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 in Madrid for the period 1995-1998, of how these methods are employed.Time-series; Poisson; GLM; GAM; autocorrelation; overdispersion; air pollution

A Modification Of The EM Algorithm To Estimate An Andersen-Gill Gamma Frailty Model For Multivariate Failure Time Data

A modification of the Andersen-Gill gamma shared frailty model is presented. The variance of the frailty is directly modeled by means of a generalized linear model, the EM algorithm is modified in order to simultaneously estimate a semiparametric model for the failure times and a model for the variance of the frailty. A simulation study is conducted to evaluate the performance of the proposed algorithm (EMB algorithm) and compared with other methods, a marginal model, and a conditional model. Multivariate data from a nosocomial infection study is used to illustrate the methods. The EMB fit turned out to be better than the fit obtained from a marginal model or from a conditional model. The EMB provided the best fit (being the least over-dispersed and having the highest AIC and the highest pseudo-R square) and estimated the parameters most efficiently. The proposed method is able to capture and to take into account unobservable random effects in semiparametric models

Compositional representation (CoDa) of the Agenda-setting of the political opinion makers in the main Spanish media groups in the 2015 general election

This article presents an innovative proposal to represent the agenda-setting of the main Spanish media groups (RTVE, Prisa, Vocento, Unidad Editorial, COPE, Atresmedia, Planeta, Godó, Mediaset España and Libertad Digital) in the context of the 2015 Spanish general election. The study uses a quantitative method based on a content analysis of the main Spanish radio and TV programmes with political opinion spaces and the main newspapers linked to these media groups. Furthermore, the compositional methodological approach the CoDa-biplot is used to visualise and emphasise the relative salience of the components under study. The article not only provides a mapping of the agenda of the main media groups in the electoral context, but it also reaches the representation of the effects of priming and the spiral of silence in the proportional study of the issues and senders of the agenda.Este artículo presenta un estudio innovador sobre la representación de la agenda-setting de los principales grupos de medios españoles (RTVE, Prisa, Vocento, Unidad Editorial, COPE, Atresmedia, Planeta, Godó, Mediaset España y Libertad Digital), en el contexto de las Elecciones Generales en España del año 2015. El estudio emplea una metodología cuantitativa con base a un análisis de contenido de los principales programas con espacios de opinión sobre política en la radio y televisión española, así como de los principales periódicos vinculados a estos grupos mediáticos. Asimismo, se emplea la herramienta metodológica composicional del CoDa-biplot, que contribuye a visualizar y enfatizar la relevancia relativa de los componentes en estudio. El artículo proporciona un mapeo de la agenda de los principales grupos de medios en el contexto electoral, al tiempo que alcanza la representación de los efectos del priming y la espiral del silencio en el estudio proporcional de los issues y emisores de la agenda

Meta-Analysis Of Results And Individual Patient Data In Epidemiologal Studies

Epidemiological information can be aggregated by combining results through a meta-analysis technique, or by pooling and analyzing primary data. Common approaches to analyzing pooled studies through an example on the effect of occupational exposure to wood dust on sinonasal cancer are described. Results were combined applying a meta-analysis technique. Alternatively, primary data from all studies were pooled and re-analyzed using mixed effect models. The combination of individual information rather than results is desirable to facilitate interpretations of epidemiological findings, leading also to more precise estimations and more powerful statistical tests for study heterogeneity

Spatiotemporal modeling of traffic risk mapping: A study of urban road networks in Barcelona, Spain

Accidents on the road have always been a major concern in modern society. According to the World Health Organization, globally road traffic collisions are one of the leading and fastest growing causes of disability and death. The present research work is conducted on ten years of traffic accident data in an urban environment to explore and analyze spatial and temporal variation in the accidents and related injuries. The proposed spatiotemporal model can make predictions regarding the number of injuries incurred on individual road segments. Bayesian methodology using Integrated Nested Laplace Approximation (INLA) with Stochastic Partial Differential Equations (SPDE) has been applied to generate a predicted risk map for the entire road network. The current study introduces INLA- SPDE modeling to perform spatiotemporal predictive analysis on selected areas, precisely on road networks instead of traditional continuous regions. Additionally, the result risk maps act as a baseline to identify the safe routes in a spatiotemporal context. The methodology can be adapted and applied to enhanced INLA-SPDE modeling of spatial point processes precisely on road networks

Longitudinal panel data study of self-rated health among migrants in French-speaking Switzerland, 2003–2017

Introduction Studies have documented poorer health among migrants than natives of several European countries, but little is known for Switzerland. We assessed the association between country of birth, socioeconomic factors and self-reported health (SRH) in a prospective cohort of adults living in Lausanne, Switzerland.Methods We used the data from the Colaus panel data study for three periods: 2003–2006 (n=6733), 2009–2012 (n=5064) and 2014–2017 (n=4555) corresponding to 35% of the source population. The response variable was SRH. Main explanatory variables were socioeconomic status, educational level, professional status, income, gender, age and years in Switzerland. The main covariate was country of birth, dichotomised as born in Switzerland or not. We specified random effects logistic regressions and used Bayesian methods for the inference.Results Being born outside of Switzerland was not associated with worse SRH (OR 1.09, 95% CI 0.52 to 2.31). Several other patient variables were, however, predictive of poor health. Educational level was inversely associated with the risk of reporting poor health. Monthly household income showed a gradient where higher income was associated with lower odds of reporting poor SRH, for both for migrants and non-migrants. Migrant women had lower odds of reporting poor SRH than men (OR 0.73, 95% CI 0.55 to 0.98). Migrant people living in couple have less risk of reporting poor SRH than people who live alone and the risk is lower for migrant people living in couple with children (OR 0.66, 95% CI 0.55 to 0.80).Discussion Migrant status was not associated with poorer SRH. However, differences in SRH were observed based on gender, age and several social determinants of health

Simulations of a variable structure-based algorithm for adaptive cruise control

The aim of this work is to test in simulations a variable structure algorithm for adaptive cruise control in vehicles. The simulation tests will include two scenarios: first, the platooning problem in vehicles, that consists in to achieve a stable flow for N controlled vehicles and, secondly, the trafic flow behaviour when this ACC algorithm is implemented in an heterogenous fleet mixing human drivers and autonomous vehicles

Evaluation of the Biases in the Studies that Assess the Effects of the Great Recession on Health: a Systematic Review

Background: Our main objective was to evaluate the fundamental biases detected in studies assessing the effects the Great Recession had on health for the case of Spain. As secondary objectives we presented methods to control these biases and to discuss the results of the studies in question if they had controlled for them. Methods: We carried out a systematic review of the literature published up to June 2018. We evaluated the biases that could have happened in all the eligible studies. Results: From the review, we finally selected 53 studies. Of the studies we reviewed, 60.38% or 32 out of 53, were evaluated as having a high risk of bias. The main biases our review revealed were problems with evaluation, time bias, lack of control of unobserved confounding, and non-exogeneity when defining the onset of the Great Recession. Conclusions: The results from the studies that controlled the biases were quite consistent. Summing up, the studies reviewed found that the Great Recession increased the risk of declaring poor self-rated health and the deterioration of mental health. Both the mortality rate and the suicide rate may well have increased after the Great Recession, probably after a three- to four-year delay