dataesr/scientific-paragraphs-categorization huggingface dataset:
background Adverse events (AEs) epidemiology is the first step to improve practice in the healthcare system. Usually, the preferred method used to estimate the magnitude of the problem is the retrospective cohort study design, with retrospective reviews of the medical records. However this data collection involves a sophisticated sampling plan, and a process of intensive review of sometimes very heavy and complex medical records. Cross-sectional survey is also a valid and feasible methodology to study AEs. Objectives The aim of this study is to compare AEs detection using two different methodologies: crosssectional versus retrospective cohort design. setting Secondary and tertiary hospitals in five countries: Argentina, Colombia, Costa Rica, Mexico and Peru. Participants The IBEAS Study is a cross-sectional survey with a sample size of 11 379 patients.
Search strategy and selection criteria Meta-analyses of study-level observational data were undertaken and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRIMSA) guidelines  [6]. Electronic searches were carried out for relevant studies in the PubMed database, as well as in a comprehensive tuberculosis studies database housed at the Global TB Programme of the WHO. The search strategies for these databases were identical to those operationalised in the prior meta-analysis by LÖNNROTH et al. [3]. As the prior meta-analysis by LÖNNROTH et al. [3]  included studies published up to 2007, the present search identified studies published between January 2007 and June 2016. In addition to the above-mentioned database searches, the reference lists of all included studies were reviewed and expert consultations (with K.
Uncertainty intervals were generated by using 40 000 Monte Carlo-like draws of all lowest level parameters  [20]. The alcohol-attributable fractions were then applied to the tuberculosis incidence and mortality data from 2014, as obtained from the publically available Global Tuberculosis Database  [21]. The alcohol-attributable tuberculosis burden of disease was estimated for the 196 member states, organised into six regions of the WHO in 2014. Comparisons of changes between 2000 and 2014 were also performed for the 22 high tuberculosis burden countries (same definition as above)  [13]. Although both crude numbers and crude rates were generated, only the latter are discussed throughout the results. Compliance with the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) Statement was ensured  [22].
To be consistent with the meta-analysis conducted by LÖNNROTH et al. [3] , the database searches were limited to the two databases specified, which may have missed other relevant studies. A distinction between drug-susceptible tuberculosis and multidrug-resistant tuberculosis was not made, as alcohol consumption was considered to influence the risk in a similar manner. There is nonetheless some evidence to suggest that alcohol consumption impacts tuberculosis treatment compliance, leading to subsequent acquired drug resistance  [59]. Several measurements of alcohol consumption in the included studies were susceptible to social desirability and recall biases (e. g. measurements from self-reported questionnaires), as well as misclassification bias (e. g. measurements from health administrative databases).
Measurements of alcohol consumption in the included cohort studies did not account for changes over time, whereas not all measurements of alcohol-related problems were based on standardised diagnostic criteria. Additionally, there were a number of included studies that ascertained tuberculosis diagnosis from health administrative databases. These limitations may also have resulted also in misclassification bias. Apart from the limitations concerning measurements, substantial between-study heterogeneity was detected, which persisted in the subgroup meta-analyses. This can be attributed to differences between the included studies, such as sources of recruitment of participants, types of participants, definitions of alcohol consumption and definitions of tuberculosis. The low number of included studies also resulted in some underpowered subgroup meta-analyses, and precluded the quantification of sex-specific RRs in meta-analyses of alcohol dosage and alcohol-related problems.
There are also limitations of the burden of disease analyses. Alcohol consumption data were based on surveys and government statistics, with the former susceptible to issues of coverage and social desirability and recall biases. Dynamic modelling was not operationalised in these analyses, which precluded determination of the secondary effects of tuberculosis transmission from alcohol users to others. Additionally, the impact on mortality resulting from delayed diagnosis or treatment incompliance due to alcohol consumption was not considered. Based on these modelling decisions, the alcohol-attributable fractions should be considered conservative.
Acknowledgements The authors wish to thank  Maximilien Rehm  ( Ritsumeikan University, Kyoto, Japan ),  Kwun (Bryan)   Hung Chan  ( The Chinese University of Hong Kong, Hong Kong, China ) and  Joyce Cheng  ( Centre for Addiction and Mental Health, Toronto, Canada ) for assisting with the translations of non-English studies, as well as  Michelle Tortolo  ( Centre for Addiction and Mental Health, Toronto, Canada ) for referencing the manuscript. The study had no funding source. It was carried out as part of the  WHO Collaborating Centre on Mental Health and Addiction , which has been responsible for estimating the alcohol-attributable burden of disease for the Global Status Reports on Alcohol and Health [14,18].
