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Preterm birth etiological pathways: a Bayesian networks and mediation analysis approach

Abstract

Background

The aim of this study was to determine the mediating effect of spontaneous preterm birth (PTB) main predictors that would allow to suggest etiological pathways.

Methods

We carried out a case–control study, including sociodemographic characteristics, habits, health care, and obstetric data of multiparous women who gave birth at a maternity hospital from Tucumán, Argentina, between 2005 and 2010: 998 women without previous PTB who delivered at term and 562 who delivered preterm. We selected factors with the greatest predictive power using a penalized logistic regression model. A data-driven Bayesian network including the selected factors was created where we identified pathways and performed mediation analyses.

Results

We identified three PTB pathways whose natural indirect effect was greater than zero with a 95% confidence interval: maternal age less than 20 years mediated by few prenatal visits, vaginal bleeding in the first trimester mediated by vaginal bleeding in the second trimester, and urinary tract infection mediated by vaginal bleeding in the second trimester. The effect mediated in these pathways showed greater sensitivity to confounders affecting the variables mediator–outcome and exposure–mediator in the same direction.

Conclusion

The identified pathways suggest PTB etiological lines related to social disparities and exposure to genitourinary tract infections.

Impact

  • Few prenatal visits (<5) and vaginal bleeding are two of the main predictors for spontaneous preterm birth in the studied population.

  • Few prenatal visits mediates part of the risk associated with maternal age less than 20 years and vaginal bleeding in the second trimester mediates part of the risk associated with vaginal bleeding in the first trimester and with urinary tract infection.

  • Social disparities and exposure to genitourinary tract infections would be etiological lines of spontaneous preterm birth.

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Fig. 1: Bayesian network of preterm birth predictors.
Fig. 2: Sensitivity analyses of preterm birth pathways.

References

  1. 1.

    Chawanpaiboon, S. et al. Global, regional, and national estimates of levels of preterm birth in 2014: a systematic review and modelling analysis. Lancet Glob. Health 7, e37–e46 (2019).

    PubMed  Article  Google Scholar 

  2. 2.

    Dirección de Estadísticas e Información de Salud—Ministerio de Salud de Argentina. Estadísticas vitales Información Básica 2018. (2019, accessed 17 February 2021); http://www.deis.msal.gov.ar/wp-content/uploads/2020/01/Serie5Nro62.pdf.

  3. 3.

    UNICEF, WHO, World Bank Group, United Nations. Levels and Trends in Child Mortality: Report 2019 (United Nations Children’s Fund, 2019).

  4. 4.

    Muglia, L. J. & Katz, M. The enigma of spontaneous preterm birth. N. Engl. J. Med. 362, 529–535 (2010).

    CAS  PubMed  Article  Google Scholar 

  5. 5.

    Krupitzki, H. B. et al. Environmental risk factors and perinatal outcomes in preterm newborns, according to family recurrence of prematurity. Am. J. Perinatol. 30, 451–461 (2013).

    PubMed  Google Scholar 

  6. 6.

    Gimenez, L. G. et al. Maternal and neonatal epidemiological features in clinical subtypes of preterm birth. J. Matern. Fetal Neonatal Med. 29, 3153–3161 (2016).

    PubMed  Article  Google Scholar 

  7. 7.

    Gimenez, L. G. et al. Association of candidate gene polymorphisms with clinical subtypes of preterm birth in a Latin American population. Pediatr. Res. 82, 554–559 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  8. 8.

    Koller, D. & Friedman, N. Probabilistic Graphical Models: Principles and Techniques (MIT Press, 2009).

  9. 9.

    Beresniak, A. et al. A Bayesian network approach to the study of historical epidemiological databases: modelling meningitis outbreaks in the Niger. Bull. World Health Organ. 90, 412–417A (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  10. 10.

    Fuster-Parra, P. et al. Bayesian network modeling: a case study of an epidemiologic system analysis of cardiovascular risk. Comput. Methods Prog. Biomed. 126, 128–142 (2016).

    CAS  Article  Google Scholar 

  11. 11.

