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Association between dyslipidemias and health costs at population level: Polygenic risk scores and modeling through computational microsimulation.

Grant number: 20/15899-2
Support Opportunities:Scholarships in Brazil - Doctorate
Effective date (Start): February 01, 2022
Effective date (End): March 31, 2026
Field of knowledge:Applied Social Sciences - Economics - Social Welfare Economics
Principal Investigator:Flávia Mori Sarti
Grantee:Jean Michel Rocha Sampaio Leite
Host Institution: Escola de Artes, Ciências e Humanidades (EACH). Universidade de São Paulo (USP). São Paulo , SP, Brazil
Associated research grant:17/05125-7 - Lifestyle, biochemical and genetic markers as cardiometabolic risk factors: Health Survey in São Paulo City, AP.TEM
Associated scholarship(s):22/14123-6 - Beyond the prediction of health care costs related to dyslipidemias and other cardiometabolic risk factors: explainable analysis through causal structure learning and inference algorithms, BE.EP.DR

Abstract

Cardiovascular diseases (CVD) are the major causes of mortality worldwide, leading to significant financial burden in national health systems of diverse countries, including Brazil. Direct costs with treatment and recovery of patients comprise substantial part of annual expenditures related to CVD. Strategies for monitoring and intervention on risk factors for CVD have been priority in the world context, considering the expressive prevalence and negative consequences to population health, quality of life and productivity, and to countries' economy. The pathophysiology of CVD is related to dyslipidemias, i.e., altered lipid profile which present high prevalence in the population, which are associated with other morbidities, e.g., obesity and type 2 diabetes. Thus, the project aims to perform an empirical study on healthcare costs in relation to the occurrence of dyslipidemias and associated risk factors in the context of the Thematic Project entitled "Lifestyle, biochemical and genetic markers as cardiometabolic risk factors: Health Survey in the municipality of Sao Paulo". The empirical strategy adopted will be based on the analysis of data from a sample representative at population level of individuals living in the municipality of Sao Paulo, considering risk and protection factors related to lifestyle and its association with biochemical and genetic markers, using: (1) genome-wide association study (GWAS) and polygenic risk score (PRS) through machine learning algorithms; (2) estimation of multiple linear regression models including instrumental variables to control for omission bias; and (3) computational simulation of scenarios for analysis of future trends in the context of the Brazilian Health System. The results will allow to identify increases in healthcare costs according to dyslipidemia risk profiles, suggesting strategies for risk reduction based on disease prevention with increased financial sustainability in the medium and long run for adoption in the context of public policies towards population's health promotion.

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