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ObjectivesWe sought to identify changes in non-profit hospitals’ community benefit spending from 2014 to 2019. Secondly, we wanted to find novel predictors of spending in the most recent available data.MethodsFor our longitudinal analysis, we used tax filing data for 1072 hospitals from 2014-2019 and time-based ANOVA. We gathered information about hospital characteristics, the social determinants of health, and hospital partnerships with local communities for 1192 hospitals for the year 2019. We employed multivariate regression to identify significant factors.ResultsTotal community benefit spending rose from 8.1% in 2014 to 9.1% of operating expenses in 2019, driven by increased spending on patient care. There was no such increase in spending on activities targeted at improving community health. The presence and strength of partnerships between hospitals and their communities were associated with higher community benefit spending.ConclusionsWe found no evidence of dramatic shifts in community benefit spending from 2014 to 2019. Further, we identified partnerships for population health improvement as an effective, novel predictor of community benefit spending. Supporting partnerships between hospitals and communities may help facilitate strategic investments in community health improvement.

Natural and induced somatic mutations that accumulate in the genome during development record the phylogenetic relationships of cells; however, whether these lineage barcodes can capture the dynamics of complex progenitor fields remains unclear. Here, we introduce quantitative fate mapping, an approach to simultaneously map the fate and quantify the commitment time, commitment bias, and population size of multiple progenitor groups during development based on a time-scaled phylogeny of their descendants. To reconstruct time-scaled phylogenies from lineage barcodes, we introduce Phylotime, a scalable maximum likelihood clustering approach based on a generalizable barcoding mutagenesis model. We validate these approaches using realistically-simulated barcoding results as well as experimental results from a barcoding stem cell line. We further establish criteria for the minimum number of cells that must be analyzed for robust quantitative fate mapping. Overall, this work demonstrates how lineage barcodes, natural or synthetic, can be used to obtain quantitative fate maps, thus enabling analysis of progenitor dynamics long after embryonic development in any organism.

Natural and induced somatic mutations that accumulate in the genome during development record the phylogenetic relationships of cells; whether these lineage barcodes capture the complex dynamics of progenitor states remains unclear. We introduce quantitative fate mapping, an approach to reconstruct the hierarchy, commitment times, population sizes, and commitment biases of intermediate progenitor states during development based on a time-scaled phylogeny of their descendants. To reconstruct time-scaled phylogenies from lineage barcodes, we introduce Phylotime, a scalable maximum likelihood clustering approach based on a general barcoding mutagenesis model. We validate these approaches using realistic in silico and in vitro barcoding experiments. We further establish criteria for the number of cells that must be analyzed for robust quantitative fate mapping and a progenitor state coverage statistic to assess the robustness. This work demonstrates how lineage barcodes, natural or synthetic, enable analyzing progenitor fate and dynamics long after embryonic development in any organism.

Recent OPTN proposals to address geographic disparity in liver allocation have involved circular boundaries: the policy selected 12/17 allocated to 150-mile circles in addition to DSAs/regions, and the policy selected 12/18 allocated to 150-mile circles eliminating DSA/region boundaries. However, methods to reduce geographic disparity remain controversial, within the OPTN and the transplant community. To inform ongoing discussions, we studied center-level supply/demand ratios using SRTR data (07/2013-06/2017) for 27 334 transplanted deceased donor livers and 44 652 incident waitlist candidates. Supply was the number of donors from an allocation unit (DSA or circle), allocated proportionally (by waitlist size) to the centers drawing on these donors. We measured geographic disparity as variance in log-transformed supply/demand ratio, comparing allocation based on DSAs, fixed-distance circles (150- or 400-mile radius), and fixed-population (12- or 50-million) circles. The recently proposed 150-mile radius circles (variance = 0.11, P = .9) or 12-million-population circles (variance = 0.08, P = .1) did not reduce the geographic disparity compared to DSA-based allocation (variance = 0.11). However, geographic disparity decreased substantially to 0.02 in both larger fixed-distance (400-mile, P