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Mental health, quality of life, and nutritional status of adolescents in Dhaka, Bangladesh: Comparison between an urban slum and a non-slum area
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Social Science & Medicine 63 (2006) 1477–1488 www.elsevier.com/locate/socscimed
Mental health, quality of life, and nutritional status of adolescents in Dhaka, Bangladesh: Comparison between an urban slum and a non-slum area Takashi Izutsua,, Atsuro Tsutsumib, Akramul Md. Islamc, Seika Katod, Susumu Wakaie, Hiroshi Kuritaf a
National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan b Hyogo Institute for Traumatic Stress, Japan c BRAC, Dhaka, Bangladesh d Faculty of Health Sciences, Tokyo Metropolitan University, Japan e Department of International Community Health, Graduate School of Medicine, The University of Tokyo, Japan f Department of Mental Health, Graduate School of Medicine, The University of Tokyo, Japan Available online 9 June 2006
Abstract This study aims to clarify the quality of life (QOL), mental health, and nutritional status of adolescents in Dhaka city, Bangladesh by comparing non-slum areas and slums, and to find the factors associated with their mental health problems. A sample of 187 boys and 137 girls from non-slum areas, and 157 boys and 121 girls from slums, between 11–18 years old were interviewed with a questionnaire consisting of a Bangla translation of the World Health Organization Quality of Life Assessment Instrument (WHOQOL-BREF), Self Reporting Questionnaire (SRQ), Youth Self-Report (YSR) and other questions. The height and weight of the respondents were measured. All significant differences in demographic characteristics, anthropometric measures, and WHOQOL-BREF were found to reflect worse conditions in slum than in non-slum areas. Contrarily, all differences in SRQ and YSR were worse in non-slum areas for both genders, except that the ‘‘conduct problems’’ score for YSR was worse for slum boys. Mental states were mainly associated with school enrolment and working status. Worse physical environment and QOL were found in slums, along with gender and area specific mental health difficulties. The results suggest gender specific needs and a requirement for area sensitive countermeasures. r 2006 Elsevier Ltd. All rights reserved. Keywords: Bangladesh; Developing countries; Mental health; Slum; Quality of life; Urbanization; Gender
Introduction According to the World Health Organization (WHO), about 450 million of the world’s population Corresponding author. Tel.: +81 90 9391 8888.
E-mail addresses: [email protected] (T. Izutsu), [email protected] (A. Tsutsumi). 0277-9536/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.socscimed.2006.04.013
suffer from mental or neurological disorders or from psychosocial problems; while one in every four persons is affected by a mental disorder at some stage in their life (WHO, 2001a). In particular, major depression, with a life time prevalence of 10–25% for females and 5–12% for males (American Psychiatric Association, 2000), is ranked fourth in terms of the global burden of disease, and is projected to be the
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second cause of the global disease burden within 20 years (WHO, 2001a). However, mental health issues tend to be overtaken by other health problems, especially in developing countries. Particularly, such situations in the ever increasing slum areas is a global concern with rapid urbanization taking place in developing countries, where slum inhabitants today are estimated to constitute 60% of the inhabitants in big cities (Hussain, Ali, & Kvale, 1999). Urbanization has been shown to be associated with higher incidents of injuries from traffic accidents (United Nations Environment Programme, & WHO, 1994), respiratory illness (Lovik, Dybing & Smith, 1996; SIDRIA, 1997), cancer (Schouten, Meijer, Huveneers, & Kiemeney, 1996), cardiovascular diseases and hypertension (Barnett, Strogatz, Armstrong, & Wing, 1996; Kaufman, Owoaje, James, Rotimi, & Cooper, 1996; Mufundu, Sigola, Chifamba, & Vengesa, 1994), deterioration in infant and child mortality (Backmann, London, & Barron, 1996; Kuate Defo, 1996; Sastry, 1997), and mental health problems (Cheng, 1989; Gillis, Welman Kock, & Joyi, 1991; Mueller, 1981; Rahim & Cederblad, 1986; Shepherd, 1984; Sijuwade, 1995; Varma et al., 1997). As for children’s and adolescents’ mental health, Takano, Nakamura, and Watanabe (1996) indicated that in young people urbanization increased risks such as drug, alcohol, tobacco, and