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Background Contemporary pulmonary embolism (PE) research, in many cases, relies on data from electronic health records (EHRs) and administrative databases that use International Classification of Diseases (ICD) codes. Natural language processing (NLP) tools can be used for automated chart review and patient identification. However, there remains uncertainty with the validity of ICD-10 codes or NLP algorithms for patient identification. Methods The PE-EHR+ study has been designed to validate ICD-10 codes as Principal Discharge Diagnosis, or Secondary Discharge Diagnoses, as well as NLP tools set out in prior studies to identify patients with PE within EHRs. Manual chart review by two independent abstractors by predefined criteria will be the reference standard. Sensitivity, specificity, and positive and negative predictive values will be determined. We will assess the discriminatory function of code subgroups for intermediate- and high-risk PE. In addition, accuracy of NLP algorithms to identify PE from radiology reports will be assessed. Results A total of 1,734 patients from the Mass General Brigham health system have been identified. These include 578 with ICD-10 Principal Discharge Diagnosis codes for PE, 578 with codes in the secondary position, and 578 without PE codes during the index hospitalization. Patients within each group were selected randomly from the entire pool of patients at the Mass General Brigham health system. A smaller subset of patients will also be identified from the Yale-New Haven Health System. Data validation and analyses will be forthcoming. Conclusions The PE-EHR+ study will help validate efficient tools for identification of patients with PE in EHRs, improving the reliability of efficient observational studies or randomized trials of patients with PE using electronic databases.

The net effect of the pandemic mitigation strategies on childhood mortality is not known. During the first year of the COVID-19 pandemic, mitigation policies and behaviors were widespread, and although vaccinations and effective treatments were not yet widely available, the risk of death from SARS-CoV-2 infection was low. In that first year, there was a 7% decrease in medical (“natural causes”) mortality among children ages 0-9 during the first pandemic year (5% among infants 1 year was absent. However, smaller increases in external (“non-natural causes”) mortality were also observed during the study period, which decreased the overall number of pediatric deaths averted during both years and the pandemic period. In total, 1,468 fewer all-cause pediatric deaths than expected occurred in the United States during the first 24 months of the COVID-19 pandemic.

IntroductionThe US continued to record all-cause excess mortality after the rollout of vaccines. We sought to quantify excess mortality by state and compare these rates to primary series vaccination completion levels.MethodsObservational cohort, US and state-level data. Expected monthly deaths were modeled using pre-pandemic US and state-level data (2015-2020). Mortality data was accessed from CDC public reporting.ResultsWe find that in a two-year period since the rollout of vaccines, the US recorded >874,000 excess deaths. Vaccination rates and excess mortality were most strongly correlated in first two periods before the Omicron variant.ConclusionThe association between vaccination and lower excess mortality rates was strongest in 2021 and early 2022, prior to high population rates of infection-acquired immunity. The findings underscore the benefits of the rapid vaccination rollout campaign and the continued need to boost at-risk populations.

IntroductionThe US has continued to see excess mortality through the Delta and Omicron periods. We sought to quantify excess mortality on a state level and calculate potential deaths averted if all states matched the excess mortality rates of those with the 10 lowest excess mortality rates.MethodsObservational cohort, US and state-level data. Expected monthly deaths were modeled using pre-pandemic US and state-level data (2015-2020). Mortality data was accessed from CDC public reporting.ResultsWe find that during the Delta and Omicron waves, the US recorded over 596,000 excess deaths. 60% of the nation’s total excess mortality during these periods could have been averted if all states had excess mortality rates equal to those with the 10 lowest excess mortality rates.ConclusionWith large differences in excess mortality across US states in this 15-month study period, we note that a significant portion of deaths could have been averted with higher vaccination rates, improved mitigation policies, and adherence to other behaviors.

