dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments: The authors are grateful to the former Minister of State for Pensions Steven J. Webb, to Carol Alexander (The Editor), Michael Arghyrou, M. Shahid Ebrahim, Mauricio Fiaschetti, Christopher Geczy, Benjamin Guin, Rhys ap Gwilym, Christos Floros, Michael Haliassos, Alexandros Kontonikas, Alexander Michaelides, Steven Ongena, Thien Nguyen, Ania Zalewska, Jan Zimmerman, three anonymous referees, the participants at the 2015 Conference on "Institutional and Individual Investors: Saving for Old Age" at the University of Bath (Bath, United Kingdom), the Vietnam 2015 International Conference on Finance, seminar participants at the University of Sussex and three anonymous reviewers for the helpful comments and suggestions. Nikos Paltalidis is grateful to Gioia Pescetto, Andy Thorpe, Alan Collins, and Renatas Kizys for their support and encouragement during his Doctoral and Post-Doctoral studies at the University of Portsmouth Business School.
Specifically, unrestricted VARs use information very sparsely in choosing the variables, in selecting the correct lag length of the model, and in imposing identification restrictions. As a result, unrestricted VAR models may lead to poor forecasting due to overfitting the dataset (see,  Koop, 2013). Structural and Bayesian methods provide a reliable solution for these problems as identified by De Mol et al. (  2008 ) and  George et al. (2008). By using Bayesian inference, we allow informative priors so that prior knowledge and results can be used to inform the current model. We also avoid problems with model identification by manipulating prior distributions. Therefore, this is the most suitable technique to employ for statistical regions of flat density. Moreover, an important assumption in Bayesian inference is that the data are fixed and the parameters are random.
Data analysis and descriptive statistics We collect detailed information about the characteristics, pension plans, and asset allocations. 13 Our sample includes at least one pension fund from each state, while also it contains the largest plans based on their assets. More precisely, Table  1  shows that there are 224 state pension plans, with 151 included in our sample. In addition, there are 3,761 local pension plans. 14 The total number of assets for all the state and local plans is about $3,2 billion, while our sample contains information for about $3,0 billion of assets, which is approximately 92% of the total assets invested in the US public pension fund industry. Figure  1  shows the dynamics of the federal funds target rate and the 10-year US Treasury yield. Throughout the 1998-2013 period the Treasury yield continuously declined from 6.
for 151 US pension funds from January 1998 to December 2013 from the Public Plans Database (PPD) obtained from the Center for Retirement Research at Boston College. The full sample includes 2,416 observations and consists of the historical yearly asset allocation in various asset classes for each pension fund and the yearly return by asset class from 1998 to 2013, the latest year for which all data are available. Moreover, we collect, from Bloomberg database, yearly data for the 10-year US Treasury yield and the federal funds target rate (upper bound).
Descriptive statisticsThis table presents the descriptive statistics for the 151 US pension funds from 50 states, with 2,416 observations. Panel A provides the summary statistics for pension plan return assumption, investment returns and the funding ratio, from 1998 to 2013. Panel B provides the summary statistics for the allocation of assets for the whole time period. The major data sources are the Public Plans Database, obtained from the Center for Retirement Research at Boston College and the Bloomberg database.
This table depicts the detailed asset allocation and the portfolio beta for 151 pension funds from 50 US States, with 2,416 observations. Panel A provides the allocation from 1998 to 2000. Panel B presents the allocation of assets from 2001 to 2006. Panel C shows the allocation of assets from 2007 to 2008 and Panel D exhibits the allocation of assets from 2009 to 2013. The major data sources are the Public Plans Database, obtained from the Center for Retirement Research at Boston College and the Bloomberg database.
This table presents the results of the regression of the change in the percentage of allocation to bond securities, short-term cash and equity assets on the mean investment return per period. It also provides the change in the portfolio's beta and Treasury yield based on the percentage of changes in the allocation of assets, for 151 US pension funds from 50 States resulting in 2,416 observations. Panel A exhibits results for well-funded pension plans. In contrast, Panel B presents results for the most underfunded pension plans, from 1998 to 2013. The major data source is the Public Plans Database obtained from the Center for Retirement Research at Boston College and the Bloomberg database.