risky sexual behaviours, and that these behaviours were linked to the rise of psychological problems. Also, Harpham (1994) pointed out that urbanization initiated rapid social changes, disintegration, dissolution of social relations, and decreased social control, and that these factors contributed to mental diseases particularly in young people. However, research regarding the mental health status of slumliving adolescents is lacking. In Bangladesh, there has been no official data on mental health. As to mental health research in Bangladesh, Islam, Ali, Ferroni, Underwood and Alam (2003) conducted a community survey in urban middle-class adult population in Dhaka, and presented prevalence of psychiatric disorders as 28%. A study of a married rural population utilizing MOS-Short Form 36 (SF-36) by Ahmed, Rana, Chowdhury and Bhuiya (2002) showed consistent negative self-evaluation of mental health status, and its deterioration with the advancement of age. Khan (2002) indicated suicides in the countries of the Indian subcontinent including Bangladesh had differences from those in Western
countries, showing higher organophosphate insecticides use, a higher ratio of married women, fewer elder subjects, and more problems in interpersonal relationship and life events as causative factors. As to children, Rabbani and Hossain (1999), using teachers’ reports, showed that 13.4% of urban primary school children in Dhaka had emotional, conduct or undifferentiated disorders. As to adolescents, though the reliability and validity of the Strengths and Difficulties Questionnaire (SDQ) has been assessed utilizing adolescent subjects (Goodman, Renfrew, & Mullick, 2000; Mullick & Goodman, 2001), the mental health status of such subjects remains unclear. To add to the prevailing poverty and severe health status; 33% of the total population of approximately 129 million is malnourished and the infant mortality rate in 2000 was 54 in 1000 births (United Nations Development Programme, 2002; World Bank & Bangladesh Centre for Advanced Studies, 1998), the urban growth rate in Bangladesh is projected to be 5.4% in the period 1999–2010 (Bangladesh Bureau of Statistics, 2002), and Bangladeshi urban population in 2020 is estimated to be nearly half the national total; and that of its capital Dhaka will be doubled by 2010 (WHO, 1997). A good percentage of this population increase will reside in slums due to lack of job opportunities, income and housing. Thus the mental health status of children and adolescents in urban slum population in Bangladesh needs to be evaluated. Therefore, this study aimed at (A) clarifying the QOL, general mental health, behavioural, emotional and social problems and nutritional status of adolescents in Dhaka city, Bangladesh in comparisons between non-slum area adolescents and slum area adolescents using internationally comparable scales; and (B) finding factors (age, sex, nutritional status, educational status, job status, and family income) associated with mental health problems. Methods Subjects General population subjects This study was conducted in Dhaka, the capital of Bangladesh. To obtain a representative cross sample of Dhaka city, six wards were randomly selected, with one non-slum area and one slum area being selected from each ward as study sites. A UN expert group established an operational definition
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of slums as being an area that to various extents combines the following characteristics: inadequate access to safe water; inadequate access to sanitation and other infrastructure; poor structural quality of housing; overcrowding; and insecure residential status (United Nations Human Settlements Programme, 2003). The slum areas we selected were consistent with this definition, whereas the nonslum areas referred to here are general urban residential areas. We conducted a household survey by visiting the houses clockwise from a post office as the starting point in non-slum areas, and from the entrance of slums. All adolescents from 11–18 years old were asked to participate in the study. Where there was more than one adolescents of this age range in a family, just one adolescent was selected. Before the interview, verbal consent was obtained from the adolescent and his/her parent. Interviews with respondents were performed in separate rooms or places away from other people for privacy and confidentiality. From a total of 653 adolescents who were identified as potential subjects, 602 questionnaires were collected (response rate 92.19%). The mean ages of the subjects’ were 