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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.

The COVID-19 pandemic has disproportionately affected Hispanic or Latino, non-Hispanic Black (Black), non-Hispanic American Indian or Alaska Native (AI/AN), and non-Hispanic Native Hawaiian or Other Pacific Islander (NH/PI) populations in the United States. These populations have experienced higher rates of infection and mortality compared with the non-Hispanic White (White) population (1-5) and greater excess mortality (i.e., the percentage increase in the number of persons who have died relative to the expected number of deaths for a given place and time) (6). A limitation of existing research on excess mortality among racial/ethnic minority groups has been the lack of adjustment for age and population change over time. This study assessed excess mortality incidence rates (IRs) (e.g., the number of excess deaths per 100,000 person-years) in the United States during December 29, 2019-January 2, 2021, by race/ethnicity and age group using data from the National Vital Statistics System. Among all assessed racial/ethnic groups (non-Hispanic Asian [Asian], AI/AN, Black, Hispanic, NH/PI, and White populations), excess mortality IRs were higher among persons aged ≥65 years (426.4 to 1033.5 excess deaths per 100,000 person-years) than among those aged 25-64 years (30.2 to 221.1) and those aged25 years (-2.9 to 14.1). Among persons aged65 years, Black and AI/AN populations had the highest excess mortality IRs. Among adults aged ≥65 years, Black and Hispanic persons experienced the highest excess mortality IRs of1,000 excess deaths per 100,000 person-years. These findings could help guide more tailored public health messaging and mitigation efforts to reduce disparities in mortality associated with the COVID-19 pandemic in the United States,* by identifying the racial/ethnic groups and age groups with the highest excess mortality rates.

BACKGROUND: Rural communities face challenges in accessing healthcare services due to physician shortages and limited unscheduled care capabilities in office settings. As a result, rural hospital-based Emergency Departments (ED) may disproportionately provide acute, unscheduled care needs. We sought to examine differences in ED utilization and the relative role of the ED in providing access to unscheduled care between rural and urban communities. METHODS: Using a 20% sample of the 2012 Medicare Chronic Condition Warehouse, we studied the overall ED visit rate and the unscheduled care rate by geography using the Dartmouth Atlas’ hospital referral regions (HRR). We calculated HRR urbanicity as the proportion of beneficiaries residing in an urban zip code within each HRR. We report descriptive statistics and utilize K-means clustering based on the ED visit rates and unscheduled care rates. RESULTS: We found rural ED use is more common and disproportionately the site of unscheduled care delivery when compared to urban communities. The ED visit and unscheduled care proportions were negatively correlated with increased urbanicity (r = −0.48, p < 0.001; r = −0.58, p < 0.001). CONCLUSION: he use and role of EDs by Medicare beneficiaries appears to be substantially different between urban and rural areas. This suggests that the ED may play a distinct role within the healthcare delivery system of rural communities that face disproportionate barriers to care access.

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.

Disease clinical treatment measures, such as inpatient length of stay (LOS), have been examined for most if not all diseases. Such analysis has important implications for the management and planning of health care, financial, and human resources. In addition, clinical treatment measures can also informatively reflect intrinsic disease properties such as severity. The existing studies mostly focus on either a single disease (or a few pre-selected and closely related diseases) or all diseases combined. In this study, we take a new and innovative perspective, examine the interconnections in length of stay (LOS) among diseases, and construct the very first disease clinical treatment network on LOS. To accommodate uniquely challenging data distributions, a new conditional network construction approach is developed. Based on the constructed network, the analysis of important network properties is conducted. The Medicare data on 100 000 randomly selected subjects for the period of January 2008 to December 2018 is analyzed. The network structure and key properties are found to have sensible biomedical interpretations. Being the very first of its kind, this study can be informative to disease clinical management, advance our understanding of disease interconnections, and foster complex network analysis.

