Prior research and the popular press have anecdotally reported inadequate nursing home staffing levels during the COVID-19 pandemic. Maintaining adequate staffing levels is critical to ensuring high-quality nursing home care and an effective response to the pandemic. We therefore sought to examine nursing home staffing levels during the first nine months of 2020 (compared with the same period in 2019), using auditable daily payroll-based staffing data from the Centers for Medicare and Medicaid Services. We found that the total number of hours of direct care nursing declined in nursing homes during the COVID-19 pandemic, as did the average nursing home census. When we accounted for changes in census, the number of nurse staff hours per resident day remained steady or, if anything, increased slightly during the pandemic. The observed increases in staff hours per resident day were small but concentrated in nursing homes operating in counties with high COVID-19 prevalence, in nursing homes with low Medicaid census (which typically have more financial resources), and in not-for-profit nursing homes (which typically invest more in staffing). These findings raise concerns that although the number of staff hours in nursing homes did not decline, the perception of shortages has been driven by increased stresses and demands on staff time due to the pandemic, which are harder to quantify.
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http://dx.doi.org/10.1377/hlthaff.2020.02351 | DOI Listing |
J Med Syst
January 2025
Unitat de Suport a la Recerca Metropolitana Nord, Institut Universitari d'Investigació en Atenció Primària Jordi Gol (IDIAP Jordi Gol), C/ Mare de Déu de Guadalupe, 2, Mataró, 08303, Barcelona, Spain.
Predicting health-related outcomes can help with proactive healthcare planning and resource management. This is especially important on the older population, an age group growing in the coming decades. Considering longitudinal rather than cross-sectional information from primary care electronic health records (EHRs) can contribute to more informed predictions.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Physiological Controls Research Center, University Research and Innovation Center, Obuda University, 1034 Budapest, Hungary.
In light of the demographic shift towards an aging population, there is an increasing prevalence of dementia among the elderly. The negative impact on mental health is preventing individuals from taking proper care of themselves. For individuals requiring hospital care, those receiving home care, or as a precaution for a specific individual, it is advantageous to utilize monitoring equipment to track their biological parameters on an ongoing basis.
View Article and Find Full Text PDFSensors (Basel)
January 2025
German Center for Vertigo and Balance Disorders (DSGZ), LMU University Hospital, LMU Munich, 81377 Munich, Germany.
Instrumented gait analysis is widely used in clinical settings for the early detection of neurological disorders, monitoring disease progression, and evaluating fall risk. However, the gold-standard marker-based 3D motion analysis is limited by high time and personnel demands. Advances in computer vision now enable markerless whole-body tracking with high accuracy.
View Article and Find Full Text PDFLife (Basel)
January 2025
Spaulding Rehabilitation Hospital, Department of Physical Medicine & Rehabilitation, Harvard Medical School, Boston, MA 02115, USA.
Chronic non-cancer pain (CNCP) is one of the leading causes of disability. The use of strong opioids (SOs) in the management of CNCP is increasing, although evidence supporting their use remains limited. Primary care (PC) plays a key role in this context.
View Article and Find Full Text PDFHealthcare (Basel)
January 2025
Nursing Department, Ashkelon Academic College, Shikmim 78211, Israel.
Purpose: To investigate community-acquired pressure injuries (CAPIs) in older people by utilizing big data.
Design: Retrospective data curation and analysis of inpatient data from two general medical centers between 1 January 2016 and 31 December 2018.
Methods: Nursing assessments from 44,449 electronic medical records of patients admitted to internal medicine departments were retrieved, organized, coded by data engineers, and analyzed by data scientists.
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