Content uploaded by Naghmana Ghafoor
Author content
All content in this area was uploaded by Naghmana Ghafoor on Jan 04, 2022
Content may be subject to copyright.
URBAN ANNOYANCES AND MENTAL HEALTH
IN THE CITY OF LAHORE, PAKISTAN
NUZRAT YAR KHAN, NAGHMANA GHAFOOR,
RABIA IFTIKHAR, and MARIA MALIK
Sustainable Development Study Center, Government College
University, Lahore, Pakistan
ABSTRACT: Lahore has undergone rapid urbanization in recent decades. Population growth
has far exceeded carrying capacity of municipal infrastructure, causing stress. We conducted a
survey to assess citizens’ responses to urban annoyances and prevalence of depression, loss of
self-esteem, and loss of resilience. An 84-item questionnaire was used to interview a sample of
370 respondents. Statistical analyses included correlations, ANOVA, factor analysis, and Multiple
Regression Analysis. Results showed that respondents were disturbed by urban annoyances. Those
disturbed were also depressed, had low self-esteem, low resilience, and an external locus of control.
Depression was strongly affected by education, population density, and household congestion. We
concluded that a degraded urban environment has caused psychological stress among citizens as
reflected in the prevalence of depression, loss of self-esteem, and low resilience. There is an urgent
need for strategic planning aimed at checking unbridled urban growth, improving civic services,
and ensuring better mental health of citizens.
Urbanization is a global phenomenon and a dominant demographic trend, which is bringing
about significant changes to the planet’s landscape. At the present time, slightly more than half
the population of the world lives in urban areas (United Nations, 2009), which occupy only 4%
or less of the world’s terrestrial surface (Grimm et al., 2008). The urban population of the world
is growing at an alarming average annual rate of 1.8% and is projected to increase by 84% by
2050 (United Nations, 2009). With this rate of increase, the world’s urban population will double
in 39 years and more than 95% of this net increase in the global population will be in cities of the
developing world (United Nations, 2005). This phenomenal growth is reflected in the existence of
21 megacities (population 10 million or more in each) in the world at the present time in contrast
with the 1975 figure of only three megacities, that is, New York, Tokyo, and Mexico City (United
Nations, 2009).
In the developing world, the process of urbanization poses huge socioeconomic (Bloom,
Canning, & G¨
unther, 2008; Montgomery, 2008) and environmental (Grimm et al., 2008) prob-
lems. According to the Population Division of the United Nations, the urban population of the
developing world will exceed rural population by 2030 (United Nations, 2005). Urbanization
Direct correspondence to: N. Y. Khan, 12, Nanook Crescent, Ottawa, Ontario K2L 2A7, Canada. E-mail:
nuzrat1600@yahoo.ca.
JOURNAL OF URBAN AFFAIRS, Volume 34, Number 3, pages 297–315.
Copyright C
2011 Urban Affairs Association
All rights of reproduction in any form reserved.
ISSN: 0735-2166. DOI: 10.1111/j.1467-9906.2011.00585.x
298 I
IJOURNAL OF URBAN AFFAIRS I
I
Vol. 34/No. 3/2012
in the developing world is almost invariably accompanied by congestion, pollution, deficits in
the provision of civic services, formation of periurban slums, increase in crime rate, and urban
poverty, adding to the stress of life in the city. The combined effect of these stressors increases the
risk of mental disorders in city dwellers. A number of publications since the 1980s have reported
higher rates of mental illnesses associated with living in urban areas as compared to rural areas
(Boydell & McKenzie, 2008; Krabbendam & van Os, 2005; McKenzie, 2008; Sundquist, Frank,
& Sundquist, 2004; van Os, Hanssen, & Bijl, 2001).
The estimated urban population growth rate of Pakistan is about 6%, which is one of the fastest
in the world (World Bank, 2006); among South Asian countries, Pakistan is the most urban.
Unfortunately, infrastructure development and maintenance in Pakistani cities, such as Karachi,
Lahore, Rawalpindi, and Faisalabad, has not kept pace with the population growth and, as such,
these urban centers are showing the symptoms of overpopulation and decaying infrastructure
(Haider & Badami, 2010). The quality of life in these cities has deteriorated and poses serious
risks to human health and socioeconomic well-being (Arif & Hamid, 2009).
Lahore is a very congested city with an estimated population of over 8 million (Sajjad, Shirazi,
Khan, & Raza, 2009) and an estimated population density of about 14,450 persons/km2(Almas,
Rahim, Butt, & Shah, 2005). Since the creation of Pakistan in 1947, the total area of Lahore city
has grown from 13 to 2,306 km2at the present time (Lahore Development Authority [LDA],
2004a, 2004b). In developing countries, such as Pakistan, migration from rural areas to urban
centers is a major cause for population increase in cities (Siddiqi, 2004) and the basic driver of
this migration is poverty (Farooq, Mateen, & Cheema, 2005).
The rapid urbanization of Lahore, especially after the 1980s, has introduced a variety of envi-
ronmental stressors: untreated industrial and municipal effluents have degraded the waterways,
uncollected solid wastes in residential areas have become a serious health risk to communities,
traffic congestion on city roads has a distressing effect on commuters, and vehicle exhaust poses
a serious health threat to the residents of the city (Haider & Badami, 2010). There has been
an astronomical increase in the number of registered motorized vehicles in Lahore in recent
decades; the number of registered vehicles in 1974 was only 39,205 but increased to 1,464,344
in 2006 (Punjab Development Statistics, 2007). The infrastructure deficit is clearly visible in
traffic congestion, uncollected garbage in residential areas, flooding in the rainy season, loss
of green spaces, and many other environmental annoyances (Haider & Badami, 2010; Saj-
jad et al., 2009). The loss of green spaces and the accompanying surface imperviousness has
caused the average annual temperature to rise sharply. A recent study has reported that since
1950 the mean annual temperature and mean minimum temperature have risen by 0.89◦C and
2.51◦C, respectively, and most of this increase took place between 1975 and 2007 (Sajjad et al.,
2009).
In recent years, a number of papers have reported a high prevalence of mental disorders in
both urban and rural populations in Pakistan. A relatively recent review (Mirza & Jenkins, 2004)
suggested an overall 34% prevalence of anxiety and depressive disorders in Pakistan and attributed
it to social and economic causes such as gender inequality, financial difficulties, and arguments
with spouses. A study of three capital cities (Lahore, Karachi, and Quetta) reported that Lahore
had the highest number of depressives (53.4%) as opposed to Quetta (43.9%) and Karachi (35%)
(Gadit & Mugford, 2007).
The higher prevalence of depressives in Lahore is attributed to high population density, among
other socioeconomic variables such as expressive nature of people, wider exposure to psychiatric
services, and relatively higher rates of unemployment and poverty. Another study on urban
Rawalpindi found that the level of emotional distress and psychiatric morbidity in a poor district
of the city was less than half those in a nearby rural village (Mumford, Minhas, Akhtar, Akhter,
I
I
Urban Annoyances and Mental Health
I
I299
& Mubbashar, 2000). However, these results need to be treated with caution (Mirza, 2001). The
“slum” population sampled in the study (Mumford et al., 2000) was relatively prosperous and
as such did not represent the population of Rawalpindi at large (Mirza, 2001); therefore, these
results could not be generalized to the urban population.