14.61 years ðSD ¼ 2:10Þ for 187 boys and 15.16 (2.02) for 137 girls in non-slum areas, and 13.76 (2.07) for 157 boys and 13.44 (2.09) for 121 girls in slum areas. They were, respectively, grouped as the Non-Slum group and the Slum group. The mean age was significantly higher in the Non-Slum group than in the Slum group for both genders (po0:01 each). Instruments The World Health Organization Quality of Life Assessment Instrument (WHOQOL-BREF) was used to assess respondents’ Quality of Life; QOL. The Bangla version of WHOQOL-BREF was shown to have reliability and validity (Izutsu et al., 2005b). It has 26 items, all with 1–5 response sets. In addition to total scores, there are four domains each of which has scores on; physical, psychological, social relationships and environment domains, with higher score indicating better QOL. Permission for use and translation of WHOQOLBREF was obtained from the WHO. The translation process and examination of reliability and validity have been noted elsewhere (Izutsu et al., 2005b). Additionally, the Self Reporting Questionnaire (SRQ) was administered. This is a scale for screening psychiatric disturbances, especially in develop-
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ing countries. It includes 20 items covering the major components of general mental health. Each item has a 0 (no) or 1 (yes) response set. Its composite score ranges from 0 to 20 with higher scores indicating a worse general mental health status. Like the original English version, the Bangla version showed good reliability and validity. Furthermore, the Achenbach system of empirically based assessment (ASEBA) form, the Youth Self-Report (YSR) was employed. The YSR is internationally one of the most used self-rating questionnaires (translated into more than 60 languages) for assessing competence or adaptive functioning and emotional and behavioural problems of adolescents. In this study, only the problem scale was employed since competence or adaptive functioning is difficult to assess in different cultures. For the scale, respondents rated each problem item as 0 (not true), 1 (somewhat or sometimes true), and 2 (very true or often true). By summing 1s and 2s on all items, eight syndromes (anxious/depressed, withdrawn/depressed, somatic complaints, social problems, thought problems, attention problems, rule-breaking behaviour, and aggressive behaviour) as well as internalizing, externalizing and total problems scores can be calculated. Also DSM-Oriented Scales comprising affective problems, anxiety problems, somatic problems, attention deficit/hyperactivity problems, oppositional defiant problems, and conduct problems are computed to infer respondents’ diagnoses. There has been research using ASEBA scales across different countries and cultures, and for these cross cultural validity and reliability have been shown in many countries including non-western countries, including for the YSR in Bangladesh (Achenbach & Rescorla, 2001; Izutsu et al., 2005a). Furthermore, demographic and other information, which is not included in the scales above; about each adolescent’s religion and ethnicity, history of education, literacy, occupational status, current disease, annual family income, and the number of family members who lived together was requested. Further, adolescent weights and heights were assessed to the nearest 0.1 kg/cm. Participants were required to take off shoes and socks but not all clothes when measuring weight and height. One and half kg was subtracted from their weight as the weight of clothing. The body mass index (BMI) (weight (kg)Cheight (m)Cheight (m)) was calculated to infer nutritional status.
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The questionnaire consisting of the above scales was answered through interviews because the literacy rate in Bangladesh is low (57% for males and 29.9% for females) (United Nations Development Programme, 2002). Nine highly experienced interviewers (5 males and 4 females) were recruited and undertook one week’s training, and were under the working supervision of experienced public health doctors. Female interviewers were principally in charge of interviewing female respondents. All procedures were accepted by the ethical committee of the School of Medicine, The University of Tokyo. Data analysis Analysis of covariance (ANCOVA), with age as the covariate between the Non-Slum and Slum groups was conducted for each gender. Also, ANCOVA with age being the covariate was employed for comparison between genders in the Non-Slum group, and a t-test was used to compare genders