Thais Rangel Bousquet Carrilho

Postdoctoral Fellow

 

Maternal weight gain is closely monitored during pregnancy, because as pregnancy weight gain increases, so does the risk of maternal postpartum weight retention, diabetes, and high blood pressure. While lower weight gain may prevent these health outcomes, it may also increase risk of fetal growth restriction and perinatal death. Thus, public health recommendations on optimal pregnancy weight gain that balances these risks are important, especially in countries as Canada, where the proportion of overweight and obesity among women and children has been increasing. The goal of this project is to establish the optimal range of pregnancy weight gain for Canadian women. We will use existing medical records from approximately 560,000 women who delivered in British Columbia, Canada, between 2004 and 2018, that have been linked with longer-term health records (such as prescriptions and hospital visits). We will obtain information on total pregnancy weight gain from the obstetrical chart, and link this will 11 short- and longer-term health complications for mother and newborn, such as excess postpartum weight retention, longer-term maternal diabetes and heart disease, cesarean delivery, fetal growth restriction, and stillbirth or infant death. We will use a statistical modelling approach that is able to consider all possible adverse health outcomes at the same time, while accounting for the fact that some outcomes are more serious than others. We will also examine how recommendations change according to a woman’s pre-pregnancy weight. Our findings could provide the basis for new recommendations on pregnancy weight gain to be used during prenatal care. Such recommendations could improve both maternal and child health, and, in the long term, help to reduce overweight and obesity in Canadian mothers and their children.

 

Supervisor

Research Interests

perinatal epidemiology
Nutritional Epidemiology
Maternal and child health

Research Methodology

Longitudinal Data Analysis
Regression models
Big Data
Missing data

Research Options

I am available and interested in collaborations (e.g. clusters, grants).
I am interested in and conduct interdisciplinary research.
I am interested in working with undergraduate students on research projects.
 
 

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