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Self-reported health and the well-being paradox among community-dwelling older adults: a cross-sectional study using baseline data from the Canadian Longitudinal Study on Aging (CLSA)

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Background Self-reported health is a widely used epidemiologic measure, however, the factors that predict self-reported health among community-dwelling older adults (≥65 years), especially those with multimorbidity (≥2 chronic conditions), are poorly understood. Further, it is not known why some older adults self-report their health positively despite the presence of high levels of multimorbidity, a phenomenon known as the well-being paradox. The objectives of this study were to: 1) examine the factors that moderate or mediate the relationship between multimorbidity and self-reported health; 2) identify the factors that predict high self-reported health; and 3) determine whether these same factors predict high self-reported health among those with high levels of multimorbidity to better understand the well-being paradox. Methods A cross-sectional analysis of baseline data from the Canadian Longitudinal Study on Aging was completed (n = 21,503). Bivariate stratified analyses were used to explore whether each factor moderated or mediated the relationship between multimorbidity and self-reported health. Logistic regression was used to determine the factors that predict high self-reported health in the general population of community-dwelling older adults and those displaying the well-being paradox. Results None of the factors explored in this study moderated or mediated the relationship between multimorbidity and self-reported health, yet all were independently associated with self-reported health. The ‘top five’ factors predicting high self-reported health in the general older adult population were: lower level of multimorbidity (odds ratio [OR] 0.75, 95% confidence interval [CI] 0.74-0.76), female sex (OR 0.62, CI 0.57-0.68), higher Life Space Index score (OR 1.01, CI 1.01-1.01), higher functional resilience (OR 1.16, CI 1.14-1.19), and higher psychological resilience (OR 1.26, CI 1.23-1.29). These same ‘top five’ factors predicted high self-reported health among the subset of this population with the well-being paradox. Conclusions The factors that predict high self-reported health in the general population of older adults are the same for the subset of this population with the well-being paradox. A number of these factors are potentially modifiable and can be the target of future interventions to improve the self-reported health of this population.
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Whitmoreetal. BMC Geriatrics (2022) 22:112
https://doi.org/10.1186/s12877-022-02807-z
RESEARCH
Self-reported health andthewell-being
paradox amongcommunity-dwelling older
adults: across-sectional study using baseline
data fromtheCanadian Longitudinal Study
onAging (CLSA)
Carly Whitmore1* , Maureen Markle‑Reid1 , Carrie McAiney2 , Jenny Ploeg1 , Lauren E. Griffith3 ,
Susan P. Phillips4, Andrew Wister5 and Kathryn Fisher1
Abstract
Background: Self‑reported health is a widely used epidemiologic measure, however, the factors that predict self‑
reported health among community‑dwelling older adults (65 years), especially those with multimorbidity (2
chronic conditions), are poorly understood. Further, it is not known why some older adults self‑report their health
positively despite the presence of high levels of multimorbidity, a phenomenon known as the well‑being paradox.
The objectives of this study were to: 1) examine the factors that moderate or mediate the relationship between mul‑
timorbidity and self‑reported health; 2) identify the factors that predict high self‑reported health; and 3) determine
whether these same factors predict high self‑reported health among those with high levels of multimorbidity to bet‑
ter understand the well‑being paradox.
Methods: A cross‑sectional analysis of baseline data from the Canadian Longitudinal Study on Aging was completed
(n = 21,503). Bivariate stratified analyses were used to explore whether each factor moderated or mediated the rela‑
tionship between multimorbidity and self‑reported health. Logistic regression was used to determine the factors that
predict high self‑reported health in the general population of community‑dwelling older adults and those displaying
the well‑being paradox.
Results: None of the factors explored in this study moderated or mediated the relationship between multimorbidity
and self‑reported health, yet all were independently associated with self‑reported health. The ‘top five factors predict‑
ing high self‑reported health in the general older adult population were: lower level of multimorbidity (odds ratio
[OR] 0.75, 95% confidence interval [CI] 0.74‑0.76), female sex (OR 0.62, CI 0.57‑0.68), higher Life Space Index score (OR
1.01, CI 1.01‑1.01), higher functional resilience (OR 1.16, CI 1.14‑1.19), and higher psychological resilience (OR 1.26, CI
1.23‑1.29). These same ‘top five factors predicted high self‑reported health among the subset of this population with
the well‑being paradox.
© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
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to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this
licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. The Creative Commons Public Domain Dedication waiver (http:// creat iveco
mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
Open Access
*Correspondence: whitmorc@mcmaster.ca
1 School of Nursing, McMaster University, 1280 Main Street W, Hamilton,
Ontario L8S 4K1, Canada
Full list of author information is available at the end of the article
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Whitmoreetal. BMC Geriatrics (2022) 22:112
Background
While definitions of multimorbidity vary in number
(e.g., 2 or more versus 3 or more chronic conditions)
and in the chronic conditions considered [1], there is
consistent and strong evidence in the literature that an
increasing level of multimorbidity is associated with
lower self-reported health [26]. Self-reported health
is a commonly used and reliable measure in health
research because of its demonstrated association with
morbidity and mortality [7, 8]. Since the 1950s, hun-
dreds of studies have demonstrated that lower self-
reported health is associated with higher levels of both
morbidity and mortality – especially among older
adults [9]. Self-reported health captures individual sub-
jective assessments of health [10] by asking one simple
question, “In general, would you say that your health is
excellent, very good, good, fair, or poor?”. e response
to this question is known to be influenced by knowl-
edge of one’s own health, social norms, or expectations
of illness, as well as illness acceptance [1114].
As adults age, the likelihood of developing chronic
conditions such as cardiovascular disease, arthritis,
and diabetes increases [15, 16]. Multimorbidity (> 2
chronic conditions) is highly prevalent among older
adults and is associated with decreased health-related
quality of life, increased use of medical and social ser-
vices, and increased risk for adverse events [15, 17].
Increasing longevity and an associated increase in mul-
timorbidity among older adults has resulted in a change
in the way that successful aging has been conceptual-
ized. Traditionally, successful aging measures revolve
around the absence of disease, the presence of physi-
cal and cognitive capacity, and ongoing social engage-
ment [18]. More recently research emphasis has shifted
from objective to subjective indices of health, including
those that consider the presence of positive emotions
such as happiness or satisfaction in aging – despite the
presence of multimorbidity [19]. is is due, in part,
to a subset of the older adult population, who despite
having poorer health according to objective indicators,
report positive levels of subjective health (e.g., self-
reported health) [18]. is phenomenon is known as
the well-being paradox and may be indicative of ‘mul-
timorbidity resilience’ (i.e., resilience in responding to
and coping with multimorbidity) [20]. Multimorbidity
resilience is shaped by coping strategies and previous
life experiences acquired throughout the lifecourse and
related to health and illness at the individual, social,
and environmental level [20].
