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Long-Term Conditions in Older People are Linked with Loneliness, but a Sense of Coherence Buffers the Adverse Effects on Quality of Life: A Cross-Sectional Study

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Background: The impact of disability, long-term conditions, rurality, living alone, and being a carer on health has some evidence base, but the extent to which a strong sense of coherence (SoC), a factor hypothesised to promote wellbeing, may moderate these associations is unknown. A model of physical, environmental and social factors on quality of life was tested, with particular emphasis on whether a strong SoC buffered (mitigated) these determinants of quality of life. Material and methods: A cross-sectional postal survey was undertaken of a random sample of 1471 respondents aged over 65 years, across a population of rural individuals. Physical, environmental, and psychological variables were assessed against quality of life using ANOVA and a generalised linear model including the interaction effects of SoC. Results: ANOVA demonstrated that age, gender, long-term conditions or disability (LTC-D), living alone, >20 hours unpaid care for others per week, SoC, and loneliness, were associated with lower quality of life (p<0.01). There were strong correlations (p>0.01), between age and LTC-D, living alone, and poor SoC. Living alone was correlated with emotional and social loneliness; but those with higher SoC were less likely to experience loneliness. In an adjusted generalised linear model, significant associations with a lower quality of life were observed from: LTC-D, emotional loneliness and social loneliness (B= -0.44, -0.30, and -0.39, respectively, all p<0.001). The only interaction with SoC that was statistically significant (at p<0.05) was LTC-D. A stronger sense of coherence buffered the negative effects of long-term condition/disability on quality of life. Discussion: The physical, environmental and social factors examined, identified LTC-D and loneliness to be the strongest factors associated with poor quality of life. Conclusion: SoC somewhat buffered the adverse effect of LTC-D on quality of life, but did not do so for loneliness.
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ORIGINAL RESEARCH
Long-Term Conditions in Older People are Linked
with Loneliness, but a Sense of Coherence Buffers
the Adverse Effects on Quality of Life: A
Cross-Sectional Study
Hugo C van Woerden
1,2
Neil Angus
2
Vasiliki Kiparoglou
3
Iain Atherton
4
Janni Leung
5
1
University of the Highlands and Islands,
Centre for Health Science, Inverness, IV2
3JH, UK;
2
Ulster University, Coleraine,
County Londonderry, BT52 1SA,
Northern Ireland, UK;
3
National Institute
of Health Research Oxford Biomedical
Research Centre, Unipart House
Business Centre, Oxford, OX4 2PG, UK;
4
Edinburgh Napier University, Edinburgh,
EH11 4BN, UK;
5
University of
Queensland, St Lucia, QLD, 4067,
Australia
Background: The impact of disability, long-term conditions, rurality, living alone, and
being a carer on health has some evidence base, but the extent to which a strong sense of
coherence (SoC), a factor hypothesised to promote wellbeing, may moderate these associa-
tions is unknown. A model of physical, environmental and social factors on quality of life
was tested, with particular emphasis on whether a strong SoC buffered (mitigated) these
determinants of quality of life.
Material and Methods: A cross-sectional postal survey was undertaken of a random
sample of 1471 respondents aged over 65 years, across a population of rural individuals.
Physical, environmental, and psychological variables were assessed against quality of life
using ANOVA and a generalised linear model including the interaction effects of SoC.
Results: ANOVA demonstrated that age, gender, long-term conditions or disability (LTC-
D), living alone, >20 hours unpaid care for others per week, SoC, and loneliness, were
associated with lower quality of life (p<0.01). There were strong correlations (p>0.01),
between age and LTC-D, living alone, and poor SoC. Living alone was correlated with
emotional and social loneliness; but those with higher SoC were less likely to experience
loneliness. In an adjusted generalised linear model, signicant associations with a lower
quality of life were observed from: LTC-D, emotional loneliness and social loneliness (B=
−0.44, −0.30, and −0.39, respectively, all p<0.001). The only interaction with SoC that was
statistically signicant (at p<0.05) was LTC-D. A stronger sense of coherence buffered the
negative effects of long-term condition/disability on quality of life.
Discussion: The physical, environmental and social factors examined, identied LTC-D and
loneliness to be the strongest factors associated with poor quality of life.
