Two distinct halo populations in the solar neighborhood. Evidence from stellar abundance ratios and kinematics

Precise abundance ratios are determined for 94 dwarf stars with 5200 < Teff < 6300 K, -1.6 < [Fe/H] < -0.4, and distances D < 335 pc. Most of them have halo kinematics, but 16 thick-disk stars are included. Equivalent widths of atomic lines are measured from VLT/UVES and NOT/FIES spectra with resolutions R = 55000 and R = 40000, respectively. An LTE abundance analysis based on MARCS models is applied to derive precise differential abundance ratios of Na, Mg, Si, Ca, Ti, Cr, and Ni with respect to Fe. The halo stars fall into two populations, clearly separated in [alpha/Fe], where alpha refers to the average abundance of Mg, Si, Ca, and Ti. Differences in [Na/Fe] and [Ni/Fe] are also present with a remarkably clear correlation between these two abundance ratios. The `high-alpha' stars may be ancient disk or bulge stars `heated' to halo kinematics by merging satellite galaxies or they could have formed as the first stars during the collapse of a proto-Galactic gas cloud. The kinematics of the `low-alpha' stars suggest that they have been accreted from dwarf galaxies, and that some of them may originate from the omega Cen progenitor galaxy.Comment: Accepted for publication in A&A as a four-page Letter with five pages of online materia

Validation of asthma recording in electronic health records: protocol for a systematic review.

BACKGROUND: Asthma is a common, heterogeneous disease with significant morbidity and mortality worldwide. It can be difficult to define in epidemiological studies using electronic health records as the diagnosis is based on non-specific respiratory symptoms and spirometry, neither of which are routinely registered. Electronic health records can nonetheless be valuable to study the epidemiology, management, healthcare use and control of asthma. For health databases to be useful sources of information, asthma diagnoses should ideally be validated. The primary objectives are to provide an overview of the methods used to validate asthma diagnoses in electronic health records and summarise the results of the validation studies. METHODS: EMBASE and MEDLINE will be systematically searched for appropriate search terms. The searches will cover all studies in these databases up to October 2016 with no start date and will yield studies that have validated algorithms or codes for the diagnosis of asthma in electronic health records. At least one test validation measure (sensitivity, specificity, positive predictive value, negative predictive value or other) is necessary for inclusion. In addition, we require the validated algorithms to be compared with an external golden standard, such as a manual review, a questionnaire or an independent second database. We will summarise key data including author, year of publication, country, time period, date, data source, population, case characteristics, clinical events, algorithms, gold standard and validation statistics in a uniform table. ETHICS AND DISSEMINATION: This study is a synthesis of previously published studies and, therefore, no ethical approval is required. The results will be submitted to a peer-reviewed journal for publication. Results from this systematic review can be used to study outcome research on asthma and can be used to identify case definitions for asthma. PROSPERO REGISTRATION NUMBER: CRD42016041798

Validation of asthma recording in the Clinical Practice Research Datalink (CPRD)

