Marie Hélène Metzger

The prevalence of celiac disease in Europe: Results of a centralized, international mass screening project

Abstract Introduction. Although the prevalence of celiac disease (CD) has been extensively investigated in recent years, an accurate estimate of CD frequency in the European population is still lacking. The aims of this study were: 1) to establish accurately the prevalence of CD in a large sample of the European population (Finland, Germany, Italy, and UK), including both children and adults; and 2) to investigate whether the prevalence of CD significantly varies between different areas of the European continent. Materials and methods. Samples were drawn from the four populations. All 29,212 participants were tested for CD by tissue transglutaminase (tTG) antibody test. Positive and border-line findings were further tested for serum endomysial antibodies (EMA). All serological determinations were centrally performed. Small-bowel biopsies were recommended to autoantibody-positive individuals. Previously diagnosed cases were identified. Results. The overall CD prevalence (previously diagnosed plus anti-tTG and EMA positives) was 1.0% (95% CI 0.9–1.1). In subjects aged 30–64 years CD prevalence was 2.4% in Finland (2.0–2.8), 0.3% in Germany (0.1–0.4), and 0.7% in Italy (0.4–1.0). Sixty-eight percent of antibody-positive individuals showed small-bowel mucosal changes typical for CD (Marsh II/III lesion). Conclusions. CD is common in Europe. CD prevalence shows large unexplained differences in adult age across different European countries.

Reducing surgical site infection incidence through a network: results from the French ISO-RAISIN surveillance system.

Surgical-site infections (SSIs) are a key target for nosocomial infection control programmes. We evaluated the impact of an eight-year national SSI surveillance system named ISO-RAISIN (infection du site opératoire - Réseau Alerte Investigation Surveillance des Infections). Consecutive patients undergoing surgery were enrolled during a three-month period each year and surveyed for 30 days following surgery. A standardised form was completed for each patient including SSI diagnosis according to standard criteria, and several risk factors such as wound class, American Society of Anesthesiologists (ASA) score, operation duration, elective/emergency surgery, and type of surgery. From 1999 to 2006, 14,845 SSIs were identified in 964,128 patients (overall crude incidence: 1.54%) operated on in 838 participating hospitals. The crude overall SSI incidence decreased from 2.04% to 1.26% (P<0.001; relative reduction: -38%) and the National Nosocomial Infections Surveillance system (NNIS)-0 adjusted SSI incidence from 1.10% to 0.74% (P<0.001; relative reduction: -33%). The most significant SSI incidence reduction was observed for hernia repair and caesarean section, and to a lesser extent, cholecystectomy, hip prosthesis arthroplasty, and mastectomy. Active surveillance striving for a benchmark throughout a network is an effective strategy to reduce SSI incidence.

Hospital-acquired influenza: a synthesis using the Outbreak Reports and Intervention Studies of Nosocomial Infection (ORION) statement.

Nosocomial influenza outbreaks occur in almost all types of hospital wards, and their consequences for patients and hospitals in terms of morbidity, mortality and costs are considerable. The source of infection is often unknown, since any patient, healthcare worker (HCW) or visitor is capable of transmitting it to susceptible persons within hospitals. Nosocomial influenza outbreak investigations should help to identify the source of infection, prevent additional cases, and increase our knowledge of disease control to face future outbreaks. However, such outbreaks are probably underdetected and underreported, making routes of transmission difficult to track and describe with precision. In addition, the absence of standardised information in the literature limits comparison between studies and better understanding of disease dynamics. In this study, reports of nosocomial influenza outbreaks are synthesised according to the ORION guidelines to highlight existing knowledge in relation to the detection of influenza cases, evidence of transmission between patients and HCWs and measures of disease incidence. Although a body of evidence has confirmed that influenza spreads within hospitals, we should improve clinical and virological confirmation and initiate active surveillance and quantitative studies to determine incidence rates in order to assess the risk to patients.

