Use of Statistical Analyses in the Ophthalmic Literature

Purpose: To identify the most commonly used statistical analyses in the ophthalmic literature and to determine the likely gain in comprehension of the literature that readers could expect if they were to add knowledge of more advanced techniques sequentially to their statistical repertoire.Design: Cross-sectional study.Methods: All articles published from January 2012 through December 2012 in Ophthalmology, the American Journal of Ophthalmology, and Archives of Ophthalmology were reviewed. A total of 780 peer-reviewed articles were included. Two reviewers examined each article and assigned categories to each one depending on the type of statistical analyses used. Discrepancies between reviewers were resolved by consensus.Main Outcome Measures: Total number and percentage of articles containing each category of statistical analysis were obtained. Additionally, we estimated the accumulated number and percentage of articles that a reader would be expected to be able to interpret depending on their statistical repertoire.Results: Readers with little or no statistical knowledge would be expected to be able to interpret the statistical methods presented in only 20.8% of articles. To understand more than half (51.4%) of the articles published, readers would be expected to be familiar with at least 15 different statistical methods. Knowledge of 21 categories of statistical methods was necessary to comprehend 70.9% of articles, whereas knowledge of more than 29 categories was necessary to comprehend more than 90% of articles. Articles related to retina and glaucoma subspecialties showed a tendency for using more complex analysis when compared with articles from the cornea subspecialty.Conclusions: Readers of clinical journals in ophthalmology need to have substantial knowledge of statistical methodology to understand the results of studies published in the literature. the frequency of the use of complex statistical analyses also indicates that those involved in the editorial peer-review process must have sound statistical knowledge to appraise critically the articles submitted for publication. the results of this study could provide guidance to direct the statistical learning of clinical ophthalmologists, researchers, and educators involved in the design of courses for residents and medical students. (C) 2014 by the American Academy of Ophthalmology.National Eye Institute, National Institutes of Health, Bethesda, MarylandBrazilian National Research CouncilCarl-Zeiss Meditec, Inc (Jena, Germany)Heidelberg Engineering, GmBH (Dosseinheim, Germany)Alcon (Hunenberg, Switzerland)Allergan (Irvine, California)Topcon (Itabashi, Tokyo, Japan)Reichert, Inc (Depew, New York)Univ Calif San Diego, Hamilton Glaucoma Ctr, La Jolla, CA 92093 USAUniv Calif San Diego, Dept Ophthalmol, La Jolla, CA 92093 USAUniversidade Federal de São Paulo, Dept Ophthalmol, São Paulo, BrazilBoston Univ, Sch Med, Boston, MA 02118 USAUniversidade Federal de São Paulo, Dept Ophthalmol, São Paulo, BrazilNational Eye Institute, National Institutes of Health, Bethesda, Maryland: EY021818National Eye Institute, National Institutes of Health, Bethesda, Maryland: P30EY022589Brazilian National Research Council: 200178/2012-1Web of Scienc

SINPHONIE (Schools Indoor Pollution and Health Observatory Network in Europe): Executive Summary of the Final Report

This report is the executive summary of the final report of the SINPHONIE (Schools Indoor Pollution and Health: Observatory Network in Europe) project. SINPHONIE was funded by the European Parliament and carried out under a contract with the European Commission’s Directorate-General for Health and Consumers (DG SANCO) (SANCO/2009/C4/04, contract SI2.570742). The SINPHONIE project established a scientific/technical network to act at the EU level with the long-term perspective of improving air quality in schools and kindergartens, thereby reducing the risk and burden of respiratory diseases among children and teachers potentially due to outdoor and indoor air pollution. At the same time, the project supports future policy actions by formulating guidelines, recommendations and risk management options for better air quality and associated health effects in schools.JRC.I.1-Chemical Assessment and Testin

Evaluation of land use regression models for NO2 and particulate matter in 20 European study areas : the ESCAPE project

Land use regression models (LUR) frequently use leave-one-out-cross-validation (LOOCV) to assess model fit, but recent studies suggested that this may overestimate predictive ability in independent data sets. Our aim was to evaluate LUR models for nitrogen dioxide (NO2) and particulate matter (PM) components exploiting the high correlation between concentrations of PM metrics and NO2. LUR models have been developed for NO2, PM2.5 absorbance, and copper (Cu) in PM10 based on 20 sites in each of the 20 study areas of the ESCAPE project. Models were evaluated with LOOCV and "hold-out evaluation (HEV)" using the correlation of predicted NO2 or PM concentrations with measured NO2 concentrations at the 20 additional NO2 sites in each area. For NO2, PM2.5 absorbance and PM10 Cu, the median LOOCV R(2)s were 0.83, 0.81, and 0.76 whereas the median HEV R(2) were 0.52, 0.44, and 0.40. There was a positive association between the LOOCV R(2) and HEV R(2) for PM2.5 absorbance and PM10 Cu. Our results confirm that the predictive ability of LUR models based on relatively small training sets is overestimated by the LOOCV R(2)s. Nevertheless, in most areas LUR models still explained a substantial fraction of the variation of concentrations measured at independent sites

Development of land use regression models for particle composition in twenty study areas in Europe

