Objectives: During the Coronavirus disease 2019 (COVID-19) outbreak in the United States, nursing homes became the hotbed for the spread of COVID-19. States developed different policies to mitigate the COVID-19 risks at nursing homes, including limiting nursing home visitation and mandating staff screening. The purpose of this study is to examine whether COVID-19 cases and deaths are related to the nursing home reported quality. Design: We combined the COVID-19 data reported by the California Department of Public Health, quality ratings provided by Nursing Home Compare, and patient racial information from Long-Term Care Focus to examine the association between nursing home reported quality and COVID-19 cases and deaths. Settings and Participants: Cross-sectional data from 1223 California skilled nursing facilities with reported quality and longitudinal data of COVID-19 cases were used. Methods: The dependent variable is COVID-19 residents' cases and deaths. The main independent variable is nursing home reported quality. Nursing home ownership, size, years of operation, and patient race composition are also included. Results: Nursing home star ratings and greater percentage of residents from different racial and ethnicity groups were significantly (P < .01) related to increased probability of having a COVID-19 residents' case or death. Conclusions and Implications: Nursing homes with 5-star ratings were less likely to have COVID-19 cases and deaths after adjusting for nursing home size and patient race proportion.

Quantile regression (QR) has received increasing attention in recent years and applied to wide areas such as investment, finance, economics, medicine and engineering. Compared with conventional mean regression, QR can characterize the entire conditional distribution of the outcome variable, may be more robust to outliers and misspecification of error distribution, and provides more comprehensive statistical modeling than traditional mean regression. QR models could not only be used to detect heterogeneous effects of covariates at different quantiles of the outcome, but also offer more robust and complete estimates compared to the mean regression, when the normality assumption violated or outliers and long tails exist. These advantages make QR attractive and are extended to apply for different types of data, including independent data, time-to-event data and longitudinal data. Consequently, we present a brief review of QR and its related models and methods for different types of data in various application areas.Citation: Huang Q, Zhang H, Chen J, He M (2017) Quantile Regression Models and Their Applications: A Review. J Biom Biostat 8: 354.

U.S. health care facilities have been encountering a recurrence of medical supply shortage since COVID-19 exploded in March 2020. There is an urgent need for important Personal Protective Equipment (PPE) such as N95 and surgical masks. This project examined the factors that were associated with nursing homes’ N95 and surgical mask supply. We analyzed data from the Nursing Home COVID-19 Public File and conducted a multivariate logistic regression estimating the association between nursing home characteristics and county-level demographic parameters with mask supply. We found that a high number of resident COVID-19 cases contributed to the supply of N95, but not surgical masks, whereas a high number of staff cases did not lead to an adequate supply of either N95 or surgical masks. Compared with not-for-profit (NFP) facilities, for-profit (FP) nursing homes were less likely to get enough masks. A better supply distribution plan is needed to prepare for future possible PPE shortage.

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