Maternal and Child Health Journal

, Volume 16, Issue 6, pp 1151–1163 | Cite as

Behavioral Influences on Preterm Birth: Integrated Analysis of the Pregnancy, Infection, and Nutrition Study

  • David A. Savitz
  • Quaker Harmon
  • Anna Maria Siega-Riz
  • Amy H. Herring
  • Nancy Dole
  • John M. ThorpJr.
Article

Abstract

Most previous studies of preterm birth have considered risk factors in isolation rather than examining the collective impact of multiple candidate determinants. In order to examine the combined impact of a set of behavioral risk factors on the risk of preterm birth, we analyzed data collected for the Pregnancy, Infection, and Nutrition Study on a range of sociodemographic, behavioral, and related factors. Women who received prenatal care at selected clinics in central North Carolina and gave birth in the period 1995–2005 were recruited into a prospective cohort study, with 4,251 women providing the required information on risk factors and pregnancy outcome. A number of demographic and behavioral attributes were modestly associated with preterm birth, with odds ratios of 1.3–1.5, including age >35, African-American ethnicity, height of 63 inches or less, parity 2+, and delivery at the academic medical center. Despite weak associations for individual risk factors, changes in a constellation of behaviors during pregnancy predict substantial shifts in the risk of preterm birth, suggesting a reduction from 8 to 3% preterm among those with a low-risk baseline profile, and a reduction from 18 to 7% preterm among those with a high-risk baseline profile. While inferences are limited by the incomplete range of available predictors, uncertainty regarding whether observed associations are causal, and substantial challenges in changing component behaviors, the possibility of substantial reduction in risk merits more serious consideration of whether behavioral interventions could markedly reduce the risk of preterm birth.