Land Use Regression (LUR) models have been used to describe and model spatial variability of annual mean concentrations of traffic related pollutants such as nitrogen dioxide (NO2), nitrogen oxides (NOx) and particulate matter (PM). No models have yet been published of elemental composition. As part of the ESCAPE project, we measured the elemental composition in both the PM10 and PM2.5 fraction sizes at 20 sites in each of 20 study areas across Europe. LUR models for eight a priori selected elements (copper (Cu), iron (Fe), potassium (K), nickel (Ni), sulfur (S), silicon (Si), vanadium (V), and zinc (Zn)) were developed. Good models were developed for Cu, Fe, and Zn in both fractions (PM10 and PM2.5) explaining on average between 67 and 79% of the concentration variance (R2) with a large variability between areas. Traffic variables were the dominant predictors, reflecting nontailpipe emissions. Models for V and S in the PM10 and PM2.5 fractions and Si, Ni, and K in the PM10 fraction performed moderately with R2 ranging from 50 to 61%. Si, NI, and K models for PM2.5 performed poorest with R2 under 50%. The LUR models are used to estimate exposures to elemental composition in the health studies involved in ESCAPE

Exploring the relationship between corporate branding, internal branding and employer branding: an empirical study

Atmospheric pollutants and meteorological conditions are suspected to be causes of preterm birth. We aimed to characterize their possible association with the risk of preterm birth (defined as birth occurring before 37 completed gestational weeks). We pooled individual data from 13 birth cohorts in 11 European countries (71,493 births from the period 1994-2011, European Study of Cohorts for Air Pollution Effects (ESCAPE)). City-specific meteorological data from routine monitors were averaged over time windows spanning from 1 week to the whole pregnancy. Atmospheric pollution measurements (nitrogen oxides and particulate matter) were combined with data from permanent monitors and land-use data into seasonally adjusted land-use regression models. Preterm birth risks associated with air pollution and meteorological factors were estimated using adjusted discrete-time Cox models. The frequency of preterm birth was 5.0%. Preterm birth risk tended to increase with first-trimester average atmospheric pressure (odds ratio per 5-mbar increase = 1.06, 95% confidence interval: 1.01, 1.11), which could not be distinguished from altitude. There was also some evidence of an increase in preterm birth risk with first-trimester average temperature in the -5°C to 15°C range, with a plateau afterwards (spline coding, P = 0.08). No evidence of adverse association with atmospheric pollutants was observed. Our study lends support for an increase in preterm birth risk with atmospheric pressure

Development of Land Use Regression Models for PM2.5, PM2.5 Absorbance, PM10 and PMcoarse in 20 European Study Areas; Results of the ESCAPE Project

ABSTRACT: Land Use Regression (LUR) models have been used increasingly for modeling small-scale spatial variation in air pollution concentrations and estimating individual exposure for participants of cohort studies. Within the ESCAPE project, concentrations of PM2.5, PM2.5 absorbance, PM10 and PMcoarse were measured in 20 European study areas at 20 sites per area. GIS-derived predictor variables (e.g. traffic intensity, population, and land-use) were evaluated to model spatial variation of annual average concentrations for each study area. The median model explained variance (R2) was 71% for PM2.5 (range across study areas 35%-94%). Model R2 was higher for PM2.5 absorbance (median 89%, range 56-97%) and lower for PMcoarse (median 68%, range 32- 81%). Models included between two and five predictor variables, with various traffic indicators as the most common predictors. Lower R2 was related to small concentration variability or limited availability of predictor variables, especially traffic intensity. Cross validation R2 results were on average 8-11% lower than model R2. Careful selection of monitoring sites, examination of influential observations and skewed variable distributions were essential for developing stable LUR models. The final LUR models are used to estimate air pollution concentrations at the home addresses of participants in the health studies involved in ESCAP

Development of land use regression models for PM2.5, PM2.5 absorbance, PM10 and PMcoarse in 20 European study areas; results of the ESCAPE project

Aplinkotyros katedraVytauto Didžiojo universiteta

The influence of meteorological factors and atmospheric pollutants on the risk of preterm birth

Atmospheric pollutants and meteorological conditions are suspected to be causes of preterm birth. We aimed to characterize their possible association with the risk of preterm birth (defined as birth occurring before 37 completed gestational weeks). We pooled individual data from 13 birth cohorts in 11 European countries (71,493 births from the period 1994–2011, European Study of Cohorts for Air Pollution Effects (ESCAPE)). City-specific meteorological data from routine monitors were averaged over time windows spanning from 1 week to the whole pregnancy. Atmospheric pollution measurements (nitrogen oxides and particulate matter) were combined with data from permanent monitors and land-use data into seasonally adjusted land-use regression models. Preterm birth risks associated with air pollution and meteorological factors were estimated using adjusted discrete-time Cox models. The frequency of preterm birth was 5.0%. Preterm birth risk tended to increase with first-trimester average atmospheric pressure (odds ratio per 5-mbar increase = 1.06, 95% confidence interval: 1.01, 1.11), which could not be distinguished from altitude. There was also some evidence of an increase in preterm birth risk with first-trimester average temperature in the −5°C to 15°C range, with a plateau afterwards (spline coding, P = 0.08). No evidence of adverse association with atmospheric pollutants was observed. Our study lends support for an increase in preterm birth risk with atmospheric pressureAplinkotyros katedraVytauto Didžiojo universiteta

The influence of meteorological factors and atmospheric pollutants on the risk of preterm birth

Atmospheric pollutants and meteorological conditions are suspected to be causes of preterm birth. We aimed to characterize their possible association with the risk of preterm birth (defined as birth occurring before 37 completed gestational weeks). We pooled individual data from 13 birth cohorts in 11 European countries (71,493 births from the period 1994-2011, European Study of Cohorts for Air Pollution Effects (ESCAPE)). City-specific meteorological data from routine monitors were averaged over time windows spanning from 1 week to the whole pregnancy. Atmospheric pollution measurements (nitrogen oxides and particulate matter) were combined with data from permanent monitors and land-use data into seasonally adjusted land-use regression models. Preterm birth risks assoc