MEASUREMENT FRAMEWORK
QUANTITATIVE STUDY
The quantitative analysis was completed using data generated through the randomized control trial. The data were collected according to the study protocols. All analyses were completed using IBM SPSS Version 21 software (IBM, 2012).
105 Population Sample and Protocol Adherence. The randomization of patients to the intervention (n = 136) and control groups (n = 133) is illustrated in Figure 5.1 below. Patients were entered into the trial from June 2013 to June 2014. There were n = 123 patients in the intervention group and n = 128 patients in the control group that were discharged home or to a home-like setting. Patients compliant with the protocols numbered n = 113 in the intervention group and n = 110 patients in the control group.
The data were analyzed using the intention-to-treat principle. The intention-to-treat principle provides an “unbiased assessment of the efficacy of the intervention at the level of adherence observed in the trial,” similar to that which might be observed in the community (Montori & Guyatt, 2001). Therefore, all 123 patients in the intervention group and all 128 patients in the control group, that were discharged home or to a home-like setting, were analyzed on the basis of how the patients were allocated.
Baseline descriptions of the intervention and control groups are provided in the sections which follow. It is assumed that the differences which might exist between the intervention and control groups were random differences as a result of chance (Pocock, Assmann, Enos, &
Kasten, 2002). Therefore, statistical analysis was not completed to measure the differences in population attributes. Analysis was completed to understand the intervening impact of social isolation and frailty on the outcomes of interest.
106 Figure 5.1: Randomization and Protocol Adherence Intervention and Control
Consented Study Population
n=269
Control n =133 Intervention n=136
Lost to death n= 13 Lost to death n= 5
Total intervention discharged
n = 123
Total control discharged n = 128
Compliance with intervention protocols
n = 113
Compliance with intervention protocols
n =110 Randomization
n=269
Sample Demographic Characteristics. Table 5.1 summarizes the basic demographic information of patients enrolled in the trial. All patients resided in the City of Greater Sudbury.
107 Table 5.1 Sample Population Demographic Characteristics, Intervention and Control
Randomized Assignment Intervention n (%) Control n (%)
Characteristic 136 133
Health Characteristics. The characteristics of the patient population in the intervention and control groups are compared in Table 5.2 below. The number of medications taken differed between the intervention and control groups and the presence of dementia was higher in the intervention group.
The aim of the screening process was to identify a patient population that was considered to be at high risk for readmission or other post-discharge adverse events. Based on the
population health status results, this was accomplished. The population identified was multi-morbid with 80% of the population having three (3) or more chronic diseases. The population utilized a significant number of medications, with 81% of the intervention and 87% of the control group utilizing more than 10 medications. In terms of the targeted chronic diseases, COPD was the most frequently occurring with 58% of the sample having the disease. Forty-nine
108 percent (49%) of the sample had diabetes and/or congestive heart failure. Twenty percent (23%) of the population had dementia. Eleven percent (11%) of the population did not have access to a primary care provider.
Table 5.2: Health Characteristics: Medication, Primary Care and Chronic Diseases Characteristic Intervention n (%) Control n (%)
136 133
Previous Health System Utilization. The population demonstrated a high percentage of hospitalization in the six months preceding entry into the trial, with 32% of the intervention group and 25% of the control group having more than one admission in the last six months.
Similarly, emergency department utilization in the previous six months was high, with 23% of
109 the intervention and 26% of the control group having more than one emergency department visit in the last six months. This information is summarized in Table 5.3 below.
Table 5.3: Previous 6 month Admissions and Emergency Department Visits Characteristic Intervention n (%)
LACE. LACE is the mnemonic for: length of stay (“L”); acuity of admission (“A”);
comorbidity of patient (“C”); and emergency department use (“E”) and is a measure of the expected risk for readmission and death within 30 days (Walraven et al., 2010). The LACE for the intervention group and control were, respectively, Mean (M) = 13.750, Standard Deviation (SD) = 2.090; M = 13.870, SD = 2.302. A LACE index score in this range would be considered high. A LACE index score of 13 has a 30-day predicted probability of readmission or death of 19.8% and a LACE index score of 14, twenty-three percent (23%) (Walraven et al., 2010). The median score and the distribution of LACE scores at the 25th and 75th percentile for the
intervention and control groups are shown in the box plot in Figure 5.2 below. The whiskers in Figure 5.2 represent the highest and lowest scores. There were no outlier scores.
