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Deconstructing the smoking-preeclampsia paradox through a counterfactual framework

  • PERINATAL EPIDEMIOLOGY
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Abstract

Although smoking during pregnancy may lead to many adverse outcomes, numerous studies have reported a paradoxical inverse association between maternal cigarette smoking during pregnancy and preeclampsia. Using a counterfactual framework we aimed to explore the structure of this paradox as being a consequence of selection bias. Using a case–control study nested in the Icelandic Birth Registry (1309 women), we show how this selection bias can be explored and corrected for. Cases were defined as any case of pregnancy induced hypertension or preeclampsia occurring after 20 weeks’ gestation and controls as normotensive mothers who gave birth in the same year. First, we used directed acyclic graphs to illustrate the common bias structure. Second, we used classical logistic regression and mediation analytic methods for dichotomous outcomes to explore the structure of the bias. Lastly, we performed both deterministic and probabilistic sensitivity analysis to estimate the amount of bias due to an uncontrolled confounder and corrected for it. The biased effect of smoking was estimated to reduce the odds of preeclampsia by 28 % (OR 0.72, 95 %CI 0.52, 0.99) and after stratification by gestational age at delivery (<37 vs. ≥37 gestation weeks) by 75 % (OR 0.25, 95 %CI 0.10, 0.68). In a mediation analysis, the natural indirect effect showed and OR > 1, revealing the structure of the paradox. The bias-adjusted estimation of the smoking effect on preeclampsia showed an OR of 1.22 (95 %CI 0.41, 6.53). The smoking-preeclampsia paradox appears to be an example of (1) selection bias most likely caused by studying cases prevalent at birth rather than all incident cases from conception in a pregnancy cohort, (2) omitting important confounders associated with both smoking and preeclampsia (preventing the outcome to develop) and (3) controlling for a collider (gestation weeks at delivery). Future studies need to consider these aspects when studying and interpreting the association between smoking and pregnancy outcomes.

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Funding

This research was supported by the Rose Traveling Fellowship from the departments of Epidemiology and Biostatistics of the Harvard T.H. Chan School of Public Health and by the START Reintegration fellowship (#130814-051) from the Icelandic Centre for Research (Rannis).

Authors’ contributions

MALF, UV and HZ are the guarantors of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. All authors designed the protocol. HZ provided the data. MALF wrote the manuscript. MALF developed and completed the statistical analysis. MW, UV, and HZ reviewed, edited and accepted the last version of the manuscript.

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Authors and Affiliations

  1. Department of Non-Communicable Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK

    Miguel Angel Luque-Fernandez & Unnur Valdimarsdottir

  2. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Miguel Angel Luque-Fernandez & Michelle A. Williams

  3. Faculty of Medicine, Center of Public Health Sciences, University of Iceland, Reykjavik, Iceland

    Helga Zoega & Unnur Valdimarsdottir

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  1. Miguel Angel Luque-Fernandez
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  2. Helga Zoega
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Correspondence to Miguel Angel Luque-Fernandez.

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Appendix (stata syntax)

Appendix (stata syntax)

  1. 1.

    Simulation of preeclampsia cumulative incidence distribution by gestation weeks

    set seed 12345

    gen pe = 24 * (1-rbeta(2,5)) + 15

    hist pe

  2. 2.

    Classic logistic regression, stratification and mediation analysis

    *CLASSICAL APPROACH: collider bias

    logistic PE SMOKING

    logistic PE SMOKING BMI AGE

    logistic PE SMOKING PARITY BMI AGE

    *STRATIFICATION: collider stratification bias

    logistic PE SMOKING PARITY BMI AGE if PTD ==1

    logistic PE SMOKING PARITY BMI AGE if PTD ==0

    *MEDIATION: collider bias

    paramed PE, avar(SMOKING) mvar(PTD) cvars(AGE BMI PARITY) a0(0) a1(1) m(1) yreg(logistic) mreg(logistic) boot seed(1234)

    paramed PE, avar(SMOKING) mvar(PTD) cvars(AGE BMI PARITY) a0(0) a1(1) m(1) yreg(logistic) mreg(logistic) nointer boot seed(1234)

  3. 3.

    Sensitivity analysis

    cc PE SMOKING

    episensi 57 319 186 747, st(cc) reps(100) nodots dpexp(uni(.01 .08)) dpunexp(uni(0.01 0.30)) drrcd(log-n(ln(20) + ln(2.2)/(2) ln(20)-ln(2.2)/2*1.96)) sed(123)

    episensi 57 319 186 747, st(cc) reps(1000) nodots dpexp(uni(.01 .08)) dpunexp(uni(0.01 0.30)) drrcd(log-n(ln(20) + ln(2.2)/(2) ln(20)-ln(2.2)/2*1.96)) sed(123)

    episensi 57 319 186 747, st(cc) reps(10000) nodots dpexp(uni(.01 .08)) dpunexp(uni(0.01 0.30)) drrcd(log-n(ln(20) + ln(2.2)/(2) ln(20)-ln(2.2)/2*1.96)) sed(123)

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Luque-Fernandez, M.A., Zoega, H., Valdimarsdottir, U. et al. Deconstructing the smoking-preeclampsia paradox through a counterfactual framework. Eur J Epidemiol 31, 613–623 (2016). https://doi.org/10.1007/s10654-016-0139-5

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  • DOI: https://doi.org/10.1007/s10654-016-0139-5

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