Biomedical subjects

Jon Wakefield

Publications and source records attributed to Jon Wakefield.

5 recordsLinked to original sources

PubMed2003

Trends in drug overdose deaths in England and Wales 1993-98: methadone does not kill more people than heroin.

AIMS: To test the hypothesis that methadone is responsible for a greater increase in overdose deaths than heroin, and causes proportionally more overdose deaths than heroin at weekends. DESIGN AND SETTING: Multivariate analysis of 3961 death certificates mentioning heroin, morphine and/or methadone held on the Office for National Statistics drug-related poisoning mortality database from 1993 to 1998 in England and Wales. MEASUREMENTS: Percentage increase in deaths by year by drug, odds ratio (OR) of dying at the weekend from methadone-related overdose compared to dying from heroin/morphine overdose. FINDINGS: From 1993 to 1998, annual opiate overdose deaths increased from 378 to 909. There was a 24.7% (95% confidence interval (CI) 22-28%) yearly increase in heroin deaths compared to 9.4% (95% CI 6-13%) for methadone only. This difference was significant (P < 0.001 by test of interaction) after adjustment for sex, age group, polydrug use, area of residence and underlying cause of death. The largest number of deaths occurred on Saturday (673). The OR of death from methadone overdose on Saturday and Sunday was 1.48 (95% CI 1.29-1.71) for methadone-only deaths compared to dying from heroin/morphine at the weekend after adjustment for other covariates, but the OR was not significant (1.09, 95% CI 0.95-1.25) if the weekend was defined as Friday and Saturday. CONCLUSIONS: There was no evidence that the threefold increase in deaths over time was due to methadone. There was equivocal support only for the hypothesis that there was an excess of deaths from methadone at weekends. Increased interventions to prevent overdose among injectors in England and Wales are long overdue.

Adolescent↗

PubMed2003

Sensitivity analyses for ecological regression.

In many ecological regression studies investigating associations between environmental exposures and health outcomes, the observed relative risks are in the range 1.0-2.0. The interpretation of such small relative risks is difficult due to a variety of biases--some of which are unique to ecological data, since they arise from within-area variability in exposures/confounders. The potential for residual spatial dependence, due to unmeasured confounders and/or data anomalies with spatial structure, must also be considered, though it often will be of secondary importance when compared to the likely effects of unmeasured confounding and within-area variability in exposures/confounders. Methods for addressing sensitivity to these issues are described, along with an approach for assessing the implications of spatial dependence. An ecological study of the association between myocardial infarction and magnesium is critically reevaluated to determine potential sources of bias. It is argued that the sophistication of the statistical analysis should not outweigh the quality of the data, and that finessing models for spatial dependence will often not be merited in the context of ecological regression.

Bias↗

PubMed2002

Geographical epidemiology of prostate cancer in Great Britain.

Prostate cancer incidence has increased during recent years, possibly linked to environmental exposures. Exposure to environmental carcinogens is unlikely to be evenly distributed geographically, which may give rise to variations in disease occurrence that is detectable in a spatial analysis. The aim of our study was to examine the spatial variation of prostate cancer in Great Britain at ages 45-64 years. Spatial variation was examined across electoral wards from 1975-1991. Poisson regression was used to examine regional, urbanisation and socioeconomic effects, while Bayesian mapping techniques were used to assess spatial variability. There was an indication of geographical differences in prostate cancer risk at a regional level, ranging from 0.83 (95% CI: 0.78-0.87) to 1.2 (95% CI: 1.1-1.3) across regions. There was significant heterogeneity in the risk across wards, although the range of relative risks was narrow. More detailed spatial analyses within 4 regions did not indicate any clear evidence of localised geographical clustering for prostate cancer. The absence of any marked geographical variability at a small-area scale argues against a geographically varying environmental factor operating strongly in the aetiology of prostate cancer.

Bayes Theorem↗

PubMed2002

Bayesian analysis of population PK/PD models: general concepts and software.

Markov chain Monte Carlo (MCMC) techniques have revolutionized the field of Bayesian statistics by enabling posterior inference for arbitrarily complex models. The now widely used WinBUGS software has, over the years, made the methodology accessible to a great many applied scientists, in all fields of research. Despite this, serious application of MCMC methods within the field of population PK/PD has been comparatively limited. We appreciate that for many applied pharmacokineticists the prospect of conducting a Bayesian analysis will require numerous alien concepts to be taken on board and it may be difficult to justify investing the time and effort required in order to understand them (especially since the approach is so computer-intensive). For this reason we provide here a thorough (but often informal) discussion of all aspects of Bayesian inference as they apply specifically to population PK/PD. We also acknowledge that while the WinBUGS software is general purpose, model specification for some types of problem, population PK/PD being a prime example, can be very difficult, to the extent that a specialized interface for describing the problem at hand is often a practical necessity. In the latter part of this paper we describe such an interface, namely PKBugs. A principal aim of the paper is to offer sufficient technical background, in an easy to follow format, that the reader may develop both the confidence and know-how to make appropriate use of the PKBugs/WinBUGS framework (or similar software) for their own data analysis needs, should they choose to adopt a Bayesian approach.

Bayes Theorem↗

PubMed2002

A hierarchical aggregate data model with spatially correlated disease rates.

The aggregate data study design (Prentice and Sheppard, 1995, Biometrika 82, 113-125) estimates individual-level exposure effects by regressing population-based disease rates on covariate data from survey samples in each population group. In this work, we further develop the aggregate data model to allow for residual spatial correlation among disease rates across populations. Geographical variation that is not explained by model predictors and has a spatial component often arises in studies of rare chronic diseases, such as breast cancer. We combine the aggregate and Bayesian disease-mapping models to provide an intuitive approach to the modeling of spatial effects while drawing correct inference regarding the exposure effect. Based on the results of simulation studies, we suggest guidelines for use of the proposed model.

Bayes Theorem↗