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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 Consent-to-Contact (C2C) registry at the University of California, Irvine collects data from community participants to aid in the recruitment to clinical research studies. Self-selection into the C2C likely leads to bias due in part to enrollees having more years of education relative to the US general population. Salazar et al. (Alzheimer's Dementia Transl Res Clin Interv 6(1):e120023, 2020, https://doi.org/10.1002/trc2.12023) recently used the C2C to examine associations of race/ethnicity with participant willingness to be contacted about research studies. To obtain representative estimates from C2C we use weighted estimation of associations of interest where the weights are related to the probability of self-selection into the convenience sample. The selection probabilities are estimated using data from the National Health and Nutrition Examination Survey (NHANES). We create a combined dataset of C2C and NHANES subjects and evaluate the trade-offs of different approaches (logistic regression, covariate balancing propensity score, entropy balancing, and random forest) for estimating the probability of membership in C2C relative to NHANES. We further propose methods to estimate the variance of parameter estimates that account for uncertainty that arises from estimating propensity weights. Simulation studies explore the impact of propensity weight estimation on uncertainty. We demonstrate the approach by repeating the analysis by Salazar et al. (Alzheimer's Dementia Transl Res Clin Interv 6(1):e120023, 2020, https://doi.org/10.1002/trc2.12023) with the deduced propensity weights for the C2C subjects and contrast the results of the two analyses. This method can be implemented using our estweight package in R available on GitHub. [ABSTRACT FROM AUTHOR]