110 Figure 5.2: LACE Scores: Median and Distribution at 25th and 75th Percentiles
Friendship Scale (Social Isolation Measure). The Friendship score is a measure of social isolation. The Friendship scores can be categorized into five levels. Those who are very socially isolated having scores of 0-11; isolated, 12-15; some social supports, 16-18; socially connected, 19-21; and very socially connected, 22-24 (Hawthorne, 2006). The distribution of Friendship scores by social isolation category are shown in Figure 5.3 below.
111 Figure 5.3: Frequency Distribution of Friendship Scores by Social Isolation Category (n=269)
The mean and standard deviation of the Friendship scores for the intervention and control group were, respectively, M = 17.60, SD 6.225; M = 18.18, SD = 6.014. The group in this case would best be characterized as having some social supports or being marginally socially connected (Hawthorne, 2006).
Men living alone were more socially isolated (M = 12.6, SD = 8.329) than men not living alone (M = 19.0, SD = 5.389), than women living alone (M = 18.2, SD = 5.657) and women not living alone (M = 19.0, SD = 5.178). Figure 5.4 below graphically shows the Friendship scores for each of the above mentioned groups.
The two-way analysis of variance demonstrated that a statistically significant interaction effect exists between sex and living alone: F (1, 265) = 11.511, p < 0.001. The pairwise
comparisons demonstrated no differences in men and women not living alone, (MD = 0.022,
112 Standard Error (SE) = 0.906, p = 0.980) whereas men living alone compared to women living alone was significantly different (MD = 5.002, SE = 1.155, p = 0.000). The difference between men living alone and not living alone was also significantly different, (MD = 5.759, SE = 1.102, p = 0.000).
Figure 5.4: Friendship Score by Gender and Lives Alone
Frailty Measures. The frailty scores for the intervention and control groups were respectively, M = 0.591, SD = 0.124; M = 0.592, SD = 0.119. The mean frailty score on admission of 0.59 is considered to be high, characterizing a population that is very frail
(Mitnitski et al., 2001). The distribution of frailty is normally distributed as illustrated in Figure 5.5 below.
Gender Differences in Frailty. The independent t- test demonstrated between gender differences in frailty scores, t(265) = -3.05, p = 0.003. The frailty scores for men and women are shown in Table 5.4 below.
113 Table 5.4: Mean Frailty Score by Gender
Group Statistics
Sex N Mean Std. Deviation Std. Error Mean
Frailty Score M 123 0.567 0.125 0.011
F 146 0.612 0.115 0.009
Figure 5.5: Frailty Score Distribution Population Sample
The mean frailty and standard deviation scores for those living alone and not living alone were, respectively, M = 0.607, SD 0.111; M = 0.582, SD = 0.127. No significant differences were found based on the independent t-test: t(265) = 1.624, p = 0.106. The two-way analysis of variance demonstrated significant differences in frailty between gender: F (1, 265) = 8.658, p = 0.004, but frailty was not significantly associated with living alone: F (1, 265) = 1.690, p = 0.061. Figure 5.6 below shows the association between frailty, living alone and gender.
114 Figure 5.6 Associations of Frailty, Lives Alone and Gender
Frailty was also significantly correlated with a number of other variables. Frailty is significantly correlated with length of stay of index admission, r(265) = 0.193, p = 0.002, number of medications, r(265) = 0.373, p < 0.000, friendship score (social isolation), r(265) = -0.270, p < 0.000 and patient’s age, r(265) = 0.181, p = 0.003. There was a moderate and
significant correlation between LACE index scores and frailty scores r(265) = 0.224, p < 0.000.
OUTCOMES
Mortality. The mortality rate for those patients who entered the trial was 22.7%.
Patients were entered into the trial from June 2013 to June 2014. Mortality frequency was based on the period from the beginning of the trial in June 2013 to 90 days after the last patient was entered into the trial in June 2014. The results are summarized in Table 5.5 below.
