they were due to such biases. Where there are inconsistencies, understanding the key sources of bias of each approach can help to identify what further research is required to address the causal question. The aim of this paper is to illustrate how triangulation might be used to improve causal inference in aetiological epidemiology. We propose a minimum set of criteria for use in triangulation in aetiological epidemiology, summarize the key sources of bias of several approaches and describe how these might be integrated within a triangulation framework. We emphasize the importance of being explicit about the expected direction of bias within each approach, whenever this is possible, and seeking to identify approaches that would be expected to bias the true causal effect in different directions. We also note the importance, when comparing results, of taking account of differences in the duration and timing of exposures. We provide three examples to illustrate these points.
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                Author and article information

                Contributors
                Journal
                Int J Epidemiol
                Int J Epidemiol
                ije
                International Journal of Epidemiology
                Oxford University Press
                0300-5771
                1464-3685
                December 2023
                18 July 2023
                18 July 2023
                : 52
                : 6
                : 1878-1886
                Affiliations
                Division of Psychiatry, University College London , London, UK
                Department of Statistical Sciences, University College London , London, UK
                K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology , Tronheim, Norway
                Medical Research Council Integrative Epidemiology Unit, University of Bristol , Bristol, UK
                Institute for Policy Research, University of Bath , Bath, UK
                Medical Research Council Integrative Epidemiology Unit, University of Bristol , Bristol, UK
                Population Health Sciences, University of Bristol , Bristol, UK
                Medical Research Council Integrative Epidemiology Unit, University of Bristol , Bristol, UK
                Department of Statistics and Nuffield College, University of Oxford , Oxford, UK
                Department of Economics, University of Groningen, Groningen , The Netherlands
                Department of Epidemiology, University Medical Center Groningen, Groningen , The Netherlands
                Author notes
                Corresponding author. Division of Psychiatry and Department of Statistical Sciences, University College London, Maple House, 149 Tottenham Court Rd, London W1T 7NF, UK. E-mail: neil.m.davies@ 123456ucl.ac.uk
                Author information
                https://orcid.org/0000-0002-2460-0508
                https://orcid.org/0000-0002-1407-8314
                Article
                dyad104
                10.1093/ije/dyad104
                10749779
                37463867
                80765cde-d3e4-4bf0-929e-3c993b51e419
                © The Author(s) 2023. Published by Oxford University Press on behalf of the International Epidemiological Association.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 05 September 2022
                : 08 June 2023
                : 04 July 2023
                Page count
                Pages: 9
                Funding
                Funded by: Medical Research Council, DOI 10.13039/501100000265;
                Funded by: University of Bristol, DOI 10.13039/501100000883;
                Funded by: MRC, DOI 10.13039/100018645;
                Funded by: Integrative Epidemiology Unit;
                Award ID: MC_UU_00011/1
                Funded by: Economics and Social Research Council;
                Funded by: Future Research Leaders;
                Award ID: ES/N000757/1
                Funded by: Norwegian Research Council, DOI 10.13039/501100005416;
                Award ID: 295989
                Categories
                Social Epidemiology
                AcademicSubjects/MED00860

                Public health
                raising of school leaving age (rosla),instrumental variable analysis,education,genomic confounding

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