    Richiardi, L., Bellocco, R. & Zugna, D. Mediation analysis in epidemiology: methods, interpretation and bias. Int. J. Epidemiol. 42, 1511–1519 (2013).

    PubMed  Article  Google Scholar 

  12. 12.

    Castilla, E. E. & Orioli, I. M. ECLAMC: the Latin-American collaborative study of congenital malformations. Community Genet. 7, 76–94 (2004).

    PubMed  Google Scholar 

  13. 13.

    Dietz, P. M. et al. A comparison of LMP‐based and ultrasound‐based estimates of gestational age using linked California livebirth and prenatal screening records. Paediatr. Perinat. Epidemiol. 21, 62–71 (2007).

    PubMed  Article  Google Scholar 

  14. 14.

    Kuhn, M. Building predictive models in R using the caret package. J. Stat. Softw. 28, 1–26 (2008).

    Article  Google Scholar 

  15. 15.

    Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010).

    PubMed  PubMed Central  Article  Google Scholar 

  16. 16.

    Firth, D. Bias reduction of maximum likelihood estimates. Biometrika 80, 27–38 (1993).

    Article  Google Scholar 

  17. 17.

    Heinze, G., Ploner, M., Dunkler, D. & Southworth, H. Package “logistf” (2020, accessed 17 February 2021); https://cran.r-project.org/web/packages/logistf/logistf.pdf.

  18. 18.

    Scutari, M., Graafland, C. E. & Gutiérrez, J. M. Who learns better Bayesian network structures: accuracy and speed of structure learning algorithms. Int. J. Approx. Reasoning 115, 235–253 (2019).

    Article  Google Scholar 

  19. 19.

    Heckerman, D., Geiger, D. & Chickering, D. M. Learning Bayesian networks: the combination of knowledge and statistical data. Mach. Learn. 20, 197–243 (1995).

    Google Scholar 

  20. 20.

    Henrion, M. Propagating uncertainty in Bayesian networks by probabilistic logic sampling. Mach. Intell. Pattern Recognit. 5, 149–163 (1988).

    Google Scholar 

  21. 21.

    Scutari, M. Learning Bayesian networks with the bnlearn R package. J. Stat. Softw. 35, 1–22 (2010).

    Article  Google Scholar 

  22. 22.

    Csardi, G. & Nepusz, T. The igraph software package for complex network research. InterJournal Complex Syst. 1695, 1–9 (2006).

    Google Scholar 

  23. 23.

    Robins, J. M. & Greenland, S. Identifiability and exchangeability for direct and indirect effects. Epidemiology 3, 143–155 (1992).

    CAS  PubMed  Article  Google Scholar 

  24. 24.

    Pearl, J. Direct and indirect effects. In: Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence, 2–5 August 2001 (Morgan Kaufmann Publishers Inc., 2001).

  25. 25.

    Lindmark, A., de Luna, X. & Eriksson, M. Sensitivity analysis for unobserved confounding of direct and indirect effects using uncertainty intervals. Stat. Med. 37, 1744–1762 (2018).

    PubMed  Article  Google Scholar 

  26. 26.

    Yang, J. et al. Vaginal bleeding during pregnancy and preterm birth. Am. J. Epidemiol. 160, 118–125 (2004).

    PubMed  Article  Google Scholar 

  27. 27.

    Hossain, R., Harris, T., Lohsoonthorn, V. & Williams, M. A. Risk of preterm delivery in relation to vaginal bleeding in early pregnancy. Eur. J. Obstet. Gynecol. Reprod. Biol. 135, 158–163 (2007).

    PubMed  PubMed Central  Article  Google Scholar 

  28. 28.

    Sharami, S. H. et al. The relationship between vaginal bleeding in the first and second trimester of pregnancy and preterm labor. Iran. J. Reprod. Med. 11, 385–390 (2013).

    PubMed  PubMed Central  Google Scholar 

  29. 29.

    Murray, S. R., Stock, S. J., Cowan, S., Cooper, E. S. & Norman, J. E. Spontaneous preterm birth prevention in multiple pregnancy. Obstet. Gynaecol. 20, 57–63 (2018).

