Approaching precision public health by automated syndromic surveillance in communities.

<h4>Background</h4>Sentinel physician surveillance in communities has played an important role in detecting early signs of epidemics. The traditional approach is to let the primary care physician voluntarily and actively report diseases to the health department on a weekly basis. However...

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Autores principales:	Ta-Chien Chan, Jia-Hong Tang, Cheng-Yu Hsieh, Kevin J Chen, Tsan-Hua Yu, Yu-Ting Tsai
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/33cd8788e1514cdca3a16a166970e77c
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spelling	oai:doaj.org-article:33cd8788e1514cdca3a16a166970e77c2021-12-02T20:15:08ZApproaching precision public health by automated syndromic surveillance in communities.1932-620310.1371/journal.pone.0254479https://doaj.org/article/33cd8788e1514cdca3a16a166970e77c2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0254479https://doaj.org/toc/1932-6203<h4>Background</h4>Sentinel physician surveillance in communities has played an important role in detecting early signs of epidemics. The traditional approach is to let the primary care physician voluntarily and actively report diseases to the health department on a weekly basis. However, this is labor-intensive work, and the spatio-temporal resolution of the surveillance data is not precise at all. In this study, we built up a clinic-based enhanced sentinel surveillance system named "Sentinel plus" which was designed for sentinel clinics and community hospitals to monitor 23 kinds of syndromic groups in Taipei City, Taiwan. The definitions of those syndromic groups were based on ICD-10 diagnoses from physicians.<h4>Methods</h4>Daily ICD-10 counts of two syndromic groups including ILI and EV-like syndromes in Taipei City were extracted from Sentinel plus. A negative binomial regression model was used to couple with lag structure functions to examine the short-term association between ICD counts and meteorological variables. After fitting the negative binomial regression model, residuals were further rescaled to Pearson residuals. We then monitored these daily standardized Pearson residuals for any aberrations from July 2018 to October 2019.<h4>Results</h4>The results showed that daily average temperature was significantly negatively associated with numbers of ILI syndromes. The ozone and PM2.5 concentrations were significantly positively associated with ILI syndromes. In addition, daily minimum temperature, and the ozone and PM2.5 concentrations were significantly negatively associated with the EV-like syndromes. The aberrational signals detected from clinics for ILI and EV-like syndromes were earlier than the epidemic period based on outpatient surveillance defined by the Taiwan CDC.<h4>Conclusions</h4>This system not only provides warning signals to the local health department for managing the risks but also reminds medical practitioners to be vigilant toward susceptible patients. The near real-time surveillance can help decision makers evaluate their policy on a timely basis.Ta-Chien ChanJia-Hong TangCheng-Yu HsiehKevin J ChenTsan-Hua YuYu-Ting TsaiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0254479 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ta-Chien Chan Jia-Hong Tang Cheng-Yu Hsieh Kevin J Chen Tsan-Hua Yu Yu-Ting Tsai Approaching precision public health by automated syndromic surveillance in communities.
description	<h4>Background</h4>Sentinel physician surveillance in communities has played an important role in detecting early signs of epidemics. The traditional approach is to let the primary care physician voluntarily and actively report diseases to the health department on a weekly basis. However, this is labor-intensive work, and the spatio-temporal resolution of the surveillance data is not precise at all. In this study, we built up a clinic-based enhanced sentinel surveillance system named "Sentinel plus" which was designed for sentinel clinics and community hospitals to monitor 23 kinds of syndromic groups in Taipei City, Taiwan. The definitions of those syndromic groups were based on ICD-10 diagnoses from physicians.<h4>Methods</h4>Daily ICD-10 counts of two syndromic groups including ILI and EV-like syndromes in Taipei City were extracted from Sentinel plus. A negative binomial regression model was used to couple with lag structure functions to examine the short-term association between ICD counts and meteorological variables. After fitting the negative binomial regression model, residuals were further rescaled to Pearson residuals. We then monitored these daily standardized Pearson residuals for any aberrations from July 2018 to October 2019.<h4>Results</h4>The results showed that daily average temperature was significantly negatively associated with numbers of ILI syndromes. The ozone and PM2.5 concentrations were significantly positively associated with ILI syndromes. In addition, daily minimum temperature, and the ozone and PM2.5 concentrations were significantly negatively associated with the EV-like syndromes. The aberrational signals detected from clinics for ILI and EV-like syndromes were earlier than the epidemic period based on outpatient surveillance defined by the Taiwan CDC.<h4>Conclusions</h4>This system not only provides warning signals to the local health department for managing the risks but also reminds medical practitioners to be vigilant toward susceptible patients. The near real-time surveillance can help decision makers evaluate their policy on a timely basis.
format	article
author	Ta-Chien Chan Jia-Hong Tang Cheng-Yu Hsieh Kevin J Chen Tsan-Hua Yu Yu-Ting Tsai
author_facet	Ta-Chien Chan Jia-Hong Tang Cheng-Yu Hsieh Kevin J Chen Tsan-Hua Yu Yu-Ting Tsai
author_sort	Ta-Chien Chan
title	Approaching precision public health by automated syndromic surveillance in communities.
title_short	Approaching precision public health by automated syndromic surveillance in communities.
title_full	Approaching precision public health by automated syndromic surveillance in communities.
title_fullStr	Approaching precision public health by automated syndromic surveillance in communities.
title_full_unstemmed	Approaching precision public health by automated syndromic surveillance in communities.
title_sort	approaching precision public health by automated syndromic surveillance in communities.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/33cd8788e1514cdca3a16a166970e77c
work_keys_str_mv	AT tachienchan approachingprecisionpublichealthbyautomatedsyndromicsurveillanceincommunities AT jiahongtang approachingprecisionpublichealthbyautomatedsyndromicsurveillanceincommunities AT chengyuhsieh approachingprecisionpublichealthbyautomatedsyndromicsurveillanceincommunities AT kevinjchen approachingprecisionpublichealthbyautomatedsyndromicsurveillanceincommunities AT tsanhuayu approachingprecisionpublichealthbyautomatedsyndromicsurveillanceincommunities AT yutingtsai approachingprecisionpublichealthbyautomatedsyndromicsurveillanceincommunities
_version_	1718374574730510336

Approaching precision public health by automated syndromic surveillance in communities.

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