Evaluation de la tenue du partogramme dans une maternité universitaire

Introduction: La mortalité maternelle est un problème majeur de santé mondiale. Une grande proportion de ces décès serait évitable par des soins adéquats, une aide à l'accouchement, la disponibilité des soins d'urgence et l'utilisation des outils d'aide à la décision tels que le partogramme. L'objectif était d'évaluer l'écart entre ce qui est censé être fait et ce qui est fait réellement pour les différents paramètres situés dans le partogramme au sein d'une maternité de 3ème niveau et élaborer des recommandations pour la mise en place d'un plan d'action. Méthodes: Il s'agit d'une étude descriptive rétrospective par audit clinique, effectuée sur un échantillon de 400 dossiers obstétricaux des parturientes ayant accouchées dans la maternité du CHU Farhat Hached durant l'année 2011. Le référentiel utilisé est celui réalisé par l'Agence Nationale d'Accréditation et d'Evaluation en Santé en l'an 2000, concernant la qualité de la tenue du partogramme. Résultats: La majorité des critères d'évaluation portant sur la présentation du partogramme était conforme. Deux critères concernant la variété de la présentation et le rythme cardiaque foetal étaient non conformes parmi ceux portant sur la surveillance du foetus. Plusieurs critères en rapport avec la surveillance de la mère étaient non conformes. Aucun des critères portant sur les traitements administrés et les marqueurs d'évènements n'est conforme. Les critères portant sur la naissance et la surveillance immédiate qui étaient non conformes sont : le début des efforts expulsifs, le mode d'accouchement, l'état du périnée, la délivrance et la révision utérine. Conclusion: La véritable démarche de l'audit clinique se doit d'aller au-delà du recueil et de l'analyse des données, le but final étant l'amélioration des pratiques

Design of a FAIR digital data health infrastructure in Africa for COVID-19 reporting and research

The limited volume of COVID-19 data from Africa raises concerns for global genome research, which requires a diversity of genotypes for accurate disease prediction, including on the provenance of the new SARS-CoV-2 mutations. The Virus Outbreak Data Network (VODAN)-Africa studied the possibility of increasing the production of clinical data, finding concerns about data ownership, and the limited use of health data for quality treatment at point of care. To address this, VODAN Africa developed an architecture to record clinical health data and research data collected on the incidence of COVID-19, producing these as human- and machine-readable data objects in a distributed architecture of locally governed, linked, human- and machine-readable data. This architecture supports analytics at the point of care and-through data visiting, across facilities-for generic analytics. An algorithm was run across FAIR Data Points to visit the distributed data and produce aggregate findings. The FAIR data architecture is deployed in Uganda, Ethiopia, Liberia, Nigeria, Kenya, Somalia, Tanzania, Zimbabwe, and Tunisia.Computer Systems, Imagery and Medi

Occupational Noise Exposure and Diabetes Risk

Introduction. Noise is one of the most common worldwide environmental pollutants, especially in occupational fields. As a stressor, it affects not only the ear but also the entire body. Its physiological and psychological impacts have been well established in many conditions such as cardiovascular diseases. However, there is a dearth of evidence regarding diabetes risk related to noises. Aim. To evaluate the relationship between occupational exposure to noise and the risk of developing diabetes. Methods. This is a cross-sectional analytical study enrolling two groups of 151 workers each. The first group (noise exposed group: EG) included the employees of a Tunisian power plant, who worked during the day shift and had a permanent position. The second group (unexposed to noise group: NEG) included workers assigned to two academic institutions, who were randomly selected in the Occupational Medicine Department of the Farhat Hached University Hospital in Sousse, during periodical fitness to work visits. Both populations (exposed and unexposed) were matched by age and gender. Data collection was based on a preestablished questionnaire, a physical examination, a biological assessment, and a sonometric study. Results. The mean equivalent continuous sound level was 89 dB for the EG and 44.6 dB for the NEG. Diabetes was diagnosed in 24 workers from EG (15.9%) and 14 workers from NEG (9.3%), with no statistically significant difference (p=0.08). After multiple binary logistic regression, including variables of interest, noise did not appear to be associated with diabetes. Conclusion. Our results did not reveal a higher risk of developing diabetes in workers exposed to noise. Further studies assessing both level and duration of noise exposure are needed before any definitive conclusion

Design of a FAIR

The limited volume of COVID‐19 data from Africa raises concerns for global genome research, which requires a diversity of genotypes for accurate disease prediction, including on the provenance of the new SARS‐CoV‐2 mutations. The Virus Outbreak Data Network (VODAN)‐Africa studied the possibility of increasing the production of clinical data, finding concerns about data ownership, and the limited use of health data for quality treatment at point of care. To address this, VODAN Africa developed an architecture to record clinical health data and research data collected on the incidence of COVID‐19, producing these as human‐ and machine‐readable data objects in a distributed architecture of locally governed, linked, human‐ and machine‐readable data. This architecture supports analytics at the point of care and—through data visiting, across facilities—for generic analytics. An algorithm was run across FAIR Data Points to visit the distributed data and produce aggregate findings. The FAIR data architecture is deployed in Uganda, Ethiopia, Liberia, Nigeria, Kenya, Somalia, Tanzania, Zimbabwe, and Tunisia

ESICM LIVES 2016: part two : Milan, Italy. 1-5 October 2016.

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