Conclusion: SoC somewhat buffered the adverse effect of LTC-D on quality of life, but did
not do so for loneliness.
Keywords: loneliness, social loneliness, disability, rurality, quality of life
Introduction
A signicant proportion of older people are affected by a long-term condition/
disability. This issue is of signicant policy interest, as there is increasing recogni-
tion that there is a need to understand the different factors that contribute to quality
of life in this population. Loneliness is an important potential factor in this context,
1
but quality of life in older populations is affect by a wide range of other factors
Correspondence: Hugo C van Woerden
Email Hugo.VanWoerden@uhi.ac.uk
Journal of Multidisciplinary Healthcare 2021:14 2467–2475 2467
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including social isolation,
2
disability,
3
long-term
conditions,
4
living alone, or being a carer.
5
Rurality is
another factor that may be associated with poorer health
outcomes,
6
but the picture is complicated, as there is also
evidence that rural communities can provide each other
with greater support.
7
Relatively little research has been
undertaken to understand the interactions between these
factors, and they have therefore been incorporated in this
study.
Measuring “quality of life” as an outcome is complex,
and it should be acknowledged that simplistic assumptions
that a long-term condition/disability inevitably leads to
a poor quality of life is incorrect.
8
Although quality of
life is sometimes restricted to health-related quality of
life,
9
we have used the term in a much wider sense
based on a single validated question.
10,11
This may be
because the components that constitute quality of life
reect changing life goals, and an inherent capacity to
adjust to loss during the life course. Quality of life in the
presence of a long-term condition/disability may also be
inuenced by factors such as a sense of meaning, purpose,
and a sense of being valued, which are incorporated in the
model underpinning salutogenesis.
12
Salutogenesis in
effect refers to something that generates health and well-
being. The term was developed to describe the capacity to
cope in the face of adversity, which was studied by
Antonovsky, who investigated holocaust survivors after
the Second World War, and sought to understand the
characteristics that had been most signicant in those
who survived.
13
He characterised these factors as “saluto-
genic”, and emphasised the importance of a personal sense
of coherence.
A sense of coherence may be dened as,
The extent to which one has a pervasive, enduring though
dynamic, feeling of condence that one’s environment is
predictable and that things will work out as well as can
reasonably be expected.
14,15
Antonovsky suggested that sense of coherence is com-
posed of three factors: comprehensibility, manageability,
and meaningfulness. Expressed in greater detail,
comprehensibility is the extent to which events are perceived
as making logical sense, that they are ordered, consistent, and
structured. Manageability is the extent to which a person feels
they can cope. Meaningfulness is how much one feels that life
makes sense, and challenges are worthy of commitment.
16
There are a wide range of concepts that overlap with sense
of coherence including mastery, resilience and hardiness.
17
However, the concept of a sense of coherence has stood
the test of time and has therefore been used in this paper.
Loneliness is most often assessed using the De Jong-
Gierveld loneliness scale, which has six items and can be
split into two three-item scales covering emotional lone-
liness and social loneliness.
21
The distinction is important
in certain contexts, as someone can be socially lonely, with
few friends or family but not feel emotionally lonely. The
reverse is also possible.
The geographical context for this study, NHS
Highland, is very rural. NHS has a low population density,
covering 41% of the land mass of Scotland, but with only
a population of 320,000. There is one small city, a number
of market towns, many small towns and villages, and 26
inhabited islands. The effect of rurality on the interplay
between different factors affecting quality of life was
therefore of interest to this study.
The impact of disability, long-term conditions, rurality,
living alone, and being a carer on health has some evi-
dence base, however, the extent to which these factors
might be buffered by a strong SoC is unknown. To explore
this, we hypothesised a model of physical, environmental
and social factors, and sought to examine whether SOC
buffers any of these factors, in terms of their impact on
quality of life, in the context of older people (65+) in
a rural Scottish Health Board.
Methods
Study Design
A cross-sectional survey was undertaken of a random
sample of 3000 households across a dened area of the
north of Scotland (NHS Highland). The survey sample
frame was drawn from the set of all patients registered
with GP practices within the health board, where there was
known to be at least one individual in the household over
65 years. The sample size was designed to be adequate to
identify differences in characteristics in relevant sub-
groups, based on a power analysis undertaken by an epi-
demiologist, based on a minimum 25% response rate to
achieve a minimum sample size of 750 respondents with
consent and valid data. A questionnaire was drawn up
which included the De Jong-Gierveld loneliness scale,
21
the three-item sense of coherence scale,
20,23
demographic
data, and a single item quality of life question, “How is
your quality of life?” with responses, excellent, good, fair,
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poor, very poor.
