{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T03:45:29Z","timestamp":1791776729655,"version":"4.3.4"},"reference-count":33,"publisher":"Cambridge University Press (CUP)","issue":"7","license":[{"start":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T00:00:00Z","timestamp":1778716800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":["cambridge.org"],"crossmark-restriction":true},"short-container-title":["Infect. Control Hosp. Epidemiol."],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec id=\"S0899823X26104061_as1\">\n                    <jats:title>Objective:<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S0899823X26104061_as2\">\n                    <jats:title>Design:<\/jats:title>\n                    <jats:p>A longitudinal k-means clustering approach followed by exploratory regression analyses.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S0899823X26104061_as3\">\n                    <jats:title>Setting:<\/jats:title>\n                    <jats:p>U.S. Nursing homes reporting to the Centers for Disease Control and Prevention\u2019s National Healthcare Safety Network (NHSN).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S0899823X26104061_as4\">\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S0899823X26104061_as5\">\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>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\u20131.807), with high staff turnover (OR = 1.292, 95% BCI 1.154\u20131.451), or located in areas with higher social vulnerability (OR = 1.457, 95% BCI 1.239\u20131.880) were more likely to experience high infection peaks. Nursing homes with higher residents\u2019 vaccination coverage (OR = .321, 95% BCI .248\u2013.380) and located in urban areas were less likely to experience high infection peaks.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S0899823X26104061_as6\">\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1017\/ice.2026.10406","type":"journal-article","created":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T07:51:32Z","timestamp":1778745092000},"page":"685-691","update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":0,"title":["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"],"prefix":"10.1017","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4078-8480","authenticated-orcid":false,"given":"Lu","family":"Meng","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/042twtr12","id-type":"ROR","asserted-by":"publisher"}],"name":"Division of Healthcare Quality Promotion, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3771-8126","authenticated-orcid":false,"given":"Jeneita M.","family":"Bell","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/042twtr12","id-type":"ROR","asserted-by":"publisher"}],"name":"Division of Healthcare Quality Promotion, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and 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