Group 4 had the most social risks and most unemployed. The social risk groups demonstrated meaningful differences in health, acute care utilization, and health care costs with group 1 having the best health outcomes and group 4 the worst (P<0.05).
 
  LCA is a practical method of aggregating correlated SDH data into a finite number of distinct social risk groups. Understanding the constellation of social challenges that patients face is critical when attempting to address their social needs and improve health outcomes.
 LCA is a practical method of aggregating correlated SDH data into a finite number of distinct social risk groups. Understanding the constellation of social challenges that patients face is critical when attempting to address their social needs and improve health outcomes.
  Studying team-based primary care using 100% national outpatient Medicare data is not feasible, due to limitations in the availability of this dataset to researchers.
 
  We assessed whether analyses using different sets of Medicare data can produce results similar to those from analyses using 100% data from an entire state, in identifying primary care teams through social network analysis. First, we used data from 100% Medicare beneficiaries, restricted to those within a primary care services area (PCSA), to identify primary care teams. Second, we used data from a 20% sample of Medicare beneficiaries and defined shared care by 2 providers using 2 different cutoffs for the minimum required number of shared patients, to identify primary care teams.
 
  The team practices identified with social network analysis using the 20% sample and a cutoff of 6 patients shared between 2 primary care providers had good agreement with team practices identified using statewide data (F measure 90.9%). Use of 100% data within a small area geographic boundary, such as PCSAs, had an F measure of 83.4%. The percent of practices identified from these datasets that coincided with practices identified from statewide data were 86% versus 100%, respectively.
 
  Depending on specific study purposes, researchers could use either 100% data from Medicare beneficiaries in randomly selected PCSAs, or data from a 20% national sample of Medicare beneficiaries to study team-based primary care in the United States.
 Depending on specific study purposes, researchers could use either 100% data from Medicare beneficiaries in randomly selected PCSAs, or data from a 20% national sample of Medicare beneficiaries to study team-based primary care in the United States.
  The Medicare comprehensive care for joint replacement (CJR) model, a mandatory bundled payment program started in April 2016 for hospitals in randomly selected metropolitan statistical areas (MSAs), may help reduce postacute care (PAC) use and episode costs, but its impact on disparities between Medicaid and non-Medicaid beneficiaries is unknown.
 
  To determine effects of the CJR program on differences (or disparities) in PAC use and outcomes by Medicare-Medicaid dual eligibility status.
 
  Observational cohort study of 2013-2017, based on difference-in-differences (DID) analyses on Medicare data for 1,239,452 Medicare-only patients, 57,452 dual eligibles with full Medicaid benefits, and 50,189 dual eligibles with partial Medicaid benefits who underwent hip or knee surgery in hospitals of 75 CJR MSAs and 121 control MSAs.
 
  Risk-adjusted differences in rates of institutional PAC [skilled nursing facility (SNF), inpatient rehabilitation, or long-term hospital care] use and readmissions; and for the subgroup ots. Among patients discharged to SNF, the CJR program showed no effect on successful community discharge, transition to long-term care, or their persistent disparities.
 
  The CJR program did not help reduce persistent disparities in readmissions or SNF-specific outcomes related to Medicare-Medicaid dual eligibility, likely due to its lack of financial incentives for reduced disparities and improved SNF outcomes.
 The CJR program did not help reduce persistent disparities in readmissions or SNF-specific outcomes related to Medicare-Medicaid dual eligibility, likely due to its lack of financial incentives for reduced disparities and improved SNF outcomes.
  Although recent research suggests that primary care provided by nurse practitioners costs less than primary care provided by physicians, little is known about underlying drivers of these cost differences.
 
  Identify the drivers of cost differences between Medicare beneficiaries attributed to primary care nurse practitioners (PCNPs) and primary care physicians (PCMDs).
 
  Cross-sectional cost decomposition analysis using 2009-2010 Medicare administrative claims for beneficiaries attributed to PCNPs and PCMDs with risk stratification to control for beneficiary severity. Cost differences between PCNPs and PCMDs were decomposed into payment, service volume, and service mix within low-risk, moderate-risk and high-risk strata.
 
  Overall, the average PCMD cost of care is 34% higher than PCNP care in the low-risk stratum, and 28% and 21% higher in the medium-risk and high-risk stratum.  https://www.selleckchem.com/products/LAQ824(NVP-LAQ824).html  In the low-risk stratum, the difference is comprised of 24% service volume, 6% payment, and 4% service mix. In the high-risk stratum, the difference is composed of 7% service volume, 9% payment, and 4% service mix. The cost difference between PCNP and PCMD attributed beneficiaries is persistent and significant, but narrows as risk increases. Across the strata, PCNPs use fewer and less expensive services than PCMDs. In the low-risk stratum, PCNPs use markedly fewer services than PCMDs.
 
  There are differences in the costs of primary care of Medicare beneficiaries provided by nurse practitioners and MDs. Especially in low-risk populations, the lower cost of PCNP provided care is primarily driven by lower service volume.
 There are differences in the costs of primary care of Medicare beneficiaries provided by nurse practitioners and MDs. Especially in low-risk populations, the lower cost of PCNP provided care is primarily driven by lower service volume.
  Previous studies have suggested that highly fragmented ambulatory care increases the risk of subsequent hospitalization, but those studies used claims only and were not able to adjust for many clinical potential confounders.
 
  The objective of this study was to determine the association between fragmented ambulatory care and subsequent hospitalization, adjusting for demographics, medical conditions, medications, health behaviors, psychosocial variables, and physiological variables.
 
  Longitudinal analysis of data (2003-2016) from the nationwide REasons for Geographic And Racial Differences in Stroke (REGARDS) study, linked to Medicare fee-for-service claims.
 
  A total of 12,693 Medicare beneficiaries 65 years and older from the REGARDS study who had at least 4 ambulatory visits in the first year of observation and did not have a hospitalization in the prior year.
 
  We defined high fragmentation as a reversed Bice-Boxerman score above the 75th percentile. We used Cox proportional hazards models to determine the association between fragmentation as a time-varying exposure and incident hospitalization in the 3 months following each exposure period.