Health Outcome Changes in Individuals With Type 1 Diabetes After a State-Level Insulin Copayment Cap.

Importance: Many insulin users ration doses due to high out-of-pocket costs. Starting January 2020 with Colorado, 25 states and the District of Columbia enacted laws that cap insulin copayments. Objective: To estimate the association of Colorado's $100 copayment cap with out-of-pocket spending, medication adherence, and health care services utilization for diabetes-related complications. Design, Setting, and Participants: In this cohort study using Colorado's All-Payer Claims Database, nonelderly insulin users with type 1 diabetes were analyzed from January 2019 to December 2020. Outcome changes were compared in the prepolicy and postpolicy period among individuals continuously enrolled in state-regulated and non-state-regulated plans using difference-in-differences regressions. Subgroup analyses were conducted based on individuals' prepolicy spending (low: never ≥$100 out-of-pocket vs high: ≥$100 out-of-pocket cost at least once). Data were analyzed from June 2023 to May 2024. Exposure: Enrollment in state-regulated health insurance plans subject to the copayment cap legislation. Main Outcomes and Measures: Adherence to basal and bolus insulin treatment was evaluated using the proportion of days covered measure, out-of-pocket spending reflected prescription cost for a 30-day supply, and health care utilization for diabetes-related complications was identified using primary diagnosis codes from medical claims data. Results: The panel included 1629 individuals with type 1 diabetes (39 096 person-months), of which 924 were male (56.7%), 540 (33.1%) had 1 or more comorbidities, and the mean (SD) age was 40.6 (15.9) years. Overall, the copayment cap was associated with out-of-pocket spending declines of $17.3 (95% CI, -$27.3 to -$7.3) for basal and $11.5 (95% CI, -$24.7 to $1.7) for bolus insulins and increases in adherence of 3.2 (95% CI, 0.0 to 6.5) percentage points for basal and 3.3 (95% CI, 0.3 to 6.4) percentage points for bolus insulins. Changes in adherence were associated with increases within the prepolicy high-spending group (basal, 9.9; 95% CI, 2.4 to 17.4 percentage points; bolus, 13.0; 95% CI, 5.1 to 20.9 percentage points). The policy was also associated with a mean reduction of -0.09 (95% CI, -0.16 to -0.02) medical claims for diabetes-related complications per person per month among high spenders, a 30% decrease. Conclusions and Relevance: In this cohort study of Colorado's insulin copayment cap among individuals with type 1 diabetes, the policy was associated with an overall decline in out-of-pocket spending, an increase in medication adherence, and a decline in claims for diabetes-related complications only among insulin users who spent more than $100 in the prepolicy period at least once.

Colorado Insulin Copay Cap: Lower Out-Of-Pocket Payments, Increased Prescription Volume And Days' Supply.

In 2020, Colorado became the first state to cap out-of-pocket spending for insulin prescriptions, requiring fully insured health plans to cap out-of-pocket spending at $100 for a thirty-day supply. We provide the first evidence on the association of Colorado's Insulin Affordability Program with patient out-of-pocket spending, the amounts paid by plans per insulin prescription, and prescription filling. Using statewide claims data from the period 2018-21, we focused on the first two years that the copay cap law was in effect. We found that Colorado's Insulin Affordability Program was associated with significant reductions in out-of-pocket spending for insulin prescriptions, with the mean out-of-pocket payment per thirty-day supply falling nearly in half (from $62.59 to $35.64). Average plan payments increased slightly more ($31.39) than the decrease in out-of-pocket spending, as the total amount paid per prescription increased by about 1 percent. The average insulin user realized annual savings of $184, while the mean number of fills and the mean days' supply per year increased by 4.2 percent and 11.4 percent, respectively.

Effectiveness of COVID-19 booster vaccination, morbidity and absenteeism among healthcare personnel during the 2022-2023 season dominated by Omicron BA.5 and BA.2 subvariants.

