dataesr/scientific-paragraphs-categorization huggingface dataset:
Type of Database Linkage All (n=15, 93. 7%) but 1 (6. 3%) study  [35]  reported indirect linkage using French claims databases (SNIIRAM, the PMSI, or the SNDS) at the national or the regional level. Registries are the most frequently linked databases with French claims data, with 6 (37. 5%) studies based on the Renal Epidemiology and Information Network (REIN) registry  [28] [29] [30] [31] [32] [33]. The REIN registry records information about all patients with end-stage renal disease who start renal replacement therapy (dialysis or pre-emptive kidney transplantation) in France  [43]. It includes data on patient and center identification, primary kidney disease, initial clinical characteristics, comorbidities, and management of end-stage renal disease, but it does not contain data on health care consumption.
In addition, 2 (12. 5%) studies involved registries of cardiological procedures  [27, 42] , and 1 (6. 3%) study involved cancer registries  [35]. Regarding cohort databases, Mechtouff et al  [34]  linked the AVC69 cohort (patients with suspected acute stroke admitted to an emergency department or stroke unit in the Rhône area) and a MAD to assess the use of and adherence to secondary prevention drugs 3 and 6 years after a transient ischemic attack or ischemic stroke. Ad hoc emergency hospital clinical databases from 5 regions in France (Angers, Brest, Grenoble, Nantes, and Rennes) were used to study the risk of major bleeding with anticoagulant and antiplatelet agents  [36] [37] [38]. Finally, 3 (18. 8%) studies focused on outpatient data. The results of biological explorations (Gleason score on prostate biopsy sample) from pathological anatomy laboratories in Brittany were linked with a MAD (SNIIRAM) in 2 (12.
5%) studies in order to assess the risk of prostate cancer in patients treated with 5-alpha-reductase inhibitors for symptomatic benign prostatic hyperplasia  [39, 41]. Perlbarg et al  [40]  reported on the feasibility of matching a general practice database from ambulatory care with the French health insurance database (SNIIRAM), which represents a novel approach to analyze treatment, comorbidities, medical practices, and care pathways. No studies looked at linking with a hospital data warehouse, a clinical trial database, or patient self-reported databases.
Quality of the Linkage As the publication of Raffray et al  [28]  was a study protocol in which the results of data linkage were not specified, the quality of the data linkage was not assessed. Analysis of the characteristics of matched versus unmatched records to assess the completeness of the linkage or to identify a potential selection bias (ie, a specific population might be missed by the algorithm) was used in 26. 7% (4/15) of the studies  [27, 29, 30, 42]. Didier et al  [27]  used comparisons of survival curves between merged populations, registries, and the SNDS to assess the quality of the linkage results. Another method to determine whether a pair is a true match was to check that the comorbidity recorded in database 1 was also recorded in database 2 for all linked patients. This method was used by Raffray et al  [29]  to compare diabetes in matched patients.
The sensitivity of linkage (ie, the proportion of truly matched records detected), specificity (ie, the proportion of truly unmatched records detected), and the positive predictive value and negative predictive value were reported in only 1 (6. 7%) of 15 studies at 99. 9%, 97. 9%, 99. 9%, and 96. 9%, respectively  [42]. Finally, adherence to the Bohensky framework for evaluating data linkage studies  [4]  is presented in Table  2. This analysis 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. Bouget et al  [36] [37] [38]  Ferrerira et al  [31]  Lesaine et al  [42]  Scailteux et al  [39]  Mechtouff et al  [34]  Sitruk et al  [32]  Hogan et al  [33]  Béchade et al  [35]  Perlbarg et al  [40]  1.
Principal Findings Health data linkage is a powerful research resource that is being increasingly developing worldwide but with national disparities  [12, 44]. This review aimed to share research publications on indirect health data linkage focusing on health product use and care trajectories in a country with a centralized health data system. As the French claims database is 1 of the largest in the world, covering almost the whole population (99%) from birth to death, irrespective of provider, socioeconomic status, or retirement  [14] , linkage with other data offers great research potential to study the safety and effectiveness of drugs in routine care  [16]. Nevertheless, only 16 studies describing their indirect record linkage methodology were included in our review. They were mainly published since 2017, which corresponds to the start of facilitated access to health data, which was also illustrated with the increase in accepted health data access projects after the reform  [20].
