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The current status of urban–rural differences in psychiatric disorders


Abstract and Figures

Reviews of urban-rural differences in psychiatric disorders conclude that urban rates may be marginally higher and, specifically, somewhat higher for depression. However, pooled results are not available. A meta-analysis of urban-rural differences in prevalence was conducted on data taken from 20 population survey studies published since 1985. Pooled urban-rural odds ratios (OR) were calculated for the total prevalence of psychiatric disorders, and specifically for mood, anxiety and substance use disorders. Significant pooled urban-rural OR were found for the total prevalence of psychiatric disorders, and for mood disorders and anxiety disorders. No significant association with urbanization was found for substance use disorders. Adjustment for various confounders had a limited impact on the urban-rural OR. Urbanization may be taken into account in the allocation of mental health services.
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The current status of urban-rural differences
in psychiatric disorders
Generally, social problems and environmental
stressors are more prevalent in cities than in the
country. Areas with high population densities are
characterized, for instance, by higher rates of
criminality, mortality, social isolation, air pollution
and noise (1). As the extent of various social
problems is related to urbanization, it is often
assumed that rates of psychiatric disorders are also
correlated with urbanization. A frequently cited
milestone in the study area of urban–rural differ-
ences in the prevalence of psychiatric disorders is
the study by Dohrenwend and Dohrenwend (2).
Peen J, Schoevers RA, Beekman AT, Dekker J. The current status of
urban–rural differences in psychiatric disorders.
Objective: Reviews of urban–rural differences in psychiatric disorders
conclude that urban rates may be marginally higher and, specifically,
somewhat higher for depression. However, pooled results are not
Method: A meta-analysis of urban–rural differences in prevalence was
conducted on data taken from 20 population survey studies published
since 1985. Pooled urban–rural odds ratios (OR) were calculated for
the total prevalence of psychiatric disorders, and specifically for mood,
anxiety and substance use disorders.
Results: Significant pooled urban–rural OR were found for the total
prevalence of psychiatric disorders, and for mood disorders and
anxiety disorders. No significant association with urbanization was
found for substance use disorders. Adjustment for various confounders
had a limited impact on the urban–rural OR.
Conclusion: Urbanization may be taken into account in the allocation
of mental health services.
J. Peen
, R. A. Schoevers
A. T. Beekman
, J. Dekker
Research Department, Arkin Mental Health Institute
Department of Clinical Psychology, VU
University Amsterdam and
Department of Psychiatry,
VU University Amsterdam Medical Centre, Amsterdam,
the Netherlands
Key words: meta-analysis; mental illness; prevalence;
rural health; urban health
J. Peen, Research Department, Arkin Mental Health
Institute Amsterdam, PO Box 75848, 1070 AV,
Amsterdam, the Netherlands.
Accepted for publication June 10, 2009
Pooled total prevalence rates for psychiatric disorders were found to be significantly higher in urban
areas compared with rural areas. Specific pooled rates for mood disorders and anxiety disorders were
also significantly higher in urban areas, while rates for substance use disorders did not show a
Adjustment for confounders had limited impact on urban–rural odds ratios found, which shows that
urban–rural differences in prevalence rates are only partly explained by population characteristics.
Urbanization may be taken into account in the allocation of mental health services.
There was heterogeneity in the dataset which might not be explained by urban–rural differences.
However, possible sources of this heterogeneity that were analysed (culture, diagnostic method,
diagnostic variation within diagnostic categories analysed) did not show significant differences in
The meta-analysis was limited to developed countries.
Schizophrenia was not included as a separate category.
Acta Psychiatr Scand 2010: 121: 84–93
All rights reserved
DOI: 10.1111/j.1600-0447.2009.01438.x
2009 John Wiley & Sons A/S
This review of nine urban–rural comparisons was
based on studies from 1942 to 1969 from quite
diverse countries. The authors concluded that there
was a tendency towards higher total rates of
psychiatric disorders in urban areas. However,
there was a variation in the difference depending
upon the specific diagnostic category. Rates for
neurosis and personality disorders were higher in
urban areas, while rates for functional psychoses
combined and manic-depressive psychoses sepa-
rately were higher in rural areas. There was no
clear trend in the rates for schizophrenia.
Since Dohrenwend and Dohrenwend (2) a
number of reviews have followed (3–7), generally
showing marginally higher overall rates in urban
areas and, specifically, somewhat higher rates for
depression. However, there is no clear trend in the
outcomes, which often lack statistical significance
as the studies were not pooled.
Furthermore, a number of factors may have
complicated the study of a possible association
between urbanization and psychopathology. First
of all, definitions of ÔurbanÕand ÔruralÕmay vary
(4). Generally, ÔurbanÕrefers to large conglomer-
ates of people, usually in a relatively small area,
resulting in relatively high population densities.
The use of the term ÔrelativelyÕmakes it clear that
what some countries define as ÔurbanÕusing defi-
nitions from national statistical institutions or
research may be defined as ÔruralÕin another
country. The United Nations have defined an
Ôurban localityÕas having at least 20 000 people,
and a city as having at least 100 000 people (8).
However, this definition was not used in any study
cited here. Secondly, the concrete manifestation of
urban and rural phenomena varies widely around
the world. The Netherlands, for instance, does not
have any metropolis such as London or New York,
and the Dutch countryside is much more popu-
lated than the countryside of Arkansas.
Thirdly, there may be other cultural differences
between studies and countries. The Dohrenwend &
Dohrenwend review (2) covers a wide variety of
cultures (7), and this may detract from the external
validity of its findings.
Fourthly, there is considerable heterogeneity in
the methods used in the available literature.
Outcome measures vary from self-report psycho-
logical wellbeing scales to case definition by struc-
tured interviews, and prevalence rates may or may
not be adjusted for different types of confounders.
Since 1984, study designs have gradually improved,
enhancing the validity of results. The five reviews
from Dohrenwend and Dohrenwend to Marsella
(2–7) were based partially on older designs, and
partially on more recent, and more sophisticated
designs. In line with this heterogeneity, none of the
previous reviews was able to pool the data and
perform meta-analyses.
Aims of the study
This study sought to investigate the links between
urbanization and psychopathology in a meta-
analysis using only studies of higher methodolog-
ical quality with adjustment for important con-
founders. Bias through cultural and environmental
variation was limited by including only studies
from developed countries. This allowed us to
establish more accurately the existence and mag-
nitude of potential urban–rural differences in levels
of psychopathology. Establishing urban–rural dif-
ferences for psychiatric disorders not only has
scientific value – by extending our models with
factors that affect the onset of mental disorders –
but may also have consequences for the allocation
of mental health resources to areas with higher
levels of urbanization.
Material and methods
Selection criteria
We included population surveys presenting urban–
rural differences in psychiatric disorders since 1985.
We restricted our study to developed countries.
The studies included were all based on reliable
diagnostic processes using standardized structured
We present studies dealing with total rates of
psychiatric morbidity, mood disorders, anxiety
disorders and substance use disorders. For Ômood
disordersÕ, rates for major depressive episodes were
used when available. In the absence of rates for
major depressive episodes, rates for combinations of
mood disorder were used. In the area of Ôsubstance
use disordersÕ, rates for alcohol abuse or alcohol
dependence (combined in some cases) were used
when there were no total rates for substance use
disorders. As stated above, there was variation in
the diagnostic content within the diagnostic groups
of which prevalence rates were pooled in this study.
