Construction and Validation of an Individual Deprivation Index: a Study Based on a Representative Cohort of the Paris Metropolitan Area.

The association between health status and deprivation is well established. However, it is difficult to measure deprivation at an individual level and already-existing indices in France are not validated or do not meet the needs of health practitioners. The aim of this work was to establish a validated, easy-to-use, multidimensional, relevant index that was representative of the population in the Paris metropolitan area. From the SIRS 2010 cohort study, 14 socio-economic characteristics were selected: health insurance, educational background, socio-professional category, professional status, feelings of loneliness, emotional situation, household type, income, perceived financial situation, social support (support in daily life, financial and emotional), housing situation, and migration origin. In addition, a total of 12 health status, healthcare use, and nutrition-related variables were also selected. Content validity and internal validity of the index were explored. The 14 socio-economic indicators were associated to varying degrees with poorer health status, less use of healthcare, and poorer nutrition and were distributed across the 14 multiple-choice questions of the index. Each answer was rated from 0 to 2. The index value of 10 that isolates 20% of the most deprived individuals was used as threshold. "Being deprived," as defined with this value, was significantly associated with 9 of the 12 studied health variables. This index could be a relevant instrument in the assessment of deprivation and social inequalities of health.

Impact de la pandémie COVID-19 sur les évènements indésirables associés aux soins.

Since early 2020, the onset of the COVID-19 pandemic, physicians have continued to report adverse events associated with care. Patients also continued to participate in the hospital satisfaction surveys. To date, no study in France has measured the impact of the pandemic on adverse events and patient satisfaction. We looked at the characteristics of these adverse events in relation to the pandemic and put patients' feelings into perspective. A qualitative and observational retrospective study of the REX and MCO48 databases was carried out. The quantitative study of the REX database was supplemented by a qualitative analysis of the declarations. The adverse events more often affects middle-aged men aged 60 years, while deaths occur in older patients with more complex pathologies and more urgent management. The nature of these events is different depending on the reporting period: Those reported in the first wave are more urgent, occur less frequently in the operating room than in the emergency room, and are considered less preventable than those reported in the second wave. The latter are more similar to the events that usually occur. The implementation of effective barriers, particularly within the teams, has made it possible to reduce the impact of the second wave on the occurrence of these events, the role of communication seems essential. The overall patient satisfaction score as well as those for medical and paramedical care has increased, which may reflect patient solidarity with caregivers. The attitude of active resilience on the part of all actors has been a major element in risk management during this crisis and it is essential to capitalize on these collaborative processes for the future.

National Variations in Recent Trends of Sudden Unexpected Infant Death Rate in Western Europe.

OBJECTIVE: To study recent epidemiologic trends of sudden unexpected death in infancy (SUDI) in Western Europe. STUDY DESIGN: Annual national statistics of death causes for 14 Western European countries from 2005 to 2015 were analyzed. SUDI cases were defined as infants younger than 1 year with the underlying cause of death classified as "sudden infant death syndrome," "unknown/unattended/unspecified cause," or "accidental threats to breathing." Poisson regression models were used to study temporal trends of SUDI rates and source of variation. RESULTS: From 2005 to 2015, SUDI accounted for 15 617 deaths, for an SUDI rate of 34.9 per 100 000 live births. SUDI was the second most common cause of death after the neonatal period (22.2%) except in Belgium, Finland, France, and the UK, where it ranked first. The overall SUDI rate significantly decreased from 40.2 to 29.9 per 100 000, with a significant rate reduction experienced for 6 countries, no significant evolution for 7 countries, and a significant increase for Denmark. The sudden infant death syndrome/SUDI ratio was 56.7%, with a significant decrease from 64.9% to 49.7% during the study period, and ranged from 6.1% in Portugal to 97.8% in Ireland. We observed between-country variations in SUDI and sudden infant death syndrome sex ratios. CONCLUSIONS: In studied countries, SUDI decreased during the study period but remained a major cause of infant deaths, with marked between-country variations in rates, trends, and components. Standardization is needed to allow for comparing data to improve the implementation of risk-reduction strategies.

Validation of a natural language processing algorithm using national reporting data to improve identification of anesthesia-related ADVerse evENTs: The "ADVENTURE" study.

BACKGROUND: Reporting and analysis of adverse events (AE) is associated with improved health system learning, quality outcomes, and patient safety. Manual text analysis is time-consuming, costly, and prone to human errors. We aimed to demonstrate the feasibility of novel machine learning and natural language processing (NLP) approaches for early predictions of adverse events and provide input to direct quality improvement and patient safety initiatives. METHODS: We used machine learning to analyze 9559 continuously reported AE by clinicians and healthcare systems to the French National Health accreditor (HAS) between January 1, 2009, and December 31, 2020 . We validated the labeling of 135,000 unique de-identified AE reports and determined the associations between different system's root causes and patient consequences. The model was validated by independent expert anesthesiologists. RESULTS: The machine learning (ML) and Artificial Intelligence (AI) model trained on 9559 AE datasets accurately categorized 8800 (88%) of reported AE. The three most frequent AE types were "difficult orotracheal intubation" (16.9% of AE reports), "medication error" (10.5%), and "post-induction hypotension" (6.9%). The accuracy of the AI model reached 70.9% sensitivity, 96.6% specificity for "difficult intubation", 43.2% sensitivity, and 98.9% specificity for "medication error." CONCLUSIONS: This unsupervised ML method provides an accurate, automated, AI-supported search algorithm that ranks and helps to understand complex risk patterns and has greater speed, precision, and clarity when compared to manual human data extraction. Machine learning and Natural language processing (NLP) models can effectively be used to process natural language AE reports and augment expert clinician input. This model can support clinical applications and methodological standards and used to better inform and enhance decision-making for improved risk management and patient safety. TRIAL REGISTRATION: The study was approved by the ethics committee of the French Society of Anesthesiology (IRB 00010254-2020-20) and the CNIL (CNIL: 118 58 95) and the study was registered with ClinicalTrials.gov (NCT: NCT05185479).

