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Environmental footprints of food consumption: Protocol for a systematic literature review.

Camila Valdejane Silva de SouzaLarissa Mont'Alverne Jucá SeabraMaria HatjiathanassiadouDiogo ValeGidyenne Christine Bandeira Silva de MedeirosDirce Maria Lobo MarchioniSeverina Carla Vieira Cunha LimaClélia de Oliveira Lyra
Published in: PloS one (2022)
Environmental footprints are indicators that can be used to estimate the impacts of diet on the environment. Since contemporary dietary practices are related to negative environmental impacts, this paper aims to describe a systematic review protocol to investigate the environmental footprints of food consumption by adults and elderly individuals worldwide. This protocol was developed based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Search strategies and records of evidence searched in previously defined electronic databases will be defined. Original, population-based articles investigating the environmental footprints of food consumption by adults and the elderly will be included. Two independent reviewers will conduct the study selection and data extraction steps. Critical appraisal of the included studies will be based on the Newcastle-Ottawa Scale. For data synthesis, a narrative synthesis and, if possible, also a meta-analysis will be performed. The systematic review produced from this protocol will provide evidence for data synthesis of the environmental impact through environmental footprints of food consumption of the adult and elderly population from different territories and the footprint assessment tools used around the world. Therefore, it is a gap that needs to be filled because knowing these impacts will be important to inform the development of public policies that encourage healthy and sustainable food in the face of climate and epidemiological changes. PROSPERO registration number: CRD42021281488.
Keyphrases
  • human health
  • meta analyses
  • systematic review
  • risk assessment
  • randomized controlled trial
  • life cycle
  • healthcare
  • electronic health record
  • big data
  • middle aged
  • primary care
  • emergency department
  • deep learning