Biomedical subjects

C Pascutto

Publications and source records attributed to C Pascutto.

5 recordsLinked to original sources

PubMed1998

The Sardinian IDDM study: 1. Epidemiology and geographical distribution of IDDM in Sardinia during 1989 to 1994.

Analysis of the geographical variation of risk for a disease is a key issue in descriptive epidemiology and may provide useful suggestions for planning further studies to identify the underlying causes. We adopted a Bayesian approach to investigate the geographical distribution of insulin-dependent diabetes mellitus (IDDM) incidence rate across Sardinia. Data on incidence of IDDM in children aged under 15 years (619 IDDM cases) in Sardinia was obtained by the Sardinian Eurodiab ACE register. The overall completeness of ascertainment was: 91.3%. The average yearly standardized incidence rate for the years 1989-1994 was 33.24 per 100000 (95% C.I. 30.60, 35.88), which is the second highest in Europe after Finland. Sex and age-specific risks were higher in males than in females. Considering the variation of IDDM risk according to the age at diagnosis, the risk profile increased up to the 13th year of age for both sexes, being steeper in males. The degree of geographical variation in IDDM risk was small with a slight difference between the highest and the lowest standardized rate across the map. Indeed, even the municipalities at lowest risk in Sardinia showed a risk higher than most European countries. The Sardinian population is genetically atypical, characterized by genetic homogeneity and marked susceptibility to autoimmune diseases. Our finding of a small geographical variation within the island coupled with a marked temporal trend previously observed in data on military conscripts could be interpreted as evidence of a relatively recent environmental aetiological factor that was uniformly distributed across the island and had its effect in a genetically predisposed population.

Age Factors↗

PubMed1997

Disease mapping with errors in covariates.

We describe Bayesian hierarchical-spatial models for disease mapping with imprecisely observed ecological covariates. We posit smoothing priors for both the disease submodel and the covariate submodel. We apply the models to an analysis of insulin Dependent Diabetes Mellitus incidence in Sardinia, with malaria prevalence as a covariate.

Bayes Theorem↗

PubMed1995

[Bayesian analysis of ecological studies].

Studying the space-time variation of risk for a given disease may give etiological clues and suggestions for planning further studies to investigate the underlying causes. When the observed events are rare, approaches based on maximum likelihood may lead to unstable and largely uninformative estimates of risk and of its time trend due to Poisson sampling variation. In this paper we propose a general Bayesian model for analyzing the variation of risk in space and time. We applied the Bayesian model to the analysis of the geographical variation of breast cancer mortality, to an ecological study on the correlation between lung cancer mortality and degree of urbanization and industrialization and to the analysis of the space-time variation of cumulative prevalence of Insulin Dependent Diabetes Mellitus (IDDM) as observed in military examinations between 1954 and 1989.

Bayes Theorem↗

PubMedDate not supplied

Statistical issues in the analysis of disease mapping data.

In this paper we discuss a number of issues that are pertinent to the analysis of disease mapping data. As an illustrative example we consider the mapping of larynx cancer across electoral wards in the North West Thames region of the U.K. Bayesian hierarchical models are now frequently employed to carry out such mapping. In a typical situation, a three-stage hierarchical model is specified in which the data are modelled as a function of area-specific relative risks at stage one; the collection of relative risks across the study region are modelled at stage two; and at stage three prior distributions are assigned to parameters of the stage two distribution. Such models allow area-specific disease relative risks to be 'smoothed' towards global and/or local mean levels across the study region. However, these models contain many structural and functional assumptions at different levels of the hierarchy; we aim to discuss some of these assumptions and illustrate their sensitivity. When relative risks are the endpoint of interest, it is common practice to assume that, for each of the age-sex strata of a particular area, there is a common multiplier (the relative risk) acting upon each of the stratum-specific risks in that area; we will examine this proportionality assumption. We also consider the choices of models and priors at stages two and three of the hierarchy, the effect of outlying areas, and an assessment of the level of smoothing that is being carried out. For inference, we concentrate on the description of the spatial variability in relative risks and on the association between the relative risks of larynx cancer and an area-level measure of socio-economic status.

Age Factors↗

PubMedDate not supplied

Bayesian analysis of space-time variation in disease risk.

The analysis of variation of risk for a given disease in space and time is a key issue in descriptive epidemiology. When the data are scarce, maximum likelihood estimates of the area-specific risk and of its linear time-trend can be seriously affected by random variation. In this paper, we propose a Bayesian model in which both area-specific intercept and trend are modelled as random effects and correlation between them is allowed for. This model is an extension of that originally proposed for disease mapping. It is illustrated by the analysis of the cumulative prevalence of insulin dependent diabetes mellitus as observed at the military examination of 18-year-old conscripts born in Sardinia during the period 1936-1971. Data concerning the genetic differentiation of the Sardinian population are used to interpret the results.

Adolescent↗