24
The three items in the sense of coher-
ence scale are: “Do you usually see a solution to problems
and difculties that other people nd hopeless?”; “Do you
usually feel that the things that happen to you in your daily
life are hard to understand?”; and “Do you usually feel that
your daily life is a source of personal satisfaction?” with
response choices of “yes usually, yes sometimes, no”.
Population surveys need to be kept short to improve
completion rates. It is therefore welcome that the original
29-item sense of coherence scale was later reduced to 13
items and has more recently been reduced to a three item
scale.
19,20
The three item sense of coherence scale has
consequently been used in this study. Measuring rurality
in Scotland is generally undertaken using an eight, six or
three category index. For the purposes of this study, the
index was collapsed into three categories, which is stan-
dard practice for this index.
22
The survey was posted with a reply envelope. A single
reminder was also sent. We asked that the questionnaire be
completed by or on behalf of the oldest member of the
household.
Participants
Participants had a mean age of 74.4 (SD=7.01). From the
3000 surveys issued, 1547 were returned, a response rate
of 51.6%. Sixty-ve responses were excluded due to con-
sent for research purposes being withheld, and a further 11
were excluded due to missing data on the quality of life
question (our key outcome measure). The nal sample size
for this study was N = 1471 (see ow chart in Figure 1).
Variables
Physical variables examined included age (10-year age
intervals), gender, and long-term condition/disability (yes
or no). Environmental variables examined included rural-
ity (“other urban areas”; “small towns, accessible rural, or
remote rural”; “very remote rural”), living alone, and
being a carer for others (“no or <20 hours”; “yes 20
hours+”). The majority (96%) of participants in the “no
or <20 hours” belonged in the “no care” category. Among
those who provided some but <20 hours of care, the
sample size was small, with the majority providing only
a small number of hours of care, and their prole was
more similar to those in the “no care” category, therefore
they were combined into one group for analytical pur-
poses. Psychological variables examined included sense
of coherence (classed as “weak” (scores 6–9), “intermedi-
ate” (scores 4–5) and “strong” (score 3 or less), and three
loneliness scales - “emotional loneliness”, “social loneli-
ness” and “overall loneliness” (in each loneliness scale
scores >1 SD above the mean were categorised as
“high”). Quality of life (scored 1–5, from very poor to
excellent) was used as the outcome variable.
A range of studies have demonstrated that sense of
coherence can have main, moderating, and mediating
effects on health and quality of life,
18
and this project
therefore sought to assess any buffering effect that
a sense of coherence might have on associations with
quality of life, in the context of a population survey.
Statistical Analysis
Descriptive statistics analysis was undertaken and the pat-
tern of missing data in participants was examined. Missing
data were below 5% for the physical and environmental
factors, and sense of coherence (Table S1 in the supple-
mentary le). However, there was a moderately high level
of missing data for the loneliness scales, ranging from
22.9% - 29.8%, due to the absence of at least one answer
to the six relevant answers making up the three scales for
“social loneliness” (3 questions), “emotional loneliness” (3
questions) and “overall loneliness” (combined 6
questions).
To address potential bias from missing data across the
loneliness scales, missing data analyses were conducted.
Missing data in the loneliness scales were associated with
Figure 1 Flowchart of participants.
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older age (p<0.001), female gender (p=0.012), disability
(p<0.001), living alone (p=0.001), and lower quality of life
(p=0.006). Multiple imputations to handle missing data
was the best option because we did not have very high
levels of missing data (<40%) and we had identied cor-
relates of the missing data. We also compared levels of
loneliness in the baseline data set, against the data set
including imputed values, where multiple imputation was
used to impute missing values drawing on all the variables
of interest in our a priori dened model. In conrmation of
our assumption, levels of loneliness were lower in the
baseline data set than in the imputed data set. Based on
this nding, multiple imputation was used for the analysis
presented in this paper.
