OBJECTIVE: The French National Health Data System (SNDS) comprises healthcare data that cover 99% of the population (over 67 million individuals) in France. The aim of this study was to present an overview of published pharmacoepidemiological studies using the SNDS in its maturation phase.
METHODS: We conducted a systematic literature review of original research articles in the Pubmed and EMBASE databases from January 2012 until August 2018.
RESULTS: A total of 316 full-text articles were included, with an annual increase over the study period. Only 16 records were excluded after screening because they did not involve the SNDS but other French healthcare databases. The study design was clearly reported in only 66% of studies of which 57% were retrospective cohorts and 22% cross-sectional studies. The reported study objectives were drug utilization (65%), safety (22%) and effectiveness (9%). Almost all ATC groups were studied but the most frequent ones concerned the nervous system in 149 studies (49%), cardiovascular system drugs in 104 studies (34%) and anti-infectives for systemic use in 50 studies (16%).
CONCLUSIONS: The SNDS is of growing interest for studies on drug use and safety, which could be conducted more in specific populations, including children, pregnant women and the elderly, as these populations are often not included in clinical trials.

OBJECTIVE: Real-world evidence (RWE) is increasingly used for medical regulatory decisions, yet concerns persist regarding its reproducibility and hence validity. This study addresses reproducibility challenges associated with diversity across real-world data sources (RWDS) repurposed for secondary use in pharmacoepidemiologic studies. Our aims were to identify, describe and characterize practices, recommendations and tools for collecting and reporting diversity across RWDSs, and explore how leveraging diversity could improve the quality of evidence.
METHODS: In a preliminary phase, keywords for a literature search and selection tool were designed using a set of documents considered to be key by the coauthors. Next, a systematic search was conducted up to December 2021. The resulting documents were screened based on titles and abstracts, then based on full texts using the selection tool. Selected documents were reviewed to extract information on topics related to collecting and reporting RWDS diversity. A content analysis of the topics identified explicit and latent themes.
RESULTS: Across the 91 selected documents, 12 topics were identified: 9 dimensions used to describe RWDS (organization accessing the data source, data originator, prompt, inclusion of population, content, data dictionary, time span, healthcare system and culture, and data quality), tools to summarize such dimensions, challenges, and opportunities arising from diversity. Thirty-six themes were identified within the dimensions. Opportunities arising from data diversity included multiple imputation and standardization.
CONCLUSIONS: The dimensions identified across a large number of publications lay the foundation for formal guidance on reporting diversity of data sources to facilitate interpretation and enhance replicability and validity of RWE.

BACKGROUND: Fractures have serious health consequences in older adults. While some medications are individually associated with increased risk of falls and fractures, it is not clear if this holds true for the use of many medications (polypharmacy). We aimed to identify what is known about the association between polypharmacy and the risk of fractures in adults aged ≥65 and to examine the methods used to study this association.
METHODS: We conducted a systematic review with narrative synthesis of studies published up to October 2023 in PubMed, Embase, CINAHL, PsychINFO, Cochrane Library, Web of Science, and the grey literature. Two independent reviewers screened titles, abstracts, and full texts, then performed data extraction and quality assessment.
RESULTS: Among the 31 studies included, 11 different definitions of polypharmacy were used and were based on three medication counting methods (concurrent use 15/31, cumulative use over a period 6/31, daily average 3/31, and indeterminate 7/31). Overall, polypharmacy was frequent and associated with higher fracture risk. A dose-response relationship between increasing number of medications and increased risk of fractures was observed. However, only seven studies adjusted for major confounders (age, sex, and chronic disease). The quality of the studies ranged from poor to high.
CONCLUSIONS: Polypharmacy appears to be a relevant modifiable risk factor for fractures in older individuals that can easily be used to identify those at risk. The diversity of medication calculation methods and definitions of polypharmacy highlights the importance of a detailed methodology to understand and compare results.

