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Coastal proximity and mental health among urban adults in England: The moderating effect of household income

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After adjusting for covariates, self-reported general health in England is higher among populations living closer to the coast, and the association is strongest amongst more deprived groups. We explored whether similar findings were present for mental health using cross-sectional data for urban adults in the Health Survey for England (2008-2012, N ≥25,963). For urban adults, living ≤1 km from the coast, in comparison to >50 km, was associated with better mental health as measured by the GHQ12. Stratification by household income revealed this was only amongst the lowest-earning households, and extended to ≤5 km. Our findings support the contention that, for urban adults, coastal settings may help to reduce health inequalities in England.
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Health & Place
journal homepage: www.elsevier.com/locate/healthplace
Coastal proximity and mental health among urban adults in England: The
moderating eect of household income
Joanne K. Garrett
a,
, Theodore J. Clitherow
a
, Mathew P. White
a
, Benedict W. Wheeler
a
,
Lora E. Fleming
a
a
European Centre for Environment and Human Health, European Centre for Environment and Human Health, University of Exeter Medical School, Knowledge Spa, Royal
Cornwall Hospital Treliske, Truro, Cornwall, TR1 3HD, UK
ABSTRACT
After adjusting for covariates, self-reported general health in England is higher among populations living closer to the coast, and the association is strongest amongst
more deprived groups. We explored whether similar ndings were present for mental health using cross-sectional data for urban adults in the Health Survey for
England (20082012, N 25,963). For urban adults, living 1 km from the coast, in comparison to > 50 km, was associated with better mental health as measured
by the GHQ12. Stratication by household income revealed this was only amongst the lowest-earning households, and extended to 5 km. Our ndings support the
contention that, for urban adults, coastal settings may help to reduce health inequalities in England.
1. Introduction
1.1. Overview
Poor mental health is among the leading causes of disability
worldwide (World Health Organisation, 2018). In England, approxi-
mately one in six adults (17 %) surveyed were suering symptoms of a
common mental disorder (CMD), such as anxiety or depression
(McManus et al., 2016). However, there is mounting evidence that
exposure to natural environments is associated with various benets for
mental health and wellbeing (hereby referred to as mental health; see
reviews by Bratman et al., 2012;Frumkin et al., 2017;Hartig et al.,
2014;Trostrup et al., 2019). Much of this work reports positive asso-
ciations between green space and mental health, where measured or
tested exposures include neighbourhood vegetation, green exercise, and
residential proximity to green space (e.g. Barton and Pretty, 2010;
Beyer et al., 2014;Cox et al., 2017b;de Vries et al., 2013;Gascon et al.,
2015;McEachan et al., 2016). This may be particularly the case for
those in urban areas where nature exposures can be limited (Cox et al.,
2017a).
Concurrently, a smaller, yet growing, amount of research suggests
that blue spaces (aquatic environments such as coasts, rivers, and lakes)
are associated with a range of aspects related to improved mental
health. These include: enhanced general health and wellbeing (re-
viewed by Gascon et al., 2017; see also Wheeler et al., 2012;White
et al., 2013a;Völker et al., 2018;Volker and Kistemann, 2011;Wood
et al., 2016); increased physical activity levels (White et al., 2014);
improved psychological restoration (White et al., 2010;White et al.,
2013b); reduced psychological distress (Nutsford et al., 2016); and
lower mortality rates (Crouse et al., 2018). A range of blue space ex-
posures have been explored in these studies including area coverage,
presence/absence, visibility and perceived and objective proximity.
There is also evidence that socioeconomic status may act as an ef-
fect-modier, or moderator, of the nature-health relationship (see
Hartig et al., 2014;Markevych et al., 2017;Mitchell et al., 2015). For
example, several cross-sectional studies nd that the association be-
tween natural environments and mental health is stronger within more
deprived areas, or that health inequality gradients are lessened where
green/blue space is more available (e.g. Wheeler et al., 2012;Maas
et al., 2006;McEachan et al., 2016;Mitchell and Popham, 2008;
Mitchell et al., 2015;van den Berg et al., 2016;Ward Thompson et al.,
2012; however, see also Mitchell and Popham, 2007).
Again, however, most of this work has examined socioeconomic
deprivation as a moderator of health regarding various measures of
green space, with exposure to blue spaces receiving less empirical in-
vestigation (Markevych et al., 2017). Indeed, to the best of the authors
knowledge, only two studies have explicitly tested this relationship.
First, Wheeler et al. (2012) found that the relationship between living
closer to the coast in England and self-reported general health was
strongest amongst communities within areas of higher socioeconomic
deprivation. More recently, Crouse et al. (2018) examined the asso-
ciation between blue space and mortality in Canada, with results sug-
gesting a similar pattern of eect-modication but lacking statistical
power for some outcomes. A further study investigated the moderating
https://doi.org/10.1016/j.healthplace.2019.102200
Received 21 January 2019; Received in revised form 16 July 2019; Accepted 22 August 2019
Corresponding author. ECEHH, University of Exeter Medical School, Knowledge Spa, Royal Cornwall Hospital Treliske, Truro, Cornwall, TR1 3HD, UK.
E-mail address: j.k.garrett@exeter.ac (J.K. Garrett).
Health and Place xxx (xxxx) xxxx
1353-8292/ © 2019 Published by Elsevier Ltd.
Please cite this article as: Joanne K. Garrett, et al., Health and Place, https://doi.org/10.1016/j.healthplace.2019.102200
eect of educational attainment, one aspect of socioeconomic status, on
the relationship between both blue spaces and green spaces on various
mental and physical health outcomes. They found a signicant inter-
action between blue space and both health outcomes for those with the
lowest educational attainment (de Vries et al., 2016).
Thus, although there have been encouraging ndings, research ex-
amining the links between blue space and mental health remains lim-
ited (Gascon et al., 2017). Furthermore, despite growing health in-
equalities (Barr et al., 2015;Thomson et al., 2018), we currently have a
poor understanding of how this relationship might vary between dif-
ferent levels of socioeconomic deprivation (Mitchell et al., 2015).
1.2. The current research
The aim of the present research was to investigate: (1) the asso-
ciation between mental health, as measured using two dierent in-
dicators of common mental disorders (CMD), and the blue space ex-
posure of coastal proximity, as used in Wheeler et al. (2012) for urban
residents; and (2) variations in this association according to household
income. The study therefore aimed to directly build on work by
Wheeler et al. (2012) through focusing on self-reported mental health
(as opposed to general health) as the dependent variable and at
household level (instead of area level) deprivation in the form of
household income, as the moderating variable. We used the Health
Survey for England (HSE), a comprehensive, nationally representative
survey which includes various measures of health, health behaviours
and socio-demographics (Aresu et al, 2009,2010,2011;Boniface et al.,
2012;Bridges et al., 2013). Based on the literature previously in-
troduced, we hypothesised that: (a) CMD likelihood would decrease as
coastal proximity increased; and (b) this association would be stronger
amongst lower income households.
2. Methods
2.1. Sample
Secondary cross-sectional data were utilised from the HSE for
English adults for the years 20082012 (pooled; aged 16+; adults
N = 45,063). All inhabitants of selected households are eligible for
interview and full sampling details can be found in (Aresu et al, 2009,
2010,2011;Boniface et al., 2012;Bridges et al., 2013). Trained inter-
viewers ask respondents a set of core questions related to their health,
lifestyle, and background, with additional sections which vary each
year.
The GHQ12 was not included in 2011 (n adults 20082010 and
2012 = 36,453), or the EQ5D in 2009 (n adults 2008 and
20102012 = 40,418), reducing the available samples for our analyses.
Both the prevalence of mental health disorders and the relationships
between natural environments and health have been found to vary by
urbanity (see Alcock et al., 2015;Maas et al., 2006;Mitchell and
Popham, 2007;Peen et al., 2010;Wheeler et al., 2012;Wood et al.,
2016). Further, access to health services (Bauer et al., 2018;Chukwusa
et al., 2019) and characteristics of natural environments are very dif-
ferent between urban and rural areas. We therefore focused only on
urban residents, which are those individuals categorised by the trained
interviewers as living in an Urbansetting, as opposed to Rural or
isolated dwellingsor Town and Fringe(Bibby and Brindley, 2013).