in the Slum group. Then, for each scale, subjects were divided into two groups, above or below the 93 percentile of all subjects (above indicates the possibility of having a problem, while being below indicates the possibility of being in a normal range), and the ratio between Non-Slum and Slum groups was compared for each scale using w2 analysis. Next, to reveal factors associated with SRQ and YSR scores, standard logistic regression analysis using above and below the cutoff of the borderline clinical range (93 percentile) of each scale as dependent variables and factors such as site (non-slum or slum); age; gender; BMI; educational status (school-attending or not); literacy (can both read and write or not); job status (working or not) and income as independent factors, were employed. SPSS version 11.0J was used in the analyses. Statistical significance was set at 0.05. Results Demographic characteristics Demographic data are shown in Table 1. Since there was a significant age difference between the Non-Slum and Slum groups for both genders ðpo0:01Þ; parametric analyses between the groups were performed using age as the covariate. Almost all of the respondents in this research were Muslim
and of Bangla ethnicity. Slum adolescents had significantly lower school enrolment, literacy, family income and higher employment rates than non-slum area adolescents ðpo0:01Þ. There was no significant difference between the groups in the ratio of currently having a disease and the number of family members who lived together. Comparison between the Non-Slum and Slum groups Comparisons between non-slum and slum adolescents on the WHOQOL-BREF, SRQ, YSR and anthropometric measures are presented in Table 2. As to the WHOQOL-BREF, scores of ‘‘WHOQOLBREF Domain 4 (environmental)’’ and ‘‘WHOQOL-BREF Total’’ were significantly worse for slum adolescents of both genders, and the ‘‘WHOQOL-BREF Domain 3 (social relationships)’’ score was significantly worse for male slum adolescents (po0:01 for all). On the other hand, with respect to mental health status, there were no significant differences in ‘‘SRQ total score’’. Contrarily, in the ‘‘YSR total score’’ (male; po0:05, female; po0:01), subscales such as ‘‘YSR Thought Problems’’ (male; po0:01, female; po0:05), ‘‘YSR Attention Problems’’ (po0:01 for both genders), ‘‘YSR DSM-Oriented Anxiety Problems’’ (po0:01 for both genders), ‘‘YSR DSMOriented Attention Deficit/Hyperactivity Problems’’ (male; po0:05, female; po0:01) for both genders, and ‘‘YSR Somatic Complaints’’ (po0:05) and ‘‘YSR Social Problems’’ ðpo0:01Þ for females, the Non-Slum group presented worse (higher) scores than did the Slum group. Only ‘‘YSR DSMOriented Conduct Problems’’ in males had a tendency for slum adolescents to score worse ðpo0:1Þ. As for nutritional status, slum adolescents of both genders had a significantly lower weight ðpo0:01Þ. Female height was also significantly lower in the Slum than the Non-Slum group ðpo0:01Þ. Male BMI ðpo0:01Þ was significantly lower in the Slum than the Non-Slum group, and the same tendency was observed for females ðpo0:1Þ. Outcomes of comparisons of rates above the cutoff at the 93rd percentile in the whole study population are shown in Table 3. The ratios of having ‘‘YSR Thought Problems’’ were higher in the Non-Slum group for both genders (male; po0:05, female; po0:01). The ratios of people who were assumed to have ‘‘YSR Attention Problems’’ ðpo0:05Þ, ‘‘YSR Aggressive Problems’’
(2.1)
(97.9) (2.1)
(100.0) (0.0)
(95.2) (4.8)
(0.0)
(100.0)
(0.0) (0.0)
(95.1) (4.9)
(6.0) (94.0) (96 000–240 000)
(2.2)
14.6
183 4
187 0
178 9
0
187
0 0
173 9
11 173 146 000
4.6
4.5
7 150 4 8000
79 73
26 22
106
15
82 60
157 0
156 1
13.8
(2.1)
(4.5) (95.5) (36 000–60 000)
(52.0) (48.0)
(18.0) (14.1)
(67.9)
(9.6)
(52.2) (38.2)
(100.0) (0.0)
(99.4) (0.6)
(2.1)
SDc/%d/1st– 3rd precentilee
0.4
1924.5**
0.4
83.0
**
70.2
**
86.2**
–
3.8** 1.3
tc/w2d/Ue
4.7
14 122 144 000
124 9
0 0
137
1
133 9
137 0
134 3
15.2
Meanc/ Frequencyd/ Mediane
Non-Slum ðn ¼ 137Þ
Female
(2.8)
(10.3) (89.7) (100 000–240 000)
(93.2) (6.8)
(0.0) (0.0)
(100.0)
(0.7)
(97.1) (2.2)
(100.0) (0.0)
(97.8) (2.2)
(2.0)
SDc/%d/1st– 3rd precentilee
4.7
19 101 36 000
94 25
17 13
91
15
75 31
114 6
115 6
13.4
Meanc/ Frequencyd/ Mediane
Slum ðn ¼ 121Þ
1$ ¼ 56 Tk. *** po0:1, *po0:05, **po0:01. a The outcome of comparison between genders in the Non-Slum group utilizing t-tests, w2-test, and Mann–Whitney U-test. b The outcome of comparison between genders in the Slum group utilizing t-tests, w2-test, and Mann–Whitney U-test. c t-test. d 2 w analysis. e Mann–Whitney U-test.