AIM: We assessed the vaccination effectiveness (VE) of a COVID-19 booster vaccine dose and the association between morbidity and absenteeism with COVID-19 booster vaccine receipt among healthcare personnel (HCP) in 2022-2023 in Greece. METHODS: We followed 5752 HCP from November 14, 2022 through May 28, 2023 for episodes of absenteeism. Absenteeism for non-infectious causes, pregnancy leave, or annual leave was not recorded. Full vaccination was defined as a primary vaccination series plus one booster dose within the past six months. Multivariable regression models were used to estimate the association of full COVID-19 vaccination with the outcomes of interest. RESULTS: A total of 1029 episodes of absenteeism occurred during the study period (17.9 episodes per 100 HCP). The mean duration of absence per episode was 5.2 days, and the total duration of absence was 5237 days. COVID-19 was diagnosed in 736 (12.8 %) HCP, asymptomatic SARS-CoV-2 infection in 62 (1.1 %) HCP, and influenza in 95 (1.7 %) HCP. Overall, COVID-19, influenza, and asymptomatic SARS-CoV-2 infection accounted for 71.5 %, 9.2 %, and 6.0 % of episodes of absenteeism, respectively. Multivariable regression models indicated that fully vaccinated HCP were absent from work for shorter periods [adjusted odds ratio (aOR): 0.42; 95 % confidence interval (CI): 0.21-0.83], were less likely to develop COVID-19 [aOR: 0.37; 95 % CI: 0.17-0.81)], and were more likely to develop an asymptomatic SARS-CoV-2 infection (aOR: 5.90; 95 % CI: 1.27-27.45). The adjusted full VE against COVID-19 was 62.8 % (95 % CI: 18.6 %-83.0 %). CONCLUSIONS: COVID-19 remains a significant cause of morbidity and absenteeism among HCP. Full COVID-19 vaccination status conferred significant protection against COVID-19 and was associated with shorter periods of absence from work.

Comparison of morbidity and absenteeism due to COVID-19 and seasonal influenza in a large cohort of health care personnel in the 2022 to 2023 season.

BACKGROUND: To compare the morbidity and work absenteeism associated with coronavirus disease 2019 (COVID-19) and influenza among health care personnel (HCP) in 2022 to 2023. METHODS: We followed 5,752 hospital-based HCP in Greece from November 14, 2022 through May 28, 2023. Symptomatic HCP was tested for SARS-CoV-2 and influenza by real-time polymerase chain reaction and/or rapid antigen detection test. The association between the duration of absenteeism and the type of disease was estimated by multivariable regression models. RESULTS: A total of 734 COVID-19 cases and 93 influenza cases were studied. The mean duration of absence per COVID-19 case was 5.8days compared with a mean of absence of 3.6days per influenza case (P value <.001). Overall, COVID-19 accounted for 4,245days missed during the study period compared with 333days missed due to influenza. Multivariable regression estimates indicated that HCP with COVID-19 had 1.91 more days of absenteeism (95% confidence interval 1.67-2.15) compared with those with influenza, on average. CONCLUSIONS: As SARS-CoV-2 becomes endemic, COVID-19 remains the prevalent cause of morbidity and absenteeism among HCP, accounting for considerably more workdays missed compared with influenza. HCP should be up-to-date with COVID-19 booster vaccinations and annual influenza vaccination in order to protect them as well as health care systems from HCP absenteeism.

Hospital Readmissions in Patients Supported with Durable Centrifugal-Flow Left Ventricular Assist Devices.

Background: Centrifugal-flow left ventricular assist devices (CF-LVADs) have improved morbidity and mortality for their recipients. Hospital readmissions remain common, negatively impacting quality of life and survival. We sought to identify risk factors associated with hospital readmissions among patients with CF-LVADs. Methods: Consecutive patients receiving a CF-LVAD between February 2011 and March 2021 were retrospectively evaluated using prospectively maintained institutional databases. Hospital readmissions within three years post-LVAD implantation were dichotomized into heart failure (HF)/LVAD-related or non-HF/LVAD-related readmissions. Multivariable Cox regression models augmented using a machine learning algorithm, the least absolute shrinkage and selection operator (LASSO) method, for variable selection were used to estimate associations between HF/LVAD-related readmissions and pre-, intra- and post-operative clinical variables. Results: A total of 204 CF-LVAD recipients were included, of which 138 (67.7%) had at least one HF/LVAD-related readmission. HF/LVAD-related readmissions accounted for 74.4% (436/586) of total readmissions. The main reasons for HF/LVAD-related readmissions were major bleeding, major infection, HF exacerbation, and neurological dysfunction. Using pre-LVAD variables, HF/LVAD-related readmissions were associated with substance use, previous cardiac surgery, HF duration, pre-LVAD inotrope dependence, percutaneous LVAD/VA-ECMO support, LVAD type, and the left ventricular ejection fraction in multivariable analysis (Harrell's concordance c-statistic; 0.629). After adding intra- and post-operative variables in the multivariable model, LVAD implant hospitalization length of stay was an additional predictor of readmission. Conclusions: Using machine learning-based techniques, we generated models identifying pre-, intra-, and post-operative variables associated with a higher likelihood of rehospitalizations among patients on CF-LVAD support. These models could provide guidance in identifying patients with increased readmission risk for whom clinical strategies to mitigate this risk may further improve LVAD recipient outcomes.

