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. 2018 May;32(3):281-286.
doi: 10.1111/ppe.12459. Epub 2018 Mar 22.

Assessment of recording bias in pregnancy studies using health care databases: An application to neurologic conditions

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Assessment of recording bias in pregnancy studies using health care databases: An application to neurologic conditions

Sarah C MacDonald et al. Paediatr Perinat Epidemiol. 2018 May.

Abstract

Background: Pre-existing conditions are imperfectly recorded in health care databases. We assessed whether pre-existing neurologic conditions (epilepsy, multiple sclerosis [MS]) were differentially recorded in the presence of major obstetric outcomes (Caesarean delivery, preterm delivery, preeclampsia) in delivery records. We also evaluated the impact of differential recording on measures of frequency and association between the conditions and outcomes.

Methods: The 2011-2014 Truven Health MarketScan® Commercial Claims Dataset was used to identify pregnancies. We calculated the relative recording of epilepsy and MS at delivery hospitalization compared with a 270-day pre-delivery window both overall and by the presence of major obstetric outcomes. We estimated risk ratios for the association between epilepsy and MS with the outcomes for each ascertainment window.

Results: We identified 909 065 pregnancies in women continuously enrolled from 270-days before the delivery date. Of women with epilepsy identified in the pre-delivery window, 73% had the condition coded at delivery. For MS, the proportion was 60%. MS recording at delivery did not vary by obstetric outcomes, however, delivery-coded epilepsy was less likely confirmed in the pre-delivery window in the presence of preeclampsia. Generally, the period of ascertainment did not meaningfully impact risk ratios, however, the risk ratio for preeclampsia associated with epilepsy was 1.67 (95% CI 1.47, 1.90) when epilepsy was ascertained at delivery and 1.26 (95% CI 1.07, 1.48) when epilepsy was ascertained in the pre-delivery window (heterogeneity, P = .007).

Conclusions: Ascertainment of epilepsy and MS in delivery hospitalization records underestimated prevalence. However, the window of recording generally did not impact risk ratio estimates of associations with obstetric outcomes.

Keywords: administrative claims; bias (epidemiology); epilepsy; health care; multiple sclerosis; pregnancy.

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Figures

Figure 1
Figure 1
Relative Sensitivity (A-B) and Predictive Values (C-D) for Epilepsy (A, C) and Multiple Sclerosis (B, D) coded in the Delivery versus the Pre-Delivery Interval (Truven Health MarketScan® Commercial Claims and Encounters Database, 2011-2014 USA, N=909,065). Darker solid bars represent those with the delivery event; lighter hatched bars represent those without the delivery event. Error bars represent 95% confidence intervals.
Figure 2
Figure 2
Adjusted Risk Ratios of Obstetric Outcomes by (A) Epilepsy and (B) Multiple Sclerosis Defined in the Delivery and Pre-Delivery Interval (Truven Health MarketScan® Commercial Claims and Encounters Database, 2011-2014, USA, N=909,065). Risk ratios adjusted for maternal age, region (Northeast, Midwest/North Central, South, and West) and year of delivery. Pre-Delivery = Pre-existing condition recorded on at least two days in the 270 days before delivery until, and including, the delivery date (and at least one anti-convulsant prescription for epilepsy). Delivery = Pre-existing condition recorded on at least one day during the delivery hospitalization. RR= risk ratio, LCI= lower confidence interval, UCI= upper confidence interval.
Figure 3
Figure 3
Directed acyclic graph indicating possible confounding. E = Pre-existing condition (e.g. epilepsy); D= Obstetric outcome (e.g. preeclampsia); E*1 = Recording of pre-existing condition in pregnancy; E*2 = Recording of pre-existing condition at delivery; L=Other covariates (e.g. Healthcare utilization). Dashed lines indicate the association of interest.

Comment in

  • Making the best use of data not created for research.
    Palmsten K, Chambers CD. Palmsten K, et al. Paediatr Perinat Epidemiol. 2018 May;32(3):287-289. doi: 10.1111/ppe.12466. Epub 2018 Mar 25. Paediatr Perinat Epidemiol. 2018. PMID: 29575116 Free PMC article. No abstract available.

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