European Journal of Epidemiology

, Volume 31, Issue 6, pp 613–623 | Cite as

Deconstructing the smoking-preeclampsia paradox through a counterfactual framework

  • Miguel Angel Luque-Fernandez
  • Helga Zoega
  • Unnur Valdimarsdottir
  • Michelle A. Williams
PERINATAL EPIDEMIOLOGY

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.

Keywords

Preeclampsia Smoking Selection bias Epidemiology methods Perinatal mortality 

References

  1. 1.
    Savitz DA, Danilack VA, Engel SM, Elston B, Lipkind HS. Descriptive epidemiology of chronic hypertension, gestational hypertension, and preeclampsia in New York State, 1995–2004. Matern Child Health J. 2014;18(4):829–38.CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Jeyabalan A. Epidemiology of preeclampsia: impact of obesity. Nutr Rev. 2013;71(Suppl 1):S18–25.CrossRefPubMedGoogle Scholar
  3. 3.
    Cnattingius S, Mills JL, Yuen J, Eriksson O, Salonen H. The paradoxical effect of smoking in preeclamptic pregnancies: smoking reduces the incidence but increases the rates of perinatal mortality, abruptio placentae, and intrauterine growth restriction. Am J Obstet Gynecol. 1997;177(1):156–61.CrossRefPubMedGoogle Scholar
  4. 4.
    Marcoux S, Brisson J, Fabia J. The effect of cigarette smoking on the risk of preeclampsia and gestational hypertension. Am J Epidemiol. 1989;130(5):950–7.PubMedGoogle Scholar
  5. 5.
    Perni UC, Wikstrom AK, Cnattingius S, Villamor E. Interpregnancy change in smoking habits and risk of preeclampsia: a population-based study. Am J Hypertens. 2012;25(3):372–8.CrossRefPubMedGoogle Scholar
  6. 6.
    Wikstrom AK, Stephansson O, Cnattingius S. Tobacco use during pregnancy and preeclampsia risk: effects of cigarette smoking and snuff. Hypertension. 2010;55(5):1254–9.CrossRefPubMedGoogle Scholar
  7. 7.
    England L, Zhang J. Smoking and risk of preeclampsia: a systematic review. Front Biosci. 2007;12:2471–83.CrossRefPubMedGoogle Scholar
  8. 8.
    Wilcox AJ, Weinberg CR, Basso O. On the pitfalls of adjusting for gestational age at birth. Am J Epidemiol. 2011;174(9):1062–8.CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    VanderWeele TJ, Mumford SL, Schisterman EF. Conditioning on intermediates in perinatal epidemiology. Epidemiology. 2012;23(1):1–9.CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Hernandez-Diaz S, Schisterman EF, Hernan MA. The birth weight “paradox” uncovered? Am J Epidemiol. 2006;164(11):1115–20.CrossRefPubMedGoogle Scholar
  11. 11.
    Cole SR, Platt RW, Schisterman EF, et al. Illustrating bias due to conditioning on a collider. Int J Epidemiol. 2010;39(2):417–20.CrossRefPubMedGoogle Scholar
  12. 12.
    Hill G, Connelly J, Hebert R, Lindsay J, Millar W. Neyman’s bias re-visited. J Clin Epidemiol. 2003;56(4):293–6.CrossRefPubMedGoogle Scholar
  13. 13.
    Delgado-Rodriguez M, Llorca J. Bias. J Epidemiol Commun Health. 2004;58(8):635–41.CrossRefGoogle Scholar
  14. 14.
    Rothman KJ, Greenland S, Lash TL. Modern epidemiology. 3rd ed. Philadelphia; London: Lippincott Williams & Wilkins; 2008.Google Scholar
  15. 15.
    Pearce N, Richiardi L. Commentary: three worlds collide: Berkson’s bias, selection bias and collider bias. Int J Epidemiol. 2014;43(2):521–4.CrossRefPubMedGoogle Scholar
  16. 16.
    Martin CL, Hall MH, Campbell DM. The effect of smoking on pre-eclampsia in twin pregnancy. BJOG. 2000;107(6):745–9.CrossRefPubMedGoogle Scholar
  17. 17.
    Misra DP, Kiely JL. The effect of smoking on the risk of gestational hypertension. Early Hum Dev. 1995;40(2):95–107.CrossRefPubMedGoogle Scholar
  18. 18.
    Savitz DA, Zhang J. Pregnancy-induced hypertension in North Carolina, 1988 and 1989. Am J Public Health. 1992;82(5):675–9.CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Engel SM, Janevic TM, Stein CR, Savitz DA. Maternal smoking, preeclampsia, and infant health outcomes in New York City, 1995–2003. Am J Epidemiol. 2009;169(1):33–40.CrossRefPubMedGoogle Scholar
  20. 20.