Bivariate associations between “quality of life” and
physical, environmental, and psychological variables
were examined by conducting ANOVAs for categorical
variables. A correlation matrix was used to examine colli-
nearity among physical, environmental, and psychological
variables.
Our level of missing data for the loneliness scale
was higher than that of our other measures but were
lower than the upper threshold of >40% where multiple
imputation would be inappropriate. In our case where
we had 22.9–29.8% of missing data on loneliness, <5%
of missing data on other variables, and that we were
able to identify correlates of our missing data, we
conclude that multiple imputation was the best
option.
25
The main analysis for this paper was a generalised
linear model with the physical, environmental, and psy-
chological variables as the exposure variables, and “qual-
ity of life” as the outcome. The model included testing for
interaction effects between physical, environmental, and
psychological exposure variables and “sense of coherence”
in relation to the outcome of “quality of life”. The analysis
was undertaken in SPSS 25.
Results
Descriptive Statistics and Characteristics
of Sample
There was a spread of age across participants, with
about half in the 70–79 age group, and the sex distribu-
tion was fairly equal (Males 48.4%) (see Table 1. Over
half had a long-term condition/disability. Proportions of
participants by rurality of residence were: 21.5% living
in “other urban areas”; 42.4% in “small towns,
accessible rural, and remote rural”; and 36% in “very
remote rural areas”. Seventy percent of the participants
were not living alone, and 6.6% of participants provided
20 hours or more per week of unpaid care for others.
Overall, participants reported moderate levels of sense
of coherence, and moderately high levels of quality of
life. Approximately 20% reported high levels of
loneliness.
Bivariate Associations with Quality of Life
Lower levels of quality of life were signicantly asso-
ciated with older age, female gender, having a long-
term condition/disability, living alone, providing
unpaid care for others for 20 hours or more per week,
low sense of coherence, and high levels of loneliness
(Table 2). Quality of life did not differ by level of
rurality.
Table 1 Descriptive Statistics of Participants’ Physical,
Environmental, and Psychological Characteristics
Physical %
Age groups
60–69 29.7
70–79 47.3
80+ 23.0
Male 48.4
Long-term condition or disability 61.4
Environmental
Rurality
Other urban areas 21.5
Small towns, accessible rural, and remote rural 42.4
Very remote rural 36.0
Living alone 30.1
Unpaid care for others 20+ hrs per week 6.6
Psychological
Sense of coherence
Weak 20.5
Intermediate 46.8
Strong 32.7
High levels of loneliness (1 SD or more above the
mean)
Overall 21.8
Emotional 17.2
Social 20.9
Quality of life; Mean [SD] 3.97 [0.77]
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Inter-Correlation Between Exposure
Variables
Inter-correlation between exposures were moderate for the
loneliness scales, and very low to moderately low for all
other variables (see Table 3). Sense of coherence was
negatively associated with age, long-term condition/dis-
ability, unpaid care for others of 20+ hours per week,
and loneliness, but positively associated with male gender
and not living alone. Overall loneliness was more strongly
associated with emotional loneliness subscale than social
loneliness subscale of the De Jong-Gierveld loneliness
scale, while the correlation between emotional and social
loneliness was weak and not statistically signicant, allow-
ing both types of loneliness to be entered into
a generalised linear model as separate constructs.
Main and Interaction Effects on Quality of
Life
In the fully adjusted model, signicant main effects indi-
cated that those who were female, had a long-term condi-
tion/disability, and had higher emotional or social
loneliness, were associated with a lower quality of life
(see Table 4).
Discussion
Some aspects of our a priori model were conrmed by our
statistical analysis, whilst others were not. Model variables
including age, gender, long-term condition/disability, liv-
ing alone, providing more than 20 hours unpaid care for
others per week, sense of coherence, overall loneliness,
social loneliness and emotional loneliness, were strongly
associated with quality of life, but perhaps surprisingly
level of rurality was not. There are challenges in measur-
ing rurality, as any approach inevitably averages house-
holds over a given geographical area and there may be
subtle effects that have been overlooked by our current
categorization into three levels of rurality.
