title: Development of criteria for identifying neonatal near-miss cases: analysis of two WHO multicountry cross-sectional studies.
abstract: OBJECTIVE
  To develop and test markers of neonatal severe morbidity for the identification of neonatal near-miss cases.
  
  
  METHODS
  This is a database analysis of two World Health Organization cross-sectional studies: the Global Survey on Maternal and Perinatal Health (WHOGS) and the Multicountry Survey on Maternal and Newborn Health (WHOMCS).
  
  
  METHODS
  The WHOGS was performed in 373 health facilities in 24 countries (2004-2008). The WHOMCS was conducted in 359 health facilities in 29 countries (2010-2011).
  
  
  METHODS
  Data were collected from hospital records of all women admitted for delivery and their respective neonates.
  
  
  METHODS
  Pragmatic markers (birthweight <1750 g, Apgar score at 5 minutes <7, and gestational age <33 weeks) were developed with WHOGS data and validated with WHOMCS data. The diagnostic accuracy of neonatal characteristics and management markers of severity was determined in the WHOMCS.
  
  
  RESULTS
  This analysis included 290 610 liveborn neonates from WHOGS and 310 436 liveborn neonates from WHOMCS. The diagnostic accuracy of pragmatic and management markers of severity for identifying early neonatal deaths was very high: sensitivity, 92.8% (95% CI 91.8-93.7%); specificity, 92.7% (95% CI 92.6-92.8%); positive likelihood ratio, 12.7 (95% CI 12.5-12.9); negative likelihood ratio, 0.08 (95% CI 0.07-0.09); diagnostic odds ratio, 163.4 (95% CI 141.6-188.4). A positive association was found between the frequency of neonatal near-miss cases and Human Development Index.
  
  
  CONCLUSIONS
  Newborn infants presenting selected markers of severity and surviving the first neonatal week could be considered as neonatal near-miss cases. This definition and criteria may be seen as a basis for future applications of the near-miss concept in neonatal health. These tools can be used to inform policy makers on how best to apply scarce resources for improving the quality of care and reducing neonatal mortality.
authors.affiliation: Department of Paediatrics, University of São Paulo, Ribeirão Preto, Brazil.
authors.forename: C
authors.initials: C
authors.lastname: Pileggi-Castro
authors.forename: J S
authors.initials: JS
authors.lastname: Camelo
authors.forename: G C
authors.initials: GC
authors.lastname: Perdoná
authors.forename: M M
authors.initials: MM
authors.lastname: Mussi-Pinhata
authors.forename: J G
authors.initials: JG
authors.lastname: Cecatti
authors.forename: R
authors.initials: R
authors.lastname: Mori
authors.forename: N
authors.initials: N
authors.lastname: Morisaki
authors.forename: K
authors.initials: K
authors.lastname: Yunis
authors.forename: J P
authors.initials: JP
authors.lastname: Vogel
authors.forename: Ö
authors.initials: Ö
authors.lastname: Tunçalp
authors.forename: J P
authors.initials: JP
authors.lastname: Souza
mesh_terms: D000293:Adolescent; D000328:Adult; D000349:Africa / Q000453:epidemiology; D001034:Apgar Score; D001208:Asia / Q000453:epidemiology; D015415:Biomarkers; D003430:Cross-Sectional Studies; D005260:Female; D005865:Gestational Age; D019538:Health Care Surveys; D006801:Humans; D007226:Infant Mortality; D007230:Infant, Low Birth Weight; D007231:Infant, Newborn; D007843:Latin America / Q000453:epidemiology; D050498:Live Birth / Q000453:epidemiology*; D008427:Maternal Health Services / Q000706:statistics & numerical data*; D008877:Middle East / Q000453:epidemiology; D011237:Predictive Value of Tests; D011247:Pregnancy; D015203:Reproducibility of Results; D014944:World Health Organization; D055815:Young Adult
chemical_list: D015415:Biomarkers
keywords: Early neonatal death; neonatal morbidity assessment; neonatal near-miss criteria; perinatal care assessment; quality of care