The rationale for this was that we wanted to include
a reasonable number of studies in each diagnostic
group. Furthermore, we have performed additional
analyses if possible, to check for within-group
variation in urban–rural associations due to differ-
ences in diagnostic content.
As reliable rates are generally difficult to estab-
lish for schizophrenia in standard population
surveys due to the low prevalence of schizophrenia
in the non-institutionalized community, we did not
Urban–rural differences
include results for schizophrenia. Finally, we
included only studies of adults or of all age groups.
Search strategy
Our database search comprised all publications
from 1985 onwards containing the subject headings
Ômental healthÕor Ômental disordersÕand i) ÔurbanÕ
and ÔruralÕor with ii) Ôcity residenceÕ,Ôcity bornÕ,
Ôcity livingÕ. The databases used were: all EBM
reviews, Embase psychiatry, Medline and Psycinfo.
A selection based on the abstracts was made from
the initial search results (n= 620). Studies con-
cerning less developed countries were also left out.
We were left with 110 studies relating to the
subject. Figure 1 shows the subsequent stepwise
exclusion process.
Data extraction and statistical analysis
All the selected studies provided basic urban and
rural prevalence rates or urban–rural odds ratios
(OR) which had been at least controlled for age
and gender. However, most studies also presented
rates or OR adjusted for a wider range of variables
(these are summed up for each study in Table 1). In
this study, we refer to the first group of rates as
Ôunadjusted ORÕ(controlled for age and gender at
best) and to the second group as Ôadjusted ORÕ
(adjusted for more than age and gender). If
available, a 12-month rate was chosen as the
outcome measure. Another available rate was used
in other cases.
Unadjusted and adjusted OR with 95% confi-
dence intervals were collected for all included
studies. Some OR and confidence intervals could
be calculated from the available numbers, even
though they were not stated in the studies. Some
stated only that there was no significant difference
for urbanization or that urbanization was not a
significant predictor in a logistic model. An OR of
1 is used for these cases in the figures.
When studies provided more than two categories
of urbanization, the most extreme dichotomy –
metropolis vs. rural, for instance – was chosen for
the analysis. In all selected studies, the level of
urbanization concerns the level or urbanization at
the time of measurement.
The Review Manager (RevMan 4.2, Cochrane
IMS, Oxford, UK) was used to perform meta-
analyses. Log OR and their standard errors were
entered in the program. The generic inverse vari-
ance option was used. Pooled ORs were estimated
using random effect modelling as there was a high
level of heterogeneity between included studies.
Two authors (JP and JD) acting independently
were responsible for the reading and the extraction
of data (including cross-checking) from the studies
selected for the meta-analysis. Any differences in
outcome were resolved by discussion.
Table 1 lists the 20 studies that were included.
Looking at the number of studies per country,
Great Britain, the Netherlands, Canada and the
USA appear to be well represented. As far as the
year of publication is concerned, 12 of the 20
studies were published after 2000, six in the 1990s
and two in the period 1985–1989. Two European
multi-country studies are presented in the table.
The first is the ODIN study of depression covering
Norway, Finland, Great Britain and Ireland (13).
The second is the Esemed study covering France,
Italy, Spain, Belgium, Germany and the Nether-
lands (15). Most studies presented 12-month
prevalence rates (13 20). The age ranges 18 and
18–64 years were most common. Ten studies used
the composite international diagnostic interview as
the diagnostic instrument, three studies used the
general health questionnaire screening instrument
(other n= 7). The distinction between urban and
rural areas was made in different ways. Straight-
forward approaches are Ôinterviewer judgementÕ
(separately for each respondent), Ôpopulation
sizeÕand Ôpopulation densityÕ.ÔConcentration of
addressesÕis a measure of human activity, includ-
ing industrial activity. ÔDemographic characteris-
ticsÕwas also used for area classification.
Most studies used two categories to differentiate
between urban and rural (the maximum number of
Selected from database/literature search: 110
1) No urban–rural comparison related to the subject: 58
2) No population survey (utilization data): 11
3) Restricted to a diagnostic group outside our focus: 10
4) Restricted to a demographic subgroup: 1
5) No clear urban–rural distinction: 1
6) No dichotomous outcome measure: 1
7) Duplicate use of same data in different publications: 8
emaining urban–rural comparisons for meta–analysis: 2
Fig. 1. The selection process within the initial search result.
Peen et al.
Table 1. Population-based prevalence studies included in meta-analysis of urban–rural differences in psychiatric disorders
Year of
publication Country
Urban rural
based on
categories Adjusted for
Madianos & Stefanis
1992 Greece 2 Point prev 18–64 CES-D DSM-III-R 3706 Demogr. charact. 4
Hodiamont et al. (10) 1992 Netherlands 1 Point prev 18–65 GHQ PSE 3232 Demogr. charact. 2
Lewis & Booth (11) 1994 Great Britain 1 1 Point prev >18 GHQ 6572 Interviewer judgement 3 1,2,4,8,chronic illness
Paykell et al. (12) 2000 Great Britain 1.4 1.4 1 wk prev 16–64 CIS-R US-NAS 9777 Interviewer judgement 3 1,2,3,4,5,6,8,life events,prim. supp.
group, perceived soc.
support,tenure,accomm. type
et al. (13)
2001 Finland, Great
Britain, Ireland,
2 12 m prev 18–64 BDI SCAN DSM-IV 7622 Demogr. charact. 2
et al. (14)
2005 France 2 2 12 m prev 18 CIDI-S DSM-IV 2628 Demogr. charact. 2 1,2,3,life events
et al. (15)
2005 Belgium, France,
Germany, Italy,
The Netherlands,
1,2,3,4 1,2,3,4 12 m prev 18 CIDI DSM-IV 21425 Pop. size 2 1,3,8
Weich et al. (16) 2006 Great Britain 1 1 12 m inc 16–74 GHQ 7659 Pop. density demogr.
2 1,2,3,4,6,8,9,curr. health
probl.,housing tenure,
overcrowding,housing probl.,
househ. type
Kringlen et al. (17) 2006 Norway 1,2,3,4 12 m prev 18–65 CIDI DSM-III-R 3146 Demogr. charact. 2
Peen et al. (18) 2007 The Netherlands 1,2,3,4 1,2,3,4 12 m prev 18–64 CIDI DSM-III-R 7076 Concentration of
5 1,2,5,9,occup. status,househ.
Dekker et al. (19) 2008 Germany 1,2,3,4 1,2,3,4 12 m prev 18–64 CIDI DSM-IV 4181 Pop. size demogr.
2 1,2,3,4, and interactions with urb.
North America
Blazer et al. (20) 1985 United States 2,3,4 2,3,4 12 m prev 18 DIS DSM-III 3798 Demogr. charact. 2 1,2,3,5,7,residential mob.
