dataesr/scientific-paragraphs-categorization huggingface dataset:
Data management A total of 59,657 CT examinations were recorded in 37,871 non-cancer pediatric patients younger than 15 years in participating hospitals between 2001 and 2010 in Germany. Details on sex were missing for 46 patients with 69 examinations, 15 patients with 17 examinations had a missing deprivation status due to invalid postal code information. We conducted a complete case analysis which included 37,810 pediatric patients with 59,571 CT examinations.
Acknowledgments This work was supported by the  German Federal Ministry of Education and Research  (grant numbers  02NUK016A  and  02NUK016CX ). The funding agency was not involved in any of the following: the study design, the analysis, the interpretation of data, the writing of the manuscript or the decision to submit the manuscript for publication. LK received a research-stipend from the  French Embassy. The authors would like to thank the radiology staff involved in the data collection in the participating hospitals and all individuals for taking part in the study. We thank (in alphabetic order)  T Albrecht ,  M Asmussen ,  J. Barkhausen ,  LD Berthold ,  C Bremensdorfer ,  A Chavan ,  C Claussen ,  M Forsting ,  K Jablonka ,  M Langer ,  M Laniado ,  J Lotz ,  HJ Mentzel ,  P Mildenberger ,  O Rompel ,  J Schaefer ,  J Schlick ,  K Schneider ,  M Schumacher ,  C Spix ,  B Spors ,  T Vogl ,  J Wagner ,  G Weisser  who made this study possible.
Accessibility to health care The objective is now to synthesize current evidence regarding how space is taken into account in healthrelated accessibility studies. Three ecological articles were selected and screened in full text journal. Two articles are from Canada  [26, 27] , and one article is from France  [28]. The aim of the three articles is to assess the spatial accessibility to facilities and more precisely propose the measurement of accessibility on a regional scale using aggregated data in Strasbourg for the methodological article of Salze et al. [28] , assess spatial accessibility to healthcare facilities for senior residents in Montreal, in the article of Paez et al. [27] , and assess the spatial accessibility to healthy and affordable food in food deserts in a Canadian city for Larsen et al.
). In our two biggest firms, they are carefully monitored by the CHSCT. Nevertheless, we will see later that there are indices of underestimation. Data reveal a similar increase in illness and accidents for all transport firms, albeit at a slightly lower level. Five companies in the division were analyzed in depth. They are of varying size and operate in large urban areas and medium-sized towns. One of the criteria for selecting them was the variety of absence rates, which is an indicator usually regarded in the literature as reflecting varying levels of ill health, or a certain degree of well-being or malaise at work. In each of the five companies, we analyzed the main documents relating to their economic situation and their characteristics in terms of HR and policies on health and safety at work.
Study Population and Measurements The study was carried out as a cross-sectional analysis of baseline information from the GCS. Details of the cohort enrollment are described elsewhere. 18 Baseline data were collected during 2004 to 2008, and total enrollment included 50,045 adults aged 40 to 75 years residing in the Golestan Province, in northern Iran. Of the total participants, 49,946 (99. 8%) were of Turkmen, Persian, Turkish, Sistani, Baluch, or Kurdish ethnicities; the other 99 enrollees were foreign nationals, who were excluded from this study. Trained physician and nonphysician interviewers administered different parts of a lifestyle questionnaire to each participant in face-to-face interviews to collect information on age, sex, ethnicity, marital status, years of education, employment status, ownership of several appliances, physical activity, body mass index (BMI), smoking, opium and hookah (water pipe) use, alcohol consumption, and Decayed, Missing, Filled Teeth (DMFT) index.