26
An interesting cluster of relationships identied in this
study was the relationship between “long-term condition/
disability” with: age; living alone; overall loneliness; emo-
tional loneliness; social loneliness; and a low sense of
coherence. This is an extensive list of negative attributes,
Table 2 Bivariate Associations of Physical, Environmental, and
Psychological Variables with Quality of Life
Characteristics Quality of
Life
Anova
Mean SD F p
Age group
60–69 4.12 0.72 22.67 <0.001
70–79 3.98 0.74
80+ 3.75 0.83
Gender
Female 3.90 0.77 8.58 <0.001
Male 4.05 0.75
Long-term condition/disability
No 4.26 0.74 143.35 <0.001
Yes 3.79 0.70
Rurality
Other urban areas 3.98 0.79 0.44 0.668
Small towns, accessible rural,
and remote rural
3.98 0.76
Very remote rural 3.95 0.76
Living alone
Yes 3.78 0.77 37.56 <0.001
No 4.05 0.78
Unpaid care for others per
week
0–<20 hrs 3.99 0.76 16.79 <0.001
20 hrs+ 3.66 0.77
Sense of coherence
Weak 3.31 0.79 246.41 <0.001
Intermediate 3.96 0.65
Strong 4.40 0.59
Overall loneliness
Low 4.08 0.75 175.11 <0.001
High (1 SD or more above
the mean)
3.48 0.74
Emotional loneliness
Low 4.09 0.75 212.97 <0.001
High (1 SD or more above
the mean)
3.36 0.73
Social loneliness
Low 4.10 0.76 195.60 <0.001
High (1 SD or more above
the mean)
3.46 0.74
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describing the huge challenges faced by an aging popula-
tion who have a long-term condition/disability. Similarly,
in our generalized linear model, a contributor to lower
quality of life was long-term condition/disability.
Those with a high sense of coherence were less likely
to have emotional or social loneliness. This is perhaps
unsurprising, as salutogenesis proposes that “general resis-
tance resources” underpin a sense of coherence. Resistance
resources include interpersonal-relational skills, which
would be expected to lead to more extensive social net-
works and hence lower levels of loneliness.
27
The response rate, at 51.6%, was reasonable for
a survey of this nature, but still leaves the possibility of
bias in relation to those who did not respond.
A comparison of the characteristics of the survey
population, the responding population, and the underlying
sample frame, is provided in the Supplementary Material
(Table S2 in the supplementary le), and provides some
evidence that the respondents were representative of the
population.
We note as a limitation that the six questions on lone-
liness were variably completed, perhaps because some
participants appear to have thought that only some of the
set of six loneliness questions needed to be answered. This
misunderstanding may have arisen because each of the six
loneliness questions did not have a specic question num-
ber assigned within the questionnaire, they were all clus-
tered under one question number. There is potential
learning for the way in which questions in
a questionnaire are numbered that could be drawn from
Table 3 Correlation Matrix of Physical, Environmental, and Psychological Exposure Variables (P<0.05 in Blue and P<0.01 in Red)
Age Male
Gender
Long-
Term
Condition/
Disability
Rurality Not
Living
Alone
Unpaid
Care for
Others
20+ hrs
per
Week
Overall
Loneliness
Emotional
Loneliness
Social
Loneliness
Sense of
Coherence
Age 1.00
Male
gender
0.04 1.00
Long-term
condition/
disability
0.16 0.06 1.00
Rurality 0.01 0.02 0.02 1.00
Not living
alone
0.16 0.21 0.08 0.10 1.00
Unpaid
care for
others 20
+ hrs per
week
0.01 0.04 0.01 0.02 0.14 1.00
Overall
loneliness
0.04 0.10 0.12 0.01 0.17 0.06 1.00
Emotional
loneliness
0.07 0.12 0.13 0.03 0.21 0.02 0.67 1.00
Social
loneliness
0.01 0.00 0.08 0.01 0.12 0.09 0.48 0.24 1.00
Sense of
coherence
0.11 0.09 0.15 0.01 0.12 0.07 0.25 0.34 0.28 1.00
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this study. As a result of missing data, some interpolation
was undertaken, which should be noted as having some
risk of bias associated with it, although our investigation
of the issue indicates that it would have diluted any nd-
ings, and the risk of a Type 1 error, identifying an associa-
tion that is not present, therefore appears to be relatively
low. It can be argued that failure to address missing data
would provide a greater risk of bias than the use of multi-
ple interpolation. We asked that the questionnaire be com-
pleted by or on behalf of the oldest member of the
household, but do not know what percentage were lled
in by proxies, which may have affected the results.
