Objective Correspondence Analysis (CA) is a multivariate graphical technique designed to explore relationships among categorical variables. Epidemiologists frequently collect data on multiple categorical variables with to the goal of examining associations amongst these variables. Nevertheless, despite its usefulness in this context, CA appears to be an underused technique in epidemiology. The objective of this paper is to present the utility of CA in an epidemiological context. Study Design and Setting The theory and interpretation of CA in the case of two variables and more than two variables is illustrated through two examples. Results The outcome from correspondence analysis is a graphical display of the rows and columns of a contingency table that is designed to permit visualization of the salient relationships among the variable responses in a low-dimensional space. Such a representation reveals a more global picture of the relationships among row-column pairs which would otherwise not be detected through a pairwise analysis. Conclusion When the study variables of interest are categorical, CA is an appropriate technique to explore relationships amongst variable response categories and can play a complementary role in analyzing epidemiological data.

Findings on the relationship between frailty and sociodemographic variables, morbidity and disability, support previous studies, providing further evidence that although frailty seems to be a distinct geriatric concept, it also overlaps with other concepts.

BackgroundMultiple comorbidity measures have been developed for risk-adjustment in studies using administrative data, but it is unclear which measure is optimal for specific outcomes and if the measures are equally valid in different populations. This research examined the predictive performance of five comorbidity measures in three population-based cohorts.MethodsAdministrative data from the province of Saskatchewan, Canada, were used to create the cohorts. The general population cohort included all Saskatchewan residents 20+ years, the diabetes cohort included individuals 20+ years with a diabetes diagnosis in hospital and/or physician data, and the osteoporosis cohort included individuals 50+ years with diagnosed or treated osteoporosis. Five comorbidity measures based on health services utilization, number of different diagnoses, and prescription drugs over one year were defined. Predictive performance was assessed for death and hospitalization outcomes using measures of discrimination (c-statistic) and calibration (Brier score) for multiple logistic regression models.ResultsThe comorbidity measures with optimal performance were the same in the general population (n = 662,423), diabetes (n = 41,925), and osteoporosis (n = 28,068) cohorts. For mortality, the Elixhauser index resulted in the highest c-statistic and lowest Brier score, followed by the Charlson index. For hospitalization, the number of diagnoses had the best predictive performance. Consistent results were obtained when we restricted attention to the population 65+ years in each cohort.ConclusionsThe optimal comorbidity measure depends on the health outcome and not on the disease characteristics of the study population.

Objective To examine the relationships among seven frailty domains: nutrition, physical activity, mobility, strength, energy, cognition, and mood, using data from three studies. Study Design and Setting Data from three studies were separately analyzed using Multiple Correspondence Analysis (MCA). The graphical output of MCA was used to assess 1) if the presence of deficits in the frailty domains separate from the absence of deficits on the graph, 2) the dimensionality of the domains, 3) the clustering of domains within each dimension and 4) their relationship with age, sex and disability. Results were compared across the studies. Results In two studies, presence of deficits for all domains separated from absence of deficits. In the third study, there was separation in all domains except cognition. Three main dimensions were retained in each study however assigned dimensionality of domains differed. The clustering of mobility with energy and/or strength was consistent across studies. Deficits were associated with older age, female sex and disability. Conclusion Our results suggest that frailty is a multidimensional concept for which the relationships among domains differ according to the population characteristics. These domains, with the possible exception of cognition, appear to aggregate together and share a common underlying construct.