Urban annoyances and their cumulative stress are known to have profound behavioral responses
in urban inhabitants (Guite, Clark, & Ackrill, 2006) with far-reaching socioeconomic implications
(Insel, 2008; Knapp, 2003). Our aim in this study, therefore, was: (1) to assess the behavioral
responses of the citizens of Lahore to various urban annoyances; (2) to assess their psychological
well-being as reflected in the prevalence of depression, loss of self-esteem, the locus of control,
and psychological resilience; (3) to determine the relative contribution of urban annoyances and
socioeconomic conditions to the mental well-being of respondents; (4) to review and discuss the
socioeconomic implications of mental disorders in the context of the prevailing urban conditions
in the city of Lahore; and (5) to suggest policy implications to improve the urban environment
and ensure the mental health of the citizens.
METHODOLOGY
Study Area
The historic city of Lahore was the focus of the study. It is a city of over 8 million people and
growing rapidly to join the league of megacities (i.e., cities with a population of 10 million or
more) of the world. Our sampling was purposive using a snowball technique. We targeted local
businesses including financial institutions, government offices, and academic institutions.1Our
aim was to recruit individuals from different ranks and gender to provide a balanced represen-
tation of various socioeconomic groups, age groups, and levels of education. We first contacted
the heads of institutions to assist us in the selection of individuals based on our criteria. We also
requested the heads of institutions refer us to other institutions where we could get a similar
level of assistance in the recruitment of individuals. The total sample for the study was 370 re-
spondents, representing the three major sectors: government, private, and academic institutions.
The respondents were residents of a variety of residential areas of Lahore, from the low-income
neighborhoods such as Ravi Town to the high-income neighborhoods such as Lahore Canton-
ment. As such, our sample represented a broad cross-section of the population of Lahore. The
study was carried out between January and July 2009. There were no refusals from the indi-
viduals selected for our study. For the illiterate and less educated respondents, the questions
were rephrased when we thought that the respondent may not have understood the questions
correctly.
Instrument
An 84-item questionnaire was used as the survey instrument. It was designed for an overall
assessment of (1) the behavioral responses of the sampled population to various urban annoyances
(stressors) associated with rapid urbanization, such as congestion, pollution, noise, and deficits
in the provision of municipal amenities, and (2) the status of the mental health of the population
manifested as depression, self-esteem, locus of control, solastalgia (or feelings related to the
loss of environment once enjoyed and cherished), and psychological resilience. Some sections
used dichotomous scales and others were in Likert scales from 0–5. It was assumed that people
living in a degraded urban environment had low self-esteem, were less resilient, more depressed,
300 I
IJOURNAL OF URBAN AFFAIRS I
I
Vol. 34/No. 3/2012
and had high external locus of control. The items in the questionnaire were organized under the
following eight domains:
1. Demographics and socioeconomic background of the sampled population.
2. Responses to urban annoyances: A total of 25 major annoyances were selected from a
scale of perceived environmental annoyances in urban settings in France (Robin, Matheau-
Police, & Couty, 2007). The urban stressors selected were in our opinion more relevant to
the sociocultural and physical environment of the city of Lahore. The scale was reliable at
Cronbach’s α=0.87.
3. Depression: Level of depression was measured by a shorter 10-item version (GDS10 ) derived
from a validated 30-item Geriatric Depression Scale (GDS30) (Yesavage et al., 1982–1983)
and a shorter validated 15-item scale (GDS15) (Sheikh & Yesavage, 1986). The validity
of the GDS10 and even shorter versions as a screening tool for depression has been tested
by several authors (Almeida & Almeida, 1999; Cheng et al., 2010; Shah, Phongsathorn,
Bielawska, & Katona, 1996; van Marwijk et al., 1995).
4. The performance of GDS scales have also been found to have comparable validity in both
younger and older persons in a study measuring the effect of age on GDS in persons with
Parkinson’s Disease (Weintraub, Saboe, & Stern, 2007).
5. Self-esteem: We used the Rosenberg Self-Esteem Scale (RSES), which is made up of 10
items that refer to self-respect and self-acceptance rated on a 4-point Likert-type scale,
ranging from 1 (totally disagree)to4(totally agree). The ratings assigned to all the items
after reverse scoring the positively worded items are summed. Scores range from 10 to 40,
with higher scores indicating higher self-esteem. The scale generally has high reliability:
test–retest correlations are typically in the range of 0.82 to 0.88, and Cronbach’s alphas for
various samples are in the range of 0.77 to 0.88 (Blascovich & Tomaka, 1993; Rosenberg,
1986).
6. Locus of control: We used the 12-item scale for measuring Locus of Control (Rotter, 1966).
It measures generalized expectancies for internal versus external control of reinforcement.
People with an internal locus of control believe that their own actions determine the rewards
that they obtain, while those with an external locus of control believe that their own behavior
does not matter much and that rewards in life are generally outside of their control. Scores
range from 0 to 13. A low score indicates an internal control while a high score indicates
external control. The scale has a very good internal reliability, with a Cronbach’s alpha of
0.85.
7. Rresilience (psychological): Psychological resilience is a quality that helps a person through
daily stressors and major life crises. We used a shortened 15-item version of a 25-item
psychological resilience scale (Wagnild & Young, 1993) derived from a factor analysis
(Neil & Dias, 2001). Cronbach’s alpha coefficients range from 0.72 to 0.94 supporting the
internal consistency and reliability of the Resilience Scale.
8. Solastalgia: Solastalgia is a phrase to denote the sense of people distressed by the loss
of valued and cherished environment (Albrecht, 2005). Based on Albrecht’s innovative
concept of “solastalgia,” an environmental distress scale (EDS) was successfully measured
and validated (Higginbotham, Connor, Albrecht, Freeman, & Agho, 2007). Psychometric
analyses found that the EDS subscales were highly intercorrelated (r=0.83), and they
demonstrated both strong internal consistency and reliability (Cronbach’s alpha =0.96),
and test–retest reliability (=0.73). In this study, we used four of the nine items of the
solastalgia scale, which, in our judgment, were relevant to the urban environmental changes
that have taken place in the last couple of decades in Lahore. Our intent of including these
I
I
Urban Annoyances and Mental Health
I
I301
items in our questionnaire was to get some measure of the feelings of our respondents about
the way the urban environment and culture of the city of Lahore has changed as a result of
rapid urbanization.
9. Prevalence of headaches: Respondents were asked about the frequency of the occurrence
of headaches: very often, sometimes, or not at all. This question was included to determine
the correlation of this important somatic symptom, which has been reported to have strong
correlations with anxiety and depression (Husain, Chaudhry, Afsar, & Creed, 2004a; Minhas
& Nizami, 2006; Mumford et al., 1991).
Procedure
A pilot study of 20 volunteers from different socioeconomic groups was conducted to fine-
tune the questionnaire. The interviews were held with the questionnaire translated into the Urdu
language. The respondents were informed that their participation was voluntary and confidential.
The results of the pilot study necessitated rephrasing certain questions in the instrument, so that
they were more clearly understood by the respondents, especially those who were illiterate or
had low levels (primary and middle) of education.
Statistical Methods
Data were analyzed using SPSS 16. For investigating the relationship between variables,
we analyzed our data by Pearson’s Correlations. One-way analysis of variance (ANOVA) was
used to analyze the effects of independent variables on the dependent variables and Tukey’s
HSD procedure was performed to determine which groups differed from each other. For the 25
urban annoyances (Cronbach’s alpha =0.87), we conducted Factor Analysis as a data reduction
technique and to determine the reliability of the four arbitrary categories of annoyances. The
analysis grouped the 25 annoyances into only two groups, which we named degraded urban
environment and crowding and congestion. A Multiple Regression Model was used to analyze
the effects of the two factors of urban annoyances obtained from the factor analysis as well as the
socioeconomic variables on mental health, that is, depression, self-esteem, resilience, and locus
of control as follows:
Mental Health =f(education of the respondent,household income,household congestion,
residential area density,urban annoyances,and prevalence of severe headaches).