OBJECTIVES: The optimal method of identifying people with asthma from electronic health records in primary care is not known. The aim of this study is to determine the positive predictive value (PPV) of different algorithms using clinical codes and prescription data to identify people with asthma in the United Kingdom Clinical Practice Research Datalink (CPRD). METHODS: 684 participants registered with a general practitioner (GP) practice contributing to CPRD between 1 December 2013 and 30 November 2015 were selected according to one of eight predefined potential asthma identification algorithms. A questionnaire was sent to the GPs to confirm asthma status and provide additional information to support an asthma diagnosis. Two study physicians independently reviewed and adjudicated the questionnaires and additional information to form a gold standard for asthma diagnosis. The PPV was calculated for each algorithm. RESULTS: 684 questionnaires were sent, of which 494 (72%) were returned and 475 (69%) were complete and analysed. All five algorithms including a specific Read code indicating asthma or non-specific Read code accompanied by additional conditions performed well. The PPV for asthma diagnosis using only a specific asthma code was 86.4% (95% CI 77.4% to 95.4%). Extra information on asthma medication prescription (PPV 83.3%), evidence of reversibility testing (PPV 86.0%) or a combination of all three selection criteria (PPV 86.4%) did not result in a higher PPV. The algorithm using non-specific asthma codes, information on reversibility testing and respiratory medication use scored highest (PPV 90.7%, 95% CI (82.8% to 98.7%), but had a much lower identifiable population. Algorithms based on asthma symptom codes had low PPVs (43.1% to 57.8%)%). CONCLUSIONS: People with asthma can be accurately identified from UK primary care records using specific Read codes. The inclusion of spirometry or asthma medications in the algorithm did not clearly improve accuracy. ETHICS AND DISSEMINATION: The protocol for this research was approved by the Independent Scientific Advisory Committee (ISAC) for MHRA Database Research (protocol number15_257) and the approved protocol was made available to the journal and reviewers during peer review. Generic ethical approval for observational research using the CPRD with approval from ISAC has been granted by a Health Research Authority Research Ethics Committee (East Midlands-Derby, REC reference number 05/MRE04/87).The results will be submitted for publication and will be disseminated through research conferences and peer-reviewed journals

Concomitant diagnosis of asthma and COPD:a quantitative study in UK primary care

Background: Asthma and chronic obstructive pulmonary disease (COPD) share many characteristics and symptoms, and the differential diagnosis between the two diseases can be difficult in primary care. This study explored potential overlap between both diseases in a primary care environment.Aim: To quantify how commonly patients with COPD have a concomitant diagnosis of asthma, and how commonly patients with asthma have a concomitant diagnosis of COPD in UK primary care. Additionally, the study aimed to determine the extent of possible misdiagnosis and missed opportunities for diagnosis.Design and setting: Patients with validated asthma and patients with validated COPD in primary care were identified from the UK Clinical Practice Research Datalink (CPRD) in separate validation studies, and the diseases were confirmed by review of GP questionnaires.Method: The prevalence of concurrent asthma and COPD in validated cases of either disease was examined based on CPRD coding, GP questionnaires, and requested additional information.Results: In total, 400 patients with COPD and 351 patients with asthma in primary care were identified. Of the patients with validated asthma, 15% (n = 52) had previously received a diagnostic COPD Read code, although COPD was only likely in 14.8% (95% confidence interval [CI] = 11.3 to 19.0) of patients with validated asthma. More than half (52.5%, n = 210) of patients with validated COPD had previously received a diagnostic asthma Read code. However, when considering additional evidence to support a diagnosis of asthma, concurrent asthma was only likely in 14.5% (95% CI = 11.2 to 18.3) of patients with validated COPD.Conclusion: A concurrent asthma and COPD diagnosis appears to affect a relative minority of patients with COPD (14.5%) or asthma (14.8%). Asthma diagnosis may be over-recorded in people with COPD.</p

How to validate a diagnosis recorded in electronic health records

Systematic measurement errors in electronic health record databases can lead to large inferential errors. Validation techniques can help determine the degree of these errors and therefore aid in the interpretation of findings. http://ow.ly/iHQ630np4xU

Identifying clinically important COPD sub-types using data-driven approaches in primary care population based electronic health records.

