Dataset Information

Mapping influenza activity in emergency departments in France using Bayesian model-based geostatistics.

ABSTRACT: BACKGROUND:Maps of influenza activity are important tools to monitor influenza epidemics and inform policymakers. In France, the availability of a high-quality data set from the Oscour® surveillance network, covering 92% of hospital emergency department (ED) visits, offers new opportunities for disease mapping. Traditional geostatistical mapping methods such as Kriging ignore underlying population sizes, are not suited to non-Gaussian data and do not account for uncertainty in parameter estimates. OBJECTIVE:Our objective was to create reliable weekly interpolated maps of influenza activity in the ED setting, to inform Santé publique France (the French national public health agency) and local healthcare authorities. METHODS:We used Oscour® data of ED visits covering the 2016-2017 influenza season. We developed a Bayesian model-based geostatistical approach, a class of generalized linear mixed models, with a multivariate normal random field as a spatially autocorrelated random effect. Using R-INLA, we developed an algorithm to create maps of the proportion of influenza-coded cases among all coded visits. We compared our results with maps obtained by Kriging. RESULTS:Over the study period, 45 565 (0.82%) visits were coded as influenza cases. Maps resulting from the model are presented for each week, displaying the posterior mean of the influenza proportion and its associated uncertainty. Our model performed better than Kriging. CONCLUSIONS:Our model allows producing smoothed maps where the random noise has been properly removed to reveal the spatial risk surface. The algorithm was incorporated into the national surveillance system to produce maps in real time and could be applied to other diseases.

SUBMITTER: Paireau J

PROVIDER: S-EPMC6185885 | biostudies-other | 2018 Nov

REPOSITORIES: biostudies-other

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Mapping influenza activity in emergency departments in France using Bayesian model-based geostatistics.

Paireau Juliette J Pelat Camille C Caserio-Schönemann Céline C Pontais Isabelle I Le Strat Yann Y Lévy-Bruhl Daniel D Cauchemez Simon S

Influenza and other respiratory viruses 20180821 6

<h4>Background</h4>Maps of influenza activity are important tools to monitor influenza epidemics and inform policymakers. In France, the availability of a high-quality data set from the Oscour<sup>®</sup> surveillance network, covering 92% of hospital emergency department (ED) visits, offers new opportunities for disease mapping. Traditional geostatistical mapping methods such as Kriging ignore underlying population sizes, are not suited to non-Gaussian data and do not account for uncertainty in ...[more]

PMID: 30055089

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Project description:BackgroundFor several decades, emergency departments (EDs) utilization has increased, inducing ED overcrowding in many countries. This phenomenon is related partly to an excessive number of nonurgent patients. To resolve ED overcrowding and to decrease nonurgent visits, the most common solution has been to triage the ED patients to identify potentially nonurgent patients, i.e. which could have been dealt with by general practitioner. The objective of this study was to measure agreement among ED health professionals on the urgency of an ED visit, and to determine if the level of agreement is consistent among different sub-groups based on following explicit criteria: age, medical status, type of referral to the ED, investigations performed in the ED, and the discharge from the ED.MethodsWe conducted a multicentric cross-sectional study to compare agreement between nurses and physicians on categorization of ED visits into urgent or nonurgent. Subgroups stratified by criteria characterizing the ED visit were analyzed in relation to the outcome of the visit.ResultsOf 1,928 ED patients, 350 were excluded because data were lacking. The overall nurse-physician agreement on categorization was moderate (kappa = 0.43). The levels of agreement within all subgroups were variable and low. The highest agreement concerned three subgroups of complaints: cranial injury (kappa = 0.61), gynaecological (kappa = 0.66) and toxicology complaints (kappa = 1.00). The lowest agreement concerned two subgroups: urinary-nephrology (kappa = 0.09) and hospitalization (kappa = 0.20). When categorization of ED visits into urgent or nonurgent cases was compared to hospitalization, ED physicians had higher sensitivity and specificity than nurses (respectively 94.9% versus 89.5%, and 43.1% versus 30.9%).ConclusionsThe lack of physician-nurse agreement and the inability to predict hospitalization have important implications for patient safety. When urgency screening is used to determine treatment priority, disagreement might not matter because all patients in the ED are seen and treated. But using assessments as the basis for refusal of care to potential nonurgent patients raises legal, ethical, and safety issues. Managed care organizations should be cautious when applying such criteria to restrict access to EDs.

Project description:BackgroundFrail older adults require specific, targeted care and expedited shared decision making in the emergency department (ED) to prevent poor outcomes and minimise time spent in this chaotic environment. The Geriatric Emergency Department Intervention (GEDI) model was developed to help limit these undesirable consequences. This qualitative study aimed to explore the ways in which two hospital implementation sites implemented the structures and processes of the GEDI model and to examine the ways in which the i-PARIHS (innovation-Promoting Action on Research Implementation in Health Services) framework influenced the implementation.MethodsUsing the i-PARIHS approach to implementation, the GEDI model was disseminated into two hospitals using a detailed implementation toolkit, external and internal facilitators and a structured program of support. Following implementation, interviews were conducted with a range of staff involved in the implementation at both sites to explore the implementation process used. Transcribed interviews were analysed for themes and sub-themes.ResultsThere were 31 interviews with clinicians involved in the implementation, conducted across two hospitals, including interviews with the two external facilitators. Major themes identified included: (i) elements of the GEDI model adopted or (ii) adapted by implementation sites and (iii) factors that affected the implementation of the GEDI model. Both sites adopted the model of care and there was general support for the GEDI approach to the management of frail older people in the ED. Both sites adapted the structure of the GEDI team and the expertise of the team members to suit their needs and resources. Elements such as service focus, funding, staff development and service evaluation were initially adopted but adaptation occurred over time. Resourcing and cost shifting issues at the implementation sites and at the site providing the external facilitators negatively impacted the facilitation process.ConclusionsThe i-PARIHS framework provided a pragmatic approach to the implementation of the evidenced-based GEDI model. Passionate, driven clinicians ensured that successful implementation occurred despite unanticipated changes in context at both the implementation and host facilitator sites as well as the absence of sustained facilitation support.

Dataset Information

Mapping influenza activity in emergency departments in France using Bayesian model-based geostatistics.

Publications

Mapping influenza activity in emergency departments in France using Bayesian model-based geostatistics.

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