RESULTS
Demographic and Socioeconomic Profile
The demographic and socioeconomic profile of the sampled population is summarized in
Table 1. The age of was relatively young. The 20–35 group constituted 67.8% of the population
and only 32.2% were between the ages of 36–50 years and above. Males represented 59.2%
and a majority (58.4%) of the respondents were married; only 3% were widowed, divorced,
or separated, and 38.6% were unmarried. Income levels varied considerably (<Rs.10,000 to
302 I
IJOURNAL OF URBAN AFFAIRS I
I
Vol. 34/No. 3/2012
TABLE 1
Demographic and Socioeconomic Profile of the Sampled Population
Var iab le s N %
Age (years)
20–35 251 67.8
36–above 50 119 32.2
Gender
Male 219 59.2
Female 151 40.8
Marital Status
Married 260 58.4
Widowed/divorced/separated 11 3.0
Unmarried 143 38.6
Income (PK Rs.)
<10,000 38 10.2
11,000–30,000 104 28.1
31,000–50,000 80 21.6
51,000–70,000 65 17.6
71,000–90,000 48 13.0
>90,000 35 9.5
Level of Education
Illiterate 18 4.9
Below matriculation 18 4.9
Secondary 35 9.5
Graduation 80 21.6
Masters 124 45.7
Above masters 95 13.5
Employment
Government 203 54.9
Private sector 129 34.9
Semigovernment 38 10.3
Population Density of Residential Area (Persons/ha)
High density (351–600) 124 33.5
Medium density (151–350) 92 24.9
Low density (150 approx.) 154 41.6
Household Size
1–4 119 32.2
5–8 221 59.7
>8308.1
Household Congestion (Persons/Room)
1 172 46.5
2 120 32.4
3ormore 78 21.1
Home Ownership
Own 274 74.1
Rented 96 25.9
Prevalence of Severe Headaches
Very often 160 43.2
Sometimes 120 32.4
Not at all 90 24.3
I
I
Urban Annoyances and Mental Health
I
I303
above Rs. 90,000 per month). The level of education also varied significantly (illiterate to above
masters level) but a majority (80%) had graduation or higher levels of education. The majority
(54.9%) were public servants, the private sector employed 34.9%, and 10.3% were employed
by semigovernment institutions. Most (41.6%) lived in low-density (up to 150 persons/ha) res-
idential areas while 24.1% and 24.9% lived in medium (151–350 persons/ha) and high-density
(351–600 persons/ha) areas. The household size of a majority (59.7%) of respondents was 5–8
persons/household. A majority (78.9%) of the households had 1–2 persons/room and only 21.1%
had 3 or more persons per room. The classification of residential-density zones was adopted after
the Lahore Development Authority (2004a, 2004b). Most (74.1%) respondents lived in houses
owned by them or their parents and only 25.9% lived in rental accommodations.
Response to Urban Annoyances
Responses to the 25 urban annoyances (stressors) in our instrument were recorded on a 5-item
Likert Scale (Not Applicable, Not Disturbed, A Bit Disturbed, Disturbed, and A Lot Disturbed)
as shown in Table 2. Responses of respondents showed that most people were “a lot disturbed”
by all of the 25 annoyances as shown in the column for Total Disturbed. Only a small percentage
of respondents were either not disturbed by urban annoyances, or the annoyances were “not
applicable” to them because they did not experience them in their residential areas or places
of work. The percent responses in each domain indicated that crowding and congestion were
the most annoying urban stressors to respondents. These findings strongly suggest that Lahore’s
municipal infrastructure has far exceeded its carrying capacity and, as a result, an overwhelming
majority of its citizens are subjected to a variety of stressors associated with the city’s unbridled
and rapid urbanization.
Response to Questions Associated with Solastalgia
The response to questions relating to solastalgia showed that most respondents demonstrated
symptoms of solastalgia (90.3% felt sad when looking at the degraded city environment, 87.6%
missed peace and quiet once enjoyed, 83.8% were ashamed of the way their city looked now, and
80% thought that the degraded environment of their city had undermined their sense of belonging
to it; see Table 3). These responses are consistent with our results relating to the prevalence of
depressive tendencies accompanied by a loss of self-esteem, reduced resilience, and prevalence
of the external locus of control in the sampled population.
Statistical Analyses
Factor Analysis
For the 25 urban annoyances, we conducted Factor Analysis using Principal Component
Analysis with Varimax Rotation as a data reduction technique and to determine the reliability of
the four arbitrary categories of annoyances. The analysis grouped the 25 annoyances into only
two groups, which we named degraded urban environment and crowding and congestion,as
shown in Table 4.
Correlations
Table 5 shows the correlations of demographic, socioeconomic, and urban annoyances variables
with mental health. The results showed that age of the respondents had a positive correlation with
self-esteem ( p<0.01) indicating the respect and regard offered to elders in the Pakistani society,
304 I
IJOURNAL OF URBAN AFFAIRS I
I
Vol. 34/No. 3/2012
TABLE 2
Response to Urban Annoyances
Urban Annoyances (%)
Not A Bit A Lot Total
Var iab le s N .A .1Disturbed Disturbed Disturbed Disturbed Disturbed2
Overcrowding in public
transport
18.64.16.213.257.877.2
Being caught in traffic jam 0.35.113.815.765.194.6
Aggression between drivers 2.43.814.917.061.993.8
Thinking of being aggressed
in public places
6.85.123.520.544.188.1
Traffic a risk to pedestrians &
cyclists
6.55.715.118.953.887.8
Queuing up for administrative
services
5.97.311.919.555.486.8
Smell in public transport 15.92.714.615.451.481.4
Getting stuck in crowd 2.42.712.718.164.194.9
Encountering homeless
people & beggars
5.77.620.816.249.786.7
Encountering people who
provoke you or marginalized
people
3.85.726.225.738.690.5
Rubbish lying around 7.62.415.411.962.790.0
Poor quality of products in
market
11.46.210.511.960.082.4
Lack of entertainment facilities 7.611.924.317.338.980.5
Poor street lighting 11.912.418.419.537.875.7
Availability of water 21.112.214.618.433.866.8
Quality of water 13.011.413.215.147.375.6
Having to wait for public
transport
21.93.212.717.344.974.9
Stray animals & their mess 9.24.120.517.049.286.7
Pollution caused by traffic and
industry
10.33.811.914.359.785.9
Street noise 12.43.515.717.351.184.1
Lack of open spaces 10.37.010.521.650.582.6
Teens hanging around on
street
8.410.318.420.043.081.4
Homes and gardens in bad
condition
13.28.917.020.340.577.8
Graffiti on public buildings &
transport
14.113.020.019.233.893.0
Noisy neighbors 15.914.116.517.635.970.8
1Not applicable; 2Total disturbed excluding N.A. and not disturbed.
