UNASSIGNED: Chronic obstructive pulmonary disease (COPD) is the most common indication for long-term domiciliary non-invasive ventilation (NIV) but there is uncertainty in data supporting current guidelines. This study described health trajectories before initiation of at-home NIV in people with COPD, and compared mortality outcomes between groups with different pre-NIV health trajectories.
UNASSIGNED: Data were from the French national health insurance reimbursement system database for individuals with COPD aged ≥40 years and ≥1 reimbursement for NIV between 1 January 2015 and 31 December 2019. Common health trajectories were determined using time sequence analysis through K-clustering (TAK analysis).
UNASSIGNED: Data from 54,545 individuals were analysed; the population was elderly (median age 70 years) with multiple comorbidities. Four clusters were generated. Cluster 1 (n = 35,975/54,545; 66%) had NIV initiated in ambulatory settings or after the first acute event/exacerbation. Cluster 2 (6653/54,545; 12%) started NIV after ≥2 severe exacerbations in the previous 6 months. Cluster 3 (11,375/54,545; 21%) started NIV after frequent severe COPD-related exacerbations in the previous year. Cluster 4 (652/54,545; 1%) started NIV after many long-lasting severe exacerbations. The four clusters differed in age, sex, comorbidities, pre-NIV investigations, and prescriber/location of NIV initiation. Mortality differed significantly between clusters: highest in Cluster 4 and lowest in Cluster 1.
UNASSIGNED: The significant heterogeneity in clinical initiation of NIV probably reflects the current lack of strong evidence and guideline recommendations. Knowledge about the characteristics and outcomes in different clusters should be used to address inequities and facilitate more consistent and personalised use domiciliary NIV in COPD.
UNASSIGNED: JLP and SB are supported by the French National Research Agency in the framework of the \"Investissements d\'avenir\" program (ANR-15-IDEX-02) and the \"e-health and integrated care and trajectories medicine and MIAI artificial intelligence (ANR-19-P3IA-0003)\" Chairs of excellence from the Grenoble Alpes University Foundation. This work was supported by ResMed.

European national disparities in the integration of data linkage (ie, being able to match patient data between databases) into routine public health activities were recently highlighted. In France, the claims database covers almost the whole population from birth to death, offering a great research potential for data linkage. As the use of a common unique identifier to directly link personal data is often limited, linkage with a set of indirect key identifiers has been developed, which is associated with the linkage quality challenge to minimize errors in linked data.
The aim of this systematic review is to analyze the type and quality of research publications on indirect data linkage on health product use and care trajectories in France.
A comprehensive search for all papers published in PubMed/Medline and Embase databases up to December 31, 2022, involving linked French database focusing on health products use or care trajectories was realized. Only studies based on the use of indirect identifiers were included (ie, without a unique personal identifier available to easily link the databases). A descriptive analysis of data linkage with quality indicators and adherence to the Bohensky framework for evaluating data linkage studies was also realized.
In total, 16 papers were selected. Data linkage was performed at the national level in 7 (43.8%) cases or at the local level in 9 (56.2%) studies. The number of patients included in the different databases and resulting from data linkage varied greatly, respectively, from 713 to 75,000 patients and from 210 to 31,000 linked patients. The diseases studied were mainly chronic diseases and infections. The objectives of the data linkage were multiple: to estimate the risk of adverse drug reactions (ADRs; n=6, 37.5%), to reconstruct the patient\'s care trajectory (n=5, 31.3%), to describe therapeutic uses (n=2, 12.5%), to evaluate the benefits of treatments (n=2, 12.5%), and to evaluate treatment adherence (n=1, 6.3%). Registries are the most frequently linked databases with French claims data. No studies have looked at linking with a hospital data warehouse, a clinical trial database, or patient self-reported databases. The linkage approach was deterministic in 7 (43.8%) studies, probabilistic in 4 (25.0%) studies, and not specified in 5 (31.3%) studies. The linkage rate was mainly from 80% to 90% (reported in 11/15, 73.3%, studies). Adherence to the Bohensky framework for evaluating data linkage studies showed that the description of the source databases for the linkage was always performed but that the completion rate and accuracy of the variables to be linked were not systematically described.
This review highlights the growing interest in health data linkage in France. Nevertheless, regulatory, technical, and human constraints remain major obstacles to their deployment. The volume, variety, and validity of the data represent a real challenge, and advanced expertise and skills in statistical analysis and artificial intelligence are required to treat these big data.