    Engel SM, Scher E, Wallenstein S, et al. Maternal active and passive smoking and hypertensive disorders of pregnancy: risk with trimester-specific exposures. Epidemiology. 2013;24(3):379–86.CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Greenland S. Quantifying biases in causal models: classical confounding vs collider-stratification bias. Epidemiology. 2003;14(3):300–6.PubMedGoogle Scholar
  22. 22.
    Gudnadotir TA, Bateman BT, Hernandez-Diaz S, Luque-Fernandez MA, Geirs DP, Valdimarsdottir U, Zoega H. Body mass index, smoking and hypertensive disorders during pregnancy: a population based case-control study. PlosOne. 2016.Google Scholar
  23. 23.
    World Health Organization. International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10) Version for 2010; 2010.Google Scholar
  24. 24.
    Greenland S. Useful methods for sensitivity analysis of observational studies. Biometrics. 1999;55(3):990–1.CrossRefPubMedGoogle Scholar
  25. 25.
    Greenland S. Basic methods for sensitivity analysis of biases. Int J Epidemiol. 1996;25(6):1107–16.CrossRefPubMedGoogle Scholar
  26. 26.
    VanderWeele TJ. Bias formulas for sensitivity analysis for direct and indirect effects. Epidemiology. 2010;21(4):540–51.CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Vanderweele TJ, Arah OA. Bias formulas for sensitivity analysis of unmeasured confounding for general outcomes, treatments, and confounders. Epidemiology. 2011;22(1):42–52.CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    VanderWeele TJ, Hernan MA. From counterfactuals to sufficient component causes and vice versa. Eur J Epidemiol. 2006;21(12):855–8.CrossRefPubMedGoogle Scholar
  29. 29.
    Emsley R, Liu H. PARAMED: stata module to perform causal mediation analysis using parametric regression models. 2013.Google Scholar
  30. 30.
    Valeri L, Vanderweele TJ. Mediation analysis allowing for exposure-mediator interactions and causal interpretation: theoretical assumptions and implementation with SAS and SPSS macros. Psychol Methods. 2013;18(2):137–50.CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Robins JM, Gail MH, Lubin JH. More on “Biased selection of controls for case-control analyses of cohort studies”. Biometrics. 1986;42(2):293–9.CrossRefPubMedGoogle Scholar
  32. 32.
    Lubin JH, Gail MH. Biased selection of controls for case-control analyses of cohort studies. Biometrics. 1984;40(1):63–75.CrossRefPubMedGoogle Scholar
  33. 33.
    Neyman J. Statistics; servant of all sciences. Science. 1955;122(3166):401–6.CrossRefPubMedGoogle Scholar
  34. 34.
    Harmon QE, Huang L, Umbach DM, et al. Risk of fetal death with preeclampsia. Obstet Gynecol. 2015;125(3):628–35.CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Richiardi L, Bellocco R, Zugna D. Mediation analysis in epidemiology: methods, interpretation and bias. Int J Epidemiol. 2013;42(5):1511–9.CrossRefPubMedGoogle Scholar
  36. 36.
    VanderWeele TJ, Chiba Y. Sensitivity analysis for direct and indirect effects in the presence of exposure-induced mediator-outcome confounders. Epidemiol Biostat Public Health. 2014;11(2):e9027.PubMedPubMedCentralGoogle Scholar
  37. 37.
    Wisborg K, Kesmodel U, Henriksen TB, Olsen SF, Secher NJ. Exposure to tobacco smoke in utero and the risk of stillbirth and death in the first year of life. Am J Epidemiol. 2001;154(4):322–7.CrossRefPubMedGoogle Scholar
  38. 38.
    Kesmodel U, Wisborg K, Olsen SF, Henriksen TB, Secher NJ. Moderate alcohol intake during pregnancy and the risk of stillbirth and death in the first year of life. Am J Epidemiol. 2002;155(4):305–12.CrossRefPubMedGoogle Scholar
  39. 39.
    Hyland A, Piazza KM, Hovey KM, et al. Associations of lifetime active and passive smoking with spontaneous abortion, stillbirth and tubal ectopic pregnancy: a cross-sectional analysis of historical data from the Women’s Health Initiative. Tob Control. 2015;24(4):328–35.CrossRefPubMedGoogle Scholar
  40. 40.
