Maternal and child nutrition programme of investigation within the 100 Million Brazilian Cohort: study protocol

Author:

Carrilho Thais Rangel BousquetORCID,Silva Natanael de JesusORCID,Paixão Enny SantosORCID,Falcão Ila Rocha,Fiaccone Rosemeire Leovigildo,Rodrigues Laura Cunha,Katikireddi Srinivasa VittalORCID,Leyland Alastair H,Dundas Ruth,Pearce Anna,Velasquez-Melendez Gustavo,Kac Gilberto,Silva Rita de Cássia RibeiroORCID,Barreto Mauricio L

Abstract

IntroductionThere is a limited understanding of the early nutrition and pregnancy determinants of short-term and long-term maternal and child health in ethnically diverse and socioeconomically vulnerable populations within low-income and middle-income countries. This investigation programme aims to: (1) describe maternal weight trajectories throughout the life course; (2) describe child weight, height and body mass index (BMI) trajectories; (3) create and validate models to predict childhood obesity at 5 years of age; (4) estimate the effects of prepregnancy BMI, gestational weight gain (GWG) and maternal weight trajectories on adverse maternal and neonatal outcomes and child growth trajectories; (5) estimate the effects of prepregnancy BMI, GWG, maternal weight and interpregnancy BMI changes on maternal and child outcomes in the subsequent pregnancy; and (6) estimate the effects of maternal food consumption and infant feeding practices on child nutritional status and growth trajectories.Methods and analysisLinked data from four different Brazilian databases will be used: the 100 Million Brazilian Cohort, the Live Births Information System, the Mortality Information System and the Food and Nutrition Surveillance System. To analyse trajectories, latent-growth, superimposition by translation and rotation and broken stick models will be used. To create prediction models for childhood obesity, machine learning techniques will be applied. For the association between the selected exposure and outcomes variables, generalised linear models will be considered. Directed acyclic graphs will be constructed to identify potential confounders for each analysis investigating potential causal relationships.Ethics and disseminationThis protocol was approved by the Research Ethics Committees of the authors’ institutions. The linkage will be carried out in a secure environment. After the linkage, the data will be de-identified, and pre-authorised researchers will access the data set via a virtual private network connection. Results will be reported in open-access journals and disseminated to policymakers and the broader public.

Publisher

BMJ

Subject

General Medicine

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. The use of machine learning in paediatric nutrition;Current Opinion in Clinical Nutrition & Metabolic Care;2024-02-01

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