Judicial Decisions Constraining Public Health Powers During COVID-19: Implications For Public Health Policy Making.

Public health legal powers are increasingly under pressure from the courts in the United States. During the COVID-19 pandemic, individuals and organizations successfully challenged many community mitigation orders (for example, mask mandates, vaccination mandates, and restrictions on gatherings), demonstrating the legal vulnerability of disease control measures. Analyzing 112 judicial decisions in which the plaintiff prevailed from March 2020 through March 2023, we examined the ways in which courts constrained public health powers during the COVID-19 pandemic. We found that in these 112 decisions, courts shifted how they analyze religious liberty claims and reviewed challenges to the exercise of statutory powers by health officials in novel ways. We discuss implications for public health policy going forward, and we recommend ways in which legislatures and health officials can design policies to maximize their prospects of surviving legal challenges.

Integration of Febio as an Instructional Tool in the Undergraduate Biomechanics Curriculum.

Computer simulations play an important role in a range of biomedical engineering applications. Thus, it is important that biomedical engineering students engage with modeling in their undergraduate education and establish an understanding of its practice. In addition, computational tools enhance active learning and complement standard pedagogical approaches to promote student understanding of course content. Herein, we describe the development and implementation of learning modules for computational modeling and simulation (CM&S) within an undergraduate biomechanics course. We developed four CM&S learning modules that targeted predefined course goals and learning outcomes within the febio studio software. For each module, students were guided through CM&S tutorials and tasked to construct and analyze more advanced models to assess learning and competency and evaluate module effectiveness. Results showed that students demonstrated an increased interest in CM&S through module progression and that modules promoted the understanding of course content. In addition, students exhibited increased understanding and competency in finite element model development and simulation software use. Lastly, it was evident that students recognized the importance of coupling theory, experiments, and modeling and understood the importance of CM&S in biomedical engineering and its broad application. Our findings suggest that integrating well-designed CM&S modules into undergraduate biomedical engineering education holds much promise in supporting student learning experiences and introducing students to modern engineering tools relevant to professional development.

Enrollment in High-Deductible Health Plans and Incident Diabetes Complications.

Importance: Preventing diabetes complications requires monitoring and control of hyperglycemia and cardiovascular risk factors. Switching to high-deductible health plans (HDHPs) has been shown to hinder aspects of diabetes care; however, the association of HDHP enrollment with microvascular and macrovascular diabetes complications is unknown. Objective: To examine the association between an employer-required switch to an HDHP and incident complications of diabetes. Design, Setting, and Participants: This retrospective cohort study used deidentified administrative claims data for US adults with diabetes enrolled in employer-sponsored health plans between January 1, 2010, and December 31, 2019. Data analysis was performed from May 26, 2022, to January 2, 2024. Exposures: Adults with a baseline year of non-HDHP enrollment who had to switch to an HDHP because their employer offered no non-HDHP alternative in that year were compared with adults who were continuously enrolled in a non-HDHP. Main Outcomes and Measures: Mixed-effects logistic regression models examined the association between switching to an HDHP and, individually, the odds of myocardial infarction, stroke, hospitalization for heart failure, lower-extremity complication, end-stage kidney disease, proliferative retinopathy, treatment for retinopathy, and blindness. Models were adjusted for demographics, comorbidities, and medications, with inverse propensity score weighting used to account for potential selection bias. Results: The study included 42 326 adults who switched to an HDHP (mean [SD] age, 52 [10] years; 19 752 [46.7%] female) and 202 729 adults who did not switch (mean [SD] age, 53 [10] years; 89 828 [44.3%] female). Those who switched to an HDHP had greater odds of experiencing all diabetes complications (odds ratio [OR], 1.11; 95% CI, 1.06-1.16 for myocardial infarction; OR, 1.15; 95% CI, 1.09-1.21 for stroke; OR, 1.35; 95% CI, 1.30-1.41 for hospitalization for heart failure; OR, 2.53; 95% CI, 2.38-2.70 for end-stage kidney disease; OR, 2.23; 95% CI, 2.17-2.29 for lower-extremity complication; OR, 1.17; 95% CI, 1.13-1.21 for proliferative retinopathy; OR, 2.35; 95% CI, 2.18-2.54 for blindness; and OR, 2.28; 95% CI, 2.15-2.41 for retinopathy treatment). Conclusions and Relevance: This study found that an employer-driven switch to an HDHP was associated with increased odds of experiencing all diabetes complications. These findings reinforce the potential harm associated with HDHPs for people with diabetes and the importance of affordable and accessible chronic disease management, which is hindered by high out-of-pocket costs incurred by HDHPs.

