dataesr/scientific-paragraphs-categorization huggingface dataset:
Context and study design The study was conducted in the implementation phase of the MONOD ANRS 12206 clinical trial (ClinicalTrial. gov registry nuNCT01127204), which was approved by the national ethics committee and the Burkina Faso Ministry of Health. The study was conducted in the capital of the country (Ouagadougou). Health professionals who were interviewed and parents of children who were enrolled for treatment provided a clear written consent. The ethics committee approved the consent procedure. The informed consent was waived for the use of aggregate register data. The ANRS 12206 MONOD trial is a randomized controlled trial whose aim is to assess a simplified once daily antiretroviral treatment in virologically suppressed HIV infected children initially treated with a triple therapy containing lopinavir/ritonavir before the age of two (Appendix S1).
Study site and population We included all the health care facilities providing PMTCT services, infant HIV diagnosis and antiretroviral treatments in Ouagadougou. We first used their 2011 aggregate data to document the PMTCT cascade. Then, in each health care facility, we interviewed all the heads of the various services: health districts, health care facilities, PMTCT services, laboratories, pharmacies, pediatric services, statistics and epidemiology surveillance division, and human resource services.
Data collection We used 2010 and 2011 Ministry of Health statistical yearbook, as the reference figures  [14, 16]. We designed a semi-structured questionnaire with three sections according to the staff targeted: pediatric, laboratory and pharmacy services. Two medical epidemiologists, one midwife and one sociologist carried out the desk review and interviewed the selected health professionals. The questionnaire reviewed variables related to PMTCT statistics (cascade of HIV care from antenatal services to EID of HIVexposed children at six weeks of age and antiretroviral treatment care for HIV-infected children), infrastructures, laboratory reagents, essential drug management and health professionals staffing (doctors, nurses, pharmacists, and laboratory technicians). The staff interviews helped to check registers and identify difficulties faced in providing early HIV infant diagnosis and treatment, and possible solutions.
In 2011, there were 103 health care facilities providing PMTCT services in Ouagadougou and we collected data from all of them. In each health care facility, there was a statistics manager who was in charge of collecting data monthly in a register provided by the Ministry of Health. Pregnant women received for antenatal consultation were recorded in a register that was later used by the statistics manager. A report was then sent to the district head of statistics and epidemiology surveillance division, who compiled the different health care facility reports with Excel software. In our study, we monthly recorded data in term of aggregate number of the different variables related to the PMTCT cascade, from the health care facility registers as well as the district registers, for comparison. In case of discrepancies, we monitored the data recording process to correct the errors.
Finally, we were able to document individual data for the HIV-infected children diagnosed and transferred to HIV pediatric care for antiretroviral treatment in the MONOD trial. For drug management, we checked the registers where the drug management was recorded to determine the availability of drugs and stock-outs. We checked the availability of antiretroviral drugs needed for the national guideline treatment: zidovudine or stavudine or abacavir, lamivudine or emtricitabine, nevirapine or efavirenz, and lopinavir/ritonavir. For opportunistic infection treatment and prophylaxis, we checked the availability of the following drugs: cotrimoxazole, nistatine, miconazole, amphotericine B, ciprofloxacine, ceftriaxone, acyclovir, and anti-tuberculosis drugs. Finally, we checked the laboratory reagent management and availability with the responsible of laboratories in the corresponding registers.
Statistical analysis The prevalence of HIV infected pregnant women was calculated by dividing the number of HIV infected pregnant women by the total number of pregnant women screened for HIV infection. The 95% confidence intervals were determined according to the following formula: (P{Z 1{ a 2 ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P(1{P) n r z 1 2n , PzZ 1{ a 2 ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P(1{P) n r z 1 2n )  [20]  where p = prevalence, n = sample size. We described the coverage of pediatric HIV services and the flow from HIV-exposed children to access to ART, of HIV-infected children. The cascade of care was compiled on Microsoft Excel software using proportions. All the proportions of the PMTCT cascade were calculated by dividing the total number of favorable cases by the number of eligible cases with their 95% confidence intervals according to the formula previously mentioned.