BackgroundThe popular beta-binomial approach to estimate the reliability of healthcare quality measures (Adams et al. 2010New England Journal of Medicine) may yield grossly over-estimated reliabilities for providers with event rates equal to 0% or 100%.ObjectiveImprove the beta-binomial approach to yield more reasonable reliability estimates for providers with event rates equal to 0% or 100%.MethodWe revise the beta-binomial approach by substituting Bayesian estimates with various priors for the crude event rates. We evaluate the new reliability estimates using Monte Carlo studies and two real-world measure examples.Results and conclusionThe revised beta-binomial approach based on Jeffreys non-informative prior yields more reasonable reliability estimates for providers with event rates equal to 0% or 100% and statistically outperforms the original beta-binomial approach regarding bias and standard errors.

Long-term sequelae after symptomatic SARS-CoV-2 infection may impact well-being, yet existing data primarily focus on discrete symptoms and/or health care use.To compare patient-reported outcomes of physical, mental, and social well-being among adults with symptomatic illness who received a positive vs negative test result for SARS-CoV-2 infection.This cohort study was a planned interim analysis of an ongoing multicenter prospective longitudinal registry study (the Innovative Support for Patients With SARS-CoV-2 Infections Registry [INSPIRE]). Participants were enrolled from December 11, 2020, to September 10, 2021, and comprised adults (aged ≥18 years) with acute symptoms suggestive of SARS-CoV-2 infection at the time of receipt of a SARS-CoV-2 test approved by the US Food and Drug Administration. The analysis included the first 1000 participants who completed baseline and 3-month follow-up surveys consisting of questions from the 29-item Patient-Reported Outcomes Measurement Information System (PROMIS-29; 7 subscales, including physical function, anxiety, depression, fatigue, social participation, sleep disturbance, and pain interference) and the PROMIS Short Form-Cognitive Function 8a scale, for which population-normed T scores were reported.SARS-CoV-2 status (positive or negative test result) at enrollment.Mean PROMIS scores for participants with positive COVID-19 tests vs negative COVID-19 tests were compared descriptively and using multivariable regression analysis.Among 1000 participants, 722 (72.2%) received a positive COVID-19 result and 278 (27.8%) received a negative result; 406 of 998 participants (40.7%) were aged 18 to 34 years, 644 of 972 (66.3%) were female, 833 of 984 (84.7%) were non-Hispanic, and 685 of 974 (70.3%) were White. A total of 282 of 712 participants (39.6%) in the COVID-19-positive group and 147 of 275 participants (53.5%) in the COVID-19-negative group reported persistently poor physical, mental, or social well-being at 3-month follow-up. After adjustment, improvements in well-being were statistically and clinically greater for participants in the COVID-19-positive group vs the COVID-19-negative group only for social participation (β = 3.32; 95% CI, 1.84-4.80; P .001); changes in other well-being domains were not clinically different between groups. Improvements in well-being in the COVID-19-positive group were concentrated among participants aged 18 to 34 years (eg, social participation: β = 3.90; 95% CI, 1.75-6.05; P .001) and those who presented for COVID-19 testing in an ambulatory setting (eg, social participation: β = 4.16; 95% CI, 2.12-6.20; P .001).In this study, participants in both the COVID-19-positive and COVID-19-negative groups reported persistently poor physical, mental, or social well-being at 3-month follow-up. Although some individuals had clinically meaningful improvements over time, many reported moderate to severe impairments in well-being 3 months later. These results highlight the importance of including a control group of participants with negative COVID-19 results for comparison when examining the sequelae of COVID-19.

