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Prevalence of iron-deficiency anaemia in Brazilian children under 5 years of age: a systematic review and meta-analysis.

Victor Nogueira da Cruz SilveiraCarolina Abreu de CarvalhoPoliana Cristina de Almeida Fonseca ViolaElma I S MagalhãesLuana L PadilhaSueli I O ConceiçãoMaria Tereza B A FrotaIsabela L CaladoNayra A C CantanhedeSylvia C C FranceschiniAna Karina T C França
Published in: The British journal of nutrition (2020)
Fe-deficiency anaemia is considered an important public health problem both in wealthier countries and in those of medium and low income, especially in children under 5 years of age. The shortage of studies with national representativity in medium-income countries, such as Brazil, prevents the knowledge of the current situation and its associated factors. We conducted a systematic review and meta-analysis to estimate the pooled prevalence of Fe-deficiency anaemia in Brazilian children under 5 years of age and determined the factors involved in the variability of the estimates of prevalence. We collected fifty-seven studies from the databases MEDLINE, LILACS and Web of Science, along with the reference lists of included articles. We contacted authors for unpublished data. We did not restrict publication timespan and language. This systematic review and meta-analysis was reported according to the guidelines by PRISMA. The pooled prevalence of anaemia in Brazil was 40·2 (95 % CI 36·0, 44·8) %. The age range of the child and the period of data collection were associated with the anaemia prevalence. The pooled prevalence of anaemia was higher in children under 24 months of age (53·5 v. 30·7 %; P < 0·001) and in studies with data collected before 2004 (51·8 v. 32·6 %; P = 0·001). The efforts made by the Brazilian government were successful in the reduction of anaemia in children under 5 years of age in Brazil in the evaluated period. However, prevalence remains beyond acceptable levels for this population group.
Keyphrases
  • iron deficiency
  • risk factors
  • public health
  • young adults
  • healthcare
  • big data
  • electronic health record
  • mental health
  • systematic review
  • machine learning
  • artificial intelligence
  • phase iii
  • data analysis