Airline Schedule Recovery after Airport Closures: Empirical Evidence Since September 11th

Since the September 11, 2001 terrorist attacks, repeated airport closures due to potential security breaches have imposed substantial costs on travelers, airlines, and government agencies in terms of flight delays and cancellations. Using data from the year following September 11th, this study examines how airlines recover flight schedules upon reopening of airports that have been closed for security reasons. As such, this is the first study to examine service quality during irregular operations. Our results indicate that while outcomes of flights scheduled during airport closures are difficult to explain, a variety of factors, including potential revenue per flight and logistical variables such as flight distance, seating capacity and shutdown severity, significantly predict outcomes of flights scheduled after airports reopen. Given the likelihood of continued security-related airport closings, understanding the factors that determine schedule recovery is potentially important.

Does School Choice Increase School Quality?

Federal No Child Left Behind' legislation, which enables students of low-performing schools to exercise public school choice, exemplies a widespread belief that competing for students will spur public schools to higher achievement. We investigate how the introduction of school choice in North Carolina, via a dramatic increase in the number of charter schools across the state, affects the performance of traditional public schools on statewide tests. We find test score gains from competition that are robust to a variety of specifications. The introduction of charter school competition causes an approximate one percent increase in the score, which constitutes about one quarter of the average yearly growth.

Developing Physician Migration Estimates for Workforce Models

OBJECTIVE: To understand factors affecting specialty heterogeneity in physician migration. DATA SOURCES/STUDY SETTING: Physicians in the 2009 American Medical Association Masterfile data were matched to those in the 2013 file. Office locations were geocoded in both years to one of 293 areas of the country. Estimated utilization, calculated for each specialty, was used as the primary predictor of migration. Physician characteristics (e.g., specialty, age, sex) were obtained from the 2009 file. Area characteristics and other factors influencing physician migration (e.g., rurality, presence of teaching hospital) were obtained from various sources. STUDY DESIGN: We modeled physician location decisions as a two-part process: First, the physician decides whether to move. Second, conditional on moving, a conditional logit model estimates the probability a physician moved to a particular area. Separate models were estimated by specialty and whether the physician was a resident. PRINCIPAL FINDINGS: Results differed between specialties and according to whether the physician was a resident in 2009, indicating heterogeneity in responsiveness to policies. Physician migration was higher between geographically proximate states with higher utilization for that specialty. CONCLUSIONS: Models can be used to estimate specialty-specific migration patterns for more accurate workforce modeling, including simulations to model the effect of policy changes

An updated model of rural hospital financial distress

PURPOSE: To create a model that predicts future financial distress among rural hospitals. METHODS: The sample included 14,116 yearly observations of 2311 rural hospitals recorded between 2013 and 2019. We randomly separated all sampled hospitals into a training set and test set at the start of our analysis. We used hospital financial performance, government reimbursement, organizational traits, and market characteristics to predict a given hospital's risk of experiencing one of three financial distress outcomes-negative cash flow margin, negative equity, or closure. FINDINGS: The model's area under the receiver operating characteristic curve (AUC) equaled 0.87 within the test set, indicating good predictive ability. We classified 30.55% of the observations in our sample as lowest risk of experiencing financial distress over the next 2 years. In comparison, we classified 32.52% of observations as mid-lowest risk of distress, 26.40% of observations as mid-highest risk, and 10.52% of observations as highest risk. Among test set observations classified as lowest-risk, 5.78% experienced negative cash flow margin within 2 years, 1.50% experienced negative equity within 2 years, and zero observations experienced closure within 2 years. Within the highest-risk group, 61.57% of observations experienced negative cash flow margin, 43.02% experienced negative equity, and 3.33% experienced closure. CONCLUSIONS: Given the ongoing challenges and consequences of rural hospital unprofitability, there is a clear need for accurate assessments of financial distress risk. The financial distress model can be used by researchers, policymakers, and rural health advocates as a screening tool to identify at-risk rural hospitals for closer monitoring

A Methodology for Using Workforce Data to Decide Which Specialties and States to Target for Graduate Medical Education Expansion