OBJECTIVE: Drug utilization studies (DUS) provide a framework for drug utilization at the national or targeted population level and important information on unmet medical needs, particularly in assessing the rationality of drug use. We aimed to systematically review DUS conducted in Turkiye.
METHODS: We examined 162 DUS with an accessible full-text, published as \"research articles\" and conducted in Turkiye between 2000 and 2021 using medical records and prescription data. We included English or Turkish papers with English abstracts. We examined the scientific characteristics of the publications, source of the data, place/time of collection, research designs, and studied drug groups.
RESULTS: We found that 79.6% of articles were in English, 45.1% were listed in SCI/SCIE, and 63.0% were on the WOS platform with 3.5 (interquartile range: 1-15) citations. The mean study period and publication time were 2.9±3.1 and 2.9±2.1 years, respectively. The highest number of studies (17.9%) were published in 2021 and (26.5%) were conducted nationwide. We identified that 93.8% of the studies had retrospective design, 67.8% were conducted in secondary/tertiary health-care institutions, and 54.9% used direct hospital data. We detected that 68.5% of the studies were conducted on the general population, 19.1% on adults, 12.4% on children, and 44.4% were antibiotic oriented.
CONCLUSIONS: Our study showed that a significant portion of the DUS, the trend of which has gained momentum in recent years, was antibiotic focused and conducted with a retrospective design from hospital-based data collected on the general patient population. This situation points to the necessity of expanding the existing DUS range by effectively using the new advantages provided by medical record databases and conducting more DUS that can provide critical clues for specific patients and drug groups.

It is important to examine the psychotropic prescription practices in schizophrenia, as it can inform regarding changing treatment choices and related patient profiles. No recent reviews have evaluated the global neuropsychopharmacological prescription patterns in adults with schizophrenia. A systematic search of the literature published from 2002 to 2023 found 88 empirical papers pertinent to the utilization of psychotropic agents. Globally, there were wide inter-country and inter-regional variations in the prescription of psychotropic agents. Overall, over time there was an absolute increase in the prescription rate of second-generation antipsychotics (up to 50%), mood stabilizers (up to 15%), and antidepressants (up to 17%), with an observed absolute decrease in the rate of antipsychotic polypharmacy (up to 15%), use of high dose antipsychotic (up to 12% in Asia), clozapine (up to 9%) and antipsychotic long-acting injectables (up to 10%). Prescription patterns were mainly associated with specific socio-demographic (such as age), illness (such as illness duration), and treatment factors (such as adherence). Further work, including more evidence in adjunctive neuropsychopharmacological treatments, pharmaco-economic considerations, and examination of cohorts in prospective studies, can proffer insights into changing prescription trends relevant to different treatment settings and predictors of such trends for enhancement of clinical management in schizophrenia.

OBJECTIVE: Most pregnant people take at least one medication during gestation or while breastfeeding, however data are lacking on the safety of medication use in these populations. We conducted a landscape review of real-world data sources specific to medication use in pregnancy and breastfeeding populations that have met, or have potential to meet, health authorities\' requirements for post-authorization safety studies.
METHODS: A 2-phase approach identified data sources from literature, publicly available registers of non-interventional post-authorization studies of pregnant women, existing database inventories, and emerging data sources known to the authors.
RESULTS: Required key attributes were assessed according to current regulatory guidance, resulting in selection of 49 suitable data sources. All global regions were represented, with North America (37%) and Europe (33%) most common; 12% of the data sources included pregnancy information from low-to middle-income countries. Administrative healthcare claims (25%) and electronic healthcare records (21%) comprised the largest types of data sources. Across data sources, 53% were managed by national or regional governments, 27% by industry, and 20% by academic institutions. Maternal age, diagnoses, prenatal care, and reproductive history were available in most, whereas fewer included demographic data (e.g., race/ethnicity). Breastfeeding data were collected in 37% of the final data sources.
CONCLUSIONS: We conducted a systematic approach to data source evaluation of pregnancy and breastfeeding to be used as a resource for investigators to consider when designing pregnancy-related research studies to satisfy regulatory requirements.

The weighted cumulative exposure (WCE) method has been used in a number of fields including pharmacoepidemiology where it can account for intensity, duration and timing of exposures on the risk of an outcome. The method uses a data driven approach with flexible cubic B-splines to assign weights to past doses and select an aetiologically appropriate time window. Predictions of risk are possible for common exposure patterns encountered in real-world studies. The purpose of this study was to describe applications of the WCE method to pharmacoepidemiology and assess the strengths and limitations of the method.
A literature search was undertaken to find studies applying the WCE method to the study of medicines. Articles published in PubMed using the search term \'weighted cumulative exposure\' and articles citing Sylvestre et al. (2009) in Google Scholar or Scopus up to March 2023 were subsequently reviewed. Articles were selected based on title and review of abstracts.
Seventeen clinical applications using the data-driven WCE method with flexible cubic splines were identified in the review. These included 3 case-control studies and 14 cohort studies, of which 12 were analysed with Cox proportional hazards models and 2 with logistic regression. Thirteen studies used time windows of 1 year or longer. Of 11 studies which compared conventional models with the WCE method, 10 (91%) studies found a better fit with WCE models while one had an equivalent fit. The freely available \'WCE\' software package has facilitated the applications of the WCE method with flexible cubic splines.
The WCE method allows additional insights into the effect of cumulative exposure on outcomes, including the timing and intensity (dose) of the exposure on the risk. The flexibility of the method is particularly well suited to studies with long-term exposures that vary over time or where the current risk of an event is affected by how far the exposure is in the past, which is difficult to model with conventional definitions of exposure. Interpretation of the results can be more complex than for conventional models and would be facilitated by a standardised reporting framework.