    PubMed  PubMed Central  Google Scholar 

  30. 30.

    Wudie, F. T. et al. Determinants of preterm delivery in the central zone of Tigray, northern Ethiopia: a case-control study. South Afr. J. Child Health 13, 108–114 (2019).

    Article  Google Scholar 

  31. 31.

    Wehby, G. L., Murray, J. C., Castilla, E. E., Lopez-Camelo, J. S. & Ohsfeldt, R. L. Prenatal care effectiveness and utilization in Brazil. Health Policy Plan 24, 175–188 (2009).

    PubMed  PubMed Central  Article  Google Scholar 

  32. 32.

    Ratowiecki, J. et al. Inequidades sociales en madres adolescentes y la relación con resultados perinatales adversos en poblaciones sudamericanas. Cad. Saude Publica 36, e00247719 (2021).

    PubMed  Article  Google Scholar 

  33. 33.

    Ketterlinus, R. D., Henderson, S. H. & Lamb, M. E. Maternal age, sociodemographics, prenatal health and behavior: influences on neonatal risk status. J. Adolesc. Health Care 11, 423–431 (1990).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  34. 34.

    Kalinderi, K., Delkos, D., Kalinderis, M., Athanasiadis, A. & Kalogiannidis, I. Urinary tract infection during pregnancy: current concepts on a common multifaceted problem. J. Obstet. Gynaecol. 38, 448–453 (2018).

    PubMed  Article  Google Scholar 

  35. 35.

    Basso, O. & Baird, D. D. Infertility and preterm delivery, birthweight, and caesarean section: a study within the Danish National Birth Cohort. Hum. Reprod. 18, 2478–2484 (2003).

    PubMed  Article  PubMed Central  Google Scholar 

  36. 36.

    Konishi, S., Sakata, S., Watanabe, C. & Ng, C. F. S. Conception delay and spontaneous and indicated preterm birth among primiparous women in Japan. Jpn. J. Health Hum. Ecol. 84, 117–128 (2018).

    Article  Google Scholar 

  37. 37.

    Gimenes, F. et al. Male infertility: a public health issue caused by sexually transmitted pathogens. Nat. Rev. Urol. 11, 672 (2014).

    PubMed  Article  Google Scholar 

  38. 38.

    Tsevat, D. G., Wiesenfeld, H. C., Parks, C. & Peipert, J. F. Sexually transmitted diseases and infertility. Am. J. Obstet. Gynecol. 216, 1–9 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  39. 39.

    Melamed, Y. et al. Differences in preterm delivery rates and outcomes in Jews and Bedouins in Southern Israel. Eur. J. Obstet. Gynecol. Reprod. Biol. 93, 41–46 (2000).

    CAS  PubMed  Article  Google Scholar 

  40. 40.

    Eliyahu, S., Weiner, E., Nachum, Z. & Shalev, E. Epidemiologic risk factors for preterm delivery. Isr. Med. Assoc. J. 4, 1115–1117 (2002).

    PubMed  Google Scholar 

  41. 41.

    Dotters-Katz, S. K., Grotegut, C. A. & Heine, R. P. The effects of anemia on pregnancy outcome in patients with pyelonephritis. Infect. Dis. Obstet. Gynecol. 2013, 5 (2013).

    Google Scholar 

  42. 42.

    Leifert, J. A. Anaemia and cigarette smoking. Int J. Lab. Hematol. 30, 177–184 (2008).

    CAS  PubMed  Article  Google Scholar 

  43. 43.

    Stevens-Simon, C., Beach, R. K. & McGregor, J. A. Does incomplete growth and development predispose teenagers to preterm delivery? A template for research. J. Perinatol. 22, 315–323 (2002).

    PubMed  Article  Google Scholar 

  44. 44.

    Wehby, G. L. & López-Camelo, J. S. Maternal education gradients in infant health in four South American countries. Matern Child Health J. 21, 2122–2131 (2017).

    PubMed  Article  Google Scholar 

  45. 45.

    Zapata, M. E., Soruco, A. I. & Carmuega, E. Malnutrition in all its forms and socio-economic indicators in Argentina. Public Health Nutr. 23, s13–s20 (2020).