TABLE 3
Response to Questions Relating to Solastalgia (Feelings Associated with the Loss of Environ-
ment Once Enjoyed and Cherished)
Questions Yes (%) No (%)
Feeling sad looking at degraded environment 334 (90.3) 36 (9.8)
Missing peace once enjoyed 324 (87.6) 46 (12.4)
Feeling ashamed the way this area looks 310 (83.8) 60 (16.2)
Sense of belonging undermined by the environmental change 296 (80.0) 74 (20.0)
I
I
Urban Annoyances and Mental Health
I
I305
TABLE 4
Factor Analysis Showing the Extracted Components Using Varimax Rotation
Components
Rotated Component Matrix Degraded Urban Urban Crowding
Urban Annoyances Environment and Congestion
Noisy neighbors 0.494
Graffiti on buildings & transport 0.580
Teenagers hanging around on street 0.590
Encountering homeless people & beggars 0.554
Rubbish lying around 0.511
Stray animals & their mess 0.612
Homes and gardens in bad conditions 0.673
Quality of water 0.533
Availability of water 0.529
Poor street lighting 0.645
Street noise 0.773
Pollution caused by traffic and industry 0.704
Lack of open spaces 0.514
Lack of entertainment facilities 0.410
Traffic a risk to pedestrians & cyclists 0.502
Being caught in a traffic jam . . . .
Aggression between drivers 0.518
Having to wait for public transportation 0.687
Overcrowding in public transport 0.761
Smell in public transport 0.751
Thinking of being aggressed in public places 0.609
Encountering people who provoke you or marginalized people 0.367
Queuing up for administrative services 0.316
Getting stuck in crowd 0.422
Poor quality of products in the markets . . . .
but gender had no significant correlations with mental health variables. Respondents with higher
levels of education were less depressed ( p<0.01), had more self-esteem ( p<0.01), were more
resilient ( p<0.01), and had an internal locus of control ( p<0.05); surprisingly, the household
income showed no significant correlations with mental health variables. Household congestion
(measured as persons/room) had a positive correlation with depression ( p<0.01), but a negative
correlation with resilience ( p<0.01). Surprisingly, residential area density showed a negative
correlation with depression ( p<0.01), but positive correlation with self-esteem (p<0.01).
It could be that the respondents living in congested areas were so used to living under those
conditions that they have accepted it as a norm.
The factors of environmental annoyances and the mental health variables also provided sig-
nificant results. A degraded urban environment was significantly associated with depression
(p<0.05), low self-esteem and resilience ( p<0.01), and external locus of control. Crowding
and congestion were also associated with depression ( p<0.01) and loss of self-esteem ( p<
0.05). The mental health variables were also significantly correlated among themselves at p<
0.01 and p<0.05, and with the prevalence of headaches ( p<0.05, p<0.01).
Analysis of Variance
One-way ANOVA revealed significant effects of level of education, household income,
household congestion (persons per room), density of the area of residence and environmental
306 I
IJOURNAL OF URBAN AFFAIRS I
I
Vol. 34/No. 3/2012
TABLE 5
Pearson’s Correlations Showing the Relationship of Urban Annoyances, Demographic and So-
cioeconomic Variables with Mental Health
Variables Depression Self-Esteem Resilience Locus of Control
Demographics & Socioeconomics
Age 0.37 0.13∗∗ 0.06 0.07
Gender −0.06 0.009 0.03 −0.03
Education −0.26∗∗ 0.25∗∗ 0.17∗∗ 0.12∗
Household income −0.10 0.09 0.06 0.0
Household congestion 0.14∗∗ −0.06 −0.16∗∗ −0.02
Density of the area −0.14∗∗ 0.34∗∗ 0.03 0.05
Urban Annoyances
Degraded urban environment 0.131∗−0.17∗∗ −0.16∗∗ −0.16∗∗
Urban crowding and congestion 0.18∗∗ −0.14∗∗ −0.10 −0.15∗∗
Prevalence of severe headaches −0.13∗∗ 0.10∗0.17∗∗ 0.15∗∗
Mental Health
Depression . . . . 0.27∗∗ −0.20∗∗ −0.13∗
Self-esteem .... .... 0.34∗∗ 0.23∗∗
Resilience . . . . . . . . . . . . 0.34∗∗
Locus of control . . . . . . . . . . . . . . ..
∗∗
p
<0.01, ∗
p
<0.05.
degradation, and crowding and congestion on mental health variables (depression, self-esteem,
locus of control, and resilience). But ANOVA did not show any significant gender differences in
mental health variables.
The effect of the level of education (Table 6) was significant for all variables of mental health—
that is, depression (F=4.21, p<0.001); self-esteem (F=2.55, p<0.001), resilience (F=2.15,
p<0.05), and locus of control (F=2.15, p<0.05)—while the significant effects of income
levels were limited to depression (F=3.39, p<0.001) and self-esteem (F=1.76, p<0.05).
The significant effects of the density of residential area were limited to self-esteem (F=2.82,
p<0.001), while the effects of household congestion were limited to depression (F=1.95,
p<0.05), and self-esteem (F=1.84, p<0.05). The urban annoyances of environmental
degradation and crowding and congestion affected mental health, and environmental degradation
had significant effects on depression (F=2.43, p<0.01), psychological resilience (F=5.80, p
<0.001), and locus of control (F=2.08, p<0.05). Crowding and congestion were significantly
related to depression (F=2.89, p<0.01), resilience (F=2.53, p<0.001), and locus of control
(F=2.06, p<0.05).
Tukey’s HSD Test
Tukey’s post hoc test was conducted to ascertain which groups of respondents were causing
significance in ANOVA (Table 7). The test showed that respondents who were illiterate and had
education only up to the secondary level were significantly more depressed than those with higher
levels of education. Those illiterate and with levels of education less than matriculation (grade
10) had significantly less self-esteem than those who were educated at the master’s and above
master’s levels. No educational groups showed any significant difference for locus of control and
had an external locus of control. Illiterate respondents were significantly less resilient than those
having higher levels of education.
I
I
Urban Annoyances and Mental Health
I
I307
TABLE 6
ANOVA Showing the Effects of Socioeconomics, Feelings of Solastalgia, and Factors of Urban
Annoyances on Mental Health
Factors Dependent Variables SS df MS
Fp
-value
Depression Socioeconomics
Gender 3.35 10 0.35 1.48 0.142
Education 70.41 10 7.041 4.21 0.000
Household income 18.64 10 1.86 3.39 0.000
Household congestion 29.47 10 2.95 1.95 0.038
Density of the area 11.33 10 1.13 1.54 0.124
Urban Annoyances
Environmental degradation 3387.87 10 338.80 2.43 0.008
Crowding and congestion 1117.99 10 111.80 2.89 0.002
Self-esteem Socioeconomics
Gender 3.36 23 0.14 0.58 0.936
Education 97.22 23 4.22 2.55 0.000
Household income 22.58 23 0.98 1.76 0.018
Household congestion 62.55 23 2.72 1.84 0.011
Density of the area 43.49 23 1.89 2.82 0.000
Psychological resilience Socioeconomics
Gender 6.40 21 0.30 1.28 0.186
Education 41.59 21 3.78 2.15 0.016
Urban Annoyances
Environmental degradation 13861.16 21 660.05 5.80 0.000
Crowding and congestion 1988.24 21 94.67 2.53 0.000
Locus of control Socioeconomics
Gender 3.03 11 0.27 1.14 0.328
Education 41.59 11 3.78 2.15 0.016
Prevalence of severe headaches 14.82 11 1.35 2.17 0.015
Urban Annoyances
Environmental degradation 3207.60 11 291.60 2.08 0.021
Crowding and congestion 893.94 11 81.268 2.06 0.022
Considerable overlap was observed among household income groups, but those with the lowest
levels of income were clearly more depressed and had lower self-esteem. All income groups had
an external locus of control and were equally resilient.