Kovess et al. (21) 1987 Canada 2 2 12 m prev 18 SCL29 Wellb DSM-III 3080 Demogr. charact. 3 1,2,3,5,life events
Kessler et al. (22) 1994 United States 1,2,3,4 12 m prev 15–54 CIDI DSM-III-R 8098 Demogr. charact. 3 1,2,3,5,7,living arrangem.,region
Parikh et al. (23) 1996 Canada 2 12 m prev 15 UM-CIDI DSM-III-R 9953 Demogr. charact. 2
Wang (24) 2004 Canada 2 2 12 m prev 12 CIDI DSM-III-R 17244 Demogr. charact. pop.
2 3,7,8,immigr. st.
Kessler et al. (25) 2005 United States 2,4 12 m prev 18 CIDI DSM-IV 3199 Demogr. charact. 6 1,2,3,5,6,7,9
Rohrer et al. (26) 2005 United States 1 1 1 m prev 18 BRFSS FMD 5757 Demogr. charact. 3 1,2,3,5,7,9,BMI
Lee et al. (27) 1990 South Korea 1,2,3,4 Lifet. prev 18–65 DIS-III DSM-III 5100 Demogr. charact. 2
Andrews et al. (28) 2001 Australia 1,2,3,4 1,2,3,4 12 m prev 18 CIDI ICD-10 10641 Pop. size 3 1,2,3,5,8,country of birth
*1 = total rate of psychiatric disorders; 2 = mood disorders; 3 = anxiety disorders; 4 = substance use disorders.
1 = age; 2 = gender; 3 = marital status; 4 = social class; 5 = educational level; 6 = ethnicity; 7 = race; 8 = unemployment; 9 = income.
CIBI, composite international diagnostic interview; GHQ, general health questionnaire.
Urban–rural differences
categories used was six). Eighteen of the 20 studies
presented unadjusted OR, while 14 out of 20
presented adjusted ratios (12 presented both). Of
the six studies without adjusted ratios, four dated
from before 2000. Adjusted odds were generally
adjusted for a large number of confounders (up to
a maximum of 14). In Wang (24), the adjusted
odds were not adjusted for age and sex in a logistic
regression model, because these factors were not
found to be a potential confounder in a preceding
bivariate analysis.
In Table 2 the contents of the prevalence rates
used in the pooled analyses are specified. Concern-
ing prevalence rates for Ôany disorderÕsome rates
were based on diagnoses while other rates were
based on cut-off scores. Concerning mood disor-
ders some studies report total prevalence rates for
mood disorders, while other studies report figures
of major depression plus dysthymia or only major
depression. Two of the studies reporting anxiety
disorders only reported prevalence rates of distinct
anxiety categories, as a total of anxiety disorders
was not available. The studies reporting on sub-
stance use disorders can be divided in a group
reporting on both alcohol and drug abuse and
dependence, and in a second group only reporting
on alcohol abuse and dependence.
Figure 2 presents a forest plot of unadjusted OR
for Ôany disorderÕ(16 comparisons), ordered by year
of publication. The number of comparisons from
European countries was much higher (n= 13) than
from outside Europe (n= 3). Of the unadjusted
OR, 56% indicated an urban–rural OR significantly
higher than 1. Thirty-eight per cent of the studies
presented no significant OR and one Belgian study
(6%) found an urban–rural OR significantly less
than 1 (15). Given the heterogeneity of the 14
studies, we used random effect modelling for the
pooled result. The pooled unadjusted OR was 1.38
(1.17–1.64), P< 0.001. The pooled adjusted OR
was slightly lower: 1.21 (1.09–1.34), P< 0.001 (14
comparisons; data not shown).
Figure 3 shows the unadjusted OR for mood
disorders (21 comparisons). By contrast to the
unadjusted odds for Ôany disorderÕ, the propor-
tion of non-European comparisons was higher
(29%; n= 6 non-European and n= 15 Euro-
pean). Thirty-three per cent of the studies found
a significant urban–rural unadjusted OR higher
than 1 for urban areas compared to rural areas,
Table 2. Specific contents of prevalence rates used in the meta-analysis of urban–rural differences in psychiatric disorders
Unadjusted rates Adjusted rates
use disorders Total
use disorders
Madianos & Stefanis (9) 1a
Hodiamont et al. (10) GHQ-30 10 PSE >4
Lewis & Booth (11) GHQ-30 5 GHQ-30 5
Paykell et al. (12) CIS-R 12 US-NAS-12 3 CIS-R 12 US-NAS-12 3
Ayuso-Mateos et al. (13) 1a
Kovess-Masfety et al. (14) 1a 1a
Kovess-Masfety et al. (15) 1a,b,2a,b,c,d,e,g,3a,b 1a,b 2a,b,c,d,e,g 3a,b 1a,b,2a,b,c,d,e,g,3a,b 1a,b 2a,b,c,d,e,g 3a,b
Weich et al. (16) GHQ-12 3 GHQ-12 3
Kringlen et al. (17) 1,2a,b,c,d,e,f,3a,b,c,d,4,5a,6 1a 2c 3a,b
Peen et al. (18) 1,2a,b,c,d,e,f,3a,b,c,d,4,6 1 2a,b,c,d,e,f 3a,b,c,d 1,2a,b,c,d,e,f,3a,b,c,d,4,6 1 2a,b,c,d,e,f 3a,b,c,d
Dekker et al. (19) 1,2a,b,c,d,e,f,3a,b,e,4c,5 1 2a,b,c,d,e,f 3a,b,e 1,2a,b,c,d,e,f,3a,b,e,4c,5 1 2a,b,c,d,e,f 3a,b,e
North America
Blazer et al. (20) 1a 2a 3a,b 1a 2a 3a,b
Kovess et al. (21) 1a,b 1a,b
Kessler et al. (22) 1,2a,b,c,d,e,3a,b,c,d,4,8 1 2a,b,c,d,e 3a,b,c,d
Parikh et al. (23) 1
Wang (24) 1a 1a
Kessler et al. (25) 1a 3a,b,c,d
Rohrer et al. (26) FMD 14 van 30 FMD 14 van 30
Lee et al. (27) 1,2a,b,c,d,e,f,3a,b,c,d,4a,b,
1 2a,b,c,d,e,f 3a,b,c,d
Andrews et al. (28) 1a,b,2a,b,d,e,f,g,3a,b,c,d 1a,b 2a,b,d,e,f,g 3a,b,c,d 1a,b,2a,b,d,e,f,g,3a,b,c,d 1a,b 2a,b,d,e,f,g 3a,b,c,d
1 = mood disorders; 1a = major depression; 1b = dysthymia; 1c = bipolar disorder; 2 = anxiety disorder; 2a = agoraphobia; 2b = social phobia; 2c = simple phobia;
2d = panic disorder; 2e = GAD; 2f = OCD; 2g = PTSD; 3 = substance use; 3a = alcohol dependence; 3b = alcohol abuse; 3c = drug dependence; 3d = drug abuse; 3e = illicit
drug use; 4 = non-affective psychosis; 4a = schizophrenia; 4b = schizophreniform disorder; 4c = possible psychotic disorder; 5 = somatoform disorder; 5a = somatization
disorder; 6 = eating disorder; 6a = anorexia; 7 = gambling; 8 = antisocial personality disorder; 9a = mild cognitive impairment; 9b = severe cognitive impairment.
GHQ, general health questionnaire.
