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.
Table of Contents
1. Introduction
1.1 Background
1.2 Research Scope
1.3 Structure of this Paper
2. Literature Review
2.1 COVID-19 and US Nursing Homes
2.2 Factors Influencing the Number of COVID-19 Cases
3. Research Methodology
3.1 Description of the Datasets
3.2 Data Processing in Python
3.3 Statistical Analyses
3.4 Prediction of Nursing Homes with COVID-19 Cases
4. Findings and Discussion
4.1 Evaluation and Interpretation of the Developed Models
4.2 Discussion of Results
5. Conclusion
5.1 Summary of Key Findings
5.2 Limitations of the Analyses
5.3 Implications for Practice and Future Research
Research Objective and Key Themes
The primary objective of this study is to identify factors that increase the probability of COVID-19 infections in United States nursing homes. By integrating epidemiological data from multiple sources and employing machine learning techniques, the research aims to establish a predictive model that enables early identification of facilities susceptible to virus outbreaks, thereby providing a basis for targeted countermeasures.
- Analysis of facility-specific characteristics and quality ratings
- Evaluation of nurse staffing levels and their impact on resident outcomes
- Assessment of external community-level infection and demographic factors
- Comparison and performance testing of various machine learning classification models
- Development of a practical decision-support concept for facility surveillance
Excerpt from the Book
2.1 COVID-19 and US Nursing Homes
Nursing homes, also known as long-term care facilities or skilled-care facilities, play an important role in providing care for dependent older people. Such facilities help vulnerable people who have difficulty living independently due to chronic illness or old age. Especially because of an ageing population in many places, the need for elderly care will increase (ECDC, 2020; National Institute on Aging, 2017; World Health Organization, 2017). According to a recent report by Comas-Herrera et al. (2020), the effects of COVID-19 on residents and staff in nursing homes have become mainly apparent in two ways: (1) nursing homes are overcrowding due to a large number of fatalities in a short period, and (2) too many staff members are becoming infected.
In recent months, there have been numerous scientific publications on the new Coronavirus. While a majority of these are medically focussed on understanding its symptoms and finding a cure (e.g., Holshue et al., 2020), there is also an increasing body of studies re-creating the dynamics of the virus and predicting its geographical distribution (e.g., Dowd et al., 2020; McMichael et al., 2020; Ren et al., 2020). The latter is also being investigated with respect to nursing homes, although only a handful of related publications have been issued to date (e.g., Abrams et al., 2020; Harrington et al., 2020; He et al., 2020; Li et al., 2020). In contrast to the academic work, both governmental institutions and non-profit organisations provide regular updates on the number of infections and fatalities and offer analyses, predictions and in some cases also recommendations for necessary countermeasures (Comas-Herrera et al., 2020; Dawson et al., 2020; Mollalo et al., 2020).
Summary of Chapters
1. Introduction: This chapter contextualizes the high COVID-19 mortality rates in nursing homes and outlines the research scope, including objectives and sub-goals for the empirical analysis.
2. Literature Review: The chapter provides a foundational overview of COVID-19 in US nursing homes and synthesizes existing academic research regarding key variables that influence case numbers.
3. Research Methodology: This section details the multi-step approach involving data procurement, Python-based processing, statistical assessment, and the selection and optimization of machine learning algorithms.
4. Findings and Discussion: The chapter presents the results of the statistical models, evaluates their performance, and discusses the implications of facility, staff, and external factors in relation to existing literature.
5. Conclusion: The final chapter summarizes central empirical findings, addresses study limitations, and suggests implications for policymakers and future research directions.
Keywords
COVID-19, Nursing Homes, Machine Learning, Predictive Modelling, Data Analysis, Facility Characteristics, CMS Rating, Nurse Staffing, Epidemiology, Healthcare Quality, Public Health, Infection Control, US Long-Term Care, Data Pre-processing, Random Forest.
Frequently Asked Questions
What is the core focus of this research?
The work investigates the underlying factors that contribute to the likelihood of COVID-19 infections in US nursing homes by analyzing a wide range of facility and community data.
Which thematic fields are analyzed in this study?
The research clusters variables into five major categories: facility characteristics, quality ratings (including deficiencies and fines), nurse staffing metrics, resident demographics, and external county-level factors.
What is the primary objective of this work?
The main goal is to identify specific drivers of COVID-19 probability and to develop a robust machine learning concept that can assist in classifying vulnerable facilities and improving future infection control strategies.
What scientific methods were employed?
The study utilized data preparation, statistical analysis (univariate and bivariate), and a comparative evaluation of seven distinct machine learning algorithms, ultimately selecting a Random Forest model for its superior accuracy.
What does the main body of the paper cover?
The main body covers a comprehensive literature review, the technical methodology for data processing and model building, the evaluation of results, and an in-depth discussion regarding how factors like facility size, ownership type, and staffing levels interact with infection outcomes.
Which keywords define this paper best?
Key terms include COVID-19, Nursing Homes, Machine Learning, Random Forest, Predictive Modelling, and Healthcare Data Analysis.
How were missing data handled during processing?
To maximize the utility of the available datasets, missing values were treated by filling them with the median of their respective columns, while facilities with insufficient data after preprocessing were excluded from the final analysis.
What was the result of the machine learning model comparison?
Among the seven models tested, the Random Forest algorithm demonstrated the best predictive performance, achieving an average 10-fold cross-validation accuracy of 74.6%.
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- Metin Baki (Autor:in), 2020, Predicting COVID-19 Cases in US Long-Term Care Facilities, München, GRIN Verlag, https://www.grin.com/document/950064