Influence of material parameter variability on the predicted coronary artery biomechanical environment via uncertainty quantification.

Central to the clinical adoption of patient-specific modeling strategies is demonstrating that simulation results are reliable and safe. Indeed, simulation frameworks must be robust to uncertainty in model input(s), and levels of confidence should accompany results. In this study, we applied a coupled uncertainty quantification-finite element (FE) framework to understand the impact of uncertainty in vascular material properties on variability in predicted stresses. Univariate probability distributions were fit to material parameters derived from layer-specific mechanical behavior testing of human coronary tissue. Parameters were assumed to be probabilistically independent, allowing for efficient parameter ensemble sampling. In an idealized coronary artery geometry, a forward FE model for each parameter ensemble was created to predict tissue stresses under physiologic loading. An emulator was constructed within the UncertainSCI software using polynomial chaos techniques, and statistics and sensitivities were directly computed. Results demonstrated that material parameter uncertainty propagates to variability in predicted stresses across the vessel wall, with the largest dispersions in stress within the adventitial layer. Variability in stress was most sensitive to uncertainties in the anisotropic component of the strain energy function. Moreover, unary and binary interactions within the adventitial layer were the main contributors to stress variance, and the leading factor in stress variability was uncertainty in the stress-like material parameter that describes the contribution of the embedded fibers to the overall artery stiffness. Results from a patient-specific coronary model confirmed many of these findings. Collectively, these data highlight the impact of material property variation on uncertainty in predicted artery stresses and present a pipeline to explore and characterize forward model uncertainty in computational biomechanics.

Influence of Material Parameter Variability on the Predicted Coronary Artery Biomechanical Environment via Uncertainty Quantification.

Central to the clinical adoption of patient-specific modeling strategies is demonstrating that simulation results are reliable and safe. Indeed, simulation frameworks must be robust to uncertainty in model input(s), and levels of confidence should accompany results. In this study, we applied a coupled uncertainty quantification-finite element (FE) framework to understand the impact of uncertainty in vascular material properties on variability in predicted stresses. Univariate probability distributions were fit to material parameters derived from layer-specific mechanical behavior testing of human coronary tissue. Parameters were assumed to be probabilistically independent, allowing for efficient parameter ensemble sampling. In an idealized coronary artery geometry, a forward FE model for each parameter ensemble was created to predict tissue stresses under physiologic loading. An emulator was constructed within the UncertainSCI software using polynomial chaos techniques, and statistics and sensitivities were directly computed. Results demonstrated that material parameter uncertainty propagates to variability in predicted stresses across the vessel wall, with the largest dispersions in stress within the adventitial layer. Variability in stress was most sensitive to uncertainties in the anisotropic component of the strain energy function. Moreover, unary and binary interactions within the adventitial layer were the main contributors to stress variance, and the leading factor in stress variability was uncertainty in the stress-like material parameter that describes the contribution of the embedded fibers to the overall artery stiffness. Results from a patient-specific coronary model confirmed many of these findings. Collectively, these data highlight the impact of material property variation on uncertainty in predicted artery stresses and present a pipeline to explore and characterize forward model uncertainty in computational biomechanics.

The geometric evolution of aortic dissections: Predicting surgical success using fluctuations in integrated Gaussian curvature.

