Predicting COVID-19 Cases in US Long-Term Care Facilities

Master's Thesis from the year 2020 in the subject Health - Nursing Science - Nursing Management, grade: 1.0, , language: English, abstract: The focus of this paper was to identify factors that increase the probability of COVID-19 cases in nursing homes and to provide an exemplary concept for the application of the findings using machine learning algorithms to allow future research to derive appropriate countermeasures in practice. The findings are based on 13,069 US nursing homes, and the results are mostly consistent with most recent studies around this topic.
Thus, this study provides not only additional evidence for previously studied factors based on a larger population of nursing homes with a holistic approach but also complements these with features not yet examined, such as most importantly the competitive environment of a nursing home.
The findings show evidence of a relationship between COVID-19 infections and fatalities and (1) the size of a nursing home, (2) a facility's age, (3) whether a nursing home is for-profit, (4) whether a nursing home is urban or rural, (5) the number of federal deficiencies, (6) the total amount of fines, (7) the concentration of residents with Medicaid, (8) the share of residents from a racial or ethnic minority, (9) the excess of beds in the respective county of a nursing home, (10) the number of infections per 100,000 people in a county, and (11) the number of deaths per 100,000 people in a county, (12) the occupancy rate, (13) the overall CMS facility rating, (14) the total reported RN staffing levels, (15) the total reported nurse staffing levels and (16) the Herfindahl Index.