Biomedical subjects

P Bernillon

Publications and source records attributed to P Bernillon.

2 recordsLinked to original sources

PubMed2000

Record-linkage between two anonymous databases for a capture-recapture estimation of underreporting of AIDS cases: France 1990-1993. The Clinical Epidemiology Group from Centres d'Information et de Soins de l'Immunodéficience Humaine.

OBJECTIVE: To estimate the completeness of the French mandatory AIDS surveillance system (Declaration Obligatoire DO) over the 1990-1993 period using a capture-recapture approach, by matching the mandatory reports with the AIDS cases present in the French Hospital Database on HIV infection (FHDH). METHODS: An anonymous record-linkage algorithm was developed to identify those cases common to both anonymous surveillance systems. The linkage was based on sex, date of birth, and infection risk group, all strictly matched, and on the dates of AIDS diagnosis and of death, the places of diagnosis and residence, and the AIDS-defining diseases at diagnosis. The total number of AIDS cases and completeness of both surveillance systems were estimated using a capture-recapture approach, assuming independence of the ascertainment sources. RESULTS: The completeness of the mandatory reporting was estimated at 83.6% (95% CI: 82.9-84.3), and that of the FHDH at 47.6% (95% CI: 46.9-48.3) for the surveillance of AIDS cases diagnosed among adults in France between 1990 and 1993. The completeness of the system based on FHDH increased over the study period as more hospitals joined the project, while the completeness of the DO surveillance system remained stable. CONCLUSION: This approach was useful in estimating the underreporting of AIDS cases in France. Regularly performed, it will allow the impact of underreporting to be monitored over time.

Acquired Immunodeficiency Syndrome↗

PubMed2000

Statistical issues in toxicokinetic modeling: a bayesian perspective.

Determining the relationship between an exposure and the resulting target tissue dose is a critical issue encountered in quantitative risk assessment (QRA). Classical or physiologically based toxicokinetic (PBTK) models can be useful in performing that task. Interest in using these models to improve extrapolations between species, routes, and exposure levels in QRA has therefore grown considerably in recent years. In parallel, PBTK models have become increasingly sophisticated. However, development of a strong statistical foundation to support PBTK model calibration and use has received little attention. There is a critical need for methods that address the uncertainties inherent in toxicokinetic data and the variability in the human populations for which risk predictions are made and to take advantage of a priori information on parameters during the calibration process. Natural solutions to these problems can be found in a Bayesian statistical framework with the help of computational techniques such as Markov chain Monte Carlo methods. Within such a framework, we have developed an approach to toxicokinetic modeling that can be applied to heterogeneous human or animal populations. This approach also expands the possibilities for uncertainty analysis. We present a review of these efforts and other developments in these areas. Appropriate statistical treatment of uncertainty and variability within the modeling process will increase confidence in model results and ultimately contribute to an improved scientific basis for the estimation of occupational and environmental health risks.

Bayes Theorem↗