Waning effectiveness of mRNA COVID-19 vaccines against inpatient and emergency department encounters.

In the United States, most real-world estimates of COVID-19 vaccine effectiveness are based on data drawn from large health systems or sentinel populations. More data is needed to understand how the benefits of vaccination may vary across US populations with disparate risk profiles and policy contexts. We aimed to provide estimates of mRNA COVID-19 vaccine effectiveness against moderate and severe outcomes of COVID-19 based on state population-level data sources. Using statewide integrated administrative and clinical data and a test-negative case-control study design, we assessed mRNA COVID-19 vaccine effectiveness against SARS-CoV-2-related hospitalizations and emergency department visits among adults in South Carolina. We presented estimates of vaccine effectiveness at discrete time intervals for adults who received one, two or three doses of mRNA COVID-19 vaccine compared to adults who were unvaccinated. We also evaluated changes in vaccine effectiveness over time (waning) for the overall sample and in subgroups defined by age. We showed that while two doses of mRNA COVID-19 vaccine were initially highly effective, vaccine effectiveness waned as time elapsed since the second dose. Compared to protection against hospitalizations, protection against emergency department visits was found to wane more sharply. In all cases, a third dose of mRNA COVID-19 vaccine conferred significant gains in protection relative to waning protection after two doses. Further, over more than 120 days of follow-up, the data revealed relatively limited waning of vaccine effectiveness after a third dose of mRNA COVID-19 vaccine.

Effectiveness of COVID-19 vaccination against school absenteeism in children and adolescents hospitalized with COVID-19.

BACKGROUND: COVID-19 vaccination has been recommended for children to protect them and to enable in-person educational and social activities. METHODS: We estimated COVID-19 vaccination effectiveness (VE) against school absenteeism in children 5-17 years old hospitalized from September 1, 2021 through May 31, 2023. Full vaccination was defined as two vaccine doses. RESULTS: We studied 231 children admitted to hospital with COVID-19, including 206 (89.2 %) unvaccinated/partially vaccinated and 25 (10.8 %) fully vaccinated. Unvaccinated/partially vaccinated children were absent from school for longer periods compared to fully vaccinated children (median absence: 14 versus 10 days; p-value = 0.05). Multivariable regression showed that full COVID-19 vaccination was associated with fewer days of absence compared to no/partial vaccination on average (adjusted relative risk: 0.77; 95 % CI: 0.61 to 0.98). COVID-19 VE was 50.7 % (95 % CI: -11.3 % to 78.2 %) for school absenteeism above the median duration of absenteeism. CONCLUSIONS: Full COVID-19 vaccination conferred protection against school absenteeism in hospitalized school-aged children with COVID-19.

Machine Learning Multicenter Risk Model to Predict Right Ventricular Failure After Mechanical Circulatory Support: The STOP-RVF Score.