    Cnattingius S, Haglund B, Meirik O. Cigarette smoking as risk factor for late fetal and early neonatal death. BMJ. 1988;297(6643):258–61.CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Orsini N, Bellocco R, Bottai M, Wolk A, Greenland S. A tool for deterministic and probabilistic sensitivity analysis of epidemiologic studies. Stata J. 2008;8(1):29–48.Google Scholar
  42. 42.
    Palei AC, Spradley FT, Warrington JP, George EM, Granger JP. Pathophysiology of hypertension in pre-eclampsia: a lesson in integrative physiology. Acta Physiol (Oxf). 2013;208(3):224–33.CrossRefGoogle Scholar
  43. 43.
    Nejatizadeh A, Stobdan T, Malhotra N, Pasha MA. The genetic aspects of pre-eclampsia: achievements and limitations. Biochem Genet. 2008;46(7–8):451–79.CrossRefPubMedGoogle Scholar
  44. 44.
    Ananth CV. Ischemic placental disease: a unifying concept for preeclampsia, intrauterine growth restriction, and placental abruption. Semin Perinatol. 2014;38(3):131–2.CrossRefPubMedGoogle Scholar
  45. 45.
    Castles A, Adams EK, Melvin CL, Kelsch C, Boulton ML. Effects of smoking during pregnancy. Five meta-analyses. Am J Prev Med. 1999;16(3):208–15.CrossRefPubMedGoogle Scholar
  46. 46.
    Cnattingius S. The epidemiology of smoking during pregnancy: smoking prevalence, maternal characteristics, and pregnancy outcomes. Nicotine Tob Res. 2004;6(Suppl 2):S125–40.CrossRefPubMedGoogle Scholar
  47. 47.
    Pueyo V, Guerri N, Oros D, et al. Effects of smoking during pregnancy on the optic nerve neurodevelopment. Early Human Dev. 2011;87(5):331–4.CrossRefGoogle Scholar
  48. 48.
    Ram FS, McDonald EM. Response to ‘Inhibitory effects of maternal smoking on the development of severe retinopathy of prematurity’. Eye (Lond). 2011;25(1):123–124; author reply 124.Google Scholar
  49. 49.
    Hogberg L, Cnattingius S, Lundholm C, D’Onofrio BM, Langstrom N, Iliadou AN. Effects of maternal smoking during pregnancy on offspring blood pressure in late adolescence. J Hypertens. 2012;30(4):693–9.CrossRefPubMedPubMedCentralGoogle Scholar
  50. 50.
    Conde-Agudelo A, Althabe F, Belizan JM, Kafury-Goeta AC. Cigarette smoking during pregnancy and risk of preeclampsia: a systematic review. Am J Obstet Gynecol. 1999;181(4):1026–35.CrossRefPubMedGoogle Scholar
  51. 51.
    Feng D, Liu T, Su DF, et al. The association between smoking quantity and hypertension mediated by inflammation in Chinese current smokers. J Hypertens. 2013;31(9):1798–805.PubMedGoogle Scholar
  52. 52.
    D’Elia L, De Palma D, Rossi G, et al. Not smoking is associated with lower risk of hypertension: results of the Olivetti Heart Study. Eur J Public Health. 2014;24(2):226–30.CrossRefPubMedGoogle Scholar
  53. 53.
    Mons U, Muezzinler A, Gellert C, et al. Impact of smoking and smoking cessation on cardiovascular events and mortality among older adults: meta-analysis of individual participant data from prospective cohort studies of the CHANCES consortium. BMJ. 2015;350:h1551.CrossRefPubMedPubMedCentralGoogle Scholar
  54. 54.
    Karumanchi SA, Levine RJ. How does smoking reduce the risk of preeclampsia? Hypertension. 2010;55(5):1100–1.CrossRefPubMedPubMedCentralGoogle Scholar
  55. 55.
    Venditti CC, Casselman R, Young I, Karumanchi SA, Smith GN. Carbon monoxide prevents hypertension and proteinuria in an adenovirus sFlt-1 preeclampsia-like mouse model. PLoS ONE. 2014;9(9):e106502.CrossRefPubMedPubMedCentralGoogle Scholar
  56. 56.
    Zhang F, Kaide JI, Rodriguez-Mulero F, Abraham NG, Nasjletti A. Vasoregulatory function of the heme-heme oxygenase-carbon monoxide system. Am J Hypertens. 2001;14(6 Pt 2):62S–7S.CrossRefPubMedGoogle Scholar
  57. 57.
    Fujita T, Toda K, Karimova A, et al. Paradoxical rescue from ischemic lung injury by inhaled carbon monoxide driven by derepression of fibrinolysis. Nat Med. 2001;7(5):598–604.CrossRefPubMedGoogle Scholar
  58. 58.
    Brouard S, Otterbein LE, Anrather J, et al. Carbon monoxide generated by heme oxygenase 1 suppresses endothelial cell apoptosis. J Exp Med. 2000;192(7):1015–26.CrossRefPubMedPubMedCentralGoogle Scholar