This scoping review explores and summarizes the existing literature on the use of chatbots to support and promote health in Africa.
The primary aim was to learn where, and under what circumstances, chatbots have been used effectively for health in Africa; how chatbots have been developed to the best effect; and how they have been evaluated by looking at literature published between 2017 and 2022. A secondary aim was to identify potential lessons and best practices for others chatbots. The review also aimed to highlight directions for future research on the use of chatbots for health in Africa.
Using the 2005 Arksey and O\'Malley framework, we used a Boolean search to broadly search literature published between January 2017 and July 2022. Literature between June 2021 and July 2022 was identified using Google Scholar, EBSCO information services-which includes the African HealthLine, PubMed, MEDLINE, PsycInfo, Cochrane, Embase, Scopus, and Web of Science databases-and other internet sources (including gray literature). The inclusion criteria were literature about health chatbots in Africa published in journals, conference papers, opinion, or white papers.
In all, 212 records were screened, and 12 articles met the inclusion criteria. Results were analyzed according to the themes they covered. The themes identified included the purpose of the chatbot as either providing an educational or information-sharing service or providing a counselling service. Accessibility as a result of either technical restrictions or language restrictions was also noted. Other themes that were identified included the need for the consideration of trust, privacy and ethics, and evaluation.
The findings demonstrate that current data are insufficient to show whether chatbots are effectively supporting health in the region. However, the review does reveal insights into popular chatbots and the need to make them accessible through language considerations, platform choice, and user trust, as well as the importance of robust evaluation frameworks to assess their impact. The review also provides recommendations on the direction of future research.

To investigate the association of the 2019-2020 influenza vaccine with prognosis of patients positive for SARS-CoV-2A, a large multi-database cohort study was conducted in four Italian regions (i.e., Lazio, Lombardy, Veneto, and Tuscany) and the Reggio Emilia province (Emilia-Romagna). More than 21 million adults were residing in the study area (42% of the population). We included 115,945 COVID-19 cases diagnosed during the first wave of the pandemic (February-May, 2020); 34.6% of these had been vaccinated against influenza. Three outcomes were considered: hospitalization, death, and intensive care unit (ICU) admission/death. The adjusted relative risk (RR) of being hospitalized in the vaccinated group when compared with the non-vaccinated group was 0.87 (95% CI: 0.86-0.88). This reduction in risk was not confirmed for death (RR = 1.04; 95% CI: 1.01-1.06), or for the combined outcome of ICU admission or death. In conclusion, our study, conducted on the vast majority of the population during the first wave of the pandemic in Italy, showed a 13% statistically significant reduction in the risk of hospitalization in some geographical areas and in the younger population. No impact of seasonal influenza vaccination on COVID-19 prognosis in terms of death and death or ICU admission was estimated.

SARS-CoV-2 is one of the most threatening pandemics in human history. As of the date of this analysis, it had claimed about 2 million lives worldwide, and the number is rising sharply. Governments, societies, and scientists are equally challenged under this burden.
This study aimed to map global coronavirus research in 2020 according to various influencing factors to highlight incentives or necessities for further research.
The application of established and advanced bibliometric methods combined with the visualization technique of density-equalizing mapping provided a global picture of incentives and efforts on coronavirus research in 2020. Countries\' funding patterns and their epidemiological and socioeconomic characteristics as well as their publication performance data were included.
Research output exploded in 2020 with momentum, including citation and networking parameters. China and the United States were the countries with the highest publication performance. Globally, however, publication output correlated significantly with COVID-19 cases. Research funding has also increased immensely.
Nonetheless, the abrupt decline in publication efforts following previous coronavirus epidemics should demonstrate to global researchers that they should not lose interest even after containment, as the next epidemiological challenge is certain to come. Validated reporting worldwide and the inclusion of low-income countries are additionally important for a successful future research strategy.

