Three alternative methods to resolve paradoxical associations of exposures before term

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• 作者：Nathalie Auger ; Ashley I. Naimi ; William D. Fraser…
• 关键词：Epidemiology ; Perinatal mortality ; Preeclampsia ; Preterm birth ; Selection bias
• 刊名：European Journal of Epidemiology
• 出版年：2016
• 出版时间：October 2016
• 年：2016
• 卷：31
• 期：10
• 页码：1011-1019
• 全文大小：424 KB
• 刊物类别：Medicine
• 刊物主题：Medicine & Public Health
  Epidemiology
  Public Health
  Infectious Diseases
  Cardiology
  Oncology
  
• 出版者：Springer Netherlands
• ISSN：1573-7284
• 卷排序：31

文摘

To propose three methods to estimate associations between pregnancy exposures and outcomes before term, including the association between preeclampsia and preterm fetal-infant mortality, while avoiding the selection bias found in conventional analytic designs. Population-level analysis of 1,099,839 women who delivered live or stillborn infants in Quebec hospitals from 1989 to 2012, covering nearly a quarter of Canadian births. The exposure of interest was preeclampsia at 20–29, 30–33, 34–36, and ≥37 weeks of gestation. We compared preeclamptic with non-preeclamptic pregnancies in parametric survival models with a Weibull distribution using three analytic designs: (1) fetuses-at-risk; (2) bias correction factors; and (3) analysis of pregnancies at high risk of preterm delivery. Main outcome measures were occurrence of fetal or postnatal infant death before discharge. In models affected by bias due to selection of preterm births, women with preeclampsia early in gestation had paradoxically lower risks of fetal and infant mortality than women without preeclampsia. All three analytic approaches reversed the associations, showing a harmful effect of preeclampsia before term. At 20–29 weeks, for example, preeclampsia was associated with 1.35 times the risk of infant mortality (95 % confidence interval 1.14–1.60) and 1.40 times the risk of stillbirth (95 % confidence interval 1.18–1.67) with the fetuses-at-risk approach. Paradoxically protective benefits of exposures such as preeclampsia before term are artifacts of inappropriate analytic design. Outcomes before term should be analyzed with care, using methods that address bias due to selection of preterm deliveries.