We described continuous variables (age, comorbidity score, IMSA score) using mean +/− standard deviation (SD) and categorical variables (sex, primary language, occupation, marital status, income, and all questions to develop the case-finding tool) using proportions. We tested the 12 selected questions to develop the case-finding tool in bivariate logistic regressions with complexity as measured by the measurement standard (IMSA), as the dependent variable. Significant variables were dichotomized using 2 × 2 tables, based on statistics and team consensus.

These dichotomized variables were then included in a multivariate logistic regression analysis, adjusted for age and sex, and a backward elimination procedure was applied to eliminate those that ceased to be significant in the presence of others. We computed variance inflated factors to check for multicollinearity among the independent variables [59]. We estimated sensitivity and specificity of the different scoring thresholds (number of yes responses) of the case-finding tool when compared to the complex/non-complex classification established by the measurement standard (IMSA). A ROC curve was developed and the area under the curve (AUC) was calculated. We identified the most appropriate threshold score to identify patients with complex health needs [60]. The selected threshold score was the one offering the best compromise between sensitivity and specificity.

To estimate a sensitivity of at least 70% with a 95% confidence level and an accuracy of 10%, 81 complex cases were required (nQuery Advisor® 7.0). Based on previous experience, estimating that the prevalence of patients with complex needs would represent 30% of patients identified, 270 participants had to be recruited. The 189 patients with non-complex needs would provide an accuracy of 6.5% to estimate a specificity of at least 70% [61].

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