Neural Translation and Automated Recognition of ICD-10 Medical Entities From Natural Language: Model Development and Performance Assessment.

BACKGROUND: The recognition of medical entities from natural language is a ubiquitous problem in the medical field, with applications ranging from medical coding to the analysis of electronic health data for public health. It is, however, a complex task usually requiring human expert intervention, thus making it expansive and time-consuming. Recent advances in artificial intelligence, specifically the rise of deep learning methods, have enabled computers to make efficient decisions on a number of complex problems, with the notable example of neural sequence models and their powerful applications in natural language processing. However, they require a considerable amount of data to learn from, which is typically their main limiting factor. The Centre for Epidemiology on Medical Causes of Death (CépiDc) stores an exhaustive database of death certificates at the French national scale, amounting to several millions of natural language examples provided with their associated human-coded medical entities available to the machine learning practitioner. OBJECTIVE: The aim of this paper was to investigate the application of deep neural sequence models to the problem of medical entity recognition from natural language. METHODS: The investigated data set included every French death certificate from 2011 to 2016. These certificates contain information such as the subject's age, the subject's gender, and the chain of events leading to his or her death, both in French and encoded as International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) medical entities, for a total of around 3 million observations in the data set. The task of automatically recognizing ICD-10 medical entities from the French natural language-based chain of events leading to death was then formulated as a type of predictive modeling problem known as a sequence-to-sequence modeling problem. A deep neural network-based model, known as the Transformer, was then slightly adapted and fit to the data set. Its performance was then assessed on an external data set and compared to the current state-of-the-art approach. CIs for derived measurements were estimated via bootstrapping. RESULTS: The proposed approach resulted in an F-measure value of 0.952 (95% CI 0.946-0.957), which constitutes a significant improvement over the current state-of-the-art approach and its previously reported F-measure value of 0.825 as assessed on a comparable data set. Such an improvement makes possible a whole field of new applications, from nosologist-level automated coding to temporal harmonization of death statistics. CONCLUSIONS: This paper shows that a deep artificial neural network can directly learn from voluminous data sets in order to identify complex relationships between natural language and medical entities, without any explicit prior knowledge. Although not entirely free from mistakes, the derived model constitutes a powerful tool for automated coding of medical entities from medical language with promising potential applications.

A Deep Artificial Neural Network-Based Model for Prediction of Underlying Cause of Death From Death Certificates: Algorithm Development and Validation.

BACKGROUND: Coding of underlying causes of death from death certificates is a process that is nowadays undertaken mostly by humans with potential assistance from expert systems, such as the Iris software. It is, consequently, an expensive process that can, in addition, suffer from geospatial discrepancies, thus severely impairing the comparability of death statistics at the international level. The recent advances in artificial intelligence, specifically the rise of deep learning methods, has enabled computers to make efficient decisions on a number of complex problems that were typically considered out of reach without human assistance; they require a considerable amount of data to learn from, which is typically their main limiting factor. However, the CépiDc (Centre d'épidémiologie sur les causes médicales de Décès) stores an exhaustive database of death certificates at the French national scale, amounting to several millions of training examples available for the machine learning practitioner. OBJECTIVE: This article investigates the application of deep neural network methods to coding underlying causes of death. METHODS: The investigated dataset was based on data contained from every French death certificate from 2000 to 2015, containing information such as the subject's age and gender, as well as the chain of events leading to his or her death, for a total of around 8 million observations. The task of automatically coding the subject's underlying cause of death was then formulated as a predictive modelling problem. A deep neural network-based model was then designed and fit to the dataset. Its error rate was then assessed on an exterior test dataset and compared to the current state-of-the-art (ie, the Iris software). Statistical significance of the proposed approach's superiority was assessed via bootstrap. RESULTS: The proposed approach resulted in a test accuracy of 97.8% (95% CI 97.7-97.9), which constitutes a significant improvement over the current state-of-the-art and its accuracy of 74.5% (95% CI 74.0-75.0) assessed on the same test example. Such an improvement opens up a whole field of new applications, from nosologist-level batch-automated coding to international and temporal harmonization of cause of death statistics. A typical example of such an application is demonstrated by recoding French overdose-related deaths from 2000 to 2010. CONCLUSIONS: This article shows that deep artificial neural networks are perfectly suited to the analysis of electronic health records and can learn a complex set of medical rules directly from voluminous datasets, without any explicit prior knowledge. Although not entirely free from mistakes, the derived algorithm constitutes a powerful decision-making tool that is able to handle structured medical data with an unprecedented performance. We strongly believe that the methods developed in this article are highly reusable in a variety of settings related to epidemiology, biostatistics, and the medical sciences in general.