Importance: The existing models predicting right ventricular failure (RVF) after durable left ventricular assist device (LVAD) support might be limited, partly due to lack of external validation, marginal predictive power, and absence of intraoperative characteristics. Objective: To derive and validate a risk model to predict RVF after LVAD implantation. Design, Setting, and Participants: This was a hybrid prospective-retrospective multicenter cohort study conducted from April 2008 to July 2019 of patients with advanced heart failure (HF) requiring continuous-flow LVAD. The derivation cohort included patients enrolled at 5 institutions. The external validation cohort included patients enrolled at a sixth institution within the same period. Study data were analyzed October 2022 to August 2023. Exposures: Study participants underwent chronic continuous-flow LVAD support. Main Outcome and Measures: The primary outcome was RVF incidence, defined as the need for RV assist device or intravenous inotropes for greater than 14 days. Bootstrap imputation and adaptive least absolute shrinkage and selection operator variable selection techniques were used to derive a predictive model. An RVF risk calculator (STOP-RVF) was then developed and subsequently externally validated, which can provide personalized quantification of the risk for LVAD candidates. Its predictive accuracy was compared with previously published RVF scores. Results: The derivation cohort included 798 patients (mean [SE] age, 56.1 [13.2] years; 668 male [83.7%]). The external validation cohort included 327 patients. RVF developed in 193 of 798 patients (24.2%) in the derivation cohort and 107 of 327 patients (32.7%) in the validation cohort. Preimplant variables associated with postoperative RVF included nonischemic cardiomyopathy, intra-aortic balloon pump, microaxial percutaneous left ventricular assist device/venoarterial extracorporeal membrane oxygenation, LVAD configuration, Interagency Registry for Mechanically Assisted Circulatory Support profiles 1 to 2, right atrial/pulmonary capillary wedge pressure ratio, use of angiotensin-converting enzyme inhibitors, platelet count, and serum sodium, albumin, and creatinine levels. Inclusion of intraoperative characteristics did not improve model performance. The calculator achieved a C statistic of 0.75 (95% CI, 0.71-0.79) in the derivation cohort and 0.73 (95% CI, 0.67-0.80) in the validation cohort. Cumulative survival was higher in patients composing the low-risk group (estimated <20% RVF risk) compared with those in the higher-risk groups. The STOP-RVF risk calculator exhibited a significantly better performance than commonly used risk scores proposed by Kormos et al (C statistic, 0.58; 95% CI, 0.53-0.63) and Drakos et al (C statistic, 0.62; 95% CI, 0.57-0.67). Conclusions and Relevance: Implementing routine clinical data, this multicenter cohort study derived and validated the STOP-RVF calculator as a personalized risk assessment tool for the prediction of RVF and RVF-associated all-cause mortality.

Concordance in Medical Urgency Classification of Discharge Diagnoses and Reasons for Visit.

Importance: Current policies to divert emergency department (ED) visits for less medically urgent conditions to more cost-effective settings rely on retrospective adjudication of discharge diagnoses. However, patients present to the ED with concerns, making it challenging for clinicians. Objective: To characterize ED visits based on the medical urgency of the presenting reasons for visit and to explore the concordance between discharge diagnoses and reasons for visit. Design, Setting, and Participants: In this retrospective, cross-sectional study, a nationwide sample of ED visits by adults (aged ≥18 years) in the US from the 2018 and 2019 calendar years' ED data of the National Hospital Ambulatory Medical Care Survey was used. An algorithm to probabilistically assign ED visits into medical urgency categories based on the presenting reason for visit was developed. A 3-step, look-back method was applied using an updated version of the New York University ED algorithm, and a map of all possible discharge diagnoses to the same reasons for visit was developed. Analyses were conducted in July and August 2023. Main Outcomes and Measures: The main outcome was probabilistic medical urgency classification of reasons for visits and discharge diagnoses and their concordance. Results: We analyzed 27 068 ED visits (mean age, 48.2% years [95% CI, 47.5%-48.9% years]) representing 190.7 million visits nationwide. Women (mean, 57.0% [95% CI, 55.9%-58.1%]) and patients with public health insurance coverage, including Medicare (mean, 24.9% [95% CI, 21.9%-28.0%]) and Medicaid (mean, 25.1% [95% CI, 21.0%-29.2%]), accounted for the largest share of ED visits, and a mean of 13.2% (95% CI, 11.4%-15.0%) of all visits resulted in a hospital admission. Overall, about 38.5% and 53.9% of all ED visits were classified with 100% and 75% probabilities, respectively, as injury related, emergency care needed, emergent but primary care treatable, nonemergent, or mental health or substance use disorders related based on discharge diagnosis compared with 0.4% and 12.4%, respectively, of all encounters based on patients' reason for visit. Among discharge diagnoses assigned with high certainty to only 1 urgency category using the New York University ED algorithm, between 38.0% (95% CI, 36.3%-39.6%) and 57.4% (95% CI, 56.0%-58.8%) aligned with the probabilistic categorical assignments of their corresponding reasons for visit. Conclusions and Relevance: In this cross-sectional study of 190.7 million ED visits among adults aged 18 years or older, a smaller percentage of reasons for visit could be prospectively categorized with high accuracy to a specific medical urgency category compared with all visits based on discharge diagnoses, and a limited concordance between reasons for visit and discharge diagnoses was found. Alternative methods are needed to identify the medical necessity of ED encounters more accurately.