Statistical Analysis
Joinpoint regression was used to identify and describe temporal changes in the rates of PDM and GDM among pregnancy-related hospitalizations during the 15-year study period. This type of statistical regression analysis is valuable in identifying key periods that denote a statistically significant change in the rate of events over time. 24 The iterative model-building process began by fitting the annual rate data to a straight line with no joinpoints, which assumed a single trend best described the data. Then a joinpoint, reflecting a change in the trend, was added to the model and a Monte Carlo permutation test assessed the improvement in model fit. The process continued until a final model with an optimal (best-fitting) number of joinpoints was selected, with each joinpoint indicating a change in the trend, and an annual percent change (APC) estimated to characterize how the rate was changing within each distinct trend segment.
Descriptive statistics, including frequencies and percentages, were used to describe the distribution of pregnancy-related hospitalizations across patient- and hospital-level characteristics, stratified by exposure group (PDM and GDM) across racial/ethnic groups: NH-White, NH-Blacks and Hispanics. Since national estimates were desired, all statistical analyses were weighted using an HCUP-provided discharge-level weight that accounted for the sampling design and appropriately generated variance estimates. Furthermore, we calculated the stillbirth rates in women with PDM and GDM across various racial/ethnic groups.
Multivariable survey logistic regression was also used to produce adjusted odds ratios (OR) that quantified the magnitude of the association between the exposures, PDM and GDM, and the outcome stillbirth, across various racial/ethnic groups. Statistical analyses were performed with R version 3∙5∙ 1 (University of Auckland, Auckland, New Zealand) and R Studio Version 1∙1∙ 423 (Boston, MA). We assumed a 5% type I error rate for all hypothesis tests (two-sided). Due to the de-identified, publicly available nature of NIS data, the analyses performed for this study were considered exempt by the Baylor College of Medicine Institutional Review Board.