19, 20 ultimorbidity in this study refers to the presence of 2 or more chronic diseases. 8, 10, 15 Similar to several comparable studies, the ascertainment of diseases was based on selfreports. 19, 20 The morbidities included cardiovascular diseases (CVDs), including hypertension, coronary heart disease and stroke, diabetes, chronic obstructive pulmonary disease (COPD), chronic kidney disease (CKD), liver disease, gastroesophageal reflux disease (GERD), tuberculosis, and cancers. ] [23] [24] [25] [26] [27] [28] [29] [30]  The study was approved by the ethics committee of the Digestive Diseases Research Institute, Tehran University of Medical Sciences (OHRP-IRB-00001641). All participants signed an informed consent during original cohort study allowing investigators to use their anonymized data for further analysis.
We conducted a retrospective cohort study of all consecutive pregnant women seeking ANC at 23 rural primary health facilities in Zomba District, Malawi, between 1 October 2011 and 31 March 2012 and collected data from standardized Malawi MOH monitoring and evaluation tools. At the time of study, all health facilities kept individual patient-level data on ANC service provision in ANC registers, and individual patient-level data on ART service provision in standardized ART master cards and ART registers. Facility-level data were aggregated at all health facilities every three months and entered into an electronic database for cohort reporting by the MOH Department of HIV and AIDS M&E team. Patient-level data on ANC and ART services were not linked as part of the existing M&E system. We collected health facility-level data on the number of registrations, HIV testing, new HIV-positive diagnoses and HIV-positive women already on ART from ANC registers.
In order to further determine true uptake of ART, for every patient who had documentation of ART registration in the ANC register, a trained research assistant searched for corresponding evidence of registration in the facility ART register using linkage either by ART number or patient name (and matching age for confirmation). To best optimize determination of primary patient outcomes, the research assistant extracted individual patient outcome data from corresponding ART master cards. Only data from patients whose ART master cards could be found were included, as these outcomes are not recorded in ANC registers and were irregularly updated in ART registers. Lost to follow up was defined as not attending clinic greater than two months after the date that last dispensed antiretroviral drugs would run out.
We described the Option B' service delivery ''model of care'' in terms of the degree of integration of ANC, HTC and ART. Full integration (Model 1) of HTC and ART initiation at ANC was compared with integration of HTC only into ANC services (Model 2) with subsequent referral to an existing ART clinic for treatment initiation. The model of care used at a health facility was determined by PMTCT and ART Program Coordinators and Clinical Mentors who visit each health facility regularly and confirmed by the Zomba District Health Office. Data analyses were conducted with IBM SPSS Statistics 20 (IBM, Armonk, NY, USA). Facility-and patient-level data were described with proportions and 95% confidence intervals (CI) or medians with interquartile ranges (IQR) by site and by model of ANCÁART integration.
Facility-level data included number of women (newly) registered and tested for HIV, number of previous and new HIV positives and number of HIV positives already on ART. Patientlevel data included timing and uptake of and retention on ART at three and six months from initiation. As per the national guideline, women on ART were counted as ''lost to follow up'' if they were expected to have run out of ART for two or more months (based on the number of tablets given at the last visit) and were not known to have transferred out, stopped or died. Comparisons between models were made using chi-square tests and nonparametric independent sample median tests. In order to consider a cascade of Option B' services at each facility, we extrapolated all women who were projected to be eligible for Option B' ART to be defined as follows: (HIV prevalence observed in model x the number not tested in model) ' new HIV positives in model.
Presidents of all 31 member countries were asked to invite 5 obesity experts from their country to participate in the survey. Those, who agreed to participate, included researchers, general or specialized physicians, dietitians, psychologists, exercise physiologists, social workers, and other relevant professions. Participation was anonymous. While the presidents of the national associations were known to the researchers, the data did not reveal whether they took part themselves nor whom they had invited to participate. Surveys were sent in October 2013 through an online link attached to an invitation email. The online survey tool used was www. q-set. eu. Participants could complete the survey anonymously. The survey took between 10 and 15 min per person and included 41 questions. Questions addressed the availability of guidelines, the aspects of care described in the national guidelines, multidisciplinary approaches, patient involvement, the type of care given, involvement of professionals in long-term care, monitoring practices, goals and objectives for the future in obesity management, the link between prevention and management, the quality of obesity management and finally reimbursement of treatment.