BACKGROUND: COPD is a highly heterogeneous disease composed of different phenotypes with different aetiological and prognostic profiles and current classification systems do not fully capture this heterogeneity. In this study we sought to discover, describe and validate COPD subtypes using cluster analysis on data derived from electronic health records. METHODS: We applied two unsupervised learning algorithms (k-means and hierarchical clustering) in 30,961 current and former smokers diagnosed with COPD, using linked national structured electronic health records in England available through the CALIBER resource. We used 15 clinical features, including risk factors and comorbidities and performed dimensionality reduction using multiple correspondence analysis. We compared the association between cluster membership and COPD exacerbations and respiratory and cardiovascular death with 10,736 deaths recorded over 146,466 person-years of follow-up. We also implemented and tested a process to assign unseen patients into clusters using a decision tree classifier. RESULTS: We identified and characterized five COPD patient clusters with distinct patient characteristics with respect to demographics, comorbidities, risk of death and exacerbations. The four subgroups were associated with 1) anxiety/depression; 2) severe airflow obstruction and frailty; 3) cardiovascular disease and diabetes and 4) obesity/atopy. A fifth cluster was associated with low prevalence of most comorbid conditions. CONCLUSIONS: COPD patients can be sub-classified into groups with differing risk factors, comorbidities, and prognosis, based on data included in their primary care records. The identified clusters confirm findings of previous clustering studies and draw attention to anxiety and depression as important drivers of the disease in young, female patients

Kinematics of Stellar Populations with RAVE Data

We study the kinematics of the Galactic thin and thick disk populations using stars from the RAVE survey's second data release together with distance estimates from Breddels et al. (2009). The velocity distribution exhibits the expected moving groups present in the solar neighborhood. We separate thick and thin disk stars by applying the X (stellar-population) criterion of Schuster et al. (1993), which takes into account both kinematic and metallicity information. For 1906 thin disk and 110 thick disk stars classified in this way, we find a vertical velocity dispersion, mean rotational velocity and mean orbital eccentricity of (sigma_W, Vphi, e)_thin = (18\pm0.3 km/s, 223\pm0.4 km/s, 0.07\pm0.07) and (sigma_W, Vphi, e)_thick = (35\pm2 km/s, 163\pm2 km/s, 0.31\pm0.16), respectively. From the radial Jeans equation, we derive a thick disk scale length in the range 1.5-2.2 kpc, whose greatest uncertainty lies in the adopted form of the underlying potential. The shape of the orbital eccentricity distribution indicates that the thick disk stars in our sample most likely formed in situ with minor gas-rich mergers and/or radial migration being the most likely cause for their orbits. We further obtain mean metal abundances of _thin = +0.03 \pm 0.17, and _thick = -0.51\pm0.23, in good agreement with previous estimates. We estimate a radial metallicity gradient in the thin disk of -0.07 dex/kpc, which is larger than predicted by chemical evolution models where the disk grows insideout from infalling gas. It is, however, consistent with models where significant migration of stars shapes the chemical signature of the disk, implying that radial migration might play at least part of a role in the thick disk's formation.Comment: 27 pages, 7 figures, accepted for publication in New Astronom

Clinical profile of predefined asthma phenotypes in a large cohort of UK primary care patients (Clinical Practice Research Datalink).

BACKGROUND: Distinct asthma phenotypes have previously been suggested, including benign asthma, atopic asthma and obese non-eosinophilic asthma. This study aims to establish if these phenotypes can be identified using data recorded in primary care clinical records and reports on patient characteristics and exacerbation frequency. METHODS: A population-based cohort study identified 193,999 asthma patients in UK primary care from 2007 to 2017. We used linked primary and secondary care data from the Clinical Practice Research Datalink, Hospital Episode Statistics and Office for National Statistics. Patients were classified into predefined phenotypes or included in an asthma "not otherwise specified" (NOS) group. We used negative binomial regression to calculate the exacerbation rates and adjusted rate ratios. Rate ratios were further stratified by asthma treatment step. RESULTS: In our cohort, 3.9% of patients were categorized as benign asthma, 28.6% atopic asthma and 4.8% obese non-eosinophilic asthma. About 62.7% of patients were asthma NOS, including asthma NOS without treatment (10.4%), only on short-acting beta agonist (6.1%) and on maintenance treatment (46.2%). Crude severe exacerbation rates per 1,000 person-years were lowest for benign asthma (106.8 [95% CI: 101.2-112.3]) and highest for obese non-eosinophilic asthma (469.0 [451.7-486.2]). Incidence rate ratios for all phenotype groups decreased when stratified by treatment step but remained raised compared to benign asthma. CONCLUSION: Established phenotypes can be identified in a general asthma population, although many patients did not fit into the specific phenotypes which we studied. Phenotyping patients and knowledge of asthma treatment step could help anticipate clinical course and therefore could aid clinical management but is only possible in a minority of primary care patients based on current phenotypes and electronic health records (EHRs)