Numerous studies have identified factors other than
multimorbidity that are associated with self-reported
health [5, 21] including demographic (e.g., sex), health-
related (e.g., performance of activities of daily living
or fewer depressive symptoms), and behavioural (e.g.,
greater social participation) factors. However, little is
known about how these factors shape self-reported
health or whether the relationship between multimorbid-
ity and self-reported health changes in the presence of
these other factors. is study was designed to address
these gaps by exploring the interaction of these factors
with multimorbidity in predicting self-reported health
and accordingly creating a model to predict high self-
reported health among community-dwelling older adults
and the subset of this population with the well-being
paradox.
Purpose
e objectives of this study were to: 1) examine whether
sociodemographic, health-related, or resilience factors
moderate or mediate the relationship between multimor-
bidity and self-reported health; 2) identify the factors that
predict self-reported health, and; 3) determine whether
these same factors predict high self-reported health in
those with high levels of multimorbidity to better under-
stand the well-being paradox.
Methods
A detailed study protocol, including the methods and
measures used, has been published elsewhere [22].
erefore, we only briefly summarize these below.
Data source
A cross-sectional analysis of baseline data from the
Canadian Longitudinal Study on Aging was completed.
e CLSA is a national population-based study that fol-
lows 51,338 community-dwelling individuals recruited at
baseline aged 45 to 85 years for a 20-year duration [23].
Interviews were conducted in English and French. Par-
ticipants were excluded from CLSA if they resided in
one of Canada’s three territories, lived on a federal First
Nations reserve, were full-time members of the Canadian
Armed Forces, lived in an institutional setting, or had a
Conclusions: The factors that predict high self‑reported health in the general population of older adults are the
same for the subset of this population with the well‑being paradox. A number of these factors are potentially modifi‑
able and can be the target of future interventions to improve the self‑reported health of this population.
Keywords: Self‑rated health, Multimorbidity, Chronic disease, Older adult, Community, CLSA
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Whitmoreetal. BMC Geriatrics (2022) 22:112
cognitive impairment precluding them from providing
informed consent or providing data on their own at the
time of recruitment [23]. e overall participation rate
for CLSA was approximately 45% and the response rate
was 10% [24].
e CLSA includes two cohorts: a tracking cohort and
a comprehensive cohort. e tracking cohort includes
a stratified random sample of 21,241 individuals from
10 Canadian provinces who provide data via telephone
interview. e comprehensive cohort includes a strati-
fied random sample of 30,097 individuals from the geo-
graphical area surrounding 11 data collection sites who
provide questionnaire data via an in-home interview and
take part in a physical assessment at a CLSA data collec-
tion site [24]. Full details of the CLSA are described in
the published protocol [23].
Sample
A subset of the full CLSA sample was used for these anal-
yses. All participants 65 years of age and older from both
the CLSA baseline tracking (version 3.4) and comprehen-
sive (version 4.0) (n = 21,503) datasets were included in
the analysis. Due to limitations on variables available (i.e.,
those variables that required in-person data collection),
for some analyses, only the comprehensive participants
(n = 12,658) were utilized. Data sources from the CLSA
datasets for each of the study objectives are displayed in
Fig.1.
Measures
Self‑reported health
Self-reported health in the CLSA is evaluated as a five-
item question, with respondents reporting their health
as 1 = excellent, 2 = very good, 3 = good, 4 = fair, or
5 = poor. In addition to this ordinal scale, self-reported
health was further dichotomized as either high self-
reported health (responses of excellent and very good)
or low self-reported health (responses of good, fair, and
poor).
Level ofmultimorbidity
e level of multimorbidity was defined in this study as
the number of chronic conditions and based on a list of
20 common chronic conditions [25], 18 of which were
available in the CLSA. ese included: the presence of
hypertension, mood disorder (anxiety or depression),
chronic musculoskeletal conditions, arthritis (rheuma-
toid or osteoarthritis), osteoporosis, respiratory condi-
tions (asthma or chronic obstructive pulmonary disease),
cardiovascular disease (angina, myocardial infarction, or
peripheral vascular disease), heart failure, stroke, stom-
ach conditions (ulcer), colon conditions, diabetes, thy-
roid disorder, cancer (did not include non-melanoma
skin cancer), kidney disease, chronic urinary conditions,
dementia, and obesity. By measuring the level of multi-
morbidity based on the number of chronic conditions,
gradient effects could be explored.
Well‑being paradox
Older adults were classified as having the well-being par-
adox if they reported high self-reported health (excellent
or very good) and a high level of multimorbidity (four or
more chronic conditions). Four or more chronic condi-
tions was selected based on a clinical understanding of
the burden of these conditions. is is because while
some of the conditions could be described as risk factors
(e.g., hypertension, obesity) or symptoms (e.g., incon-
tinence, colon disorder) [26], those older adults with
four or more chronic conditions are likely to experience
greater challenges with their health than those with fewer
than four conditions.
Sociodemographic andhealth‑related
Independent sociodemographic variables identified from
the literature [21] and available in CLSA included: sex
(female or male), age (continuous variable and 65-69,
70-74, 75-79, 80+ years), marital status (single, mar-
ried, or widowed, divorced, or separated), education (
secondary school, degree or diploma, or greater than
a degree or diploma), household income (<$20,000,
$20,000-49,999, $50,000-99,000, $100,000-149,999,
$150,000), and current dwelling type (house, apart-
ment/condominium, or retirement home, assisted living).
In addition to sociodemographic factors, health-related
factors were examined and included a depressive symp-
tom score, a depression screen, as well as a Life Space
Index score. Depressive symptom score was obtained
from the Centre for Epidemiologic Studies Depression
Scale 10-item (CES-D-10) and analyzed as both continu-
ous (i.e., reflective of the severity of depressive symp-
toms) and categorical (i.e., reflective of the presence of
depression) [27]. e CES-D-10 contains ten questions
related to feelings of depression, loneliness, hopefulness,
and other related physical symptoms such as decreased
sleep [27] and provides a measure of the severity of
depressive symptoms. For each question, participants
respond with either “all of the time”, “occasionally”, “some
of the time”, and “rarely or never”. Total scores range from
0 to 30, with higher scores indicating higher levels of
depressive symptoms. is score was also used to screen
for the presence of depressive symptoms (10/30) [27].
CES-D-10 scores were included in the demographic and
bivariate stratified analyses but were excluded from the
logistic regression analyses because the multimorbidity
resilience measures used in the regressions included this
measure as described below.