Acknowledging that Europe is a highly diverse world region known for health inequalities, the EU-funded "Developing methodologies to reduce inequalities in the determinants of health" project (DEMETRIQ) aimed to construct a harmonized dataset of health outcomes by indicators of socioeconomic position over the last decades  [24]. The study offered the unique possibility to systematically assess the trends in socioeconomic inequalities in obesity in 15 European countries between 1990 and 2010.
Data sources We obtained nationally representative health surveys from 15 European countries with more than two surveys available between the time period 1990 and 2010. Data came from the same survey over time for most of the countries, except for Austria, France, Hungary, and Italy (Table  1 ). The different data sources within these four countries have a high comparability  [25] [26] [27] [28] [29] [30] [31] , and thus could be included to analyze the trends over time. The age range used for all countries in the analysis was 30-64 years. In Polish data information on age was provided in 10-year groups and thus for Poland the upper limit of 69 was used instead. Older respondents were excluded because the upper age limits differed between countries, which could have influenced the comparability of the level of inequalities in obesity.
A previous study based on the same data sources and with self-assessed health as the outcome found essentially similar results if weighted or unweigthed data were used  [35]. To study the trends over time in each country and in the ensemble of countries as a whole, we employed meta-regression with random effects models, using the DerSimonian and Laird method  [36]. The year of data collection was used as the only independent variable in the models and the total prevalence of obesity, the prevalence of obesity by level of socioeconomic position; the absolute (RD) and relative inequalities (RR) by socioeconomic position were included as the dependent variables. The country-specific regression parameters and their 95% confidence intervals were displayed as forest plots and meta-analyses were performed to calculate overall random effect estimates for all countries.
Acknowledgements The author thanks the DEMETRIQ study group for the permission to use of the data. The authors thank  Stefaan Demarest  ( Scientific Institute of Public Health, Belgium ),  Satu Helakorpi  and  Anni Helldàn  (both  Finnish National Institute for Health and Welfare THL, Finland ),  Eszter Balku  ( National Institute for Health Development, Hungary ) for providing data, and  Catherin Bosle  ( Mannheim Institute of Public Health, Germany ) for organizational assistance in preparing this manuscript.
40 Based on these results drug release analysis was performed on batch B only. A series of factors can inuence the drug release from a given system resulting in different release rates, 1,41-43 such as temperature, the host shape and porosity as well as solubility and diffusivity of the drug molecule itself. 2] [43]  Here we applied the semi empirical Korsmeyer-Peppas model  44, 45  to describe the CIPRO's release from LiFh. This is a power law which relates the fraction of drug released (f t ) to time (t) and is used to t data within 60% of the fraction of drug released as follows: f t ¼ k KP t n (1) where, n and k KP are respectively the release exponent and constant, which are related to the release mechanism and to the physical and geometric characteristics of the drug delivery system.
Acknowledgements E. C. dos Santos research is nanced by the  Norwegian Research Council (RCN) ,  SYNKØYT Program  (project number  228551 ), and the  University of Copenhagen, Niels Bohr Institute. H. N. Bordallo ,  T. Plivelic  and  J. O. Fossum  acknowledge the nancial support by the  Norwegian Research Council (RCN) ,  SYNKØYT Program  (project number  228551 ). L. Michels,  Z. Rozynek  and  J. O. Fossum  acknowledge the nancial support by the  Norwegian Research Council (RCN) ,  CLIMIT Program  (project number  200041 ). A. Mikkelsen  acknowledges nancial support from the  European Union 's  Horizon 2020 research and innovation framework programme  under the  Marie Skłodowska-Curie  grant agreement No. 752896. Zbigniew Rozynek  acknowledges the nancial support of the  Polish National Science Centre  through the  OPUS programme  ( 2015/19/B/ST3/03055 ).
R. N. Klitgaard  and  A. Løbner-Olesen  were supported by the  University of Copenhagen Centre for Control of Antibiotic Resistance  ( UC-Care ) and  A. Løbner-Olesen  was supported by the  Center for Bacterial Stress Response and Persistence (BASP)  funded by a grant from the  Danish National Research Foundation  ( DNRF120 ). The I911-SAXS beamline at  MAX IV Laboratory  is acknowledged for the beamtime provided, as well as the  CALIPSO exchange program  for funding  E. L. Hansen  travels to perform for these experiments. C. Dicko  is acknowledged for assistance on the UV-VIS experiments performed at  MAX IV Laboratory. We also gratefully thank  A. Rivera ,  L. Valdés ,  I. Pérez ,  A. Lam , and  E. Altshuler  from the  University of Havana, Cuba , for very fruitful discussions.
MeThODS This study of patients with type 2 diabetes ('diabetes') formed part of the French multicentre ECOGEN study (Etude des Eléments de COnsultation en médecine GENérale), which was conducted from December 2011 to April 2012. Details of the cross-sectional ECOGEN study design have been published previously. 26 The ECOGEN study was conducted in a nationally representative sample of 128 GP practices across France, where participants received their usual care from their GP. The study investigators were 54 voluntary GP residents (ie, trainees specialising in family practice under direct supervision), who observed the consultation and collected data on the usual care provided by their 128 GP trainers. Each resident was placed in two or three different practices and no practice had more than one resident.
