Table 1 provides an overview of study population’s demographics, health and social characteristics. From our baseline sample 81.1% (2039/2507) had complete data. Those with complete data were less likely to be poor (χ2=8.77, 2df, p=0.01) and less likely to have low educational attainment (χ2=8.77, 2df, p=0.01).
Table 1. Demographic, social and health characteristics, showing differences between baseline population sample and analytic sample.
Characteristic
|
Total population at baseline (n=2507)
|
Participants with complete data (n-2039)
|
Difference between samples*
|
Demographics
|
Percent Women (n)
|
62.0 % (1555)
|
61.8% (1260)
|
χ2=0.25, 1df, P=0.62
|
Mean age (std. dev)
|
51.9 years (12.6)
|
52.3 years (12.5)
|
T
t=-4.26, p<.0001
|
Percent rural residents
|
43.7% (1082)
|
44.3% (892)
|
χ2=1.36, 1df, p=0.24
|
Health
|
Compared to others your age, how do you rate your health in general, % (n) :
Poor
Fair
Good
Very good
Excellent
|
3.98% (99)
17.08% (425)
34.31% (854)
31.50% (784)
13.14% (327)
|
3.78% (77)
16.34% (333)
34.59% (705)
31.80% (648)
13.49% (275)
|
χ2=6.35, 4 df, p=0.17
|
Multimorbidity : Percent with 3+ chronic illnesses (list of 14)
|
25.5% (636)
|
26.1% (530)
|
χ2=1.82, 1df, p=0.18
|
Social characteristics (in order of increasing vulnerability)
|
Highest education completed, % (n)
Primary or partial secondary
Secondary or technical
University
|
21.4% (531)
55.1% (1366)
23.5% (582)
|
19.8% (403)
55.7% (1138)
24.5% (500)
|
χ2=20.8, 2df, p<.0001
|
Self-perceived financial status, % (n)
Very poor to very tight
Tight to Moderately comfortable
Comfortable or very comfortable
|
9.7% (238)
59.9% (1476)
30.5% (751)
|
8,9% (181)
60,1% (1225)
31.0% (633)
|
χ2=8.77, 2df, p=0.01
|
Social Support - How many persons, family or friends…
…could help you with activities of daily living (e.g. dressing, driving)?
More than one
One
None
… can you freely confide in or talk about yourself or your problems?
More than one
One
None
|
50.9% (1262)
31.6% (784)
17.5% (434)
73.3% (1822)
21.2% (526)
5.5% (137)
|
49.4% (1007)
33.1% (675)
17.5% (357)
73.4% (1496)
21.5% (438)
5.1% (105)
|
χ2=13.14, 2 df, p=0.001
χ2=3.26, 2df, p=0.20
|
Language spoken at home, % (n)
Other
French or English
French and English
|
1.2% (30)
94.0% (2339)
4.7% (118)
|
0.9% (19)
94.9% (1933)
4.1% (84)
|
χ2=16.95, 2 df, p=0.0002
|
* Two-tailed statistical test of difference between baseline and between baseline sample and those included in the study: χ2 = value of chi-squared, with degrees of freedom, t = value of t-test.
|
Model specification
The four variables that we judged to best reflect the latent variable presumed to be healthcare self-efficacy are shown in Table 2, along with Spearman correlations between them. Each indicator has a median value of 4 but the full range 1-to-5 was present in the study sample. Correlations between all indicators are statistically significant, and medium strength between information agency and health efficacy and between the two self-management indicators.
Table 2 also shows the Spearman correlations between the indicators of limited healthcare efficacy and the candidate formative social indicators. Indicators of healthcare self-efficacy correlate negatively with low social support, poor financial status, and low educational achievement. The small to medium correlations are of similar magnitude, thus meeting the proportional effect assumption for MIMIC. We also examined correlations with age, sex, rurality and language, but they were very small or not statistically significant, so they were not included in the model. Nonetheless, we note that males, older adults, and rural residents tend to have lower values of healthcare self-efficacy indicators. A proxy of limited language proficiency – not speaking English or French at home – was not statistically significant and was not included in our model, because only 1% of the sample (n=19/2039) expressed this vulnerability risk, so we did not have statistical power to reliably estimate the weight despite its conceptual relevance.