This table reveals the effects of monetary policy shocks on pension fund asset allocation decisions and risk-taking behavior. The time periods are split based on the drastic changes in monetary policy to capture the full effects and the changes in the characteristics of the pension funds. Three scenarios are simulated: i) 100 basis point increase in the Treasury yield; ii) 120 basis point increase in the Treasury yield; and iii) 200 basis point increase in the Treasury yield, for 151 US pension funds from 50 States, making 2,416 observations. The major data sources are the Public Plans Database, obtained from the Center for Retirement Research at Boston College and the Bloomberg database.
This table presents the effects of monetary policy shocks on pension fund asset allocation decisions and risk-taking behavior, based on the scenario that the allocation of assets in bond securities and short-term cash does not change from period 1 to period 4. The mean portfolio return represents 151 US pension funds from 50 States, making 2416 observations. The major data sources are the Public Plans Database, obtained from the Center for Retirement Research at Boston College and the Bloomberg database.
). The major data sources are the Public Plans Database, obtained from the Center for Retirement Research at Boston College and the Bloomberg database. Panel A: Top-fifteen pension funds by liabilities Pension fund Liabilities (U. S. $) Funding coverage ratio (%) Inv. 5 year re-turn (%) Inv. 10 year re-turn (%) % of in-vestment in equities % of in-vestment in bonds Portfolio beta California Teachers 222,280,992 67. 0 3. 72 7. 53 53. 6 16. 79 0. 57 Florida RS 154,125,952 85. 4 5. 04 7. 44 59. 09 22 0. 62 Texas Teachers 150,666,000 80. 8 5. 4 7. 2 49. 7 14. 3 0. 64 New York State Teachers 94,538,800 87. 5 5. 2 7. 5 58. 89 18. 99 0. 52 Ohio Teachers 94,366,696 66. 3 4. 87 8. 08 52. 78 20. 19 0. 61 Illinois Teachers 93,886,992 40. 5 4. 2 7. 2 43. 9 24. 79 0. 60 Pennsylvania School Emp. 89,951,816 63.
Tendência e causa múltipla de óbito por insuficiência renal crônica em município da Amazônia brasileira Trends and multiple causes of death due to chronic renal failure in a municipality in the Brazilian Amazon Resumo O presente estudo buscou observar a tendência de mortalidade por insuficiência renal crônica (IRC) e verificar as causas básicas e associadas na capital do Acre, Amazônia brasileira. Para tanto, foi realizado um estudo ecológico com dados de óbitos por IRC do DATASUS ocorridos entre 1986 e 2012, em ambos os sexos de residentes em Rio Branco, Acre. Estimou-se a variação anual percentual (Estimated Annual Percentage Chance -EAPC) com a técnica de regressão log-linear de Poisson do programa Joinpoint. Os resultados demonstraram que as taxas de mortalidade ajustadas da IRC com.
These fee payments are to be transformed into allotted interest payments on tranches of securitization, which are then to garner investments from large professional investors. Here, the technical expertise ascribed to development banks unfold as envisaged by the EFSI's complementary pillars of the EIAH and the EIPP. 13 However, to assess the potential political implications of this nexus, we must first understand the key risk-sharing devices that development banks have created regarding EFSI and CMU. For this reason, we now turn to examine development bank activity in the Juncker Plan. Using EFSI's publicly accessible project database with information on funded projects, project volumes and participant investors since 2015, we point to the significant expansion of PPPs and the multilevel use of financial engineering to shape European capital markets.
htm, last accessed on 17 March 2016. 2. Instead of 'development banks', the EU policy context refers to 'promotional banks', which describes the very same institutions. 3. Investors are often called 'anchor investors' when they pursue a long-term strategy, adding value and attracting other investors through their reputation and capabilities, see  Espenlaub et al. (2016) ;  Hirshleifer and Hong (2003). 4. A list of interviewees is provided in the Appendix 1. These semi-structured interviews informed the direction and the discussion of the study, allowing us to both corroborate data from official documents and better understand the political intentions of policy makers and development bankers alike when formulating and implementing EFSI. However, they are not referenced in the analysis for the reconstruction of a narrative or a policy process, but only occasionally as additional data points.