Available urban adults sample sizes were 28,662 (GHQ12) and 31,906
(EQ5D). For respective analyses, we excluded those with missing re-
sponses for GHQ12 and the anxious/depression dimension of the EQ5D,
therefore the full samples were 26,099 and 28,885 for GHQ12 and
anxious/depression respectively.
The richness of the dataset enabled the inclusion of a range of po-
tential confounding factors which may also relate to mental health,
including: income, age, sex and the presence of limiting longstanding
illnesses. We also included the health risk factors smoking status and
body mass index (BMI), as these have received limited attention in
previous studies exploring environment-health relationships (Mitchell,
2013).
We categorised responses of Item not applicable,No answer/
refusedand Don't knowas missing and calculated the sample sizes.
Missing data categories were excluded where there were < 20 re-
spondents in a category (see Supplemental Table 2). This led to nal
analysis sample sizes of 25,963 (GHQ12) and 28,723 (EQ5D Anxiety/
depression).
2.2. Coastal proximity
Following previous approaches (Wheeler et al., 2012;White et al.,
2013a), coastal proximity was measured in terms of the Euclidean
distance (km) from the population density weighted centroid of re-
spondents' Lower-layer Super Output Area (LSOA, as at 2001 Census) to
the nearest coastline. There are approximately 32,500 LSOAs in Eng-
land, each with a mean area of 4 km
2
and containing an average po-
pulation of around 1500 (Wheeler et al., 2012). Following Wheeler
et al. (2012), we operationalised coastal proximity using ve cate-
gories: (1) 01 km; (2) > 15 km; (3) > 520 km; (4) > 2050 km;
(5) > 50 km. As with previous studies (Wheeler et al., 2012;White
et al., 2013a), we used > 50 km as the reference category to enable us
to test if the likelihood of having a CMD decreases with proximity to the
coast. This also allowed us to compare coastal(i.e. < 50 km; as used in
EU denitions e.g. dening coastal regions (Eurostat, 2018)) re-
spondents with inland(i.e. 50 km) respondents (White et al.,
2013b).
2.3. Self-reported mental health
Mental health was measured through two outcomes. The rst was
the 12-item version of the General Health Questionnaire (GHQ12;
Goldberg et al., 1997), available 20082010 and 2012, a self-reported
measure widely used by health practitioners and researchers to indicate
the likelihood or casenessof an individual having a high risk of a CMD.
Following established recommendations for the GHQ12, results were
dichotomised with scores of four or above widely considered predictive
of a high risk of common mental health disorders such as anxiety or
depression (Fryers et al., 2004;Katikireddi et al., 2012;Mann et al.,
2011;Semlyen et al., 2016). The two outcome categories for this
measure were therefore: high likelihood of a CMD (GHQ12 score 4);
and low likelihood of a CMD (GHQ12 score < 4).
The second outcome was the anxiety and depression dimension of
the EQ-5D-3L (hereafter referred to as the EQ5D; EuroQol Research
Foundation, 2018). The EQ5D is a standardised measure of health-re-
lated quality of life (EuroQol Research Foundation, 2018), in-
corporating ve dimensions, which has been used by practitioners and
researchers (EuroQol, 2018;Hulme et al., 2004;Park et al., 2011) and
utilised in studies exploring environmental characteristics (de Oliveira
et al., 2013;Kyttä et al., 2011). Although the intended use is as a
composite scale, here we use a single dimension anxiety and de-
pression. This dimension has been associated with anxiety and/or de-
pression measured using a diagnostic scale (Mini International Neu-
ropsychiatric Interview (Supina et al., 2007)), found to align with the
GHQ12 (Bohnke and Croudace, 2016) and used for the same purpose in
other studies (Semlyen et al., 2016). It should be noted that it was not
found to be responsive to changes in anxiety or depression for those
clinically diagnosed (Crick et al., 2018) and therefore not necessarily a
measure of clinical diagnoses of anxiety and depression. As with the
GHQ12, it is a self-completed scale. There are three possible response
options, with respondents reporting whether they are not anxious or
depressed; moderately anxious or depressed; or extremely anxious or
depressed at the time of completion. These responses were dichot-
omised into the categories Not anxious/depressedand At least mod-
erately anxious/depressed(moderate or extreme anxiety/depression)
J.K. Garrett, et al. Health and Place xxx (xxxx) xxxx
2
to account for the skewness in data and low sample sizes within the
extremely anxious depressed category (n = 648; 2 % of total).
2.4. Area level controls
In line with previous research (Wheeler et al., 2012;White et al.,
2017;Mitchell and Popham, 2007), we controlled for area level de-
privation (English Index of Multiple Deprivation, Noble et al., 2007), as
well as green space and freshwater coverage at LSOA level to explore
the unique eect of coastal proximity. The English Index of Multiple
Deprivation (IMD) consists of area measures of crime, employment,
education, and income and has been found to be related to mental
health (Bellis et al., 2012) and moderate the coastal-health relationship
(Wheeler et al., 2012). Percentage greenspace coverage was based on
the generalised land use database (GLUD; Department for Communities
and Local Government, 2007) for LSOAs and incorporated all area level
green spaces, not including private gardens. Percentage freshwater
coverage of the LSOA was derived from the CEH Land Cover Map 2007
(Morton et al., 2011).
2.5. Household level controls
We also included household and individual level covariates which
may also relate to mental health. Equivalised household income, which
takes into account the number of household members, was used to
assess household level deprivation (reference category = highest in-
come quintile). Household income has been found to be related to a
range of mental health disorders (Domenech-Abella et al., 2018;Kahn
et al., 2000;Sareen et al., 2011). The upper and lower bounds of each
quintile vary by year and are given in Supplemental Table 1. Car access
was also included at the household level (ref = access).
2.6. Individual level controls
Individual level controls were based on confounders of mental
health identied by similar research with large survey datasets
(Wheeler et al., 2012;White et al., 2013a;White et al., 2013b;Beyer
et al., 2014;Crouse et al., 2017;Stranges et al., 2014). These included:
sex (reference = female), age (reference = 1634 years old), highest
qualication level (reference = none/foreign/other), economic status
(reference = in employment/student), relationship status (re-
ference = single), year (reference = 2008), presence of limiting long-
standing illnesses (reference = no limiting longstanding illness), ci-
garette smoking status (reference = never smoked cigarettes at all), and
weight (body mass index; BMI; reference = normal weight).
2.7. Data linkage
Standard licence versions of HSE data only include large area geo-
graphical identiers to preserve anonymity. In order to allocate higher
resolution measures of coastal proximity, green space and freshwater,
these three variables at LSOA level were supplied by the authors to the
data providers (NatCen Social Research) and linked anonymously to
HSE data under agreement from the NHS Health and Social Care
Information Centre (now NHS Digital). To prevent identication of any
individual LSOA of residence, the three environmental variables were
constrained to relatively coarse categories; and LSOA and regional
identiers were removed from the linked data and returned to the au-
thors.
2.8. Analyses
Data were analysed using the surveypackage (version 3.34;
Lumley, 2018) in R Studio Version 3.4.2. Generalised linear models
(GLM) using a quasi-binomial error structure (appropriate when ana-
lysing complex survey data (Lumley, 2018)) and household clusters, to
account for multiple respondents within households and provide robust
standard errors, were used to identify correlations between coastal
proximity and mental health. We were not able to include clustering by
LSOA as this had been removed by the data providers for anonymity.
The data were weighted using the interview weights provided in the
dataset to account for selection, non-response and population biases
(Aresu et al, 2009,2010,2011;Boniface et al., 2012;Bridges et al.,
2013). We calculated the odds ratios (OR) and 95% condence intervals
(CI) of participants having either a high likelihood of a CMD
(GHQ12 4) or of reporting a status of at least moderately anxious/
depressed for this dimension of the EQ5D.
We present unadjusted models (nature exposures only) and fully-
adjusted models to examine how coastal proximity was associated with
mental health before and after adding the controls. A sensitivity ana-
lysis was also carried out with > 20 km as a reference category. We
then stratied our analysis by household income, whereby we analysed
the relationships between coastal proximity and mental health using
fully-adjusted (unweighted) GLMs for each household income quintile.
