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Seasonality patterns are reported for various psychiatric disorders. Concerning adolescents, there is an increased frequency of general emergency department visits for mental health disorders observed between March and May, as well as in October and November. We conducted a retrospective cohort study using the French health insurance medico-administrative database. We extracted psychotropic drug deliveries occurring between 2015 and 2019 for patients aged between 12 and 18 years old. Each drug delivery was classified as occurring during a school period (Sc), the summer holidays (SumH) or other shorter holidays periods (ShH). We compared the number of distinct patients, as well as the proportion of new consumers, according to week status. Anxiolytics and hypnotics were more frequently dispensed during the school periods and short breaks than during the summer holidays. Conversely, antidepressants were more commonly dispensed during the short breaks rather than school periods and summer holidays. The stressful effects induced by schooling appear to be addressed in the first line by anxiolytics and hypnotics, while antidepressants are more frequently introduced during school holidays. [ABSTRACT FROM AUTHOR]

Data Science emerged as a new cross-disciplinary discipline at the intersection of statistics, computer science, and expertise in a specific domain, such as health and biology. The data science field, alongside other data-related professions, is continuously evolving. We conducted a study examining tasks assigned to first-year internship students pursuing a Master's degree in Health Data Science, exploring the missions, technologies employed and skills required, and internship alignment with students' training through semi-structured interviews with 32 participants. Three quarters of the students were placed in teams within the public sector. Among these entities, there were 11 hospitals and 12 universities. Although the majority of students did their internship as part of a methodological team, they often had a healthcare professional on their team. Nearly half of the missions involved descriptive analysis, followed by 9 missions focused on etiology or prediction and 8 missions on implementing a data warehouse. The majority of students had to perform data management and produce graphs, while only half conducted statistical analysis. The findings highlighted that data management remains a major challenge, and it should be taken into consideration when designing training programs. In future, it remains to determine whether this trend will continue with second-year students or if, with experience, they are more often assigned statistical analyses. [ABSTRACT FROM AUTHOR]

Adverse drug reaction are defined as "harmful or unpleasant reaction, resulting from an intervention related to the use of a medicinal product". In France, adverse effects due to medicines are reported to the French National Agency of Medicines (ANSM) by the healthcare professionals or consumers. The objective of this study was to implement a tool that facilitates the utilization of ANSM reports by synthesizing information to effectively inform prescribers and users. We focused on 3 psychotropic classes: antidepressants, antipsychotics and anxiolytics. We extracted relevant data from the ANSM website through a webscraping process, based on the names of molecules in these 3 classes: antidepressants, antipsychotics, and anxiolytics. We implemented a web interface with R Shiny that provides three panels: (i) a presentation of the active ingredient with the fewest reports for a selected adverse effect category, (ii) the adverse reactions for a selected active ingredient ranked in descending order, and (iii) a comparison of two active ingredients where, for each adverse effect, the active ingredient with the fewest reported adverse drug events (ADEs) is displayed. Our application allows for synthesizing information to effectively inform prescribers and users. In the ANSM existing interface, molecules can only be viewed one by one, and the ratio needs to be calculated manually, making it difficult to compare molecules. It is important to note that this is not a prescription assistance device but rather for informational purposes. In the future, the application may be expanded to include other categories of molecules. Finally, the indicators provided by our tool could be compared to those from other pharmacovigilance databases. [ABSTRACT FROM AUTHOR]

Intensive care units (ICUs) provide care for critical patients at high risk of morbidity and mortality, and require continuous monitoring of clinical, biological and, imaging parameters. Collaborative ventures have enabled the emergence of large open access databases for the secondary use of Electronic Health Records (EHRs). The objective of this work was to evaluate the availability of scripts and datasets in publications based on ICU open-access databases. We included 910 original articles based on four ICU open-access databases (Amsterdam University Medical Centers Database, eICU Collaborative Research Database, High time resolution ICU dataset, and Medical Information Mart for Intensive Care). The majority of the studies did not provide their data management scripts (n=839, 92.9%), neither the analysis script (n=843, 93.4%) in the article. Attempts to contact the 845 corresponding authors in question resulted in 89.11% (n=753) of our e-mail requests going unanswered over a two-month period. We received 51 automated messages (55.43%) indicating that emails have not been delivered, while 6 messages (6.52%) redirected to alternative email addresses. Only 20 corresponding authors (18.18%) answered, finally providing the requested materials. Despite scientific journals recommendations to share materials, our study unveils the absence of crucial components for the replication of studies by other research teams. [ABSTRACT FROM AUTHOR]

