Article ; Online: Geospatial Distribution of Neurosurgeons Age 60 and Older Relative to the Spread of COVID-19.
2020 Volume 145, Page(s) e259–e266
Abstract: ... ≥60 years old and compare these data with the spread of 2019 novel coronavirus disease (COVID-19 ... across the United States.: Methods: Data regarding distribution of COVID-19 cases were collected ... 6%-38.9%. Among states with the highest number of COVID-19 deaths, the older workforce proportions ...
| Abstract | Objective: To perform an ecological study to analyze the geospatial distribution of neurosurgeons ≥60 years old and compare these data with the spread of 2019 novel coronavirus disease (COVID-19) across the United States. Methods: Data regarding distribution of COVID-19 cases were collected from the Environmental Systems Research Institute, and demographic statistics were collected from the American Association of Medical Colleges 2019 State Workforce Reports. These figures were analyzed using geospatial mapping software. Results: As of July 5, 2020, the 10 states with the highest number of COVID-19 cases showed older neurosurgical workforce proportions (the proportion of active surgeons ≥60 years old) of 20.6%-38.9%. Among states with the highest number of COVID-19 deaths, the older workforce proportions were 25.0%-43.4%. Connecticut demonstrated the highest with 43.4% of neurosurgeons ≥60 years old. Conclusions: Regional COVID-19 hotspots may coincide with areas where a substantial proportion of the neurosurgical workforce is ≥60 years old. Continuous evaluation and adjustment of local and national clinical practice guidelines are warranted throughout the pandemic era. |
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| MeSH term(s) | Age Factors ; Aged ; COVID-19/epidemiology ; Cross-Sectional Studies ; Female ; Geography ; Humans ; Male ; Middle Aged ; Neurosurgeons/statistics & numerical data ; Pandemics ; Surveys and Questionnaires ; United States/epidemiology ; Workforce |
| Keywords | covid19 |
| Language | English |
| Publishing date | 2020-10-14 |
| Publishing country | United States |
| Document type | Journal Article |
| ZDB-ID | 2534351-8 |
| ISSN | 1878-8769 ; 1878-8750 |
| ISSN (online) | 1878-8769 |
| ISSN | 1878-8750 |
| DOI | 10.1016/j.wneu.2020.10.037 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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