Objectives The objectives of this study were to compare risk-standardized hospital visit ratios of the predicted to expected number of unplanned hospital visits within 7 days of same-day surgeries performed at US hospital outpatient departments (HOPDs) and to describe the causes of hospital visits. Summary of background data More than half of procedures in the US are performed in outpatient settings, yet little is known about facility-level variation in short-term safety outcomes. Methods The study cohort included 1,135,441 outpatient surgeries performed at 4058 hospitals between October 1, 2015 and September 30, 2016 among Medicare Fee-for-Service beneficiaries aged ≥65 years. Hospital-level, risk-standardized measure scores of unplanned hospital visits (emergency department visits, observation stays, and unplanned inpatient admissions) within 7 days of hospital outpatient surgery were calculated using hierarchical logistic regression modeling that adjusted for age, clinical comorbidities, and surgical procedural complexity. Results Overall, 7.8% of hospital outpatient surgeries were followed by an unplanned hospital visit within 7 days. Many of the leading reasons for unplanned visits were for potentially preventable conditions, such as urinary retention, infection, and pain. We found considerable variation in the risk-standardized ratio score across hospitals. The 203 best-performing HOPDs, at or below the 5th percentile, had at least 22% fewer unplanned hospital visits than expected, whereas the 202 worst-performing HOPDs, at or above the 95th percentile, had at least 29% more post-surgical visits than expected, given their case and surgical procedure mix. Conclusions Many patients experience an unplanned hospital visit within 7 days of hospital outpatient surgery, often for potentially preventable reasons. The observed variation in performance across hospitals suggests opportunities for quality improvement.

Background To estimate, prior to finalization of claims, the national monthly numbers of admissions and rates of 30-day readmissions and post-discharge observation-stays for Medicare fee-for-service beneficiaries hospitalized with acute myocardial infarction (AMI), heart failure (HF), or pneumonia. Methods The centers for Medicare & Medicaid Services (CMS) Integrated Data Repository, including the Medicare beneficiary enrollment database, was accessed in June 2015, February 2017, and February 2018. We evaluated patterns of delay in Medicare claims accrual, and used incomplete, non-final claims data to develop and validate models for real-time estimation of admissions, readmissions, and observation stays. Results These real-time reporting models accurately estimate, within 2 months from admission, the monthly numbers of admissions, 30-day readmission and observation-stay rates for patients with AMI, HF, or pneumonia. Conclusions This work will allow CMS to track the impact of policy decisions in real time and enable hospitals to better monitor their performance nationally.

Background Given the inclusion of orthopaedic quality measures in the Centers for Medicare & Medicaid Services national hospital payment programs, the present study sought to assess whether the public reporting of total hip arthroplasty (THA) and total knee arthroplasty (TKA) risk-standardized readmission rates (RSRRs) and complication rates (RSCRs) was temporally associated with a decrease in the rates of these outcomes among Medicare beneficiaries. Methods Annual trends in national observed and hospital-level RSRRs and RSCRs were evaluated for patients who underwent hospital-based inpatient hip and/or knee replacement procedures from fiscal year 2010 to fiscal year 2016. Hospital-level rates were calculated with use of the same measures and methodology that were utilized in public reporting. Annual trends in the distribution of hospital-level outcomes were then examined with use of density plots. Results Complication and readmission rates and variation declined steadily from fiscal year 2010 to fiscal year 2016. Reductions of 33% and 25% were noted in hospital-level RSCRs and RSRRs, respectively. The interquartile range decreased by 18% (relative reduction) for RSCRs and by 34% (relative reduction) for RSRRs. The frequency of risk variables in the complication and readmission models did not systematically change over time, suggesting no evidence of widespread bias or up-coding. Conclusions This study showed that hospital-level complication and readmission rates following THA and TKA and the variation in hospital-level performance declined during a period coinciding with the start of public reporting and financial incentives associated with measurement. The consistently decreasing trend in rates of and variation in outcomes suggests steady improvements and greater consistency among hospitals in clinical outcomes for THA and TKA patients in the 2016 fiscal year compared with the 2010 fiscal year. The interactions between public reporting, payment, and hospital coding practices are complex and require further study. Level of evidence Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Objective: Early diagnosis and treatment of children with congenital hypothyroidism (CH) through newborn screening can effectively prevent delayed development. This study was designed to investigate the pathogenesis and factors that influence CH in urban areas of China between 2009 and 2018. Methods: A retrospective analysis of newborn screening data and diagnosis and treatment information for CH diagnosed in the information database of the neonatal disease screening center in one of China's five special economic zones from 2009 to 2018. Results: Of the 947,258 newborns screened between 2009 and 2018, 829 (406 girls) were diagnosed with CH at birth (1 diagnosis/1,136 births). Among the 608 cases of CH diagnosed at birth and re-evaluated at the age of 3 years, 487 were permanent congenital hypothyroidism (PCH, 1/1,429), and 121 were transient congenital hypothyroidism (TCH, 1/5,882). A total of 83.2% of infants with PCH (405/487) underwent thyroid imaging in the neonatal period, of which thyroid dysgenesis accounted for 28.64% (116/405) and functional defects accounted for 71.36% (289/405). The incidence of CH changed significantly in infants with initial serum thyroid-stimulating hormone concentrations of 41 to 100 mIU/L and ≥100 mIU/L, whereas the incidence of mild CH showed a slight increase. The incidence of CH was significantly higher in postterm infants (1/63) and low-birth-weight infants (1/370). Conclusion: In the past decade, the incidence of CH has increased, mainly due to the increase in the incidence of PCH and TCH. The incidence of mild CH has increased slightly. Postterm birth and low birth weight are important factors affecting the incidence of CH. Abbreviations: CH = congenital hypothyroidism; FT4 = free thyroxine; L-T4 = levothyroxine sodium; PCH = permanent congenital hypothyroidism; TCH = transient congenital hypothyroidism; TSH = thyroid-stimulating hormone; TT4 = total thyroxine.