Acknowledgements We wish to thank  Mr. Euan Woodward  for his practical and moral support, without which we would not have been able to conduct this study. The support of EASO's executive, all national associations for the study of obesity, EASO's three vice-presidents,  Dr. Gabriela Roman ,  Dr. Dragan Micic , and  Prof. Johannes Hebebrand , EASO's Obesity Management, Prevention and  Public Health and Childhood Obesity Taskforces  and of all national experts is gratefully acknowledged. The feedback of EASOs' Patient Council on the final report is gratefully appreciated and their input is deemed to be of crucial importance in future strategies.
Data collection Data collection has been extensively described previously. 1  A standardized questionnaire was used to collect demographic data (age, sex, body mass index [BMI]), smoking history (current status and cumulative smoking), comorbidities, symptoms of dyspnea using the modified Medical Research Council (mMRC) scale, symptoms of chronic bronchitis defined as daily cough and sputum production for at least 3 months per year during the past 2 years, hospital anxiety and depression scale, and the number of exacerbations in the previous year. HRQoL was assessed using the SGRQ. Pulmonary function tests were performed according to American Thoracic Society/European Respiratory Society standards. 19, 20 ugh and sputum assessment questionnaire CASA-Q was added to the standardized questionnaires in the Initiatives BPCO cohort for the purpose of this study.
21, 22 atistical analysis Quantitative variables are expressed as median and quartiles and/or number and percentages. Univariate analyses were performed to determine the relationship between SGRQ total score and the following variables: age, sex, smoking status, cumulative smoking, BMI, FEV 1 % predicted, dyspnea mMRC grade, number of exacerbations in the previous year, hospital anxiety and depression scale (cutoff $10), cardiovascular comorbidities, chronic bronchitis, and CASA-Q domains. Multivariate models were built to assess the contribution of CASA-Q domains to quality of life impairment. Patients with complete data on the CASA-Q and the variables identified as being associated with the total SGRQ score in univariate analyses were included in the multivariate analyses. The first model was built with SGRQ total score as the explained variable and only the four CASA-Q domains as the possible explanatory variables (ie, no other variable was introduced in the model) (model 1).
Independent contribution of cough and sputum production to quality of life impairment One hundred and forty-eight COPD patients with insightful data on the CASA-Q and the variables identified as associated with the total SGRQ score in univariate analyses were   Notes: The Casa-Q assessed symptoms and impact in the previous 7 days. Chronic bronchitis was defined as daily cough and sputum production for at least 3 months each year during the past 2 years. all four Casa-Q domains (cough symptoms, cough impact, sputum symptoms, and sputum impact) were lower when chronic bronchitis was present (*P,0. 0001), indicating more cough and sputum symptoms or impact. Abbreviation: Casa-Q, cough and sputum assessment questionnaire. included in the multivariate analyses (Table  3 ). There were no statistically significant differences between the clinical characteristics of the 148 patients included in the multivariate analyses and the 30 patients not included because of incomplete data.
Chronic cough is often assessed by simple questions on daily or usual cough and sputum production, eventually completed by data related to chronology parameters such as date of onset and duration (at least 3 months each year during the past 2 years). In the present study, each domain score of the CASA-Q was strongly lower when chronic bronchitis was present, but multivariate analyses demonstrated that the association of HRQoL impairment was greater with the cough impact domain score than with the usual definition of chronic bronchitis. These results demonstrate that a dedicated questionnaire assessing cough in the previous 7 days is more accurate to identify cough-associated HRQoL impairment in COPD than the usual definition of chronic bronchitis assessing cough and sputum production during the past 2 years.