Clinical imaging modalities are a mainstay of modern disease management, but the full utilization of imaging-based data remains elusive. Aortic disease is defined by anatomic scalars quantifying aortic size, even though aortic disease progression initiates complex shape changes. We present an imaging-based geometric descriptor, inspired by fundamental ideas from topology and soft-matter physics that captures dynamic shape evolution. The aorta is reduced to a two-dimensional mathematical surface in space whose geometry is fully characterized by the local principal curvatures. Disease causes deviation from the smooth bent cylindrical shape of normal aortas, leading to a family of highly heterogeneous surfaces of varying shapes and sizes. To deconvolute changes in shape from size, the shape is characterized using integrated Gaussian curvature or total curvature. The fluctuation in total curvature (δK) across aortic surfaces captures heterogeneous morphologic evolution by characterizing local shape changes. We discover that aortic morphology evolves with a power-law defined behavior with rapidly increasing δK forming the hallmark of aortic disease. Divergent δK is seen for highly diseased aortas indicative of impending topologic catastrophe or aortic rupture. We also show that aortic size (surface area or enclosed aortic volume) scales as a generalized cylinder for all shapes. Classification accuracy for predicting aortic disease state (normal, diseased with successful surgery, and diseased with failed surgical outcomes) is 92.8±1.7%. The analysis of δK can be applied on any three-dimensional geometric structure and thus may be extended to other clinical problems of characterizing disease through captured anatomic changes.

Evaluation of High-Deductible Health Plans and Acute Glycemic Complications Among Adults With Diabetes.

Importance: Optimal diabetes care requires regular monitoring and care to maintain glycemic control. How high-deductible health plans (HDHPs), which reduce overall spending but may impede care by increasing out-of-pocket expenses, are associated with risks of severe hypoglycemia and hyperglycemia is unknown. Objective: To examine the association between an employer-forced switch to HDHP and severe hypoglycemia and hyperglycemia. Design, Setting, and Participants: This retrospective cohort study used deidentified administrative claims data for privately insured adults with diabetes from a single insurance carrier with multiple plans across the US between January 1, 2010, and December 31, 2018. Analyses were conducted between May 15, 2020, and November 3, 2022. Exposures: Patients with 1 baseline year of enrollment in a non-HDHP whose employers subsequently forced a switch to an HDHP were compared with patients who did not switch. Main Outcomes and Measures: Mixed-effects logistic regression models were used to examine the association between switching to an HDHP and the odds of severe hypoglycemia and hyperglycemia (ascertained using diagnosis codes in emergency department [ED] visits and hospitalizations), adjusting for patient age, sex, race and ethnicity, region, income, comorbidities, glucose-lowering medications, baseline ED and hospital visits for hypoglycemia and hyperglycemia, and baseline deductible amount, and applying inverse propensity score weighting to account for potential treatment selection bias. Results: The study population was composed of 42 326 patients who switched to an HDHP (mean [SD] age: 52 [10] years, 19 752 [46.7%] women, 7375 [17.4%] Black, 5740 [13.6%] Hispanic, 26 572 [62.8%] non-Hispanic White) and 202 729 patients who did not switch (mean [SD] age, 53 [10] years, 89 828 [44.3%] women, 29 551 [14.6%] Black, 26 689 [13.2%] Hispanic, 130 843 [64.5%] non-Hispanic White). When comparing all study years, switching to an HDHP was not associated with increased odds of experiencing at least 1 hypoglycemia-related ED visit or hospitalization (OR, 1.01 [95% CI, 0.95-1.06]; P = .85), but each year of HDHP enrollment did increase these odds by 2% (OR, 1.02 [95% CI, 1.00-1.04]; P = .04). In contrast, switching to an HDHP did significantly increase the odds of experiencing at least 1 hyperglycemia-related ED visit or hospitalization (OR, 1.25 [95% CI, 1.11-1.42]; P < .001), with each year of HDHP enrollment increasing the odds by 5% (OR, 1.05 [95% CI, 1.01-1.09]; P = .02). Conclusions and Relevance: In this cohort study, employer-forced switching to an HDHP was associated with increased odds of potentially preventable acute diabetes complications, potentially because of delayed or deferred care. These findings suggest that employers should be more judicious in their health plan offerings, and health plans and policy makers should consider allowing preventive and high-value services to be exempt from deductible requirements.