Background Readmission rates after acute myocardial infarction (AMI) and heart failure (HF) hospitalizations have decreased in the United States since the implementation of the Hospital Readmissions Reduction Program. Objectives This study was designed to examine the temporal trends of readmission and mortality after AMI and HF in Ontario, Canada, where reducing hospital readmissions has not had a policy incentive. Methods The cohort was comprised of AMI or HF patients 65 years of age or older who had been hospitalized from 2006 to 2017. Primary outcomes were 30-day readmission and post-discharge mortality. Secondary outcomes included in-hospital mortality, 30-day mortality from admission, and in-hospital mortality or 30-day mortality post-discharge. Adjusted monthly trends for each outcome were examined over the study period. Results Our cohorts included 152,808 AMI and 223,283 HF patients. Age- and sex-standardized AMI hospitalization rates in Ontario declined 32% from 2006 to 2017 while HF hospitalization rates declined slightly (9.1%). For AMI, risk-adjusted 30-day readmission rates declined from 17.4% in 2006 to 14.7% in 2017. All AMI risk-adjusted mortality rates also declined from 2006 to 2017 with 30-day post-discharge mortality from 5.1% to 4.4%. For HF, overall risk-adjusted 30-day readmission was largely unchanged from 2006 to 2014 at 21.9%, followed by a decline to 20.8% in 2017. Risk-adjusted 30-day post-discharge mortality declined from 7.1% in 2006 to 6.6% in 2017. Conclusions The patterns of outcomes in Ontario are consistent with the United States for AMI, but diverge for HF. For AMI and HF, admissions, readmissions, and mortality rates declined over this period. The reasons for the country-specific patterns for HF need further exploration.

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.

Background Despite its clinical significance, the risk of severe infection requiring hospitalization among outpatients with severe acute respiratory syndrome coronavirus 2 infection who receive angiotensin‐converting enzyme (ACE) inhibitors and angiotensin receptor blockers (ARBs) remains uncertain. Methods and Results In a propensity score–matched outpatient cohort (January–May 2020) of 2263 Medicare Advantage and commercially insured individuals with hypertension and a positive outpatient SARS‐CoV‐2, we determined the association of ACE inhibitors and ARBs with COVID‐19 hospitalization. In a concurrent inpatient cohort of 7933 hospitalized with COVID‐19, we tested their association with in‐hospital mortality. The robustness of the observations was assessed in a contemporary cohort (May–August). In the outpatient study, neither ACE inhibitors (hazard ratio [HR], 0.77; 0.53–1.13, P =0.18) nor ARBs (HR, 0.88; 0.61–1.26, P =0.48) were associated with hospitalization risk. ACE inhibitors were associated with lower hospitalization risk in the older Medicare group (HR, 0.61; 0.41–0.93, P =0.02), but not the younger commercially insured group (HR, 2.14; 0.82–5.60, P =0.12; P ‐interaction 0.09). Neither ACE inhibitors nor ARBs were associated with lower hospitalization risk in either population in the validation cohort. In the primary inpatient study cohort, neither ACE inhibitors (HR, 0.97; 0.81–1.16; P =0.74) nor ARBs (HR, 1.15; 0.95–1.38, P =0.15) were associated with in‐hospital mortality. These observations were consistent in the validation cohort. Conclusions ACE inhibitors and ARBs were not associated with COVID‐19 hospitalization or mortality. Despite early evidence for a potential association between ACE inhibitors and severe COVID‐19 prevention in older individuals, the inconsistency of this observation in recent data argues against a role for prophylaxis.