The effects of household congestion and the density of the residential areas on the four
mental health variables had considerable overlap between groups but indicated, generally, that
respondents residing in low- and medium-density areas were less depressed than those living in
high-density residential areas.
Multiple Regression Analysis
Multiple regressions were conducted to evaluate the determinants (i.e., age, educational level,
household income, household congestion, population density of the residential area, prevalence
of severe headaches, degraded urban environment, and urban crowding and congestion) of mental
health (depression, self-esteem, resilience, and locus of control). Four models were constructed
representing the four mental health variables. The results of the four models are presented in
Table 8.
The results show that Model 1 had an R2explaining 12% of the variation in depression caused
by the explanatory variables of the model. The adjusted R2and F-statistic show that the model was
308 I
IJOURNAL OF URBAN AFFAIRS I
I
Vol. 34/No. 3/2012
TABLE 7
Results of Tukey’s Post Hoc Test Showing Groups Causing Significance in ANOVA
Depression Self-Esteem Locus of Control Resilience
Subset for Subset for Subset for Subset for
Alpha =0.05 Alpha =0.05 Alpha =0.05 Alpha =0.05
TukeyHSD N123N1 2 3N1 2 N1 2
Education
Illiterate 18 . . .. 7.0 . . . 18 15.7 . . . . . . 18 38.3 . . . 18 33.3 . . . .
Less than matric 17 . . . . 6.6 . . . 17 16.6 . . . . . . . 17 34.9 . . . 17 35.0 35.0
Secondary 36 . . . . 6.6 . . . 36 17.6 17.6 . . . 36 38.8 . . . . 36 34.8 34.8
Graduation 89 4.7 . . . . . . . 89 19.2 19.2 . . . 89 36.9 . . . 89 35.9 35.9
Masters 116 4.9 . . . . . . . 116 . . . 20.3 . . . 116 37.8 . . . 116 . . . . 36.3
Above masters 94 4.9 . . . . . . 94 . . . 20.5 . . . 94 41.0 . . . 94 . . . . 36.4
Income
Below 10,000 37 . . . . . . . . 6.4 37 17.5 . . . . . . 37 39.7 . . . 37 34.9 . . . .
10,000–30,000 105 . . . . 5.6 5.6 105 18.4 . . . . . . 105 37.1 . . . 105 35.3 . . . .
31,000–50,000 80 4.9 4.9 . . . 80 19.8 19.8 . . . 80 38.1 . . . 80 36.1 . . . .
51,000–70,000 65 . . .. 5.2 5.2 65 19.1 19.1 . . . 65 39.6 . . . 65 35.7 . . . .
71,000–90,000 48 4.8 4.8 . . . 48 . . . 21.5 . . . 48 38.7 . . . 48 36.6 . . . .
91,000 and above 35 3.9 . . . . . . 35 . . . 21.6 . . . 35 39.1 . . . 35 37.1 . . . .
Household Congestion
1 P/R 172 4.8 . . . . . . 172 19.6 . . . . . . 172 38.3 . . . 172 . . . . 36.5
2 P/R 120 5.5 . . . . . . 120 19.1 . . . . . . 120 38.6 . . . 120 35.6 35.6
3 or More P/R 78 5.4 . . . . . . 78 19.5 . . . . . . 78 38.3 . . . 78 34.8 . . . .
Population Density of Residential Area
Low 154 4.9 . . . . . . . 154 . . . . . . . . 21.3 154 38.6 . . . 154 36.1 . . .
Medium 92 4.9 . . . . . . . 92 . . . . 19.3 . . . . 92 38.7 . . . 92 35.8 . . .
High 124 . . . . 5.7 . . . 124 17.3 . . . . . . . . 124 37.8 . . . 124 35.6 . . .
I
I
Urban Annoyances and Mental Health
I
I309
TABLE 8
Multiple Regression Analysis Showing Estimates of the Determinants of Mental Health
Explained
Variables Explanatory Variables Coeff. (β)S.E.
t
-ratio
p
-value
Model 1 Depression Constant 4.70 0.85 5.54 0.000
Socioeconomics
Age 0.02 0.01 2.24 0.026
Education −0.22 0.10 −2.23 0.026
Household income −0.53 0.18 −2.99 0.003
Urban Annoyances
Crowding and congestion 0.05 0.02 2.94 0.004
Model summary
R
2=0.12, adjusted
R
2=0.11, S.E. of estimate =2.11,
F
-statistic =10.33,
p
-value =0.000
Model 2 Self-esteem Constant 13.53 1.79 7.54 0.000
Socioeconomics
Age 0.05 0.02 2.28 0.023
Education 0.75 0.18 4.08 0.000
Density of the area 1.82 0.29 6.34 0.000
Urban Annoyances
Degraded environment −0.05 0.02 −2.63 0.009
Model summary
R
2=0.20, adjusted
R
2=0.18, S.E. of estimate =4.70,
F
-statistic =21.05,
p
-value =0.000
Model 3 Psychological Constant 38.41 1.79 21.45 0.000
resilience Socioeconomics
Education 0.38 0.16 2.36 0.018
Urban Annoyances
Degraded environment −0.04 0.02 −2.46 0.014
Crowding and congestion −0.72 0.27 −2.58 0.010
Model summary
R
2=0.30, adjusted
R
2=0.09, S.E. of estimate =4.01,
F
-statistic =7.19,
p
-value =0.001
Model 4 Locus of control Constant 36.72 1.54 23.85 0.000
Socioeconomics
Education 0.36 0.16 2.22 0.027
Household congestion −0.72 0.27 −2.61 0.009
Prevalence of severe headaches 0.73 0.26 2.75 0.006
Urban Annoyances
Degraded environment −0.04 0.02 −2.52 0.012
Model summary
R
2=0.09, adjusted
R
2=0.08, S.E. of estimate =4.02,
F
-statistic =8.68,
p
-value =0.000
a good fit at p<0.001. The age of the respondents had a significant positive impact on depression,
while their educational level and household income had significant negative impacts on level of
depression. Crowding and congestion were also significant contributors to the respondent’s level
of depression.
The results of Model 2 suggest that age, educational level, and density of the area had significant
positive impacts on self-esteem, whereas the degraded environment had a significant negative
effect. Overall, the model was a good fit, explaining 20% of the variation in self-esteem caused
by the explanatory variables.
The estimates of Model 3 suggest that the respondents’ educational level positively in-
creased resilience, while degraded environment and crowding and congestion negatively impacted
resilience. The R2shows 30% of the variation in psychological resilience explained by the ex-
planatory variables of the model, and the model was a good fit at p<0.01.
310 I
IJOURNAL OF URBAN AFFAIRS I
I
Vol. 34/No. 3/2012
The results of Model 4 suggest that the educational level of the respondent and the prevalence
of severe headaches had a positive relationship with locus of control, and also indicated an internal
locus of control. But household congestion and degraded environment had significant negative
impacts on locus of control, demonstrating an external locus of control for the respondents.
Overall, the model is a good fit explaining 9% of the variation in locus of control caused by the
explanatory variables of the model, and with a significant F-statistic at p<0.001.