OBJECTIVE: Access to literature on clozapine in Russian language remains strikingly limited. We aimed to identify and translate clinical evidence on clozapine-based treatment outcomes.
METHODS: We performed a systematic review in PubMed, Embase and scientific indexes from former USSR states searching for articles published in Russian from the database inception till January 2023 and summarized the data in a scoping review (PROSPERO Reg. Number CRD42023386737).
RESULTS: A total of 60 papers were included comprising eight main topic categories: 1) clozapine-related intoxications (n = 20), 2) effectiveness/efficacy and safety of clozapine treatment (n = 14), 3) adverse drug-induced reactions (ADRs) related to clozapine treatment (n = 9), 4) therapeutic drug monitoring for clozapine (n = 5), 5) combination of clozapine and non-pharmacological treatments (n = 4), 6) pharmacoepidemiology of clozapine (n = 3), 7) effects of clozapine on the brain electrical activity (n = 3), and 8) novel clozapine formulations (n = 2). Among clozapine-related intoxications there were reports of criminal poisoning, which was associated with low lethality. Worse outcomes were accompanied by systemic reactions to intoxications. Clinical benefits of hemoadsorption were reported in the management of clozapine-related intoxications. Only half of studies reporting clozapine effectiveness used standardized scales to assess outcomes. Clozapine superiority in treatment-resistant schizophrenia (TRS) was replicated in one trial. No reports of clozapine-related agranulocytosis were identified. Clozapine ranked among most prescribed antipsychotics for TRS and non-TRS.
CONCLUSIONS: As clinical research in former USSR states is advancing to adopt western clinical research standards, comparability and extrapolation of findings is expected to increase, with transfer of older findings to clinical practice being particularly challenging.

The U.S. Food and Drug Administration\'s Sentinel System is a national medical product safety surveillance system consisting of a large multisite distributed database of administrative claims supplemented by electronic health-care record data. The program seeks to improve data capture of race and ethnicity for pharmacoepidemiology studies.
We conducted a narrative literature review of published research on data augmentation and imputation methods to improve race and ethnicity capture in U.S. health-care systems databases. We focused on methods with limited (five-digit ZIP codes only) or full patient identifiers available to link to external sources of self-reported data. We organized the literature by themes: (1) variation in data capture of self-reported data, (2) data augmentation from external sources of self-reported data, and (3) imputation methods, including Bayesian analysis and multiple regression.
Researchers reduced data missingness with high validity for Asian, Black, White, and Pacific Islander racial groups and Hispanic ethnicity. Native American and multiracial groups were difficult to validate due to relatively small sample sizes.
Limitations on accessible self-reported data for validation will dictate methods to improve race and ethnicity data capture. We recommend methods leveraging multiple sources that account for variations in geography, age, and sex.

To perform a systematic review on the use of Artificial Intelligence (AI) techniques for predicting COVID-19 hospitalization and mortality using primary and secondary data sources.
Cohort, clinical trials, meta-analyses, and observational studies investigating COVID-19 hospitalization or mortality using artificial intelligence techniques were eligible. Articles without a full text available in the English language were excluded.
Articles recorded in Ovid MEDLINE from 01/01/2019 to 22/08/2022 were screened.
We extracted information on data sources, AI models, and epidemiological aspects of retrieved studies.
A bias assessment of AI models was done using PROBAST.
Patients tested positive for COVID-19.
We included 39 studies related to AI-based prediction of hospitalization and death related to COVID-19. The articles were published in the period 2019-2022, and mostly used Random Forest as the model with the best performance. AI models were trained using cohorts of individuals sampled from populations of European and non-European countries, mostly with cohort sample size <5,000. Data collection generally included information on demographics, clinical records, laboratory results, and pharmacological treatments (i.e., high-dimensional datasets). In most studies, the models were internally validated with cross-validation, but the majority of studies lacked external validation and calibration. Covariates were not prioritized using ensemble approaches in most of the studies, however, models still showed moderately good performances with Area under the Receiver operating characteristic Curve (AUC) values >0.7. According to the assessment with PROBAST, all models had a high risk of bias and/or concern regarding applicability.
A broad range of AI techniques have been used to predict COVID-19 hospitalization and mortality. The studies reported good prediction performance of AI models, however, high risk of bias and/or concern regarding applicability were detected.