Within each practice, all consecutive patients seeking care, including children and adults, were recruited to participate in the ECOGEN study. The ECOGEN database only recorded encounters and any related health problems managed, provided that the patient encounter involved its management through at least one process of care. Patient morbidity which did not result in any process of care during the encounter (past medical history or health problems not related to the encounter) were not recorded. During data analysis we selected patients from the ECOGEN database who had a diagnosis of type 2 diabetes for inclusion in this nested study. A total of 168 (0. 8%) patients declined to participate in the ECOGEN study due to the requirement that a trainee doctor be present during the consultation. During a 2-day seminar the study investigators (ie, resident trainees) were trained to collect and code structured consultation data according to the requirements of the International Classification of Primary Care (ICPC-2) to analyse the potential for task delegation and to record data in a central database using a digital encoding engine.
Data collection and recording The ECOGEN study investigators directly observed 20 613 patient-GP encounters ('encounters'), regardless of the Open Access reasons for each encounter, over a total number of 20 days during the study period (ie, 1 day a week over 5 months). The processes of care ('processes') were all tasks carried out by the GP relating to the health problems that were managed during the encounters. The term 'process of care' includes preventive, diagnostic, curative, administrative and coordinative tasks, referred to as processes or procedures in the ICPC-2 (see online Supplementary appendix 1). 27 ealth problems, which were assessed as being either a symptom or a diagnosed health issue, were recorded in the study if they generated at least one process of care. Study investigators collected data relating to individual encounters (place, date and reasons for the encounter, diagnostic assessment, processes), individual patient characteristics (gender, age, existing or new patient, profession, fee-exemption status) and individual GP characteristics (age, gender, solo or group practice, location, number of annual patient encounters, use of fixed fees vs variable fees).
Analysis of data Study authors parsed and analysed the data on all encounters for patients with diabetes being managed by their GP. Transferability was assessed for each process of care (ie, task) and analysed according to health problems, processes and involved professionals. The unit of analysis was the process of care. We measured the mean transferability of the processes of care associated with each health problem managed, including diabetes and any comorbidity. It was not possible to link patient records across the various encounters because the unit of analysis was the processes of care of the encounter and not the patient. For univariate analysis, the transferability according to mean age, number of health problems managed and processes was analysed with a Student's t-test and the categorical variables were compared with a χ 2 test.
The generalised linear mixedmodel fit by maximum likelihood (Laplace approximation) was adjusted for the clustering of patients by resident. The transferability of processes formed the dependent dichotomous variable. Explanatory independent variables were introduced into the model according to a forward stepwise selection. Processes of care were categorised into the following five groups: preventive, diagnostic, curative, coordinative and administrative. No interaction was found between the variables in the multivariate model. Analyses were computed using SPSS 25 software for univariate analysis and R 3. 3. 2 software for mixed models. This study received ethical approval and associated registration numbers from both the National Committee on Informatics and Freedom (CNIL, no 1549782) and the Committee for the Protection of Persons (CPP, no L11-149).
ReSulTS Among the 20 613 encounters recorded in the ECOGEN database, 1088 (5. 3%) included the management of patients with diabetes. There were 1088 encounters, 8572 processes of care and 4038 health problems. These 1088 encounters were conducted by 128 GPs who assessed 4038 managed health problems including diabetes (a mean of 3. 7 different health problems managed per encounter). GPs performed or prescribed 8572 processes of care (7. 9 processes per encounter). Among these processes, 37. 0% were related to diabetes and 63. 0% were related to comorbidities. These encounters concerned patients with a mean (SD) age of 68. 3 (11. 6) years (range 28-100 years) Open Access Transferability according to health professional A total of 78. 1% of all potentially transferable processes were considered transferable to nurses and 36.
Strengths and weaknesses of the study A strength of the ECOGEN study lies in its comprehensive dataset (only two missing data) of health problems managed in day-to-day practice with their associated processes, which included health education and coordination of care between healthcare professionals. The 128 GP trainers in this study were representative of GPs at the national level in terms of age, gender, type of contract with the social security system (fixed or variable fees), practice location (population size of the city) and annual number of encounters. In a previous study, French GP trainers were found to be representative of standard GPs in terms of patients and activities,  30  therefore patients in our study can probably be considered representative of French patients. As no previous working experience in the interprofessional teams or Open Access with task delegation was required for the residents or for the GP trainers, transferability evaluation by investigators (ie, residents), who were specifically trained for the ECOGEN study, reflects their current views.