Clinical treatment outcomes are the quality and cost targets that healthcare providers aim to improve. Most existing outcome analysis focuses on a single disease or all diseases combined. Motivated by the success of molecular and phenotypic human disease networks (HDNs), this article develops a clinical treatment network that describes the interconnections among diseases in terms of inpatient length of stay (LOS) and readmission. Here one node represents one disease, and two nodes are linked with an edge if their LOS and number of readmissions are conditionally dependent. This is the very first HDN that jointly analyzes multiple clinical treatment outcomes at the pan-disease level. To accommodate the unique data characteristics, we propose a modeling approach based on two-part generalized linear models and estimation based on penalized integrative analysis. Analysis is conducted on the Medicare inpatient data of 100,000 randomly selected subjects for the period of 01/2010-12/2018. The resulted network has 1,008 edges for 106 nodes. We analyze key network properties including connectivity, module/hub, and temporal variation. The findings are biomedically sensible. For example, high connectivity and hub conditions, such as disorders of lipid metabolism and essential hypertension, are identified. There are also findings that are less/not investigated in the literature. Overall, this study can provide additional insight into diseases' properties and their interconnections and assist more efficient disease management and healthcare resources allocation. This article is protected by copyright. All rights reserved.

To propose and evaluate a novel approach for measuring hospital-level disparities according to the effect of a continuous, polysocial risk factor on those outcomes.Our cohort consisted of Medicare Fee-for-Service (FFS) patients 65 years and older admitted to acute care hospitals for one of six common conditions or procedures. Medicare administrative claims data for six hospital readmission measures including hospitalizations from July 2015 to June 2018 were used.We adapted existing methodologies that were developed to report hospital-level disparities using dichotomous social risk factors (SRFs). The existing methods report disparities within and across hospitals; we developed and tested modified approaches for both methods using the Agency for Healthcare Research and Quality Socioeconomic Status Index. We applied the adapted methodologies to six 30-day hospital readmission measures included in the Centers for MedicareMedicaid Services Hospital Readmissions Reduction Program measures. We compared the within- and across-hospital results for each to those obtained from using the original methods and dichotomizing the AHRQ SES Index into "low" and "high" scores.We used Medicare FFS administrative claims data linked to U.S. Census data.For all six readmission measures we find that, when compared with the existing methods, the methods for continuous SRFs provide disparity results for more facilities though across a narrower range of values. Measures of disparity based on this approach are moderately to highly correlated with those based on a dichotomous version of the same risk factor, while reflecting a fuller spectrum of risk. This approach represents an opportunity for detection of provider-level results that more closely align with underlying social risk.We have demonstrated the feasibility and utility of estimating hospital disparities of care using a continuous, polysocial risk factor. This approach expands the potential for reporting hospital-level disparities while better accounting for the multifactorial nature of social risk on hospital outcomes.

Procedural complications limit the clinical benefit of transcatheter left atrial appendage occlusion (LAAO). Next-generation devices incorporate design modifications intended to improve procedural safety, but their clinical impact has not been described.The aim of this study was to compare in-hospital outcomes for the Watchman FLX with the predicate Watchman 2.5 device.The National Cardiovascular Data Registry LAAO Registry was used to identify patients who received the Watchman FLX and an identical number of patients receiving the Watchman 2.5 at the same sites directly preceding the first Watchman FLX case at each site. The primary endpoint was in-hospital major adverse events (MAE), defined as a composite of death, cardiac arrest, stroke, transient ischemic attack, intracranial hemorrhage, systemic arterial embolism, major bleeding, major vascular complication, myocardial infarction, pericardial effusion requiring intervention (percutaneous or surgical), and device embolization. A secondary analysis was performed using 2:1 propensity score matching of patients receiving the Watchman 2.5 or Watchman FLX.The study cohort consisted of 27,013 patients receiving each device. The rate of in-hospital MAE was significantly lower for the Watchman FLX compared with the Watchman 2.5 (1.35% vs 2.40%; adjusted OR: 0.57; 95% CI: 0.50-0.65; P 0.0001), driven largely by fewer pericardial effusions requiring intervention (0.42% vs 1.23%; adjusted OR: 0.34; 95% CI: 0.28-0.42; P 0.0001). The Watchman FLX was also associated with significant lower rates of the individual endpoints of in-hospital mortality (0.12% vs 0.24%; P 0.0001), major bleeding (1.08% vs 2.05%; P 0.0001), cardiac arrest (0.13% vs 0.24%; P = 0.006), and device embolization (0.02% vs 0.06%; P = 0.028), while myocardial infarction, stroke, and major vascular complications did not differ between groups. Propensity score matching analysis demonstrated similar results, with lower rates of MAE with the Watchman FLX (1.34% vs 2.58%; OR: 0.51; 95% CI: 0.46-0.58; P 0.0001).Transcatheter LAAO with the Watchman FLX was associated with lower rates of in-hospital MAE compared with the predicate Watchman device, including mortality, pericardial effusion, major bleeding, cardiac arrest, and device embolization. This may favorably influence the balance of risks and benefits of transcatheter LAAO for stroke prevention in patients with atrial fibrillation.