  59. 59.
    Liu XM, Chapman GB, Peyton KJ, Schafer AI, Durante W. Carbon monoxide inhibits apoptosis in vascular smooth muscle cells. Cardiovasc Res. 2002;55(2):396–405.CrossRefPubMedGoogle Scholar
  60. 60.
    Llurba E, Sanchez O, Dominguez C, et al. Smoking during pregnancy: changes in mid-gestation angiogenic factors in women at risk of developing preeclampsia according to uterine artery Doppler findings. Hypertens Pregnancy. 2013;32(1):50–9.CrossRefPubMedGoogle Scholar
  61. 61.
    Cudmore M, Ahmad S, Al-Ani B, et al. Negative regulation of soluble Flt-1 and soluble endoglin release by heme oxygenase-1. Circulation. 2007;115(13):1789–97.CrossRefPubMedGoogle Scholar
  62. 62.
    Hogberg L, Cnattingius S. The influence of maternal smoking habits on the risk of subsequent stillbirth: is there a causal relation? BJOG. 2007;114(6):699–704.CrossRefPubMedPubMedCentralGoogle Scholar
  63. 63.
    Raymond EG, Cnattingius S, Kiely JL. Effects of maternal age, parity, and smoking on the risk of stillbirth. Br J Obstet Gynaecol. 1994;101(4):301–6.CrossRefPubMedGoogle Scholar
  64. 64.
    Gray R, Bonellie SR, Chalmers J, et al. Contribution of smoking during pregnancy to inequalities in stillbirth and infant death in Scotland 1994–2003: retrospective population based study using hospital maternity records. BMJ. 2009;339:b3754.CrossRefPubMedPubMedCentralGoogle Scholar
  65. 65.
    Geneletti S, Richardson S, Best N. Adjusting for selection bias in retrospective, case-control studies. Biostatistics. 2009;10(1):17–31.CrossRefPubMedGoogle Scholar
  66. 66.
    Richardson DB. An incidence density sampling program for nested case-control analyses. Occup Environ Med. 2004;61(12):e59.CrossRefPubMedPubMedCentralGoogle Scholar
  67. 67.
    Azzato EM, Greenberg D, Shah M, et al. Prevalent cases in observational studies of cancer survival: do they bias hazard ratio estimates? Br J Cancer. 2009;100(11):1806–11.CrossRefPubMedPubMedCentralGoogle Scholar
  68. 68.
    Kyrklund-Blomberg NB, Cnattingius S. Preterm birth and maternal smoking: risks related to gestational age and onset of delivery. Am J Obstet Gynecol. 1998;179(4):1051–5.CrossRefPubMedGoogle Scholar
  69. 69.
    Breslow NE, Day NE. Statistical methods in cancer research. Volume I—the analysis of case-control studies. IARC Sci Publ. 1980;32:5–338.PubMedGoogle Scholar
  70. 70.
    Hernan MA, Schisterman EF, Hernandez-Diaz S. Invited commentary: composite outcomes as an attempt to escape from selection bias and related paradoxes. Am J Epidemiol. 2014;179(3):368–70.CrossRefPubMedGoogle Scholar
  71. 71.
    Hernan MA, Hernandez-Diaz S, Robins JM. A structural approach to selection bias. Epidemiology. 2004;15(5):615–25.CrossRefPubMedGoogle Scholar
  72. 72.
    Greenland S. Sensitivity analysis, Monte Carlo risk analysis, and Bayesian uncertainty assessment. Risk Anal. 2001;21(4):579–83.CrossRefPubMedGoogle Scholar
  73. 73.
    Arah OA, Chiba Y, Greenland S. Bias formulas for external adjustment and sensitivity analysis of unmeasured confounders. Ann Epidemiol. 2008;18(8):637–46.CrossRefPubMedGoogle Scholar
  74. 74.
    Lisonkova S, Joseph KS. Left truncation bias as a potential explanation for the protective effect of smoking on preeclampsia. Epidemiology. 2015;26(3):436–40.CrossRefPubMedPubMedCentralGoogle Scholar
  75. 75.
    Hernan MA, Schisterman EF, Hernandez-Diaz S. Invited commentary: composite outcomes as an attempt to escape from selection bias and related paradoxes. Am J Epidemiol. 2014;179(3):368–70.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.Department of Non-Communicable Disease Epidemiology, Faculty of Epidemiology and Population HealthLondon School of Hygiene and Tropical MedicineLondonUK
  2. 2.Department of EpidemiologyHarvard T.H. Chan School of Public HealthBostonUSA
  3. 3.Faculty of Medicine, Center of Public Health SciencesUniversity of IcelandReykjavikIceland

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