This enabled us to observe variations in the relationship between
coastal proximity and mental health by household income. We had an a
priori prediction that the eects would be strongest in the lowest in-
come quintiles, however, we also carry out analyses interacting coastal
proximity and household income.
3. Results
3.1. Full model/sample results
Table 1 presents descriptive statistics of the un-stratied mental
health models. In the GHQ12 model sample, the proportion of people
with a high risk of a common mental disorder (CMD) closely resembled
previous national averages (McManus et al., 2016), with approximately
15 % of participants reporting a high likelihood of suering from a
CMD. In comparison, CMD prevalence was slightly higher in the EQ5D
model, likely due to the dierent method of measurement, with ap-
proximately 22 % of respondents reporting at least moderate anxiety or
depression. CMD prevalence was also greater amongst more deprived
areas and lower earning households (Table 1). For the years 2008, 2010
and 2012 where both the GHQ12 and anxiety and depression were
present, the correlation was 0.50 (kendall's τ,p< 0.001).
Table 2 displays the unadjusted and adjusted odds ratios (OR) with
95% condence intervals (CI) of respondents having a high risk of CMD
for the full model samples (un-stratied) of both outcomes. Re-
spondents were less likely to report an at risk GHQ12 score of 4if
they lived up to 1 km of the coast compared to > 50km (OR
adj
= 0.78,
95 % CI = 0.65 0.95).
No signicant (p< 0.05) associations were found between coastal
proximity and CMD likelihood for either the GHQ12 outcome or an-
xiety/depression EQ5D dimension in the unadjusted models. Similarly,
there were no signicant associations between coastal proximity and
the anxious/depression dimension of the EQ5D in the adjusted model.
Respondents living in areas of 80100 % greenspace were less likely
to report at risk scores of the GHQ12 and being at least moderately
anxious or depressed in the unadjusted models (and 6080 % with the
EQ5D dimension). However, neither of these associations held in the
adjusted models.
Freshwater coverage of > 5100 % was found to be related to the
anxious/depression dimension of the EQ5D in both the unadjusted and
adjusted models (OR
adj
= 0.78, 95 % CI = 0.63 0.96). However, it
was not related to GHQ12 in either the unadjusted or adjusted model.
The results from our sensitivity analysis with > 20 km as a reference
category were similar, giving us condence in our results
(Supplementary Table 3). As with a reference category of > 50 km, we
nd signicant associations between living 1 km from the coast and
the GHQ12 (1km vs. >20km OR
adj
= 0.79, 95 % CI = 0.660.94).
J.K. Garrett, et al. Health and Place xxx (xxxx) xxxx
3
Table 1
Descriptive statistics for the un-stratied GHQ12 (N = 25,963) and EQ5D (N = 28,723) models.
Variables GHQ12 EQ5D
Full model sample Low risk of CMD
a
(score < 4) High risk of CMD
a
(score 4) Full model sample Not anxious or depressed At least moderately anxious or
depressed
Unweighted N Weighted %
age
Unweighted N Weighted %
age
Unweighted N Weighted %
age
Unweighted N Weighted %
age
Unweighted N Weighted %
age
Unweighted N Weighted %age
Total Ns 25963 21984 84.88 3979 15.12 28723 22275 78.38 6448 21.62
LSOA
b
level variables
Coastal proximity
01km 1532 5.75 1315 86.03 217 13.97 1826 6.17 1413 78.01 413 21.99
>15km 3202 11.68 2729 85.70 473 14.30 3394 11.27 2635 78.88 759 21.12
>520 km 3781 13.46 3186 84.77 595 15.23 4400 13.93 3372 77.70 1028 22.30
>2050 km 7252 30.10 6171 85.07 1081 14.93 7921 29.71 6216 79.13 1705 20.87
> 50 km (ref) 10196 39.01 8583 84.36 1613 15.64 11182 38.92 8639 77.97 2543 22.03
Freshwater coverage
>5100 % 712 2.74 602 84.83 110 15.17 776 2.67 627 81.89 149 18.11
>15% 1607 6.09 1386 86.81 221 13.19 1790 6.10 1393 78.19 397 21.81
>01% 1459 5.42 1244 85.38 215 14.62 1658 5.63 1278 77.81 380 22.19
0 % (ref) 22185 85.75 18752 84.72 3433 15.28 24499 85.59 18977 78.32 5522 21.68
Greenspace coverage
80100 % 1697 6.03 1478 87.33 219 12.67 1897 6.15 1516 80.20 381 19.80
60 - < 80 % 3239 11.84 2788 86.23 451 13.77 3539 11.76 2817 80.21 722 19.79
40 - < 60 % 4570 17.04 3842 84.41 728 15.59 5108 17.15 3912 77.26 1196 22.74
20 - < 40 % 7630 29.15 6398 84.11 1232 15.89 8545 29.45 6547 77.97 1998 22.03
0 - < 20 % (ref) 8827 35.93 7478 84.88 1349 15.12 9634 35.49 7483 78.34 2151 21.66
IMD
c
Most deprived 5549 21.65 4410 80.26 1139 19.74 6219 21.97 4495 73.83 1724 26.17
2nd most deprived 5540 21.90 4640 84.23 900 15.77 6133 21.82 4674 77.70 1459 22.30
Medium deprived 5051 19.79 4298 85.01 753 14.99 5566 19.67 4343 78.60 1223 21.40
2nd least deprived 4573 17.24 3986 87.30 587 12.70 5118 17.53 4086 80.53 1032 19.47
Least deprived (ref) 5250 19.43 4650 88.49 600 11.51 5687 19.00 4677 82.21 1010 17.79
Household level variables
Household income quintile
Lowest 3922 14.76 2969 76.12 953 23.88 4347 14.86 2890 68.07 1457 31.93
Second lowest 4172 15.14 3465 83.27 707 16.73 4662 15.37 3501 76.05 1161 23.95
Middle 4167 15.74 3576 86.22 591 13.78 4640 15.73 3677 80.06 963 19.94
Second highest 4434 17.48 3917 88.18 517 11.82 4853 17.27 3982 82.15 871 17.85
Missing data 4923 19.76 4159 84.38 764 15.62 5478 19.97 4191 77.40 1287 22.60
Highest (ref) 4345 17.12 3898 89.85 447 10.15 4743 16.80 4034 85.34 709 14.66
Car access
No 5503 20.79 4277 78.04 1226 21.96 6165 20.91 4173 68.92 1992 31.08
Yes (ref) 20460 79.21 17707 86.68 2753 13.32 22558 79.09 18102 80.88 4456 19.12
Individual level variables
Age categories
75+ 2615 8.13 2188 83.63 427 16.37 2875 8.01 2110 73.24 765 26.76
55 - 74 7344 23.65 6328 86.01 1016 13.99 8036 23.56 6132 76.47 1904 23.53
35 - 54 9049 35.18 7558 83.93 1491 16.07 10060 35.25 7702 77.15 2358 22.85
1634 (ref) 6955 33.05 5910 85.40 1045 14.60 7752 33.18 6331 82.28 1421 17.72
Highest qualication
Higher ed/Degree 8203 32.73 7130 86.83 1073 13.17 9294 33.27 7596 81.89 1698 18.11
NVQ3/A level 3944 16.98 3364 85.64 580 14.36 4429 17.19 3609 82.27 820 17.73
NVQ1/NVQ2/GCSE 6989 26.97 5888 84.66 1101 15.34 7650 26.81 5858 77.46 1792 22.54
Other/none (ref) 6827 23.33 5602 81.86 1225 18.14 7350 22.73 5212 71.38 2138 28.62
Working status
(continued on next page)
J.K. Garrett, et al. Health and Place xxx (xxxx) xxxx
4