IntroductionExcess mortality does not depend on labeling the cause of death and is an accurate representation of the pandemic population-level effects. A comprehensive evaluation of all-cause excess mortality in the United States during the first two years of the COVID-19 pandemic, stratified by age, sex, region, and race/ethnicity can provide insight into the extent and variation in harm.MethodsWith Centers for Disease Control and Prevention (CDC)/National Center for Health Statistics (NCHS) data from 2014-2022, we use seasonal autoregressive integrated moving averages (sARIMA) to estimate excess mortality during the pandemic, defined as the difference between the number of observed and expected deaths. We continuously correct monthly expected deaths to reflect the decreased population owing to cumulative pandemic-associated excess deaths recorded. We calculate excess mortality for the total US population, and by age, sex, US census division, and race/ethnicity.ResultsFrom March 1, 2020, through February 28, 2022, there were 1.17 million excess deaths in the United States. Overall, mortality was 20% higher than expected during the study period. Of the excess deaths, 799,477 (68%) were among residents aged 65 and older. The largest relative increase in all-cause mortality was 27% among adults ages 18-49 years. Males comprised most of the excess mortality (57%), but this predominance declined with age. A higher relative mortality occurred among non-Hispanic American Indian/Alaskan Native, non-Hispanic Black, non-Hispanic Native Hawaiian and Other Pacific Islander, Hispanic people. Excess mortality differed by region; the highest rates were in the South, including in the population ages ≥65 years. Excess mortality rose and fell contemporaneously with COVID-19 waves.ConclusionIn the first two years of the pandemic, the US experienced 1.17 million excess deaths, with greater relative increases in all-cause mortality among men, in American Indian/Alaskan Native, Black and Hispanic people, and the South.

ImportanceAdjusting quality measures used in pay-for-performance programs for social risk factors remains controversial.ObjectiveTo illustrate a structured, transparent approach to decision-making about adjustment for social risk factors for a measure of clinician quality that assesses acute admissions for patients with multiple chronic conditions (MCCs).Design, Setting, and ParticipantsThis retrospective cohort study used 2017 and 2018 Medicare administrative claims and enrollment data, 2013 to 2017 American Community Survey data, and 2018 and 2019 Area Health Resource Files. Patients were Medicare fee-for-service beneficiaries 65 years or older with at least 2 of 9 chronic conditions (acute myocardial infarction, Alzheimer disease/dementia, atrial fibrillation, chronic kidney disease, chronic obstructive pulmonary disease or asthma, depression, diabetes, heart failure, and stroke/transient ischemic attack). Patients were attributed to clinicians in the Merit-Based Incentive Payment System (MIPS; primary health care professionals or specialists) using a visit-based attribution algorithm. Analyses were conducted between September 30, 2017, and August 30, 2020.ExposuresSocial risk factors included low Agency for Healthcare Research and Quality Socioeconomic Status Index, low physician-specialist density, and Medicare-Medicaid dual eligibility.Main Outcomes and MeasuresNumber of acute unplanned hospital admissions per 100 person-years at risk for admission. Measure scores were calculated for MIPS clinicians with at least 18 patients with MCCs assigned to them.ResultsThere were 4 659 922 patients with MCCs (mean [SD] age, 79.0 [8.0] years; 42.5% male) assigned to 58 435 MIPS clinicians. The median (IQR) risk-standardized measure score was 38.9 (34.9-43.6) per 100 person-years. Social risk factors of low Agency for Healthcare Research and Quality Socioeconomic Status Index, low physician-specialist density, and Medicare-Medicaid dual eligibility were significantly associated with the risk of hospitalization in the univariate models (relative risk [RR], 1.14 [95% CI, 1.13-1.14], RR, 1.05 [95% CI, 1.04-1.06], and RR, 1.44 [95% CI, 1.43-1.45], respectively), but the association was attenuated in adjusted models (RR, 1.11 [95% CI 1.11-1.12] for dual eligibility). Across MIPS clinicians caring for variable proportions of dual-eligible patients with MCCs (quartile 1, 0%-3.1%; quartile 2, >3.1%-9.5%; quartile 3, >9.5%-24.5%, and quartile 4, >24.5%-100%), median measure scores per quartile were 37.4, 38.6, 40.0, and 39.8 per 100 person-years, respectively. Balancing conceptual considerations, empirical findings, programmatic structure, and stakeholder input, the Centers for Medicare & Medicaid Services decided to adjust the final model for the 2 area-level social risk factors but not dual Medicare-Medicaid eligibility.Conclusions and RelevanceThis cohort study demonstrated that adjustment for social risk factors in outcome measures requires weighing high-stake, competing concerns. A structured approach that includes evaluation of conceptual and contextual factors, as well as empirical findings, with active engagement of stakeholders can be used to make decisions about social risk factor adjustment.