This study uses national death certificate data to characterize trends in death and excess mortality from drug overdoses, homicides, unintentional injuries, motor vehicle crashes, and suicide during the first 6 months of the pandemic in the US.

ObjectiveReal-world data have been critical for rapid-knowledge generation throughout the COVID-19 pandemic. To ensure high-quality results are delivered to guide clinical decision making and the public health response, as well as characterize the response to interventions, it is essential to establish the accuracy of COVID-19 case definitions derived from administrative data to identify infections and hospitalizations.MethodsElectronic Health Record (EHR) data were obtained from the clinical data warehouse of the Yale New Haven Health System (Yale, primary site) and 3 hospital systems of the Mayo Clinic (validation site). Detailed characteristics on demographics, diagnoses, and laboratory results were obtained for all patients with either a positive SARS-CoV-2 PCR or antigen test or ICD-10 diagnosis of COVID-19 (U07.1) between April 1, 2020 and March 1, 2021. Various computable phenotype definitions were evaluated for their accuracy to identify SARS-CoV-2 infection and COVID-19 hospitalizations.ResultsOf the 69,423 individuals with either a diagnosis code or a laboratory diagnosis of a SARS-CoV-2 infection at Yale, 61,023 had a principal or a secondary diagnosis code for COVID-19 and 50,355 had a positive SARS-CoV-2 test. Among those with a positive laboratory test, 38,506 (76.5%) and 3449 (6.8%) had a principal and secondary diagnosis code of COVID-19, respectively, while 8400 (16.7%) had no COVID-19 diagnosis. Moreover, of the 61,023 patients with a COVID-19 diagnosis code, 19,068 (31.2%) did not have a positive laboratory test for SARS-CoV-2 in the EHR. Of the 20 cases randomly sampled from this latter group for manual review, all had a COVID-19 diagnosis code related to asymptomatic testing with negative subsequent test results. The positive predictive value (precision) and sensitivity (recall) of a COVID-19 diagnosis in the medical record for a documented positive SARS-CoV-2 test were 68.8% and 83.3%, respectively. Among 5,109 patients who were hospitalized with a principal diagnosis of COVID-19, 4843 (94.8%) had a positive SARS-CoV-2 test within the 2 weeks preceding hospital admission or during hospitalization. In addition, 789 hospitalizations had a secondary diagnosis of COVID-19, of which 446 (56.5%) had a principal diagnosis consistent with severe clinical manifestation of COVID-19 (e.g., sepsis or respiratory failure). Compared with the cohort that had a principal diagnosis of COVID-19, those with a secondary diagnosis had a more than 2-fold higher in-hospital mortality rate (13.2% vs 28.0%, PConclusionsCOVID-19 diagnosis codes misclassified the SARS-CoV-2 infection status of many people, with implications for clinical research and epidemiological surveillance. Moreover, the codes had different performance across two academic health systems and identified groups with different risks of mortality. Real-world data from the EHR can be used to in conjunction with diagnosis codes to improve the identification of people infected with SARS-CoV-2.

Introduction The COVID-19 pandemic has been associated with substantial rates of all-cause excess mortality. The contribution of external causes of death to excess mortality including drug overdose, homicide, suicide, and unintentional injuries during the initial outbreak in the United States is less well documented. Methods Using public data published by the National Center for Health Statistics on February 10, 2021, we measured monthly excess mortality (the gap between observed and expected deaths) from five external causes using national-level data published by National Center for Health Statistics; assault (homicide); intentional self-harm (suicide); accidents (unintentional injuries); and motor vehicle accidents. We used seasonal autoregressive integrated moving average (sARIMA) models developed with cause-specific monthly mortality counts and US population data from 2015-2019 and estimated the contribution of individual cause-specific mortality to all-cause excess mortality from March-July 2020. Results From March-July, 2020, 212,825 (95% CI 136,236-290,776) all-cause excess deaths occurred in the US). There were 8,540 excess drug overdoses (all intents) (95% CI 5,106 to 11,975), accounting for 4% of all excess mortality; 1,455 excess homicide deaths (95% CI 708 to 2202, accounting for 0.7% of excess mortality; 5,492 excess deaths due to unintentional accidents occurred (95% CI 85 to 10,899, accounting for 2.6% of excess mortality. Though a non-significantly 135 (95% CI -1361 to 1,630) more MVA deaths were recorded during the study period, a significant decrease in April (525; 95% CI -817 to -233) and significant increases in June-July (965; 95% CI 348 to 1,587) were observed. Suicide deaths were statistically lower than projected by 2,067 (95% CI 941-3,193 fewer deaths). Meaning Excess deaths from drug overdoses, homicide, and addicents occurred during the pandemic but represented a small fraction of all-cause excess mortality. The excess external causes of death, however, still represent thousands of lives lost. Notably, deaths from suicide were lower than expected and therefore did not contribute to excess mortality.