Background: Few studies on the risk of incident major adverse cardiac and cerebrovascular events (MACCEs) in sarcopenia have been reported. The objective was to assess the association between presarcopenia and sarcopenia and a higher risk of MACCEs. Methods: This study on the UK Biobank prospective cohort, used data collected between 2006 and 2021. Community‐dwelling Caucasian participants aged 37 to 73 years were included if values for Handgrip Strength (HGS) and Skeletal Muscle Index (SMI) were available and if no history of MACCEs was reported. Exposure was assessed using the European Working Group on Sarcopenia in Older People 2 (EWGSOP2) criteria. Muscle strength was measured using HGS, and muscle mass using the SMI. Presarcopenia was defined through the two definitions available in the literature, as low HGS with normal SMI and as normal HGS with low SMI, whereas sarcopenia was defined as low HGS with low SMI. The main outcome was to determine whether presarcopenia and/or sarcopenia were predictors of MACCEs (composite events). Results: A total of 406,411 included participants (women: 55.7%) were included. At baseline, there were 18,257 (4.7%) presarcopenics—subgroup n°1 (low HGS only), 7940 (2.1%) presarcopenics—subgroup n°2 (low SMI only), and 1124 (0.3%) sarcopenics. Over a median follow‐up of 12.1 years (IQR: [11.4; 12.8]), 28,300 participants (7.0%) were diagnosed with at least one event. Compared to NonSarc, presarcopenic (subgroups n°1 and n°2) and sarcopenic status were significantly associated with a higher risk of MACCEs (respectively fully adjusted HRs: HR = 1.25 [95% CI: 1.19; 1.31], HR = 1.33 [95% CI: 1.23; 1.45] and HR = 1.62 [95% CI: 1.34; 1.95]). Conclusions: In a community‐dwelling population, the risk of MACCEs was higher in both presarcopenic and sarcopenic participants. See related editorial by Mirzai and Tang in this issue. [ABSTRACT FROM AUTHOR]

This study explores the role of home care nurses in managing long-term illnesses (L-TI) within the French healthcare system, utilizing data from the SNDS. Focused on data from 2022, it categorizes nursing actions into medical procedures, care procedures, and nursing processes, revealing significant involvement in patient care. The findings highlight the crucial, evolving role of home care nurses in addressing the complex needs of millions suffering from chronic conditions like diabetes and cardiovascular diseases in France. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic has not been associated with increases in suicidal behavior at the national, regional, or county level. However, previous studies were not conducted on a finer scale or adjusted for ecological factors., Objective: Our objective was to assess the fine-scale spatiotemporal association between self-harm and COVID-19 hospitalizations, while considering ecological factors., Methods: Using the French national hospital discharge database, we extracted data on hospitalizations for self-harm of patients older than 10 years (from 2019 to 2021) or for COVID-19 (from 2020 to 2021) in metropolitan France. We first calculated monthly standardized incidence ratios (SIRs) for COVID-19 between March 2020 and December 2021, using a Besag, York, and Mollié spatiotemporal model. Next, we entered the SIRs into an ecological regression in order to test the association between hospital admissions for self-harm and those for COVID-19. Lastly, we adjusted for ecological variables with time lags of 0 to 6 months., Results: Compared with a smoothed SIR of ≤1, smoothed SIRs from 1 to 3, from 3 to 4, and greater than 4 for COVID-19 hospital admissions were associated with a subsequent increase in hospital admissions for self-harm, with a time lag of 2 to 4 months, 4 months, and 6 months, respectively., Conclusions: A high SIR for hospital admissions for COVID-19 was a risk factor for hospital admission for self-harm some months after the epidemic peaks. This finding emphasizes the importance of monitoring and seeking to prevent suicide attempts outside the epidemic peak periods., (© Maëlle Baillet, Marielle Wathelet, Antoine Lamer, Camille Frévent, Thomas Fovet, Fabien D'Hondt, Charles-Edouard Notredame, Guillaume Vaiva, Michael Génin. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org).)