Table 1 (continued)
Variables GHQ12 EQ5D
Full model sample Low risk of CMD
a
(score < 4) High risk of CMD
a
(score 4) Full model sample Not anxious or depressed At least moderately anxious or
depressed
Unweighted N Weighted %
age
Unweighted N Weighted %
age
Unweighted N Weighted %
age
Unweighted N Weighted %
age
Unweighted N Weighted %
age
Unweighted N Weighted %age
ILO unemployed
d
803 3.54 576 72.26 227 27.74 923 3.58 601 66.91 322 33.09
Retired/other inactive 9532 30.82 7634 79.43 1898 20.57 10430 30.64 7248 68.96 3182 31.04
In work/student (ref) 15628 65.64 13774 88.12 1854 11.88 17370 65.77 14426 83.40 2944 16.60
Sex
Male 11497 48.75 9986 86.90 1511 13.10 12668 48.76 10275 81.54 2393 18.46
Female (ref) 14466 51.25 11998 82.96 2468 17.04 16055 51.24 12000 75.37 4055 24.63
Relationship status
In a relationship 16207 61.27 14132 87.25 2075 12.75 17870 61.25 14427 81.06 3443 18.94
Widow/separated./
divorced
4484 14.60 3530 78.33 954 21.67 4980 14.58 3394 67.89 1586 32.11
Single (ref) 5272 24.13 4322 82.84 950 17.16 5873 24.17 4454 77.91 1419 22.09
Limiting illness presence
Limiting illness 22.13 4341 67.77 2024 32.23 7046 22.09 3981 56.64 3065 43.36
Non-limiting longstanding illness 18.22 4498 89.38 514 10.62 5442 17.95 4355 79.86 1087 20.14
No longstanding illness (ref) 59.65 13145 89.86 1441 10.14 16235 59.95 13939 85.95 2296 14.05
BMI-
e
Obese 5869 21.71 4837 82.65 1032 17.35 6517 21.87 4824 75.10 1693 24.90
Overweight 8412 31.93 7316 86.95 1096 13.05 9237 31.65 7328 79.84 1909 20.16
Underweight 373 1.70 294 80.26 79 19.74 399 1.63 298 77.55 101 22.45
Missing data 3386 12.37 2736 81.61 650 18.39 3835 12.76 2846 75.17 989 24.83
Normal weight (ref) 7923 32.30 6801 85.84 1122 14.16 8735 32.10 6979 80.49 1756 19.51
Smoking status
Current smoker 5587 22.19 4398 79.16 1189 20.84 6151 22.02 4279 70.53 1872 29.47
Used to smoke 8099 29.21 6915 85.46 1184 14.54 8952 29.20 6981 78.68 1971 21.32
Never smoked (ref) 12277 48.61 10671 87.15 1606 12.85 13620 48.79 11015 81.74 2605 18.26
Year
2012 5591 21.53 4702 84.31 889 15.69 5696 19.92 4500 79.80 1196 20.20
2011 - ––5891 20.35 4277 73.78 1614 26.22
2010 5857 22.80 4956 84.71 901 15.29 5865 20.76 4473 76.94 1392 23.06
2009 3308 12.99 2728 82.68 580 17.32 ––––– –
2008 (ref 11207 42.68 9598 85.94 1609 14.06 11271 38.96 9025 80.82 2246 19.18
a
CMD = common mental disorder.
b
LSOA = Lower-layer Super Output Area;
c
IMD = Indices of Multiple Deprivation;
d
ILO = International Labour Organisation;
e
BMI = Body Mass Index.
J.K. Garrett, et al. Health and Place xxx (xxxx) xxxx
5
Table 2
Unadjusted and adjusted regression models predicting the likelihood of respondents having poor metal health as assessed using the GHQ12 and anxiety/depression
component of the EQ5D. Signicant results are highlighted in bold type.
GHQ12 (4) EQ5D: at least moderately anxious/depressed
Unadjusted Adjusted Unadjusted Adjusted
term OR 95 % CI pOR 95 % CI pOR 95 % CI pOR 95 % CI p
LSOA level variables
a
Coastal proximity
01km 0.87 0.731.04 0.121 0.78 0.650.95 0.011 0.99 0.851.14 0.836 0.90 0.781.05 0.195
>15km 0.90 0.801.02 0.091 0.90 0.791.02 0.108 0.95 0.851.05 0.330 0.93 0.831.04 0.188
>520 km 0.97 0.861.09 0.592 0.99 0.871.12 0.845 1.02 0.931.12 0.709 1.01 0.921.11 0.818
>2050 km 0.94 0.861.04 0.229 0.97 0.881.07 0.507 0.93 0.861.01 0.083 0.97 0.891.05 0.451
> 50 km (ref)
Freshwater coverage
>5100 % 0.99 0.781.26 0.949 0.95 0.741.21 0.656 0.79 0.650.97 0.022 0.78 0.630.96 0.020
>15% 0.87 0.741.03 0.098 0.85 0.721.02 0.074 1.03 0.891.19 0.709 1.05 0.911.21 0.541
>01% 1.03 0.861.24 0.758 1.03 0.851.26 0.768 1.09 0.951.25 0.239 1.12 0.961.31 0.141
0 % (ref)
Greenspace coverage
80100 % 0.82 0.690.97 0.023 0.90 0.751.08 0.267 0.86 0.750.99 0.043 0.87 0.751.02 0.082
60 - < 80 % 0.90 0.791.02 0.103 0.97 0.851.11 0.643 0.88 0.79 0.98 0.019 0.91 0.821.02 0.122
40 - < 60 % 1.04 0.931.16 0.504 1.02 0.911.15 0.720 1.06 0.971.16 0.218 1.02 0.921.12 0.763
20 - < 40 % 1.06 0.961.16 0.263 1.05 0.951.16 0.387 1.01 0.941.10 0.741 0.99 0.911.07 0.781
0 - < 20 % (ref)
IMD
b
Most deprived 1.21 1.051.38 0.008 1.04 0.921.17 0.518
2nd most deprived 1.08 0.941.24 0.257 0.99 0.891.11 0.922
Medium deprived 1.17 1.031.34 0.020 1.07 0.961.20 0.230
2nd least deprived 1.05 0.911.21 0.509 1.00 0.901.13 0.939
Least deprived (ref)
Household level variables
Household income quintile
Lowest 1.40 1.191.64 < 0.001 1.37 1.191.56 < 0.001
Second lowest 1.24 1.061.44 0.007 1.19 1.051.36 0.007
Middle 1.15 0.991.33 0.074 1.12 0.991.27 0.082
Second highest 1.13 0.971.31 0.119 1.20 1.061.36 0.004
Missing data 1.21 1.041.40 0.012 1.22 1.081.39 0.002
Highest (ref)
Car access
No 1.15 1.031.27 0.010 1.24 1.141.36 < 0.001
Yes (ref)
Individual level variables
Age categories
75+ 0.41 0.340.50 < 0.001 0.55 0.460.65 < 0.001
55 - 74 0.54 0.470.63 < 0.001 0.78 0.690.88 < 0.001
35 - 54 1.00 0.891.12 0.984 1.23 1.121.35 < 0.001
1634 (ref)
Highest qualication
Higher ed/Degree 1.19 1.051.34 0.005 0.97 0.881.08 0.601
NVQ3/A level 1.12 0.981.29 0.105 0.85 0.760.96 0.007
NVQ1/NVQ2/GCSE 1.03 0.921.15 0.649 0.95 0.861.04 0.251
Other/none (ref)
Working status
ILO unemployed
c
1.97 1.622.39 < 0.001 1.72 1.462.03 < 0.001
Retired/other inactive 1.47 1.321.64 <0.001 1.55 1.421.70 < 0.001
In work/student (ref)
Sex
Male 0.79 0.730.86 < 0.001 0.75 0.700.80 < 0.001
Female (ref)
Relationship status
In a relationship 0.80 0.710.89 < 0.001 0.78 0.710.86 < 0.001
Widow/separated./divorced 1.12 0.981.28 0.098 1.07 0.951.20 0.282
Single (ref)
Limiting illness presence
Limiting illness 4.28 3.904.69 < 0.001 4.15 3.843.50 < 0.001
Non-limiting longstanding illness 1.19 1.061.34 0.003 1.62 1.481.77 <0.001
No longstanding illness (ref)
BMI
d
Obese 1.08 0.971.21 0.140 1.07 0.981.17 0.154
Overweight 0.95 0.861.06 0.373 1.03 0.941.12 0.511
Underweight 1.23 0.921.65 0.161 1.04 0.791.36 0.794
Missing data 1.12 0.991.26 0.071 1.03 0.921.15 0.608
Normal weight (ref)
Smoking status
Current smoker 1.40 1.261.54 < 0.001 1.55 1.431.69 <0.001
(continued on next page)
J.K. Garrett, et al. Health and Place xxx (xxxx) xxxx
6
3.2. Results stratied by household income
We nd some signicant interactions between coastal proximity and
household income for both the GHQ12 measure and the anxiety/de-
pression of the EQ5D (Supplemental Table 4).