115 Table 5.5: Mortality Frequencies and Percentages during the Study Period
Mortality Count and Percentage
Frequency Percent Valid Percent Cumulative Percent
Death Yes 61 22.7 22.7 22.7
No 208 77.3 77.3 100.0
Total 269 100.0 100.0
The mean frailty score for those who died was (M = 0.638, SD = 0.104)and was
significantly higher than those who did not (M = 0.579, SD = 0.123), t (267) = 3.401, p = 0.001.
This is illustrated in figure 5.7 below.
Figure 5.7: Mean Frailty Scores Death vs. No Death
Post-Discharge Utilization. The post-discharge utilization for the intervention and control groups for the 30-day, 60-day and 90-day readmission count, the total hospital bed-days used and emergency department visits for the same periods are presented below. Patients enrolled in the study, but who died while still in the hospital were not included in this analysis.
A total of 18 patients died in hospital prior discharge.
116 The number of individual patients admitted during each of the post discharge periods is summarized in Table 5.6 below. It is important to note that an individual admitted and counted in one period may be counted in a subsequent period, if they were readmitted during that subsequent period.
Table 5.6: 30-Day, 60-Day, 90-Day Readmission Count and Percentage Cases
Readmitted Not Readmitted Total
n Percent n Percent n Percent
30-day post-discharge
readmission –cases 51 20.3% 200 79.7% 251 100.0%
Readmission cases 30-60
days post-discharge 36 14.3% 215 85.7% 251 100.0%
Readmission cases 60-90
days post-discharge 28 11.2% 223 88.8% 251 100.0%
Table 5.7 summarizes the results of the Fisher’s exact test, comparing the intervention and control groups on the measures of readmission at 30 days, 60 days, and 90 days in the post-discharge period. The post-post-discharge readmission date was calculated by measuring the
difference between the index admission discharge date and the readmission date. Total
readmissions were used, meaning that an individual patient could be readmitted more than once during a period. The analysis confirms the null hypothesis, that there is no difference between the control and the intervention groups, in terms of their rates of readmission, based on the results of the Fisher’s exact test.
117 Table 5.7: 30-Day, 60-Day, 90-Day Readmissions
Assignment Admitted
Readmission Rates. Readmission rates are defined as the number of readmissions in a period for a group of n individuals. In this case, the sample of interest is the 251 patients who were discharged home or to a homelike setting. The 18 patients enrolled in the trial but who died in hospital were excluded from the analysis. The cumulative 30-day, 60-day and 90-day
readmission rates are shown in Table 5.8 below. The 30-day readmission rate of 22.31% is consistent with the expected readmission rate based on the LACE scores of the sample (Walraven et al., 2010).
Table 5.8: Cumulative Readmission Rates at 30-Day, 60-Day & 90-Day Post Discharge
Post Discharge Period Readmission Rate (%)
30-day readmission rate 56/251 (22.31%)
60-day readmission rate 93/251 (37.05%)
90-day readmission rate 123/251 (49.00%)
Figure 5.8 below shows the 30-day, 30 to 60-day and 60 to 90-day readmission frequencies. The highest frequency of readmissions occurred in the 30-day post-discharge period. There is a clear decline in the number of readmissions between the time intervals. Trend analysis of the period differences was found to be statistically significant. The linear term contrast was F (1, 752) = 8.598, p = 0.003; and after adjusting for violations of homogeneity of variance, a significant linear contrast result was achieved of t(464.81) = 2.833, p = 0.005. Its eta value is 0.012 which can be interpreted as a weak effect.
118 Figure 5.8: 30-Day, 30-60-Day & 60-90-Day Readmission Events
Emergency Department Visits. Table 5.9 below summarizes the frequency of post-discharge emergency department visits. The count of visits represents the cumulative emergency department visits that did not result in a hospital admission. As with readmissions, an individual who visits the emergency department (ED) in one period can be counted in a subsequent period.
The cumulative visits represent the total number of individual patients who had a visit to the emergency department. Comparing the intervention and control groups at 30 days, 60 days and 90 days in the post-discharge period; no statistically significant differences in the utilization of emergency department visits was found during the cumulative or interval time periods based on the results of the Fisher’s exact test.
