Due to rapid growth in computing power, the collection of high dimensional and complex datasets is increasingly feasible. To reap their full benefit, novel analytic strategies may be required. Application of such methods remains limited in certain epidemiologic research areas. The overarching aim of this dissertation was to apply novel analytic strategies with close ties to causal inference and statistical learning theory to gain new insights into well-studied areas of perinatal epidemiology. In Study 1, we explored whether the association between short interpregnancy intervals (i.e., the end of one pregnancy to the start of the next) and increased risk of preterm birth may be due to residual confounding in three populations (n=693 American Indian and n=728 white women from the Northern Plains, U.S., and n=783 mixed ancestry women from the Western Cape, South Africa). Using data from the prospective Safe Passage cohort (2007-2015), we applied propensity score methods to control for a variety of sociodemographic and reproductive factors. A third-to-half of women with <6 months intervals had propensity scores that largely did not overlap with those of women with 18-23 months intervals. Since the propensity score models included factors related to both interpregnancy interval and preterm birth, these findings suggest the possibility of strong confounding in all three populations. The pooled associational estimate with preterm birth was attenuated in the propensity score trimmed and weighted data (risk ratio 1.4, 95% CI 0.75-2.6) compared with the crude results (risk ratio 1.7, 95% CI 1.1-2.7). However, the sample size and precision were reduced after propensity score trimming, and several covariates remained imbalanced. The data demonstrated the complexity of the processes leading to interpregnancy interval length. These issues may have been difficult to identify without comprehensive confounder data and with other methods, such as traditional regression adjustment. In Study 2, we examined the relative importance of timing (first trimester versus second/third trimesters) and degree of gestational weight gain in relation to infant size at birth (small-and-large-for-gestational age) among women with obesity using data from a medical records-based case-cohort study (Pittsburgh, PA, 1998-2010). We operationalized serial antenatal weight measurements as above, below, or within the current recommended ranges for U.S. pregnancies, i.e., 0.2-2.0 kg total gain in the first trimester and 0.17-0.27 kg per week in the second and third trimesters (based on group based trajectory modeling). Data were analyzed by obesity class (n=1290 in the class I subcohort, n=1247 class II, n=1198 class III). Our findings supported the current clinical guidelines, except for women with class III obesity. Among women with class III obesity, lower than recommended gain in the second and third trimesters was associated with decreased risk of having a large-for-gestational age infant (adjusted risk ratio 0.76, 95% CI 0.51-1.1), while not increasing small-for-gestational age (SGA) risk (adjusted risk ratio 1.0, 95% CI 0.63-1.7). Our results were in agreement with findings from several other studies of women with obesity using other methodologies to operationalize gestational weight gain. In Study 3, we used hierarchical clustering to explore latent groups of placental pathology features. We also investigated whether the placental clusters, in addition to birthweight percentiles, were beneficial to explain the variability of select adverse pregnancy outcomes. Data were from the Safe Passage Study (same as Study 1, n=2005). We identified one cluster with low prevalence of abnormalities (60.9%) and three clusters that mapped well to the expert consensus-based Amsterdam criteria: severe maternal vascular malperfusion (5.8%), fetal vascular malperfusion (11.1%), and inflammation (22.1%). The clusters were weakly-to-moderately associated with certain antenatal risk factors, pregnancy complications, and neonatal outcomes. Birthweight percentiles plus the placental clusters was better able to explain the variance of select adverse outcomes, compared with using small-for-gestational age only. This study serves as proof-of-concept that machine learning methods, and placental data, may aid in the identification and etiologic study of certain adverse pregnancy outcomes. In sum, all three studies support that the application of novel analytic methods to high-dimensional datasets may expand our understanding of certain causal questions, even ones that have been broached before, although, as seen in Study 2, such research may not always yield novel insights.
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/43007 |
| Date | 10 September 2021 |
| Creators | Petersen, Julie Margit |
| Contributors | Werler, Martha M. |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
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