Challenges of COVID-19 Case Forecasting in the US, 2020-2021.

During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and government entities led by the Centers for Disease Control and Prevention and the US COVID-19 Forecast Hub (https://covid19forecasthub.org). We evaluated approximately 9.7 million forecasts of weekly state-level COVID-19 cases for predictions 1-4 weeks into the future submitted by 24 teams from August 2020 to December 2021. We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. Overall, we found high variation in skill across individual models, with ensemble-based forecasts outperforming other approaches. Forecast skill relative to the baseline was generally higher for larger jurisdictions (e.g., states compared to counties). Over time, forecasts generally performed worst in periods of rapid changes in reported cases (either in increasing or decreasing epidemic phases) with 95% prediction interval coverage dropping below 50% during the growth phases of the winter 2020, Delta, and Omicron waves. Ideally, case forecasts could serve as a leading indicator of changes in transmission dynamics. However, while most COVID-19 case forecasts outperformed a naïve baseline model, even the most accurate case forecasts were unreliable in key phases. Further research could improve forecasts of leading indicators, like COVID-19 cases, by leveraging additional real-time data, addressing performance across phases, improving the characterization of forecast confidence, and ensuring that forecasts were coherent across spatial scales. In the meantime, it is critical for forecast users to appreciate current limitations and use a broad set of indicators to inform pandemic-related decision making.

The Rising Costs of Alcohol-Associated Liver Disease in the United States.

INTRODUCTION: Alcohol-associated liver disease (ALD) is rising in the United States because of an increase in high-risk drinking, but population-level ALD cost is unknown. Our aim was to project the direct and indirect costs associated with ALD in the US population through 2040. METHODS: We used a previously validated microsimulation model of alcohol consumption and ALD with model parameters estimated from publicly available data sources, including the National Epidemiologic Survey Alcohol and Related Conditions-III, the Center for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research, the Bureau of Labor Statistics, and published studies informing the impact of alcohol consumption on ALD severity in the United States resident population. The simulated scenario included current and projected ALD-associated costs. RESULTS: From 2022 to 2040, the ALD is projected to cost $880 billion, $355 billion in direct healthcare-related costs, and $525 billion in lost labor and economic consumption. The annual cost of ALD is projected to increase from $31 billion in 2022 to $66 billion (118% increase) in 2040. Although the female population makes up 29% of these costs in 2022, by 2040 on a per annum basis, female costs would be 43% of the total annual expenditure. DISCUSSION: Increased consumption of alcohol in the US population, especially in females, will cause a steep rise in the economic burden of ALD in the United States. These findings highlight the need for planners and policymakers to plan for the increased impact of liver disease in the United States.

Telemental Health Utilization in Commercial Health Insurance Plans in the United States From 2010 Through 2019.

Objective: We sought to characterize patterns of utilization of telemental health among commercially insured individuals over the decade preceding COVID-19.Methods: We developed telemental health service groups from the US PharMetrics Plus database, using diagnostic codes to identify those diagnosed with mental health conditions and procedure codes to capture mental health visits delivered via telehealth sessions. We analyzed 2 indicators of utilization between January 1, 2010, and December 31, 2019: (1) the percentage of patients with mental health needs who used telemental health services and (2) the percentage of all mental health services provided via telehealth. We stratified our analyses by year, patient gender, patient age, and geographic region.Results: The proportion of mental health visits delivered via telemental health increased from 0.002% to 0.162% between 2010 and 2019. A larger proportion of males received telemental health services as compared to females; however, the proportion of mental health visits delivered via telehealth was higher for females than for males. Patients aged 18 to 34 years and those in the western US had the highest utilization compared to other age groups and geographic regions.Conclusions: Telemental health utilization comprised a small fraction of overall mental health services and beneficiaries in the IQVIA PharMetrics Plus claims data, but increased over time, with differences documented in utilization based on patient gender, patient age, geographic region, and type of telemental health claim. Evidence from this study may serve as a pre-pandemic baseline for comparison against future evaluations of telehealth expansion policies.

Effectiveness of HCC surveillance programs using multitarget blood test: A modeling study.