Limitations One limitation is the cross-sectional nature of the study, which prevents from linking CASA-Q data with longitudinally collected outcomes. The sample size is also a potential limitation; indeed, the generalizability of these results should be tested in larger populations. The 7-day period assessed with the CASA-Q does not provide information regarding the impact of cough and sputum symptoms on HRQoL in a shorter or longer than 7-day period. It would be interesting to investigate the relationships between the CASA-Q scores and the COPD assessment test questionnaire that includes eight items with two items assessing cough and sputum production and which is used in routine clinical practice. It could be argued that cough and sputum production are likely to relate to HRQoL as measured by the SGRQ, since this questionnaire actually comprises questions on cough and sputum production.
Procedures Data were collected between June 2005 and December 2007, on the day of a routine clinical evaluation by their pediatrician. Participants were interviewed by one trained clinical psychologist and completed several self-report questionnaires at the end of the interview. The psychologist had not previously met the adolescents. Socio-demographic and medical information was extracted from the medical record and completed by the pediatrician on standardized case report forms. Viral load, CD4%, Centers for Disease Control (CDC) stage, and antiretroviral therapy were assessed and recorded during the same time period, ±1. 5 month, as the psychological evaluation. The 1994 Revised Classification System for Human Immunodeficiency Virus Infection in Children Less Than 13 years of Age was used with the following categories: N: No sign/symptom, A: Mild signs/symptoms, B: Moderate signs/symptoms, C: severe signs/symptoms  [28].
Demographics and Medical Characteristics Among 79 eligible adolescents, 58 gave their assent/consent to participate, representing 74% of those eligible; four of these dropped out prior to completing the study measures, leaving a final sample of 54 participants. No significant differences were found in terms of demographic and medical variables between the 58 adolescents enrolled and the 21 eligible subjects who declined participation (data not shown). The 54 adolescents ranged in age from 14 to 20 ans. All of them had been living between 4 to 10 years before HAART introduction. Of them 63% had never been sick (N or A coding for CDC). At the time of psychological evaluation, 50 had been under HAART treatment since 1996 and were currently receiving HAART. Viral load was controlled (VL<50copies/mL) in 63%.
Strengths and Limitations A strength of this study was the use of multiple methods and sources of data including the medical record, pediatrician/psychologist ratings, qualitative interviews with a psychologist, and self-report questionnaires supporting confidence in the validity of our data. In addition, we evaluated family relationships and psychosocial factors that might contribute to resilience. Findings from our qualitative interviews provide the healthcare community with data on the transition of perinatally HIV-infected adolescents into adult life. To our knowledge, ours is the first report on how those adolescents view their future. Although sociodemographic and medical data in our study did not differ between the adolescents assessed and the non assessed, resilience of 26% of the eligible population could not be assessed.
It helped make entrepreneurialism possible. [31] The literature on this topic focuses on answering how potential entrepreneurs are affected by access to health insurance. Early work by Holtz-Eakin, Penrod and Rosen  [19]  estimated the propensity to become self-employed as a function of access to health insurance. More recent work refined these types of estimates for specific subpopulations; Fairlie, Kapur and Gates [9] is an example that uses different datasets to generate estimates by sex and for workers nearing retirement. We are deepening our understanding of how access health insurance impacts various types of entrepreneurs. We are not aware of research asking the complementary question of what potential enterprises are affected by access to health insurance. To illustrate, consider the response of one high-tech entrepreneur in Cambridge, MA when probed about the entrepreneurial impact of the 2006 health insurance reform in Massachusetts: [The] top barrier  [s]  to people starting companies are [a] lack of drive and lack of capital.
I The rest of the article proceeds as follows. Section 2 reviews our current knowledge on the importance of both access to health care and entrepreneurship. Section 3 describes the Massachusetts health reform we use as a policy shock. Section 4 outlines a model that considers how the reform should heterogeneously affect entrepreneurship by industry. Section 5 describes the data sources we use in our analysis. Section 6 explains our empirical approach. Section 7 presents the effects of the reform on entrepreneurship and Section 8 concludes.