    PubMed  Article  Google Scholar 

  46. 46.

    Axelsen, S. M., Henriksen, T. B., Hedegaard, M. & Secher, N. J. Characteristics of vaginal bleeding during pregnancy. Eur. J. Obstet. Gynecol. Reprod. Biol. 63, 131–134 (1995).

    CAS  PubMed  Article  Google Scholar 

  47. 47.

    Elovitz, M. A., Baron, J. & Phillippe, M. The role of thrombin in preterm parturition. Am. J. Obstet. Gynecol. 185, 1059–1063 (2001).

    CAS  PubMed  Article  Google Scholar 

  48. 48.

    Gómez, R. et al. Idiopathic vaginal bleeding during pregnancy as the only clinical manifestation of intrauterine infection. J. Matern Fetal Neonatal Med. 18, 31–37 (2005).

    PubMed  Article  Google Scholar 

  49. 49.

    Nielson, E. C., Varner, M. W. & Scott, J. R. The outcome of pregnancies complicated by bleeding during the second trimester. Surg. Gynecol. Obstet. 173, 371–374 (1991).

    CAS  PubMed  Google Scholar 

  50. 50.

    Oyelese, Y. & Smulian, J. C. Placenta previa, placenta accreta, and vasa previa. Obstet. Gynecol. 107, 927–941 (2006).

    PubMed  Article  Google Scholar 

  51. 51.

    Molina, P. E., Happel, K. I., Zhang, P., Kolls, J. K. & Nelson, S. Focus on: alcohol and the immune system. Alcohol Res. Health 33, 97 (2010).

    PubMed  PubMed Central  Google Scholar 

  52. 52.

    Gonçalves, L. F., Chaiworapongsa, T. & Romero, R. Intrauterine infection and prematurity. Ment. Retard. Dev. Disabil. Res. Rev. 8, 3–13 (2002).

    PubMed  Article  Google Scholar 

  53. 53.

    Rosen, T. et al. Thrombin-enhanced matrix metalloproteinase-1 expression: a mechanism linking placental abruption with premature rupture of the membranes. J. Matern. Fetal Neonatal Med. 11, 11–17 (2002).

    CAS  PubMed  Article  Google Scholar 

  54. 54.

    Elias, D. et al. Preterm birth and genitourinary tract infections: assessing gene–environment interaction. Pediatr. Res. 1–7, https://doi.org/10.1038/s41390-020-01200-z (2020).

  55. 55.

    Vittinghoff, E. & McCulloch, C. E. Relaxing the rule of ten events per variable in logistic and Cox regression. Am. J. Epidemiol. 165, 710–718 (2007).

    PubMed  Article  Google Scholar 

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Acknowledgements

The authors want to thank Mrs. Mariana Piola and Alejandra Mariona. This work was supported by Agencia Nacional de Promoción Científica y Tecnológica (ANPCyT-MINCyT), PICT-2018-4275 to J.S.L.C. and PICT-2018-4285 to L.G.G., and Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET).

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Each author has met the Pediatric Research authorship requirements. D.E. made substantial contributions to design, acquisition of data, analysis and interpretation of data, drafting the article, and approving the final manuscript as submitted. H.C., F.A.P., J.A.G., J.R., M.P., M.R.S., V.C., R.U., C.S., and M.R. made substantial contributions to design, acquisition of data, analysis and interpretation of data, and approving the final manuscript as submitted. S.L.H. made draft review and manuscript edition. H.B.K. and J.S.L.C. made substantial contributions to design, acquisition of data, critical manuscript revision for important intellectual content, and approving of the final version as submitted. L.G.G. made substantial contributions to design, acquisition of data, analysis and interpretation of data, drafting the article, and approving the final manuscript as submitted.

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Correspondence to Dario Elias.

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Elias, D., Campaña, H., Poletta, F.A. et al. Preterm birth etiological pathways: a Bayesian networks and mediation analysis approach. Pediatr Res (2021). https://doi.org/10.1038/s41390-021-01659-4

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