Full results for each income quintile are presented in supplementary
materials (Supplemental Tables 59), with a summary of the key coastal
proximity ndings in Fig. 1. As can be seen, living near the coast
(5 km) is associated with lower ORs (than living > 50 km) of poor
mental health as measured by both the GHQ12 (01km OR
adj
= 0.58,
95 % CI = 0.39 0.87; > 15km OR
adj
= 0.76, 95 % CI = 0.59 0.98)
and the anxiety/depression sub-scale of the EQ5D (01km
Table 2 (continued)
GHQ12 (4) EQ5D: at least moderately anxious/depressed
Unadjusted Adjusted Unadjusted Adjusted
term OR 95 % CI pOR 95 % CI pOR 95 % CI pOR 95 % CI p
Used to smoke 1.13 1.031.24 0.011 1.09 1.011.17 0.033
Never smoked (ref)
Year
2012 1.17 1.061.30 0.003 1.12 1.021.23 0.022
2011 1.62 1.481.77 < 0.001
2010 1.11 1.001.23 0.053 1.32 1.211.45 < 0.001
2009 1.30 1.151.47 < 0.001
2008 (ref)
Intercept 1.68 2.52 1.25 2.16
N 25963 25963 28723 28723
Households 16592 16592 18419 18419
AIC
e
22056.47 19951.43 29987.95 26890.79
Cox & Snell pseudo-R
2
(%) 0.1 8.1 0.1 10.5
a
LSOA = Lower-layer Super Output Area; bIMD = Indices of Multiple Deprivation; cILO = International Labour Organisation; dBMI = Body Mass Index;
eAIC = Akaike's Information Criterion.
Fig. 1. The relationship between coastal proximity (reference category > 50 km) and mental health for each household income quintile. Note: results are fully
adjusted; CMD likelihood presented as odds ratios with 95% condence intervals. Full model results in Supplemental Tables 59.
J.K. Garrett, et al. Health and Place xxx (xxxx) xxxx
7
OR
adj
= 0.72, 95 % CI = 0.53 0.99; > 15km OR
adj
= 0.78, 95 %
CI = 0.62 0.99) for individuals in the lowest household income
quintile only. There were no other signicant associations between
coastal proximity and mental health for those in the higher household
income quintiles.
4. Discussion
In sum, we have explored the association between two measures of
mental health and coastal proximity for urban English adults using four
years of pooled data from the Health Survey for England. After ad-
justing for a range of relevant covariates, those living 01 km from the
coast had signicantly lower odds of being at high risk of a CMD, as
measured by the GHQ12 and compared to those living further than
50 km. Coastal proximity was not found to be related to the anxiety/
depression EQ5D dimension.
As predicted, income quintile was a strong predictor of mental
health outcomes, and other socioeconomic factors (e.g. employment,
relationship and smoking status) were also largely consistent with
earlier work (Katikireddi et al., 2016;Stranges et al., 2014). However,
we nd BMI not to be signicantly related to mental health contrasting
with research by Stranges et al. (2014) using the HSE. Conversely, we
nd that those who used to smoke were more likely to have poorer
mental health whereas this was not found by Stranges et al. (2014).We
also nd not having access to a car was signicantly related with worse
mental health whilst this was not found in earlier HSE years (Riva et al.,
2011).
Stratifying by household income revealed that the relationship be-
tween coastal proximity and mental health outcomes was present only
for those with the lowest household incomes and extended to < 5 km.
Specically, the results imply that people living in urban areas in the
lowest household income quintile are less likely to suer from a
common mental disorder (CMD) such as anxiety or depression if they
live within 5 km of the coast, compared to those living in urban areas
further inland (> 50 km). In particular, living within 1 km of the coast
is associated with the strongest reductions in CMD likelihood for people
from the most economically deprived households. Respondents from
this category reported symptoms consistent with a CMD according to
the GHQ12 measure with odds that were 40 % less than those living
further than 50 km. This is a greater reduction in comparison to being
in a relationship (vs. single OR
adj
= 0.78, 95 % CI = 0.630.98).
These ndings add to the growing evidence base linking blue
spaces, particularly coastal environments, with better health and
wellbeing (White et al., 2010;Wheeler et al., 2012;White et al., 2013a;
Crouse et al., 2018;Gascon et al, 2015;Gascon et al., 2017;Nutsford
et al., 2016;Volker and Kistemann, 2011). This study also highlights
the potentially benecial link between coastal proximity and common
mental disorders, which have been highlighted as growing issues in
countries such as England (McManus et al., 2016). Given that increas-
ingly many people live by and visit the coast in many countries, and
even more of them reside in cities, such research is vital for environ-
mental and social policy (Elliott et al., 2018;Pelling and Blackburn,
2014).
This research also supports previous work which suggests that the
positive relationship between living in more natural environments and
mental health is stronger within more socioeconomically deprived
groups (e.g. Wheeler et al., 2012;Maas et al., 2009;Maas et al., 2006;
McEachan et al., 2016;Mitchell and Popham, 2008). It also extends
prior research that investigated the interaction with area level depri-
vation (Wheeler et al., 2012), by demonstrating that household income
moderates the association between coastal proximity and health, in this
case specically mental health. This suggests that access to the natural
environment may, at least partly, oset the adverse health and well-
being outcomes associated with low incomes. Indeed, recent work by
Elliott et al. (2018) nds that recreational visits to the English coast,
particularly walking, are more likely to be made by people from some
lower socioeconomic backgrounds as compared to other natural en-
vironments. Subsequently, ensuring coastal environments are accessible
to more socioeconomically deprived communities could therefore help
to reduce health inequalities (Elliott et al., 2018).
Although not established in this study, it is plausible that there is a
causal relationship between coastal living and mental health. Indeed, it
could be that exposure to coastal environments improves mental health
through a range of potential mechanisms in the same way as has been
proposed for green space, such as through reduced stress, improved air
quality and immune functioning, and increased opportunities for social
contact and physical activity (Hartig et al., 2014;Markevych et al.,
2017). In support of this, de Bell et al. (2017) sought to test whether the
same mechanisms that have been proposed to explain the relationship
between green space and health also applied in blue space visits. Most
people identied psychological benets or social interactions as the
most important perceived benet from their most recent blue space
visit. Similarly, higher levels of blue space visibility were associated
with lower levels of psychological distress in Wellington, New Zealand,
whilst green space visibility was not found to be related (Nutsford et al.,
2016) and, in Ireland, a sea view was found to be related to lower
depression scores (Dempsey et al., 2018). Earlier work by (Bauman
et al., 1999) also suggests that living by the coast is associated with
increased opportunities for physical activity.
More recently, White et al. (2014) found that people in England who
lived closer to the coast were more likely to visit the coast and, sub-
sequently, achieve their recommended weekly physical activity levels.
Combined with the nding that approximately 271 million recreational
visits are made each year to coastal environments in England (Elliott
et al., 2018), this suggests that the mental health of English coastal
urban dwellers (who are more likely to visit the coast) is better than
those in urban areas inland because of certain salutary mechanisms,
such as physical activity.
There were several unexpected ndings in our research. For in-
stance, in contrast to previous research (e.g. de Bell et al., 2017;
MacKerron and Mourato, 2013;Völker et al., 2018), we found that
whilst living in closer proximity to coastal environments was sig-
nicantly linked to improved mental health outcomes, living in areas
with more freshwater coverage was not related overall according to the
GHQ12 measure. However, freshwater coverage was related to the
anxiety/depression EQ5D dimension. Freshwater coverage may be
specically related to anxiety and depression, while the GHQ12 mea-
sure is slightly broader (Jackson, 2007). Further, we found more people
were at least moderately anxious or depressed under the EQ5D measure
than were at high risk of a CMD as measured under the GHQ12, sug-
gesting that this measure is perhaps a more sensitive measure of mental
health.