Coincident, 100 kpc-scale damped Lyman alpha absorption towards a binary QSO: how large are galaxies at z ~ 3?

We report coincident damped Lyman alpha (DLA) and sub-DLA absorption at z = 2.66 and z = 2.94 towards the z ~ 3 13.8 arcsecond separation binary quasar SDSS 1116+4118 AB. At the redshifts of the absorbers, this angular separation corresponds to a proper transverse separation of ~ 110 kpc. A third absorber, a sub-DLA at z = 2.47, is detected towards SDSS 1116+4118 B, but no corresponding high column density absorber is present towards SDSS 1116+4118 A. We use high resolution galaxy simulations and a clustering analysis to interpret the coincident absorption and its implications for galaxy structure at z ~ 3. We conclude that the common absorption in the two lines of sight is unlikely to arise from a single galaxy, or a galaxy plus satellite system, and is more feasibly explained by a group of two or more galaxies with separations ~ 100 kpc. The impact of these findings on single line of sight observations is also discussed; we show that abundances of DLAs may be affected by up to a few tenths of a dex by line of sight DLA blending. From a Keck ESI spectrum of the two quasars, we measure metal column densities for all five absorbers and determine abundances for the three absorbers with log N(HI) > 20. For the two highest N(HI) absorbers, we determine high levels of metal enrichment, corresponding to 1/3 and 1/5 solar. These metallicities are amongst the highest measured for DLAs at any redshift and are consistent with values measured in Lyman break galaxies at 2 < z < 3. For the DLA at z = 2.94 we also infer an approximately solar ratio of alpha-to-Fe peak elements from [S/Zn] = +0.05, and measure an upper limit for the molecular fraction in this particular line of sight of log f(H_2)< -5.5.Comment: Accepted for publication in MNRAS. Full resolution simulation images available in pdf copy of the manuscript at http://www.astro.uvic.ca/~sara/1116.pd

Thick disk kinematics from RAVE and the solar motion

Radial velocity surveys such as the Radial Velocity Experiment (RAVE) provide us with measurements of hundreds of thousands of nearby stars most of which belong to the Galactic thin, thick disk or halo. Ideally, to study the Galactic disks (both thin and thick) one should make use of the multi-dimensional phase-space and the whole pattern of chemical abundances of their stellar populations. In this paper, with the aid of the RAVE Survey, we study the thin and thick disks of the Milky Way, focusing on the latter. We present a technique to disentangle the stellar content of the two disks based on the kinematics and other stellar parameters such as the surface gravity of the stars. Using the Padova Galaxy Model, we checked the ability of our method to correctly isolate the thick disk component from the Galaxy mixture of stellar populations. We introduce selection criteria in order to clean the observed radial velocities from the Galactic differential rotation and to take into account the partial sky coverage of RAVE. We developed a numerical technique to statistically disentangle thin and thick disks from their mixture. We deduce the components of the solar motion relative to the Local Standard of Rest (LSR) in the radial and vertical direction, the rotational lag of the thick disk component relative to the LSR, and the square root of the absolute value of the velocity dispersion tensor for the thick disk alone. The analysis of the thin disk is presented in another paper. We find good agreement with previous independent parameter determinations. In our analysis we used photometrically determined distances. In the Appendix we show that similar values can be found for the thick disk alone as derived in the main sections of our paper even without the knowledge of photometric distances.Comment: accepted on A&A, please see companion paper "THIN disk kinem...