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Whitmoreetal. BMC Geriatrics (2022) 22:112
Fig. 1 Data sources for each study objective
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Page 5 of 16
Whitmoreetal. BMC Geriatrics (2022) 22:112
Life-space mobility was measured using the Life Space
Index [28], which was available within the CLSA com-
prehensive dataset only. is is a self-report of the fre-
quency and extent of movement within and from one’s
home to the neighbourhood and beyond [28]. e Life
Space Index reports mobility across different locations,
such as rooms in the house, yard, neighbourhood, alter-
native neighbourhoods, and outside of one’s city/town,
frequency of going from place to place, and whether
assistance was needed [28]. Scores are calculated for each
level of mobility with a maximum score of 120 (e.g., going
out of town without assistance) [28, 29].
Multimorbidity resilience
Multimorbidity resilience was measured using a resil-
ience index developed by Wister and colleagues [30]
using CLSA data. is index maps functional, social, and
psychological factors to multimorbidity resilience with a
composite score of the three subdomains, each of which
is comprised of three index measures representing adver-
sity and adaptation [30].
Functional resilience was measured using the Older
Americans Resources and Services (OARS) Activity of
Daily Living (ADL) Scale, the OARS Instrumental Activi-
ties of Daily Living (IADL) Scale, as well as the Summary
Performance Score [31]. e OARS ADL Scale consists
of 7 indicators of daily tasks such as eating and personal
hygiene. Each of the 7 tasks is scored on a scale from 0
(i.e., completely unable) to 2 (i.e., completely able). Total
scores range from 0 to 14, with higher scores indicating
higher functional status [31]. e OARS IADL Scale,
also a measure of functional ability, consists of 7 meas-
ures of instrumental activities such as taking medication
and preparing meals [31]. e Summary Performance
Score was calculated from individual scores of a stand-
ing balance measure, a walk time measure, and a timed
chair raise measure. For each of these, a score from 1 to 4
based on statistical quartiles was assigned. If participants
did not complete a task, they were assigned a 0. e over-
all Summary Performance Score ranged from 0 to 12
with a higher score reflecting greater physical ability [31].
Social resilience was measured using the Medical Out-
comes Study (MOS) Social Support Survey, a social par-
ticipation variable, and a perceived loneliness measure.
e MOS Social Support Survey is a 19-item tool that
measures emotional or informational support, affection
support, tangible support, and positive social interac-
tion [32]. For each of the questions, a score of 1 (“none of
the time”) to 5 (“all of the time”) is assigned. Total scores
range from 19 to 95, with higher scores indicating higher
levels of social support [32]. Social participation is a
measure developed by the research team at CLSA which
asks participants to report how often they engaged in
activities with friends or family over the past 12 months.
Possible responses to this measure are “once a day”, “at
least once a week”, “at least once a month”, “at least once
a year”, to “never” [23]. Lastly, using the CES-D-10, per-
ceived loneliness is measured with responses of “all of
the time”, “occasionally”, “some of the time”, and “rarely or
never” [27].
Psychological resilience was measured using the CES-
D-10, the Kessler Psychological Distress K10 Scale, and
the Diener Satisfaction with Life Scale. e Kessler Psy-
chological Distress Scale is a 10-item scale that meas-
ure global distress including symptoms of anxiety [33].
Answers to these questions can range from 0 (“never”)
to 3 (“most of the time”) with a total score of 30 repre-
senting the greatest distress [33]. e Diener Satisfaction
with Life Scale involves 5 items that assess global satisfac-
tion with responses ranging from 1 (“strongly disagree”)
to 7 (“strongly agree”) [34]. Total scale scores range from
5 to 35, with higher score indicating higher levels of life
satisfaction [34].
A total resilience score, consisting of the functional,
social, and psychological sub-domain scores, provides
a total scale score capturing multimorbidity resilience.
Calculation of the functional, social, and psychological
composite scores, along with the total resilience score,
is described elsewhere [30]. e resilience variables were
available for comprehensive participants only as some of
the component variables were not collected in the track-
ing cohort. In addition to the scores for each subdomain,
as well as the total resilience score, individual measures
within each of the sub-domains (e.g., Satisfaction with
Life, social participation, ADLs) were analyzed to explore
how these items shape self-reported health. Full descrip-
tions of the CLSA dataset and variables are available in
the CLSA cohort profile [24] and in the paper by Wister
and colleagues [30].
Statistical analysis
To examine the relationship between multimorbidity
and self-reported health and whether the factors mod-
erated or mediated this relationship, bivariate stratified
analyses were completed. Analyses began with examin-
ing the relationship between the level of multimorbidity
and self-reported health. Stratified analyses were then
performed to explore whether each factor (e.g., demo-
graphic, health, or resilience factors) modified or medi-
ated the relationship between the level of multimorbidity
and self-reported health. ese analyses were guided by
work on effect modification, interaction, and mediation
by Corraini and colleagues [35] as well as Frazier and
colleagues [36]. Two-way analysis of variance (ANOVA)
were used to determine the statistical significance of the
associations factor-by-factor. Two-way ANOVA models
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Whitmoreetal. BMC Geriatrics (2022) 22:112
included the level of multimorbidity, the additional inde-
pendent variable, and the interaction of the two in
predicting self-reported health. Where statistical signifi-
cance of a relationship was noted, visual inspection of the
interaction plots was completed to assess whether mean-
ingful interactions were present due to the large sample
size.
For a comprehensive understanding of the factors that
predict high self-reported health, a multiple, complete
case logistic regression was used. e regression model
included sociodemographic, health-related, and resil-
ience factors that were independently and individually
significantly associated with self-reported health. is
was preceded by tests of multicollinearity to confirm that
factors included in the models were not highly correlated
with one another.
Tests of model fit were completed including Cragg
Uhler’s R2 [37] and Wald test [38]. In addition, a variable
importance function was used to estimate the contribu-
tion of each independent variable in the model using the
absolute value of the t-statistic for the model parameter.
e same analytical methods described above were used
to determine the predictors of high self-reported health
among the subset of individuals within this population
with high levels of multimorbidity (i.e., 4+ chronic con-
ditions). Regression analyses used only the CLSA com-
prehensive dataset (n = 12,658) because some of the
factors (life space index and resilience index scores) are
only available in this dataset. Due to the large sample size
available for these analyses and the potential for statisti-
cally significant but not clinically significant findings, the
relative effect size was calculated using Cohen’s d (where
d = [LogOddsRatio x (3/π)]). Findings are reported
using Cohen’s classification criteria to determine small
(d= > 0.2), moderate (d= > 0.5), or large (d= > 0.8) effect
sizes (Cohen, 1988). Analyses were completed using SAS
(version 3.8) and R (version 4.0.2).