DISCUSSION
The prevalence of depressive and anxiety disorders in Pakistan is highest among developing
countries. About 67% of women and 25% of men in Pakistan suffer from mental disorders at
any given time and about 6 out of 1,000 children aged 3–9 suffer from severe mental retardation
(Husain, Creed, & Tomenson, 2000). Husain et al. (2000) attributed the higher prevalence of
depressives in Lahore to high population density among other socioeconomic variables, such as
relatively higher rates of unemployment and poverty. The provision of mental health for the nearly
180 million population of Pakistan is grossly inadequate and relies on only 320 psychiatrists based
in major urban centers, 400 clinical psychologists, and only 52 trained psychiatric nurses (Gadit,
2007).
Given the prevailing degraded urban environmental conditions in Lahore as seen in our study,
it is not surprising that our respondents were very disturbed by a variety of urban annoyances. But
the depressive tendencies were more prevalent among the relatively less-educated and those who
had low household income. Household congestion, measured as persons/room, was positively
correlated with depression. Generally, those depressed also had low self-esteem, had an external
locus of control, and were psychologically less resilient. In our study, depression, self-esteem, and
locus of control were strongly correlated, which is consistent with numerous studies conducted on
the relationship of these mental health variables (Fathi-Ashtiani, Ejei, Khodapanahi, & Tarkho-
rani, 2007; Goodman, Cooley, Sewel, & Leavitt, 1982; Harrow, Hansford, & Astrachan-Fletcher,
2009; Robin et al., 2007; Yousafzai & Siddiqi, 2007).
An important point to note in our study is that we found no significant differences between
male and female respondents in the prevalence of mental disorders. This is not consistent with
several other papers that reported higher prevalence of mental disorders in Pakistani and South
Asian women than men (Husain, Gater, Tomenson, & Creed, 2004b; Patel, 2007; Trivedi, Sareen,
& Dhyani, 2008). However, it is to be noted that a majority of the females in our study were
professional women and were as or more educated than men. Since higher education has been
demonstrated to be negatively associated with the prevalence of mental disorders, it would support
our findings and would reinforce the findings that education is a prominent determinant of mental
health disorders, that is, the higher the education the less the psychological morbidity (Husain
et al., 2000, 2004a; Patel & Kleinman, 2003).
Socioeconomic Implications of Mental Disorders and Psychological Morbidity
The prevalence of mental disorders has far-reaching social and economic implications. Be-
sides having direct effects on the sufferer, it significantly impacts those with whom the sufferer
associates, such as care givers and family members (World Health Organization, 2004, 2006).
For example, a study in Lahore showed that children of parents with mental illness had almost
twice the occurrence of mental health problems than those whose parents did not suffer from
mental disorders (Imran, Sattar, Amjad, & Bhatti, 2009). In another study, based on a sample
of 650 women at primary healthcare centers of Lahore, 64.3% were diagnosed with psychi-
atric problems and 30.4% had major depressive disorders (Ayub et al., 2009). Verbal violence,
I
I
Urban Annoyances and Mental Health
I
I311
battering, and stressful life events were positively correlated with psychiatric morbidity in these
women.
Mental disorders have been reported to cause significant burdens to individuals, families, and
communities. Mental and neurological disorders can amount to 13% of the total Disability-
Adjusted Life Years lost due to all diseases and injuries in the world (World Health Organization,
2004). According to a 1996 report (Murray & Lopez, 1996), 5 of the 10 leading causes of
disability in the world are psychiatric conditions. Projections further estimate that by the year
2020 neuropsychiatric conditions will constitute 15% of all disabilities in the world.
On a global basis, the economic burden of mental disorders is enormous and runs into billions
of dollars each year in direct and indirect costs (World Health Organization, 2006). In the United
States alone, the total direct and indirect annual economic cost of serious mental illness, excluding
incarceration, homelessness, comorbid conditions, and early mortality have been estimated to be
$317.6 billion (Insel, 2008; Kessler et al., 2008). According to the World Health Organization, the
costs in low-income countries are lower because of the low availability and coverage of mental
healthcare services, but the indirect costs as a result of loss of productivity are a larger proportion
of the overall costs (World Health Organization, 2001, 2004). Overall, it appears that the economic
costs of mental illnesses are enormous and not easily measurable, particularly if the hidden costs
of social care, education, housing, criminal justice, and social security systems are included in
the estimates (Knapp, 2003). To the best of our knowledge, there is only one publication that has
made an attempt to estimate the direct and indirect costs of morbidity relating to metal disorders
(anxiety and depression). In a semiurban population near Rawalpindi, the estimated baseline cost
is more than 3,000 Pakistani Rupees per month, which amounts to a loss of approximately 20
days of work by an agricultural worker (Chisholm et al., 2000). In an urban area such as Lahore,
it would amount to a much higher loss.
In estimating the prevalence of mental disorders and the cost of psychological morbidity, it
is important to note that many somatic complaints in patients are often correlated with mental
disorders and, as such, largely go unnoticed in primary healthcare facilities. Using the Bradford
Somatic Inventory (Mumford et al., 1991), a series of three community-based studies in both
rural and urban populations in Pakistan reported high prevalence of mental disorders in patients
complaining about somatic symptoms (Minhas & Nizami, 2006). Conservative estimates of these
authors revealed that 66% of women and 25% of men were suffering from depressive and anxiety
disorders although their complaints were predominantly of a somatic nature. Similarly, using
the Self-Report Questionnaire (SRQ), it was found that 80% of men and 55.4% of women in
outpatient clinics in Pakistan had SRQ scores of 9 and above, suggesting probable depressive
disorders (Husain et al., 2004b). These studies suggest that in estimating the total burden of mental
disorders in both urban and rural Pakistan the somatoform disorders should also be included.
The results of our study suggest that urban annoyances or stressors associated with rapid
urbanization of the city of Lahore are prominent determinants of mental health in urban dwellers
of Lahore. The study also demonstrates that socioeconomic variables such as household income
and level of education, and demographic variables such as the household size and population
density of the area of residence, are strong determinants of mental health of the citizens of
Lahore. As such, the prevalence and morbidity associated with them has the potential for causing
serious social and economic burdens to individuals, households, and communities at large.
CONCLUSIONS
Based on the results of our study and the ensuing discussion it is concluded that:
•Rapid urban growth of the city of Lahore in recent decades has introduced a variety
of annoyances associated with congestion, overcrowding, degradation of the municipal
312 I
IJOURNAL OF URBAN AFFAIRS I
I
Vol. 34/No. 3/2012
infrastructure, and urban poverty. Citizens of Lahore are very disturbed by these annoyances
and show depressive tendencies, affecting their self-esteem and psychological resilience.
They also showed an external locus of control, believing that rewards and deprivations in life
are generally outside of their control.
•Overcrowding and congestion are the most disturbing of all the annoyances since they
demonstrate highly significant relationships with depressive tendencies in the residents of
Lahore. Both household congestion and high density of residential areas are positively
correlated with depression.
•Respondents with higher levels of education and higher household income showed a lesser
tendency to be depressive and had higher self-esteem and psychological resilience.
•Most respondents appeared to have symptoms of solastalgia (a distress syndrome associated
with the loss of environment once cherished and enjoyed) and, as such, were unhappy about
the way the city has changed both aesthetically and demographically.
Recommendations
1. Deficits in governance and a lack of strategic vision and planning appear to be the cause of
the degraded urban environment and associated prevalence of depression in Lahore. There is
a dire need for strategic planning in the city, which would focus on demographic trends and
improving the degraded urban environment of Lahore and the mental health of its citizens.