Similarly, green space coverage was not consistently related to
mental health after adjusting for confounders, as with Nutsford et al.
(2016). This is despite growing evidence that living within greener
environments is positively connected to general mental health and
wellbeing (see Hartig et al., 2014), as well as more specic factors as-
sociated with mental health, such as reduced stress levels (Cox et al.,
2017c;Van den Berg et al., 2010), reduced rates of antidepressant
prescriptions (Taylor et al., 2015), and increased psychological re-
storation (White et al., 2013b).
As previously discussed, it may therefore be that coastal environ-
ments are particularly important for mental health in comparison to
green spaces. Similar conclusions were drawn in Hong Kong, where
blue space visits were linked to mental health whilst visits to green
spaces were not related (Garrett et al., 2019). Our result may also be
due to the coarse measures used here to assess green space coverage.
While the GLUD data are based on a high resolution cartographic da-
tabase, it does not capture any measures of quality or accessibility that
may be important modiers of any health benets of proximity to green
space (Wheeler et al., 2015;Markevych et al., 2017). However, a re-
lationship with self-reported health has previously been detected using
J.K. Garrett, et al. Health and Place xxx (xxxx) xxxx
8
a similar measure elsewhere (Mitchell and Popham, 2007).
4.1. Limitations and future work
Beyond the potential limitations associated with self-reporting
health (e.g. Lee and Dugan, 2015), the cross-sectional nature of our
study means the results should be interpreted cautiously before making
generalisations about a causal relationship between coastal proximity
and common mental disorders (Gascon et al., 2017). Future work
should therefore examine the potential factors mediating this link, such
as physical activity. More longitudinal and experimental research (e.g.
White et al., 2013a;White et al., 2015;Annerstedt et al., 2012) is also
needed to elucidate a causal relationship and determine whether living
by the coast for an extended period remains benecial for mental
health, as well as if these coastal benets are consistently greater than
living in areas with more green space and freshwater coverage. Further,
our measure does not capture variations in accessibility and quality
which can relate to visit frequency and mental health benets (Garrett
et al., 2019;Wyles et al, 2016,2017).
We were also not able to account for clustering at the LSOA level
which may have resulted in smaller standard errors as we cannot ac-
count for some potential non-independence within the data. However,
we have included LSOA level controls including additional nature ex-
posures and IMD.
4.2. Conclusion
To summarise, we found that the relationship between coastal
proximity and mental health was strongest for those urban adults in
more deprived households. This builds on previous research in-
vestigating coastal proximity and health inequalities at the community
level. Our results therefore add further evidence that the coast might
act as a mental health resource, particularly for people living in more
socioeconomically deprived circumstances. Ensuring access to these
environments may therefore have a role to play in reduction of health
inequalities (Allen and Balfour, 2014). At a time of increasing urbani-
sation, mental health disorders and degradation of coastal and marine
environments, such research should be developed and translated to
inform relevant environmental, planning and public health policies.
Acknowledgements
The authors would like to thank Dr Lewis Elliott for his advice and
guidance with the data analyses. The authors also thank Laura Brown at
ScotCen Social Research for support with HSE data linkage and ap-
provals from HSCIC/NHS Digital (Data Sharing Agreement NIC-09479-
J9Z4G). HSE data are copyright © 2013, re-used with the permission of
The Health and Social Care Information Centre [now NHS Digital], all
rights reserved. This work was supported by the National Institute for
Health Research Health Protection Research Unit (NIHR HPRU) in
Environmental Change and Health at the London School of Hygiene and
Tropical Medicine in partnership with Public Health England (PHE),
and in collaboration with the University of Exeter, University College
London, and the Met Oce. The funders had no role in the study de-
sign, analysis, interpretation of data, or decision to submit the article
for publication. The views expressed are those of the author(s) and not
necessarily those of the NHS, the NIHR, the Department of Health, or
Public Health England. JG's time on the manuscript was undertaken as
part of the BlueHealth project, which received funding from the
European Unions Horizon 2020 research and innovation programme
[grant agreement No.: 666773]. Analyses and interpretation are solely
the responsibility of the authors, and not the funders or data providers.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://
doi.org/10.1016/j.healthplace.2019.102200.
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... Nor did we find a similar modifying effect of living near blue space on the associations between pre-existing comorbidities, sex, age and mental health disorders. 23 reported a differential association between living near blue space and mental health. Using 12-item General Health Questionnaire (GHQ-12) scores as a mental health indicator and equivalised household income as a socio-economic status index, they found that the beneficial effect of blue space on mental health existed only for those living in the most deprived areas 23 . ...
... 23 reported a differential association between living near blue space and mental health. Using 12-item General Health Questionnaire (GHQ-12) scores as a mental health indicator and equivalised household income as a socio-economic status index, they found that the beneficial effect of blue space on mental health existed only for those living in the most deprived areas 23 . These findings suggest that the effect of blue space on mental health is moderated by socio-economic deprivation 23 . ...
... Using 12-item General Health Questionnaire (GHQ-12) scores as a mental health indicator and equivalised household income as a socio-economic status index, they found that the beneficial effect of blue space on mental health existed only for those living in the most deprived areas 23 . These findings suggest that the effect of blue space on mental health is moderated by socio-economic deprivation 23 . Our study confirms longitudinally that the beneficial effects of blue spaces are greater amongst more vulnerable and socioeconomically deprived communities. ...
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... A small but growing body of evidence demonstrates the potential health effect of engaging with blue spaces in the elderly's later life. Our results are in line with previous studies that suggested a significant association between neighborhood blue space and the elderly's general health [7,9,10,27,37,38], though such a relationship is not consistently observed [15]. However, evidence from our study on the blue space-health outcome association in elderly adults was scant and showed inconsistent findings. ...
... Thus, the living patterns and activities of this group depend considerably on the residential neighborhood's natural environment. Similarly, a study in England indicated that household income is a potentially important moderator in the linkage between coastal proximity and self-reported mental health [37]. ...
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... Although the coast has been visited by people for leisure, relaxation and thalassotherapy already since the 18th century (Verhaeghe, 1843;Strange, 1991), it is only until recently that there is empirical evidence of the relationship between the coast and wellbeing. Much of this evidence has demonstrated that residential proximity to the coast is associated with a better physical and mental health in comparison to residents living inland (Garrett et al., 2019;Hooyberg et al., 2020). ...
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... Reasons for poorer physical health could include the high mean and median age of this sample, which is 60.2 and 61.0, respectively. Additionally, living near bodies of water and coastal regions has been shown to improve mental health outcomes, especially in elderly individuals (Chen and Yuan 2020;Garrett et al. 2019;Gascon et al. 2017). This may have resulted in a slightly higher average mental health score in this population. ...
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... Street network proximity has been linked to neurological diseases such as non-Alzheimer's dementia, Parkinson's disease, Alzheimer's disease, and multiple sclerosis (Yuchi et al., 2020). In contrast, proxies for lower density, such as access to green spaces, water features, natural views, and natural light, have been found to correlate to low rates of anxiety and depression (Braubach, 2007;May et al., 2009;Garrett et al., 2019). ...
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... Reasons for poorer physical health could include the high mean and median age of this sample, which is 60.22 and 61, respectively. Additionally, living near bodies of water and coastal regions has been shown to improve mental health outcomes, especially in elderly individuals (Chen & Yuan, 2020;Garrett et al., 2019;Gascon et al., 2017). This may have resulted in a slightly higher average mental health score in this population. ...