This result suggests that the recent increase in data linkage in France is encouraging and needs to be confirmed. This review also highlights that indirect linkage mainly actually involves claims databases and disease-specific or population-based registries. However, many others database, including the use of health products, exist, emphasizing the big potential and challenges associated with data linkage. Finally, the quality of data linkage is poorly described: few studies have assessed or specified potential errors associated with data processing before, during, and after linkage. Moreover, 31% of the studies included in this review did not define the method of indirect data linkage (ie, deterministic or probabilistic approaches). Wider appropriation and dissemination of recommendations for the proper use of database linkage seem important to achieve.
Comparing our results on the use of health data linkage to other countries is difficult due to the use of different linkage models  [2, 12]. For example, Young et al  [1]  reported over 1200 publications in Australia based on linked data since 2009, without specifying the linkage method. In 2020, Haneef et al  [12]  reported in their European survey that France uses advanced data linkage for routine public health activities at national and subnational levels. As direct linkage between French health databases involves the use of a unique identification number (NIR), and because its sharing is highly protected, we focused in this review on indirect linkage. However, several large health cohorts have been directly linked to the SNDS  [26]. For example, CONSTANCES is a large prospective population-based cohort (200,000 persons included), in which an annual direct linkage is performed with 3 French social and health databases (SNIIRAM; Caisse nationale d'assurance vieillesse [CNAV], the national salaried employees retirement fund; and the National Death Registry, CépiDc)  [45] [46] [47].
Another example of a database directly linked to the SNDS, and not included in its formal framework, is Resid'EPHAD, which allows nursing homes to transmit information about the residents and their health care consumption  [13, 48]. Claims databases and disease-specific or population-based registries appeared to be the most used database for data linkage on health product use and care trajectories in this review. Nevertheless, other databases are likely to be linked in the future. Health insurance claims databases and EHRs have been identified as the preferred data sources for studying the safety and effectiveness of drugs in routine care  [49]. Surprisingly, Lin and Schneeweiss  [49]  reported in 2016 in their review only 9 papers on linking electronic medical records to claims data to study drug safety and effectiveness.
All the included studies were found to be based in the United States, although this was not part of the inclusion criteria  [49]. The development of hospital data warehouses, defined as a large computerized database that processes all data generated during hospital stays from the hospital information system (eg, medical observations and diagnoses, biology, imaging, prescribing, and drug administration), is on the rise  [15, [50] [51] [52]. These databases can quickly become powerful tools because real-time data collection is automated and reflects the clinical practice in hospitals for all inpatients  [15]. In particular, they offer the possibility of supplementing data on health products used in hospitalized patients, which are data rarely captured in other databases  [44]. The pharmaceutical record (the Dossier Pharmaceutique), which is a centralized national electronic database shared by all French community pharmacists on all dispensing, could provide added value to study over-the-counter drug consumption  [53, 54].
Another possible source of data to be linked to health databases is data generated by new tools, from various fields, such as mobile phones, social networks, eHealth, and connected medical devices. The quantified self-tracking movement, defined as the regular collection of any measurable data about oneself, such as biological, physical, behavioral, or environmental information, offers an additional opportunity to enrich knowledge about health product use and care trajectories  [55]. Patient-reported outcomes are increasingly used in routine medical care, improving patient-clinician communication, clinician knowledge of symptoms, symptom management, patient satisfaction, quality of life, and overall survival  [56]. In this context, Tran et al  [57]  proposed the "COOP' e-cohort," which aims to build a large community of patients willing to participate in research by contributing to the creation of a large database, passively enriched, at the individual level, through linkage with routinely collected care or medicoadministrative data.