Peen et al.
while 67% of the studies presented no significant
unadjusted OR. None of the studies found a
significant urban–rural OR less than 1. The
pooled unadjusted OR for mood disorders was
1.39 (1.23–1.58), P< 0.0001. The pooled
adjusted OR was somewhat lower: 1.28 (1.13–
Review: Urban rural differences
Comparison: 01 Inverse var
Outcome: 01 Any disorder - unadjusted
Study Odds ratio (random) Weight Odds ratio (random)
or sub-category 95% CI % 95% CI
01 Sub-category
15 - Belgium 4.98 0.60 (0.39, 0.92)
27 - South Korea 7.04 0.95 (0.84, 1.07)
26 - United States 6.30 1.07 (0.84, 1.37)
15 - Italy 6.23 1.07 (0.83, 1.38)
15 - Spain 6.47 1.14 (0.91, 1.42)
28 - Australia 6.99 1.19 (1.04, 1.36)
16 - Great Britain 6.45 1.25 (1.00, 1.57)
15 - The Netherlands 3.49 1.27 (0.66, 2.43)
15 - Germany 6.10 1.31 (0.99, 1.72)
15 - France 6.51 1.54 (1.24, 1.91)
11 - Great Britain 6.87 1.54 (1.32, 1.80)
19 - Germany 6.52 1.57 (1.27, 1.95)
12 - Great Britain 6.51 1.64 (1.32, 2.04)
18 - The Netherlands 6.67 1.77 (1.46, 2.14)
17 - Norway 6.71 2.47 (2.05, 2.97)
10 - The Netherlands 6.16 3.03 (2.32, 3.96)
Subtotal (95% CI) 100.00 1.38 (1.17, 1.64)
Test for heterogeneity: χ² = 153.45, df = 15 (P < 0.00001), I² = 90.2%
Test for overall effect: Z = 3.80 (P = 0.0001)
0.2 0.5 1 2 5
Fig. 2. Urban–rural comparisons of any disorder, unadjusted OR with 95% CI.
Review: Urban rural differences
Comparison: 01 Inverse var
Outcome: 02 Mood disorders - unadjusted
Study Odds ratio (random) Weight Odds ratio (random)
or sub-category 95% CI % 95% CI
01 Sub-category
15 - Belgium 3.07 0.76 (0.43, 1.35)
13 - Norway 5.91 0.81 (0.62, 1.06)
27 - South Korea 6.08 1.08 (0.84, 1.40)
13 - Finland 4.16 1.15 (0.75, 1.78)
24 - Canada 6.87 1.19 (0.99, 1.43)
15 - Spain 5.46 1.19 (0.87, 1.61)
9 - Greece 5.18 1.24 (0.89, 1.73)
28 - Australia 5.58 1.25 (0.93, 1.68)
21 - Canada 5.34 1.25 (0.91, 1.71)
23 - Canada 6.10 1.28 (1.00, 1.65)
13 - Great Britain 5.13 1.30 (0.93, 1.82)
15 - France 5.51 1.35 (1.00, 1.83)
15 - Italy 4.70 1.37 (0.94, 2.00)
15 - The Netherlands 1.20 1.61 (0.55, 4.72)
19 - Germany 5.36 1.75 (1.27, 2.39)
14 - France 4.18 1.75 (1.14, 2.69)
15 - Germany 3.99 1.90 (1.21, 2.98)
17 - Norway 4.92 2.05 (1.43, 2.93)
18 - The Netherlands 5.39 2.10 (1.54, 2.87)
20 - United States 3.27 2.96 (1.72, 5.08)
13 - Ireland 2.57 3.06 (1.59, 5.89)
Subtotal (95% CI) 100.00 1.39 (1.23, 1.58)
Test for heterogeneity: χ² = 57.37, df = 20 (P < 0.0001), I² = 65.1%
Test for overall effect: Z = 5.08 (P < 0.00001)
0.1 0.2 0.5 1 2 5 10
Rural Urban
Fig. 3. Urban–rural comparisons of mood disorders, unadjusted OR with 95% CI.
Urban–rural differences
1.44), P< 0.001 (15 comparisons; data not
Figure 4 shows the unadjusted OR for anxiety
disorders (12 comparisons). The number of com-
parisons in this figure is lower (n= 12) than those
for Ôany disorderÕ(n= 16) or Ômood disorderÕ
(n= 21). Of these 12, nine were from Europe
and three from outside Europe. The majority of
unadjusted OR indicated no difference (67%).
Thirty-three per cent indicated an urban–rural
OR significantly higher than 1. The pooled
unadjusted OR for anxiety disorders was 1.21
(1.02–1.42), P= 0.03. The pooled adjusted OR
was 1.13 (1.00–1.28), P= 0.06 (11 comparisons;
data not shown).
Figure 5 shows the unadjusted OR for substance
use disorders (13 comparisons). Of the 13 available
comparisons, 10 were from Europe and three from
outside Europe. As was the case with anxiety
disorders, the majority of unadjusted OR indicated
no difference (69%). Three studies found a signif-
icant urban–rural OR higher than 1 (23%) and
Review: Urban rural differences
Comparison: 01 Inverse var
Outcome: 04 Anxiety disorders - unadjusted
Study Odds ratio (random) Weight Odds ratio (random)
or sub-category 95% CI % 95% CI
01 Sub-category
15 - Belgium 5.25 0.62 (0.36, 1.06)
15 - Italy 8.62 0.90 (0.67, 1.21)
27 - South Korea 10.26 0.93 (0.76, 1.12)
15 - Spain 9.00 0.99 (0.75, 1.30)
20 - United States 9.54 1.13 (0.89, 1.44)
15 - Germany 8.67 1.19 (0.88, 1.59)
28 - Australia 9.39 1.22 (0.95, 1.56)
15 - The Netherlands 3.39 1.39 (0.65, 2.97)
15 - France 9.17 1.41 (1.09, 1.83)
18 - The Netherlands 9.49 1.44 (1.13, 1.83)
19 - Germany 8.76 1.48 (1.11, 1.98)
17 - Norway 8.45 2.37 (1.75, 3.22)
Subtotal (95% CI) 100.00 1.21 (1.02, 1.42)
Test for heterogeneity: χ2 = 43.53, df = 11 (P < 0.00001), I2 = 74.7%
Test for overall effect: Z = 2.22 (P = 0.03)
0.2 0.5 1 2 5
Favours treatment Favours control
Fig. 4. Urban–rural comparisons of anxiety disorders, unadjusted OR with 95% CI.