BACKGROUND: The effectiveness of ultrasound-based surveillance for HCC in patients with cirrhosis is limited by suboptimal sensitivity for early tumor detection and poor adherence. Emerging blood-based biomarkers have been proposed as an alternative surveillance strategy. We aimed to evaluate the comparative effectiveness of a multitarget HCC blood test (mt-HBT)-with and without improved adherence-against ultrasound-based HCC surveillance. METHODS: We developed a Markov-based mathematical model that simulated a virtual trial in patients with compensated cirrhosis comparing potential surveillance strategies: biannual surveillance using ultrasound, ultrasound plus AFP, and mt-HBT with or without improved adherence (+10% increase). We used published data to inform underlying liver disease progression rates, HCC tumor growth patterns, performance characteristics of surveillance modalities, and efficacy of treatments. Primary outcomes of interest were the number of early-stage HCCs detected and life years gained. RESULTS: Per 100,000 patients with cirrhosis, mt-HBT detected 1680 more early-stage HCCs than ultrasound alone and 350 more early-stage HCCs than ultrasound + AFP, yielding an additional 5720 and 1000 life years, respectively. mt-HBT with improved adherence detected 2200 more early-stage HCCs than ultrasound and 880 more early-stage HCCs than ultrasound + AFP, yielding an additional 8140 and 3420 life years, respectively. The number of screening tests needed to detect one HCC case was 139 with ultrasound, 122 with ultrasound + AFP, 119 with mt-HBT, and 124 with mt-HBT with improved adherence. CONCLUSIONS: mt-HBT is a promising alternative to ultrasound-based HCC surveillance, particularly given anticipated improved adherence with blood-based biomarkers could increase HCC surveillance effectiveness.

Interpersonal Primary Care Continuity for Chronic Conditions Is Associated with Fewer Hospitalizations and Emergency Department Visits Among Medicaid Enrollees.

BACKGROUND: Interpersonal primary care continuity or chronic condition continuity (CCC) is associated with improved health outcomes. Ambulatory care-sensitive conditions (ACSC) are best managed in a primary care setting, and chronic ACSC (CACSC) require management over time. However, current measures do not measure continuity for specific conditions or the impact of continuity for chronic conditions on health outcomes. The purpose of this study was to design a novel measure of CCC for CACSC in primary care and determine its association with health care utilization. METHODS: We conducted a cross-sectional analysis of continuously enrolled, nondual eligible adult Medicaid enrollees with a diagnosis of a CACSC using 2009 Medicaid Analytic eXtract files from 26 states. We conducted adjusted and unadjusted logistic regression models of the relationship between patient continuity status and emergency department (ED) visits and hospitalizations. Models were adjusted for age, sex, race/ethnicity, comorbidity, and rurality. We defined CCC for CACSC as at least 2 outpatient visits with any primary care physician for a CACSC in the year, and (2) more than 50% of outpatient CACSC visits with a single PCP. RESULTS: There were 2,674,587 enrollees with CACSC and 36.3% had CCC for CACSC visits. In fully adjusted models, enrollees with CCC were 28% less likely to have ED visits compared with those without CCC (aOR = 0.71, 95% CI = 0.71 - 0.72) and were 67% less likely to have hospitalization than those without CCC (aOR = 0.33, 95% CI = 0.32-0.33). CONCLUSIONS: CCC for CACSCs was associated with fewer ED visits and hospitalizations in a nationally representative sample of Medicaid enrollees.

Cervical cancer geographical burden analyzer: An interactive, open-access tool for understanding geographical disease burden in patients with recurrent or metastatic cervical cancer.

OBJECTIVE: Cervical cancer (CC) disproportionately affects women based on socioeconomic status and racial/ethnic background. There is limited research in quantifying and visualizing whether substantial geographical disparities in the US exist with respect to CC burden, and especially with respect to recurrent or metastatic CC (r/mCC) disease burden. Identifying regions with higher r/mCC burden may help inform effective healthcare resource allocation and navigating patients to appropriate care. METHODS: We conducted a retrospective analysis of the 2015-2020 MarketScan® Commercial and Supplemental Medicare claims data; r/mCC burden was estimated as the number of patients initiating r/mCC systemic therapy over CC-diagnosed patients for each of the 410 metropolitan statistical areas (MSAs) considered. We developed a public, web-based tool, the Cervical Cancer Geographical Disease Burden Analyzer (Cervical Cancer Geo-Analyzer, http://www.geo-analyzer.org), that allows users to visualize r/mCC burden across MSAs over multiple years. RESULTS: There was considerable variation in r/mCC burden across MSAs, with a range of 0-83.3%. Burden increased in Boston-Cambridge-Newton, MA (r/mCC to CC ratio: 41% in 2018 to 50% in 2020), and Sacramento-Roseville-Arden-Arcade, CA (33% in 2018 to 50% in 2020). On the other hand, while r/mCC burden remained high, it decreased in Grand Rapids, MI (55% in 2018 to 31% in 2020) and San Francisco-Oakland-Hayward, CA (40% in 2018 to 26% in 2020). There were regions with sparse or no data, suggesting a need for more representative data capture. CONCLUSION: The Cervical Geo-Analyzer is a tool to visualize areas with high need for CC interventions. It also builds the foundation for further work to understand local risk factors of disease burden, identify populations of interest, characterize health disparities of CC or r/mCC and inform targeted interventions.