Results
Sociodemographic andhealth‑related characteristics
Of the 21,503 community-dwelling older adults
(65 years) included in this study sample, 50% were
female, 55% were between the ages of 65 and 74, 62%
were married or in a common-law relationship, and 97%
were white. Even though the older adults in these analy-
ses reported an average of 3.25 chronic conditions, 58%
of the sample rated their general health as high (very
good or excellent). e most common chronic conditions
were hypertension (51%), arthritis (39%), chronic mus-
culoskeletal conditions (27%), diabetes (22%), and car-
diovascular disease (20%). In addition, 15% of the study
sample screened positive for depressive symptoms on the
CES-D-10. Key demographic and health-related data are
included in Table1.
Objective 1: factors thatmoderate ormediate
therelationship betweenlevel ofmultimorbidity
andself‑reported health amongcommunity‑dwelling
older adults
Findings indicated that as the level of multimorbid-
ity increased, self-reported health decreased. One-way
ANOVA results showed that self-reported health was
significantly different across levels of multimorbidity
(F(6) = 751.44, p = <.0001). Kruskal-Wallis results were
consistent.
Sociodemographic andhealth‑related factors
e main effects for all socio-demographic, health, and
resilience factors were significant (p = <.0001) in the
models (see Table2). Significant interaction effects were
found between multimorbidity age group (F(6,3) = 2.41,
p = .0007); education (F(6,2) = 4.43, p = <.0001); life space
index (F(6,3) = 2.22, p = .0022), and self-reported health.
Despite the statistical significance of these interactions,
visual examination of the interaction plots did not sug-
gest a meaningful interaction (see Supplementary File1).
Multimorbidity resilience factors
Independent effects for each of the factors that com-
prise the functional, social, and psychological resilience
scores as well as the scores themselves were identified
(see Table2). Further to these independent effects, sig-
nificant interactions were found between multimorbid-
ity, functional resilience score (F(6,3) = 1.65, p = .04);
IADLs (F(6,1) = 3.65, p = .0013); satisfaction with life
(F(6,3) = 1.72, p = .0289), and self-reported health. How-
ever, visual examination of the interaction plots did not
suggest a meaningful interaction between these factors.
Objective 2: factors thatpredict high self‑reported health
amongcommunity‑dwelling older adults
All factors, except household income and marital sta-
tus, were significantly associated with high self-reported
health (see Table3). Using Cohen’s classification criteria,
female sex (d = 0.26) and education level greater than
a diploma or a degree (d = 0.21) had the largest effect
sizes, although these would be classified as ‘small’ using
Cohen’s thresholds. e effect sizes for the other statisti-
cally significant factors were even smaller.
Objective 3: factors thatpredict high self‑reported health
amongthesubset ofcommunity‑dwelling older adults
withhigh multimorbidity
Using the CLSA comprehensive dataset, 18.1% (n = 2296)
of older adults with high multimorbidity had high
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Whitmoreetal. BMC Geriatrics (2022) 22:112
Table 1 Key demographic and health factors
Characteristic Total
Combined Dataset (n= 21,503) or Comprehensive
Dataset only (n= 12,658)*
Sex nProportion
Female 10,749 50.02%
Male 10,742 49.98%
Total 21,491
Age
Mean Age (SD) 73.36 (5.82)
Median Age (Range) 73 (65 – 89)
Marital / Partner Status nProportion
Single / Always lived alone 1207 5.62%
Married / Common‑law 13,287 61.83%
Widowed, Divorced / Separated 6993 32.54%
Refused 4 0.02
Total 21,491
Race (not mutually exclusive) nProportion
White 20,832 96.88%
Other Race 816 3.79%
Refused / No answer / Don’t know 26 0.12%
Education nProportion
Secondary School Graduation 13,186 61.35%
University Degree or College Diploma 5575 25.94%
> Degree / Diploma 2682 12.48%
Refused / Don’t Know 50 0.17%
Total 21,493
Economic Status (Household Income) nProportion
< $20,000 1536 7.15%
$20,000 ‑ $49,999 7440 34.62%
$50,000 – $99,999 7386 34.36%
$100,000 ‑ $149,999 2148 9.99%
$150,000 1064 4.95%
Refused / Don’t Know 1917 8.92%
Total 21,493
Current Dwelling nProportion
House 15,899 73.98%
Apartment / Condo 5058 23.54%
Retirement Home / Assisted Living, Rooming / Lodging House, Other 365 1.69%
Total 21,322
Chronic Conditions nProportion
Hypertension 10,885 50.94%
Arthritis or Rheumatoid Arthritis 8212 38.92%
Chronic Musculoskeletal Condition 5853 27.34%
Obesity 5516 25.65%
Diabetes 4637 21.64%
Cardiovascular Disease 4288 20.15%
Heart Failure 3910 18.31%
Thyroid Disorder 3742 17.72%
Asthma or Chronic Obstructive Pulmonary Disease 3542 16.58%
Cancer 3550 16.57%
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Whitmoreetal. BMC Geriatrics (2022) 22:112
self-reported health (i.e., the well-being paradox). In
comparison, 24.2% (n = 3067) of these same older adults
with high multimorbidity had low self-reported health.
Characteristics ofolder adults withthewell‑being paradox
Older adults in the well-being paradox group had
higher education (X2(2) = 42.48, p = <.0001) and house-
hold income (X2(5) = 14.98, p = 0.0204), reported fewer
depressive symptoms (t(5320) = 16.75, p = <.0001),
had a higher Life Space Index score (t(5352) = 14.54,
p = <.0001), higher overall levels of resilience
(t(5361) = 21.30, p = <.0001), as well as higher levels of
functional, social, and psychological resilience, compared
to the ‘non-well-being paradox’ group – defined as those
with low self-reported health and high multimorbidity
(see Table4). In addition, those in the well-being paradox
Table 1 (continued)
Characteristic Total
Combined Dataset (n= 21,503) or Comprehensive
Dataset only (n= 12,658)*
Depression or Anxiety 3360 15.69%
Osteoporosis 3115 14.63%
Urinary Incontinence 2621 12.24%
Stomach Ulcer 1985 9.28%
Irritable Bowel Disease 2123 9.92%
Stroke or Transient Ischemic Attack 1696 7.92%
Kidney Disease 876 4.10%
Dementia or Alzheimer’s disease 78 0.36%
Level of Multimorbidity
Mean Number of Chronic Conditions (SD) 3.25 (2.13)
Median Number of Chronic Conditions (Range) 3 (0 – 15)
Depressive Symptoms (CES‑D‑10)
Mean Score (SD) 5.16 (4.40)
Median Score (Range) 4 (0 – 30)
Life Space Index (Mobility)*
Mean Score (SD) 80.50 (18.41)
Median Score (Range) 82.00 (0 – 120)
Self‑Rated (General) Health nProportion
Excellent 4022 18.71%
Very Good 8445 39.30%
Good 6526 30.37%
Fair 2031 9.45%
Poor 438 2.04%
Did not complete 29
Total 21,491
Functional Resilience*
Mean Score (SD) 7.52 (2.45)
Median Score (Range) 7.76 (0 – 10)
Psychological Resilience*
Mean Score (SD) 2.32 (1.77)
Median Score (Range) 2.23 (0 – 6.67)
Social Resilience*
Mean Score (SD) 7.19 (1.87)
Median Score (Range) 7.23 (0 – 10)
Total Resilience*
Mean Score (SD) 5.91 (1.13)
Median Score (Range) 6.02 (0 – 8.89)
Note: * marks those data only analyzed from the CLSA comprehensive dataset
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Page 9 of 16
Whitmoreetal. BMC Geriatrics (2022) 22:112
group (i.e., high self-reported health and high level of
multimorbidity) had a lower mean number of chronic
conditions compared to the non-well-being group (5.02
vs. 5.63).