2. A major cause of urban issues in Pakistan are policies based on the assumed dichotomy
between rural and urban areas as reflected in the division of policies along spatial and sectoral
lines (e.g., rural development programs, urban management programs). None of these
initiatives have given much thought to the complex dynamics of rural–urban interactions
and interdependencies of factors governing these dynamics. The intertwining nature of urban
and rural livelihoods has seldom been a concern of policy makers. There is an urgent need
to understand the complexity of this nexus, focusing on linkages across space (such as flows
of people, goods, revenue, and information), and across sectors (such as urban agriculture
and the manufacturing activities in rural areas). Policies also need to include consideration
of the influence of globalization on all aspects (social, economic, and ecological) of the
urban–rural nexus, which has almost always been ignored in Pakistan.
3. The methodological approach in rural–urban research should include commodity chain
analysis, which would identify actors, evaluate distribution of income and profit at each
level of the chain, and determine the mechanisms by which access to benefits may be sus-
tained. Dynamic models should be used to determine the interdependencies of the plethora
of variables involved. The model should also emphasize the complex social–ecological
systems, such as a resource system (e.g., agriculture), resource units (such as wheat), re-
source users (e.g., farmers), and governance systems (e.g., institutions and rules that govern
agriculture in a particular region.
4. Comprehensive research efforts need to be made to estimate the cost of mental illness in
urban areas to individuals and households, both in terms of the diagnosis and treatment of
the disease.
5. Finally, primary healthcare practitioners should be trained to diagnose somatoform
disorders.
ACKNOWLEDGMENT: Generous funding for this research was provided by the Higher Education Commission
of Pakistan.
I
I
Urban Annoyances and Mental Health
I
I313
ENDNOTE
1 People from different backgrounds—officials of banks, government and private organizations, industry workers,
university teachers, and graduate and postgraduate university students—were interviewed for the survey. These
people participated in the survey on a voluntary basis.
REFERENCES
Albrecht, G. A. (2005). Solastalgia: A new concept in human health and identity. PAN: Philosophy Activism
Nature,3, 41–55.
Almas, A. S., Rahim, C. A., Butt, M. J. & Shah, T. I. (2005). Metropolitan growth monitoring and land use clas-
sification using geospatial techniques. Proceedings of International Workshop on Service and Application
of Spatial Data Infrastructure, Hangzhou, China, October 14–16, 2005, 277–282.
Almeida, O. P., & Almeida, S. A. (1999). Short versions of the geriatric depression scale: A study of their validity
for the diagnosis of a major depressive episode according to ICD-10 and DSM-IV. International Journal
of Geriatric Psychiatry,14, 858–869.
Arif, G. M., & Hamid, S. (2009). Urbanization, city growth and quality of life in Pakistan. European Journal of
Social Sciences,10, 196–215.
Ayub, M., Irfan, M., Nasr, T., Lutfullah, M., Kingdon, D., & Naeem, F. (2009). Psychiatric morbidity and domestic
violence: A survey of married women in Lahore. Social Psychiatry and Psychiatric Epidemiology,44, 953–
960.
Blascovich, J., & Tomaka, J. (1993). Measures of self-esteem. In J. P. Robinson, P. R. Shaver, & L. S. Wrightsman
(Eds.), Measures of personality and social psychological attitudes (3rd Ed., pp. 115–160). Ann Arbor, MI:
Institute for Social Research.
Bloom, D. E., Canning, D., & G¨
unther, F. (2008). Urbanization and the wealth of nations. Science,319(5864),
772–775.
Boydell, J., & McKenzie, K. (2008). Society, place and space. In C. Morgan, K. McKenzie, & P. Fearon (Eds.),
Society and psychosis. Cambridge, England: Cambridge University Press.
Cheng, S.-T., Lee, S. Y., Wong, J., Hin, L., Kei, C., Chan, H., & Wong, M. (2010). The Geriatric Depression Scale
as a screening tool for depression and suicide ideation: A replication and extension. American Journal of
Geriatric Psychiatry,18, 256–265.
Chisholm, D., Kumar, K., Sekar, K., James, S., Mubbashar, M., & Srinivasa, M. R. (2000). Integration of mental
health care into primary care: A demonstration cost-outcome study in India and Pakistan. British Journal
of Psychiatry,176, 581–588.
Farooq, M., Mateen, A., & Cheema, M. A. (2005). Determinants of migration in Punjab, Pakistan: A case study
of Faisalabad metropolitan. Journal of Agriculture and Social Sciences,1(3), 280–282.
Fathi-Ashtiani, A., Ejei, J., Khodapanahi, M.-K., & Tarkhorani, H. (2007). Relationship between self-concept,
self-esteem, anxiety, depression and academic achievement in adolescents. Journal of Applied Sciences,7,
995–1000.
Gadit, A. A. M. (2007). Psychiatry in Pakistan 1947–2006: A new balance sheet. Journal of Medical and Biological
Sciences, 1(2), 1–20.
Gadit, A. A. M., & Mugford, G. (2007). Prevalence of depression among households in three capital
cities of Pakistan: Need to revise the mental health policy. Available at www.plosone.org/article/
info%3Adoi%2F10.1371%2Fjournal.pone.0000209
Goodman, S. H., Cooley, E. L., Sewel, D. R., and Leavitt, N. (1982). Locus of control and self-esteem
in depressed, low income African-American women. Community Mental Health Journal,30(3), 259–
269.
Grimm, N. B., Faeth, S. H., Golubiewski, N. E., Redman, C. L., Wu, J., Bai, X., & Briggs, J. M. (2008). Global
change and the ecology of cities. Science,319(5864), 756–760.
Guite, H. F., Clark, C., & Ackrill, G. (2006). The impact of the physical and urban environment on mental
well-being. Public Health,120(12), 1117–1126.
Haider, M., & Badami, M. G. (2010). Urbanization and local governance challenges in Pakistan. Environment and
Urbanization Asia,1, 81–96.
314 I
IJOURNAL OF URBAN AFFAIRS I
I
Vol. 34/No. 3/2012
Harrow, M., Hansford, B. G., & Astrachan-Fletcher, E. B. (2009). Locus of control: Relation to schizophrenia,
to recovery, and to depression and psychosis: A 15-year longitudinal study. Psychiatry Research,168(3),
186–192.
Higginbotham, N., Connor, L., Albrecht, G., Freeman, S., & Agho, K. (2007). Validation of an environmental
distress scale. Ecohealth,3(4), 245–254.
Husain, N., Chaudhry, I., Afsar, S., & Creed, F. (2004a). Psychological distress among patients attending a general
medical outpatient clinic in Pakistan. General Hospital Psychiatry,26, 277–281.
Husain, N., Creed, F., & Tomenson, B. (2000). Depression and social stress in Pakistan. Psychological Medicine,
30(2), 395–402.
Husain, N., Gater, R., Tomenson, B., & Creed, F. (2004b). Social factors associated with chronic depression
among a population-based sample of women in rural Pakistan. Social Psychiatry and Epidemiology,39(8),
618–624.
Imran, N., Sattar, A., Amjad, N., & Bhatti, M. R. (2009). Psychological problems in children of parents with
mental illness: A comparative study from Lahore, Pakistan. Pakistan Journal of Medical Science,25(6),
895–900.
Insel, T. R. (2008). Assessing the economic costs of serious mental illness. American Journal of Psychiatry,165,
663–665.
