Ideal cardiovascular health at ELSA-Brasil: non-additivity effects of gender, race, and schooling by using additive and multiplicative interactions.
Roberta Souza FreitasItamar de Souza SantosSheila Maria Alvim de MatosEstela Maria Leão de AquinoLeila Denise Alves Ferreira AmorimPublished in: Cadernos de saude publica (2022)
This study aims to assess the non-additivity effects of gender, race, and schooling on ideal cardiovascular health among participants of the Brazilian Longitudinal Study of Adult Health - ELSA-Brasil. This is a cross-sectional study using data from the baseline of ELSA-Brasil, conducted from 2008 to 2010. The American Heart Association defined a score of ideal cardiovascular health (ICH) as the sum of indicators for the presence of seven favorable health factors and behaviors: non-smoking, ideal body mass index, physical activity and healthy diet, adequate levels of total cholesterol, normal blood pressure, and absence of diabetes mellitus. Multiplicative and additive interactions between gender, race, and schooling were assessed using the Poisson regression model to discuss intersectionality. The mean cardiovascular health score was 2.49 (SD = 1.31). This study showed a positive interaction between gender and schooling (women with high school and higher education) in both additive and multiplicative scales for the score of ideal cardiovascular health. We observed a trend towards higher mean values of cardiovascular health for increased schooling, with a marked difference among women. The lowest cardiovascular health scores observed reinforce the importance of understanding the psychosocial experiences that influence health attitudes, access to health care, and healthy lifestyle choices, which affect ICH, to reduce inequities in health and propose more adequate public policies that assist and prevent cardiovascular diseases.
Keyphrases
- mental health
- healthcare
- public health
- physical activity
- body mass index
- cardiovascular disease
- health information
- heart failure
- health promotion
- metabolic syndrome
- weight loss
- polycystic ovary syndrome
- depressive symptoms
- machine learning
- young adults
- climate change
- emergency department
- atrial fibrillation
- deep learning
- big data
- sleep quality