The Health Effects of Climate Change: A Survey of Recent Quantitative Research

44 p.

A critical review of some recent developments in quantitative research on gender and achievement in the United Kingdom

Over recent years the findings of a number of quantitative research studies have been published in the UK on gender and achievement. Much of this work has emanated from Stephen Gorard and his colleagues and has not only been highly critical of existing approaches to handling quantitative data but has also suggested a number of alternative and, what they claim to be, more valid ways of measuring differential patterns of achievement and underachievement between groups. This article shows how much of this work has been based upon rather under-developed measures of achievement and underachievement that tend, in turn, to generate a number of misleading findings that have questionable implications for practice. It will be argued that this body of work provides a useful case study in the problems of quantitative research that fails to engage adequately with the substantive theoretical and empirical literature and considers some of the implications of this for future research in this area.

A quantitative research on S- and SO2-poisoning Pt/Vulcan carbon fuel cell catalyst

A quantitative research on S and SO2 poisoning Pt/Vulcan carbon (Pt/VC) catalysts for fuel cells was conducted by the three-electrode method. Pt/VC electrodes were contaminated by submersion in a SO2- containing solution made up of 0.2 mM Na2SO3 and 0.5 M H2SO4 for different periods of time, and held at 0.05 V (vs. RHE) in 0.5 M H2SO4 solutions in order to gain zero-valence sulfur (S0) poisoned electrodes. The sulfur coverage of Pt was determined from the total charge consumed as the sulfur was oxidized from S0 at 0.05 V (vs. RHE) to sulfate at >1.1 V (vs. RHE). The summation of initial coverage of S0 (S) and coverage of H (H) are approximately equal to 1 (H + S = 1) when 0.5

Discrete Conditional Phase-type model (DC_Ph) for patient waiting time with a logistic regression component to predict patient admission to hospital

Discrete Conditional Phase-type (DC-Ph) models are a family of models which represent skewed survival data conditioned on specific inter-related discrete variables. The survival data is modeled using a Coxian phase-type distribution which is associated with the inter-related variables using a range of possible data mining approaches such as Bayesian networks (BNs), the Naïve Bayes Classification method and classification regression trees. This paper utilizes the Discrete Conditional Phase-type model (DC-Ph) to explore the modeling of patient waiting times in an Accident and Emergency Department of a UK hospital. The resulting DC-Ph model takes on the form of the Coxian phase-type distribution conditioned on the outcome of a logistic regression model.

Facteurs associés à l'alimentation traditionnelle au sein de trois communautés cries du Nord du Québec

Cette étude propose d’identifier les facteurs affectant la consommation d’aliments traditionnels à travers une perspective écologique, afin de réduire les taux de prévalence élevés de maladies chroniques et ralentir la forte diminution de consommation d’aliments traditionnels chez les Cris du nord québécois. Pour ce faire, une méthode mixte « sequential explanatory », fut utilisée, combinant quatre groupes focus (n=23) et une régression logistique (n=374) à partir de données secondaires issues de trois études transversales. Selon les résultats de la régression logistique: l’âge, chasser, marcher, le niveau d’éducation et la communauté de résidence étaient associées à une consommation d’aliments traditionnelle trois fois/semaine (p<0,05). Subséquemment, des groupes focus vinrent enrichir et contredire ces résultats. Par exemple : les participants étaient en désaccord avec le fait qu’il n’y avait aucune association entre les aliments traditionnels et l’emploi. Ils croyaient que les personnes sans emploi ont plus d’opportunités pour aller chasser mais peu d’argent pour couvrir les dépenses et inversement pour ceux avec emploi. Ce double effet aurait possiblement fait disparaître l’association dans la régression logistique. Suite aux groupes focus, plusieurs facteurs furent identifiés et distribués dans un modèle écologique suggérant que la consommation d’aliments traditionnels est principalement influencée par des facteurs sociaux, communautaires et environnementaux et ne se limite pas aux facteurs individuels. En conclusion, afin de promouvoir l’alimentation traditionnelle, quatre suggestions de priorités d’action sont proposées. L’alimentation traditionnelle doit faire partie des stratégies de santé publique pour réduire les taux de maladies chroniques et améliorer le bien-être des populations autochtones.

