Conditional Poisson Regression with Random Effects for the Analysis of Multi-site Time Series Studies.

The analysis of time series studies linking daily counts of a health indicator with environmental variables (e.g., mortality or hospital admissions with air pollution concentrations or temperature; or motor vehicle crashes with temperature) is usually conducted with Poisson regression models controlling for long-term and seasonal trends using temporal strata.

Alcohol as a trigger of migraine attacks in people with migraine. Results from a large prospective cohort study in English-speaking countries.

Objective: To assess whether alcohol intake is associated with the onset of migraine attacks up to 2 days after consumption in individuals with episodic migraine (EM).

Background: Although alcohol has long been suspected to be a common migraine trigger, studies have been inconclusive in proving this association.

Methods: This was an observational prospective cohort study among individuals with migraine who registered to use a digital health platform for headache.

Bayesian model selection for the study of Hardy-Weinberg proportions and homogeneity of gender allele frequencies.

Standard statistical tests for Hardy-Weinberg equilibrium assume the equality of allele frequencies in the sexes, whereas tests for the equality of allele frequencies in the sexes assume Hardy-Weinberg equilibrium. This produces a circularity in the testing of genetic variants, which has recently been resolved with new frequentist likelihood and exact procedures. In this paper, we tackle the same problem by posing it as a Bayesian model comparison problem.

An integrated analysis of individual and aggregated health data using estimating equations.

Analyses of individual disease-exposure data within a population are useful when exposure of interest varies sufficiently within the population. When the within-population variance of exposure is limited, however, power of the individual-data analysis is reduced. In such situations, aggregated-data analyses of disease data across populations, with a sample of individual exposure data from each population, can be powerful in estimating the exposure effect if between population variation of exposure is large.

[Mortality of gastric cancer in Catalonia, Spain: geographical distribution and time trends from 1986 to 2000].

Background And Objective: The aims of this study are to describe the time trends and the changes in the spatial distribution of stomach cancer mortality by gender, in Catalonia, Spain, in the period 1986-2000.

Material And Method: The mortality data comes from the Mortality Register for Catalonia at the Health Department and the population data from the Institute of Statistics for Catalonia. To analyze time trends, a Poisson regression model was adjusted for each gender.

[Analysis of mortality time trend using generalized linear models].

In this work it is shown how generalized linear models allow one to describe different patterns of temporary evolution of mortality data, while at the same time allow for an easy interpretation. As a practical application, the evolution of the female breast cancer mortality in Catalonia from 1986 to 2000 is analyzed. Remarkably, the mortality from breast cancer first increases and then decreases for all age groups.