Changes in leisure time and occupational physical activity over 8 years: The Cornellà health Interview Survey Follow-Up Study

Aim: To describe changes in leisure time and occupational physical activity status in an urban Mediterranean population-based cohort, and to evaluate sociodemographic, health-related and lifestyle correlates of such changes. Methods: Data for this study come from the Cornellè Health Interview Survey Follow-Up Study, a prospective cohort study of a representative sample (n¿=¿2500) of the population. Participants in the analysis reported here include 1246 subjects (567 men and 679 women) who had complete data on physical activity at the 1994 baseline survey and at the 2002 follow-up. We fitted Breslow-Cox regression models to assess the association between correlates of interest and changes in physical activity. Results: Regarding leisure time physical activity, 61.6% of cohort members with ¿sedentary¿ habits in 1994 changed their status to ¿light/moderate¿ physical activity in 2002, and 70% who had ¿light/moderate¿ habits in 1994 did not change their activity level. Regarding occupational physical activity, 74.4% of cohort members who were ¿active¿ did not change their level of activity, and 64.3% of participants with ¿sedentary¿ habits in 1994 changed to ¿active¿ occupational physical activity. No clear correlates of change in physical activity were identified in multivariate analyses. Conclusion: While changes in physical activity are evident in this population-based cohort, no clear determinants of such changes were recognised. Further longitudinal studies including other potential individual and contextual determinants are needed to better understand determinants of changes in physical activity at the population level.

Predictors of death among patients who completed tuberculosis treatment: a population-based cohort study.

Background: Mortality among patients who complete tuberculosis (TB) treatment is still high among vulnerable populations. The objective of the study was to identify the probability of death and its predictive factors in a cohort of successfully treated TB patients. Methods: A population-based retrospective longitudinal study was performed in Barcelona, Spain. All patients who successfully completed TB treatment with culture-confirmation and available drug susceptibility testing between 1995 1997 were retrospectively followed-up until December 31, 2005 by the Barcelona TB Control Program. Socio-demographic, clinical, microbiological and treatment variables were examined. Mortality, TB Program and AIDS registries were reviewed. Kaplan-Meier and a Cox regression methods with time-dependent covariates were used for the survival analysis, calculating the hazard ratio (HR) with 95% confidence intervals (CI). Results: Among the 762 included patients, the median age was 36 years, 520 (68.2%) were male, 178 (23.4%) HIV-infected, and 208 (27.3%) were alcohol abusers. Of the 134 (17.6%) injecting drug users (IDU), 123 (91.8%) were HIV-infected. A total of 30 (3.9%) recurrences and 173 deaths (22.7%) occurred (mortality rate: 3.4/100 person-years of follow-up). The predictors of death were: age between 4160 years old (HR: 3.5; CI:2.15.7), age greater than 60 years (HR: 14.6; CI:8.924), alcohol abuse (HR: 1.7; CI:1.22.4) and HIV-infected IDU (HR: 7.9; CI:4.713.3). Conclusions: The mortality rate among TB patients who completed treatment is associated with vulnerable populations such as the elderly, alcohol abusers, and HIV-infected IDU. We therefore need to fight against poverty, and promote and develop interventions and social policies directed towards these populations to improve their survival.

Positive Outcomes Influence the Rate and Time to Publication, but Not the Impact Factor of Publications of Clinical Trial Results

Objectives: Publication bias may affect the validity of evidence based medical decisions. The aim of this study is to assess whether research outcomes affect the dissemination of clinical trial findings, in terms of rate, time to publication, and impact factor of journal publications. Methods and Findings: All drug-evaluating clinical trials submitted to and approved by a general hospital ethics committee between 1997 and 2004 were prospectively followed to analyze their fate and publication. Published articles were identified by searching Pubmed and other electronic databases. Clinical study final reports submitted to the ethics committee, final reports synopses available online and meeting abstracts were also considered as sources of study results. Study outcomes were classified as positive (when statistical significance favoring experimental drug was achieved), negative (when no statistical significance was achieved or it favored control drug) and descriptive (for non-controlled studies). Time to publication was defined as time from study closure to publication. A survival analysis was performed using a Cox regression model to analyze time to publication. Journal impact factors of identified publications were recorded. Publication rate was 48·4% (380/785). Study results were identified for 68·9% of all completed clinical trials (541/785). Publication rate was 84·9% (180/212) for studies with results classified as positive and 68·9% (128/186) for studies with results classified as negative (p<0·001). Median time to publication was 2·09 years (IC95 1·61-2·56) for studies with results classified as positive and 3·21 years (IC95 2·69-3·70) for studies with results classified as negative (hazard ratio 1·99 (IC95 1·55-2·55). No differences were found in publication impact factor between positive (median 6·308, interquartile range: 3·141-28·409) and negative result studies (median 8·266, interquartile range: 4·135-17·157). Conclusions: Clinical trials with positive outcomes have significantly higher rates and shorter times to publication than those with negative results. However, no differences have been found in terms of impact factor.