Flow-based method demonstrates improved accuracy for calculating wall shear stress in arterial flows from 4D flow MRI data.

Four-dimensional flow magnetic resonance imaging (i.e., 4D flow MRI) has become a valuable tool for the in vivo assessment of blood flow within large vessels and cardiac chambers. As wall shear stress (WSS) has been correlated with the development and progression of cardiovascular disease, focus has been directed at developing techniques to quantify WSS directly from 4D flow MRI data. The goal of this study was to compare the accuracy of two such techniques - termed the velocity and flow-based methods - in the setting of simplified and complex flow scenarios. Synthetic MR data were created from exact solutions to the Navier-Stokes equations for the steady and pulsatile flow of an incompressible, Newtonian fluid through a rigid cylinder. In addition, synthetic MR data were created from the predicted velocity fields derived from a fluid-structure interaction (FSI) model of pulsatile flow through a thick-walled, multi-layered model of the carotid bifurcation. Compared to the analytical solutions for steady and pulsatile flow, the flow-based method demonstrated greater accuracy than the velocity-based method in calculating WSS across all changes in fluid velocity/flow rate, tube radius, and image signal-to-noise (p < 0.001). Furthermore, the velocity-based method was more sensitive to boundary segmentation than the flow-based method. When compared to results from the FSI model, the flow-based method demonstrated greater accuracy than the velocity-based method with average differences in time-averaged WSS of 0.31 ± 1.03 Pa and 0.45 ± 1.03 Pa, respectively (p <0.005). These results have implications on the utility, accuracy, and clinical translational of methods to determine WSS from 4D flow MRI.

Revascularizations and limb outcomes of hospitalized patients with diabetic peripheral arterial disease in the contemporary era.

BACKGROUND: Concomitant diabetes mellitus and peripheral artery disease (PAD) is a complex disease process. This retrospective analysis of the National Inpatient Sample sought to understand trends in limb outcomes of this unique and prevalent cohort of patients. METHODS: The National Inpatient Sample was queried between 2003 and 2017 for hospitalizations of patients with both type 2 diabetes mellitus and PAD. Trends in hospitalizations, limb outcomes, vascular interventions, and costs were analyzed. RESULTS: There were 10,303,673 hospitalizations of patients with concomitant diabetes mellitus and PAD that were identified between 2003 and 2017. The prevalence of hospitalizations associated with this disease process increased from 1644 to 3228 per 100,000 hospitalizations, a 96.4% increase. This included an increase of 288 to 587 per 100,000 hospitalizations of patients aged 18 to 49 years old, which was accompanied by a 10.8% increase in minor amputations. Nontraumatic lower extremity amputations decreased overall. Black and Hispanic ethnicity were associated with an increased risk for amputation, along with Medicaid insurance and lower income quartile. Inpatient endovascular revascularization has increased over time with an associated decrease in open revascularization procedures. Amputation-related hospital costs significantly increased from $6.6 billion in 2003 to $14.8 billion in 2017. CONCLUSIONS: An alarming increase of disease prevalence, negative in-hospital limb outcomes, and costs are seen in the current era in this analysis of patients with concurrent diabetes and PAD.

Out-of-Pocket Cost Burden Associated With Contemporary Management of Advanced Prostate Cancer Among Commercially Insured Patients.