Review: Urban rural differences
Comparison: 01 Inverse var
Outcome: 03 Substance use disorders - unadjusted
Study Odds ratio (random) Weight Odds ratio (random)
or sub-category 95% CI % 95% CI
01 Sub-category
15 - Belgium 5.49 0.56 (0.22, 1.40)
20 - United States 9.58 0.61 (0.42, 0.87)
19 - Germany 8.77 0.90 (0.57, 1.44)
15 - Spain 6.74 0.94 (0.45, 1.96)
27 - South Korea 10.90 0.99 (0.88, 1.12)
28 - Australia 10.32 1.20 (0.94, 1.54)
15 - The Netherlands 2.90 1.38 (0.30, 6.36)
15 - France 7.23 1.41 (0.72, 2.73)
15 - Italy 2.60 1.50 (0.29, 7.77)
12 - Great Britain 9.64 1.60 (1.12, 2.28)
15 - Germany 6.32 2.15 (0.98, 4.75)
18 - The Netherlands 10.02 2.33 (1.73, 3.14)
17 - Norway 9.50 3.71 (2.56, 5.37)
Subtotal (95% CI) 100.00 1.31 (0.97, 1.78)
Test for heterogeneity: χ2 = 87.24, df = 12 (P < 0.00001), I2 = 86.2%
Test for overall effect: Z = 1.77 (P = 0.08)
0.1 0.2 0.5 1 2 5 10
Favours treatment Favours control
Fig. 5. Urban–rural comparisons of substance use disorders, unadjusted OR with 95% CI.
Peen et al.
one study (8%) found a significant urban–rural
OR less than 1. The pooled unadjusted OR was
1.31 (0.97–1.78), P= 0.08. The adjusted OR was
1.03 (0.85–1.26), P= 0.74 (13 comparisons; data
not shown).
Several possible sources of heterogeneity, apart
from urban–rural variations, can be put forward.
These sources can be differences in culture or
socioeconomic status of the countries involved, but
also differences in the contents of the prevalence
rates used and the way in which they were estab-
lished (see Table 2). Therefore, we made some
additional comparisons within the diagnostic cate-
gories reported in this study (if the available number
of studies was sufficient to do so). To analyse
possible heterogeneity due to culture, we compared
the pooled (unadjusted) prevalence rate for mood
disorders for European studies to the pooled rate for
the North American studies (see Tables 1 and 2). No
difference was found [1.44 (CI: 1.20–1.71) and 1.40
(CI: 1.08–1.82) respectively]. Furthermore, we anal-
ysed possible heterogeneity due to method in which
prevalence rates were established in each study.
Therefore, we compared prevalence rates for Ôany
disorderÕbased on diagnostic instruments to rates
based on cut-off scores (see Tables 1 and 2). No
differences were found in both unadjusted [1.30 (CI:
1.05–1.60) and 1.59 (CI: 1.18–2.13)] and adjusted
rates [1.17 (CI: 1.01–1.35) and 1.29 (CI: 1.16–1.44)].
Subsequently, possible heterogeneity within diag-
nostic groups was analysed. First, within (unad-
justed) rates for mood disorders, studies from which
rates of major depression were used compared with
other studies (mainly containing mood disorders in
general; see Table 2). No difference was found [1.48
(CI: 1.15–1.90) and 1.36 (CI: 1.19–1.56)]. Likewise,
within (unadjusted) rates for substance use disor-
ders, we compared studies presenting rates for
alcohol dependence and abuse to studies also
including drug dependence and abuse. No difference
was found [1.33 (CI: 0.79–2.25) and 1.26 (CI: 0.86–
This is the first meta-analysis investigating urban–
rural differences in prevalence rates for common
mental disorders. Using only higher quality studies
performed since 1985 in high income countries, it
was shown for both Ôany disorderÕ(38% higher),
mood disorders (39%) and anxiety disorders (21%)
that the pooled urban prevalence rate was higher
in urban areas compared with rural areas. No
difference was found for substance use disorders.
In addition, when controlling for important
confounders, we found slightly lower, but statisti-
cally significant, pooled OR. While the number of
confounders was generally considerable, this dif-
ference between adjusted and unadjusted ratios
was limited, showing that urban–rural differences
are only partly explained by population character-
Although both the use of standardized diagnos-
tic instruments and the extent to which findings are
adjusted for potential confounders has significantly
increased since the period before 1985, the current
study thus confirms less systematically evaluated
findings from earlier reviews (2–7).
One could argue that the association with
urbanization presented here is low at 1.21 (1.09–
1.34) for Ôany disorderÕ. Compared to other factors
associated with the prevalence of psychiatric
disorders – such as being unmarried or childhood
abuse – the strength of the association with
urbanization is limited. Nevertheless, it remains
intriguing that, even when controlling for a rela-
tively large number of confounders, the urban
environment seems to be associated with the
prevalence of psychopathology. This association
does not appear to be explained solely by popula-
tion characteristics such as age, gender, marital
status, social class or ethnicity. In line with studies
examining the association between the urban
environment and schizophrenia (29), we found
that the urban environment appears to be associ-
ated with mental health. Further study is needed to
establish whether this association can partly be
explained by gene–environment interactions (30).
Furthermore, the practical implications of 34%
more cases in urbanized areas are significant in
terms of service allocation and healthcare budget.
The allocation of more services to urban areas is
not only desirable because of the prevalence rates,
but also because comorbidity rates tend to be
higher in urban areas (18, 22). Generally, the
distribution of funds does not keep up with the
extra need for services in urban areas. The conse-
quences are, for instance, relatively long waiting
lists and pressure to keep treatments and admis-
sions short, putting the quality of care at risk.
Ideally, a match between the provision of services
and demand for mental health care is the best
option. Based on our findings, urbanization may
be a useful indicator for allocating mental health
funds and services.
When interpreting these findings, a number of
potential limitations should be addressed. Several
possible sources of heterogeneity apart from
urban–rural variation can be mentioned concern-
Urban–rural differences
ing this study. As the analysis contains studies in a
period of 20 years from all over the world there is
possible heterogeneity due to diagnostic methods,
culture and socioeconomic status for instance.
Apart from this, also differences in the diagnostic
contents of the prevalence rates used may be a
source of heterogeneity. For instance, rates used
for the analysis of mood disorders containing ÔonlyÕ
major depression may have a different relation to
urbanization compared to rates containing all
mood disorders. In addition, the latter contrast
may also represent a difference in severity. In a
secondary analysis we made some comparisons
concerning possible heterogeneity due to culture
(Europe vs. North America), diagnostic method
(diagnostic instruments vs. cut-off scores) and
diagnostic content (major depression vs. mood
disorders as a whole and alcohol abuse depen-
dence vs. substance use disorders as a whole).
These comparisons did not show any significant
differences, which may lower concerns about sys-
tematic heterogeneity in this study.
It has to be taken into account that there is
comorbidity between diagnostic groups reported in
this study, for instance between anxiety and mood
disorders. This means that some research subjects
will be present in more than one comparison.
A more or less similar point is that studies which
are included in two or more diagnostic groups
analysed here, have a relatively larger weight
compared to studies which are only included in
one diagnostic group.
A limitation of the study is that schizophrenia
was not included as a separate diagnostic category.
It is difficult to generate reliable prevalence rates
for schizophrenia from general population studies
due to both the low prevalence of schizophrenia in
the non-institutionalized community, and to selec-
tive exclusion of these patients from population
surveys (31). Accordingly, most of the studies in
our analysis did not present rates for schizophre-
Our review included two multi-country studies
(13, 15) (one deals with mood disorders only), and
we presented the results for each of the individual
countries. As there is a wide variation of outcomes
between countries within these studies, and as the
findings do not systematically differ from other
studies, we believe this is the preferred strategy.
Presenting ratios for the total study area only
would have resulted in the loss of information
about variation between countries within the areas.