Cost-Effectiveness and Value-of-Information Analysis Using Machine Learning-Based Metamodeling: A Case of Hepatitis C Treatment.

BACKGROUND: Metamodels can address some of the limitations of complex simulation models by formulating a mathematical relationship between input parameters and simulation model outcomes. Our objective was to develop and compare the performance of a machine learning (ML)-based metamodel against a conventional metamodeling approach in replicating the findings of a complex simulation model. METHODS: We constructed 3 ML-based metamodels using random forest, support vector regression, and artificial neural networks and a linear regression-based metamodel from a previously validated microsimulation model of the natural history hepatitis C virus (HCV) consisting of 40 input parameters. Outcomes of interest included societal costs and quality-adjusted life-years (QALYs), the incremental cost-effectiveness (ICER) of HCV treatment versus no treatment, cost-effectiveness analysis curve (CEAC), and expected value of perfect information (EVPI). We evaluated metamodel performance using root mean squared error (RMSE) and Pearson's R2 on the normalized data. RESULTS: The R2 values for the linear regression metamodel for QALYs without treatment, QALYs with treatment, societal cost without treatment, societal cost with treatment, and ICER were 0.92, 0.98, 0.85, 0.92, and 0.60, respectively. The corresponding R2 values for our ML-based metamodels were 0.96, 0.97, 0.90, 0.95, and 0.49 for support vector regression; 0.99, 0.83, 0.99, 0.99, and 0.82 for artificial neural network; and 0.99, 0.99, 0.99, 0.99, and 0.98 for random forest. Similar trends were observed for RMSE. The CEAC and EVPI curves produced by the random forest metamodel matched the results of the simulation output more closely than the linear regression metamodel. CONCLUSIONS: ML-based metamodels generally outperformed traditional linear regression metamodels at replicating results from complex simulation models, with random forest metamodels performing best. HIGHLIGHTS: Decision-analytic models are frequently used by policy makers and other stakeholders to assess the impact of new medical technologies and interventions. However, complex models can impose limitations on conducting probabilistic sensitivity analysis and value-of-information analysis, and may not be suitable for developing online decision-support tools.Metamodels, which accurately formulate a mathematical relationship between input parameters and model outcomes, can replicate complex simulation models and address the above limitation.The machine learning-based random forest model can outperform linear regression in replicating the findings of a complex simulation model. Such a metamodel can be used for conducting cost-effectiveness and value-of-information analyses or developing online decision support tools.

Factors associated with receipt of second-line recurrent or metastatic cervical cancer treatment in the United States: A retrospective administrative claims analysis.

Purpose: Contemporary, real-world data on eligible patients receiving treatment following progression on first-line (1L) recurrent or metastatic cervical cancer (r/mCC) therapy are needed to inform treatment algorithms and identify potential gaps in the r/mCC care continuum. Methods: This study estimated the prevalence and predictors of second-line (2L) r/mCC therapy among 1L-treated patients using the 2015-2020 IBM MarketScan® commercial claims database. Women ≥ 18 years diagnosed with cervical cancer and treated with first-line systemic therapies were identified and followed for 12 months from their 1L therapy end date. Women with claims for a new therapy after 60 days but no later than 365 days from the end of 1L treatment were identified as those who progressed and received 2L therapy for r/mCC. Descriptive statistics examined baseline cohort characteristics and multivariable logistic regression model examined the factors associated with receiving 2L treatment. Results: We identified 384 1L-treated patients with r/mCC with ≥ 12 months of follow-up post-1L treatment. During follow-up, over half (51.0 %) of the 1L-treated r/mCC patients received 2L treatment. Patients from the South and Midwest had a lower likelihood of receiving 2L treatment compared with those living in the Northeast (adjusted odds ratio [aOR] = 0.43; 0.23-0.84) and (aOR = 0.52; 0.28-0.95, respectively). Patients not treated with bevacizumab in 1L were also less likely to receive 2L therapy (aOR = 0.65; 0.43-0.99). Conclusion: Additional research and targeted outreach efforts are needed to understand geography-, population-, or practice-specific barriers impacting access to 2L therapy among patients with r/mCC.