Table 2. Spearman correlation within indicators reflective of limited healthcare self-efficacy, and between reflective and formative social indicators. (All reported correlations are statistically significant at p<0.001)
Indicator statement (Likert scale)
|
Mean (SD)
|
|
Spearman Correlation
|
Inform’n efficacy
|
Health efficacy
|
Manage btn visits
|
Manage w/o help
|
1. Health Information efficacy: How easy is it for you to get healthcare information by yourself?
(1=not at all to 5=Very easy)
|
3.78
(1.09)
|
1.0
|
0.40
|
0.20
|
0.22
|
2. Health efficacy: How much do you feel you have control over your health? (1=None to 5=Complete)
|
4.04
(0.92)
|
|
|
0.31
|
0.31
|
3. Self-management between appointments: How easy is it for you to care for your health condition between appointments? (1=not at all easy to 5=Very easy)
|
4.29
(0.87)
|
|
|
|
0.54
|
4. Self-management without medical help: In your day-to-day life, how do you rate your ability to take care of yourself without medical help? (1=Very poor to 5=Very good)
|
4.09
(0.92)
|
|
|
|
1.0
|
Social (Formative) Indicators *
|
Highest education completed
(1= post-graduate university to 8= primary)
|
Mode
Secondary-technical (55%)
|
-0.17
|
-0.20
|
-0.13
|
-0.13
|
Self-perceived financial status
(1=very comfortable to 7=very poor)
|
Moderate comfortable
(60%)
|
-0.12
|
-0.18
|
-0.22
|
-0.17
|
Social Support, instrumental activities
(0=more than one person to 2=no one)
|
High -1+
(49%)
|
-0.15
|
-0.12
|
-0.15
|
-0.15
|
Social support, confidante.
(0=more than one person to 2=no one)
|
High -1+
(73%)
|
-0.17
|
-0.19
|
-0.20
|
-0.18
|
Language at home
0=Eng/Fr,1=Other
|
Fr or Eng
(94%)
|
---
|
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|
---
|
---
|
|
|
Inform’n efficacy
|
Health efficacy
|
Manage btn visits
|
Manage w/o help
|
* Social indicators in their original form, higher values reflect increased social vulnerability
|
We undertook PLS-PM and MIMIC alternately, selecting the model that best represents our conceptual model but that also preserves the highest explained variance of the latent variable in PLS-PM and provides the highest CFI and lowest Akaike Information Criterion in MIMIC. After the final models were specified and tested, we transformed the social indicators into three ordinal categories, with cut-points informed by the highest explained variance in the latent variable with PLS-PM and the best comparative fit index (CFI) for MIMIC. The transformation not only improves model stability, but also has a common scoring (Table 3) that reflects the notion that a social characteristic can be a social asset protective against vulnerability (-1=“not-vulnerable”) or a deficit leading to greater vulnerability (1=“vulnerable”).
Table 3. Components of the individual index of social vulnerability, showing indicator values and distribution in the study population.
Indicator
|
Weights & Coding
|
Percent1
|
Highest education completed
|
-1 = High (University completed or partial)
0 = Mid (Secondary completed or technical college)
1 = Low (None, primary or partial secondary)
|
24.5%
55.7%
19.8%
|
Self perceived Financial status
|
-1 = Comfortable or very comfortable
0 = Tight to moderately comfortable
1 = Very tight to Poor
|
31.0%
60,1%
8,9%
|
Instrumental social support, Number of family or friends to help with activities of daily living (e.g. dressing, driving)?
|
-1 = High social support (1+ persons)
0 = Medium social support (1 person)
1 = Low social support (0 persons)
|
49.4%
33.1%
17.5%
|
Confidant social support
Number of family or friends you freely confide in or talk about yourself or your problems?
|
0 = high social support (1+ persons)
1 =Medium social support (1 person)
2 = Low social support (0 persons)
|
73.4%
21.5%
5.1%
|
Index of social vulnerability to limited healthcare self-advocacy
(calculated by summing the value of the indicators)
|
-2 = Not vulnerable
-1 = mode, not vulnerable
0 = median, not vulnerable
1 = possibly vulnerable
2 = vulnerable
3 = very vulnerable
|
22.2%
26.1%
22.6%
15.1%
8.6%
5.4%
|
1 For simplicity we do not show 95% confidence intervals, but they vary by ±1% where prevalence<10% and by ±2% where prevalence≥10%.
|
The weight and loading estimates of the final PLS-PM model are included in Figure 3. The moderately strong path coefficient (β=-0.38) shows that social vulnerability and healthcare self-efficacy are inversely related. The moderately strong magnitude justifies combining the latent variables in a single latent variable in the MIMIC model as per the conceptual model.
The final MIMIC model is shown in Figure 4. The estimated weights, loadings and goodness-of-fit statistics are calculated with the disturbance (error) term. The single latent variable is presumed to be “Social vulnerability to low healthcare self-efficacy”.
Representing the Social Vulnerability Index
Both PLS-PM and MIMIC express social vulnerability as a weighted sum of the formative indicators. However, the model-generated weight estimates are specific to the model specification and to this empirical dataset.31 For instance, the model-generated Social Vulnerability Index for our final PLS-PM model would be: Index = (0.42 x Education status) + (0.38 x Financial status) + (0.33 X Instrumental support) + (0.51 x Social support), which is not very pragmatic for clinical practice. And since a different data set would generate slightly different weights, it is not generalizable.
