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Background Glioblastoma is an aggressive disease with a defined standard of care offering crucial survival benefits. Disparities in care may influence treatment decisions. This study seeks to evaluate potential patterns in care delivery using the National Cancer Database (NCDB). Methods We evaluated the NCDB from 1998 to 2011 for patients diagnosed with glioblastoma older than 20 years of age in order to describe current hospital-based demographics, rates of treatment modality by age, race, gender, likelihood of receiving treatment, and survival probabilities. Results From 1998 to 2011, 100672 patients were diagnosed with glioblastoma in the United States. Of these, 54% were younger than 65 years of age, while 20% were 75 years of age or older. The most common type of treatment was surgery (73%), followed by radiation (69%) and chemotherapy (50%). Eleven percent of patients did not receive any form of therapy. Patients receiving no form of treatment were more likely to be older, female, black, or Hispanic. Tumors that did not involve brainstem, ventricles, or the cerebellum were associated with more aggressive treatment and better overall survival. The median survival was 7.5 months. The use of concomitant surgical resection, chemotherapy, and radiation demonstrated greater survival benefit. Conclusions Median survival for glioblastoma is significantly less than reported in clinical trials. Sociodemographic factors such as age, gender, race, and socioeconomic status affect treatment decisions for glioblastoma. The elderly are greatly undertreated, as many elderly patients receive no treatment or significantly less than standard of care. [ABSTRACT FROM AUTHOR]

We combine COVID-19 case data with mobility data to estimate a modified susceptible-infected-recovered (SIR) model in the United States. In contrast to a standard SIR model, we find that the incidence of COVID-19 spread is concave in the number of infectious individuals, as would be expected if people have inter-related social networks. This concave shape has a significant impact on forecasted COVID-19 cases. In particular, our model forecasts that the number of COVID-19 cases would only have an exponential growth for a brief period at the beginning of the contagion event or right after a reopening, but would quickly settle into a prolonged period of time with stable, slightly declining levels of disease spread. This pattern is consistent with observed levels of COVID-19 cases in the US, but inconsistent with standard SIR modeling. We forecast rates of new cases for COVID-19 under different social distancing norms and find that if social distancing is eliminated there will be a massive increase in the cases of COVID-19. [ABSTRACT FROM AUTHOR]

Accurate predictive modeling of pandemics is essential for optimally distributing biomedical resources and setting policy. Dozens of case prediction models have been proposed but their accuracy over time and by model type remains unclear. In this study, we systematically analyze all US CDC COVID-19 forecasting models, by first categorizing them and then calculating their mean absolute percent error, both wave-wise and on the complete timeline. We compare their estimates to government-reported case numbers, one another, as well as two baseline models wherein case counts remain static or follow a simple linear trend. The comparison reveals that around two-thirds of models fail to outperform a simple static case baseline and one-third fail to outperform a simple linear trend forecast. A wave-by-wave comparison of models revealed that no overall modeling approach was superior to others, including ensemble models and errors in modeling have increased over time during the pandemic. This study raises concerns about hosting these models on official public platforms of health organizations including the US CDC which risks giving them an official imprimatur and when utilized to formulate policy. By offering a universal evaluation method for pandemic forecasting models, we expect this study to serve as the starting point for the development of more accurate models., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Chharia, Jeevan, Jha, Liu, Berman and Glorioso.)

