Dataset Information

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

ABSTRACT: BACKGROUND:New 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. OBJECTIVE:This study aimed to build and assess a national ecological NH PH surveillance system (SS). METHODS:Using 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. RESULTS:By 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). CONCLUSIONS:This 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.

SUBMITTER: Delespierre T

PROVIDER: S-EPMC6315244 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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

Delespierre Tiba T Josseran Loic L

JMIR public health and surveillance 20181213 4

<h4>Background</h4>New 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.<h4>Objective</h4>This study aimed to build and assess a national ecological NH PH surveillance system (SS).<h4>Methods</h4>Using a national network of 126 NH, ...[more]

PMID: 30545816

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Project description:BackgroundTraditional performance-based measurements of mobility fail to recognize the interaction between the individual and their environment. Life-space (LS) forms a central element in the broader context of mobility and has received growing attention in gerontology. Still, knowledge on LS in the nursing home (NH) remains sparse. The aim of this study was to identify LS trajectories in people with dementia from time of NH admission, and explore characteristics associated with LS over time.MethodsIn total, 583 people with dementia were included at NH admission and assessed biannually for 3 years. LS was assessed using the Nursing Home Life-Space Diameter. Association with individual (age, sex, general medical health, number of medications, pain, physical performance, dementia severity, and neuropsychiatric symptoms) and environmental (staff-to-resident ratio, unit size, and quality of the physical environment) characterises was assessed. We used a growth mixture model to identify LS trajectories and linear mixed model was used to explore characteristics associated with LS over time.ResultsWe identified four groups of residents with distinct LS trajectories, labelled Group 1 (n = 19, 3.5%), Group 2 (n = 390, 72.1%), Group 3 (n = 56, 10.4%), Group 4 (n = 76, 14.0%). Being younger, having good compared to poor general medical health, less severe dementia, more agitation, less apathy, better physical performance and living in a smaller unit were associated with a wider LS throughout the study period.ConclusionFrom NH admission most NH residents' LS trajectory remained stable (Group 2), and their daily lives unfolded within their unit. Better physical performance and less apathy emerged as potentially modifiable characteristics associated with wider LS over time. Future studies are encouraged to determine whether LS trajectories in NH residents are modifiable, and we suggest that future research further explore the impact of environmental characteristics.

Project description:BackgroundInternet data can be used to improve infectious disease models. However, the representativeness and individual-level validity of internet-derived measures are largely unexplored as this requires ground truth data for study.ObjectiveThis study sought to identify relationships between Web-based behaviors and/or conversation topics and health status using a ground truth, survey-based dataset.MethodsThis study leveraged a unique dataset of self-reported surveys, microbiological laboratory tests, and social media data from the same individuals toward understanding the validity of individual-level constructs pertaining to influenza-like illness in social media data. Logistic regression models were used to identify illness in Twitter posts using user posting behaviors and topic model features extracted from users' tweets.ResultsOf 396 original study participants, only 81 met the inclusion criteria for this study. Of these participants' tweets, we identified only two instances that were related to health and occurred within 2 weeks (before or after) of a survey indicating symptoms. It was not possible to predict when participants reported symptoms using features derived from topic models (area under the curve [AUC]=0.51; P=.38), though it was possible using behavior features, albeit with a very small effect size (AUC=0.53; P?.001). Individual symptoms were also generally not predictable either. The study sample and a random sample from Twitter are predictably different on held-out data (AUC=0.67; P?.001), meaning that the content posted by people who participated in this study was predictably different from that posted by random Twitter users. Individuals in the random sample and the GoViral sample used Twitter with similar frequencies (similar @ mentions, number of tweets, and number of retweets; AUC=0.50; P=.19).ConclusionsTo our knowledge, this is the first instance of an attempt to use a ground truth dataset to validate infectious disease observations in social media data. The lack of signal, the lack of predictability among behaviors or topics, and the demonstrated volunteer bias in the study population are important findings for the large and growing body of disease surveillance using internet-sourced data.

