UC Berkeley

Today, nearly 1 in 3 women giving birth will undergo cesarean delivery. This is far from the 1970s when only about 1 in 20 women have cesareans. Higher frequencies of cesarean deliveries, however, do not necessarily correspond with improved perinatal outcomes. In fact, neonatal outcomes have not improved in the past decades. It is well documented that cesarean delivery is associated with increased risk of maternal morbidity and mortality. Further, cesarean delivery can have a negative impact on perinatal outcomes of subsequent pregnancies, with higher risk of stillbirth and uterine rupture. Increasing number of repeat cesarean deliveries also correlates with increasing maternal morbidity.

Data suggest that current cesarean delivery in the U.S. could be safely lowered without increasing infant mortality. Although numerous strategies have been suggested and tried to reduce cesarean delivery, it continues to rise at a rate disproportional to the changing maternal characteristics that may be partly responsible for the increase. The goal of this research is to identify potentially modifiable physician practice factors and patient characteristics that are associated with the increased risk of cesarean delivery. Identification of these risk factors is needed to develop strategies to curtail the current upward trend in use of cesarean delivery.

As a first step to address this long term goal, this dissertation several analyses to investigated obstetric characteristics and practice patterns associated with cesarean delivery in United States based on existing datasets. Additionally, I conducted a survey study and collected clinician-level data to investigate obstetric providers' potential influence on the decision to recommend cesarean delivery.

The Background chapter presents a brief history of cesarean delivery and reviews common indications of cesarean delivery. Cesarean delivery is often considered to impose some risks to the parturient, with the tradeoff of potentially conveying benefit to the fetus. Thus, this chapter also reviews maternal and neonatal morbidity associated with cesarean delivery, as well as potential health economic impact.

First, to explore if pregnancy intervention, particularly, induction of labor, is associated with increased risk of cesarean delivery in the U.S., I used marginal structural models (MSM) to examine this research aim. In this analysis, the relation between induction of labor at a specific gestational age (e.g., 39 weeks) was compared to expectant management (delivery at a later gestational age, i.e., 40, 41 or 42 weeks, by either entering spontaneous labor or subsequently induction of labor for various medical/obstetric indications) and associated maternal/neonatal outcomes. This analytic approach is in contrast to traditional multivariable regression approaches that are pervasive in the obstetric literature. As multivariable regression analyses estimate the effect of association conditional on confounding covariates, it does not address specifically the risk of outcome for each subject under both exposed and unexposed conditions. Based on the concept of counterfactuals, MSM compares outcome frequency under different exposure distributions (exposed and non-exposed) in the same sample population and estimates the effect of exposure across the entire population. By applying causal inference framework through the use of MSM, this analysis estimated the population-level, marginal effect of induction on cesarean delivery and other perinatal outcomes that correspond to hypothetical interventions. Based on the MSM analysis, I show that induction of labor was associated with a decreased risk of cesarean delivery compared to expectant management.

Next, I examined the association between advanced maternal age and cesarean delivery in the U.S. Delayed childbearing has become increasingly common in the U.S. Increase in maternal age has been associated with higher risk of adverse pregnancy outcomes. Thus, I used the population intervention models to estimate the population attributable fraction of advanced maternal age (age >35 years at estimated date of delivery) on cesarean delivery. More specifically, population intervention models build upon the causal inference literature to model the difference of an effect between the distribution of a population in an observed environment (the actual study population) and a counterfactual treatment-specific population distribution (the population outcome that would have been observed under "intervention" such that the exposure would be at some target, optimal level). In this analysis, I used the population intervention models to estimate the potential changes in the distribution of cesarean delivery in low-risk population of nulliparous women who gave live births in the U.S. While maternal age cannot be easily "intervened" on, I chose to use population intervention models to gain insights into the potential changes in the distribution of cesarean delivery, focusing on the population prevalence of advanced maternal age as a risk factor. Through this analysis, I observed that advanced maternal age was a risk factor of cesarean delivery.

While patient characteristics may influence the decision to undergo cesarean delivery, clinicians may also play an important role. However, few studies have been published regarding this topic. Thus, I conducted a cross-sectional survey study to explore provider characteristics that might be associated with increased likelihood of recommending cesarean delivery. I used multivariable logistic regression analysis fit by maximum likelihood to assess provider factors associated with an increased likelihood of recommending cesarean delivery. Further, I also used the Deletion/Substitution/Addition (DSA) algorithm to independently assess clinician factors associated with an increased likelihood to recommend cesarean. As multivariable logistic regression analysis was based on conditional probability to estimate the effect of the exposure-outcome association, this was in contrast to the DSA algorithm that used polynomial basis functions to identify predictors for the exposure-outcomes of interest based on cross-validation and the L2 loss function.

As the current rise in cesarean delivery has profound impact on maternal and child health, there are also social and economic repercussions associated with rise in cesareans that are not yet well understood. This dissertation examined several increasingly common factors, including induction of labor, and advanced maternal age that might be associated with increased risk or increased likelihood of cesarean delivery. This work was achieved through the application of causal inference framework and analytical methods such as marginal structural models and population intervention models and the usage of nationwide birth data. Additionally, provider characteristics and experience information were collected via a cross-sectional survey to explore clinician-level information to identify factors driving the increase in cesarean delivery. These analyses serve as a first step towards the understanding of why cesarean delivery continues to increase in the U.S. and worldwide, but much work remains to be done.