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C Guihenneuc-Jouyaux

Publications and source records attributed to C Guihenneuc-Jouyaux.

5 recordsLinked to original sources
PubMed2006

Excess mortality related to the August 2003 heat wave in France.

OBJECTIVES: From August 1st to 20th, 2003, the mean maximum temperature in France exceeded the seasonal norm by 11-12 degrees C on nine consecutive days. A major increase in mortality was then observed, which main epidemiological features are described herein. METHODS: The number of deaths observed from August to November 2003 in France was compared to those expected on the basis of the mortality rates observed from 2000 to 2002 and the 2003 population estimates. RESULTS: From August 1st to 20th, 2003, 15,000 excess deaths were observed. From 35 years age, the excess mortality was marked and increased with age. It was 15% higher in women than in men of comparable age as of age 45 years. Excess mortality at home and in retirement institutions was greater than that in hospitals. The mortality of widowed, single and divorced subjects was greater than that of married people. Deaths directly related to heat, heatstroke, hyperthermia and dehydration increased massively. Cardiovascular diseases, ill-defined morbid disorders, respiratory diseases and nervous system diseases also markedly contributed to the excess mortality. The geographic variations in mortality showed a clear age-dependent relationship with the number of very hot days. No harvesting effect was observed. CONCLUSIONS: Heat waves must be considered as a threat to European populations living in climates that are currently temperate. While the elderly and people living alone are particularly vulnerable to heat waves, no segment of the population may be considered protected from the risks associated with heat waves.

Adolescent↗
PubMed2000

Biases in ecological studies: utility of including within-area distribution of confounders.

This paper is centred on studies commonly known as ecological studies, where one seeks to estimate risks from data aggregated on a geographical basis. The aggregated nature of the data creates difficulties for interpreting, at an individual level, any ecological association found, difficulties which are generically referred to as 'the ecological bias or fallacy'. Here, we address an important component of this bias related to the problem of misspecification in ecological studies. We consider how aggregated level dose-response relationships are derived from integrating individual level ones over the group, and how their correct specification requires, in general, knowledge of the within-group joint distribution of the relevant risk factors, which is rarely available. We discuss in detail the common situation where data on the proportion of persons exposed in each area to several dichotomous risk factors are available. We show that ecological regression estimates of the relative risk for each factor will be improved by including in the regression, besides the linear terms, cross-product terms of the marginal prevalences. Results from a simulation study are discussed and an example concerning the geographical analysis of lung cancer mortality in France is presented.

Adult↗
PubMed2000

Modeling markers of disease progression by a hidden Markov process: application to characterizing CD4 cell decline.

Multistate models have been increasingly used to model natural history of many diseases as well as to characterize the follow-up of patients under varied clinical protocols. This modeling allows describing disease evolution, estimating the transition rates, and evaluating the therapy effects on progression. In many cases, the staging is defined on the basis of a discretization of the values of continuous markers (CD4 cell count for HIV application) that are subject to great variability due mainly to short time-scale noise (intraindividual variability) and measurement errors. This led us to formulate a Bayesian hierarchical model where, at a first level, a disease process (Markov model on the true states, which are unobserved) is introduced and, at a second level, the measurement process making the link between the true states and the observed marker values is modeled. This hierarchical formulation allows joint estimation of the parameters of both processes. Estimation of the quantities of interest is performed via stochastic algorithms of the family of Markov chain Monte Carlo methods. The flexibility of this approach is illustrated by analyzing the CD4 data on HIV patients of the Concorde clinical trial.

Algorithms↗