Discussion This cross-sectional survey assessed for the first time the staff, the infrastructures and the PMTCT cascade from prenatal PMTCT up to pediatric HIV care, in all health care facilities providing pediatric HIV care services in Ouagadougou. We documented that only 40% of HIV-infected women received a PMTCT intervention and less than a third of HIV-exposed children were tested during the postnatal period. Moreover, it provides a description of the health care system in this country, useful to understand some of the weaknesses of the system when it comes to the issue of EID in all HIV exposed children, and their access to antiretroviral therapy. There are several drawbacks in our observations. Firstly, the incompleteness of the data collected may be the source of information bias in this study.
Acknowledgments We acknowledge the  Head of Ministry of Health division for AIDS (ComiteMinisteriel de Lutte contre le Sida ), in  Burkina Faso ,  Dr Marie-Joseph Sanou , the regional Director of Health,  Dr Ame  ´dee Prosper Djiguemdeand all heads of health care facilities in Ouagadougou and their staff for their contribution to data collection. We are grateful to the  French GIP ESTHER  for its assistance to HIV infected children in Burkina Faso. We would like to give special thanks to  Pr Louis Rachid Salmi  for his helpful suggestion on using WHO standards.
Background: The number of maternity units has declined in France, raising concerns about the possible impact of increasing travel distances on perinatal health outcomes. We investigated impact of distance to closest maternity unit on perinatal mortality. Methods: Data from the French National Vital Statistics Registry were used to construct foetal and neonatal mortality rates over 2001-08 by distance from mother's municipality of residence and the closest municipality with a maternity unit. Data from French neonatal mortality certificates were used to compute neonatal death rates after out-of-hospital birth. Relative risks by distance were estimated, adjusting for individual and municipal-level characteristics. Results: Seven percent of births occurred to women residing at !30 km from a maternity unit and 1% at !45 km.
Vital statistics registry data We used the 2001-08 national data on births according to municipality of residence for mainland France. In 2010, there were 36 570 municipalities in mainland France. Annual statistics for births come from birth certificates completed at the municipality where the birth occurred. These include information on vital status, type of pregnancy (singleton or multiple) and maternal age. We requested aggregate tables for live births and foetal deaths by type of pregnancy (singleton, multiple) and maternal age. Aggregate data on foetal deaths were only available between 2002 and 2005 because of a technical problem in 2006 with the death certificate and changes in legislation governing the registration of stillbirths in 2001-08. 16, 17 here were 26 860 stillbirths and 3 086 128 live births in 2002-05.
Neonatal mortality certificate data We used 2001-08 data from the neonatal death certificate provided by the CepiDc (Centre d'e´pide´miologie sur les causes me´dicales de de´ce`s). Certificate includes medical causes of death as well as perinatal data: type of pregnancy (singleton vs. multiple) and maternal age and where the birth occurred (in-hospital, out-ofhospital). 18 There were 14 860 neonatal deaths, 282 deaths after out-of-hospital births and 6 202 918 live births during the study period. There were high rates of missing data for type of pregnancy (9. 7%) and maternal age (16. 6%).
Our study is limited by the different periods for the measures of mortality we used and the missing data for individual characteristics. Stillbirths were measured during a shorter time period than neonatal mortality, to keep consistency in the data because of the change in their definition for registration. This did not affect the overall analysis because of the large number of deaths included. Data were missing on individual characteristics for the mother's age and type of pregnancy (singleton vs. multiple) in the neonatal death certificate, partly due to its recent introduction. The reporting of these items is progressively improving  18  ; there is no reason to believe that there is an association between distance and this missing information. Also, we cannot distinguish between medical terminations of pregnancy and spontaneous foetal deaths.
