Optimizing epidemic prevention in nursing homes using clinical surveillance of respiratory infections
Summary: Background: Respiratory tract infections (RTIs) pose a significant risk in nursing homes (NHs), which makes surveillance crucial for timely intervention. Aim: To monitor the impacts of seasonal RTIs in NHs, which include frequency, the use of rapid diagnostic tests and antibiotics, mortali...
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Elsevier
2025-03-01
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| Series: | Infection Prevention in Practice |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590088925000083 |
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| author | Philippe Gaspard Martin Martinot |
| author_facet | Philippe Gaspard Martin Martinot |
| author_sort | Philippe Gaspard |
| collection | DOAJ |
| description | Summary: Background: Respiratory tract infections (RTIs) pose a significant risk in nursing homes (NHs), which makes surveillance crucial for timely intervention. Aim: To monitor the impacts of seasonal RTIs in NHs, which include frequency, the use of rapid diagnostic tests and antibiotics, mortality, and cluster dynamics, with the use of clinical surveillance. Methods: During the winter periods from 2015 to 2019 (22 weeks), data on general signs (GSs), and respiratory signs (RSs) were collected to define three respiratory clinical sign patterns (CSPs): GS+/RS+, GS−/RS+, and GS+/RS−. Clusters (≥2 cases) were identified and classified into three intensity levels, namely, L1, L2, and L3 (2, 3–5, and ≥6 GS+/RS+/4 days, respectively). CSP frequencies and the 28-day all-cause mortality were calculated. Findings: In 13 NHs (N = 3,628 resident inclusions, median age: 87.2 years), 1,538 GS+/RS+, 1,482 GS−/RS+, and 233 GS+/RS− cases were observed, with mortality rates of 8.5%, 2.8%, and 6%, respectively. Among the GS+/RS+ cases, 63% received an antimicrobials. GS+/RS+ cluster analysis identified 141 clusters with L1, 100 with L2, and 26 with L3.A total of 209 rapid diagnostic tests for influenza were carried out, with 72.2% conducted in L2 or L3 clusters. Within clusters, the first case must be identified promptly with rapid outbreak development taking place within the first 2–3 days, and potentially less effective containment efforts following delayed detection. Conclusion: Clinical surveillance is a comprehensive method that can be utilized for the rapid implementation of preventive measures and appropriate use of antibiotics. |
| format | Article |
| id | doaj-art-c9140d29ede145cda8ab766bdb3db8a1 |
| institution | DOAJ |
| issn | 2590-0889 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
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| series | Infection Prevention in Practice |
| spelling | doaj-art-c9140d29ede145cda8ab766bdb3db8a12025-08-20T03:00:50ZengElsevierInfection Prevention in Practice2590-08892025-03-017110044410.1016/j.infpip.2025.100444Optimizing epidemic prevention in nursing homes using clinical surveillance of respiratory infectionsPhilippe Gaspard0Martin Martinot1Hospital Hygiene Service, Rouffach Hospital Center, Rouffach, France; UMR 6249 Chrono-environnement, University of Franche-Comté, Besançon, France; Corresponding author. Address: Hospital Hygiene Service, Rouffach Hospital Center, 27 rue du 4ème RSM, 68250 Rouffach, France. Tel.: +33 03 89 78 74 47; fax: +33 03 89 78 70 00.Department of Infectious Diseases, Colmar Civil Hospital, Colmar, FranceSummary: Background: Respiratory tract infections (RTIs) pose a significant risk in nursing homes (NHs), which makes surveillance crucial for timely intervention. Aim: To monitor the impacts of seasonal RTIs in NHs, which include frequency, the use of rapid diagnostic tests and antibiotics, mortality, and cluster dynamics, with the use of clinical surveillance. Methods: During the winter periods from 2015 to 2019 (22 weeks), data on general signs (GSs), and respiratory signs (RSs) were collected to define three respiratory clinical sign patterns (CSPs): GS+/RS+, GS−/RS+, and GS+/RS−. Clusters (≥2 cases) were identified and classified into three intensity levels, namely, L1, L2, and L3 (2, 3–5, and ≥6 GS+/RS+/4 days, respectively). CSP frequencies and the 28-day all-cause mortality were calculated. Findings: In 13 NHs (N = 3,628 resident inclusions, median age: 87.2 years), 1,538 GS+/RS+, 1,482 GS−/RS+, and 233 GS+/RS− cases were observed, with mortality rates of 8.5%, 2.8%, and 6%, respectively. Among the GS+/RS+ cases, 63% received an antimicrobials. GS+/RS+ cluster analysis identified 141 clusters with L1, 100 with L2, and 26 with L3.A total of 209 rapid diagnostic tests for influenza were carried out, with 72.2% conducted in L2 or L3 clusters. Within clusters, the first case must be identified promptly with rapid outbreak development taking place within the first 2–3 days, and potentially less effective containment efforts following delayed detection. Conclusion: Clinical surveillance is a comprehensive method that can be utilized for the rapid implementation of preventive measures and appropriate use of antibiotics.http://www.sciencedirect.com/science/article/pii/S2590088925000083InfluenzaAirborne virusGeriatricsNursing homeDisease outbreaksClusters |
| spellingShingle | Philippe Gaspard Martin Martinot Optimizing epidemic prevention in nursing homes using clinical surveillance of respiratory infections Infection Prevention in Practice Influenza Airborne virus Geriatrics Nursing home Disease outbreaks Clusters |
| title | Optimizing epidemic prevention in nursing homes using clinical surveillance of respiratory infections |
| title_full | Optimizing epidemic prevention in nursing homes using clinical surveillance of respiratory infections |
| title_fullStr | Optimizing epidemic prevention in nursing homes using clinical surveillance of respiratory infections |
| title_full_unstemmed | Optimizing epidemic prevention in nursing homes using clinical surveillance of respiratory infections |
| title_short | Optimizing epidemic prevention in nursing homes using clinical surveillance of respiratory infections |
| title_sort | optimizing epidemic prevention in nursing homes using clinical surveillance of respiratory infections |
| topic | Influenza Airborne virus Geriatrics Nursing home Disease outbreaks Clusters |
| url | http://www.sciencedirect.com/science/article/pii/S2590088925000083 |
| work_keys_str_mv | AT philippegaspard optimizingepidemicpreventioninnursinghomesusingclinicalsurveillanceofrespiratoryinfections AT martinmartinot optimizingepidemicpreventioninnursinghomesusingclinicalsurveillanceofrespiratoryinfections |