Comparison of a multitarget blood test to ultrasound and alpha-fetoprotein for hepatocellular carcinoma surveillance: Results of a network meta-analysis.

Ultrasound-based surveillance has suboptimal sensitivity for early detection of hepatocellular carcinoma (HCC) in patients with cirrhosis. There are several emerging alternatives, including a novel multitarget HCC blood test (Mt-HBT). We compared performance of mt-HBT against ultrasound with or without alpha-fetoprotein (AFP) for early HCC detection in patients with cirrhosis. Per the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, two reviewers searched PubMed, Cochrane, Embase, and clinicaltrials.gov databases from January 1990 through December 2020 to identify studies reporting sensitivity and/or specificity of ultrasound and AFP for overall and early stage HCC detection in patients with cirrhosis. Mt-HBT diagnostic performance was derived from a clinical validation study. A network meta-analysis model was built for comparative assessment, and pooled estimates of sensitivity at a fixed specificity were estimated based on Bayesian binormal receiver operating characteristic models for each modality. Forty-one studies (comprising 62,517 patients with cirrhosis) met inclusion criteria. Ultrasound-alone sensitivity was 51.6% (95% credible interval [CrI], 43.3%-60.5%) for early stage HCC detection, which increased with the addition of AFP to 74.1% (95% CrI, 62.6%-82.4%); however, this was offset by decreased specificity (87.9% vs. 83.9%, respectively). With specificity fixed at 90%, mt-HBT sensitivity for early stage HCC detection was higher than ultrasound alone (18.2%; 95% CrI, 0.2%-37.7%) and similar to ultrasound with AFP (-3.3%; 95% CrI, -22.3%-17.4%). Pairwise posterior probabilities suggested a preference for mt-HBT over ultrasound alone in 97.4% of cases but only 36.3% of cases versus ultrasound with AFP. Conclusion: A blood-based mt-HBT has higher sensitivity than ultrasound alone for early stage HCC detection but similar sensitivity compared to ultrasound and AFP. Mt-HBT could be a comparable alternative to existing methods for HCC surveillance in patients who are at risk.

Projecting COVID-19 Mortality as States Relax Nonpharmacologic Interventions.

Importance: A key question for policy makers and the public is what to expect from the COVID-19 pandemic going forward as states lift nonpharmacologic interventions (NPIs), such as indoor mask mandates, to prevent COVID-19 transmission. Objective: To project COVID-19 deaths between March 1, 2022, and December 31, 2022, in each of the 50 US states, District of Columbia, and Puerto Rico assuming different dates of lifting of mask mandates and NPIs. Design Setting and Participants: This simulation modeling study used the COVID-19 Policy Simulator compartmental model to project COVID-19 deaths from March 1, 2022, to December 31, 2022, using simulated populations in the 50 US states, District of Columbia, and Puerto Rico. Projected current epidemiologic trends for each state until December 31, 2022, assuming the current pace of vaccination is maintained into the future and modeling different dates of lifting NPIs. Exposures: Date of lifting statewide NPI mandates as March 1, April 1, May 1, June 1, or July 1, 2022. Main Outcomes and Measures: Projected COVID-19 incident deaths from March to December 2022. Results: With the high transmissibility of current circulating SARS-CoV-2 variants, the simulated lifting of NPIs in March 2022 was associated with resurgences of COVID-19 deaths in nearly every state. In comparison, delaying by even 1 month to lift NPIs in April 2022 was estimated to mitigate the amplitude of the surge. For most states, however, no amount of delay was estimated to be sufficient to prevent a surge in deaths completely. The primary factor associated with recurrent epidemics in the simulation was the assumed high effective reproduction number of unmitigated viral transmission. With a lower level of transmissibility similar to those of the ancestral strains, the model estimated that most states could remove NPIs in March 2022 and likely not see recurrent surges. Conclusions and Relevance: This simulation study estimated that the SARS-CoV-2 virus would likely continue to take a major toll in the US, even as cases continued to decrease. Because of the high transmissibility of the recent Delta and Omicron variants, premature lifting of NPIs could pose a substantial threat of rebounding surges in morbidity and mortality. At the same time, continued delay in lifting NPIs may not prevent future surges.

