Biomedical subjects
Publications and source records attributed to L Bernardinelli.
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.
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.
AIM AND BACKGROUND: Public concern on an increased incidence of childhood leukemia in SW Sardinia prompted the authors to an epidemiological investigation. METHODS: Incident childhood neoplasms observed in the Cagliari province (Sardinia--Italy) in 1974-1989 were registered. Expected cases of the most frequent childhood cancers were calculated for each town, based on the sex-and age-specific incidence rates in the province. RESULTS: An excess risk of childhood acute lymphoblastic leukemia (ALL) was observed in Carbonia, a town located in the SW part of the province. The risk was highest in 1983-85, when 7 cases occurred versus 0.8 expected (RR = 8.7; 95% C.I. = 4.6, 16.3). No spatial clustering of ALL cases was observed within the town. CONCLUSIONS: A significantly higher than expected incidence of childhood ALL was observed in the town of Carbonia in 1983-85. In alternative to chance, possible exposure to environmental pollutants from a near industrial settlement is discussed as the cause of the observed excess, but it is far to be proven. Other hypotheses, including a viral infection in a population with increased susceptibility, as suggested for new urban settlements, cannot be discarded.
This paper presents a statistical approach, originally developed for mapping disease risk, to ecological regression analysis in the presence of spatial autocorrelated extra-Poisson variation. An insight into the effect of allowing for spatial autocorrelation on the relationship between disease rates and explanatory variables is given. Examples based on cancer frequency in Scotland and Sardinia are used to illustrate the interpretation of regression coefficient and further methodological issues.
This paper reviews methods for mapping geographical variation in disease incidence and mortality. Recent results in Bayesian hierarchical modelling of relative risk are discussed. Two approaches to relative risk estimation, along with the related computational procedures, are described and compared. The first is an empirical Bayes approach that uses a technique of penalized log-likelihood maximization; the second approach is fully Bayesian, and uses an innovative stochastic simulation technique called the Gibbs sampler. We chose to map geographical variation in breast cancer and Hodgkin's disease mortality as observed in all the health care districts of Sardinia, to illustrate relevant problems, methods and techniques.
The purpose of the study was to describe the mortality experience of an Italian cohort of rubber workers and an attempt was made to identify any occupational cancer hazards that might currently be affecting men employed in this type of work. A total of 4917 male workers who first started working in a large rubber factory between 1962 and 1972 have been followed up until 31 January 1983. The number of deaths from all causes and from malignant neoplasms was determined and compared with the expected number of deaths calculated from mortality rates for the province in which the population of the plant lived. Mortality from all causes was 85% of that expected. A slight overall excess of deaths from cancer (SMR = 119) was found; this was entirely due to the excess mortality in the 35-44 age group. In order further to evaluate the possible existence of a cancer risk SMRs were analysed by duration of exposure, time since first exposure, and for specific sites of cancer. A trend in SMR with duration of exposure was found for employees with 10 years of follow up or more. A high risk for some tumour sites emerged.
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In the fully Bayesian (FB) approach to disease mapping the choice of the hyperprior distribution of the dispersion parameter is a key issue. In this context we investigated the sensitivity of the rate ratio estimates to the choice of the hyperprior via a simulation study. We also compared the performance of the FB approach to mapping disease risk to the conventional approach of mapping maximum likelihood (ML) estimates and p-values. The study was modelled on the incidence data of insulin dependent diabetes mellitus (IDDM) as observed in the communes of Sardinia.
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.
A brief presentation is made of the theory underlying the multistage model of Armitage and Doll and its implications in assessing the effect of exposure to a carcinogen. The theory foresees different relationships between cancer risk and the dependent time variables (time from beginning of exposure, age at start of employment, duration of exposure and time since end of exposure) according to whether the carcinogen acts in the early or late stages of the process of cell transformation leading to tumour formation. The trend in risk of death from lung cancer was studied in a cohort of workers exposed to hexavalent chromium followed up between 1948 and 1985. The results, referred to the multistage model, indicate that hexavalent chromium probably acts in the later stages of the cell transformation process; the implications of this hypothesis for prevention and/or occupational safety strategies are discussed.
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