Introduction: Joint arthroplasty is a particularly complex orthopaedic surgical procedure performed on joints, including the hip, knee, shoulder, ankle, elbow, wrist and even digit joints. Increasing evidence from volume-outcomes research supports the finding that patients undergoing joint arthroplasty in high-volume hospitals or by high-volume surgeons achieve better outcomes, and minimum case load requirements have been established in some areas. However, the relationships between hospital/surgeon volume and outcomes in patients undergoing arthroplasty are not fully understood. Furthermore, whether elective arthroplasty should be restricted to high-volume hospitals or surgeons remains in dispute, and little is known regarding where the thresholds should be set for different types of joint arthroplasties., Methods and Analyses: This is a protocol for a suite of systematic reviews and dose-response meta-analyses, which will be amended and updated in conjunction with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols. Electronic databases, including PubMed and Embase, will be searched for observational studies examining the relationship between the hospital or surgeon volume and clinical outcomes in adult patients undergoing primary or revision of joint arthroplasty. We will use records management software for study selection and a predefined standardised file for data extraction and management. Quality will be assessed using the Newcastle-Ottawa Scale, and the meta-analysis, subgroup analysis and sensitivity analysis will be performed using Stata statistical software. Once the volume-outcome relationships are established, we will examine the potential non-linear relationships between hospital/surgeon volume and outcomes and detect whether thresholds or turning points exist., Ethics and Dissemination: Ethical approval is not required, because these studies are based on aggregated published data. The results of this suite of systematic reviews and meta-analyses will be submitted to peer-reviewed journals for publication., Prospero Registration Number: CRD42017056639., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2018. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Glioblastoma in children is an aggressive disease with no defined standard therapy. We evaluated hospital-based demographic and survival patterns obtained through the National Cancer Database to better characterize children with glioblastoma. Our study identified 1173 patients from 0 to 19 years of age between 1998 and 2011. Comparisons were made among demographics, clinical characteristics, treatment, and survival variables. Fifty-four percent of patients were over 10 years of age. Approximately 80% of patients underwent either partial or complete resection. Adjuvant therapy was used variably, and its use increased with patient age. Forty-eight percent of patients received the combination of surgery, radiation, and chemotherapy, and 4% did not receive any treatment. As expected, patients ≤5 years of age had better 5-year survival than those ages 6-10 (P = 0.01) or 11-19 years (P = 0.0077). Other factors associated with poor survival included black race and central tumor location. Better outcomes were associated with treatment that included surgery, radiotherapy, and chemotherapy compared to any other treatment combinations. Radiotherapy had no impact on survival in the 0 to 10-year-old age group, but was associated with improved survival for patients 11-19 years. We report an extensive demographic and survival analysis of pediatric glioblastoma. The observed differences likely reflect variances in tumor biology and likelihood of treatment receipt. Improved survival was associated with the use of surgery, radiotherapy, and chemotherapy. Radiation therapy was not associated with survival in patients younger than 10 years of age., (© 2018 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.)

Background: The current analysis follows the implementation of Public Law 107-260, the Benign Brain Tumor Cancer Registries Amendment Act, which mandated the collection of nonmalignant brain tumors., Methods: Meningiomas were selected from the Surveillance, Epidemiology, and End Results (SEER) Program database for the years 2004 to 2011. Demographic and clinical characteristics, initial treatment patterns, and survival outcomes were evaluated using surveillance epidemiology statistical methods., Results: The average annual age-adjusted incidence rate per 100,000 population was 7.62 (95 % confidence interval [CI], 7.55-7.68) for all meningiomas, 7.18 (95% CI, 7.12-7.25) for benign meningiomas, 0.32 (95% CI, 0.31-0.33) for borderline malignant meningiomas, and 0.12 (95% CI, 0.11-0.12) for malignant meningiomas. The annual rates increased for benign and borderline malignant tumors but decreased for malignant tumors. The rates for women exceeded those for men, especially for those with benign meningiomas. Black race was associated with significantly higher rates as was advancing age. Greater than 80% of tumors were located in cerebral meninges. Diagnostic confirmation through pathology occurred for approximately 50% of benign tumors, 90% of borderline malignant tumors, and 80% of malignant tumors. No initial treatment was reported for greater than 60% of benign tumors, 29% of borderline malignant tumors, or 31% of malignant tumors. The 5-year relative survival estimates for benign tumors, borderline malignant tumors, and malignant tumors were 85.6% (95% confidence interval [CI], 85%-86.2%), 82.3% (95% CI, 79.3%-84.8%), and 66% (95% CI, 60.6%-70.9%), respectively. Predictors of poorer survival were advanced age, being male gender, black race, no initial treatment, and malignant tumor behavior., Conclusions: The current analysis demonstrates that there is an increasing incidence of nonmalignant meningiomas, probably because of reporting learning curves associated with the implementation of Public Law 107-260. The high proportion of cases who receive no initial treatment is a survival outcome concern, especially for patients with malignant meningiomas., (© 2015 American Cancer Society.)