ImportanceSelected glucagon-like peptide-1 receptor agonists (GLP-1RAs) and sodium glucose cotransporter-2 inhibitors (SGLT2i) have cardioprotective effects in patients with type 2 diabetes and elevated cardiovascular risk. Prescription of these agents by clinicians and their consistent use by patients are essential to realize their benefits.ObjectiveTo assess the patterns of use and prescription fills of GLP-1RAs and SGLT-2i.DesignCross-sectional for medication use and prospective for prescription fills in 2018-2020SettingNationwide de-identified US administrative claims database of Medicare Advantage and commercially insured adults.ParticipantsIndividuals 18 years of age and older with type 2 diabetesExposuresComorbidities representing guideline-directed indications of atherosclerotic cardiovascular disease (ASCVD) for GLP-1RAs, and ASCVD, heart failure, and diabetic nephropathy for SGLT2i.Main Outcomes and MeasuresMedication use and monthly fill rates for 12 months following initiation of therapy by calculating the proportion of days with consistent medication use.ResultsAmong 587,657 individuals with type 2 diabetes, 80,196 (13.6%) were prescribed GLP-1RAs and 68,149 (11.5%) SGLT2i during 2018-2020. This represented 12.9% and 10.5% of individuals with indications for each medication, respectively. Based on monthly counts of new prescriptions, there were no changes in the uptake of either drug class during 2019-2020. Among new initiators, fill rate was 52.5% for GLP-1RAs and 52.9% for SGLT2i one year after initiation. One-year fill rates were higher for patients with commercial insurance than those with Medicare Advantage plans for both GLP-1RAs (59.3% vs 51.0%, p-valueConclusions and RelevanceIn 2018-2020, use of GLP-1RAs and SGLT2i remained limited to fewer than 1 in 8 individuals with type 2 diabetes meeting criteria for evidence-based guideline and professional society recommendations, with one-year fill rates around 50%. The low and inconsistent use of these medications compromises their longitudinal health outcomes benefits in a period of expanding indications for their use.