In 2014, The National Institute for Health and Care Excellence (NICE) called for the development of a system to collate local data on exercise referral schemes (ERS). This database would be used to facilitate continued evaluation of ERS. The use of health databases can spur scientific investigation and the generation of evidence regarding healthcare practice. NICE\'s recommendation has not yet been met by public health bodies. Through collaboration between ukactive, ReferAll, a specialist in software solutions for exercise referral, and the National Centre for Sport and Exercise Medicine, which has its research hub at the Advanced Wellbeing Research Centre, in Sheffield, data has been collated from multiple UK-based ERS to generate one of the largest databases of its kind. This database moves the research community towards meeting NICEs recommendation. This paper describes the formation and open sharing of The National ReferAll Database, data-cleaning processes, and its structure, including outcome measures. Collating data from 123 ERSs on 39,283 individuals, a database has been created containing both scheme and referral level characteristics in addition to outcome measures over time. The National ReferAll Database is openly available for researchers to interrogate. The National ReferAll Database represents a potentially valuable resource for the wider research community, as well as policy makers and practitioners in this area, which will facilitate a better understanding of ERS and other physical-activity-related social prescribing pathways to help inform public health policy and practice.

OBJECTIVE: The purpose of this study was to compare and contrast the prevalence of rheumatoid arthritis in Northern Alberta estimated by health administrative data and data from a rheumatologist-based prescription database.
METHODS: The study was performed using administrative health data from the province of Alberta through the local health authority. The cases and population identified in the database were reported from the year 2016. Rheumatology prescribing data was accessed through the Physician Learning Program and based on Alberta health billing data of actively practicing rheumatologists between the years 2012 and 2016. Ethics was provided by the Conjoint Health Research Ethics Boards at the University of Calgary (REB 13-0459).
RESULTS: The total population of the area examined was determined to be 2,086,181. The administrative health database identified 42,354 cases of RA based on their case definition with a prevalence of 2.08%. Based on rheumatologist diagnosis and prescribing data, the number of cases identified was 11,273 cases of RA with a prevalence of 0.542%. The average percentage of identified RA patients being seen by a rheumatologist was determined to be 26.7% with the range of 19.8 to 39.9%.
CONCLUSIONS: In conclusion, this study compares and contrasts the prevalence of rheumatoid arthritis reported by administrative data versus identification by specialists. Our study again illustrates that accuracy of case definitions when studying chronic conditions such as rheumatoid arthritis is paramount. The results also suggest a lack of access to rheumatologist services in Northern Alberta and reiterate the need for ongoing recruitment of new rheumatologists as has been highlighted previously. Key Points • The main contribution of this paper is to compare and contrast the prevalence of rheumatoid arthritis as reported by administrative data versus identification by specialists. • Our study also shows the distribution of rheumatoid arthritis in a large geographical area and illustrates a lack of access to subspecialty care in certain regions.

This study describes a combined surveillance of surgical site infection implemented in an Italian region, which relies on integration of the specific surveillance (SIChER) with other sources and the targeted review of a small proportion of cases. Additional information on post-surgical follow-up was obtained from hospital discharge, microbiology laboratory and emergency department databases. Based on these data, 76 patients were reclassified as possible cases and revised by the health trust representatives. Eventually 45 new cases were confirmed, leading to an increase in the infection ratio from 1.13% to 1.45%. The proposed method appears to be accurate and sustainable over time.

Emerging health care research paradigms such as comparative effectiveness research (CER), patient-centered outcome research (PCOR), and precision medicine (PM) share one ultimate goal: constructing evidence to provide the right treatment to the right patient at the right time. We argue that to succeed at this goal, it is crucial to have both timely access to individual-level data and fine geographic granularity in the data. Existing data will continue to be an important resource for observational studies as new data sources are developed. We examined widely used publicly funded health databases and population-based survey systems and found four ways they could be improved to better support the new research paradigms: (1) finer and more consistent geographic granularity, (2) more complete geographic coverage of the US population, (3) shorter time from data collection to data release, and (4) improved environments for restricted data access. We believe that existing data sources, if utilized optimally, and newly developed data infrastructures will both play a key role in expanding our insight into what treatments, at what time, work for each patient.