119 Table 5.9: Interval & Cumulative Emergency Department visits at 30, 60 & 90-days Post Discharge
Assignment ED Visit
Emergency Department Utilization Rates. Emergency department visit rates are defined as the number of emergency department visits in a period for a group of n individuals.
In this case, the sample of interest is the 251 patients who were discharged home or to a homelike setting. The 18 patients enrolled in the trial but who died in hospital were excluded from the analysis. Table 5.10 below summarizes the results.
Table 5.10: Emergency Department Post Discharge Utilization Rates
Post Discharge Period Readmission Rate (%)
30-day readmission rate 58/251 (23.11)
60-day readmission rate 91/251 (36.25)
90-day readmission rate 122/251 (48.61)
Figure 5.9 below shows the 30-day, 30-60-day, 60-90-day emergency department visits.
The highest frequency of emergency department visits occurred in the 30-day post-discharge period. There is a clear decline in the number of emergency department visits between the time intervals of 30 days and 30-60 days post-discharge. Trend Analysis of the period differences in emergency department visits was found to be statistically significant. The linear term contrast was found to be statistically significant; F (1, 752) = 6.593, p = 0.010 and after adjusting for violations of homogeneity of variances, the contrast result remained significant, t(458.432) = 2.491, p = 0.013. Its eta value is 0.011 which can be interpreted as a weak effect.
120 Figure 5.9: 30-Day, 30 to 60-Day & 60 to 90-Day Emergency Department Visits
Total Hospital Bed-Days Used. Table 5.11 summarizes the results of the Mann- Whitney U comparing the intervention and control groups on the measure of total hospital bed-days used at 30 bed-days, 60 bed-days and 90 bed-days in the post-discharge period, calculated by measuring the difference between the index admission discharge date and the time to first readmission.
Table 5.11: 30-Day, 60-Day, 90-Day, Hospital Bed-Days Used, Intervention and Control Assignment N Mean
Time to First Readmission. The time to first readmission from the discharge date of the index admission was analysed using a Kaplan-Meier survival analysis. The median period of
121 time to the first readmission for the intervention and control group are shown in Table 5.12 below.
Table 5.12: Median Time to First Readmission
Median Time to First Readmission
Assignment Median
Estimate Std. Error 95% Confidence Interval
Lower Bound Upper Bound
Intervention 56.000 12.369 31.756 80.244
Control 58.000 10.167 38.072 77.928
Overall 58.000 7.131 44.024 71.976
a. Estimation is limited to the largest survival time if it is censored.
The plot of the time to first readmission is shown in Figure 5.10 below. Based on the log rank (Mantel-Cox) test, there was no statistically significant differences in the time to first readmission between the intervention and control groups; X2(1, N=251) = 0.165, p = 0.685.
Figure 5.10: Time to First Readmission, Intervention vs. Control
Impact of Gender and Living Alone on Time to First Readmission. Two-way analysis of variance demonstrated a main effect of gender, F (1, 137) = 0.158, p = 0.692 and those living alone, F (1, 137) = 1.8929, p = 0.178, on the time to first readmission not to be
122 statistically significant. There was no significant interaction effect of gender and living alone; F (1, 137) = 0.891, p = 0.347. Further, analysis using multiple pairwise comparison demonstrated that females living alone had a time to first readmission that was less than females who did not live alone but that this difference was not significant (MD = 34.015, SE = 19.543, p = 0 .084).
Figure 5.11 below shows the differences in time to first readmission for males and females living alone and not living alone.
Figure 5.11: Time to First Readmission: Comparison of Men and Women Living Alone
Time to First Emergency Department Visit. The time to first emergency department visit from the discharge date of the index admission was analysed using a Kaplan-Meier survival analysis. The median period of time to the first readmissions for the intervention and control group are shown in Table 5.13 below and illustrated in Figure 5.12 below.
123 Table: 5.13: Median Time to First Emergency Department Visit
Median Time to First ED Visit
Assignment Median
Estimate Std. Error 95% Confidence Interval
Lower Bound Upper Bound
Intervention 58.000 18.407 21.921 94.079
Control 52.000 11.516 29.428 74.572
Overall 52.000 8.980 34.399 69.601
a. Estimation is limited to the largest survival time if it is censored.