Já para o cenário 2 (ótimo), não foram apresentados benefícios em termos de equidade, pois o objetivo do problema de otimização é minimizar a distância média percorrida pelos usuários. Os resultados são coerentes, tendo em vista que o valor obtido para β foi maior no cenário 2 do que nos demais, e que o cenário 3 apresentou valor menor em relação ao cenário 1 (Tabelas 1 e 2). A solução do problema foi encontrada com o código implementado em MatLab R2008a (The MathWorks, Natick, Estados Unidos), usando-se as funções fminunc e fminsearch da toolbox de otimização e um microcomputador com processador Intel Core I5 com 4Gb de memória RAM e sistema operacional Windows 8 (Microsoft Corp. , Estados Unidos).
Agradecimentos Agradecimentos à Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Capes) pela concessão de bolsa de doutorado sanduíche à S. Cirino (processo n o 6283-13-6). Ao Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) pelo financiamento. O trabalho foi apresentado no XXVIII ANPET -Congresso Nacional de Pesquisa e Ensino em Transportes, Curitiba, Brasil, em 2014 (os autores declaram que o congresso é de uma área diferente da área de saúde coletiva). Cad. Saúde Pública 2018; 34(3):e00185615.
Objective Unlike several other national health agencies, French health authorities recommended that the newer direct oral anticoagulant (DOAC) agents only be prescribed as second choice for the treatment of newly diagnosed nonvalvular atrial fibrillation (NVAF), with vitamin K antagonists (VKA) remaining the first choice. We investigated the patterns of use of DOACs versus VKA in the treatment of NVAF in France over the first 5 years of DOAC availability. We also identified the changes in patient characteristics of those who initiated DOAC treatment over this time period. Methods Based on the French National Health Administrative Database, we constituted a populationbased cohort of all patients who were newly treated for NVAF between January 2011 and December 2015. Trends in drug use were described as the percentage of patients initiating each drug at the time of treatment initiation.
IntrOduCtIOn 4] [5] [6] [7]  For more than 50 years, vitamin K antagonists (VKA), such as warfarin, were the only effective therapy available for stroke prevention in patients with NVAF. 8 [10]  To respond to physicians' and patients' expectations of more user-friendly drugs, research on new drugs has intensified over the last few years. This has prompted the development of direct oral anticoagulants (DOAC) that inhibit thrombin or factor Xa. strength and limitations of this study ► With a source database covering 66 million inhabitants and exhaustive information on anticoagulant deliveries in France, our study is the largest to report penetration of direct oral anticoagulants (DOACs) on the market. ► The extensive size of the cohort comprising 814 446 individuals provides reliable information even for the most recent DOAC available at the time of the study, apixaban.
Given the initial lack of specific antidotes and of data on these drugs' efficacy and safety in real life, French health authorities recommended that VKAs remain the standard therapy. They also recommended that DOACs be offered as an alternative therapy only to patients with low adherence to VKAs or unstable international normalised ratios (INRs) on VKAs. 17 To date, it is not clear how the expectations of clinicians and the recommendations of health authorities have impacted the choice of anticoagulant for newly treated patients with NVAF. Nor is it clear how patients' characteristics have influenced treatment choice. In view of the above, we conducted a study in the French National Health administrative database. This study based on claims data, aimed to identify the initial oral anticoagulant therapy used in a cohort of patients newly diagnosed with NVAF for the prevention of stroke and systemic thromboembolism.