Factors thatpredict high self‑reported health
amongthesubset ofparticipants withhigh multimorbidity
With the exception of social resilience, the factors pre-
dicting high self-reported health among the general
population of community-dwelling older adults were the
same as those predicting high self-reported health among
older adults with high multimorbidity (see Table 5).
Using Cohen’s classification criteria, male compared to
female sex (d = 0.28) and an education beyond a bach-
elor’s degree compared to high school graduate or less
(d = 0.22) were found to have small effects on higher self-
reported health among this subset of the sample while
the remaining factors had even less impact.
Goodness-of-fit diagnostics were completed for both
models (see Table6). Findings from Cragg Uhler’s R2 test
highlight that both models are relatively weak. In exam-
ining the Wald test and the variable importance analysis,
the similarities between the two models were apparent –
i.e., level of multimorbidity, sex, Life Space Index score,
functional resilience score, and psychological resilience
score were the ‘top five’ predictors of higher self-reported
health in both models.
Discussion
Study objectives were to: 1) examine whether sociode-
mographic, health-related, or resilience factors moderate
or mediate the relationship between multimorbidity and
Table 2 Two‑Way ANOVA main effects and interaction effects
Combined Datasets (n= 21,503) or Comprehensive Dataset only
(n= 12,658)*
Factor DF Mean Square FPr >F
Sex
Main effect 1 44.29 58.49 <.0001
Interaction effect 6 0.62 0.82 0.55
Age Group
Main effect 3 7.69 10.16 <.0001
Interaction effect 18 1.82 2.41 0.0007
Education
Main effect 2 71.96 96.02 <.0001
Interaction effect 12 3.32 4.43 <.0001
Household Income
Main effect 4 51.36 69.16 <.0001
Interaction effect 24 0.46 0.62 0.92
Marital Status
Main effect 4 3.41 4.49 <.0001
Interaction effect 24 0.38 0.48 0.98
Depression Screen
Main effect 1 376.37 514.58 <.0001
Interaction effect 6 0.57 0.77 0.59
Life Space Index*
Main effect 3 53.81 76.46 <.0001
Interaction effect 18 1.56 2.22 0.0022
Functional Resilience*
Main effect 3 97.51 141.81 <.0001
Interaction effect 18 1.13 1.65 0.0411
Summary Performance Score*
Main effect 3 97.38 140.14 <.0001
Interaction effect 18 0.99 1.42 0.11
Activities of Daily Living*
Main effect 1 66.57 93.33 <.0001
Interaction effect 6 1.04 1.46 0.18
Instrumental Activities of Daily Living*
Main effect 1 151.98 216.99 <.0001
Interaction effect 6 2.55 3.65 0.0013
Social Resilience*
Main effect 3 44.51 62.63 <.0001
Interaction effect 18 0.69 0.98 0.48
Social Support*
Main effect 3 22.96 32.14 <.0001
Interaction effect 18 0.49 0.68 0.83
Loneliness*
Main effect 4 25.58 36.02 <.0001
Interaction effect 22 0.59 0.83 0.68
Social Participation*
Main effect 5 21.63 30.39 <.0001
Interaction effect 29 0.86 1.21 0.20
Psychological Resilience*
Main effect 3 142.08 208.47 <.0001
Table 2 (continued)
Combined Datasets (n= 21,503) or Comprehensive Dataset only
(n= 12,658)*
Factor DF Mean Square FPr >F
Interaction effect 18 0.85 1.25 0.21
Psychological Distress*
Main effect 3 100.31 149.55 <.0001
Interaction effect 18 0.64 0.95 0.51
Depressive Symptom Score*
Main effect 3 116.34 169.87 <.0001
Interaction effect 18 0.85 1.25 0.21
Satisfaction with Life*
Main effect 3 163.96 244.94 <.0001
Interaction effect 18 1.15 1.72 0.0289
Total Resilience*
Main effect 3 178.29 267.69 <.0001
Interaction effect 18 0.97 1.46 0.09
Note: * marks those data only analyzed from the CLSA comprehensive dataset
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Whitmoreetal. BMC Geriatrics (2022) 22:112
self-reported health; 2) identify the factors that predict
self-reported health, and; 3) determine whether these
same factors predict high self-reported health in those
with high levels of multimorbidity to better understand
the well-being paradox. is study has generated several
key findings.
None of the sociodemographic, health-related, and
resilience factors moderated or mediated the relationship
between multimorbidity and self-reported health, yet all
were independently associated with self-reported health.
is confirms existing evidence that has demonstrated
the breadth of factors that shape how older adults per-
ceive their health [21]. However, to our knowledge, this
is the first study to explore the factors that potentially
moderate or mediate the relationship between multimor-
bidity and self-reported health among a general popula-
tion of community-dwelling older adults. ese findings
highlight that the burden of multimorbidity is not only
a strong factor associated with self-reported health, but
that the association between multimorbidity and self-
reported health is seemingly not influenced by other
demographic, health-related, and resilience factors.
Our work has uniquely identified five key factors
that predict high self-reported health among a general
population of community-dwelling older adults, as well
as a subset of this population with high multimorbid-
ity (i.e., the well-being paradox). ese factors included
a lower level of multimorbidity, female sex, higher Life
Space Index score, and higher levels of functional and
psychological resilience. While other studies have iden-
tified factors predictive of high self-reported health,
including female sex [39] and physical performance (e.g.,
balance, chair stand test) [40, 41], this is the first study
to identify that the factors that predict high self-reported
health among a general population of older adults is the
same for the subset of the population with high multi-
morbidity. is finding is a unique contribution to the
literature because while the well-being paradox is com-
monly acknowledged and identified, it is poorly described
and understood. is may be because of the limited link-
age between the well-being paradox as a concept and its
relevance to clinical practice.
