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Depression prevalence based on the Edinburgh Postnatal Depression Scale compared to Structured Clinical Interview for DSM DIsorders classification: Systematic review and individual participant data meta-analysis.

Anita LyubenovaDipika NeupaneBrooke LevisYin WuYing SunChen HeAnkur KrishnanParash M BhandariZelalem NegeriMahrukh ImranDanielle B RiceMarleine AzarMatthew J ChiovittiNazanin SaadatKira E RiehmJill T BoruffJohn P A IoannidisPim CuijpersSimon GilbodyLorie A KlodaScott B PattenIan ShrierRoy C ZiegelsteinLiane ComeauNicholas D MitchellMarcello TonelliSimone N VigodFranca AcetiJacqueline BarnesAmar D BavleCheryl T BeckCarola BindtPhilip M BoyceAdomas BuneviciusLinda H ChaudronNicolas FavezBarbara FigueiredoLluïsa Garcia-EsteveLisa GiardinelliNadine HelleLouise M HowardJane KohlhoffLaima KusminskasZoltan KozinszkyLorenzo LelliAngeliki A LeonardouValentina MeutiSandra N RadošPurificación N GarcíaSusan J PawlbyChantal QuispelEmma Robertson-BlackmoreTamsen J RochatDeborah J SharpBonnie W M SiuAlan SteinRobert C StewartMeri TadinacS Darius TandonIva TendaisAnnamária TörekiAnna Torres-GiménezThach D TranKylee TrevillionKatherine TurnerJohann M Vega-DienstmaierAndrea BenedettiBrett D Thombs
Published in: International journal of methods in psychiatric research (2020)
EPDS ≥14 approximated SCID-based prevalence overall, but considerable heterogeneity in individual studies is a barrier to using it for prevalence estimation.
Keyphrases
  • systematic review
  • meta analyses
  • risk factors
  • depressive symptoms
  • machine learning
  • deep learning
  • sleep quality
  • single cell
  • big data
  • data analysis