This study is novel in examining the relationship
between the presence of a long-term condition/disability
and loneliness in an older rural population. A comparably
study was identied, which examined the relationship
between loneliness and depressive symptoms in nursing
homes, and demonstrated a relationship with resilience and
social support, which is broadly consistent with the nd-
ings in this study.
28
Another study found the moderating
effects of the “subjective perception of how long an
individual expects to live”, which is a concept that over-
laps with salutogenesis.
29
A key question is what interventions can be put in place
to address the needs of those with long-term conditions/
disability in older populations, particularly where this is
associated with living alone, and the associated risk of
loneliness. Several reviews have identied a range of health
promotion activities that can make a difference.
30,31
One
study identied public sector savings of up to £300 per year
for individuals receiving befriending support. Similarly, in
selected groups, arts-based community activities have been
shown to signicantly reduce the need for acute hospital
care.
32
Health and social care systems have a growing chal-
lenge in supporting an elderly and lonely population who
have long-term conditions or disability, who have high
care needs. Population projections indicate that the num-
bers in this sector of the population will grow rapidly in
developed countries over the next two decades.
33
From
a policy perspective, there is a growing need to understand
interactions between different factors that affect the rising
Table 4 Generalized Linear Model on Quality of Life Testing the Main Effects of Physical, Environmental, and Psychological Variables,
and Their Interaction with Sense of Coherence
B SE Lower Upper p
Main effects
Age 0.08 0.05 0.17 0.01 0.075
Male gender (a) 0.14 0.06 0.01 0.27 0.030
Long-term condition/disability (b) 0.44 0.07 0.57 0.31 <0.001
Rurality 0.02 0.04 0.07 0.10 0.705
Not living alone 0.01 0.07 0.13 0.14 0.890
Unpaid care for others 20+ hrs
per week
0.14 0.11 0.36 0.08 0.218
Emotional loneliness (b) 0.30 0.08 0.45 0.14 <0.001
Social loneliness (b) 0.39 0.07 0.52 0.25 <0.001
Sense of coherence 0.40 0.27 0.12 0.92 0.134
Interaction with sense of
coherence
Age 0.01 0.03 0.08 0.06 0.775
Male gender 0.05 0.05 0.15 0.04 0.279
Long-term condition/disability (a) 0.10 0.05 0.01 0.20 0.037
Rurality 0.04 0.03 0.11 0.03 0.268
Not living alone 0.04 0.05 0.07 0.15 0.468
Unpaid care for others 20+ hrs
per week
0.08 0.09 0.27 0.11 0.396
Emotional loneliness 0.02 0.08 0.13 0.18 0.754
Social loneliness 0.04 0.06 0.08 0.16 0.523
Note: (a) refers to p<0.05; (b) refers to p<0.01. A signicant interaction was observed between “long-term condition/disability” and “sense of coherence” in relation to the
outcome of “quality of life”.
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numbers of older people in the population who have
a long-term condition/disability. This study has sought to
explore some of these factors in a rural context, and
identied some important associations and explain the
complex interaction between different factors, which in
part explains why some very elderly and disabled indivi-
duals thrive while others do not.
Data Sharing Statement
The data set is available from the corresponding author.
Ethics Approval and Informed
Consent
Ethical approval was obtained from South Central -
Oxford B Research Ethics Committee, reference 16/SC/
0356. Consent was obtained as part of the survey form.
Participants were informed about the purpose of the study.
The study was undertaken in accordance with the
Declaration of Helsinki.
Acknowledgments
The authors wish to thank staff in NHS Highland who
supported the initial data collection within NHS Highland
including: Sam Campbell, Barry Collard, Sharon Duncan,
Sarah Grifn, Frances Hines, Elspeth Lee, Alison
McGrory, Christine Robinson, Elisabeth Smart, Cathy
Steer, Sara Huc, and Ian Douglas.
Funding
This study was largely funded by the NHS Highland. The
National Institute of Health Research (NIHR) Oxford
Biomedical Research Centre (BRC) have contributed to
publication.
Disclosure
The authors have no competing interests to declare.
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