Infection control and hospital epidemiology · 2026
Using a longitudinal k-means clustering method to explore nursing home factors associated with SARS-CoV-2 infection peak and resilience to a COVID-19 surge.
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Meng Lu, Bell Jeneita M, Barbre Kira, Guthrie Sydney, Mason Larry, Massey Jason, Wiegand Ryan, Rowe Theresa, Sillions Prosper Molly, Woods Austin, Qureshi Iram, Reses Hannah, Stuckey Matthew, Kuhar David, Lindley Megan, Hernandez-Romieu Alfonso Claudio, Benin Andrea
Аннотация
OBJECTIVE: Nursing home residents have been disproportionately impacted by respiratory virus-related morbidity and mortality due to inherent vulnerability and communal living environments. This study aims to identify nursing homes with higher infection rates during a period of intense SARS-CoV-2 transmission and explore facility-level characteristics potentially associated with infection surges.
DESIGN: A longitudinal k-means clustering approach followed by exploratory regression analyses.
SETTING: U.S. Nursing homes reporting to the Centers for Disease Control and Prevention's National Healthcare Safety Network (NHSN).
METHODS: A longitudinal k-means method (kmlShape) classified the facilities based on their weekly SARS-CoV-2 incidence rate epidemic curve, identifying two categories (low vs high infection peak) based on the magnitude of infection peaks. A logistic regression model with bootstrapping was developed to assess facility characteristics associated with higher SARS-CoV-2 infection surges.
RESULTS: Among 11,990 nursing homes analyzed, 9,058 were classified as having a low infection peak, while 2,932 had a high infection peak. Nursing homes that are for-profit (OR = 1.570, 95% bootstrap confidence interval [BCI] 1.441-1.807), with high staff turnover (OR = 1.292, 95% BCI 1.154-1.451), or located in areas with higher social vulnerability (OR = 1.457, 95% BCI 1.239-1.880) were more likely to experience high infection peaks. Nursing homes with higher residents' vaccination coverage (OR = .321, 95% BCI .248-.380) and located in urban areas were less likely to experience high infection peaks.
CONCLUSIONS: The facility-level characteristics associated with lower SARS-CoV-2 infection peaks may indicate resiliency and help evaluate the capacity of nursing homes to endure stressors such as respiratory viruses and other communicable illnesses.
DESIGN: A longitudinal k-means clustering approach followed by exploratory regression analyses.
SETTING: U.S. Nursing homes reporting to the Centers for Disease Control and Prevention's National Healthcare Safety Network (NHSN).
METHODS: A longitudinal k-means method (kmlShape) classified the facilities based on their weekly SARS-CoV-2 incidence rate epidemic curve, identifying two categories (low vs high infection peak) based on the magnitude of infection peaks. A logistic regression model with bootstrapping was developed to assess facility characteristics associated with higher SARS-CoV-2 infection surges.
RESULTS: Among 11,990 nursing homes analyzed, 9,058 were classified as having a low infection peak, while 2,932 had a high infection peak. Nursing homes that are for-profit (OR = 1.570, 95% bootstrap confidence interval [BCI] 1.441-1.807), with high staff turnover (OR = 1.292, 95% BCI 1.154-1.451), or located in areas with higher social vulnerability (OR = 1.457, 95% BCI 1.239-1.880) were more likely to experience high infection peaks. Nursing homes with higher residents' vaccination coverage (OR = .321, 95% BCI .248-.380) and located in urban areas were less likely to experience high infection peaks.
CONCLUSIONS: The facility-level characteristics associated with lower SARS-CoV-2 infection peaks may indicate resiliency and help evaluate the capacity of nursing homes to endure stressors such as respiratory viruses and other communicable illnesses.