It was also contextualised to the actual organisation of GP practices, not only the 10% who are engaged in new models of multiprofessional health clinics. Of the 128 GP trainers, 70 double-checked the assessment of transferability in the main ECOGEN study for 1710 consultations, representing 8. 2% of all consultations included in the database. This comparative evaluation of the process transferability showed no difference in the proportion of transferable processes and a fair agreement between residents and GP trainers in the main ECOGEN study (20. 5% vs 21. 1%, p=0. 21; κ=0. 37). 31 ultivariate analysis enabled an investigation of the transferability based on the type of process of care and also on the characteristics of the encounter (number of combined health problems and patient characteristics).
39 conomic aspects might favour a comprehensive teambased approach to patient care. Indeed, the efficacy of patient education and therapeutic counselling has been demonstrated  41  and some healthcare systems, such as the USA, even reward them financially through Current Procedural Terminology codes,  38 62  conversely to the French billing nomenclature. Education and counselling are regarded as non-existent in the healthcare databases and in French general practice even though, at a practical level, many GPs manage and initiate education for their patients. 26 33 63 64 The modality of intervention and funding is different between hospital and GP practice encounters based on hospital-based activity, or is given either at a flat rate or on a per capita basis in the countries where it exists.
Natsal-3 used computer-assisted personal interviews (CAPI) with computer assisted self-administered interviews (CASI) for sensitive questions. FECOND was a telephone-survey, using landlines and mobiles. Natsal-3 used a multistage, clustered and stratified probability sampling strategy. In FECOND, two samples were independently selected to include a random sample of individuals who had a landline and a random sample of mobile phone users who did not, following a two stage random probability sampling process. Details of both methodologies are published elsewhere  [25, 26]. The response rate in the Natsal-3 survey was 57. 7%. The data were weighted to adjust for unequal selection probabilities, and a non-response post-stratification weight corrected for differences in sex, age, and Government Office Region between the achieved sample and the 2011 census.
In the FECOND survey, the total response rate was 50. 2%. The data were weighted to adjust for unequal selection probabilities and post-stratification weights corrected for differences in sex, age, marital/cohabitation status, educational-level, professional situation, place of birth and dependent children between the achieved sample and the census. These surveys have the benefit of being conducted at the same point in time, and covered similar topics, facilitating comparability between the countries. The key outcome variables are age at first heterosexual intercourse (dichotomised to before/after age 16), contraceptive-use at first sex, and among women only, reporting of a conception before age 20 and reporting of an abortion before age 20, among those who had conceived. Contraceptive-use at first intercourse was selected as an indicator of contraceptive-use over current use because we are interested in behaviours relating to conceptions before 20, and current use among older respondents may not accurately represent their contraceptive-use at younger ages.
Contraception was defined as all medical methods of contraception and condoms. The key independent variables in these analyses were respondent educational-level, an indicator of respondents' individual social resources, and parental socioeconomic group, an indicator of respondents' social origin. Respondent's educational-level was defined as having completed some post-compulsory education or training versus having completed none, the latter being the reference category in logistic regression models. Sixteen year-olds were excluded from all analyses, as the school leaving age in both countries at the time of data collection was 16, so they may not have completed compulsory education at interview. Data on parental socioeconomic characteristics were collected differently in the two surveys; FECOND asked about parent's educational-level, whereas Natsal-3 derived parent's social class from parent's occupation when the respondent was 14.
Strengths and limitations Key strengths of this study include being able to analyse broadly comparable, individual-level data from national probability surveys from two countries to contrast how two commonlyused indicators of socioeconomic status relate to a number of sexual and reproductive health outcomes experienced on the pathway to abortion. Although data on parental characteristics were collected differently in the two surveys, which may capture different elements of socioeconomic position in Britain and France, we were able to create a relative measure by constructing a tiered variable. Contraceptive-use at first sex as an indicator of contraceptive-use at a young age is a crude indicator that does not capture all the nuances of contraceptive-use, for example method choice or consistency of use, both of which may change over time  [36] (.
Acknowledgments Natsal-3 is a collaboration between  University College London , the  London School of Hygiene & Tropical Medicine, NatCen Social Research, Public Health England  (formerly the  Health Protection Agency) , and the  University of Manchester. We thank the study participants, the team of interviewers from NatCen Social Research who carried out the interviews, and operations and computing staff from  NatCen Social Research. The FECOND team is made up of:  N. Bajos  and  C. Moreau  (principal investigators),  A. Bohet  (coordinator),  A. Andro ,  L. Aussel ,  J. Bouyer ,  G. Charrance ,  C. Debest ,  D. Dinova ,  D. Hassoun ,  M. Le Guen ,  S. Legleye ,  E. Marsicano ,  M. Mazuy ,  E. Moreau ,  H. Panjo ,  N. Razafindratsima ,  A. Regnier-Loilier ,  V. Ringa ,  E. de La Rochebrochard ,  V.