To make the Social Vulnerability Index both generalizable and pragmatic, we transformed weights by assigning a fixed base weight value of 1 to model-generated weights of approximately similar value (e.g. ≈0.4 = 1). In both PLS-PM and MIMIC the weight of the indicators are approximately equivalent, supporting a fixed weight=1. The result is that characteristics above the vulnerability cut-off can be summed easily to represent total social vulnerability. The proposed Social Vulnerability Index to low healthcare self-efficacy becomes = (1 x Education status) + (1 x Financial status) + (1 x Instrumental social support) + (1 x Confidant social support). Or more simply, the sum of the indicator cut-offs shown in Table 3. The Pearson correlations between the model-generated and the fixed-weight Index is very high: 0.983 with the PLS-PM-generated Index and 0.982 with the MIMIC-generated Index, indicating minimal loss of information with the fixed-weight Index.
Predicting increased likelihood of negative healthcare events
Our last step was to examine whether and how the Index predicted the likelihood of negative healthcare events in the following 12-month period. We used separate logistic regression models to determine if the Index predicted likelihood of negative healthcare events. Using administrative data we determined if the Index predicted: 2+ visits to the hospital Emergency Department (ED), any hospital admission, and, hospital admissions from the ED (presumed to be unplanned). We used survey data to predict: self-reported use of ED for system-related reasons (not have a family physician or being able to contact primary care, not knowing what to do), unmet needs for care, feeling abandoned in the system, and perception that a health problem became more serious because of delayed care. We repeated the analyses for 2 years after baseline. We examined whether the effect of the index of social vulnerability was independent of or interacted with chronic disease burden.
Graphs of the Index against the occurrence of all the negative healthcare events shows that there appears to be a threshold at Index=2, where we see an exponential increase in the likelihood of negative healthcare (Figure 5). In contrast, the relationship of negative healthcare events with chronic disease burden is ordinal, with an increased prevalence of negative events with each additional chronic illness, and no clear threshold. We confirmed with Logistic regression models that Index≥2 predicts an increased likelihood in the subsequent year of all negative healthcare events, except unmet need for care. Consequently, we consider any two of the vulnerability indicators to indicate Social Vulnerability to Poor Healthcare as a binary variable When the number of chronic diseases is added to the regression model, the effect of the Social Vulnerability Index≥2 decreases slightly, suggesting confounding by health status, but the effect of social vulnerability remains largely independent of chronic disease burden (Table 4). We explored interaction effects between social vulnerability and chronic disease burden and found a suggestive interaction (p=0.06) for increased risk of hospital admission or feeling abandoned by the health system. In these models the main effect of social vulnerability is not statistically significant, suggesting that in the absence of any chronic disease, social vulnerability alone does not increase risk of hospital admission or feeling abandoned by the health system. However, being socially vulnerable with chronic disease increases exponentially the likelihood of these negative healthcare events relative to the effect of chronic disease alone.
Table 4. Summary of logistic regression models with both Index of social vulnerability and number of chronic illnesses included in the model.
Negative healthcare event
|
Prevalence of event1
|
Model Results
|
Social Vulnerability Index ≥2
OR2
(95% CI)
|
Number chronic diseases
OR3
(95% CI)
|
Events based on administrative data:
|
|
|
|
2+ ED visits (administrative data)
|
11.1%
(197/1769)
|
2.18
(1.43, 8.10)
|
1.19
(1.09. 1.32)
|
Any hospital admission
|
16.3%
(289/1769)
|
1.50
(1.03. 2.18)
|
1.24
(1,15, 1.35)
|
Hospital admission through the emergency room
|
4.7%
(68/1368)
|
2.95
(1,71, 5.07)
|
1.38
(1.23, 1.57)
|
Self-reported events, survey data
|
|
|
|
ED use for health system reasons4
|
16.9%
(343/1685)
|
1.61
(1.19, 2.19)
|
1.04
(0.98, 1.18)
|
5+ point decline in functional health status (SF-12)5
|
23.1%
(414/1377)
|
1.26
(0.90, 1.75)
|
1.32
(1.20, 1.46)
|
Problem became worse because of delayed care
|
8.6%
(173/1837)
|
2.0
(1.36, 2.91)
|
1.17
(1.07, 1.27)
|
Feel abandoned by the system
|
23.1%
(463/1453)
|
1.37
(1.03, 1.83)
|
1.09
(1.01, 1.15)
|
Unmet need for healthcare
|
11.5%
(231/1774)
|
1.16
(0.80, 1,70)
|
1.08
(0.99, 1.17)
|
1 Varying denominator reflects missing values at Time 2.
2 Odds Ratio (OR) show adjusted likelihood of negative healthcare event in the subsequent 12 months, among patients with Index ≥ 2, compared to Index<2 , bolded values are statistically significant two-sided p<0.05.
3 Odds ratio associated with each additional chronic disease from a list of 14 stable diagnoses
4 Doctor not available, wait for appointment too long, not know what to do, confused what to do or had conflicting information, too far to clinic.
5 Controlling for baseline SF-12.