Project description:ImportanceEmpirical antibiotic prescribing in nursing homes (NHs) is often suboptimal. The potential for antibiograms to improve empirical antibiotic decision-making in NHs remains poorly understood.ObjectiveTo determine whether providing NH clinicians with a urinary antibiogram improves empirical antibiotic treatment of urinary tract infections (UTIs).Design, setting, and participantsThis was a survey study using clinical vignettes. Participants were recruited via convenience sampling of professional organization listservs of NH clinicians practicing in the US from December 2021 through April 2022. Data were analyzed from July 2022 to June 2023.InterventionsRespondents were randomized to complete vignettes using a traditional antibiogram (TA), a weighted-incidence syndromic combination antibiogram (WISCA), or no tool. Participants randomized to antibiogram groups were asked to use the antibiogram to empirically prescribe an antibiotic. Participants randomized to the no tool group functioned as controls.Main outcomes and measuresEmpirical antibiotic selections were characterized as microbiologically (1) active and (2) optimal according to route of administration and spectrum of activity.ResultsOf 317 responses, 298 (95%) were included in the analysis. Duplicate responses (15 participants), location outside the US (2 participants), and uninterpretable responses (2 participants) were excluded. Most respondents were physicians (217 respondents [73%]) and had over 10 years of NH practice experience (155 respondents [52%]). A mixed-effects logistic model found that use of the TA (odds ratio [OR], 1.41; 95% CI, 1.19-1.68; P < .001) and WISCA (OR, 1.54; 95% CI, 1.30-1.84; P < .001) were statistically superior to no tool when choosing an active empirical antibiotic. A similarly constructed model found that use of the TA (OR, 1.94; 95% CI, 1.42-2.66; P < .001) and WISCA (OR, 1.7; 95% CI, 1.24-2.33; P = .003) were statistically superior to no tool when selecting an optimal empirical antibiotic. Although there were differences between tools within specific vignettes, when compared across all vignettes, the TA and WISCA performed similarly for active (OR, 1.09; 95% CI, 0.92-1.30; P = .59) and optimal (OR, 0.87; 95% CI, 0.64-1.20; P = .69) antibiotics.Conclusions and relevanceProviding NH clinicians with a urinary antibiogram was associated with selection of active and optimal antibiotics when empirically treating UTIs under simulated conditions. Although the antibiogram format was not associated with decision-making in aggregate, context-specific effects may have been present, supporting further study of syndromic antibiograms in clinical practice.

Project description:Early detection and subsequent reduction of modifiable risk factors for cognitive decline is important for extending healthy life expectancy in the currently aging society. Although a recent increase in studies on the state or number of the teeth and cognitive function, few studies have focused on the association between posterior teeth occlusion necessary to maintain chewing function and cognitive function among older adults. This study examined the association between posterior teeth occlusion and cognitive function in nursing home older residents. In this cross-sectional study, 279 residents aged ≥60 years from eight nursing homes in Aso City, Japan participated in cognitive function and dental status assessments and completed a comprehensive questionnaire survey in 2014. Cognitive function was measured using a Mini-Mental State Examination (MMSE). Posterior teeth occlusion was assessed using a total number of functional tooth units (total-FTUs), depending on the number and location of the remaining natural and artificial teeth on implant-supported, fixed, and removable prostheses. Linear regression models were used to assess univariate and multivariate associations between total-FTUs and MMSE scores. Models were sequentially adjusted for demographic characteristics, number of natural teeth, socioeconomic status, health behaviors, comorbidities, physical function, and nutritional status. Among the 200 residents included in our analysis, mean MMSE scores and total-FTUs were 11.0 ± 8.6 and 9.3 ± 4.6, respectively. Higher total-FTUs were significantly associated with higher MMSE scores after adjustment for demographics and teeth number (B = 0.48, 95% confidence interval [CI] = 0.22-0.74). The association remained significant even after adjustment for all covariates (B = 0.25, 95% CI = 0.01-0.49). The current findings demonstrated that loss of posterior teeth occlusion was independently associated with cognitive decline in nursing home older residents in Japan. Maintenance and restoration of posterior teeth occlusion may be a preventive factor against cognitive decline in aged populations.

Dataset Information

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

Publications

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

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