PURPOSE: Out-of-pocket costs represent an important component of financial toxicity and may impact patients' receipt of care. Herein, we evaluated patient-level factors associated with out-of-pocket costs for contemporary advanced prostate cancer treatment options. MATERIALS AND METHODS: We identified all commercially insured men receiving treatment for advanced prostate cancer between 2007 and 2019 within the OptumLabs Data Warehouse®. Patients were categorized into 3 treatment groups: androgen deprivation monotherapy, novel hormonal therapy, and nonandrogen systemic therapy. The primary outcome was out-of-pocket costs in the first year of treatment. The associations of treatment and patient variables with out-of-pocket costs were assessed using multivariable regression models. All costs were adjusted to reflect 2019 U.S. dollars using the Consumer Price Index. RESULTS: In a cohort of 13,409 men 81% (n = 10,926) received androgen deprivation monotherapy, 6% (n = 832) novel hormonal therapy, and 12% (n = 1,651) nonandrogen systemic therapy. Mean treatment-related out-of-pocket costs in the first year were $165, $4,236, and $994 for androgen deprivation monotherapy, novel hormonal therapy, and nonandrogen systemic therapy, respectively. The adjusted difference in annual treatment-related out-of-pocket costs for novel hormonal therapy and nonandrogen systemic therapy were $2,581 (95% CI: $1,923-$3,240) and $752 (95% CI: $600-$903) higher than androgen deprivation monotherapy, respectively. Patient characteristics associated (P < .05) with higher treatment-related out-of-pocket costs included older age (65-74 years), Black race, lower comorbidity scores, and lower household income. CONCLUSIONS: Patients receiving novel hormonal therapy for advanced prostate cancer had substantially higher treatment-related out-of-pocket costs. In addition to raising awareness among prescribers, these data support the inclusion of treatment associated financial toxicity in shared decision making for advanced prostate cancer and call attention to subgroups of patients particularly vulnerable to financial toxicity.

Modernizing Diabetes Care Quality Measures.

The proliferation of diabetes quality measures in the US since the mid-1990s has increased the burden of measurement without commensurate improvements in the quality of care or health outcomes. Measures in use today do not represent or incentivize achievement of care goals in all domains of quality that are necessary to achieve optimal diabetes health. We recommend reimagining and improving diabetes quality measurement through the following propositions: widespread adoption of new measures and modernization of existing measures across six domains of quality; use of a subset of new and modernized metrics as top-line measures for reporting and reimbursement; and optional use of the remaining new and modernized measures for evaluative purposes at all levels of the care delivery system to identify and address gaps in care quality and outcomes. These propositions would support practices and policies at all levels of the health care system to improve the health of people with diabetes.

Insights from Expanded Adverse Childhood Experiences Screening in a Hospital-Based Outpatient Psychiatry Service.

Adverse childhood experiences (ACEs) are associated with poor mental health in adulthood. Comprehensive prevalence data encompassing all 10 ACE questionnaire items has not previously been described in a hospital-based outpatient psychiatric clinic. This study assessed the prevalence of 10 ACEs in such a clinic and correlated ACEs with indicators of case severity. For 252 patients newly evaluated in an urban clinic, a retrospective chart review was completed and data was collected on ACE questionnaire responses, psychiatric, substance-related, and medical diagnoses, psychiatric hospitalizations, suicide attempts, and suicide and violence risk. Patients in the clinic had an average of 3.4 ACEs, higher than national community sample averages of 1.6. The percentages of patients with at least one, two, and four ACEs were 82% (n = 207), 68% (n = 172), and 42% (n = 106) respectively (compared with 61%, 38%, and 15% nationally). ACEs had statistically significant correlations with an increased number of psychiatric diagnoses, substance use disorders, medical illnesses, suicide attempts, and suicide risk level. This study demonstrated that patients seeking psychiatric care from a hospital-based outpatient clinic are likely to be traumatized to a degree far exceeding what is typical in the general population. While a high prevalence of ACEs in a psychiatric population is an expected finding given the literature to date, this is the first study presenting data on the prevalence of ACEs in such a hospital-based community clinic. Additionally this study reinforces prior research correlating childhood adversity and case severity.

