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Emergency general surgery (EGS) is a critical component of emergency care in the United States. Due to the time sensitiveness of EGS conditions, ensuring adequate spatial access to EGS services is paramount for reducing patient morbidity and mortality. Past studies have used travel time to measure spatial access to EGS services, which has its limitations. The major purpose of this paper is to evaluate the utility of a gravity-based spatial access model in measuring spatial access to EGS services in California. Our data sources include the American Hospital Association 2015 Annual Survey, the American Community Survey 2013–2017 five-year average dataset, and background geospatial datasets. We implemented both the gravity-based model and the shortest travel time method and compared them in measuring spatial access to EGS-capable hospitals in California at the census block group level. We analyzed each metric's ability to identify disparities in spatial access for the population as a whole, and subsequently to identify socio-demographic disparities. Overall, we found that both methods identified similar geographic and socio-demographic patterns of the spatial access. Native Americans and rural residents experienced the greatest disadvantage in spatial access to both general EGS services and advanced EGS services. However, the gravity-based model revealed more disparities in spatial access to EGS services than the travel time model, suggesting that using travel cost alone to measure spatial access to EGS services may underestimate the magnitude of disparities. These findings call for the use of gravity-based models that incorporate measures of population demand and hospital capacity when assessing spatial access to surgical services, and have implications for reallocating surgery resources to address disparities in spatial access. [ABSTRACT FROM AUTHOR]

The quality of propensity scores is traditionally measured by assessing how well they make the distributions of covariates in the treatment and control groups match, which we refer to as 'good balance'. Good balance guarantees less biased estimates of the treatment effect. However, the cost of achieving good balance is that the variance of the estimates increases due to a reduction in effective sample size, either through the introduction of propensity score weights or dropping cases when propensity score matching. In this paper, we investigate whether it is best to optimize the balance or to settle for a less than optimal balance and use double robust estimation to adjust for remaining differences. We compare treatment effect estimates from regression, propensity score weighting, and double robust estimation with varying levels of effort expended to achieve balance using data from a study about the differences in outcomes by HIV status in heterosexually active homeless men residing in Los Angeles. Because of how costly data collection efforts are for this population, it is important to find an alternative estimation method that does not reduce effective sample size as much as methods that aggressively aim to optimize balance. Results from a simulation study suggest that there are instances in which we can obtain more precise treatment effect estimates without increasing bias too much by using a combination of regression and propensity score weights that achieve a less than optimal balance. There is a bias-variance tradeoff at work in propensity score estimation; every step toward better balance usually means an increase in variance and at some point a marginal decrease in bias may not be worth the associated increase in variance. [ABSTRACT FROM AUTHOR]