Data Sources We measure our outcome variable of entrepreneurship with two different US Census data sets. First we focus on self-employment as measured in the American Community Survey (ACS). The ACS surveys households for employment status and provides information on self-employment rates on a county basis. Although this self-employment data lacks the industry classification necessary for main findings, it connects well with previous research that estimated the impact of health reform on self-employment rates. To test our hypothesis related to industry characteristics, we use the Nonemployer Statistics (NS) series also from the US Census. The NS is based on tax filings and includes self-employed individuals as well incorporated businesses and partnerships that lack employees. Both of our data sources suffer from being measurements of net rather than gross values.
For example, if we do not obverse a change in number self-employed individuals over a period of time, this does not necessarily mean no new enterprises were created. No change happens when the rate of establishment entrance equals the rate of firm establishment. Exit and entrance are also definitional; a previously existent firm that hired its first worker would be listed as a exit in the NS data one year but an entrant in a following year if that worker was later laid off. Unfortunately we were not able to find a satisfactory public data set with gross establishment or firm numbers. However we think any change in the rate of entrepreneurship will still manifest in our dataset. To illustrate, figure  5 -1 shows how the number of nonemployers changes in construction, a traditionally cyclical industry.
Non-profit Heterogeneity Proposition 2 predicts that non-profits were more strongly impacted by the health care reform since there is no long term cost of increased taxes that balances the short term benefit of health care access. To test this prediction, we use' Economic Census data that provides information about the number of tax paying and tax-exempt establishments at the 4 digit NAICS code industry level for a subset of industries. Our first specification restricts our observations to counties in Massachusetts. For each of our industries we use the percentage of the industry's firms that were excluded from federal taxes as a measure of treatment intensity. Changecit = a, + -y + 6t + 3 x {% of industry non-profit} -1{year > 2007} + 6 ct (6. 2) Here Changecit represents the number of net new establishments created per million residents in year t, county c and industry i.
Capital Requirement Heterogeneity Proposition 3 states that as long as opportunities for low capital entrepreneurship are at least as prevalent as opportunities for high capital entrepreneurship, we should see that the health insurance shock increased low capital entrepreneurship relative to high capital entrepreneurship. We first test this proposition using model (4) within Massachusetts. Changecit = c + 7i + t + f1{low capital industry} -1{year >= 2008} + Eict (6. 4) Our observation count of industries across counties is large enough for us to deploy industry fixed effects -y at the 4 digit NAICS code level. We use the Survey of Business Owners (SBO) Public Use Microdata Sample (PUMS) from 2007 to determine which industries have low capital requirements. Since the SBO PUMS data only reports on 2 digit NAICS codes, we calculate the percentage of businesses that required less than $1000 of startup capital for each 2 digit NAICS code and match the 2 digit NAICS codes with our 4 digit industry codes.
Column 2 uses the border county procedure to attempt a closer match between our treatment and control groups. Column 3 uses the synthetic control group process. Both results in similar standard errors but magnitudes that are not distinguishable from zero. We interpret these findings to mean any possible effect our shock had on selfemployment is likely to be too small to detect using the publicly available census  Although our estimate under border control loses its significance, we believe the is an artifact of poor matching; services such as homeless shelters appear in our dataset for Massachusetts border counties but not in the border control group.
ideally we would want to measure change in probability that an individual becomes an entrepreneur. Given that our dataset that lacks observations of individuals, we focus on the changes in our variables of interest. For our first proposition, we measure this as the change in percentage of self-employed people per county (Changect). For our other propositions, we measure the change in non-employed establishments per million people in each county (Changecit) by industry. The large number of 4 digit NAICS classifications causes us to use the million people denominator; simple percentages would lead to coefficients that become cumbersome to read.
Acknowledgments We would like to thank  Scott Stern  for his advice and guidance on this research project. We are also very appreciative of the helpful suggestions and comments provided by  David Autor ,  Pierre Azoulay ,  Bill Goulding ,  Glenn Ellison ,  Daniel Fehder ,  Jorge Guzman ,  Neil Thompson ,  Heidi Williams  and  Samantha Zyontz. Any errors are our responsibility.