Biomedical subjects

Richard Burnett

Publications and source records attributed to Richard Burnett.

4 recordsLinked to original sources

PubMed2006

The effect of patella resurfacing in total knee arthroplasty on functional range of movement measured by flexible electrogoniometry.

BACKGROUND: The need for patella resurfacing remains an area of considerable controversy in total knee replacement surgery. There would appear to be no reported evidence on the effect of patella resurfacing on knee function, as measured by functional range of movement used in a series of tasks, in patients undergoing knee replacement. The object of this study was to measure knee joint motion during functional activities both prior to and following total knee replacement in a randomised group of patients with and without patella resurfacing and to compare these patient groups with a group of normal age-matched subjects. METHODS: The study design was a double blinded, randomised, prospective, controlled trial. The knee joint functional ranges of movement of a group of patients (n=50, mean age=70 years) with knee osteoarthritis were investigated prior to and following total knee arthroplasty (4 months and 18-24 months) along with a group of normal subjects (n=20, mean age=67). Patients were randomly allocated into two groups, those who received patella resurfacing (n=25) and those who did not (n=25). Flexible electrogoniometry was used to measure the flexion-extension angle of the knees with respect to time in eleven functional activities. FINDINGS: No statistically significant differences (alpha level 0.05) in joint excursion of the affected knee were found between patients who received patella resurfacing and those who did not. INTERPRETATION: Routine patella resurfacing in a typical knee arthroplasty population does not result in an increase in the functional range of movement used after knee replacement.

Aged↗

PubMed2004

Bias due to aggregation of individual covariates in the Cox regression model.

The impact of covariate aggregation, well studied in relation to linear regression, is less clear in the Cox model. In this paper, the authors use real-life epidemiologic data to illustrate how aggregating individual covariate values may lead to important underestimation of the exposure effect. The issue is then systematically assessed through simulations, with six alternative covariate representations. It is shown that aggregation of important predictors results in a systematic bias toward the null in the Cox model estimate of the exposure effect, even if exposure and predictors are not correlated. The underestimation bias increases with increasing strength of the covariate effect and decreasing censoring and, for a strong predictor and moderate censoring, may exceed 20%, with less than 80% coverage of the 95% confidence interval. However, covariate aggregation always induces smaller bias than covariate omission does, even if the two phenomena are shown to be related. The impact of covariate aggregation, but not omission, is independent of the covariate-exposure correlation. Simulations involving time-dependent aggregates demonstrate that bias results from failure of the baseline covariate mean to account for nonrandom changes over time in the risk sets and suggest a simple approach that may reduce the bias if individual data are available but have to be aggregated.

Bias↗

PubMed2003

Exploring bias in a generalized additive model for spatial air pollution data.

During the past few years, the generalized additive model (GAM) has become a standard tool for epidemiologic analysis exploring the effect of air pollution on population health. Recently, the use of the GAM has been extended from time-series data to spatial data. Still more recently, it has been suggested that the use of GAMs to analyze time-series data results in air pollution risk estimates being biased upward and that concurvity in the time-series data results in standard error estimates being biased downward. We show that concurvity in spatial data can lead to underestimation of the standard error of the estimated air pollution effect, even when using an asymptotically unbiased standard error estimator. We also show that both the magnitude and direction of the bias in the air pollution effect depend, at least in part, on the nature of the concurvity. We argue that including a nonparametric function of location in a GAM for spatial epidemiologic data can be expected to result in concurvity. As a result, we recommend caution in using the GAM to analyze this type of data.

Air Pollution↗

PubMedDate not supplied

Mortality and long-term exposure to ambient air pollution: ongoing analyses based on the American Cancer Society cohort.

This article provides an overview of previous analysis and reanalysis of the American Cancer Society (ACS) cohort, along with an indication of current ongoing analyses of the cohort with additional follow-up information through to 2000. Results of the first analysis conducted by Pope et al. (1995) showed that higher average sulfate levels were associated with increased mortality, particularly from cardiopulmonary disease. A reanalysis of the ACS cohort, undertaken by Krewski et al. (2000), found the original risk estimates for fine-particle and sulfate air pollution to be highly robust against alternative statistical techniques and spatial modeling approaches. A detailed investigation of covariate effects found a significant modifying effect of education with risk of mortality associated with fine particles declining with increasing educational attainment. Pope et al. (2002) subsequently reported results of a subsequent study using an additional 10 yr of follow-up of the ACS cohort. This updated analysis included gaseous copollutant and new fine-particle measurements, more comprehensive information on occupational exposures, dietary variables, and the most recent developments in statistical modeling integrating random effects and nonparametric spatial smoothing into the Cox proportional hazards model. Robust associations between ambient fine particulate air pollution and elevated risks of cardiopulmonary and lung cancer mortality were clearly evident, providing the strongest evidence to date that long-term exposure to fine particles is an important health risk. Current ongoing analysis using the extended follow-up information will explore the role of ecologic, economic, and, demographic covariates in the particulate air pollution and mortality association. This analysis will also provide insight into the role of spatial autocorrelation at multiple geographic scales, and whether critical instances in time of exposure to fine particles influence the risk of mortality from cardiopulmonary and lung cancer. Information on the influence of covariates at multiple scales and of critical exposure time windows can assist policymakers in establishing timelines for regulatory interventions that maximize population health benefits.

Air Pollution↗