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European Journal of Epidemiology

, Volume 33, Issue 6, pp 523–530 | Cite as

Two denominators for one numerator: the example of neonatal mortality

  • Quaker E. Harmon
  • Olga Basso
  • Clarice R. Weinberg
  • Allen J. Wilcox
METHODS
  • 122 Downloads

Abstract

Preterm delivery is one of the strongest predictors of neonatal mortality. A given exposure may increase neonatal mortality directly, or indirectly by increasing the risk of preterm birth. Efforts to assess these direct and indirect effects are complicated by the fact that neonatal mortality arises from two distinct denominators (i.e. two risk sets). One risk set comprises fetuses, susceptible to intrauterine pathologies (such as malformations or infection), which can result in neonatal death. The other risk set comprises live births, who (unlike fetuses) are susceptible to problems of immaturity and complications of delivery. In practice, fetal and neonatal sources of neonatal mortality cannot be separated—not only because of incomplete information, but because risks from both sources can act on the same newborn. We use simulations to assess the repercussions of this structural problem. We first construct a scenario in which fetal and neonatal factors contribute separately to neonatal mortality. We introduce an exposure that increases risk of preterm birth (and thus neonatal mortality) without affecting the two baseline sets of neonatal mortality risk. We then calculate the apparent gestational-age-specific mortality for exposed and unexposed newborns, using as the denominator either fetuses or live births at a given gestational age. If conditioning on gestational age successfully blocked the mediating effect of preterm delivery, then exposure would have no effect on gestational-age-specific risk. Instead, we find apparent exposure effects with either denominator. Except for prediction, neither denominator provides a meaningful way to define gestational-age-specific neonatal mortality.

Keywords

Neonatal mortality Fetal pathology Preterm delivery Gestational-age paradox 

Notes

Acknowledgements

The authors gratefully acknowledge the comments on earlier drafts by Dr. Donna Baird, Dr. David Umbach, and anonymous reviewers.

Funding

This research has been supported in part by the Intramural Research Program of the National Institute of Environmental Health Sciences, National Institutes of Health.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Informed consent

Publicly available data with no identifying details were used. For this type of study formal consent is not required.

Supplementary material

10654_2018_373_MOESM1_ESM.docx (102 kb)
Supplementary material 1 (DOCX 102 kb)
10654_2018_373_MOESM2_ESM.xlsx (70 kb)
Supplementary material 2 (XLSX 70 kb)

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Copyright information

© This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply [2018] 2018

Authors and Affiliations

  1. 1.National Institute of Environmental Health SciencesDurhamUSA
  2. 2.Department of Obstetrics and GynecologyMcGill UniversityMontrealCanada
  3. 3.Department of Epidemiology, Biostatistics and Occupational HealthMcGill UniversityMontrealCanada
  4. 4.Biostatistics and Computational Biology BranchNational Institute of Environmental Health SciencesDurhamUSA

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