All analyses will be performed with SPSS 22. 0. We aim to highlight the changes in memory problem perceptions in a longitudinal study through the IPQ-M. We anticipate that the use of IPQ-M will help gain insight into how patients and next of kin cope with memory problems and the diagnosis of MCI or early dementia. Following on from this study we plan to develop clinical guidelines on how to best adapt the process of disclosing the diagnosis to the individual belief system of the patient and next of kin.
Data management University of Ulm conducted the data management of the trial. All protocol-required information collected during the study had been entered by the investigator, or a designated representative in the CRF. CRF data were entered by University of Ulm into the database. During the study, data were exported into the statistical analysis system and checked additionally for plausibility, consistency and completeness. Based on these checks, queries have been produced. Any missing data or inconsistencies were reported back to the respective site and clarified by the responsible investigator. After all corrections were done, the database was closed and used for statistical analysis. All collected data have been processed according to the German Data Protection Law and handled in strictest confidence.
Power calculation/analysis Which number of patients was necessary to show a significant difference between therapy strains of 80% power on the one hand and p <0. 05 on the other? Retrospective HbA1c data from a database of the CF centre at Medical School Hannover were used. Measurements of n = 132 HbA1c within the first two years after diabetes diagnosis of n = 44 patients older than 10 years at the date of diagnosis resulted in an average value of HbA1c = 5. 6 ± 1. 5 (average value ± SD). Within a similar group (older than 10 years) but without diabetes n = 763 measurements yielded an HbA1c of 4. 6 ± 0. 68 (average value ± SD). A difference of 1 (which is in the range of individual variations during treatment especially in youngsters and young adults) between therapy groups was considered relevant  [12].
METHODS The Three-City (3C) is a longitudinal cohort study enrolling participants living in three French cities (Bordeaux, Dijon and Montpellier) designed to estimate the risk of dementia and cognitive impairment attributable to vascular risk factors. 27 The present analysis only uses data from participants living in Dijon because data on RLS were only collected in that city. Each participant signed an informed consent statement. To be eligible for the 3C study, the participant needed to live in Dijon, be registered on the electoral rolls in 1999, be 65 years or older and not be institutionalised. A total of 4931 individuals were recruited at the Dijon site between 1999 and 2001. For the MRI substudy, all participants recruited from the Dijon centre who were <80 years of age and enrolled between June 1999 and September 2000 were eligible to participate.
Of those eligible, 2285 (82%) participants agreed to participate in the MRI study, but only 1924 scans could be performed at baseline due to financial constraints. The process of obtaining the MRI information has been described in detail elsewhere. 28 29 In brief, MRIs were obtained using a 1. 5 T Magnetom (Siemens, Erlangen, Germany). A three-dimensional (3D) highresolution T1-weighted brain volume was obtained using a 3D inversion recovery fast spoiled-gradient echo sequence. T2-weighted and proton density (PD)-weighted brain volumes were acquired using a 2-D dual spin echo sequence with two echo times. Each participant data set (T1, T2, PD) was reconstructed and visually checked for major artefacts before being stored. A fully automated image processing software was used to detect, measure and localise WMLs.
Covariates Trained psychologists collected sociodemographic and medical data on participants during home visits. Participants were asked if they were treated for various comorbidities at baseline and in the fifth and sixth waves of the study. We used all available information from baseline to RLS assessment to determine a participant's comorbidity history. History of cardiovascular disease was defined as a history of myocardial infarction, stroke, angina, percutaneous transluminal coronary angioplasty or coronary artery bypass surgery. History of diabetes was defined as glycaemia ≥7 mmol/L or use of antidiabetic treatment. 33 High blood pressure was defined as measured systolic blood pressure ≥140 mm Hg or measured diastolic blood pressure ≥90 mm Hg. High cholesterol was defined as lipid lowering treatment or cholesterol ≥6.