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Changes to water conditions due to eutrophication and climate change have resulted in the proliferation of harmful algal blooms in freshwater and marine environments globally, including in Canadian lakes. We developed and administered an online survey to evaluate the awareness of these blooms and the accompanying health risks in a sample of New Brunswick waterfront cottage and homeowners. The survey was distributed to lake and cottage associations in New Brunswick and was completed by 186 eligible respondents. Participants were asked to about information about the water quality of their lake, awareness about algae blooms, sociodemographic and cottage characteristics, and complete a self-rated measure of physical and mental health. While approximately 75% of participants reported that the quality of their lake water was good or very good, 40% indicated that algae blooms were a concern. We found no statistically significant differences in self-reported physical or mental health between those who were aware of algae blooms at their cottage and those who weren’t (p > 0.05). Participants expressed concern about the impacts of algal blooms on the health of their pets, and wildlife. While climate change was the most identified cause of algal blooms, there was substantial heterogeneity in the responses. Taken together, the findings from our survey suggest that cottage owners in New Brunswick are aware and concerned about the impacts of algae blooms, however, there is a need to provide additional information to lake associations about the causes of these blooms.
... We performed several uncertainty tests with the fully adjusted main models. First, we tested another distance category (i.e., <6 km, 6-15 km, >15-25 km, and >25 km; a narrower first category led to too few suicide cases) (Garrett et al., 2019) to determine whether our estimates were stable. Second, to evaluate whether estimates were dependent on 300 m buffers, we repeated the modeling with 1000 m buffers. ...
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Exposure to blue spaces may promote psychological wellbeing and reduce mental distress. Whether these effects extend to suicide is unknown. We used register data from 14 million Dutch adults aged 18–64-years between 2007 and 2016 in a nested case-control study to estimate associations between blue space exposures and suicide risk. Each suicide case was matched to ten randomly selected controls. Two blue space exposures were assigned over a ten-year residential address history: distance to the closest inland blue space and distance to the coast. We fitted (gender-stratified) conditional logistic regressions to the data. Possible effect modifications by income were also examined. In total, our analyses included 9757 cases and 95,641 controls. Effect estimates for distance to the closest inland blue space in the total population showed that people living farthest away from inland blue space were at-risk. Suicide risk was lower among women who lived farther away from the coast; no significant effect was observed for men. No evidence was observed that income modified these associations. Our findings provide suggestive evidence that living close to the coast is associated with greater suicide risk for women, while living closer to inland blue spaces may add to the resilience against suicide in the total population. Past research shows that coastal proximity protects against milder forms of mental illness, but these protective effects do not appear to hold for suicide. Blue space interventions for women with severe mental illness or propensities to engage in self-harm should be approached with caution.
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The COVID-19 pandemic has considerable mental health impacts. Immersive nature-based interventions, such as swimming or snorkeling, may help mitigate the global mental health crisis caused by the pandemic. To investigate this, we collected cross-sectional data from residents of coastal villages (n = 308) in Kepulauan Selayar, Indonesia. Analysis of Covariance (ANCOVA) was used with mental well-being as the outcome variable, operationalized as the Mental Component Summary (MCS) scores from the SF-12 (12-item Short Form Health Survey). After adjusting for covariates, the activity of sea swimming or snorkeling was found to be significantly associated with better mental well-being (η² = 0.036; p < 0.01). Predictive margins analysis revealed that those who engaged in sea swimming or snorkeling for one to three days a week gained a 2.7 increase in their MCS scores, compared to those who did not. A non-linear dose-response relationship was detected: for those swimming or snorkeling more than three days per week, there was only an increase of 1.7 MCS score compared to the 0-day. Overall this study contributes to the expanding of evidence base, showing that interactions with blue spaces can be beneficial for mental health, especially in a potentially stressful time such as the current pandemic.
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Background: Coronary artery disease (CAD) is one of the crucial reasons for cardiovascular mortality in middle-aged people worldwide. The most typical tool is angiography for diagnosing CAD. The challenges of CAD diagnosis using angiography are costly and have side effects. One of the alternative solutions is the use of machine learning-based patterns for CAD diagnosis. Methods: Hence, this paper provides a new hybrid machine learning model called genetic support vector machine and analysis of variance (GSVMA). The analysis of variance (ANOVA) is known as the kernel function for the SVM algorithm. The proposed model is performed based on the Z-Alizadeh Sani dataset so that a genetic optimization algorithm is used to select crucial features. In addition, SVM with ANOVA, linear SVM (LSVM), and library for support vector machine (LIBSVM) with radial basis function (RBF) methods were applied to classify the dataset. Results: As a result, the GSVMA hybrid method performs better than other methods. This proposed method has the highest accuracy of 89.45% through a 10-fold crossvalidation technique with 31 selected features on the Z-Alizadeh Sani dataset. Conclusion: We demonstrated that SVM combined with genetic optimization algorithm could be lead to more accuracy. Therefore, our study confirms that the GSVMA method outperforms other methods so that it can facilitate CAD diagnosis.
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Background Little is known about the role of geographic access to inpatient palliative and end of life care (PEoLC) facilities in place of death and how geographic access varies by settlement (urban and rural). This study aims to fill this evidence gap. Methods Individual-level death data in 2014 (N = 430,467, aged 25 +) were extracted from the Office for National Statistics (ONS) death registry and linked to the ONS postcode directory file to derive settlement of the deceased. Drive times from patients’ place of residence to nearest inpatient PEoLC facilities were used as a proxy estimate of geographic access. A modified Poisson regression was used to examine the association between geographic access to PEoLC facilities and place of death, adjusting for patients’ socio-demographic and clinical characteristics. Two models were developed to evaluate the association between geographic access to inpatient PEoLC facilities and place of death. Model 1 compared access to hospice, for hospice deaths versus home deaths, and Model 2 compared access to hospitals, for hospital deaths versus home deaths. The magnitude of association was measured using adjusted prevalence ratios (APRs). Results We found an inverse association between drive time to hospice and hospice deaths (Model 1), with a dose–response relationship. Patients who lived more than 10 min away from inpatient PEoLC facilities in rural areas (Model 1: APR range 0.49–0.80; Model 2: APR range 0.79–0.98) and urban areas (Model 1: APR range 0.50–0.83; Model 2: APR range 0.98–0.99) were less likely to die there, compared to those who lived closer (i.e. ≤ 10 min drive time). The effects were larger in rural areas compared to urban areas. Conclusion Geographic access to inpatient PEoLC facilities is associated with where people die, with a stronger association seen for patients who lived in rural areas. The findings highlight the need for the formulation of end of life care policies/strategies that consider differences in settlements types. Findings should feed into local end of life policies and strategies of both developed and developing countries to improve equity in health care delivery for those approaching the end of life. Electronic supplementary material The online version of this article (10.1186/s12942-019-0172-1) contains supplementary material, which is available to authorized users.
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Background The effect of nature-based interventions on self-reported mental well-being in patients with physical disease is gaining increasing attention. However, there is a lack of randomized controlled trials investigating this area. Due to the massive costs in health care systems, there is a need for new strategies to address these issues and an urgent need for attention to this field. Nature-based interventions are low cost, easy to implement, and should get attention within the health care field. Therefore, the objective was to find the impact of nature interventions on mental well-being in humans with a physical disease. Methods In four major databases (PubMed, Cinahl, PsycINFO, and Cochrane Library), a systematic review of quantitative studies of nature’s impact on self-reported mental health in patients with physical disease was performed. A total of 1909 articles were retrieved but only five met the inclusion criteria and were summarized. Results All five studies were quantitative, with a control group and a nature-based intervention. A source of heterogeneity was identified: the patients in one of the five studies were psychosomatic. In the four studies with somatic patients, significant benefit of nature on self-reported mental health outcomes was found; the only study that failed to show a significant benefit was the one with psychosomatic patients. Conclusion A significant effect of nature on mental well-being of patients with somatic disease was found. The result in patients with psychosomatic disease is inconclusive, and more studies in this category are needed. Further research on the effect of nature on mental health is merited, with special attention to standardizing intervention type and dose as well as outcome measures within each medical discipline.