Occurring alongside increasing longevity and multi-
morbidity, the contradictory nature of reporting positive
perceptions of health despite living with multiple chronic
conditions challenges the way that health care profes-
sionals measure wellness in older age [18, 19]. Evidence
has shown that primary care providers often rate patient’s
Table 3 Logistic regression model for higher self‑reported health – factors and effect
Notes: CI 95%, two-sided alpha, rounded to 2 decimal places
a Where d notes small eect size
Comprehensive Dataset, Complete Cases Only (n= 11,464)
Higher Self‑Reported Health (n= 6915); Lower Self‑Reported Health (n= 4549)
Factors Notes Pr > |z| (OR) Point Estimate
[Condence Interval] d
Intercept 3.12e‑16 0.05 [0.02‑0.10] 1.652
Number of Chronic Conditions Continuous variable < 2e‑16 0.75 [0.74‑0.76] 0.1573
Age Continuous variable 1.18e‑07 1.02 [1.01‑1.03] 0.0118
SexaCategorical variable
Male (2) vs. Female (1) < 2e‑16 0.62 [0.57‑0.68] 0.2627
Level of EducationaCategorical variable
Degree/Diploma (2) vs. High School (1) 0.002702 1.16 [1.05‑1.27] 0.0808
> Degree/Diploma (3) vs.
High School (1) 8.16e‑10 1.46 [1.29‑1.65] 0.2095
Household Income Categorical variable
$20,000 ‑ 49,999 (2) vs. <$20,000 (1) 0.040579 1.20 [1.01‑1.43] 0.1011
$50,000 – 99,999 (3) vs. <$20,000 (1) 0.019588 1.24 [1.04‑1.49] 0.1198
$100,000 – 149,999 (4) vs. <$20,000 (1) 0.003941 1.36 [1.10‑1.68] 0.1709
$150,000 (5) vs. <$20,000 (1) 0.073544 1.25 [0.98‑1.59] 0.1213
Marital Status Categorical variable
Married/Common‑law (2) vs. Single/always lived alone (1) 0.177297 0.88 [0.73‑1.06] 0.0716
Widowed/Divorced/Separated (3) vs. Single/always lived alone (1) 0.583730 0.95 [0.78‑1.15] 0.0294
Life Space Index Score Continuous variable 9.09e‑16 1.01 [1.01‑1.01] 0.0057
Functional Resilience Score Continuous variable < 2e‑16 1.16 [1.14‑1.19] 0.0823
Social Resilience Score Continuous variable 0.000181 1.05 [1.02‑1.07] 0.0259
Psychological Resilience Score Continuous variable < 2e‑16 1.26 [1.23‑1.29] 0.129
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Whitmoreetal. BMC Geriatrics (2022) 22:112
health differently than they rate it themselves [42]. is
incongruence between providers’ and older adults’ per-
ceptions of health is due to the fact that physicians tend
to evaluate health based solely on the presence of disease,
while older adults are more likely to evaluate their health
based on other factors, including their illnesses, whether
or not they are feeling well [42] and the presence of hap-
piness [43]. One interpretation of these findings is that
Table 4 Differences in characteristics for those with the well‑being paradox
a Statistically signicant mean dierences at <.05
b < .0001 (chi-squared; independent t-tests)
Characteristic Comprehensive Dataset (n= 12,658) Older Adults with High Multimorbidity (4+) (n= 5363)
High Self‑Reported Health (n = 2296) Low Self‑Reported Health (n = 3067)
Sex nProportion nProportion p
Female 1303 56.75% 1660 54.12% 0.0556
Male 993 43.25% 1407 45.88%
Age Group p
Mean Age (SD) 73.81 (5.57) 73.76 (5.77) 0.7754
Median Age (Range) 74 (65 – 86) 74 (65 – 86)
Marital / Partner Status nProportion nProportion p
Single / Always lived alone 134 5.84% 179 5.84% 0.9044
Married / Common‑law 1401 61.07% 1853 60.50%
Widowed, Divorced, Separated 759 33.09% 1031 33.66%
Educationbn Proportion n Proportion p
High School 711 30.97% 1164 37.95% <.0001
Diploma or Degree 1114 48.52% 1445 47.11%
> Degree/Diploma 471 20.51% 458 14.93%
Economic Statusa(Household Income) nProportion nProportion p
< $20,000 132 5.75% 244 7.96% 0.0204
$20,000 ‑ $49,999 717 31.26% 982 32.05%
$50,000 – $99,999 833 36.31% 1068 34.86%
$100,000 ‑ $149,999 279 12.16% 313 10.22%
$150,000 115 5.01% 164 5.35%
Level of Multimorbidity p
Mean Number (SD)b5.02 (1.26) 5.63 (1.66) <.0001
Median Number (Range) 5 (4 – 11) 5 (4 – 14)
Depressive Symptoms (CES‑D‑10)bp
Mean Score (SD) 4.94 (4.14) 7.12 (5.08) <.0001
Median Score (Range) 4 (0 – 26) 6 (0 – 28)
Life Space Indexbp
Mean Score (SD) 81.43 (17.57) 73.73 (20.31) <.0001
Median Score (Range) 82 (9 – 120) 74 (6 – 120)
Functional Resiliencebp
Mean Score (SD) 7.49 (2.32) 6.16 (2.87) <.0001
Median Score (Range) 6.67 (0 – 10) 6.67 (0 – 10)
Psychological Resiliencebp
Mean Score (SD) 2.62 (1.59) 1.87 (1.62) <.0001
Median Score (Range) 2.23 (0 – 5.57) 1.1 (0 – 5.57)
Social Resiliencebp
Mean Score (SD) 6.43 (1.77) 5.92 (1.94) <.0001
Median Score (Range) 6.53 (0 – 9.17) 6.10 (0 – 9.17)
Total Resiliencebp
Mean Score (SD) 6.57 (2.45) 5.08 (2.59) <.0001
Median Score (Range) 6.67 (0 – 10) 5.53 (0 – 10)
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Whitmoreetal. BMC Geriatrics (2022) 22:112
this difference in emphasis and perceptions on multimor-
bidity between providers and individuals may contrib-
ute to the presence of the well-being paradox, not some
innate difference in the older adults themselves. From a
practice, research, and policy perspective, these findings
support the growing shift toward person-centred care
that emphasizes the importance of assessing individual
perceptions of health.
Implications
Except for female sex, all the factors that predict high
SR health are potentially modifiable. is includes the
level of multimorbidity, Life Space Index score, and
functional and psychological resilience. While the level
of multimorbidity itself may not be modifiable, aspects
of care, such as improved access to treatment, manage-
ment of symptom or disease burden, and prevention
of secondary disease can be achieved. is includes
interventions and research that aim to address the
social determinants of health [44], programs that tackle
common risk factors such as alcohol or tobacco use,
physical inactivity, and poor mental health [45], and
approaches to enhance self-management capacity [46].
