Virtual care for multiple myeloma in the COVID-19 era: interrupted time series analysis of veterans health administration data

Article Oncology

Impact of the COVID-19 Pandemic on Treatment Patterns for Patients With Metastatic Solid Cancer in the United States

Ravi B. Parikh et al.

Summary: Despite the COVID-19 pandemic causing delays in patients seeking treatment for life-threatening conditions, it did not affect the time to treatment initiation or treatment selection for newly diagnosed patients with metastatic solid cancers.

JNCI-JOURNAL OF THE NATIONAL CANCER INSTITUTE (2022)

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Letter Oncology

Changes in multiple myeloma treatment patterns during the early COVID-19 pandemic period

Natalia Neparidze et al.

LEUKEMIA (2022)

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Article Oncology

Burden of Treatment Among Older Adults With Newly Diagnosed Multiple Myeloma

Hira S. Mian et al.

Summary: The study utilized a database to explore the treatment burden among newly diagnosed multiple myeloma patients, finding a significant burden in the first year, particularly within the first three months. Factors such as high comorbidities, poor performance status, and bone disease were associated with higher treatment burden.

CLINICAL LYMPHOMA MYELOMA & LEUKEMIA (2021)

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Article Computer Science, Artificial Intelligence

Forecasting the dynamics of cumulative COVID-19 cases (confirmed, recovered and deaths) for top-16 countries using statistical machine learning models: Auto-Regressive Integrated Moving Average (ARIMA) and Seasonal Auto-Regressive Integrated Moving Average (SARIMA)

K. E. ArunKumar et al.

Summary: Most countries are reopening and facing a significant increase in daily COVID-19 cases, requiring monitoring and estimation of future cases. Time-series models, including ARIMA and SARIMA, are used to forecast the pandemic trends in top-16 countries, guiding healthcare policies for the ongoing outbreak.

APPLIED SOFT COMPUTING (2021)

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Article Health Care Sciences & Services

Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models: a guide for evaluating large-scale health interventions

Andrea L. Schaffer et al.

Summary: ARIMA modeling is a useful approach for evaluating the impact of large-scale interventions, especially when dealing with seasonality and autocorrelation. It can account for underlying trends, allow for flexible modeling of different impacts, and provide valuable insights for policy evaluation.

BMC MEDICAL RESEARCH METHODOLOGY (2021)

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Review Oncology

Telehealth for Cancer Care in Veterans: Opportunities and Challenges Revealed by COVID

Cindy Y. Jiang et al.

Summary: The Veterans Health Administration system is utilizing telehealth to provide more convenient cancer care services to veterans. There are opportunities and challenges in using telehealth for cancer care, and optimization of its role in the healthcare system can be achieved through evaluation and integration of lessons learned.

JCO ONCOLOGY PRACTICE (2021)

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Article Hematology