ObjectivesWe identify a correction for excess mortality that takes the sudden unexpected changes in the size of the United States population into account.DesignThis is a weekly cross-sectional analysis of all-cause mortality since week 5, 2020. We describe and apply a simple correction that takes population changes into account in order to provide corrected weekly estimates of expected deaths for 2020 and 2021.SettingThe United States.ParticipantsAll United States residents.InterventionsThe covid-19 pandemic.Main outcome measuresExpected and excess mortality for the United States during the covid-19 period.ResultsAs of week 53, 2020 (ending January 2, 2021), approximately >10,200 more excess deaths have occurred in the United States than could be detected if expected deaths projections were not amended to reflect population decreases during 2020. The figure is projected to rise to >12,600 (>600 weekly) by week 5, 2021. Assuming recent excess mortality and pandemic-associated visa reductions continue until the earliest time herd immunity could be approached resulting from a combination of infections and vaccinations (week 17, 2021), if point estimates of expected deaths are not corrected, expected deaths will be overestimated (and therefore potential excess mortality underestimated) by ∼43,000 during 2021, or >53,300 since the outbreak of the pandemic measurement period (beginning week 5, 2020). By late December 2021, weekly expected death differences are projected to approach 1,000 per week.ConclusionsCurrent models measuring excess mortality should be revised immediately so that public health officials do not lose the ability to detect ongoing excess mortality as the population changes continue to compound, lowering the number of weekly expected deaths. A similar approach should be used in the middle and late phases of all future pandemics.

Background: Coronary artery bypass graft (CABG) surgery is a focus of bundled and alternate payment models that capture outcomes up to 90 days postsurgery. While clinical registry risk models perform well, measures encompassing mortality beyond 30 days do not currently exist. We aimed to develop a risk-adjusted hospital-level 90-day all-cause mortality measure intended for assessing hospital performance in payment models of CABG surgery using administrative data. Methods: Building upon Centers for Medicare and Medicaid Services hospital-level 30-day all-cause CABG mortality measure specifications, we extended the mortality timeframe to 90 days after surgery and developed a new hierarchical logistic regression model to calculate hospital risk-standardized 90-day all-cause mortality rates for patients hospitalized for isolated CABG. The model was derived from Medicare claims data for a 3-year cohort between July 2014 to June 2017. The data set was randomly split into 50:50 development and validation samples. The model performance was evaluated with C statistics, overfitting indices, and calibration plot. The empirical validity of the measure result at the hospital level was evaluated against the Society of Thoracic Surgeons composite star rating. Results: Among 137 819 CABG procedures performed in 1183 hospitals, the unadjusted mortality rate within 30 and 90 days were 3.1% and 4.7%, respectively. The final model included 27 variables. Hospital-level 90-day risk-standardized mortality rates ranged between 2.04% and 11.26%, with a median of 4.67%. C statistics in the development and validation samples were 0.766 and 0.772, respectively. We identified a strong positive correlation between 30- and 90-day risk-standardized mortality rates, with a regression slope of 1.09. Risk-standardized mortality rates also showed a stepwise trend of lower 90-day mortality with higher Society of Thoracic Surgeons composite star ratings. Conclusions: We present a measure of hospital-level 90-day risk-standardized mortality rates following isolated CABG. This measure complements Centers for Medicare and Medicaid Services’ existing 30-day CABG mortality measure by providing greater insight into the postacute recovery period. It offers a balancing measure to ensure efforts to reduce costs associated with CABG recovery and rehabilitation do not result in unintended consequences.

The learning health system is a conceptual model for continuous learning and knowledge generation rooted in the daily practice of medicine. While companies such as Google and Amazon use dynamic learning systems that learn iteratively through every customer interaction, this efficiency has not materialized on a comparable scale in health systems. An ideal learning health system would learn from every patient interaction to benefit the care for the next patient. Notable advances include the greater use of data generated in the course of clinical care, Common Data Models, and advanced analytics. However, many remaining barriers limit the most effective use of large and growing health care data assets. In this review, we explore the accomplishments, opportunities, and barriers to realizing the learning health system.