Keywords

Preterm birth Prediction models Health behaviors 

References

  1. 1.
    Behrman, R. E., Butler, A. S., & Institute of Medicine. (2007). Preterm birth: Causes, consequences, and prevention. Washington, DC: National Academies Press.Google Scholar
  2. 2.
    Meis, P. J., Klebanoff, M., Thom, E., et al. (2003). Prevention of recurrent preterm delivery by 17 alpha-hydroxyprogesterone caproate. New England Journal of Medicine, 348(24), 2379–2385.PubMedCrossRefGoogle Scholar
  3. 3.
    Berkowitz, G. S., & Papiernik, E. (1993). Epidemiology of preterm birth. Epidemiologic Reviews, 15(2), 414–443.PubMedGoogle Scholar
  4. 4.
    Savitz, D. A., & Murnane, P. (2010). Behavioral influences on preterm birth: A review. Epidemiology, 21(3), 291–299.PubMedCrossRefGoogle Scholar
  5. 5.
    Savitz, D. A., Dole, N., Williams, J., et al. (1999). Determinants of participation in an epidemiological study of preterm delivery. Paediatric and Perinatal Epidemiology, 13(1), 114–125.PubMedCrossRefGoogle Scholar
  6. 6.
    Savitz, D. A., Dole, N., Terry, J. W., Jr., et al. (2001). Smoking and pregnancy outcome among African-American and white women in Central North Carolina. Epidemiology, 12(6), 636–642.PubMedCrossRefGoogle Scholar
  7. 7.
    Mumford, S. L., Siega-Riz, A. M., Herring, A., et al. (2008). Dietary restraint and gestational weight gain. Journal of the American Dietetic Association, 108(10), 1646–1653.PubMedCrossRefGoogle Scholar
  8. 8.
    Kramer, M. S., McLean, F. H., Eason, E. L., et al. (1992). Maternal nutrition and spontaneous preterm birth. American Journal of Epidemiology, 136(5), 574–583.PubMedGoogle Scholar
  9. 9.
    IOM (Institute of Medicine) and NRC (National Research Council). (2009). Weight gain during pregnancy: Reexamining the guidelines. Washington, DC: The National Academies Press.Google Scholar
  10. 10.
    Block, G., Hartman, A. M., Dresser, C. M., et al. (1986). A data-based approach to diet questionnaire design and testing. American Journal of Epidemiology, 124(3), 453–469.PubMedGoogle Scholar
  11. 11.
    Siega-Riz, A. M., Promislow, J. H., Savitz, D. A., et al. (2003). Vitamin C intake and the risk of preterm delivery. American Journal of Obstetrics and Gynecology, 189(2), 519–525.PubMedCrossRefGoogle Scholar
  12. 12.
    Siega-Riz, A. M., Savitz, D. A., Zeisel, S. H., et al. (2004). Second trimester folate status and preterm birth. American Journal of Obstetrics and Gynecology, 191(6), 1851–1857.PubMedCrossRefGoogle Scholar
  13. 13.
    Radloff, L. S. (1983). The CES-D scale: A self report depression scale for research in the general population. American Journal of Psychiatry, 140, 41–46.Google Scholar
  14. 14.
    Orr, S. T., & Miller, C. A. (1995). Maternal depressive symptoms and the risk of poor pregnancy outcome. Review of the literature and preliminary findings. Epidemiologic Reviews, 17(1), 165–171.PubMedGoogle Scholar
  15. 15.
    Hoffman, S., & Hatch, M. C. (2000). Depressive symptomatology during pregnancy: Evidence for an association with decreased fetal growth in pregnancies of lower social class women. Health Psychology, 19(6), 535–543.PubMedCrossRefGoogle Scholar
  16. 16.
    Sherbourne, C. D., & Stewart, A. L. (1991). The MOS social support survey. Social Science and Medicine, 32(6), 705–714.PubMedCrossRefGoogle Scholar
  17. 17.
    Schafer, J. L. (1997). Analysis of incomplete multivariate data. New York: Chapman and Hall.CrossRefGoogle Scholar
  18. 18.
    Rubin, D. B. (1987). Multiple imputation for nonresponse in surveys. New York: Wiley.CrossRefGoogle Scholar
  19. 19.
    SAS Institute Inc. SAS/STAT software: The MIANALYZE procedure. In: SAS OnlineDoc® 9.2. Cary, NC: SAS Institute Inc.Google Scholar
  20. 20.
    Howards, P. P., Schisterman, E. F., & Heagerty, P. J. (2007). Potential confounding by exposure history and prior outcomes: An example from perinatal epidemiology. Epidemiology, 18(5), 544–551.PubMedCrossRefGoogle Scholar
  21. 21.
    Harville, E. W., Savitz, D. A., Dole, N., et al. (2007). Psychological and biological markers of stress and bacterial vaginosis in pregnant women. BJOG, 114(2), 216–223.PubMedCrossRefGoogle Scholar
  22. 22.
    Creasy, R. K., Gummer, B. A., & Liggins, G. C. (1980). System for predicting spontaneous preterm birth. Obstetrics and Gynecology, 55(6), 692–695.PubMedGoogle Scholar
  23. 23.
    Meis, P. J., Michielutte, R., Peters, T. J., et al. (1995). Factors associated with preterm birth in cardiff, wales. I. Univariable and multivariable analysis. American Journal of Obstetrics and Gynecology, 173(2), 590–596.PubMedCrossRefGoogle Scholar
  24. 24.
    Goldenberg, R. L., Iams, J. D., Mercer, B. M., et al. (1998). The preterm prediction study: The value of new vs standard risk factors in predicting early and all spontaneous preterm births. NICHD MFMU network. American Journal of Public Health, 88(2), 233–238.PubMedCrossRefGoogle Scholar
  25. 25.
    Harlow, B. L., Frigoletto, F. D., Cramer, D. W., et al. (1996). Determinants of preterm delivery in low-risk pregnancies. The RADIUS study group. Journal of Clinical Epidemiology, 49(4), 441–448.PubMedCrossRefGoogle Scholar
  26. 26.
    Meis, P. J., Michielutte, R., Peters, T. J., et al. (1995). Factors associated with preterm birth in cardiff, wales. II. Indicated and spontaneous preterm birth. American Journal of Obstetrics and Gynecology, 173(2), 597–602.PubMedCrossRefGoogle Scholar
  27. 27.
    Siega-Riz, A. M., Adair, L. S., & Hobel, C. J. (1996). Maternal underweight status and inadequate rate of weight gain during the third trimester of pregnancy increases the risk of preterm delivery. Journal of Nutrition, 126(1), 146–153.PubMedGoogle Scholar
  28. 28.
    Nohr, E. A., Bech, B. H., Vaeth, M., et al. (2007). Obesity, gestational weight gain and preterm birth: A study within the danish national birth cohort. Paediatric and Perinatal Epidemiology, 21(1), 5–14.PubMedCrossRefGoogle Scholar
  29. 29.
    Mercer, B. M., Goldenberg, R. L., Das, A., et al. (1996). The preterm prediction study: A clinical risk assessment system. American Journal of Obstetrics and Gynecology, 174(6), 1885–1893. discussion 1893–1895.PubMedCrossRefGoogle Scholar
  30. 30.
    Meis, P. J., Goldenberg, R. L., Mercer, B. M., et al. (1998). The preterm prediction study: Risk factors for indicated preterm births. Maternal-fetal medicine units network of the national institute of child health and human development. American Journal of Obstetrics and Gynecology, 178(3), 562–567.PubMedCrossRefGoogle Scholar
  31. 31.
    Greenland, S., & Rothman, K. J. (2008). Ch. 13: Fundamentals of epidemiologic data analysis. In T. L. Lash (Ed.), Modern epidemiology (p. 219). Philadelphia: Wolters Kluwer Health/Lippincott Williams & Wilkinsp.Google Scholar
  32. 32.
    Consortium on Safe Labor, Hibbard, J. U., Wilkins, I., et al. (2010). Respiratory morbidity in late preterm births. JAMA, 304(4), 419–425.PubMedCrossRefGoogle Scholar
  33. 33.
    McIntire, D. D., & Leveno, K. J. (2008). Neonatal mortality and morbidity rates in late preterm births compared with births at term. Obstetrics and Gynecology, 111(1), 35–41.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • David A. Savitz
    • 1
    • 2
  • Quaker Harmon
    • 3
  • Anna Maria Siega-Riz
    • 3
    • 4
    • 5
  • Amy H. Herring
    • 5
    • 6
  • Nancy Dole
    • 5
  • John M. ThorpJr.
    • 5
    • 7
  1. 1.Department of EpidemiologyBrown UniversityProvidenceUSA
  2. 2.Departments of Epidemiology and Obstetrics and GynecologyBrown UniversityProvidenceUSA
  3. 3.Department of EpidemiologyUniversity of North Carolina Gillings School of Global Public HealthChapel HillUSA
  4. 4.Department of NutritionUniversity of North Carolina Gillings School of Global Public HealthChapel HillUSA
  5. 5.Carolina Population CenterUniversity of North CarolinaChapel HillUSA
  6. 6.Department of BiostatisticsUniversity of North Carolina Gillings School of Global Public HealthChapel HillUSA
  7. 7.Department of Obstetrics and GynecologyUniversity of North Carolina School of MedicineChapel HillUSA

Personalised recommendations