Time to Surgical Referral in Non-Hispanic White and Black Patients with Primary Hyperparathyroidism

Primary hyperparathyroidism is the excessive secretion of parathyroid hormone by one or more parathyroid glands, resulting in hypercalcemia. Primary hyperparathyroidism can be safely cured by parathyroidectomy, but there are well-established racial differences in disease burden at the time of parathyroidectomy with Black patients exhibiting greater serum calcium, parathyroid hormone levels and parathyroid gland size compared to non-Hispanic White patients. However, few studies investigate what factors contribute to these differences. We hypothesize that Black patients experience greater time between presentation with hypercalcemia and surgical referral date than non-Hispanic White patients. To test this hypothesis, we will carry out a retrospective review on all Black and non-Hispanic White patients in the Yale-New Haven Health System with hypercalcemia from January 2014 – December 2015. This study may offer some insight into the racially disparate disease burden in Black patients and may suggest potential interventions to minimize racial disparities in the management of primary hyperparathyroidism

Hospital Networks of Shared Patients and Engagement in Health Information Exchange

Although the healthcare delivery system is composed of an array of organizations that are linked through important, enduring, and complex ties, the healthcare delivery system is rarely explicitly conceptualized or measured as a network. In consequence, we know little about how the enduring but often informal relationships between organizations shape their behavior in terms of the decisions that they make, the quality of care that they provide, and the efficiency of that care. Using techniques developed in the multidisciplinary field of network analysis, I sought to better understand two important facets of health care that are intrinsically linked to the network perspective: the fragmentation of patients’ treatments between multiple hospitals, and hospitals engagement in electronically sharing patient information. By analyzing networks of shared Medicare patients treated at multiple hospitals, I first identified dense networks of hospitals that are closely interlinked through many high volume shared patient connections and are therefore likely linked through complex collaborative and competitive relationships. I then characterized these networks to identify arrangements of patient sharing that allowed hospitals to better manage care fragmentation. I found that more concentrated networks, in which hospitals shared most of their patients with few important partners rather than a large number of other hospitals, and more centralized networks, in which the network is arranged in a hub-and-spoke model, were associated with more efficient, higher quality care. I next described three different approaches to health information exchange and the logic of participation in each approach with specific emphasis on the value of the enterprise approach for connecting a smaller number of providers and the community approach for facilitating broader connections between more partners. I then investigated whether the choice that hospitals made about how to electronically share patient information was shaped by their networks. I found that hospitals with and within more concentrated patient sharing networks were more likely to engage in enterprise exchange while hospitals with and within less concentrated networks engaged in community exchange more frequently. Together, these findings offer novel insights into the network features of hospitals and how they relate to important healthcare processes and outcomes. More concentrated, centralized networks appear to perform better and these features may be one reason for variation in the cost and quality of care across the nation. Similarly, policy changes designed to shape how healthcare organizations interact and who they interact with—like accountable care organizations, bundled payment initiatives and patient center medical homes—may be more successful if they reinforce beneficial network attributes. Further, as policy efforts designed to facilitate the sharing of information between healthcare providers continue, it will be crucial to allow flexible adoption of different approaches to health information exchange and to support hospitals that engage in an approach to information exchange that benefits communities.PHDHealth Services Organization & PolicyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137104/1/jeverson_1.pd

Comparing methods of grouping hospitals

ObjectiveTo compare the performance of widely used approaches for defining groups of hospitals and a new approach based on network analysis of shared patient volume.Study SettingNonâ federal acute care hospitals in the United States.Study DesignWe assessed the measurement properties of four methods of grouping hospitals: hospital referral regions (HRRs), metropolitan statistical areas (MSAs), coreâ based statistical areas (CBSAs), and community detection algorithms (CDAs).Data Extraction MethodsWe combined data from the 2014 American Hospital Association Annual Survey, the Census Bureau, the Dartmouth Atlas, and Medicare data on interhospital patient travel patterns. We then evaluated the distinctiveness of each grouping, reliability over time, and generalizability across populations.Principle FindingsHospital groups defined by CDAs were the most distinctive (modularity = 0.86 compared to 0.75 for HRRs and 0.83 for MSAs; 0.72 for CBSA), were reliable to alternative specifications, and had greater generalizability than HRRs, MSAs, or CBSAs. CDAs had lower reliability over time than MSAs or CBSAs (normalized mutual information between 2012 and 2014 CDAs = 0.93).ConclusionsCommunity detection algorithmâ defined hospital groups offer high validity, reliability to different specifications, and generalizability to many uses when compared to approaches in widespread use today. They may, therefore, offer a better choice for efforts seeking to analyze the behaviors and dynamics of groups of hospitals. Measures of modularity, shared information, inclusivity, and shared behavior can be used to evaluate different approaches to grouping providers.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151847/1/hesr13188-sup-0001-AuthorMatrix.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151847/2/hesr13188_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151847/3/hesr13188.pd

The Geography of Diabetes in London, Canada: The Need for Local Level Policy for Prevention and Management

Recent reports aimed at improving diabetes care in socially disadvantaged populations suggest that interventions must be tailored to meet the unique needs of the local community—specifically, the community’s geography. We have examined the spatial distribution of diabetes in the context of socioeconomic determinants of health in London (Ontario, Canada) to characterize neighbourhoods in an effort to target these neighbourhoods for local level community-based program planning and intervention. Multivariate spatial-statistical techniques and geographic information systems were used to examine diabetes rates and socioeconomic variables aggregated at the census tract level. Creation of a deprivation index facilitated investigation across multiple determinants of health. Findings from our research identified ‘at risk’ neighbourhoods in London with socioeconomic disadvantage and high diabetes. Future endeavours must continue to identify local level trends in order to support policy development, resource planning and care for improved health outcomes and improved equity in access to care across geographic regions

Effectiveness of Simeprevir Plus Sofosbuvir, With or Without Ribavirin, in Real-World Patients With HCV Genotype 1 Infection

The interferon-free regimen of simeprevir plus sofosbuvir was recommended by professional guidelines for certain patients with hepatitis C virus (HCV) genotype 1 infection based on the findings of a phase 2 trial. We aimed to evaluate the safety and efficacy of this regimen in clinical practice settings in North America

Electronic Connectivity Among US Hospitals Treating Shared Patients

BackgroundIncreasing electronic health information exchange (HIE) between provider organizations is a top policy priority that has been pursued by establishing varied types of networks.ObjectivesTo measure electronic connectivity enabled by these networks, including community, electronic health record vendor, and national HIE networks, across US hospitals weighted by the volume of shared patients and identify characteristics that predict connectivity.Research designCross-sectional analysis of 1721 hospitals comprising 16,344 hospital pairs and 6,492,232 shared patients from 2018 CareSet Labs HOP data and national hospital surveys.SubjectsPairs of US acute care hospitals that delivered care to 11 or more of the same fee-for-service Medicare beneficiaries in 2018.MeasuresWhether a patient was treated by a pair of hospitals connected through participation in the same HIE network ("connected hospitals") or not connected because the hospitals participated in different networks, only 1 participated, or both did not participate.ResultsSixty-four percent of shared patients were treated by connected hospitals. Of the remaining shared patients, 14% were treated by hospital pairs that participated in different HIE networks, 21% by pairs in which only 1 hospital participated in an HIE network, and 2% by pairs in which neither participated. Patients treated by pairs with at least 1 for-profit hospital, and by pairs located in competitive markets, were less likely to be treated by connected hospitals.ConclusionsWhile the majority of shared patients received care from connected hospitals, remaining gaps could be filled by connecting HIE networks to each other and by incentivizing certain types of hospitals that may not participate because of competitive concerns