{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T12:54:16Z","timestamp":1766408056236,"version":"3.37.1"},"reference-count":23,"publisher":"Scientific Scholar","content-domain":{"domain":["ijmr.org.in"],"crossmark-restriction":true},"short-container-title":["Indian J Med Res"],"abstract":"<jats:sec>\n<jats:title>Background &amp; objectives<\/jats:title>\n<jats:p>Event-based surveillance (EBS) is a critical component of early warning systems for detecting and responding to infectious disease outbreaks. While EBS is widely used in public health settings, its integration into private healthcare facilities remains limited. This study undertook to pilot an EBS in private hospitals in Kasaragod, Kerala and to assess its added value in early detection of disease clusters.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title>Methods<\/jats:title>\n<jats:p>Clinical nurses abstracted the data on hospitalisation dates, places of residence, and presenting illnesses from case records of patients with acute febrile illness (AFI) admitted in six private hospitals. A software algorithm analysed the data to identify spatiotemporal clustering of case-patients or deaths (signals), for syndromes of interest [acute febrile illness with rash (AFIR), acute encephalitis syndrome (AES), acute febrile illness with haemorrhage (AFIH) and severe acute respiratory illness (SARI)]. The District Surveillance Unit (DSU) verified these signals, flagged verified signals as events, and conducted a risk assessment to determine if the events were outbreaks.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title>Results<\/jats:title>\n<jats:p>From May to December 2023, data from 3294 (73%) of 4512 AFI patients were analysed using the EBS algorithm. Of the 88 signals identified, 67 (76%) were due to SARI, 9 (10.3%) were due to AES, and 9 (9%) were due to AFIR. Ten signals were verified as events, of which nine were classified as outbreaks (dengue-1, H1N1-3, H3N2-1, H1N1 and H3N2 - 1, H1N1 and SARS-COV2 \u2013 1, no pathogen detected\u2013 2). Five outbreaks were not detected by the existing indicator-based surveillance (IBS).<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title>Interpretation &amp; conclusions<\/jats:title>\n<jats:p>EBS pilot in private health facilities complemented the IBS system by early detecting outbreaks. This EBS model has the potential for implementation in other districts, especially in districts at higher risk of zoonotic spillover.<\/jats:p>\n<\/jats:sec>","DOI":"10.25259\/ijmr_1395_2024","type":"journal-article","created":{"date-parts":[[2025,2,15]],"date-time":"2025-02-15T11:35:04Z","timestamp":1739619304000},"page":"54-63","update-policy":"https:\/\/doi.org\/10.25259\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Piloting an event-based surveillance model in private hospitals for early detection of disease clusters, Kerala, India"],"prefix":"10.25259","volume":"161","author":[{"given":"Raman Swathy","family":"Vaman","sequence":"first","affiliation":[{"name":"Department of Health and Family Welfare, Government of Kerala, Kerala, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sunil","family":"Solomon","sequence":"additional","affiliation":[{"name":"Division of Infectious Diseases, John Hopkins University School of Medicine, 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