National analysis of COVID-19 and older emergency physicians

Demographic Analysis of Financial Hardships Faced by Brain Tumor Survivors

OBJECTIVE

Quantitative analysis of the financial hardship faced by patients with brain tumors is lacking. The present study sought to conduct a longitudinal analysis of responses to the National Health Interview Survey by patients diagnosed with brain tumors and characterize the impact of demographic factors on financial hardship indices.

METHODS

National Health Interview Survey respondents between 1997 and 2018 who reported previous diagnosis with cancer of the brain and who responded to 4 survey questions that assessed financial stress were included. Sociodemographic exposures included age, ethnicity/race, marriage status, insurance status, and degree of highest educational attainment.

RESULTS

Educational attainment, marital status, and insurance status were the most significant risk factors for temporary or indefinite delays to necessary medical care. Those with only a high-school diploma had 9.6 times higher odds (adjusted odds ratio, 9.68; 95% confidence interval, 2.96-31.70; P < 0.001) of reporting that, in the past 12 months, one of their family members had to limit their medical care in an effort to save money. Similarly, patients with brain tumors who were not married had 3.94 times greater odds (adjusted odds ratio, 3.94; 95% confidence interval, 1.49-10.44; P = 0.009) of avoiding necessary medical care because of an inability to afford it.

CONCLUSIONS

Given this variation in self-reported financial burden, demographics clearly have an impact on a patient's holistic experience after a brain cancer diagnosis. Therefore, by using the comparisons in this study, we hope that medical institutions and neurosurgical societies can more accurately predict which patients are most susceptible to significant financial stress and distribute resources accordingly.

A comprehensive review of imaging findings in COVID-19 - status in early 2021

Medical imaging methods are assuming a greater role in the workup of patients with COVID-19, mainly in relation to the primary manifestation of pulmonary disease and the tissue distribution of the angiotensin-converting-enzyme 2 (ACE 2) receptor. However, the field is so new that no consensus view has emerged guiding clinical decisions to employ imaging procedures such as radiography, computer tomography (CT), positron emission tomography (PET), and magnetic resonance imaging, and in what measure the risk of exposure of staff to possible infection could be justified by the knowledge gained. The insensitivity of current RT-PCR methods for positive diagnosis is part of the rationale for resorting to imaging procedures. While CT is more sensitive than genetic testing in hospitalized patients, positive findings of ground glass opacities depend on the disease stage. There is sparse reporting on PET/CT with [18F]-FDG in COVID-19, but available results are congruent with the earlier literature on viral pneumonias. There is a high incidence of cerebral findings in COVID-19, and likewise evidence of gastrointestinal involvement. Artificial intelligence, notably machine learning is emerging as an effective method for diagnostic image analysis, with performance in the discriminative diagnosis of diagnosis of COVID-19 pneumonia comparable to that of human practitioners.