Use of multilevel logistic regression to identify the causes of differential item functioning

Resumen tomado de la publicaci??n

Quantitative research Data analysis page 2

data analysis table

A comparison of discriminant logistic regression and Item Response Theory Likelihood-Ratio Tests for Differential Item Functioning (IRTLRDIF) in polytomous short tests

Resumen tomado de la publicaci??n

Application of logistic regression for fault analysis in an industrial printing process

A statistical technique for fault analysis in industrial printing is reported. The method specifically deals with binary data, for which the results of the production process fall into two categories, rejected or accepted. The method is referred to as logistic regression, and is capable of predicting future fault occurrences by the analysis of current measurements from machine parts sensors. Individual analysis of each type of fault can determine which parts of the plant have a significant influence on the occurrence of such faults; it is also possible to infer which measurable process parameters have no significant influence on the generation of these faults. Information derived from the analysis can be helpful in the operator's interpretation of the current state of the plant. Appropriate actions may then be taken to prevent potential faults from occurring. The algorithm is being implemented as part of an applied self-learning expert system.

Prediction in Multilevel Logistic Regression

The purpose of this article is to present a new method to predict the response variable of an observation in a new cluster for a multilevel logistic regression. The central idea is based on the empirical best estimator for the random effect. Two estimation methods for multilevel model are compared: penalized quasi-likelihood and Gauss-Hermite quadrature. The performance measures for the prediction of the probability for a new cluster observation of the multilevel logistic model in comparison with the usual logistic model are examined through simulations and an application.

Logistic regression analysis of potential prognostic factors for pulmonary thromboembolism

Objective: To identify potential prognostic factors for pulmonary thromboembolism (PTE), establishing a mathematical model to predict the risk for fatal PTE and nonfatal PTE.Method: the reports on 4,813 consecutive autopsies performed from 1979 to 1998 in a Brazilian tertiary referral medical school were reviewed for a retrospective study. From the medical records and autopsy reports of the 512 patients found with macroscopically and/or microscopically,documented PTE, data on demographics, underlying diseases, and probable PTE site of origin were gathered and studied by multiple logistic regression. Thereafter, the jackknife method, a statistical cross-validation technique that uses the original study patients to validate a clinical prediction rule, was performed.Results: the autopsy rate was 50.2%, and PTE prevalence was 10.6%. In 212 cases, PTE was the main cause of death (fatal PTE). The independent variables selected by the regression significance criteria that were more likely to be associated with fatal PTE were age (odds ratio [OR], 1.02; 95% confidence interval [CI], 1.00 to 1.03), trauma (OR, 8.5; 95% CI, 2.20 to 32.81), right-sided cardiac thrombi (OR, 1.96; 95% CI, 1.02 to 3.77), pelvic vein thrombi (OR, 3.46; 95% CI, 1.19 to 10.05); those most likely to be associated with nonfatal PTE were systemic arterial hypertension (OR, 0.51; 95% CI, 0.33 to 0.80), pneumonia (OR, 0.46; 95% CI, 0.30 to 0.71), and sepsis (OR, 0.16; 95% CI, 0.06 to 0.40). The results obtained from the application of the equation in the 512 cases studied using logistic regression analysis suggest the range in which logit p > 0.336 favors the occurrence of fatal PTE, logit p < - 1.142 favors nonfatal PTE, and logit P with intermediate values is not conclusive. The cross-validation prediction misclassification rate was 25.6%, meaning that the prediction equation correctly classified the majority of the cases (74.4%).Conclusions: Although the usefulness of this method in everyday medical practice needs to be confirmed by a prospective study, for the time being our results suggest that concerning prevention, diagnosis, and treatment of PTE, strict attention should be given to those patients presenting the variables that are significant in the logistic regression model.

Logistic regression to estimate the welfare of broiler breeders in relation to environmental and behavioral variables

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Predicting smear negative pulmonary tuberculosis with classification trees and logistic regression: a cross-sectional study

Abstract Background Smear negative pulmonary tuberculosis (SNPT) accounts for 30% of pulmonary tuberculosis cases reported yearly in Brazil. This study aimed to develop a prediction model for SNPT for outpatients in areas with scarce resources. Methods The study enrolled 551 patients with clinical-radiological suspicion of SNPT, in Rio de Janeiro, Brazil. The original data was divided into two equivalent samples for generation and validation of the prediction models. Symptoms, physical signs and chest X-rays were used for constructing logistic regression and classification and regression tree models. From the logistic regression, we generated a clinical and radiological prediction score. The area under the receiver operator characteristic curve, sensitivity, and specificity were used to evaluate the model's performance in both generation and validation samples. Results It was possible to generate predictive models for SNPT with sensitivity ranging from 64% to 71% and specificity ranging from 58% to 76%. Conclusion The results suggest that those models might be useful as screening tools for estimating the risk of SNPT, optimizing the utilization of more expensive tests, and avoiding costs of unnecessary anti-tuberculosis treatment. Those models might be cost-effective tools in a health care network with hierarchical distribution of scarce resources.