Association of stay-at-home orders and COVID-19 incidence and mortality in rural and urban United States: a population-based study.

OBJECTIVE: We examined the association between stay-at-home order implementation and the incidence of COVID-19 infections and deaths in rural versus urban counties of the United States. DESIGN: We used an interrupted time-series analysis using a mixed effects zero-inflated Poisson model with random intercept by county and standardised by population to examine the associations between stay-at-home orders and county-level counts of daily new COVID-19 cases and deaths in rural versus urban counties between 22 January 2020 and 10 June 2020. We secondarily examined the association between stay-at-home orders and mobility in rural versus urban counties using Google Community Mobility Reports. INTERVENTIONS: Issuance of stay-at-home orders. PRIMARY AND SECONDARY OUTCOME MEASURES: Co-primary outcomes were COVID-19 daily incidence of cases (14-day lagged) and mortality (26-day lagged). Secondary outcome was mobility. RESULTS: Stay-at-home orders were implemented later (median 30 March 2020 vs 28 March 2020) and were shorter in duration (median 35 vs 54 days) in rural compared with urban counties. Indoor mobility was, on average, 2.6%-6.9% higher in rural than urban counties both during and after stay-at-home orders. Compared with the baseline (pre-stay-at-home) period, the number of new COVID-19 cases increased under stay-at-home by incidence risk ratio (IRR) 1.60 (95% CI, 1.57 to 1.64) in rural and 1.36 (95% CI, 1.30 to 1.42) in urban counties, while the number of new COVID-19 deaths increased by IRR 14.21 (95% CI, 11.02 to 18.34) in rural and IRR 2.93 in urban counties (95% CI, 1.82 to 4.73). For each day under stay-at-home orders, the number of new cases changed by a factor of 0.982 (95% CI, 0.981 to 0.982) in rural and 0.952 (95% CI, 0.951 to 0.953) in urban counties compared with prior to stay-at-home, while number of new deaths changed by a factor of 0.977 (95% CI, 0.976 to 0.977) in rural counties and 0.935 (95% CI, 0.933 to 0.936) in urban counties. Each day after stay-at-home orders expired, the number of new cases changed by a factor of 0.995 (95% CI, 0.994 to 0.995) in rural and 0.997 (95% CI, 0.995 to 0.999) in urban counties compared with prior to stay-at-home, while number of new deaths changed by a factor of 0.969 (95% CI, 0.968 to 0.970) in rural counties and 0.928 (95% CI, 0.926 to 0.929) in urban counties. CONCLUSION: Stay-at-home orders decreased mobility, slowed the spread of COVID-19 and mitigated COVID-19 mortality, but did so less effectively in rural than in urban counties. This necessitates a critical re-evaluation of how stay-at-home orders are designed, communicated and implemented in rural areas.

Childhood adversity and psychotic disorders: Epidemiological evidence, theoretical models and clinical considerations.

While genetic factors play a critical role in the risk for schizophrenia and other psychotic disorders, increasing evidence points to the role of childhood adversity as one of several environmental factors that can significantly impact the development, manifestations and outcome of these disorders. This paper reviews the epidemiological evidence linking childhood adversity and psychotic disorders and explores various theoretical models that seek to explain the connection. We discuss neurobiological parallels between the impact of childhood trauma and psychosis on the brain and then explore the impact of childhood adversity on different domains of clinical presentation. Finally, implications for prevention and treatment are considered, both on individual and structural levels.

The Clinical and Theranostic Values of Activated Leukocyte Cell Adhesion Molecule (ALCAM)/CD166 in Human Solid Cancers.

Activated leukocyte cell adhesion molecule (ALCAM), also known as CD166, is a cell adhesion protein that is found in multiple cell types. ALCAM has multiple and diverse roles in various physiological and pathological conditions, including inflammation and cancer. There has been compelling evidence of ALCAM's prognostic value in solid cancers, indicating that it is a potential therapeutic target. The present article overviews the recent findings and progress in ALCAM and its involvement in cancer, with a primary focus on its clinical connections in cancer and therapeutic values.