MethOd study design and source of data The retrospective population-based cohort of patients with NVAF was formed from data provided by the French National Health Insurance System (NHIS). 18 The NHIS guarantees universal health coverage to all segments of the population and includes both a drug delivery database and a hospital discharge database. The NHIS comprises health insurance schemes for salaried workers, self-employed workers, agricultural workers and farmers, as well as 12 other insurance schemes. Together, these schemes provide health insurance to approximately 66 million inhabitants, which corresponds to approximately 99% of the French population. 19 Detailed description of the NHIS database is provided elsewhere. 20 21 n France, drugs are available only in pharmacies, and a medical prescription is required to obtain anticoagulant drugs.
All reimbursement claims for prescriptions processed and filled in pharmacies are submitted to the NHIS via a single electronic system. This drug delivery database is linked to the hospital discharge database through a unique personal identifier allocated to every individual. The second database provides medical information on all patients discharged from hospitals, along with associated 10th version of International Classification of Diseases diagnosis codes. However, no clinical diagnosis is provided in this database for consultations by health professionals in an ambulatory care setting.
Cohort definition We defined a cohort of all patients 18 years and older who were newly treated for NVAF between 1 January 2011 and 31 December 2015. Cohort entry was defined by the delivery of anticoagulant therapy (VKA or DOAC) combined with either an antiarrhythmic agent (flecainide, propafenone, amiodarone, quinidine, disopyramide or sotalol) or a rate control treatment (beta-blocker, calcium channel blockers-verapamil and diltiazem-or digoxin) within a time window of +/-30 days. The date of cohort entry was the latest date of delivery of either drug, within the 30-day window. We excluded patients with less than 1 year of data available in the database before cohort entry, as well as patients who had received anticoagulant treatment or had a history of cardiac valvular replacement in the 12 months before inclusion.
To estimate the risk of major bleeding, we calculated a modified HAS-BLED Open Access score (hypertension, abnormal renal and/or liver function, stroke/transient ischaemic attack, bleeding, labile INR, age >65, antiplatelet/NSAID use or alcohol abuse). 22 abile INR was not included in the score because it is unavailable in the database. Alcohol abuse was determined based on the hospital discharge database. To estimate the risk of stroke, we calculated the CHA 2 DS 2 -VASc score (congestive heart failure, hypertension, age ≥75, diabetes mellitus, stroke/transient ischaemic attack, vascular disease, age 65-74 and sex). 23 ta analysis Descriptive statistics were computed for continuous data (mean,+/-SD or median and range) and for categorical data (frequency and proportion). Trends in drugs use were described as the number of new patients treated each month and as the percentage of each anticoagulant prescribed at the time of treatment initiation.
Open Access Our study has several strengths. The source database covers 66 million inhabitants and nearly 99% of the French population, which means that our findings are independent of individual health coverage. Moreover, we had access to exhaustive information on anticoagulant delivery because these treatments are delivered on prescription alone. As a result, our study is the largest to report penetration of DOACs on the market (particularly in the case of apixaban, which is the most recent DOAC available) and to describe variations in the characteristics of patients over time. Nevertheless, some limitations must also be acknowledged. The NHIS administrative database does not include clinical or biological results nor does it include outpatients' diagnosis codes. To capture outpatient diagnosis of AF, we based our definition of NVAF on drug dispensation, using the most likely treatment scheme for NVAF.
Insofar as the results of our sensitivity analyses are consistent, we can be confident that our findings regarding the choice of the initial therapy and the patients' characteristics are not too sensitive to the definition of NVAF. Moreover, 69% of patients who were hospitalised during follow-up had a diagnostic code of NVAF in the hospital discharge database (data not shown). We did not use long duration diseases codes to define AF as these codes have various limitation, for example, their use has been shown to differ between the insurance schemes included in the database,  41  and there was an important discrepancy between them and hospital discharge codes. These long duration disease codes were only used to define some covariates but only in combination with drugs delivery and/or hospital codes.