Efforts need to be made to improve the health scientific community's understanding of data linkage methodology and the interpretation of linked data. The recent creation of the Health Data Hub  [61] , enabled by the law of July 24, 2019, on the organization and transformation of the French health system  [21]  should improve this. Its objective is to enable the implementation of authorized innovative projects using nonnominal data via a state-of-the-art secure technological platform  [21]. Once all the regulatory authorizations relating to data security have been obtained, the Health Data Hub platform would centralize databases from patient registries, cohorts, or electronic medical records and allow the linkage of consolidated databases with SNDS data  [21].
Limitations Limitations of this review include our research strategy based on the MeSH terms "medical record linkage" and "data linkage," which probably excluded some potentially eligible studies that were not referenced with these terms. In addition, Tuppin et al  [13]  highlighted a lack of homogeneity in the English terms used to describe French national health insurance databases, which made it more difficult to detect papers. Therefore, the use of indirect data linkage may be underestimated in our review. Moreover, some interesting information on data linkage studies may have escaped a traditional bibliography (ie, reports provided to health authorities or other gray literature) and may thus contribute to the underreporting of indirect data linkage experiences in France. Wider application of RECORD guidelines  [62] , which aims to increase the discovery of publications involving the use of routinely collected data, including data linkage, would help overcome this underreporting issue.
, commentaries, book chapters, studies where the full text of the publication was not available Primary research studies Paper type Direct data linkage (ie, unique personal identifier available to easily link the databases-the NIR a for French databases); studies on data linkage between the French MADs b making up the SNDS c not identified (eg, between SNIIRAM d and the PMSI e ) Indirect data linkage Intervention N/A f Studies on health product (drugs or medical devices) use or care trajectories of numéro d'inscription au repertoire. b MAD: medicoadministrative database. c SNDS: Système National des Données de Santé. d SNIIRAM: Système National d'Information Inter-Régimes de l'Assurance Maladie. e PMSI: Programme de Médicalisation des Systèmes d'Information. f N/A: not applicable.
Data We use the SPDR S&P 500 ETF (SPY) and 20 individual stocks constituents of the S&P 500 for the period 2000 -2016, comprising 4277 trading days. The individual stocks were chosen based on their jump activity index, allowing for different levels of both finite and infinite jumps. The data source is TickData database. We employ the previous tick interpolation for aggregating down the data to the required sampling frequency. 9  Table  3  reports the descriptive statistics and average volume of the stocks under examination. This table highlights the heterogeneous composition of our dataset, with great variation in the trading volume, yet still active enough to avoid higher levels of price staleness, and different levels of volatility which ensures a great variety of jump activity across our stocks.
(B. 2) This design enables the variance of the noise to be constant throughout the day, though changing from day to day. This noise is then added to the X t,i price process to obtain the time series of actual high-frequency simulate prices Y t,i = X t,i +u t,i. ξ is the noise-to-signal ratio used to simulate the market micro-structure noise. Finally, we simulate T = 50 days, consisting of 6. 5 hours of trading per second, i. e. , n = 23400. We then sample the data every 5, 60, and 300 seconds. We use 3000 replications providing a total of 150,000 days, and the jump tests are evaluated at the 5% level. Bollerslev, and Dobrev (2007)  and our modified version that is noise-robust. The theoretical size is α = 0. 05, and the Monte Carlo settings and models are described in Appendix B. ξ represents the noise-to-signal ratio used to simulate the market micro-structure noise.
e. since the 1980s. In doing so, we complement the ACS dataset with data of the decennial Census for the period  1980-2000. Since in the Census we can only observe STEM workers and their general educational attainments, including post-graduate education, we reconstruct the time series of STEM graduates over the period 1980-2008 by assigning the average occupation-byindustry level of STEM graduate intensity in 2009-2019 to the previous decades and carefully assess the validity of this procedure (see next section for details). Our analyses shed light on five issues. First, we document a long-term reallocation of the hours worked by STEM graduates towards finance and away from other sectors. STEM reallocation exceeds the well-known reallocation of the hours worked by any other graduates towards finance.