Innovation sources and productivity in Catalonian firms: a quantile regression analysis

This paper explores the effects of two main sources of innovation -intramural and external R&D- on the productivity level in a sample of 3,267 Catalonian firms. The data set used is based on the official innovation survey of Catalonia which was a part of the Spanish sample of CIS4, covering the years 2002-2004. We compare empirical results by applying usual OLS and quantile regression techniques both in manufacturing and services industries. In quantile regression, results suggest different patterns at both innovation sources as we move across conditional quantiles. The elasticity of intramural R&D activities on productivity decreased when we move up the high productivity levels both in manufacturing and services sectors, while the effects of external R&D rise in high-technology industries but are more ambiguous in low-technology and knowledge-intensive services. JEL codes: O300, C100, O140. Keywords: Innovation sources, R&D, Productivity, Quantile regression

A priori ratemaking using bivariate poisson regression models

In automobile insurance, it is useful to achieve a priori ratemaking by resorting to gene- ralized linear models, and here the Poisson regression model constitutes the most widely accepted basis. However, insurance companies distinguish between claims with or without bodily injuries, or claims with full or partial liability of the insured driver. This paper exa- mines an a priori ratemaking procedure when including two di®erent types of claim. When assuming independence between claim types, the premium can be obtained by summing the premiums for each type of guarantee and is dependent on the rating factors chosen. If the independence assumption is relaxed, then it is unclear as to how the tari® system might be a®ected. In order to answer this question, bivariate Poisson regression models, suitable for paired count data exhibiting correlation, are introduced. It is shown that the usual independence assumption is unrealistic here. These models are applied to an automobile insurance claims database containing 80,994 contracts belonging to a Spanish insurance company. Finally, the consequences for pure and loaded premiums when the independence assumption is relaxed by using a bivariate Poisson regression model are analysed.

Innovation sources and productivity: a quantile regression analysis

This paper explores the effects of two main sources of innovation —intramural and external R&D— on the productivity level in a sample of 3,267 Catalan firms. The data set used is based on the official innovation survey of Catalonia which was a part of the Spanish sample of CIS4, covering the years 2002-2004. We compare empirical results by applying usual OLS and quantile regression techniques both in manufacturing and services industries. In quantile regression, results suggest different patterns at both innovation sources as we move across conditional quantiles. The elasticity of intramural R&D activities on productivity decreased when we move up the high productivity levels both in manufacturing and services sectors, while the effects of external R&D rise in high-technology industries but are more ambiguous in low-technology and services industries.

Factors explaining local privatization: A meta-regression analysis

Privatization of local public services has been implemented worldwide in the last decades. Why local governments privatize has been the subject of much discussion, and many empirical works have been devoted to analyzing the factors that explain local privatization. Such works have found a great diversity of motivations, and the variation among reported empirical results is large. To investigate this diversity we undertake a meta-regression analysis of the factors explaining the decision to privatize local services. Overall, our results indicate that significant relationships are very dependent upon the characteristics of the studies. Indeed, fiscal stress and political considerations have been found to contribute to local privatization specially in the studies of US cases published in the eighties that consider a broad range of services. Studies that focus on one service capture more accurately the influence of scale economies on privatization. Finally, governments of small towns are more affected by fiscal stress, political considerations and economic efficiency, while ideology seems to play a major role for large cities.

Estimation of lean meat content in pig carcasses using X-ray Computed Tomography and PLS regression

Lean meat percentage (LMP) is an important carcass quality parameter. The aim of this work is to obtain a calibration equation for the Computed Tomography (CT) scans with the Partial Least Square Regression (PLS) technique in order to predict the LMP of the carcass and the different cuts and to study and compare two different methodologies of the selection of the variables (Variable Importance for Projection — VIP- and Stepwise) to be included in the prediction equation. The error of prediction with cross-validation (RMSEPCV) of the LMP obtained with PLS and selection based on VIP value was 0.82% and for stepwise selection it was 0.83%. The prediction of the LMP scanning only the ham had a RMSEPCV of 0.97% and if the ham and the loin were scanned the RMSEPCV was 0.90%. Results indicate that for CT data both VIP and stepwise selection are good methods. Moreover the scanning of only the ham allowed us to obtain a good prediction of the LMP of the whole carcass.

