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DISTRIBUTIONAL EFFECTS OF EDUCATION ON HEALTH.

ABSTRACT: This paper studies distributional effects of education on health. In 1972, England, Scotland, and Wales raised their minimum school-leaving age from 15 to 16 for students born after 9/1/1957. Using a regression discontinuity design and objective health measures for 129,000 individuals, we find that education reduced body size and increased blood pressure in middle age. The reduction in body size was concentrated at the upper tail of the distribution with an 8 percentage point reduction in obesity. The increase in blood pressure was concentrated at the lower tail of the distribution with no effect on stage 2 hypertension.

SUBMITTER: Barcellos SH

PROVIDER: S-EPMC10361687 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature

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DISTRIBUTIONAL EFFECTS OF EDUCATION ON HEALTH.

Barcellos Silvia H SH Carvalho Leandro S LS Turley Patrick P

The Journal of human resources 20210609 4

This paper studies distributional effects of education on health. In 1972, England, Scotland, and Wales raised their minimum school-leaving age from 15 to 16 for students born after 9/1/1957. Using a regression discontinuity design and objective health measures for 129,000 individuals, we find that education reduced body size and increased blood pressure in middle age. The reduction in body size was concentrated at the upper tail of the distribution with an 8 percentage point reduction in obesit ...[more]

PMID: 37484544

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Project description:In low- and middle-income countries (LMICs), making the best use of scarce resources is essential to achieving universal health coverage. The design of health benefits packages creates the opportunity to select interventions on the basis of explicit objectives. Distributional cost-effectiveness analysis (DCEA) provides a framework to evaluate interventions based on two objectives: increasing population health and reducing health inequality. We conduct aggregate DCEA of potential health benefits package interventions to demonstrate the feasibility of this approach in LMICs, using the case of the Malawian health benefits package. We use publicly available survey and census data common to LMICs and describe what challenges we encountered and how we addressed them. We estimate that diseases targeted by the health benefits package are most prevalent in the poorest population quintile and least prevalent in the richest quintile. The survey data we use indicate socioeconomic patterns in intervention uptake that diminish the population health gain and inequality reduction from the package. We find that a similar set of interventions would be prioritized when impact on health inequality is incorporated alongside impact on overall population health. However, conclusions about the impact of individual interventions on health inequalities are sensitive to assumptions regarding the health opportunity cost, the utilization of interventions, the distribution of diseases across population groups and the level of aversion to inequality. Our results suggest that efforts to improve access to the Essential Health Package could be targeted to specific interventions to improve the health of the poorest fastest but that identifying these interventions is uncertain. This exploratory work has shown the potential for applying the DCEA framework to inform health benefits package design within the LMIC setting and to provide insight into the equity impact of a health benefits package.

Project description:MotivationAutomatically quantifying semantic similarity and relatedness between clinical terms is an important aspect of text mining from electronic health records, which are increasingly recognized as valuable sources of phenotypic information for clinical genomics and bioinformatics research. A key obstacle to development of semantic relatedness measures is the limited availability of large quantities of clinical text to researchers and developers outside of major medical centers. Text from general English and biomedical literature are freely available; however, their validity as a substitute for clinical domain to represent semantics of clinical terms remains to be demonstrated.ResultsWe constructed neural network representations of clinical terms found in a publicly available benchmark dataset manually labeled for semantic similarity and relatedness. Similarity and relatedness measures computed from text corpora in three domains (Clinical Notes, PubMed Central articles and Wikipedia) were compared using the benchmark as reference. We found that measures computed from full text of biomedical articles in PubMed Central repository (rho = 0.62 for similarity and 0.58 for relatedness) are on par with measures computed from clinical reports (rho = 0.60 for similarity and 0.57 for relatedness). We also evaluated the use of neural network based relatedness measures for query expansion in a clinical document retrieval task and a biomedical term word sense disambiguation task. We found that, with some limitations, biomedical articles may be used in lieu of clinical reports to represent the semantics of clinical terms and that distributional semantic methods are useful for clinical and biomedical natural language processing applications.Availability and implementationThe software and reference standards used in this study to evaluate semantic similarity and relatedness measures are publicly available as detailed in the article.Contactpakh0002@umn.eduSupplementary information: Supplementary data are available at Bioinformatics online.

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DISTRIBUTIONAL EFFECTS OF EDUCATION ON HEALTH.

Publications

DISTRIBUTIONAL EFFECTS OF EDUCATION ON HEALTH.

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