Finally, in shedding light on the heterogeneity of the finance wage premium, we contribute to the literature on returns to major fields of study  (Altonji et al. , 2016). This literature shows that STEM fields stand out as the best paid ones, and that there is a complementarity between field of study and occupation-specific tasks, thus STEM graduates earn more in STEM jobs. 7  We show that, in the spirit of the race between technology and education, these complementarities may change over time so that STEM skills can pay more in non-STEM jobs whose math intensity increased over time. The paper is organized as follows. Section 2 describes the dataset and the measures of STEM input. Section 3 provides evidence on the reallocation of STEM graduates, both across sectors and occupations. Section 4 focuses on the earning premium for STEM graduates in finance and the link with STEM reallocation.
Estimating the finance wage premium The rich ACS dataset allows to explore the structure of the finance wage premium and its variation across occupations, age groups and degrees. With respect to the previous section and to Figure  5 , we use individual-level microdata and control for a standard set of intervening factors to mitigate the concern that the finance wage premium for STEMs is mostly driven by differences in observable individual characteristics correlated with both earnings and the probability of obtaining a STEM degree. This approach represents an imperfect soluton of the self-selection problem, thus our estimates should be interpreted as correlations describing how the finance wage premium varies across occupations and degrees. Moreover, in light of this limitation, our study is unable to discern the two main explanations of the finance wage premium: rent seeking vs.
Methods and data An on-line survey was conducted for collecting the information on the vaccine prices, funding, and procurement systems in national vaccination programmes in European countries. Programme managers of 28 European Union (EU) countries, three European Economic Area (EEA) countries and Switzerland were invited to respond to an on-line survey using Webropol Survey Tool. The invitation to the online survey was sent in April 2017 and the completed questionnaires were collected in May-July 2017. The nonrespondents were sent e-mail reminders in 2 weeks from the initial email. If there were any clarifications to be made, respondents were contacted by e-mail or phone. We compared the responding and nonresponding countries by funding and procurement systems and income status. The survey referred to the information available on 31 December 2016 and was limited to the most common vaccines that are offered free of charge for children under 18 years of age with the exception of seasonal influenza vaccines.
Firstly, the respondents and non-respondents did not differ by the funding and procurement system. This is apparent since most of the EU member states procure the vaccines in the national vaccination programme through public tenders  [4]. In addition, the income status was rather similar. All nine non-respondents were high-income countries and of the 23 respondent countries 20 were high-income and three upper middle-income countries in 2016  [18]. The present study adds to the knowledge about the vaccine prices in European high-income countries. To improve vaccine market transparency and understand global vaccine market dynamics, World Health Organization (WHO) launched the Market Information for Access to Vaccines (MI4A) initiative  [20]  including vaccine purchase database  [21]  that contains information on vaccine prices, volumes, manufacturers and procurement modalities.
Acknowledgements We thank  Toni Lehtonen  for graphical help with Fig. 1 and Fig. 3. We also thank all the contacts in the countries who provided the data:  Carsten Böner  ( Germany ),  Agnes Danielisz  ( Hungary ),  Nadine Eckert  ( Switzerland ),  Radosveta Filipova  ( Bulgaria ),  Irina Filippova  ( Estonia ),  Top Geert  ( Belgium ),  Thorolfur Gudnason  ( Iceland ),  Magid Herida  ( France ),  Stefania Iannazzo  ( Italy ),  Leslie Isken  ( Netherlands ),  Marina Jamnicki  (Liechtenstein),  Bernard Kaić  ( Croatia ),  Cliona Kiersey  ( Ireland ),  Nerija Kupreviciene  ( Lithuania ),  Ainars Lacbergs  ( Latvia ),  Aurora Limia Sánchez  ( Spain ),  Jan Mikas  ( Slovakia ),  Birgit Neale  ( Denmark )  Adriana Pistol  ( Romania ) and  Anna Widarska  ( Poland ).