Innovation sources and productivity in Catalonian firms: a quantile regression analysis

This paper explores the effects of two main sources of innovation - intramural and external R&D— on the productivity level in a sample of 3,267 Catalonian firms. The data set used is based on the official innovation survey of Catalonia which was a part of the Spanish sample of CIS4, covering the years 2002-2004. We compare empirical results by applying usual OLS and quantile regression techniques both in manufacturing and services industries. In quantile regression, results suggest different patterns at both innovation sources as we move across conditional quantiles. The elasticity of intramural R&D activities on productivity decreased when we move up the high productivity levels both in manufacturing and services sectors, while the effects of external R&D rise in high-technology industries but are more ambiguous in low-technology and knowledge-intensive services. JEL codes: O300, C100, O140 Keywords: Innovation sources, R&D, Productivity, Quantile Regression

Modelling dependence in a ratemaking procedure with multivariate Poisson regression models

When actuaries face with the problem of pricing an insurance contract that contains different types of coverage, such as a motor insurance or homeowner's insurance policy, they usually assume that types of claim are independent. However, this assumption may not be realistic: several studies have shown that there is a positive correlation between types of claim. Here we introduce different regression models in order to relax the independence assumption, including zero-inflated models to account for excess of zeros and overdispersion. These models have been largely ignored to multivariate Poisson date, mainly because of their computational di±culties. Bayesian inference based on MCMC helps to solve this problem (and also lets us derive, for several quantities of interest, posterior summaries to account for uncertainty). Finally, these models are applied to an automobile insurance claims database with three different types of claims. We analyse the consequences for pure and loaded premiums when the independence assumption is relaxed by using different multivariate Poisson regression models and their zero-inflated versions.

Mixture of bivariate Poisson regression models with an application to insurance

In a recent paper Bermúdez [2009] used bivariate Poisson regression models for ratemaking in car insurance, and included zero-inflated models to account for the excess of zeros and the overdispersion in the data set. In the present paper, we revisit this model in order to consider alternatives. We propose a 2-finite mixture of bivariate Poisson regression models to demonstrate that the overdispersion in the data requires more structure if it is to be taken into account, and that a simple zero-inflated bivariate Poisson model does not suffice. At the same time, we show that a finite mixture of bivariate Poisson regression models embraces zero-inflated bivariate Poisson regression models as a special case. Additionally, we describe a model in which the mixing proportions are dependent on covariates when modelling the way in which each individual belongs to a separate cluster. Finally, an EM algorithm is provided in order to ensure the models’ ease-of-fit. These models are applied to the same automobile insurance claims data set as used in Bermúdez [2009] and it is shown that the modelling of the data set can be improved considerably.

A logistic regression approach to estimating customer profit loss due to lapses in insurance

This article focuses on business risk management in the insurance industry. A methodology for estimating the profit loss caused by each customer in the portfolio due to policy cancellation is proposed. Using data from a European insurance company, customer behaviour over time is analyzed in order to estimate the probability of policy cancelation and the resulting potential profit loss due to cancellation. Customers may have up to two different lines of business contracts: motor insurance and other diverse insurance (such as, home contents, life or accident insurance). Implications for understanding customer cancellation behaviour as the core of business risk management are outlined.

Time-series regression models to study the short-term effects of environmental factors on health

Time series regression models are especially suitable in epidemiology for evaluating short-term effects of time-varying exposures on health. The problem is that potential for confounding in time series regression is very high. Thus, it is important that trend and seasonality are properly accounted for. Our paper reviews the statistical models commonly used in time-series regression methods, specially allowing for serial correlation, make them potentially useful for selected epidemiological purposes. In particular, we discuss the use of time-series regression for counts using a wide range Generalised Linear Models as well as Generalised Additive Models. In addition, recently critical points in using statistical software for GAM were stressed, and reanalyses of time series data on air pollution and health were performed in order to update already published. Applications are offered through an example on the relationship between asthma emergency admissions and photochemical air pollutants

An alternative method for dating unknown tephras based on a segmented regression model

In CoDaWork’05, we presented an application of discriminant function analysis (DFA) to 4 differentcompositional datasets and modelled the first canonical variable using a segmented regression modelsolely based on an observation about the scatter plots. In this paper, multiple linear regressions areapplied to different datasets to confirm the validity of our proposed model. In addition to dating theunknown tephras by calibration as discussed previously, another method of mapping the unknown tephrasinto samples of the reference set or missing samples in between consecutive reference samples isproposed. The application of these methodologies is demonstrated with both simulated and real datasets.This new proposed methodology provides an alternative, more acceptable approach for geologists as theirfocus is on mapping the unknown tephra with relevant eruptive events rather than estimating the age ofunknown tephra.Kew words: Tephrochronology; Segmented regression

The building blocks of international ecological footprint inequality: a regression-based decomposition

This paper performs an empirical Decomposition of International Inequality in Ecological Footprint in order to quantify to what extent explanatory variables such as a country’s affluence, economic structure, demographic characteristics, climate and technology contributed to international differences in terms of natural resource consumption during the period 1993-2007. We use a Regression-Based Inequality Decomposition approach. As a result, the methodology extends qualitatively the results obtained in standard environmental impact regressions as it comprehends further social dimensions of the Sustainable Development concept, i.e. equity within generations. The results obtained point to prioritizing policies that take into account both future and present generations.