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Age, disability, and household composition of nurses and physicians who are not in the labor force.

ABSTRACT: While several areas in the United States have asked nurses and physicians who are not in the labor force to return to help with the COVID-19 pandemic, little is known about the characteristics of these clinicians that may present barriers to returning. We studied age, disability, and household composition of clinicians not in the workforce using the American Community Survey from 2014 to 2018, a nationally-representative survey of US households administered by the US Census. Overall, we found that, for nurses and physicians not in the labor force, over three-quarters were 55 and over and about 15 percent had a disability. For female nurses and physicians not in the labor force, over half of those ages 20-54 had a child under 15 at home and over half of those ages 65+ had another adult 65 and over at home. These characteristics may present challenges and risks to returning.

SUBMITTER: Ly DP

PROVIDER: S-EPMC7909633 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Age, disability, and household composition of nurses and physicians who are not in the labor force.

Ly Dan P DP

PloS one 20210226 2

While several areas in the United States have asked nurses and physicians who are not in the labor force to return to help with the COVID-19 pandemic, little is known about the characteristics of these clinicians that may present barriers to returning. We studied age, disability, and household composition of clinicians not in the workforce using the American Community Survey from 2014 to 2018, a nationally-representative survey of US households administered by the US Census. Overall, we found th ...[more]

PMID: 33635918

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Project description:BACKGROUND: The process of prognostication has not been described for acutely hospitalized older patients. OBJECTIVE: To investigate (1) which factors are associated with 90-day mortality risk in a group of acutely hospitalized older medical patients, and (2) whether adding a clinical impression score of nurses or physicians improves the discriminatory ability of mortality prediction. DESIGN: Prospective cohort study. PARTICIPANTS: Four hundred and sixty-three medical patients 65 years or older acutely admitted from November 1, 2002, through July 1, 2005, to a 1024-bed tertiary university teaching hospital. MEASUREMENTS: At admission, the attending nurse and physician were asked to give a clinical impression score for the illness the patient was admitted for. This score ranged from 1 (high possibility of a good outcome) until 10 (high possibility of a bad outcome, including mortality). Of all patients baseline characteristics and clinical parameters were collected. Mortality was registered up to 90 days after admission. MAIN RESULTS: In total, 23.8% (n = 110) of patients died within 90 days of admission. Four parameters were significantly associated with mortality risk: functional impairment, diagnosis malignancy, co-morbidities and high urea nitrogen serum levels. The AUC for the baseline model which included these risk factors (model 1) was 0.76 (95% CI 0.71 to 0.82). The AUC for the model using the risk factors and the clinical impression score of the physician (model 2) was 0.77 (0.71 to 0.82). The AUC for the model using the risk factors and the clinical impression score of the nurse (model 3) was 0.76 (0.71 to 0.82) and the AUC for the model, including the baseline covariates and the clinical impression score of both nurses and physicians was 0.77 (0.72 to 0.82). Adding clinical impression scores to model 1 did not significantly improve its accuracy. CONCLUSION: A set of four clinical variables predicted mortality risk in acutely hospitalized older patients quite well. Adding clinical impression scores of nurses, physicians or both did not improve the discriminating ability of the model.

Dataset Information

Age, disability, and household composition of nurses and physicians who are not in the labor force.

Publications

Age, disability, and household composition of nurses and physicians who are not in the labor force.

Similar Datasets

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