Kessler, R. C., Heeringa, S., Lakoma, M. D., Petukhova, M., Rupp, A. E., Schoenbaum, M., Wang, P. S., &
Zaslavsky, A. M. (2008). Individual and societal effects of mental disorders on earnings in the United
States: Results from the National Comorbidity Survey Replication. American Journal of Psychiatry,165,
703–711.
Knapp, M. (2003). Hidden costs of mental illness. British Journal of Psychiatry,183(6), 477–478.
Krabbendam, L., & van Os, J. (2005). Schizophrenia and urbanicity: A major environmental influence—
Conditional on genetic risk. Schizophrenia Bulletin,31(4), 795–799.
Lahore Development Authority (2004a). Integrated master plan for Lahore 2021. Existing scenario, Vol. I. Lahore:
National Engineering Services Pakistan (Pvt.) Ltd.
Lahore Development Authority (2004b). Integrated master plan for Lahore 2021. Analysis and Proposal, Vol. II.
Lahore: National Engineering Services Pakistan (Pvt.) Ltd.
McKenzie, K. (2008). Urbanization, social capital and mental health. Global Social Policy,8, 359–377.
Minhas, F. A., & Nizami, A. T. (2006). Somatoform disorders: Perspectives from Pakistan. International Review
of Psychiatry,18(1), 55–60.
Mirza, I. (2001). Common mental disorders in urban vs. rural Pakistan. British Journal of Psychiatry,178, 475–476.
Mirza, I., & Jenkins, R. (2004). Risk factors, prevalence, and treatment of anxiety and depressive disorders in
Pakistan: Systematic review. British Medical Journal,328, 1–5.
Montgomery, M. R. (2008). The urban transformation of the developing world. Science,319(5864), 761–764.
Mumford, D. B., Bavington, J. T., Bhatnagar, K. S., Hussain, Y., Mirza, S., & Naraghi, M. M. (1991). The
Bradford Somatic Inventory: A multi-ethnic inventory of somatic symptoms reported by anxious and
depressed patients in Britain and the Indo-Pakistan subcontinent. British Journal of Psychiatry,158, 379–
386.
Mumford, D. B., Minhas, F. A., Akhtar, I., Akhter, S., & Mubbashar, M. H. (2000). Stress and psychiatric disorder
in urban Rawalpindi. British Journal of Psychiatry,177(6), 557–562.
Murray, C. J. L., & Lopez, A. D. (1996). The global burden of disease: A comprehensive assessment of mortality
and disability from diseases, injury and risk factors in 1990 projected to 2020. Geneva: World Bank, World
Health Organization, and Harvard School of Public Health.
Neil, J. T., & Dias, K. L. (2001). Adventure education and resilience: The double-edge sword. Journal of Adventure
Education and Outdoor Leadership,1(2), 35–42.
Patel, V. (2007). Mental health in low- and middle-income countries. British Medical Bulletin, 81–82(1), 81–96.
Patel, V., & Kleinman, A. (2003). Poverty and common mental disorders in developing countries. Bulletin of the
World Health Organization,81, 609–615.
Punjab Development Statistics (2007). Number of motor vehicles registered in Punjab Province on 30th June.
2006 (pp. 371–372). Lahore: Bureau of Statistics, Government of Punjab.
Robin, M., Matheau-Police, A., & Couty, C. (2007). Development of a scale of perceivedenvironmental annoyances
in urban settings. Journal of Environmental Psychology,27, 55–68.
I
I
Urban Annoyances and Mental Health
I
I315
Rosenberg, M. (1986). Self-concept from middle childhood through adolescence. In J. Suls & A. G. Greenwald,
(Eds.), Psychological perspectives on the self (pp. 107–136). Hillsdale, NJ: Erlbaum.
Rotter, J. (1966). Generalized expectancies for internal versus external control of reinforcements. Psychological
Monographs,80, 1–28.
Sajjad, S. H., Shirazi, S. A., Khan, M. A., & Raza, A. (2009). Urbanization effects on temperature trends of Lahore
during 1950–2007. International Journal of Climate Change Strategies and Management,1(3), 274–
281.
Shah, A. K., Phongsathorn, V., Bielawska, C., & Katona, C. (1996). Screening for depression among geriatric inpa-
tients with short versions of the Geriatric Depression Scale. International Journal of Geriatric Psychiatry,
11, 915–918.
Sheikh, J. I., & Yesavage, J. A. (1986). Geriatric depression scale: Recent evidence and development of a shorter
version. Clinical Gerontologist,5, 165–173.
Siddiqi, M. W. (2004). Rural-urban migration: A case study of Lahore district. Ph.D. Thesis, University of the
Punjab, Lahore.
Sundquist, K., Frank, G., & Sundquist, J. (2004). Urbanization and incidence of psychosis and depression:
Follow-up study of 4.4 million women and men in Sweden. British Journal of Psychiatry,184, 293–298.
Trivedi, J. K., Sareen, H., & Dhyani, M. (2008). Rapid urbanization—Its impact on metal health: A South Asian
perspective. Indian Journal of Psychiatry,50(3), 161–165.
United Nations (2005). World urbanization prospects: The 2005 revision. New York: United Nations Department
of Economic and Social Affairs/Population Division.
United Nations (2009). World urbanization prospects: The 2009 revision. New York: United Nations Department
of Economic and Social Affairs/Population Division.
van Marwijk, H. W., Wallace, P., De Bock, G. H., Hernans, J. O., Kaptein, A. A., & Mulder, J. D. (1995). Evaluation
of the feasibility, reliability and diagnostic value of shortened versions of the Geriatric Depression Scale.
British Journal of General Practice, 45, 195–199.
van Os, J., Hanssen, M., & Bijl, R. V. (2001). Prevalence of psychotic disorder and community level of psychotic
symptoms: An urban–rural comparison. Archives of General Psychiatry,58(7), 663–668.
Wagnild, G. M., & Young, H. M. (1993). Development and psychometric evaluation of the resilience scale. Journal
of Nursing Measurement,1(2), 165–178.
Weintraub, D., Saboe, K., & Stern, M. B. (2007). Effect of age on Geriatric Depression Scale performance in
Parkinson’s disease. Movement Disorders,22, 1331–1335.
World Bank (2006). Pakistan: Strategic country environmental assessment. Main report. South Asia Environment
and Social Development Unit, South Asia Region, 1. Report No. 36946-PK. Washington, DC: World Bank.
World Health Organization (2001). Mental health: New understanding, new hope. Geneva: World Health Organi-
zation.
World Health Organization (2004). Prevention of mental disorders: Effective interventions and policy options.
Summary Report of the World Health Organization Dept. of Mental Health and Substance Abuse; In
collaboration with the Prevention Research Centre of the Universities of Nijmegen and Masstricht. Geneva:
World Health Organization.
World Health Organization (2006). Economic aspects of the mental health systems: Key messages to health
planners and policy makers. Geneva: WHO Department of Mental Health and Substance Abuse.
Yesavage, J. A., Brink, T. L., Rose, O. L., Huang, V., Adey, M., & Leirer, V. O. (1982–1983). Development and
validation of a geriatric depression screening scale: A preliminary report. Journal of Psychiatric Research,
17(1), 37–49.
Yousafzai, A. W., & Siddiqi, M. N. (2007). Association of lower self-esteem with depression: A case control study.
Journal of Postgraduate Medical Institute, 21(3), 192–196.