Background: Patient-monitoring software generates a large amount of data that can be reused for clinical audits and scientific research. The Observational Health Data Sciences and Informatics (OHDSI) consortium developed the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to standardize electronic health record data and promote large-scale observational and longitudinal research., Objective: This study aimed to transform primary care data into the OMOP CDM format., Methods: We extracted primary care data from electronic health records at a multidisciplinary health center in Wattrelos, France. We performed structural mapping between the design of our local primary care database and the OMOP CDM tables and fields. Local French vocabularies concepts were mapped to OHDSI standard vocabularies. To validate the implementation of primary care data into the OMOP CDM format, we applied a set of queries. A practical application was achieved through the development of a dashboard., Results: Data from 18,395 patients were implemented into the OMOP CDM, corresponding to 592,226 consultations over a period of 20 years. A total of 18 OMOP CDM tables were implemented. A total of 17 local vocabularies were identified as being related to primary care and corresponded to patient characteristics (sex, location, year of birth, and race), units of measurement, biometric measures, laboratory test results, medical histories, and drug prescriptions. During semantic mapping, 10,221 primary care concepts were mapped to standard OHDSI concepts. Five queries were used to validate the OMOP CDM by comparing the results obtained after the completion of the transformations with the results obtained in the source software. Lastly, a prototype dashboard was developed to visualize the activity of the health center, the laboratory test results, and the drug prescription data., Conclusions: Primary care data from a French health care facility have been implemented into the OMOP CDM format. Data concerning demographics, units, measurements, and primary care consultation steps were already available in OHDSI vocabularies. Laboratory test results and drug prescription data were mapped to available vocabularies and structured in the final model. A dashboard application provided health care professionals with feedback on their practice., (© Mathilde Fruchart, Paul Quindroit, Chloé Jacquemont, Jean-Baptiste Beuscart, Matthieu Calafiore, Antoine Lamer. Originally published in JMIR Medical Informatics (https://medinform.jmir.org).)

Unlabelled: The growing adoption and use of health information technology has generated a wealth of clinical data in electronic format, offering opportunities for data reuse beyond direct patient care. However, as data are distributed across multiple software, it becomes challenging to cross-reference information between sources due to differences in formats, vocabularies, and technologies and the absence of common identifiers among software. To address these challenges, hospitals have adopted data warehouses to consolidate and standardize these data for research. Additionally, as a complement or alternative, data lakes store both source data and metadata in a detailed and unprocessed format, empowering exploration, manipulation, and adaptation of the data to meet specific analytical needs. Subsequently, datamarts are used to further refine data into usable information tailored to specific research questions. However, for efficient analysis, a feature store is essential to pivot and denormalize the data, simplifying queries. In conclusion, while data warehouses are crucial, data lakes, datamarts, and feature stores play essential and complementary roles in facilitating data reuse for research and analysis in health care., (© Antoine Lamer, Chloé Saint-Dizier, Nicolas Paris, Emmanuel Chazard. Originally published in JMIR Medical Informatics (https://medinform.jmir.org).)

Background: The COVID-19 pandemic has had a significant impact on mental health, with evidence suggesting an enduring mental health crisis. Studies worldwide observed increased usage of antidepressants, anxiolytics, and hypnotics during the pandemic, notably among young people and women. However, few studies tracked consumption post-2021. Our study aimed to fill this gap by investigating whether the surge in the number psychotropic drug consumers in France persisted 2 years after the first lockdown, particularly focusing on age and gender differences., Methods: We conducted a national retrospective observational study based on the French national insurance database. We retrieved all prescriptions of anxiolytics, hypnotics, and antidepressants dispensed in pharmacies in France for the period 2015-2022. We performed interrupted time series analyses based on Poisson models for five age classes (12-18; 19-25; 26-50; 51-75; 76 and more) to assess the trend before lockdown, the gap induced and the change in trend after., Results: In the overall population, the number of consumers remained constant for antidepressants while it decreased for anxiolytics and hypnotics. Despite this global trend, a long-term increase was observed in the 12-18 and 19-25 groups for the three drug classes. Moreover, for these age classes, the increases were more pronounced for women than men, except for hypnotics where the trends were similar., Conclusions: The number of people using antidepressants continues to increase more than 2 years after the first lockdown, showing a prolonged effect on mental health. This effect is particularly striking among adolescents and young adults confirming the devastating long-term impact of the pandemic on their mental health., (© 2024. The Author(s).)