OBJECTIVE: To outline a methodology for allocating graduate medical education (GME) training positions based on data from a workforce projection model. DATA SOURCES: Demand for visits is derived from the Medical Expenditure Panel Survey and Census data. Physician supply, retirements, and geographic mobility are estimated using concatenated AMA Masterfiles and ABMS certification data. The number and specialization behaviors of residents are derived from the AAMC's GMETrack survey. DESIGN: We show how the methodology could be used to allocate 3,000 new GME slots over 5 years-15,000 total positions-by state and specialty to address workforce shortages in 2026. EXTRACTION METHODS: We use the model to identify shortages for 19 types of health care services provided by 35 specialties in 50 states. PRINCIPAL FINDINGS: The new GME slots are allocated to nearly all specialties, but nine states and the District of Columbia do not receive any new positions. CONCLUSIONS: This analysis illustrates an objective, evidence-based methodology for allocating GME positions that could be used as the starting point for discussions about GME expansion or redistribution

Vegetable Oil as an Emergency Fuel

The unstable world political situation combined with the fact that a large amount of the world\u27s oil reserves is located in the Middle East means we must continue to search for an alternate fuel source for our agricultural machinery. It is well-known that there is a limited amount of fossil fuels and even with no world political problems the demand will exceed supply in the not-too-distant future. The United States currently imports fossil fuels because we use more than we produce. Vegetable oils are a renewable resource. They offer a means of keeping our agricultural equipment operating in an oil emergency

The relationship between violence in Northern Mexico and potentially avoidable hospitalizations in the USA–Mexico border region

BACKGROUND: Substantial proportions of US residents in the USA-Mexico border region cross into Mexico for health care; increases in violence in northern Mexico may have affected this access. We quantified associations between violence in Mexico and decreases in access to care for border county residents. We also examined associations between border county residence and access. METHODS: We used hospital inpatient data for Arizona, California and Texas (2005-10) to estimate associations between homicide rates and the probability of hospitalization for ambulatory care sensitive (ACS) conditions. Hospitalizations for ACS conditions were compared with homicide rates in Mexican municipalities matched by patient residence. RESULTS: A 1 SD increase in the homicide rate of the nearest Mexican municipality was associated with a 2.2 percentage point increase in the probability of being hospitalized for an ACS condition for border county patients. Residence in a border county was associated with a 1.3 percentage point decrease in the probability of being hospitalized for an ACS condition. CONCLUSIONS: Increased homicide rates in Mexico were associated with increased hospitalizations for ACS conditions in the USA, although residence in a border county was associated with decreased probability of being hospitalized for an ACS condition. Expanding access in the border region may mitigate these effects by providing alternative sources of care

Signs of the 2009 Influenza Pandemic in the New York-Presbyterian Hospital Electronic Health Records

Background In June of 2009, the World Health Organization declared the first influenza pandemic of the 21st century, and by July, New York City's New York-Presbyterian Hospital (NYPH) experienced a heavy burden of cases, attributable to a novel strain of the virus (H1N1pdm). Methods and Results We present the signs in the NYPH electronic health records (EHR) that distinguished the 2009 pandemic from previous seasonal influenza outbreaks via various statistical analyses. These signs include (1) an increase in the number of patients diagnosed with influenza, (2) a preponderance of influenza diagnoses outside of the normal flu season, and (3) marked vaccine failure. The NYPH EHR also reveals distinct age distributions of patients affected by seasonal influenza and the pandemic strain, and via available longitudinal data, suggests that the two may be associated with distinct sets of comorbid conditions as well. In particular, we find significantly more pandemic flu patients with diagnoses associated with asthma and underlying lung disease. We further observe that the NYPH EHR is capable of tracking diseases at a resolution as high as particular zip codes in New York City. Conclusion The NYPH EHR permits early detection of pandemic influenza and hypothesis generation via identification of those significantly associated illnesses. As data standards develop and databases expand, EHRs will contribute more and more to disease detection and the discovery of novel disease associations