Statistical analysis Of the 1924 individuals in the MRI substudy, 1189 were still alive and participating in the 3C study during the waves in which RLS was assessed. We excluded 97 participants who did not answer any of the questions about RLS and the 57 participants who did not have data on total WML volume, leaving a total of 1035 participants eligible for our analyses of which 218 (21. 1%) reported RLS (figure  1 ). We performed a cross-sectional analysis using logistic regression to calculate ORs and 95% CIs of reporting RLS for each tertile of WML volume using the lowest tertile as the reference group. Analyses examining the association between tertiles of total WML volume and RLS adjusted for tertiles of total white matter as the likelihood of WML correlate with the size of the total white matter.
Further adjustment for measures of sleep quality, difficulty sleeping and taking sleep medications did not affect our results (results not shown). All covariates were measured at baseline. Less than 39 people were missing information on any covariate, except for quality of sleep and were assigned to the reference value of that covariate. We created a separate category for those missing information on quality of sleep. We also performed separate age-adjusted analyses stratified by sex or mean age (72 years). We considered a two-tailed p value of <0. 05 as statistically significant and used SAS V. 9. 3 as statistical software (Cary, North Carolina, USA).
Additionally, it is unclear if the authors adjusted for other potential confounders including age and sex. Another study using data from the Memory and Morbidity in Augsburg Elderly Study (MEMO) examined the association between RLS and brain lesions detected using MRI. They found a non-significant increase risk of silent infarction (OR=2. 11, 95% CI 0. 71 to 6. 27) and subcortical brain lesions greater than or equal to 10 mm (OR=1. 35, 95% CI 0. 56 to 3. 22) in those who reported RLS compared with those without RLS. 24 The small size of this study (26 RLS cases and 241 controls) and limited power to control for confounding by cardiovascular risk factors may explain some of the differences between the results of the MEMO study and our study. While evidence for a direct association between WML and RLS is limited, some cross-sectional studies have suggested that RLS may be associated with hypertension, 14 stroke  9 36  and migraine,  25 26  which are associated with WML.
Statistical analysis The Wilcoxon signed-rank test has been done to compare the data of the posturographic tests between morning and afternoon. A two-way repeated-measures analysis of variance after a rank transformation was performed to evaluate statistical differences between posturographic conditions and at morning vs. evening times. The level of significance has been set to 0. 05. The calculations have been done with the software SAS version 9. 1 by a biostatistician.
This observation is not only inconsistent with our finding in this regard but also surprisingly conflicting with these authors' own data on the time for optimal sports performance in the afternoon and early evening. These authors suggest that a better static postural control on unilateral stance in the morning might be associated with a better cognitive functionality, following a circadian rhythm, thus allowing for a sustained focus in the morning. Nevertheless, both this study and the study of Gribble et al. [15]  showed that the influence of eye closing was not different between early morning and late afternoon. In addition to the assessment of static postural control, we also performed the dynamic LOS test that evaluates motor and cognitive capabilities to respond to the complex task to initiate a movement, with certain accuracy, in function of a visual stimulus.
Acknowledgements The authors would like to thank all RHCP partners who contributed to the organization, implementation, and dissemination of this work, especially  Miriam Goodson ,  John Lasuba ,  Sam Ouk ,  Sheena Loth ,  Kim Sin ,  Jennifer Lopez ,  Carrie Bergstralh  and  Fatuma Omer. This publication was supported by  NIH  Grant Number  R01 HL 111407  from the  National Heart, Lung, and Blood Institute  and by  CTSA  Grant Number  UL1 TR000135  from the  National Center for Advancing Translational Science (NCATS). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the  NIH.