Factors associated with self-reporting of chronic health problems in the French GAZEL cohort

The objective of this study was to examine factors associated with self-reporting of chronic health problems. The self-reports were obtained from a questionnaire sent by mail to the French GAZEL cohort, composed of workers of a French company. The disorders reported in the questionnaire were compared with diagnoses from the sick-leave database of the company. Associations between self-reporting and characteristics were studied by multiple logistic regression analyses. Three types of characteristics were analyzed: individual, methodological and disorder-related (i.e., prevalence of chronic disorders in the general population, probable disability and probable life risk scores). In 1992, the cohort consisted of 16,534 subjects aged 38 to 53 years. The reporting rate (number of self-reports in the questionnaire divided by number of records in the sick-leave database for the disorder considered) varied from 8.9% to 100%. Self-reporting was associated with individual characteristics (gender, family status, place of residence, annual number of sick days and sick leaves), disorder-related characteristics (probable disability, prevalence) and methodological characteristics (precision of the formulation, delay between the last sick-day and the patient report). By body system, the characteristics associated with self-reporting varied greatly but the annual number of sick days, probable disability and precision of formulation were the variables which remained most often in the models. These characteristics should be particularly taken into consideration in the interpretation of epidemiological results based on self-reporting.

Automated detection of nosocomial infections: evaluation of different strategies in an intensive care unit 2000–2006

The aim of this study was to evaluate seven different strategies for the automated detection of nosocomial infections (NIs) in an intensive care unit (ICU) by using different hospital information systems: microbiology database, antibiotic prescriptions, medico-administrative database, and textual hospital discharge summaries. The study involved 1,499 patients admitted to an ICU of the University Hospital of Lyon (France) between 2000 and 2006. The data were extracted from the microbiology laboratory information system, the clinical information system on the ward and the medico-administrative database. Different algorithms and strategies were developed, using these data sources individually or in combination. The performances of each strategy were assessed by comparing the results with the ward data collected as a national standardised surveillance protocol, adapted from the National Nosocomial Infections Surveillance system as the gold standard. From 1,499 patients, 282 NIs were reported. The strategy with the best sensitivity for detecting these infections using an automated method was the combination of antibiotic prescription or microbiology, with a sensitivity of 99.3% [95% confidence interval (CI): 98.2-100] and a specificity of 56.8% (95% CI: 54.0-59.6). Automated methods of NI detection represent an alternative to traditional monitoring methods. Further study involving more ICUs should be performed before national recommendations can be established.

The use of regional platforms for managing electronic health records for the production of regional public health indicators in France.

BackgroundIn France, recent developments in healthcare system organization have aimed at strengthening decision-making and action in public health at the regional level. Firstly, the 2004 Public Health Act, by setting 100 national and regional public health targets, introduced an evaluative approach to public health programs at the national and regional levels. Meanwhile, the implementation of regional platforms for managing electronic health records (EHRs) has also been under assessment to coordinate the deployment of this important instrument of care within each geographic area. In this context, the development and implementation of a regional approach to epidemiological data extracted from EHRs are an opportunity that must be seized as soon as possible. Our article addresses certain design and organizational aspects so that the technical requirements for such use are integrated into regional platforms in France. The article will base itself on organization of the Rhône-Alpes regional health platform.DiscussionDifferent tools being deployed in France allow us to consider the potential of these regional platforms for epidemiology and public health (implementation of a national health identification number and a national information system interoperability framework). The deployment of the Rhône-Alpes regional health platform began in the 2000s in France. By August 2011, 2.6 million patients were identified in this platform. A new development step is emerging because regional decision-makers need to measure healthcare efficiency. To pool heterogeneous information contained in various independent databases, the format, norm and content of the metadata have been defined. Two types of databases will be created according to the nature of the data processed, one for extracting structured data, and the second for extracting non-structured and de-identified free-text documents.SummaryRegional platforms for managing EHRs could constitute an important data source for epidemiological surveillance in the context of epidemic alerts, but also in monitoring a number of indicators of infectious and chronic diseases for which no data are yet available in France.

Evaluation study of different strategies for detecting surgical site infections using the hospital information system at Lyon University Hospital, France.