Based on the log rank (Mantel-Cox) test, there were no statistically significant
differences in the time to first readmission between the intervention and control groups, X2(1, n=251) = 0.168, p = 0.682.
Two-way analysis of variance demonstrated a main effect of gender, F (1, 119) = 4.827, p
= 0.03, with females having a statistically significantly shorter time to first emergency department visit. There was no significant interaction effect of gender and living alone. F (1, 119) = 0.022, p = 0.882. No statistically significant differences in time to first emergency department visit were found between those who lived alone and not alone, F (1, 119) = 0.018, p
= 0.892.
Figure 5.12: Time to First Post Discharge Emergency Visit (Days)
124 Impacts of Frailty. The mean frailty scores of those readmitted in the post-discharge period were higher (M = 0.5968, Standard Error (SE) = 0.0009) than those who were not
readmitted (M = 0.5780, SE = 0.0129), but the differences were not significant, t(249) = 1.188, p
= 0.236. No correlation was found between frailty and the time to first readmission, r(141) = -0.054, p = 0.531, the time to first emergency department visit, F(2,120) = .691, p = .503 or the total inpatient bed-days, F(2,265) = 1.837, p = 0.161.
Impacts of Social Isolation. No correlation was found between friendship category and the time to first readmission, r(141) = -0.001, p = 0.994. No differences were observed in the time to first readmission by friendship category, F(4,133) = 2.366, p = 0.056, the time to first emergency department visit by friendship category, F(4,116) = 1.754, p = 0.143, or total inpatient bed utilization by friendship category, F(2,265) = 0.784, p = 0.536.
Summary of Utilization Analysis. During the 15 month study period, 251 of 269 patients enrolled in the study were discharged home after their index admission. These 251 patients experienced a total of 272 readmissions during the study period. These readmissions resulted in the utilization of 5,197 bed-days, representing the equivalent utilization of 11.7 inpatient hospital beds. No statistically significant differences were found between the
intervention and control groups at 30 days, 60 days or 90 days on the number of readmissions, or number of inpatient bed-days used, or the number of emergency department visits. No
correlation was found between frailty and the time to first hospital readmission. Additionally, no statistically significant differences on key measures of utilization by frailty category were found.
No statistically significant differences on key measures of utilization were found between social isolation categories as measured by the Friendship scale.
125 The inpatient readmission rates calculated from the discharge date of the index admission were at 30 days, 22.3%; at 60 days, 37%; and at 90 days, 49%. The median time to first
readmission after the index discharge was 58 days. The emergency department utilization rates that did not result in an inpatient admission and calculated from the discharge date of the index admission were at 30 days, 23%; at 60 days, 36%; and at 90 days, 49%. The median time to first emergency department visit in the post-discharge period after the index admission discharge was 52 days. No statistically significant differences were found between the normal care group and the intervention group in regards to these outcomes.
Models of Prediction
A secondary goal of the trial was to determine if models of prediction could be developed for the 30-day, 60-day and 90-day hospital readmissions, inpatient bed used and emergency department visits.
As a first step in developing models of prediction, multiple linear regression was
performed on all post-discharge measures of interest. In each regression model, the correlation of residuals was assessed using the Durbin-Watson statistic. In several cases, the Durbin-Watson statistic revealed that the data were serially correlated, the implication being that the
independence of the data could not be assumed (Norusis, 2012). The repeated nature of the measurement on the same subjects resulted in a situation where the data were comparatively close to one another and thus correlated (Cleophas & Zwinderman, 2012).
Linear fixed models, such as multiple linear regression, are based on the assumption of independence of observations and the regression coefficients are considered fixed. The models do not account for correlations between cases which may occur on repeated measures on the same subjects (Bickle et al., 2007) . While it is most often the fixed effects that are of primary
126 interest in most studies, it is necessary to adjust for the covariance in cases where there is a repeated measurement of subjects or where the experimental subjects are nested in a hierarchical
126 interest in most studies, it is necessary to adjust for the covariance in cases where there is a repeated measurement of subjects or where the experimental subjects are nested in a hierarchical