Duration and cost-effectiveness of hepatocellular carcinoma surveillance in hepatitis C patients after viral eradication.

BACKGROUND & AIMS: Successful treatment of chronic hepatitis C with oral direct-acting antivirals (DAAs) leads to virological cure, however, the subsequent risk of hepatocellular carcinoma (HCC) persists. Our objective was to evaluate the cost-effectiveness of biannual surveillance for HCC in patients cured of hepatitis C and the optimal age to stop surveillance. METHODS: We developed a microsimulation model of the natural history of HCC in individuals with hepatitis C and advanced fibrosis or cirrhosis who achieved virological cure with oral DAAs. We used published data on HCC incidence, tumor progression, real-world HCC surveillance adherence, and costs and utilities of different health states. We compared biannual HCC surveillance using ultrasound and alpha-fetoprotein for varying durations of surveillance (from 5 years to lifetime) vs. no surveillance. RESULTS: In virologically cured patients with cirrhosis, the incremental cost-effectiveness ratio (ICER) of biannual surveillance remained below $150,000 per additional quality-adjusted life year (QALY) (range: $79,500-$94,800) when surveillance was stopped at age 70, irrespective of the starting age (40-65). Compared with no surveillance, surveillance detected 130 additional HCCs in 'very early'/early stage and yielded 51 additional QALYs per 1,000 patients with cirrhosis. In virologically cured patients with advanced fibrosis, the ICER of biannual surveillance remained below $150,000/QALY (range: $124,600-$129,800) when surveillance was stopped at age 60, irrespective of the starting age (40-50). Compared with no surveillance, surveillance detected 24 additional HCCs in 'very early'/early stage and yielded 12 additional QALYs per 1,000 patients with advanced fibrosis. CONCLUSION: Biannual surveillance for HCC in patients cured of hepatitis C is cost-effective until the age of 70 for patients with cirrhosis, and until the age of 60 for patients with stable advanced fibrosis. LAY SUMMARY: Individuals who are cured of hepatitis C using oral antiviral drugs remain at risk of developing liver cancer. The value of lifelong screening for liver cancer in these individuals is not known. By simulating the life course of hepatitis C cured individuals, we found that ultrasound-based biannual screening for liver cancer is cost-effective up to age 70 in those with cirrhosis and up to age 60 in those with stable advanced fibrosis.

Factors associated with receipt of second-line recurrent or metastatic cervical cancer treatment in the United States: A retrospective administrative claims analysis.

Purpose: Contemporary, real-world data on eligible patients receiving treatment following progression on first-line (1L) recurrent or metastatic cervical cancer (r/mCC) therapy are needed to inform treatment algorithms and identify potential gaps in the r/mCC care continuum. Methods: This study estimated the prevalence and predictors of second-line (2L) r/mCC therapy among 1L-treated patients using the 2015-2020 IBM MarketScan® commercial claims database. Women ≥ 18 years diagnosed with cervical cancer and treated with first-line systemic therapies were identified and followed for 12 months from their 1L therapy end date. Women with claims for a new therapy after 60 days but no later than 365 days from the end of 1L treatment were identified as those who progressed and received 2L therapy for r/mCC. Descriptive statistics examined baseline cohort characteristics and multivariable logistic regression model examined the factors associated with receiving 2L treatment. Results: We identified 384 1L-treated patients with r/mCC with ≥ 12 months of follow-up post-1L treatment. During follow-up, over half (51.0 %) of the 1L-treated r/mCC patients received 2L treatment. Patients from the South and Midwest had a lower likelihood of receiving 2L treatment compared with those living in the Northeast (adjusted odds ratio [aOR] = 0.43; 0.23-0.84) and (aOR = 0.52; 0.28-0.95, respectively). Patients not treated with bevacizumab in 1L were also less likely to receive 2L therapy (aOR = 0.65; 0.43-0.99). Conclusion: Additional research and targeted outreach efforts are needed to understand geography-, population-, or practice-specific barriers impacting access to 2L therapy among patients with r/mCC.

Increasing Utilization of Emergency Department Neuroimaging From 2007 Through 2017.