Objective:To evaluate different strategies for detecting surgical site infections (SSIs) using different sources (notification by the surgeon, bacteriological results, antibiotic prescription, and discharge diagnosis codes). Background:Surveillance plays a role in reducing the risks of SSIs but the performance of case reports by surgeons is insufficient. Indirect methods of SSI detection are an alternative to increase the quality of surveillance. Methods:A retrospective cohort study of 446 patients operated consecutively during the first half of 2007 was set up in a 56-bed general surgery unit in Lyon University Hospital, France. Patients were followed up 30 days after intervention. Different methods of detection were established by combining different data sources. The sensitivity and specificity of these methods were calculated by using, as reference method, the manual review of the medical records. Results:The sensitivity and specificity of SSI detection were, respectively, 18.4% (95% confidence interval [CI]: 7.9–31.6) and 100% for surgeon notification; 63.2% (95% CI: 47.3–78.9) and 95.1% (95% CI: 92.9–97.1) for detection based on positive cultures; 68.4% (95% CI: 52.6–81.6) and 87.5% (95% CI: 84.3–90.7) using antibiotic prescription; 26.3% (95% CI: 13.2–42.1) and 99.5% (95% CI: 98.8–100) using discharge diagnosis codes. By combining the latter 3 sources, the sensitivity increased at 86.8% (95% CI: 76.3 – 97.4) and the specificity was lowered at 85.5% (95% CI: 82.1 – 89.0). Conclusions:SSI detection based on the combination of data extracted automatically from the hospital information system performed well. This strategy has been implemented gradually in Lyon University Hospital.

Evaluation of natural language processing from emergency department computerized medical records for intra-hospital syndromic surveillance

BackgroundThe identification of patients who pose an epidemic hazard when they are admitted to a health facility plays a role in preventing the risk of hospital acquired infection. An automated clinical decision support system to detect suspected cases, based on the principle of syndromic surveillance, is being developed at the University of Lyons Hôpital de la Croix-Rousse. This tool will analyse structured data and narrative reports from computerized emergency department (ED) medical records. The first step consists of developing an application (UrgIndex) which automatically extracts and encodes information found in narrative reports. The purpose of the present article is to describe and evaluate this natural language processing system.MethodsNarrative reports have to be pre-processed before utilizing the French-language medical multi-terminology indexer (ECMT) for standardized encoding. UrgIndex identifies and excludes syntagmas containing a negation and replaces non-standard terms (abbreviations, acronyms, spelling errors...). Then, the phrases are sent to the ECMT through an Internet connection. The indexers reply, based on Extensible Markup Language, returns codes and literals corresponding to the concepts found in phrases. UrgIndex filters codes corresponding to suspected infections. Recall is defined as the number of relevant processed medical concepts divided by the number of concepts evaluated (coded manually by the medical epidemiologist). Precision is defined as the number of relevant processed concepts divided by the number of concepts proposed by UrgIndex. Recall and precision were assessed for respiratory and cutaneous syndromes.ResultsEvaluation of 1,674 processed medical concepts contained in 100 ED medical records (50 for respiratory syndromes and 50 for cutaneous syndromes) showed an overall recall of 85.8% (95% CI: 84.1-87.3). Recall varied from 84.5% for respiratory syndromes to 87.0% for cutaneous syndromes. The most frequent cause of lack of processing was non-recognition of the term by UrgIndex (9.7%). Overall precision was 79.1% (95% CI: 77.3-80.8). It varied from 81.4% for respiratory syndromes to 77.0% for cutaneous syndromes.ConclusionsThis study demonstrates the feasibility of and interest in developing an automated method for extracting and encoding medical concepts from ED narrative reports, the first step required for the detection of potentially infectious patients at epidemic risk.

Quality of information: a European challenge

Since the end of the 1970s, many countries have started to set up programmes to control healthcare-associated infections (HAIs) and to achieve a safe and sustainable development of their healthcare facilities that minimises the risk of infection. Surveillance is a usual component of any organised programme to address the problem either at national, regional or local level. So a considerable effort has been made by the national Public Health Authorities of EU Member States to foster and extend the surveillance of HAI via the production of increasingly standardised indicators. This information is used by Infection Control teams to implement preventive strategies, to evaluate the magnitude of the problem and to understand variations in the risks of HAI. At the same time, Public Health authorities and healthcare financing agencies in several countries have attempted to generalise the production of such indicators at an official level and use them as a global approach for hospital quality assessment, accreditation, continuous quality improvement and communication with patients and the general population.

Linguistic and temporal processing for discovering hospital acquired infection from patient records

This paper describes the first steps of development of a rulebased system that automatically processes medical records in order to discover possible cases of hospital acquired infections (HAI). The system takes as input a set of patient records in electronic format and gives as output, for each document, information regarding HAI. In order to achieve this goal, a temporal processing together with a deep syntactic and semantic analysis of the patient records is performed. Medical knowledge used by the rules is derived from a set of documents that have been annotated by medical doctors. After a brief description of the context of this work, we present the general architecture of our document processing chain and explain how we perform our temporal and linguistic analysis. Finally, we report our preliminary results and we lay out the next steps of the project.