PLAIN LANGUAGE SUMMARY Psoriasis is a chronic disease and can have a large impact on patients' quality of life. Patients often discontinue psoriasis treatments for a number of reasons, including side effects, ineffectiveness, and inconvenience. Apremilast  (Otezla)  is a twice-daily oral tablet for the treatment of moderate-to-severe plaque psoriasis. Data on the needs of patients with psoriasis and the patient-perceived benefits of psoriasis treatments, including apremilast, are limited. The REALIZE (Real Life Data for OTEZLA Evidence) study collected data from 379 patients with moderate-to-severe psoriasis receiving apremilast for up to 12 months in clinical practice across France. Patients completed questionnaires regarding their treatment goals, how well apremilast treatment met these goals, their quality of life, and their satisfaction with apremilast treatment.
Study Design REALIZE was a longitudinal, multicenter, observational study of French clinical practice. Eligible patients were identified by dermatologists in public hospitals, private clinics, or private practice and invited to enroll up to 4 weeks after initiating apremilast. The decision to initiate apremilast was independent of, and occurred before, study enrollment, and aligned with French reimbursement criteria  [4]. Data were collected at enrollment, defined as baseline, and routine clinic visits scheduled 6 months and 12 months after apremilast initiation; there were no mandatory study visits. Physician assessments were performed by dermatologists according to their normal practice; patient assessments were conducted via questionnaires completed by patients during their dermatologist visits.
Statistical Analysis All analyses were descriptive in nature and performed using SAS 9. 4 (SAS Institute, Cary, NC, USA). Baseline values at enrollment were analyzed using the full analysis set, which included all patients who had received C 1 dose of apremilast and had C 1 post-enrollment data point. Efficacy outcomes at 6 months and 12 months were analyzed for patients remaining on apremilast at the relevant timepoint. Data for non-persistent patients were collected as possible. Patients were excluded from analyses if they had discontinued apremilast [ 61 days prior to the 6-month or 12-month assessment, if the date of discontinuation was missing or unknown, or if none of the items on a questionnaire were completed. Safety data were analyzed using the safety analysis set, which included all patients who had received C 1 dose of apremilast.
Patient Disposition Between 25 September 2018 and 24 June 2020, 430 patients were enrolled across 87 sites in France (private practice, n = 229; hospital/clinic, n = 185; site information missing, n = 16). Patient disposition is displayed in Fig. 1. The full analysis set included 365 patients, with 65 patients excluded because they did not receive apremilast treatment (n = 51) or they did not have any post-inclusion data (n = 14). The safety analysis set included 379 patients who had taken C 1 dose of apremilast. More than half of the patients from the full analysis set [200/365 (54. 8%)] remained on treatment and in the study at  12.
ACKNOWLEDGEMENTS We thank the patients and physicians who participated in the study. Funding. This study was sponsored by  Amgen Inc. , Thousand Oaks, CA,  USA. The sponsor is funding the  Rapid Service Fee. Medical Writing and Editorial Assistance. Writing support was funded by  Amgen France  and provided by  Christina Mulvihill, PharmD, of Peloton Advantage, LLC , an  OPEN Health company. Editorial support was provided by  Claire Desborough ,  Amgen UK.
Background: In CheckMate 9LA (NCT03215706), first-line nivolumab plus ipilimumab with chemotherapy (2 cycles) significantly improved overall survival versus chemotherapy (4 cycles) in patients with metastatic non-small cell lung cancer and no known sensitising epidermal growth factor receptor/anaplastic lymphoma kinase alterations. We present exploratory patient-reported outcomes (PROs; minimum follow-up, 2 years). Methods: In patients (N Z 719) randomised 1:1 to nivolumab plus ipilimumab with chemotherapy or chemotherapy alone, disease-related symptom burden and health-related quality of life were assessed using the Lung Cancer Symptom Scale (LCSS) and 3-level EQ-5D (EQ-5D-3L). Treatment-phase changes in LCSS average symptom burden index (ASBI), LCSS three-item global index (3-IGI) and EQ-5D-3L visual analogue scale (VAS) and utility index (UI) over time were analysed descriptively and using mixed-effect model repeated measures.