Multivariable Analyses

When developing the modeling approach for both outcomes, correlations between variables were examined to assess any potential issues of collinearity. Where relatively high correlations were observed between covariates, the covariate with the most significant association with the outcome of interest was included in the final, fully adjusted models (although alternate models varying in covariates retained were also explored).

4.5.3.1 Cognitive Decline among the Survived Cohort

Generalized linear models with a binomial distribution and a logit link function were used to estimate odds ratios of cognitive decline associated with social vulnerability while accounting for covariates and clustering of residents within DAL facilities. Models were created using a forward stepwise function and checked with backward selection. Preliminary covariates were selected based on published literature and bivariate findings. Unadjusted models with only the response and a single predictor variable were run first (i.e., the probability of experiencing

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cognitive decline over one-year was modeled by age only, then by sex only, and so forth). Age and sex were then added as covariates due to their prominence in the literature and their

associations with social vulnerability and cognitive decline. Baseline functional and cognitive impairment were then added to the model. Finally, health region was added to the model. These covariates were all kept in the model because they reduced the QIC value, indicating a better model fit. This model with SVI, age, sex, baseline functional and cognitive impairment, and health region was used as the adjusted model (A).

Covariates were continually added and removed to the adjusted model (A), testing their significance and noting their impact on the QIC value. Among these covariates, baseline

depression, hospital use in the past 90 days, comorbidity, number of medications used, and four classes of psychotropic drugs (i.e., antidepressants, anxiolytics, hypnotics and sedatives, and antipsychotics) were examined. With the exception of anxiolytics, all covariates added to the adjusted model (A) were not found to be statistically significant predictors of cognitive decline. Further, their inclusion did not significantly alter the estimates of other covariates, or reduce the QIC value to indicate better model fit. The potential final model for the Survived cohort adjusted for age, sex, baseline functional and cognitive impairment, anxiolytic use, and health region.

This model was then subjected to backward selection. Baseline functional impairment was removed as a covariate because no level of functional impairment was a significant predictor of cognitive decline. The removal of this covariate marginally reduced the QIC value, but

provided no change to odd ratio estimates. Because there was no significant change to the model upon removing functional impairment, and because functional impairment is plausibly related to social vulnerability and cognitive decline, it was retained in the model. The final model for the

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Survived cohort adjusted for age, sex, baseline functional and cognitive impairment, anxiolytic use, and health region. This model is the adjusted model (B).

4.5.3.2 Cognitive Decline among Residents with and without Dementia

Generalized linear models for the dementia and non-dementia subgroups were built using the same procedures as the model presented above in section 4.5.3.1. Adjusted model (A) – adjusted for age, sex, baseline functional and cognitive impairment, and health region – was the superior model for the dementia subgroup. Adjusted model (B) – adjusted for age, sex, baseline functional and cognitive impairment, anxiolytic use, and health region – was the superior model for the non-dementia subgroup. For comparison purposes between subgroups, adjusted models (A) and (B) were executed and presented for both dementia strata.

4.5.3.3 First-Event Hospitalization, Linked Cohort

Cox proportional hazards models were used to estimate hazard ratios for time to first- event hospitalization10 _{associated with residents’ SVI while adjusting for relevant covariates and} clustering of residents within DAL facilities. As a semi-parametric model, Cox proportional hazards models were appropriate in this investigation because they allowed for statistical analysis when the effects of covariates were known, the distribution of data was unknown, and censoring occurred. Robust sandwich standard errors were used when the assumption of independence was thought to be violated by clustering of residents within facilities (198).

Models were created using a forward stepwise function, and checked using backward selection by removing the least significant covariate one at a time. As noted above, preliminary

10 _{Recall that residents were classified into four groups based on the date of their first event: (1) inpatient hospital} admission, (2) LTC admission or death without prior hospital admission, (3) other transitions without prior hospital admission, and (4) no event and remained in DAL at 1-year follow up. Further, residents were censored on the date of an alternative first-event (i.e., LTC admission, death, or other transition), and those who did not experience an alternative event were censored on the date of their 1-year follow-up assessment.

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covariates were selected based on published literature (4,14,18,19,86,115,152,156,157,193–195) and bivariate findings. Bivariate models containing only a single predictor variable were tested first (i.e., the probability of first-event hospital admission was modeled by age only, then by sex only, and so forth). Age and sex were then added simultaneously as covariates due to their importance demonstrated in the literature and because of their relevance to social vulnerability and hospitalization. Baseline measures of fatigue, cognitive impairment, depressive symptoms, health instability, comorbidity, number of medications used, frequency of hospitalizations in the past year, bowel incontinence, and health region were then added one-at-a-time to the model. With a few exceptions (see below), covariates that reached a significance level of <0.10 were retained in the model. The adjusted model (A) included baseline measures of age, sex, fatigue, health instability, comorbidity, number of medications used, frequency of hospitalizations in the past year, and health region as covariates.

Although age and sex did not reach a significance level of <0.10, they were retained in the adjusted model due to their importance in model development among a geriatric population. Comorbidity was also retained in adjusted model (A) even though it did not reach a significance level of <0.10. Comorbidity was retained because it often influences the likelihood of

experiencing a hospitalization among a geriatric population and is plausibly associated with social vulnerability.

A second model, adjusted model (B), was executed to explore the influence of social vulnerability when comorbidity was removed from the model due to its failure to reach statistical significance. Adjusted model (B) included baseline measures of age, sex, fatigue, health

instability, number of medications used, frequency of hospitalizations in the past year, and health region as covariates.

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Lastly, the proportional hazards assumption was tested graphically and statistically by adding time-dependent covariates for the primary independent variable of interest – social vulnerability. Graphically, the proportional hazards assumption did seem to be violated; however, the test of interaction contradicted this observation. Given the relatively short follow- up period of one year and the fact that the proportional hazards assumption did not appear to be violated with the statistical test, no further analyses (e.g., by time of follow-up) were explored. 4.5.3.4 First-Event Hospitalization among Residents with and without Dementia

Cox proportional hazards models for the dementia and non-dementia subgroups were constructed using the same procedures as presented above for the total Linked cohort in section 4.5.3.3. Adjusted model (A) included baseline measures of age, sex, fatigue, cognitive

impairment, health instability, comorbidity, bowel incontinence, number of medications used, frequency of hospitalizations in past year, and health region as covariates. Again, although comorbidity did not reach statistical significance in the models, it was retained in the adjusted model (A) because comorbidity is plausibly related to social vulnerability and hospitalization.

Another model, adjusted model (B), was executed where comorbidity was excluded from analyses in response to its lack of statistical significance in the model. Adjusted model (B) included baseline measures of age, sex, fatigue, cognitive impairment, health instability, bowel incontinence, number of medications used, frequency of hospitalizations in past year, and health region as covariates.