BACKGROUND. The volume of emergency department (ED) visits and the number of neuroimaging examinations have increased since the start of the century. Little is known about this growth in the commercially insured and Medicare Advantage populations. OBJECTIVE. The purpose of our study was to evaluate changing ED utilization of neuroimaging from 2007 through 2017 in both commercially insured and Medicare Advantage enrollees. METHODS. Using patient-level claims from Optum's deidentified Clinformatics Data Mart database, which annually includes approximately 12-14 million commercial and Medicare Advantage health plan enrollees, annual ED utilization rates of head CT, head MRI, head CTA, neck CTA, head MRA, neck MRA, and carotid duplex ultrasound (US) were assessed from 2007 through 2017. To account for an aging sample population, utilization rates were adjusted using annual relative proportions of age groups and stratified by patient demographics, payer type, and provider state. RESULTS. Between 2007 and 2017, age-adjusted ED neuroimaging utilization rates per 1000 ED visits increased 72% overall (compound annual growth rate [CAGR], 5%). This overall increase corresponded to an increase of 69% for head CT (CAGR, 5%), 67% for head MRI (CAGR, 5%), 1100% for head CTA (CAGR, 25%), 1300% for neck CTA (CAGR, 27%), 36% for head MRA (CAGR, 3%), and 52% for neck MRA (CAGR, 4%) and to a decrease of 8% for carotid duplex US (CAGR, -1%). The utilization of head CT and CTA of the head and neck per 1000 ED visits increased in enrollees 65 years old or older by 48% (CAGR, 4%) and 1011% (CAGR, 24%). CONCLUSION. Neuroimaging utilization in the ED grew considerably between 2007 and 2017, with growth of head and neck CTA far outpacing the growth of other modalities. Unenhanced head CT remains by far the dominant ED neuroimaging examination. CLINICAL IMPACT. The rapid growth of head and neck CTA observed in the fee-for-service Medicare population is also observed in the commercially insured and Medicare Advantage populations. The appropriateness of this growth should be monitored as the indications for CTA expand.

Effect of increased alcohol consumption during COVID-19 pandemic on alcohol-associated liver disease: A modeling study.

BACKGROUND AND AIMS: Alcohol consumption increased during the COVID-19 pandemic in 2020 in the United States. We projected the effect of increased alcohol consumption on alcohol-associated liver disease (ALD) and mortality. APPROACH AND RESULTS: We extended a previously validated microsimulation model that estimated the short- and long-term effect of increased drinking during the COVID-19 pandemic in individuals in the United States born between 1920 and 2012. We modeled short- and long-term outcomes of current drinking patterns during COVID-19 (status quo) using survey data of changes in alcohol consumption in a nationally representative sample between February and November 2020. We compared these outcomes with a counterfactual scenario wherein no COVID-19 occurs and drinking patterns do not change. One-year increase in alcohol consumption during the COVID-19 pandemic is estimated to result in 8000 (95% uncertainty interval [UI], 7500-8600) additional ALD-related deaths, 18,700 (95% UI, 17,600-19,900) cases of decompensated cirrhosis, and 1000 (95% UI, 1000-1100) cases of HCC, and 8.9 million disability-adjusted life years between 2020 and 2040. Between 2020 and 2023, alcohol consumption changes due to COVID-19 will lead to 100 (100-200) additional deaths and 2800 (2700-2900) additional decompensated cirrhosis cases. A sustained increase in alcohol consumption for more than 1 year could result in additional morbidity and mortality. CONCLUSIONS: A short-term increase in alcohol consumption during the COVID-19 pandemic can substantially increase long-term ALD-related morbidity and mortality. Our findings highlight the need for individuals and policymakers to make informed decisions to mitigate the impact of high-risk alcohol drinking in the United States.

Association of Limited In-Person Attendance in US National Football League and National Collegiate Athletic Association Games With County-Level COVID-19 Cases.