We did not exclude patients with deep vein thrombosis or pulmonary embolism at inclusion. They represented 4. 4% of the study sample. We conducted a sensitivity analysis excluding these patients and obtained similar results (data not shown). Another limitation of our study is due to the 2015 data that may be partially incomplete. Indeed, for patients who do not have their NHIS card and attend a pharmacy that is not their regular pharmacy-a paper reimbursement form may be issued. The data are then recorded when the paper form is sent to the NHIS and integrated later in the database. When the 2015 data were made available, paper claims were likely to have not been all included. However, this changes the total number of users but not the proportion of users of the different drugs. The rapid and massive adoption of DOACs as initial therapy for NVAF will impact treatment expenditures because of the important increase in costs associated with these new drugs (in the USA, these costs accounted for more than 90% of insurer spending on anticoagulants in 2014).
Acknowledgements We thank the  Caisse National de l'Assurance Maladie des Travailleurs Salariés (CNAMTS)  and the  Institut des Données de Santé (IDS)  for their assistance in obtaining the study database. We personally thank  Ms Valérie EDEL  from the  IDS , as well as  Mr Laurent DUCHET  and  Mr Medhi GABBAS  from the department in charge of the SNIIRAM DATA at CNAM-TS. We are indebted to  Arianne Dorval  for proofreading the English manuscript. C Renoux  is the recipient of a  Chercheur-Boursier award  from the  Fonds de la recherche du Québe-Santé (FRQ-S). SS is the recipient of the James McGill Chair.
Methods: The resources consumed during the hospital stay and organ recovery procedure were identified and quantified retrospectively from hospital discharge abstracts and the national discharge abstract database. Identified items were valued using hospital cost accounting, followed by 2010-2011 ENC data, and then weighted using 2010-2011 ENC data. A Kruskal-Wallis test was used to determine whether at least two of the cost databases provided different results. Then, a Mann-Whitney test was used to compare the three cost databases.
Results: The costs assessed using hospital cost accounting differed significantly from those obtained using the ENC data (Mann-Whitney; P-value < 0. 001). In the ENC, the mean costs for hospital stays and organ recovery procedures were determined to be €4961 (SD €7295) and €862 (SD €887), respectively, versus €12,074 (SD €6956) and €4311 (SD €1738) for the hospital cost accounting assessment. The use of a weighted methodology reduced the differences observed between these two data sources. Conclusions: Readers, hospital managers and decision makers must know the strengths and weaknesses of each database to interpret the results in an informed context.
Background The choice of a cost data source is important because, as demonstrated in the case of colorectal cancer, using different data sources can produce widely different estimates of health care costs  [1, 2]. Currently, in a context of budget restrictions and trends toward more efficiency in resource allocation, choosing the most appropriate cost database for medico-economic evaluations appears to be a crucial determinant insofar as this choice influences not only the results of cost studies but also the decisions of hospital managers and ultimately the national directives of health policy makers  [3]. In the absence of approved guidelines and recommendations, researchers and health economists conducting cost-benefit analyses, hospital internal investigations or national reports must choose between several cost data sources with the risk of consciously or unconsciously biasing the results  [3].
Given the increasing need for and popularity of cost studies worldwide, the development of multiple cost databases to assess health care costs has raised difficulty in the choice of cost data sources. In the United States, Lund et al. [4]  identified more than 80 data sources that could be used to estimate health care costs, including data aggregated at the patient, hospital and national levels. A major difficulty in applying some of the available databases to cost analyses (e. g. , insurance databases) is that these databases were initially created and designed for reimbursement processes of hospital expenses and not for the measurement of health care costs  [5]. Moreover, each data source represents a unique population and has its own level of aggregation, periodicity and access cost, which must be considered when selecting the most appropriate data source for each specific research question  [4].
Organ recovery is not a classified procedure; thus, the hospital stay can be distributed among surgical or medical DRGs. DRGs are a medico-economic classification system that pools together hospital stays with medical and economic homogeneity. The activity-based pricing is a hospital funding method based on resource allocation according to the volume and nature of their activities. In France, the health ministry has used financial incentives to encourage implementation of a standardized cost accounting system in hospitals  [8, 9]. Hospital costs per stay are calculated using a top-down costing method that combines medical and nonmedical cost data for each stay. Despite a few particularities, the sample of French hospitals that participated in the national cost study (ENC) used the same model of cost accounting and transferred their cost data from the hospital database to the national level in the ENC  [10].