Biomedical subjects

L M Lix

Publications and source records attributed to L M Lix.

3 recordsLinked to original sources

PubMed2006

The interaction of ethnicity and chronic disease as risk factors for osteoporotic fractures: a comparison in Canadian Aboriginals and non-Aboriginals.

INTRODUCTION: Efforts to develop global methods for absolute fracture risk prediction are currently limited by uncertainty over the validity of these models in non-White populations. Aboriginal Canadians have higher fractures rates than non-Aboriginals. This analysis examined the interaction of ethnicity with diabetes mellitus, disease comorbidity and substance abuse as possible explanatory variables. METHODS: A retrospective, population-based matched cohort study of fracture rates was performed using Manitoba administrative health data (1984-2003). The study cohort consisted of 27,952 registered Aboriginal adults (aged 20 years or older) and 83,856 non-Aboriginal controls (matched three to one for year of birth and gender). Diabetes mellitus, number of ambulatory disease groups (ADGs), substance abuse and incident fractures were based upon validated definitions. Poisson regression analyses of fracture rates modelled the explanatory variables as main effects and two-way interactions with ethnicity. RESULTS: Osteoporotic fracture rates were approximately twofold higher in the Aboriginal cohort (p<0.0001). Diabetes, greater number of ADGs and substance abuse were all more common in the Aboriginal cohort (all p<0.0001). These factors were associated with increased fracture rates (all p<0.0001) and significantly higher population attributable risk percent in the Aboriginal cohort (all p<0.0001). However, no significant interactions between the risk factors and ethnicity were observed (p>0.1 for all interaction effects). CONCLUSION: Greater prevalence of diabetes, comorbidity and substance abuse contributes to higher rates of fracture. The relative risk of fracture for these factors is similar for both Aboriginal and non-Aboriginals despite large differences in absolute fracture risk and risk factor prevalence.

Adult↗

PubMed2004

A framework for modelling differences in regional mortality over time.

STUDY OBJECTIVE: To present a conceptual framework for testing differences in mortality for small geographical areas over time using the generalised linear model with generalised estimating equations. This framework can be used to test whether the magnitude of regional inequalities in health status has changed over time. DESIGN: A Poisson regression model for correlated data is used to investigate the relation of population health status to demographic, geographical, and temporal explanatory variables. Differences between regions at one or more points in time are tested with linear contrasts. SETTING AND PARTICIPANTS: A case example shows the application of the framework. All cause mortality and cause specific mortality were compared for three rural regions of Manitoba, Canada between 1985 and 1999. The data were obtained from Vital Statistics records and the provincial health registry. MAIN RESULTS: Tests of linear contrasts on the regression coefficients for time and region show an increase in the magnitude of the difference in the risk of all cause mortality and heart disease mortality between northern and southern regions of the province for the 1985-1989 and 1995-1999 time periods. No significant differences are identified for cancer, injury, or respiratory disease mortality. CONCLUSIONS: The proposed framework enables testing of a variety of hypotheses about differences between regions and time periods and can be applied to other measures of population health status.

Health Status↗

PubMed2000

Testing treatment effects in repeated measures designs: trimmed means and bootstrapping.

Non-normality and covariance heterogeneity between groups affect the validity of the traditional repeated measures methods of analysis, particularly when group sizes are unequal. A non-pooled Welch-type statistic (WJ) and the Huynh Improved General Approximation (IGA) test generally have been found to be effective in controlling rates of Type I error in unbalanced non-spherical repeated measures designs even though data are non-normal in form and covariance matrices are heterogeneous. However, under some conditions of departure from multisample sphericity and multivariate normality their rates of Type I error have been found to be elevated. Westfall and Young's results suggest that Type I error control could be improved by combining bootstrap methods with methods based on trimmed means. Accordingly, in our investigation we examined four methods for testing for main and interaction effects in a between- by within-subjects repeated measures design: (a) the IGA and WJ tests with least squares estimators based on theoretically determined critical values; (b) the IGA and WJ tests with least squares estimators based on empirically determined critical values; (c) the IGA and WJ tests with robust estimators based on theoretically determined critical values; and (d) the IGA and WJ tests with robust estimators based on empirically determined critical values. We found that the IGA tests were always robust to assumption violations whether based on least squares or robust estimators or whether critical values were obtained through theoretical or empirical methods. The WJ procedure, however, occasionally resulted in liberal rates of error when based on least squares estimators but always proved robust when applied with robust estimators. Neither approach particularly benefited from adopting bootstrapped critical values. Recommendations are provided to researchers regarding when each approach is best.

Humans↗