Additionally, Life Space Index as well as functional and
psychological resilience are potentially modifiable. For
example, the Life Space Index, a measure of community
mobility, is related to modifiable factors such as social
support and walking speed [47]. Previous research
has demonstrated links between social support, walk-
ing speed, and important health outcomes, including
known associations with walking speed and risk for
falls and hospitalization [48, 49]. Similarly, functional
resilience, captured as a composite score of physical
and functional measures (including walking speed), and
psychological resilience, comprised of depression, dis-
tress, and life satisfaction scales, are all factors that can
be targeted and modified [5052]. Building on the well-
documented links between higher self-reported health
and positive health outcomes for older adults [10],
identification of these five key drivers has the potential
to inform the development of clinical interventions that
target these modifiable factors.
Table 5 Logistic regression model for presence of well‑being paradox – factors and effect
Notes: CI 95%, two-sided alpha, rounded to 2 decimal places
a Where d notes small eect size
Comprehensive Dataset: Older Adults with4 Chronic Conditions, Complete Cases Only (n= 4837)
Well‑Being Paradox (n= 2074); Not Well‑Being Paradox (n= 2763)
Factors Notes Pr > |z| (OR) Point Estimate
[Condence Interval] d
Intercept < 2e‑16 0.01 [0.00‑0.03] 2.5131
Number of Chronic Conditions (4 or more) Continuous variable 7.30e‑15 0.83 [0.79‑0.87] 0.1008
Age Continuous variable 2.07e‑09 1.04 [1.02‑1.05] 0.0194
SexaCategorical variable
Male (2) vs. Female (1) 1.26e‑14 0.59 [0.53‑0.68] 0.2819
Level of EducationaCategorical variable
Degree/Diploma (2) vs.
High School (1)
0.00999 1.19 [1.04‑1.38] 0.1
> Degree/Diploma (3) vs.
High School (1) 1.20e‑05 1.50 [1.25‑1.79] 0.2237
Household Income Categorical variable
$20,000 ‑ 49,999 (2) vs. <$20,000 (1) 0.15808 1.20 [0.93‑1.55] 0.1014
$50,000 – 99,999 (3) vs. <$20,000 (1) 0.07632 1.27 [0.97‑1.66] 0.1319
$100,000 – 149,999 (4) vs. <$20,000 (1) 0.00747 1.52 [1.11‑2.07] 0.1912
$150,000 (5) vs. <$20,000 (1) 0.34790 1.19 [0.83‑1.71] 0.0954
Marital Status Categorical variable
Married/Common‑law (2) vs. Single/always
lived alone (1)
0.11535 0.80 [0.61‑1.06] 0.1219
Widowed/Divorced/Separated (3) vs. Single/
always lived alone (1) 0.28599 0.86 [0.65‑1.14] 0.0833
Life Space Index Score Continuous variable 6.13e‑11 1.01 [1.01‑1.02] 0.0067
Functional Resilience Score Continuous variable < 2e‑16 1.16 [1.13‑1.19] 0.0819
Social Resilience Score Continuous variable 0.05424 1.04 [0.99‑1.07] 0.0197
Psychological Resilience Score Continuous variable < 2e‑16 1.24 [1.19‑1.29] 0.1187
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Whitmoreetal. BMC Geriatrics (2022) 22:112
Strengths andlimitations
A key strength of this research involves the use of
a large, population-based sample. is provided
an opportunity to closely examine the relationship
between sociodemographic, health-related, and resil-
ience factors. However, the use of this dataset also
contributed to some notable limitations that should
be considered when interpreting findings. First, this
research was a cross-sectional analysis of baseline data
from the CLSA. As such, results cannot be interpreted
as causal, nor can temporality of the factors or direc-
tionality of associations be captured. Second, while
CLSA datasets are large and aim to be representative,
there are limitations regarding certain demographic
factors. For example, representation of race, for exam-
ple, or the exclusion of certain population groups (e.g.,
veterans, individuals living in Canadian territories).
is limits the application of these findings to broader
populations as the sample for these analyses was pre-
dominantly white, English-speaking, urban-dwelling,
and middle-income. ird, while level of multimor-
bidity is a widely used approach to measuring disease
burden, the way that chronic conditions are captured
in CLSA means that an individual may have a diagno-
sis of a specific condition (e.g., arthritis), however, that
condition may not be causing any challenge or discom-
fort while for another person, that same condition may
be very challenging or burdensome. By using a level of
multimorbidity, as opposed to using a disease burden
scale (e.g., Disability Adjusted Life Years [53]), there
are limitations on the application of these findings.
Table 6 Goodness‑of‑fit diagnostics for logistic regression models
Diagnostic Test of Model Fit Higher Self‑Reported Health
among all Older Adults
(n= 11,464)
Higher Self‑Reported Health among
Subset with Well‑Being Paradox
(n= 4837)
Cragg Uhler’s R20.25 0.18
Wald Test (p)
Number of Chronic Conditions < 2.22e‑16 8.8692e‑15
Age 1.201e‑7 2.2227e‑9
Sex < 2.22e‑16 1.5198e‑14
Level of Education 6.6387e‑9 5.6963e‑5
Household Income 0.077106 0.08751
Marital Status 0.20682 0.24972
Life Space Index Score 9.9874e‑16 6.763e‑11
Functional Resilience Score < 2.22e‑16 < 2.22e‑16
Social Resilience Score 0.00018238 0.054303
Psychological Resilience Score < 2.22e‑16 < 2.22e ‑16
Variable Importance (t)
Number of Chronic Conditions 25.18 7.78
Age 5.29 5.99
Sex 10.64 7.71
Level of Education
Degree/Diploma (2) vs. High School (1) 2.99 2.58
> Degree/Diploma (3) vs. High School (1) 6.14 4.38
Household Income
$20,000 ‑ 49,999 (2) vs. <$20,000 (1) 2.05 1.41
$50,000 – 99,999 (3) vs. <$20,000 (1) 2.33 1.77
$100,000 – 149,999 (4) vs. <$20,000 (1) 2.88 2.67
$150,000 (5) vs. <$20,000 (1) 1.79 0.94
Marital Status
Married/Common‑law (2) vs. Single/always lived alone (1) 1.35 1.57
Widowed/Divorced/Separated (3) vs. Single/always lived alone (1) 0.55 1.07
Life Space Index Score 8.04 6.54
Functional Resilience Score 14.17 10.31
Social Resilience Score 3.74 1.92
Psychological Resilience Score 16.46 10.43
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Page 14 of 16
Whitmoreetal. BMC Geriatrics (2022) 22:112
is is particularly true for those conditions which may
relapse, remit, or carry a significant burden of illness
such as stroke or chronic obstructive pulmonary dis-
ease. Fourth, there are limitations associated with the
design of the CLSA. is includes limitations related to
how the data is collected, as well as the duration of the
study. It is likely that those who are willing to partici-
pate in a study lasting up to 20 years, particularly such
a comprehensive study, may be different than those in
the general population. As well, these analyses domi-
nantly drew from the comprehensive dataset, meaning
that individuals living in more rural communities out-
side of the data collection catchment areas, would not
be included. Lastly, due to the large sample size, find-
ings should be interpreted with emphasis on the effect
of the relationship (e.g., Cohen’s classification crite-
ria) and the general weakness of the models generated
instead of solely the statistical significance reported.