BackgroundWe sought to quantify whether there were statistically significant disparities along race and ethnicity lines during the early rollout of Covid-19 vaccine booster doses in the United States. We also studied whether such disparities replicated or widened disparities that had already been observed during the initial series rollout as of 2 months earlier (Janssen) or 6 months earlier (Pfizer-BioNTech or Moderna), which comprised the booster-eligible population.MethodsThis cross-sectional study of US adults (ages ≥18 years) used public data from US Centers for Disease Control and Prevention. The observed shares of vaccine doses for each race and ethnicity were compared to the expected shares, predicted based upon the compositions of the booster-eligible and initial series-eligible populations.ResultsAs of November 16, 2021, 123.5 million US adults were eligible for a booster dose of either the Pfizer-BioNTech, Moderna, or Janssen vaccines. Of these, 21.7 million had received a booster dose, among whom race and ethnicity information was available for 18.8 million booster recipients.A statistically significant higher share of Non-Hispanic White and Non-Hispanic Multiple/Other race individuals had received a booster vaccination than projected based on the composition of the booster-eligible population. A statistically significant lower share of Hispanic, Non-Hispanic American Indian/Alaskan Native, Non-Hispanic Asian, Non-Hispanic Black, and Non-Hispanic Native Hawaiian/Other Pacific Islander individuals had received a booster vaccination than expected based on the booster-eligible population. A secondary analysis of the booster-eligible population found that some of these disparities had already occurred at the time of the initial series. However, the booster campaign widened all of those disparities and added new disparities for Non-Hispanic American Indian/Alaskan Native and Non-Hispanic Native Hawaiian/Other Pacific Islander individuals.ConclusionDisparities in Covid-19 vaccine administration on race and ethnicity lines occurred during the initial series rollout in the US. However, these disparities were not merely replicated but widened by the early booster rollout.

This cohort study uses Medicare prescription claims data to evaluate patterns of direct oral anticoagulant prescribing by US clinicians between 2013 and 2018., Key Points Question How have patterns of direct-acting oral anticoagulant (DOAC) use changed among US clinicians between 2013 and 2018? Findings In this cohort study of Medicare prescription claim data encompassing 325 666 clinicians from 2013 to 2018, most clinicians continued to use warfarin as their predominant or only anticoagulant instead of DOACs, including 1 in 5 general medicine practitioners exclusively using warfarin in 2018. Despite an increase in DOAC prescribing, those prescribing only warfarin in 2013 had lower proportionate DOAC use throughout the study than 2013 DOAC prescribers. Meaning In this study, many clinicians did not prescribe any DOACs in 2018, suggesting a need to address barriers to DOAC use., Importance Contemporary national clinical practice guidelines recommend direct-acting oral anticoagulants (DOACs) as the first-line anticoagulant strategy over warfarin for most indications, especially among older individuals with an elevated bleeding risk. Objective To evaluate anticoagulant prescribing and DOAC uptake by US clinicians in the Medicare population. Design, Setting, and Participants This retrospective cohort study included all US clinicians with more than 10 Medicare oral anticoagulant prescription claims, who were included in the national Medicare Provider Utilization and Payment Data (2013-2018). Data analyses were conducted between October 2020 and October 2021. Exposures DOAC prescription in 2013. Main Outcomes and Measures Clinicians were categorized based on 2013 prescribing as solely prescribing warfarin, DOAC, or both, and their temporal trajectories of proportionate DOAC use were examined. Results The analysis included 325 666 unique clinicians with more than 10 oral anticoagulant prescriptions between 2013 and 2018 (26 620 [8.2%] cardiologists, 85 563 [26.3%] internal medicine physicians, 84 369 [25.9%] family medicine physicians, and 81 161 [24.9%] advanced practice clinicians, including nurse practitioners and physician assistants). In 2013, among 91 837 prescribers, 54 501 (59.3%) prescribed only warfarin, 1918 (2.1%) prescribed only a DOAC, and 35 418 (38.6%) prescribed both. During the study period, the number of clinicians prescribing DOACs increased, but 19% continued to prescribe only warfarin in 2018. While 359 cardiologists prescribing anticoagulants (1.6%) were warfarin-only prescribers, 10 414 (20.0%) and 6296 (12.6%) of family and internal medicine physicians also prescribed only warfarin, respectively. Clinicians prescribing only warfarin in 2013 had lower proportionate DOAC use throughout the study compared with 2013 DOAC prescribers, which represents a median (IQR) of 41.9% (20.3%-61.9%) of their anticoagulant prescriptions in 2018 vs 67.0% (49.9%-82.8%) for DOAC prescribers. Conclusions and Relevance Despite the increase in DOAC use among Medicare beneficiaries, many clinicians in this study continued to use warfarin as their predominant or only anticoagulant instead of DOACs. There is a need to address barriers to the uptake of these medications to realize their potential benefits for patients.

