We used a nationally representative cross-sectional survey, the 2000/01 Canadian Community Health Survey, which provides a large sample size (about 110,000) and permits more comprehensive adjustment for need indicators than previous studies.
Without any adjustment, a threshold of 5.2% was calculated; with adjustment for age, sex, and blood pressure, the threshold was 5.6%, and after a more comprehensive adjustment, the cutoff was 6.0%.
While van Doorslaer and his colleagues only used self-perceived health and activity status (i.e., impact of health problems on home, work or school, and other activities) as need indicators, we used more comprehensive adjustments for need (see Table 1).
In addition to the utilisation of an improved referent group and more comprehensive covariate adjustment compared with many existing studies, analyses undertaken for this paper are subject to two additional strengths.
Overall predictive power and accurate prediction of low- and high-cost groups depend more on comprehensive adjustment of comorbidities than on disease severity, even in a disease-specific sample.
The way toward improvement is classified into the conventional E/C (Experiment/Calculation) bias method and the more comprehensive cross section adjustment based on the Bayesian theorem and the generalized least square method.
In this sample, reduced functional capacity and dependency in transfer and dressing were independently associated with the presence of more depressive symptoms, after comprehensive adjustment.
Utilisation of three variables – in addition to self-rated general health, breathlessness and depression – makes the adjustment more comprehensive than in previous studies, although the results were quite similar (odds ratios not shown) when these variables were introduced separately into the analyses.
Widespread adoption of the interRAI Home Care (RAI-HC) instrument in several North American and European jurisdictions provided very large sample sizes, which could be used to develop a more comprehensive set of risk adjusters and to introduce a two-step adjustment model involving both individual level covariates, population level stratification, and temporal adjustments [ 5, 6].
However, if the EOM does not work in the long term, despite the necessary and reasonable adjustments, a more comprehensive redesign of the electricity market needs to be considered.
The reproducibility of the sample size increased with more comprehensive reporting, primarily withdrawal rates and adjustments for multiple testing.