On the use of constrained reactive mixtures of solids to model finite deformation isothermal elastoplasticity and elastoplastic damage mechanics.

This study presents a framework for plasticity and elastoplastic damage mechanics by treating materials as reactive solids whose internal composition evolves in response to applied loading. Using the framework of constrained reactive mixtures, plastic deformation is accounted for by allowing loaded bonds within the material to break and reform in a stressed state. Bonds which break and reform represent a new generation with a new reference configuration, which is time-invariant and provided by constitutive assumption. The constitutive relation for the reference configuration of each generation may depend on the selection of a suitable yield measure. The choice of this measure and the resulting plastic flow conditions are constrained by the Clausius-Duhem inequality. We show that this framework remains consistent with classical plasticity approaches and principles. Verification of this reactive plasticity framework, which is implemented in the open source FEBio finite element software (febio.org), is performed against standard 2D and 3D benchmark problems. Damage is incorporated into this reactive framework by allowing loaded bonds to break permanently according to a suitable damage measure, where broken bonds can no longer store free energy. Validation is also demonstrated against experimental data for problems involving plasticity and plastic damage. This study demonstrates that it is possible to formulate simple elastoplasticity and elastoplastic damage models within a consistent framework which uses measures of material mass composition as theoretically observable state variables. This theoretical frame can be expanded in scope to account for more complex behaviors.

Postacute Sequelae of Severe Acute Respiratory Syndrome Coronavirus 2 Infection: A State-of-the-Art Review.

The vast majority of patients (>99%) with severe acute respiratory syndrome coronavirus 2 survive immediate infection but remain at risk for persistent and/or delayed multisystem. This review of published reports through May 31, 2021, found that manifestations of postacute sequelae of severe acute respiratory syndrome coronavirus 2 infection (PASC) affect between 33% and 98% of coronavirus disease 2019 survivors and comprise a wide range of symptoms and complications in the pulmonary, cardiovascular, neurologic, psychiatric, gastrointestinal, renal, endocrine, and musculoskeletal systems in both adult and pediatric populations. Additional complications are likely to emerge and be identified over time. Although data on PASC risk factors and vulnerable populations are scarce, evidence points to a disproportionate impact on racial/ethnic minorities, older patients, patients with preexisting conditions, and rural residents. Concerted efforts by researchers, health systems, public health agencies, payers, and governments are urgently needed to better understand and mitigate the long-term effects of PASC on individual and population health.

Impact of High Deductible Health Plans on Diabetes Care Quality and Outcomes: Systematic Review.

OBJECTIVE: To provide a review of the impact of high deductible health plans (HDHPs) on the utilizations of services required for optimal management of diabetes and subsequent health outcomes. METHODS: Systematic literature review of studies published between January 1, 2000, and May 7, 2021, was conducted that examined the impact of HDHP on diabetes monitoring (eg, recommended laboratory and surveillance testing), routine care (eg, ambulatory appointments), medication management (eg, medication initiation, adherence), and acute health care utilization (eg, emergency department visits, hospitalizations, incident complications). RESULTS: Of the 303 reviewed articles, 8 were relevant. These studies demonstrated that HDHPs lower spending at the expense of reduced high-value diabetes monitoring, routine care, and medication adherence, potentially contributing to the observed increases in acute health care utilization. Additionally, patient out-of-pocket costs for recommended screenings doubled, and total health care expenditures increased by 49.4% for HDHP enrollees compared with enrollees in traditional health plans. Reductions in disease monitoring and routine care and increases in acute health care utilization were greatest in lower-income patients. None of the studies examined the impact of HDHPs on access to diabetes self-management education, technology use, or glycemic control. CONCLUSION: Although HDHPs reduce some health care utilization and costs, they appear to do so at the expense of limiting high-value care and medication adherence. Policymakers, providers, and payers should be more cognizant of the potential for negative consequences of HDHPs on patients' health.