Importance: In 2020 and early 2021, the National Football League (NFL) and National Collegiate Athletic Association (NCAA) opted to host football games in stadiums across the country. The in-person attendance of games varied with time and from county to county. There is currently no evidence on whether limited in-person attendance of games is associated with COVID-19 case numbers on a county-level. Objective: To assess whether NFL and NCAA football games with limited in-person attendance were associated with increased COVID-19 cases in the counties they were held compared with a matched set of counties. Design, Setting, and Participants: In this time-series cross-sectional study, every county hosting NFL or NCAA games with in-person attendance (treated group) in 2020 and 2021 was matched with a county that that did not host a game on the corresponding day but had an identical game history for up to 14 days prior (control group). A standard matching method was used to further refine this matched set so that the treated and matched control counties had similar population size, nonpharmaceutical interventions in place, and COVID-19 trends. The association of hosting games with in-person attendance with COVID-19 cases was assessed using a difference-in-difference estimator. Data were analyzed from August 29 to December 28, 2020. Exposures: Hosting NFL or NCAA games. Main Outcomes and Measures: The main outcome was estimation of new COVID-19 cases per 100 000 residents at the county level reported up to 14 days after a game among counties with NFL and NCAA games with in-person attendance. Results: A total of 528 games with in-person attendance (101 NFL games [19.1%]; 427 NCAA games [80.9%]) were included. The matching algorithm returned 361 matching sets of counties. The median (interquartile range [IQR]) number of attendance for NFL games was 9949 (6000 to 13 797) people. The median number of attendance for NCAA games was not available, and attendance was recorded as a binary variable. The median (IQR) daily new COVID-19 cases in treatment group counties hosting games was 26.14 (10.77-50.25) cases per 100 000 residents on game day. The median (IQR) daily new COVID-19 cases in control group counties where no games were played was 24.11 (9.64-48.55) cases per 100 000 residents on game day. The treatment effect size ranged from -5.17 to 4.72, with a mean (SD) of 1.21 (2.67) cases per 100 000 residents, within the 14-day period in all counties hosting the games, and the daily treatment effect trend remained relatively steady during this period. Conclusions and Relevance: This cross-sectional study did not find a consistent increase in the daily COVID-19 cases per 100 000 residents in counties where NFL and NCAA games were held with limited in-person attendance. These findings suggest that NFL and NCAA football games hosted with limited in-person attendance were not associated with substantial risk for increased local COVID-19 cases.

Gene expression profiling-based risk prediction and profiles of immune infiltration in diffuse large B-cell lymphoma.

The clinical risk stratification of diffuse large B-cell lymphoma (DLBCL) relies on the International Prognostic Index (IPI) for the identification of high-risk disease. Recent studies suggest that the immune microenvironment plays a role in treatment response prediction and survival in DLBCL. This study developed a risk prediction model and evaluated the model's biological implications in association with the estimated profiles of immune infiltration. Gene-expression profiling of 718 patients with DLBCL was done, for which RNA sequencing data and clinical covariates were obtained from Reddy et al. (2017). Using unsupervised and supervised machine learning methods to identify survival-associated gene signatures, a multivariable model of survival was constructed. Tumor-infiltrating immune cell compositions were enumerated using CIBERSORT deconvolution analysis. A four gene-signature-based score was developed that separated patients into high- and low-risk groups. The combination of the gene-expression-based score with the IPI improved the discrimination on the validation and complete sets. The gene signatures were successfully validated with the deconvolution output. Correlating the deconvolution findings with the gene signatures and risk score, CD8+ T-cells and naïve CD4+ T-cells were associated with favorable prognosis. By analyzing the gene-expression data with a systematic approach, a risk prediction model that outperforms the existing risk assessment methods was developed and validated.

Factors Associated With First Medication Time for Children Treated in the Emergency Department for Asthma.

OBJECTIVES: Acute asthma exacerbations are among the most common reasons for childhood emergency department (ED) visits and hospitalizations. Although early ED administration of asthma medication has been shown to decrease hospitalizations, studies of factors associated with early ED asthma medication delivery have been limited. The objective of our study was to identify patient- and ED-related factors associated with early medication delivery among children treated in the ED for asthma exacerbations. METHODS: This retrospective study used electronic health record data from all encounters for a primary diagnosis of asthma in an academic children's hospital ED during the study period 2009 to 2013. Using multivariate logistic regression, we identified the association between patient- and ED-related factors and the time to first medication defined as a binary outcome using a threshold of 1 hour from ED arrival. We then stratified our analysis by triage level (Emergency Severity Index [ESI]). RESULTS: Of the 4846 encounters during the study period, 62% were male, mean age was 7.30 years, 76% had public insurance, and 57% had an ESI level of 3. Medication was administered within 1 hour of arrival in 2236 encounters (46%). After adjusting for covariates, multivariate logistic regression revealed that patients were less likely to have medications within 1 hour when they had less severe ESI (ESI 2 vs ESI 4: odds ratio [OR], 0.139; confidence interval [CI], 0.114-0.170), arrived via non-emergency medical services (OR, 0.525; CI, 0.413-0.665), or arrived to a crowded ED (OR, 0.574; CI, 0.505-0.652). Age, sex, and insurance type were not associated with timeliness of initial medication administration. Stratified analyses demonstrated that the crowding effect was larger for less severely ill patients. CONCLUSIONS: Our study found that patient severity (acuity level, arrival mode) and level of ED crowing-but not demographic factors-are associated with the administration of medication in the first hour to pediatric patients with asthma. Our findings may be helpful in redesigning asthma care management strategies.