Background: Evidence from large randomized clinical trials supports the benefit of SGLT2i (sodium-glucose cotransporter-2 inhibitors) to improve cardiovascular and kidney outcomes in patients with type 2 diabetes with or at high risk for atherosclerotic cardiovascular disease or chronic kidney disease. Considering this evidence, which has been expanding since the product label indication for empagliflozin to reduce risk of cardiovascular death in 2016, clinician-level variation in the prescription of SGLT2i among US Medicare beneficiaries was evaluated. Methods: Antihyperglycemic medication prescribers were identified as those physicians and advanced practice providers prescribing metformin in Medicare part D prescriber data. In this cross-sectional study, the proportion prescribing SGLT2i was assessed overall and across specialties in 2018, with changes assessed from 2014 to 2018. SGLT2i use was compared with other second-line antihyperglycemic medication classes, sulfonylureas and DPP4is (dipeptidyl peptidase-4 inhibitors). Results: Among 232 523 unique clinicians who prescribed metformin for Medicare beneficiaries in 2018 (diabetes-treating clinicians), 45 255 (19.5%) prescribed SGLT2i. There was substantial variation across specialties—from 72% of endocrinologists to 14% of cardiologists who prescribed metformin also prescribed SGLT2i. Between 2014 and 2018, the number prescribing SGLT2i increased 5-fold from 9048 in 2014 to 45 255 in 2018. Among clinicians who prescribed both sulfonylureas and SGLT2i in 2018, SGLT2i was prescribed to a median 33 beneficiaries for every 100 prescribed sulfonylureas (interquartile range, 18–67). SGLT2i use relative to sulfonylureas increased from 19 (interquartile range, 11–34) per 100 in 2014 to 33 (interquartile range, 18–67) per 100 in 2018 ( P trend Conclusions: Eighty percent of clinicians prescribing metformin to Medicare beneficiaries did not prescribe SGLT2i in 2018. Moreover, sulfonylureas prescriptions were 3 times more frequent than those of SGLT2is, although a pattern of increasing uptake may portend future trends. These findings highlight a baseline opportunity to improve care and outcomes for patients with type 2 diabetes.

BackgroundAll-cause excess mortality (the number of deaths that exceed projections in any period) has been widely reported during the Covid-19 pandemic. Whether excess mortality has occurred during the Delta wave is less well understood.MethodsWe performed an observational study using data from the Massachusetts Department of Health. Five years of US Census population data and CDC mortality statistics were applied to a seasonal autoregressive integrated moving average (sARIMA) model to project the number of expected deaths for each week of the pandemic period, including the Delta period (starting in June 2021, extending through August 28th 2021, for which mortality data are >99% complete). Weekly Covid-19 cases, Covid-19-attributed deaths, and all-cause deaths are reported. County-level excess mortality during the vaccine campaign are also reported, with weekly rates of vaccination in each county that reported 100 or more all-cause deaths during any week included in the study period.ResultsAll-cause mortality was not observed after March 2021, by which time over 75% of persons over 65 years of age in Massachusetts had received a vaccination. Fewer deaths than expected (which we term ‘deficit mortality’) occurred both during the summer of 2020, the spring of 2021 and during the Delta wave (beginning June 13, 2021 when Delta isolates represented >10% of sequenced cases). After the initial wave in the spring of 2020, more Covid-19-attributed deaths were recorded that all-cause excess deaths, implying that Covid-19 was misattributed as the underlying cause, rather than a contributing cause of death in some cases.ConclusionIn a state with high vaccination rates, excess mortality has not been recorded during the Delta period. Deficit mortality has been recorded during this period.