IntroductionCoronavirus disease-19 (COVID-19) has caused a marked increase in all-cause deaths in the United States, mostly among adults aged 65 and older. Because younger adults have far lower infection fatality rates, less attention has been focused on the mortality burden of COVID-19 in this demographic.MethodsWe performed an observational cohort study using public data from the National Center for Health Statistics at the United States Centers for Disease Control and Prevention, and CDC Wonder. We analyzed all-cause mortality among adults ages 25-44 during the COVID-19 pandemic in the United States. Further, we compared COVID-19-related deaths in this age group during the pandemic period to all drug overdose deaths and opioid-specific overdose deaths in each of the ten Health and Human Services (HHS) regions during the corresponding period of 2018, the most recent year for which data are available.ResultsAs of September 6, 2020, 74,027 all-cause deaths occurred among persons ages 25-44 years during the period from March 1st to July 31st, 2020, 14,155 more than during the same period of 2019, a 23% relative increase (incident rate ratio 1.23; 95% CI 1.21–1.24), with a peak of 30% occurring in May (IRR 1.30; 95% CI 1.27-1.33). In HHS Region 2 (New York, New Jersey), HHS Region 6 (Arkansas, Louisiana, New Mexico, Oklahoma, Texas), and HHS Region 9 (Arizona, California, Hawaii, Nevada), COVID-19 deaths exceeded 2018 unintentional opioid overdose deaths during at least one month. Combined, 2,450 COVID-19 deaths were recorded in these three regions during the pandemic period, compared to 2,445 opioid deaths during the same period of 2018.MeaningWe find that COVID-19 has likely become the leading cause of death—surpassing unintentional overdoses—among young adults aged 25-44 in some areas of the United States during substantial COVID-19 outbreaks.NoteThe data presented here have since been updated. As a result, an additional 1,902 all-cause deaths occurring among US adults ages 25-44 during the period of interest are not accounted for in this manuscript.

Background The electronic health record (EHR) holds the prospect of providing more complete and timely access to clinical information for biomedical research, quality assessments, and quality improvement compared to other data sources, such as administrative claims. In this study, we sought to assess the completeness and timeliness of structured diagnoses in the EHR compared to computed diagnoses for hypertension (HTN), hyperlipidemia (HLD), and diabetes mellitus (DM). Methods We determined the amount of time for a structured diagnosis to be recorded in the EHR from when an equivalent diagnosis could be computed from other structured data elements, such as vital signs and laboratory results. We used EHR data for encounters from January 1, 2012 through February 10, 2019 from an academic health system. Diagnoses for HTN, HLD, and DM were computed for patients with at least two observations above threshold separated by at least 30 days, where the thresholds were outpatient blood pressure of ≥ 140/90 mmHg, any low-density lipoprotein ≥ 130 mg/dl, or any hemoglobin A1c ≥ 6.5%, respectively. The primary measure was the length of time between the computed diagnosis and the time at which a structured diagnosis could be identified within the EHR history or problem list. Results We found that 39.8% of those with HTN, 21.6% with HLD, and 5.2% with DM did not receive a corresponding structured diagnosis recorded in the EHR. For those who received a structured diagnosis, a mean of 389, 198, and 166 days elapsed before the patient had the corresponding diagnosis of HTN, HLD, or DM, respectively, recorded in the EHR. Conclusions We found a marked temporal delay between when a diagnosis can be computed or inferred and when an equivalent structured diagnosis is recorded within the EHR. These findings demonstrate the continued need for additional study of the EHR to avoid bias when using observational data and reinforce the need for computational approaches to identify clinical phenotypes.

Objectives: This study aimed to characterize the distribution of acute care visits among Medicare beneficiaries receiving skilled nursing facility (SNF) services. Methods: We conducted a cross-sectional analysis of a 20% sample of continuously enrolled Medicare beneficiaries in the 2012 Chronic Condition Warehouse data set. Beneficiaries were grouped by the number of days of SNF services, and acute care visits were categorized as “before SNF,” “during SNF,” or “after SNF.” Results: Among the 10,717,786 Medicare beneficiaries analyzed, 384,312 (3.6%) had at least one SNF stay. Discussion: Beneficiaries who received SNF services had a higher proportion of acute care visits made to emergency departments (EDs) than beneficiaries who did not receive SNF services. Also, a higher proportion of acute care visits were made to EDs by beneficiaries after a SNF stay in comparison to residents actively residing in a SNF. The acute care capabilities of SNFs and post-SNF transitions of care to the community setting are discussed.