Objective To define the association between large-scale obstetric unit closures and relative changes in maternal and neonatal outcomes. Data Sources/Study Setting Birth and death certificates were linked to maternal and neonatal hospital discharge records for all births between 1/1/1995 and 6/30/2005 in Philadelphia, which experienced the closure of 9 of 19 obstetric units between 1997 and 2005, and five surrounding counties and eight urban counties that did not experience a similar reduction in obstetric units. Design A before-and-after study design with an untreated control group compared changes in perinatal outcomes in Philadelphia to five surrounding control counties and eight urban control counties after controlling for casemix differences and secular trends (N=3,140,782). Results Relative to the pre-closure years, the difference in neonatal mortality (odds ratio (OR) 1.49, 95% CI 1.12–2.00) and all perinatal mortality (OR 1.53, 95% CI 1.14–2.04) increased for Philadelphia residents compared to both control groups between 1997 and 1999. After 2000, there was no statistically significant change in any outcome in Philadelphia county compared to the pre-closure epoch. Conclusions Obstetric unit closures were initially associated with adverse changes in perinatal outcomes, but these outcomes ameliorated over time.

Flight en route time is expected to be longer when traffic demand exceeds airspace capacities. When demand is high, flights can often be subjected to rerouting, Miles in Trail (MIT) and vectoring, etc. During summer months, convective weather, which could severely affect the capacities of major air routes and airports, accounts for the most significant share of weather-relate delays. Flight en route time prediction could help airline dispatchers and traffic management coordinators make strategic plans, and alert travelers of potential delays. This paper aims to use machine learning techniques to model traffic volume and especially the effect of convective weather within 100 miles of great circle line between city pairs on the en route time. Several best off-the-shelf algorithms including LightGBM, XGBoost and Random Forest are tested and compared.