Conclusion
Self-reported health is one of the most commonly used
outcome measures in epidemiology, health research,
and clinical practice [54]. Findings from this study have
highlighted that while many factors are associated with
self-reported health, these factors do not seem to influ-
ence the relationship between multimorbidity and self-
reported health. Findings have additionally identified
the factors that predict high self-reported health are the
same for the general population of older adults and a sub-
set of this population with high multimorbidity. Further,
this study has identified that of these five key factors, four
of them are potentially modifiable including the level of
multimorbidity, the Life Space Index score, and the func-
tional and psychological resilience scores. Findings from
this work have generated several additional research
opportunities. is a need to leverage longitudinal stud-
ies using data from the CLSA to explore causal relation-
ships (e.g., further examination of the temporality of
factors), to repeat these analyses in differing populations
(e.g., more diverse sample, a sample that includes more
rural and remote participants), as well as to compare
these findings to those who have the opposite of the well-
being paradox (i.e., those with few or no chronic condi-
tions and lower self-reported health). In addition, future
qualitative research is warranted to explore how these
key factors predict high self-reported health among com-
munity-dwelling older adults. Moving beyond an explor-
atory understanding of self-reported health and the
well-being paradox, our findings have advanced under-
standing of the factors that predict high self-reported
health among community-dwelling older adults.
Abbreviations
ADL: Activities of daily living; ANOVA: Analysis of variance; CES‑D‑10: Centre for
Epidemiologic Studies Depression Scale 10‑item; CI: Confidence interval (95%,
two‑sided alpha); CLSA: Canadian Longitudinal Study on Aging; IADL: Instru‑
mental activities of daily living; MOS: Medical Outcomes Study; OARs: Older
Americans Resources and Services; OR: Odds Ratio; SD: Standard Deviation.
Supplementary Information
The online version contains supplementary material available at https:// doi.
org/ 10. 1186/ s12877‑ 022‑ 02807‑z.
Additional le1. Interaction effect figures.
Acknowledgements
This research was made possible using the data collected by the Canadian
Longitudinal Study on Aging (CLSA). Funding for the Canadian Longitudinal
Study on Aging (CLSA) is provided by the Government of Canada through
the Canadian Institutes of Health Research (CIHR) under grant reference: LSA
94473 and the Canada Foundation for Innovation as well as the following
provinces: Newfoundland, Nova Scotia, Quebec, Ontario, Manitoba, Alberta,
and British Columbia. This research has been conducted using the CLSA data‑
set [Baseline Comprehensive Dataset version 4.0, Follow‑up 1 Comprehensive
Dataset version 1.0], under Application Number 190241. The CLSA is led by
Drs. Parminder Raina, Christina Wolfson, and Susan Kirkland.
Disclaimer
The opinions expressed in this manuscript are the authors’ own and do not
reflect the views of the Canadian Longitudinal Study on Aging.
Authors’ contributions
CW, MMR, KF, CM, and JP conceptualized the initial study with LG, SP, and
AW contributing further design considerations. All authors contributed to
the data analysis plan. CW and KF implemented the analyses and all authors
contributed to the interpretation of the results. CW, MMR, and KF contributed
to the first draft of the manuscript. All authors reviewed and approved the
manuscript. CW is the lead and corresponding author on this publication;
MMR is the senior author.
Funding
This study was funded through a Canadian Institutes of Health Research
Catalyst Grant (funding reference #170309). CW is grateful for the funding sup‑
port awarded through CIHR and the Vanier Canada Graduate Scholarship. This
research was undertaken, in part, thanks to funding from MMR’s Tier 2 Canada
Research Chair.
Availability of data and materials
The data that support the findings of this study are available through the
Canadian Longitudinal Study on Aging (CLSA) (www. clsa‑ elcv. ca) for research‑
ers who meet the criteria for access to de‑identified data.
Declarations
Ethics approval and consent to participate
The CLSA has been reviewed and approved by local research ethics boards.
All participants provided informed written consent and study procedures
were performed in accordance with the World Medical Declaration of Helsinki
ethical principles for medical research. Institutional ethics approval was addi‑
tionally granted for these specific analyses through the Hamilton Integrated
Research Ethics Board (#8337 V4).
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
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Whitmoreetal. BMC Geriatrics (2022) 22:112
Author details
1 School of Nursing, McMaster University, 1280 Main Street W, Hamilton,
Ontario L8S 4K1, Canada. 2 School of Public Health Sciences, University
of Waterloo & Schlegel‑University of Waterloo Research Institute for Aging, 200
University Ave W, Waterloo, Ontario N2L 3G1, Canada. 3 Department of Health
Research Methods, Evidence, and Impact, McMaster University, 1280 Main
Street W, Hamilton, Ontario L8S 4K1, Canada. 4 School of Medicine, Queen’s
University, 220 Bagot St, Kingston, Ontario K7L 5E9, Canada. 5 Department
of Gerontology, Simon Fraser University, 515 W Hastings St, Vancouver, British
Columbia V6B 5K3, Canada.
Received: 24 November 2021 Accepted: 2 February 2022
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Self-reported health is a common measure predictive of morbidity and mortality among adults. Many factors are known to be associated with self-reported health including the number of chronic conditions (i.e., multimorbidity). While the association between self-reported health and morbidity and mortality has been well-established, the factors that shape the relationship with self-reported health (e.g., modify and mediate) are poorly understood. Further, it is unknown why some older adults, despite having high numbers of chronic conditions, continue to rate their health positively. This is known as the well-being paradox. This mixed methods research study was designed to address these knowledge gaps. The objectives of the proposed research are to (1) determine what factors shape the relationship between multimorbidity and self-reported health and how they do so; (2) describe the ways that older adults define and perceive their individual health; and (3) explain the well-being paradox. Informed by a multimorbidity resilience framework, the quantitative component of research will analyze Canadian Longitudinal Study on Aging data while the qualitative component will collect and analyze interview data from 12 to 20 community-dwelling older adults using a case study design. Findings from this study have the potential to inform and advance future health intervention programs or services aimed at improving health-related quality of life for community-dwelling older adults.
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