Low-income older adults who are dually eligible (DE) for Medicare and Medicaid often experience worse outcomes following hospitalization. Among other federal policies aimed at improving health for DE patients, Medicare has recently begun reporting disparities in within-hospital readmissions. The degree to which disparities for DE patients are owing to differences in community-level factors or, conversely, are amenable to hospital quality improvement, remains heavily debated.To examine the extent to which within-hospital disparities in 30-day readmission rates for DE patients are ameliorated by state- and community-level factors.In this retrospective cohort study, Centers for MedicareMedicaid Services (CMS) Disparity Methods were used to calculate within-hospital disparities in 30-day risk-adjusted readmission rates for DE vs non-DE patients in US hospitals participating in Medicare. All analyses were performed in February and March 2019. The study included Medicare patients (aged ≥65 years) hospitalized for acute myocardial infarction (AMI), heart failure (HF), or pneumonia in 2014 to 2017.Within-hospital disparities, as measured by the rate difference (RD) in 30-day readmission between DE vs non-DE patients following admission for AMI, HF, or pneumonia; variance across hospitals; and correlation of hospital RDs with and without adjustment for state Medicaid eligibility policies and community-level factors.The final sample included 475 444 patients admitted for AMI, 898 395 for HF, and 1 214 282 for pneumonia, of whom 13.2%, 17.4%, and 23.0% were DE patients, respectively. Dually eligible patients had higher 30-day readmission rates relative to non-DE patients (RD0) in 99.0% (AMI), 99.4% (HF), and 97.5% (pneumonia) of US hospitals. Across hospitals, the mean (IQR) RD between DE vs non-DE was 1.00% (0.87%-1.10%) for AMI, 0.82% (0.73%-0.96%) for HF, and 0.53% (0.37%-0.71%) for pneumonia. The mean (IQR) RD after adjustment for community-level factors was 0.87% (0.73%-0.97%) for AMI, 0.67% (0.57%-0.80%) for HF, and 0.42% (0.29%-0.57%) for pneumonia. Relative hospital rankings of corresponding within-hospital disparities before and after community-level adjustment were highly correlated (Pearson coefficient, 0.98).In this cohort study, within-hospital disparities in 30-day readmission for DE patients were modestly associated with differences in state Medicaid policies and community-level factors. This suggests that remaining variation in these disparities should be the focus of hospital efforts to improve the quality of care transitions at discharge for DE patients in efforts to advance equity.

Background Whether the volume of coronavirus disease 2019 (COVID-19) hospitalizations is associated with outcomes has important implications for the organization of hospital care both during this pandemic and future novel and rapidly evolving high-volume conditions. Methods We identified COVID-19 hospitalizations at US hospitals in the American Heart Association COVID-19 Cardiovascular Disease Registry with ≥10 cases between January and August 2020. We evaluated the association of COVID-19 hospitalization volume and weekly case growth indexed to hospital bed capacity, with hospital risk-standardized in-hospital case-fatality rate (rsCFR). Results There were 85 hospitals with 15,329 COVID-19 hospitalizations, with a median hospital case volume was 118 (interquartile range, 57, 252) and median growth rate of 2 cases per 100 beds per week but varied widely (interquartile range: 0.9 to 4.5). There was no significant association between overall hospital COVID-19 case volume and rsCFR (rho, 0.18, P = .09). However, hospitals with more rapid COVID-19 case-growth had higher rsCFR (rho, 0.22, P = 0.047), increasing across case growth quartiles (P trend = .03). Although there were no differences in medical treatments or intensive care unit therapies (mechanical ventilation, vasopressors), the highest case growth quartile had 4-fold higher odds of above median rsCFR, compared with the lowest quartile (odds ratio, 4.00; 1.15 to 13.8, P = .03). Conclusions An accelerated case growth trajectory is a marker of hospitals at risk of poor COVID-19 outcomes, identifying sites that may be targets for influx of additional resources or triage strategies. Early identification of such hospital signatures is essential as our health system prepares for future health challenges.