Analysis of medication patterns for pediatric asthma patients in emergency department: Does the sequence placement of glucocorticoids administration matter?

Objectives: Timely glucocorticoid administration is associated with decreased admission rate and is thus a common quality metric for ED asthma care; less is known about the impact of the timing of glucocorticoids in the context of the sequence of asthma medications administered. Therefore, we investigated the distribution of asthma medication sequences in one ED and analyzed the effect of the sequence placement of glucocorticoids administration on treatment outcomes.Methods: A retrospective study using five-year electronic health record data obtained from an academic urban children's hospital ED was conducted. We clustered the sequences of medication administration using an exact string-matching algorithm to identify the most frequently used asthma medication sequences. Then, we used the identified patterns to perform statistical tests to examine the effect of the sequence placement of glucocorticoids administration on the outcomes length-of-stay and ED disposition.Results: A total of 4,844 encounters were included in our study. The ten most common treatment sequences accounted for 43% of all encounters. Stratified analyses confirmed that treatment sequences pattern was correlated with patient severity, but ED crowding does not impact treatment sequences. In multivariable models, glucocorticoids administration earlier in the treatment sequence was associated with shorter length of stay and lower hospital admission rates.Conclusions: By analyzing medication sequence patterns for the ED encounter of pediatric asthma, we found that the earlier sequence placement of glucocorticoids administration is associated with improved outcomes. Our findings can help inform quality improvement and clinical guideline development related to ED asthma care for children.

Assessment of Incidence of and Surveillance Burden for Hepatocellular Carcinoma Among Patients With Hepatitis C in the Era of Direct-Acting Antiviral Agents.

Importance: In the US, hepatocellular carcinoma (HCC), primarily associated with hepatitis C virus (HCV) infection, is the fastest rising cause of cancer-related death. Wider use of highly effective direct-acting antiviral agents (DAAs) substantially reduces the burden of chronic HCV infection, but the subsequent impacts with HCV-associated HCC remain unknown. Objective: To assess projected changes in the incidence rate of and surveillance burden for HCC in the era of DAA treatment for HCV. Design, Setting, and Participants: This decision analytical model study was performed from January 2019 to February 2020, using an individual-level state-transition simulation model to simulate disease progression, screening, and different waves of antiviral treatments for HCV in the US from 2012 to 2040. Interventions: Current clinical management for chronic HCV infection. Main Outcomes and Measures: Model outcomes were projected temporal trends and age distribution of incident HCC cases and candidates for HCC surveillance among patients with viremia and patients with virologically cured HCV. Results: The simulation model projected that the annual incidence of HCC among patients with viremia and patients with virologically cured HCV will continue increasing to 24 000 (95% uncertainty interval [UI], 18 000-31 000) cases until 2021. In patients with virologically cured HCV, incident HCC cases are projected to increase from 1000 (95% UI, 500-2100) in 2012 to the peak of 7000 (95% UI, 5000-9600) in 2031 with a subsequent decrease to 6000 (95% UI, 4300-8300) by 2040. The proportion of incident HCC cases that occur in individuals with virologically cured HCV is estimated to increase from 5.3% in 2012 to 45.8% in 2040. The number of candidates for HCC surveillance in the population with virologically cured HCV is projected to increase from 106 000 (95% UI, 70 000-178 000) in 2012 to the peak of 649 000 (95% UI, 512 000-824 000) in 2030 and decrease to 539 000 (95% UI, 421 000-687 000) by 2040, while the proportion of all candidates for surveillance who are virologically cured is estimated to increase from 8.5% to 64.6% during the same period. The average age of HCC incidence and surveillance candidates is estimated to increase from 55 in 2012 to 72 and 71, respectively, by 2040. Conclusions and Relevance: The results of this study suggest that the burden of HCC will shift from patients with viremia to patients with virologically cured HCV, and to older populations. Appropriate management may be warranted for early detection of HCC in patients who may no longer be receiving specialty care for liver conditions.