The Esemed study, for example, found that
Belgium, which has higher total rural rates com-
pared to urban rates, differs substantially from its
neighbouring countries (15).
One could argue that using dichotomized
measures for urbanization would underestimate
the influence of this factor on levels of psycho-
pathology. Using continuous measures or com-
paring the extremes of more than two categories
of urbanization, would probably yield a signifi-
cant difference more easily. However, most
studies did not provide such data. Furthermore,
this rule applies only to studies of large con-
nected areas (countries, for example). However,
the choice of either one or the other separate
area in a Ôtwin studyÕhas implications for the
possibility of finding differences (7). When one
chooses to compare one typically rural area
with a metropolitan area, the initial differences
in urbanization are probably greater than
between the extremes of a division into five
categories of a whole country. After all, ÔurbanÕ
and ÔruralÕare relative concepts, and their oper-
ationalization will probably always differ between
To explain inner-city and urban–rural varia-
tions in psychiatric morbidity, there are two main
theoretical concepts, which originated from the
early ecological research of schizophrenia (32) and
from the Chicago School of Sociology (33): the
drift hypothesis and the breeder hypothesis. The
drift hypothesis assumes on the one hand that
sick and vulnerable people are more or less
doomed to remain in socially unstable, deprived
neighbourhoods, while better off people move
away (social residue theory; 34). On the other
hand, socially deprived neighbourhoods can also
have a Ôpull-functionÕon sick and vulnerable
people, as they move to these areas with low
social control and greater tolerance towards
deviant behaviour (social drift hypothesis). Evi-
dence concerning drift processes is still sparse (6,
35). However, concentration of schizophrenic
patients in deprived inner-city areas has been
described in numerous ecological studies (32, 36).
It remains to be seen however, if these supposed
drift processes apply to all psychiatric illnesses.
The second theory, the breeder hypothesis,
assumes that various environmental factors
cause illness. These can be physical factors (air
pollution, small housing, population density) and
also social factors (stress, life events, perinatal
aspects, social isolation). A lot of the stress
factors mentioned above are more common in
urbanized areas (1, 37).Urbanization is modestly
but consistently associated with the prevalence
of psychopathology. This should be further
examined in studies of the aetiology of mood
and anxiety disorders in particular. Levels of
urbanization should also be taken into account
Peen et al.
when planning the allocation of mental health
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Urban–rural differences
... 14 Some reviews on rural-urban differences for psychiatric disorders show urbanization as risk factor while others contradict this concept. 15 Islamabad, the capital of Pakistan has five zones among which two are designated for urban development and other three for rural development. 16 Islamabad is well developed in health care facilities and its rural population can conveniently approach these facilities in contrast to other deprived rural areas of Pakistan. ...
... 18 In a meta-analytic study conducted to find urban-rural differences in prevalence of psychiatric disorders published since 1985, it was exhibited that pooled total prevalence was significantly higher in urban population as compared to rural population for psychiatric disorders. 15 In another study conducted in US no significant association was seen between urbanity and prevalence of depression. In this study no significant differences were found in adults of large metropolitan and rural areas. ...
... 19 In another study conducted on US population, contrary to expectation, the prevalence of most psychiatric disorders was similar across rural-urban continuum which concluded rurality as not being a risk factor for any psychiatric disorder or trauma exposure. 20 Few studies link Urbanization as a risk factor for mental disorders 15 and on contrary literature also reveals high prevalence of major depressive disorders in rural areas. 21 Prevalence of anxiety disorders was reported to be 18% in US 1 and in European Union more than sixty million people gets affected per year. ...
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Background: Depression, anxiety and stress are most prevalent causes of disease burden in common population. Due to rapid economic and social changes same increasing trend of these disorders has also been observed in Pakistan. Objective: To compare level of depression, anxiety and stress between rural and urban population of Islamabad, Pakistan. Methodology: A cross-sectional comparative survey was administered on a sample of n=386 participants. A total of n=193 participants belonged to rural population and n=193 belonged to urban population of Islamabad. Participants were interviewed and responses were rated on Depression Anxiety Stress 21 scale (DASS). Data was analyzed by using frequency, percentages, mean and standard deviation. To compare rural and urban population independent t-test was used. Results: The mean age of study participants was 32.46±9 years and 33.75±9.98 years in urban and rural population respectively. There was no significant difference at p≥0.05 between total scores of depression, anxiety and stress among rural and urban participants. Conclusion: Rural and urban population of Islamabad does not vary significantly on the basis of depression, anxiety and stress level. Keywords: Anxiety, DASS 21, Depression, Anxiety, Islamabad, Pakistan
... Psychiatric disorders cause significant burden to affected individuals, their friends and family, and the healthcare system [2]. However, the burden of mental health problems is not felt equally across the population and may not be equal between people living in urban vs. rural locations [e.g., [3][4][5] Some studies, including a meta-analysis, find that any psychiatric disorder, mood disorders, and anxiety disorders are more common in people living in urban settings than rural settings [4][5][6]. Other studies find that hazardous alcohol use, alcohol use-related harms, and PTSD are more common in rural communities as compared to urban ones [6,7], whereas some studies find that rates of all psychiatric disorders are similar between urban vs. rural dwellers [8]. ...
... Psychiatric disorders cause significant burden to affected individuals, their friends and family, and the healthcare system [2]. However, the burden of mental health problems is not felt equally across the population and may not be equal between people living in urban vs. rural locations [e.g., [3][4][5] Some studies, including a meta-analysis, find that any psychiatric disorder, mood disorders, and anxiety disorders are more common in people living in urban settings than rural settings [4][5][6]. Other studies find that hazardous alcohol use, alcohol use-related harms, and PTSD are more common in rural communities as compared to urban ones [6,7], whereas some studies find that rates of all psychiatric disorders are similar between urban vs. rural dwellers [8]. ...
... The current study used data from a cohort of treatment-seeking patients to address whether rates of psychiatric disorders and trauma exposure, psychiatric symptom severity, and functioning differed between people living in rural vs. urban municipalities. We hypothesized that patients residing in urban areas would have higher prevalence of mood and anxiety disorders than patients residing in rural areas [4], but that patients residing in rural areas would have greater psychiatric symptom severity than patients residing in urban areas [10]. ...
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Objective Identifying whether certain groups of people experience elevated rates or severities of psychiatric symptoms provides information to guide healthcare allocation. People living in urban areas have higher rates of some psychiatric disorders relative to people living in rural settings, however, it is unclear if psychiatric severity is more elevated in urban vs. rural settings. This study investigates the urban vs. rural differences in rates of psychiatric disorders and severity of psychiatric symptoms. Method A cohort of patients (63% women, 85% White) presenting to an outpatient psychiatric treatment center in the U.S. completed patient-reported outcomes at all clinic visits as part of standard care. Rurality was determined by municipality population density. Sociodemographic characteristics, psychiatric diagnoses, trauma exposure, psychiatric symptom severity, functioning, and suicidality were compared by rural vs. urban municipality. Results There were virtually no differences between patients living in rural vs. urban municipalities on rates of psychiatric disorders, severity of psychiatric symptoms, functional impairment, and suicidality ( p s≥.09). The only difference was that patients living in rural municipalities had higher exposure to serious accidents than patients living in urban municipalities ( p < .01); exposure to nine other traumatic events did not differ between groups ( p ≥.07). Conclusions People living in urban and rural municipalities have a similar need for mental health treatment. Access to care may be one explanatory factor for the occasional rural-urban differences in rates of psychiatric disorders. In other words, if people living in rural areas can access care, their symptom presentations appear unlikely to differ from those of people living in urban areas.
