Issues in Building a Nursing Home Syndromic Surveillance System with Textmining: Longitudinal Observational Study (Preprint)

Delespierre, Tiba; Josseran, Loïc

doi:10.2196/preprints.9022

Cited by 1 publication

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References 39 publications

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“…This syndromic IS offers a real opportunity for finding new ways to seniors’ functioning modelization and opens, hopefully, the path toward specific clinical hypotheses formulation. Other works included studying the use of this IS applied to other public health problems such as frequent falls or falls with casualties [102] but also working toward a better life ending with cancer [103]. Ultimately, the aims are removing all preventable deaths and improving the residents’ end of life with more autonomy, less pain, and an improved quality of life, translating this new knowledge into health benefits for seniors everywhere.…”

Section: Discussionmentioning

confidence: 99%

Issues in Building a Nursing Home Syndromic Surveillance System with Textmining: Longitudinal Observational Study

Delespierre¹,

Josseran²

2018

JMIR Public Health Surveill

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BackgroundNew nursing homes (NH) data warehouses fed from residents’ medical records allow monitoring the health of elderly population on a daily basis. Elsewhere, syndromic surveillance has already shown that professional data can be used for public health (PH) surveillance but not during a long-term follow-up of the same cohort.ObjectiveThis study aimed to build and assess a national ecological NH PH surveillance system (SS).MethodsUsing a national network of 126 NH, we built a residents’ cohort, extracted medical and personal data from their electronic health records, and transmitted them through the internet to a national server almost in real time. After recording sociodemographic, autonomic and syndromic information, a set of 26 syndromes was defined using pattern matching with the standard query language-LIKE operator and a Delphi-like technique, between November 2010 and June 2016. We used early aberration reporting system (EARS) and Bayes surveillance algorithms of the R surveillance package (Höhle) to assess our influenza and acute gastroenteritis (AGE) syndromic data against the Sentinelles network data, French epidemics gold standard, following Centers for Disease Control and Prevention surveillance system assessment guidelines.ResultsBy extracting all sociodemographic residents’ data, a cohort of 41,061 senior citizens was built. EARS_C3 algorithm on NH influenza and AGE syndromic data gave sensitivities of 0.482 and 0.539 and specificities of 0.844 and 0.952, respectively, over a 6-year period, forecasting the last influenza outbreak by catching early flu signals. In addition, assessment of influenza and AGE syndromic data quality showed precisions of 0.98 and 0.96 during last season epidemic weeks’ peaks (weeks 03-2017 and 01-2017) and precisions of 0.95 and 0.92 during last summer epidemic weeks’ low (week 33-2016).ConclusionsThis study confirmed that using syndromic information gives a good opportunity to develop a genuine French national PH SS dedicated to senior citizens. Access to senior citizens’ free-text validated health data on influenza and AGE responds to a PH issue for the surveillance of this fragile population. This database will also make possible new ecological research on other subjects that will improve prevention, care, and rapid response when facing health threats.

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Section: Discussionmentioning

confidence: 99%