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Objective It is known that mental health deteriorated following the 2008 global financial crisis, and that subsequent UK austerity policies post-2010 disproportionately impacted women and those in deprived areas. We aimed to assess whether gender and socioeconomic inequalities in poor mental health have changed since the onset of austerity policies. Design Repeat cross-sectional analysis of survey data. Setting England. Participants Nationally and regionally representative samples of the working-age population (25–64 years) from the Health Survey for England (1991–2014). Outcome measures Population-level poor mental health was measured by General Health Questionnaire-12 (GHQ) caseness, stratified by gender and socioeconomic position (area-level deprivation and highest educational attainment). Results The prevalence of age-adjusted male GHQ caseness increased by 5.9% (95% CI 3.2% to 8.5%, p<0.001) from 2008 to 2009 in the immediate postrecession period, but recovered to prerecession levels after 2010. In women, there was little change in 2009 or 2010, but an increase of 3.0% (95% CI 1.0% to 5.1%, p=0.004) in 2012 compared with 2008 following the onset of austerity. Estimates were largely unchanged after further adjustment for socioeconomic position, employment status and household income as potential mediators. Relative socioeconomic inequalities in GHQ caseness narrowed from 2008 to 2010 immediately following the recession, with Relative Index of Inequality falling from 2.28 (95% CI 1.89 to 2.76, p<0.001) to 1.85 (95% CI 1.43 to 2.38, p<0.001), but returned to prerecession levels during austerity. Conclusions Gender inequalities in poor mental health narrowed following the Great Recession but widened during austerity, creating the widest gender gap since 1994. Socioeconomic inequalities in poor mental health narrowed immediately postrecession, but this trend may now be reversing. Austerity policies could contribute to widening mental health inequalities.
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Background: Anxiety and depression disorders are associated with significantly lower health-related quality of life (HRQL). The EQ-5D is a commonly used generic measure of HRQL; it captures mental health through a single domain-the anxiety/depression dimension. Evidence on the responsiveness of this measure in assessing changes in mental health changes is limited. Objective: To examine the performance of the anxiety/depression dimension (A/D) of the 3- and 5-level (3L and 5L) versions of the EQ-5D in assessing changes in mental health. Methods: Data from two patient populations were used: 495 adults post-discharge from general internal medicine ward (EQ-5D-3L), and 225 type 2 diabetes patients who screened positive for depressive symptoms (EQ-5D-5L). Anchor-based approach along with effect sizes (ES) and ROC analysis was used. Anchors included patient health questionnaire 9-items "PHQ9" and generalized anxiety disorder 2-item questionnaire "GAD2" for EQ-5D-3L, and PHQ9 and SF-12 mental composite summary scores (MCS) for EQ-5D-5L. A/D change was quantified as the difference between follow-up and baseline levels. Results: The A/D dimension of the EQ-5D-3L showed limited responsiveness to changes in depressive symptoms measured by PHQ9 and for anxiety symptoms measured by GAD2, whereby in those who improved or deteriorated in either symptom, more than half of the patients did not have an A/D change. In the ROC analysis, the A/D dimension of the EQ-5D-3L showed weak performance with C-indices ranging from 0.58 to 0.63 and probability of detection of depressive or anxiety symptoms ranging between 20 and 40%, which are all well below acceptable ranges. Similar results were observed for the A/D dimension of the EQ-5D-5L; although the performance was slightly better, it was still below acceptable range. In patients who improved or deteriorated based on the PHQ9 or MCS, around a third had no changes on the A/D dimension. The performance of the A/D dimension of the EQ-5D-5L was also very limited with C-indices ranging between 0.67 and 0.76, and probability of detection between 50 and 67%, slightly better than that of the 3L, yet unsatisfactory. Conclusions: Although the A/D of both EQ-5D-3L and 5L was limited in capturing changes in mental health in these populations, the 5L was slightly more responsive than the 3L. While the performance was better for depressive than anxiety symptoms, it varied by the direction of change. Further research using other measures of mental health in other populations is warranted.
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The potential benefits of aquatic environments for public health have been understudied in Asia. We investigated the relationships between blue space exposures and health outcomes among a sample of predominantly older adults in Hong Kong. Those with a view of blue space from the home were more likely to report good general health, while intentional exposure was linked to greater odds of high wellbeing. Visiting blue space regularly was more likely for those within a 10–15 min walk, and who believed visit locations had good facilities and wildlife present. Longer blue space visits, and those involving higher intensity activities, were associated with higher recalled wellbeing. Our evidence suggests that, at least for older citizens, Hong Kong's blue spaces could be an important public health resource. Full article available at https://www.sciencedirect.com/science/article/pii/S1353829218303745?via%3Dihub or on request.
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This paper tests whether higher exposure to coastal blue space is associated with lower risk of depression using data from The Irish Longitudinal Study on Ageing (TILDA), a nationally representative longitudinal study of people aged fifty and over in Ireland. We contribute to the literature on blue space and health by (i) using scores from the Center for Epidemiologic Studies Depression Scale (CES-D) to measure depression outcomes (ii) using new measures of coastal blue space visibility (iii) studying the association in an older population (iv) using data from Ireland. Our results indicate that exposure to coastal blue space is associated with beneficial mental health outcomes: TILDA respondents with the highest share of sea view visibility have lower depression (CES-D) scores, while distance from coastline is not statistically significant when views and proximity are both included in the model. This finding supports the idea that the primary channel through which coastal blue space operates to reduce depression scores is visual rather than related to physical proximity.
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Background: Socioeconomic status, as measured by education, occupation or income, is associated with depression. However, data are lacking on the psychosocial, material and behavioral mediators of these associations. We have examined the association of education, occupation and income with depression and the potential mediations using community-based data. Methods: A total of 7,966 older adults were interviewed in Finland, Poland and Spain. The differential associations between depression and SES, mediator variables, country of residence and cofounder variables, such as chronic physical conditions, were assessed through logistic regression models. Meditation analyses were carried out using khb method for Stata 13.1. Results: Education, followed by household income, were the SES indicators most frequently significantly associated with depression. These SES markers, but not occupation, showed an independent effect in this association. Psychosocial factors and loneliness in particular showed the strongest associations with depression among mediator variables. However, material factors and, especially, financial strain had a higher mediating function in the association between SES and depression. Overall, SES markers, chronic conditions and mediation factors were more positive in Finland than in Poland and Spain. Conclusion: Improving psychosocial and material dimensions as well as access to the educational system for older adults might result in a reduction in the prevalence of depression in the general population and particularly among individuals with low SES.
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Health and economic benefits may accrue from marine and coastal recreation. In England, few national-level descriptive analyses exist which examine predictors of recreation in these environments. Data from seven waves (2009–2016) of a representative survey of the English population (n = 326,756) were analysed to investigate how many recreational visits were made annually to coastal environments in England, which activities were undertaken on these visits, and which demographic, motivational, temporal, and regional factors predict them. Inland environments are presented for comparison. Approximately 271 million recreational visits were made to coastal environments in England annually, the majority involving land-based activities such as walking. Separately, there were around 59 million instances of water-based recreation undertaken on recreational visits (e.g. swimming, water sports). Visits to the coast involving walking were undertaken by a wide spectrum of the population: compared to woodland walks, for instance, coastal walks were more likely to be made by females, older adults, and individuals from lower socioeconomic classifications, suggesting the coast may support reducing activity inequalities. Motivational and temporal variables showed distinct patterns between visits to coastal and inland comparator environments. Regional variations existed too with more visits to coastal environments made by people living in the south-west and north-east compared to London, where more visits were made to urban open spaces. The results provide a reference for current patterns of coastal recreation in England, and could be considered when making policy-level decisions with regard to coastal accessibility and marine plans. Implications for future public health and marine plans are discussed.
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Exposure to nature can strengthen an individual’s sense of connectedness (i.e., emotional/cognitive bonds to the natural world) and enhance psychological restoration (e.g., feeling relaxed/refreshed). To date, there have been few large studies looking at the role that type and quality of natural environments may have on these outcomes. The present study used data from a large survey in England (sample analyzed = 4,515), which asked participants to recall a recent visit to nature. After controlling for covariates, respondents recalled greater connectedness to nature and restoration following visits to rural and coastal locations compared with urban green space, and to sites of higher environmental quality (operationalized by protected/designated area status, for example, nature reserves). A series of structural equation analyses provided evidence for a bidirectional association between connectedness and restoration. Consideration of the psychological benefits associated with different types and quality of environment has implications for human health, environmental management, and conservation.