The Association Between Short Interpregnancy Interval and Preterm Birth in Louisiana: A Comparison of Methods
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There is growing interest in the application of propensity scores (PS) in epidemiologic studies, especially within the field of reproductive epidemiology. This retrospective cohort study assesses the impact of a short interpregnancy interval (IPI) on preterm birth and compares the results of the conventional logistic regression analysis with analyses utilizing a PS. The study included 96,378 singleton infants from Louisiana birth certificate data (1995–2007). Five regression models designed for methods comparison are presented. Ten percent (10.17 %) of all births were preterm; 26.83 % of births were from a short IPI. The PS-adjusted model produced a more conservative estimate of the exposure variable compared to the conventional logistic regression method (β-coefficient: 0.21 vs. 0.43), as well as a smaller standard error (0.024 vs. 0.028), odds ratio and 95 % confidence intervals [1.15 (1.09, 1.20) vs. 1.23 (1.17, 1.30)]. The inclusion of more covariate and interaction terms in the PS did not change the estimates of the exposure variable. This analysis indicates that PS-adjusted regression may be appropriate for validation of conventional methods in a large dataset with a fairly common outcome. PS’s may be beneficial in producing more precise estimates, especially for models with many confounders and effect modifiers and where conventional adjustment with logistic regression is unsatisfactory. Short intervals between pregnancies are associated with preterm birth in this population, according to either technique. Birth spacing is an issue that women have some control over. Educational interventions, including birth control, should be applied during prenatal visits and following delivery.
KeywordsBirth interval Interpregnancy interval Logistic regression Pregnancy interval Preterm birth Propensity score
This work was supported by the Eunice Kennedy Shriver National Institute of Child Health And Human Development (T32HD057780). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
- 1.The Henry J. Kaiser Family Foundation. (2008). Statehealthfacts.org. Accessed 10 April 2011.
- 12.Sturmer, T., Joshi, M., Glynn, R. J., et al. (2006). A review of the application of propensity score methods yielded increasing use, advantages in specific settings, but not substantially different estimates compared with conventional multivariable methods. Journal of Clinical Epidemiology, 59, 437–447.PubMedCrossRefGoogle Scholar