... Recently, living condition, including urbanisation and deprivation, has become regarded as important social determinants of health. 18 Contemporary evidence is growing about the relationship between urbanisation and mental health, [19][20][21][22][23][24][25] which a review suggested the negative effect of urbanisation on mental health. 20 Regarding loneliness, studies using simple measurement (eg, density) found no associations with loneliness especially when the perceived quality of neighbourhood is controlled. ...
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Objectives The aim of this study was to investigate the association of living condition (deprivation and urbanisation level) with loneliness and social isolation. Study design Cross-sectional study. Methods Data were retrieved from the Japan COVID-19 and Society Internet Survey, a nationwide online cohort study, conducted from September to October 2021. Area Deprivation Index and Densely Inhabited District data were used as indicators of living condition. The Japanese version of the University of California, Los Angeles Loneliness Scale and Lubben Social Network Scale-6 were used to assess loneliness and social isolation, respectively. Analysis of covariance was performed to compare the difference in outcome variables by living conditions (four categories), adjusting for gender, age, educational attainment, income, marital status, people living with, work status and subjective health status. Results A total of 27 520 community dwelling people were included in this study. For loneliness, living condition did not show a significant difference in the adjusted model, while deprived area had a tendency to show high loneliness. For social isolation, there was a significant group difference in the adjusted model (p<0.001), and living in a higher urbanisation level and lower deprivation showed the highest score for social isolation. Conclusion Overall, the effects of the living condition on loneliness and social isolation were small. Further study is needed to explore more comprehensive environment factors affecting loneliness and social isolation.
... Gray space refers to the presence of impervious land including buildings, concrete, parking lots, roads, and rooftops (see Figure 1; Acevedo-Garcia et al., 2020). While urbanization has many benefits to society, it is associated with increased levels of chronic mental health outcomes (e.g., anxiety and depression; Wang, 2004;Peen et al., 2010;Lederbogen et al., 2011) and biological stress responses (e.g., hypertension) that lead to oxidative stress, inflammation, and neuronal injury (Bernabe-Ortiz et al., 2017;Xu et al., 2018), which in turn may impact brain development among youth. Studies have provided evidence that built environmental factors during childhood and adolescence impact brain function and structure. ...
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Introduction: Aspects of the built environment relate to health factors and equity in living conditions, and may contribute to racial, ethnic, or economic health disparities. For example, urbanicity is linked with negative factors including exposure to gray space (e.g., impervious surfaces such as concrete, streets, or rooftops). While there is existing research on access to green space and urbanicity on some mental health and cognitive outcomes, there is limited research on the presence of gray space linked with cognitive functioning in youth. The goal of this study was to investigate the link between gray space and amygdala-default mode network (DMN) connectivity. Methods: This study used data from the ABCD Study. Participants (n = 10,144; age M = 119.11 months, female = 47.62%) underwent resting-state fMRI acquisition at baseline. Impervious surfaces (gray space) were measured via the Child Opportunity Index (COI). To examine the relationship between presence of gray space and-amygdala-DMN (left/right) connectivity, we employed linear mixed effects models. Correlations were run between amygdala-DMN connectivity and internalizing and externalizing symptoms. Finally, post hoc sensitivity analyses were run to assess the impact of race. Results: More gray space, adjusting for age, sex, and neighborhood-level variables, was significantly associated with increased left amygdala-DMN connectivity (p = 0.0001). This association remained significant after sensitivity analyses for race were completed (p = 0.01). No significant correlations were observed between amygdala-DMN and internalizing or externalizing symptoms. Discussion: Findings suggest gray space was linked with increased left amygdala-DMN connectivity, circuits that have been implicated in affective processing, emotion regulation, and psychopathology. Thus gray space may be related to alterations in connectivity that may enhance risk for emotion dysregulation. Future investigation of these relationships is needed, as neuroimaging findings may represent early dysregulation not yet observed in the behavioral analyses at this age (i.e., the present study did not find significant relationships with parent-reported behavioral outcomes). These findings can help to inform future public policy on improving lived and built environments.
... The outcomes of our study should develop an ecological awareness about the future of nature in cities as urban liveability may be considerably compromised by reducing of large green cores and corridors in the context of ecosystem services, global climate change, biodiversity decline, physical health and even mental crisis of urban dwellers (Peen et al., 2010). ...
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Compared to pristine ecosystems, urban protected areas (PAs) are exposed to intensified pressure and deterioration due to rapid population growth and entangled stakeholders’ interests. At the same time, these valuable ecosystems provide cities with ecosystem services, including cultural ones, and enhance the quality of life. Spatial analysis of PAs’ transformations in the context of the multidisciplinary approach contributes to the detection and safeguarding of vulnerable ecosystems. The study object is the protected areas of Moscow megapolis (within boundaries until 2012), whereas the study subject is the spatial and temporal PA’s transformations established by legislative acts. The research question is to devise a model of transformations designated by law within urban PAs and affecting their borders, land use, and rate of ecosystem deterioration. To achieve the research question, three goals were set: to gather spatial data on PAs’ transformations within Moscow designated by legislative acts; to design a comprehensive and exhaustive classification of PAs’ transformations established by legislative acts; to model spatial and temporal trends in transformations of Moscow PAs (1985-2022), according to the classification devised. The 3-compound framework for the analysis of legislative transformations (downgrading, downsizing, degazettment of protected areas) was coupled by content analysis of transformation events, GIS mapping, and spatial analysis of urban vegetation through NDVI (normalized difference vegetation index) estimations and raster computations in QGIS and GDAL software. The originality of our study derives from: the analysis of the 4th transformations’ compound (design failures of new PAs); spatial comparison with positive transformations, strengthening nature conservation; uncovering detailed subtypes and levels of transformations; applying this approach to the local scale of megapolis. Our study is based on: 1985-2022 legislative acts with text and map representations of PAs’ borders, zones and land-use designated by regional government and national ministries; national and Moscow open-access spatial data hubs; Moscow online news; 2001-2021 Landsat imageries and Global Forest Change data on Moscow region. Adverse transformations affected a larger area than positive ones (53.8% of a total PA area compared to 22.6%). Positive transformations contributed by PAs’ design (49.5%) mostly, while adverse ones – by easing of restrictions on land use (60.3%) and failures in the design of new PAs (22.8%). Adverse transformations are mainly reflected in the downsizing of zones with the strictest prohibitions on land use (-68% on average) and a low share of designed PAs (54%) through the period 1985-2022. Woodland plantations dramatically expanded (+86.5%), replacing seminatural urban forests (2005-2021). Hence, PA’s ability to supply ecosystem services has been considerably diminished. In regard to Moscow, considerable adverse trends in nature protection were revealed, generally hidden from the public. The analyzed typology of Moscow PAs’ transformations is quite conventional and may be improved through comparisons with other megapolises abundant in natural heritage to advance the model devised and elicit threats to nature conservation.