Aims: To compare the burden of disease (BoD) attributable to diabetes expressed in disabilityadjusted life years (DALYs) for five European countries in 2010. Methods: DALYs lost to diabetes as the sum of years of life lost and years lived with disability were estimated by sex and age using country-specific epidemiological data and global disability weights. Data from various secondary sources were combined to estimate health loss due to diabetes for France, Germany, Italy, Spain, and the UK. National statistical databases were used and if necessary, community studies were used to derive the prevalence of diabetes by sex and age group, which were weighted proportionately for a national population burden of disease estimate. All identified data were adapted to the Global Burden of Disease methodology (2010) to calculate the burden attributable to diabetes.
input data 1] [22] [23] [24]  Similar to population size data, LE (which is not related to any specific disease) by age group, and sex were collected for the five countries from their national statistics. The epidemiological data with reference to prevalence were obtained from each national statistics database, except for France (Ricci et al)  25  and Germany (Heidemann et al). 26 Prevalence data for the UK included region-specific data for England, 27 Scotland,  28  Wales,  29  and Northern Ireland. 30 To be able to compare the UK with other countries of interest, uniform age categories for the communities within the UK were defined. For the age group 0-14 years, prevalence data only included T1DM, as no data on type 2 diabetes for this age category were available. It is assumed that 97% of the population in this age group was suffering from T1DM.
20 All the data needed to calculate H-DALYs can be measured at the time in question, so these DALYs are a true period measure. There are limitations to these estimates. Firstly, the availability of input data conditioned our analysis. Prevalence data were not directly available for all districts from one national statistics database in the UK. Therefore, prevalence data were taken from different sources and adjusted to the relevant age categories. In case prevalence information was missing for an age category, it was estimated based on the mean prevalence of an equivalent age category from another country of interest. Secondly, disability weights were equal for all age categories and for both sexes as no direct QoL data were available to obtain individual disability weights that could have affected YLDs.
In addition, for each measurement point, Figure  1  shows significant time variability. Work in this office is highly collaborative in fact, resulting in considerable movement of the employees and discussions at numerous different places during the day. The respondents were requested to complete the questionnaire at work. They were asked to answer the questions spontaneously and anonymously. The answers are therefore subjective and specific to each person. The answers collected were processed using statistical computation software (Statistica 10 -Statsoft).
Patient level activity and cost analysis The study of economic The data set was further structured by creating subgroups to allow analysis of referral patterns. Three subgroups were created based on the number of inpatient stays prior to the known date of referral to either the Midhurst service or a Hospice. Group one were those referred prior to an inpatient stay; group two, those referred after one stay; and group three those referred after two or more stays. These categories were developed as a proxy of how early a patient is identified as needing palliative or supportive care.
Postal survey of GPs A postal survey of GP practices was included to illuminate the context of care in which the Midhurst Macmillan Specialist Palliative Care Service operates. GPs are delivering palliative care in conjunction with the Midhurst service as well as referring patients to it. We used the General Practice End of Life Care Index (GP-EoLC-I): a postal questionnaire on supportive and palliative care developed for an earlier national study  (Hughes et al. 2010). The questionnaire provides data on reported clinical practice, views on palliative care services, participation in national initiatives and organisation of palliative care from the perspective of GPs. From this a summary measure of reported compliance with national guidance on palliative care can be computed. Senior partners in all GP practices in West Sussex, Surrey and Hampshire PCTs (418 practices) were sent GP-EoLC-I questionnaires.
This would depend on referrals of patients with chronic conditions and the Midhurst service finding ways to substitute for hospital inpatient or outpatient or day-care. Published work indicates that the future need for end-oflife care can only increase  (Gomes & Higginson 2008). Two significant limitations can be identified in the financial data collected on the service. First, we were not able to collect data on the extent of the involvement of primary care services, and hence this element could not be costed accurately. Second, we were not able to obtain detailed data on costs of care in the hospices surrounding the Midhurst service areas but made use of nationally available data on hospice costs (Marie Curie 2008) which may not reflect actual costs in the area of the evaluation. Regarding the interview data, we have limited material from the acute sector, and this may have altered the picture of the service that we obtained.