In this article, the author demonstrates how one can use large-scale and publicly available online review data to study the rise in anxiety in the United States. Using the anxiety keyword list from the dictionary compiled by Linguistic Inquiry and Word Count, the author analyzed the text of approximately 7 million online reviews submitted by Yelp reviewers across 13 U.S. states from 2006 to 2021. The overall pattern confirms existing discourse that anxiety has been constantly rising in Western societies since 2000. Beyond documenting the overall pattern, online review data enable the disaggregation of this pattern by geographies, price levels, and individuals, thereby providing a more comprehensive and detailed picture than previously documented in existing literature. Additional analysis shows that anxiety is increasing faster than other emotions, such as anger and sadness.
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Urban life affects us greatly and serves as more than just a backdrop to our lives. Urban planning holds the ability to influence our emotions, levels of stress, and general state of mind in addition to its functional elements. This article examines the delicate relationship between several aspects of urban design, including greenery, layout, and buildings, and how it affects people living in cities psychologically. I aim to advance knowledge of the mutually beneficial interaction between urban environments and the human psyche by exploring the role of urban design in fostering mental health. We are faced with a crucial question as we make our way through the maze-like pathways of this discourse: how might urban settings transform from passive contributors to mental discomfort to proactive catalysts for well-being? Entering this space entails investigating the dichotomy of urban life, which is a dance between opportunities and difficulties, energy, and unrest. We explore the theories of urban designers, the designs of architects, and the confusing networks of psychological health in search of solutions. This exploration reveals the complex interplay between urban environments and mental states, challenging us to think of cities as complex ecosystems that support the human locus rather than merely as concrete jungles.
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The environment influences mental health, both detrimentally and beneficially. Current research has emphasized the individual psychosocial 'microenvironment'. Less attention has been paid to 'macroenvironmental' challenges including climate change, pollution, urbanicity and socioeconomic disparity. With the advent of large-scale big-data cohorts and an increasingly dense mapping of macroenvironmental parameters, we are now in a position to characterise the relation between macroenvironment, brain, and behaviour across different geographic and cultural locations globally. This review synthesises findings from recent epidemiological and neuroimaging studies, aiming to provide a comprehensive overview of the existing evidence between the macroenvironment and the structure and functions of the brain, with a particular emphasis on its implications for mental illness. We discuss putative underlying mechanisms and address the most common exposures of the macroenvironment. Finally, we identify critical areas for future research to enhance our understanding of the aetiology of mental illness and to inform effective interventions for healthier environments and mental health promotion.
Errors in Byline, Author Affiliations, and Acknowledgment. In the Original Article titled “Prevalence, Severity, and Comorbidity of 12-Month DSM-IV Disorders in the National Comorbidity Survey Replication,” published in the June issue of the ARCHIVES (2005;62:617-627), an author’s name was inadvertently omitted from the byline on page 617. The byline should have appeared as follows: “Ronald C. Kessler, PhD; Wai Tat Chiu, AM; Olga Demler, MA, MS; Kathleen R. Merikangas, PhD; Ellen E. Walters, MS.” Also on that page, the affiliations paragraph should have appeared as follows: Department of Health Care Policy, Harvard Medical School, Boston, Mass (Drs Kessler, Chiu, Demler, and Walters); Section on Developmental Genetic Epidemiology, National Institute of Mental Health, Bethesda, Md (Dr Merikangas). On page 626, the acknowledgment paragraph should have appeared as follows: We thank Jerry Garcia, BA, Sara Belopavlovich, BA, Eric Bourke, BA, and Todd Strauss, MAT, for assistance with manuscript preparation and the staff of the WMH Data Collection and Data Analysis Coordination Centres for assistance with instrumentation, fieldwork, and consultation on the data analysis. We appreciate the helpful comments of William Eaton, PhD, Michael Von Korff, ScD, and Hans-Ulrich Wittchen, PhD, on earlier manuscripts. Online versions of this article on the Archives of General Psychiatry Web site were corrected on June 10, 2005.
Background Health planning should be based on data about prevalence, disability and services used. Aims To determine the prevalence of ICD-10 disorders and associated comorbidity, disability and service utilisation. Method We surveyed a national probability sample of Australian households using the Composite International Diagnostic Interview and other measures. Results The sample size was 10 641 adults, response rate 78%. Close to 23% reported at least one disorder in the past 12 months and 14% a current disorder. Comorbidity was associated with disability and service use. Only 35% of people with a mental disorder in the 12 months prior to the survey had consulted for a mental problem during that year, and most had seen a general practitioner. Only half of those who were disabled or had multiple comorbidity had consulted and of those who had not, more than half said they did not need treatment. Conclusions The high rate of not consulting among those with disability and comorbidity is an important public health problem. As Australia has a universal health insurance scheme, the barriers to effective care must be patient knowledge and physician competence.
This paper compares the findings in rural Korea and in the urban capital city of Seoul, Korea. In rural Korea 1966 subjects and in Seoul 3134 subjects were interviewed with the National Institute of Mental Health Diagnostic Interview Schedule. We found that the prevalence of psychiatric disorders, including alcohol dependence, was higher in rural Korea than in the urban setting. Comparing men and women, the prevalence of psychiatric disorders was higher in women. The exception was alcohol abuse/dependence, which was much higher among men, as was antisocial personality disorder. Comparison of prevalence by age groups showed that, in contrast to American studies, there was a tendency for prevalence to increase with increasing age. Alcohol abuse/dependence was much higher in rural Korea compared with St. Louis. Drug abuse/dependence was much higher in St. Louis. Other interesting similarities and differences are discussed.
Evidence regarding the recent suggestion of greater prevalence of depressive symptoms in urban than in rural communities is evaluated. National data are reviewed which indicate that, whereas depressive symptoms may be less prevalent in rural than in urban communities, mean depression scores of residents of rural farm areas outside of standard metropolitan statistical areas are almost as high as scores of center city residents. Further data are presented from a study of urban and rural Florida residents which indicate a significantly higher mean number of depressive symptoms in urban than rural areas, though this difference was eliminated by controls for income, education, age, and sex. When persistent depressive symptoms were examined, urban-rural differences were not significant. The implications of the present findings for urban-rural differences in depressive disorder are considered and the need to examine the urbanicity hypothesis on a data base allowing appropriate controls for socioeconomic status-related variables and migration is emphasized.
• We studied rural/urban differences in the prevalence of nine psychiatric disorders from a community survey (part of the Epidemiologic Catchment Area Program) of 3,921 adults living in the Piedmont of North Carolina. Crude comparisons disclosed that major depressive episodes and drug abuse and/ or dependence were more common in the urban area, whereas alcohol abuse/dependence was more common in the rural area. When prevalence for these disorders was stratified for age, sex, race, and education (factors that may confound urban/rural comparisons), a number of significant differences were identified, such as higher prevalence of major depression in female and white subjects and higher prevalence of alcohol abuse/dependence in the less educated subjects. A logisticregression analysis was used to determine if significant urban/ rural differences persisted when these potential confounders were controlled. Major depressive disorders were found to be twice as frequent in the urban area in this controlled analysis.