Agec Religiond Islam Hindu Ethnicityd Bangla Others Schoold Going to school Stopped going to school Never entered school Literacyd Both read and write Only read or write No literacy Job statusd Not working Working Current diseased Yes No Yearly family income (Tk)e Number of families who live togetherc
Meanc/ Frequencyd/ Mediane
Meanc/ Frequencyd/ Mediane
SDc/%d/1st– 3rd precentilee
Slum ðn ¼ 157Þ
Non-Slum ðn ¼ 187Þ
Male
Table 1 Demographic data for Non-Slum and Slum groups
(2.0)
(15.8) (84.2) (24 000–48 000)
(79.0) (21.0)
(14.0) (10.7)
(75.2)
(12.4)
(62.0) (25.6)
(95.0) (5.0)
(95.0) (5.0)
(2.1)
SDc/%d/1st– 3rd precentilee
0.1
458.0**
1.7
10.9**
38.4**
50.7**
7.0**
6.7** 1.5
tc/w2d/Ue
0.5
12384.5
2.0
0.5
–
2.9
–
2.4* 0.0
tc/w2d/Ue
NonSluma
0.8
6485.5**
10.3**
21.1**
4.1
5.0***
8.0**
1.3 5.2*
tc/w2d/Ue
Slumb
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Table 2 Comparison between Non-Slum and Slum adolescents on WHOQOL-BREF, SRQ, YSR and anthropometric measures by ANCOVA with age as the covariate Male Non-Slum ðn ¼ 187Þ
Female Slum ðn ¼ 157Þ
Mean (SE) Mean (SE)
F
Non-Slum ðn ¼ 137Þ
Slum ðn ¼ 121Þ
F
Non-Sluma
Slumb
F
t
Mean (SE) Mean (SE)
WHOQOL-BREF Domain 1 (Physical) 15.7 (0.1) 15.5 (0.2) 1.3 15.2 (0.2) 15.3 (0.2) Domain 2 (Psychological) 14.3 (0.1) 14.3 (0.2) 0.0 13.6 (0.2) 13.4 (0.2) Domain 3 (Social relationships) 15.4 (0.2) 14.3 (0.2) 14.8** 15.2 (0.2) 15.2 (0.2) Domain 4 (Environment) 14.0 (0.1) 11.8 (0.2) 107.9** 13.7 (0.2) 11.8 (0.2) Total 93.5 (0.8) 87.9 (0.8) 23.2** 90.0 (0.9) 86.2 (1.0) SRQ 4.4 (0.3) 4.2 (0.3) 0.3 5.7 (0.3) 5.3 (0.4) YSR Syndrome scales Anxious/depressed 8.9 (0.3) 7.4 (0.3) 15.5*** 10.1 (0.4) 9.3 (0.4) Withdrawn/depressed 5.6 (0.2) 5.9 (0.2) 1.2 5.7 (0.2) 6.3 (0.3) Somatic complaints 4.3 (0.2) 4.3 (0.3) 0.0 5.6 (0.3) 4.5 (0.3) Social problems 6.3 (0.2) 6.0 (0.3) 1.1 7.6 (0.3) 6.1 (0.3) Thought problems 3.6 (0.2) 2.3 (0.3) 14.4** 4.4 (0.3) 3.4 (0.3) Attention problems 7.3 (0.3) 5.9 (0.3) 12.4** 8.1 (0.3) 5.9 (0.3) Rule-breaking behaviour 4.5 (0.3) 4.9 (0.3) 1.4 3.4 (0.3) 3.7 (0.3) Aggressive behaviour 9.2 (0.4) 8.5 (0.4) 1.6 10.2 (0.4) 9.1 (0.5) Other problems 4.4 (0.2) 3.2 (0.2) 15.3** 5.5 (0.2) 4.1 (0.2) Internalizing 18.7 (0.6) 17.5 (0.6) 2.1 21.2 (0.7) 20.1 (0.8) Externalizing 13.7 (0.6) 13.4 (0.7) 0.1 13.6 (0.6) 12.8 (0.6) Total score 54.1 (1.6) 48.4 (1.7) 5.6* 60.5 (1.7) 52.4 (1.8) DSM-oriented scales Affective problems 5.9 (0.3) 5.4 (0.3) 0.8 7.8 (0.4) 6.9 (0.4) Anxiety problems 4.1 (0.2) 3.1 (0.2) 17.1** 4.9 (0.2) 3.8 (0.2) Somatic complaints 2.6 (0.2) 2.7 (0.2) 0.3 3.3 (0.2) 2.7 (0.2) 6.0 (0.2) 4.2 (0.3) Attention deficit/hyperactivity problems 5.2 (0.2) 4.4 (0.2) 5.1* Oppositional defiant problems 2.8 (0.1) 2.7 (0.2) 0.3 3.2 (0.2) 2.7 (0.2) Conduct problems 4.8 (0.3) 5.7 (0.3) 3.6*** 4.2 (0.3) 4.5 (0.3) Anthropometric measures Height 158.3 (6.0) 159.5 (6.5) 0.0 151.9 (0.5) 145.8 (0.6) Weight 46.8 (0.6) 38.5 (0.7) 77.2** 43.2 (0.7) 38.5 (0.7) BMI 18.4 (0.2) 16.8 (0.2) 24.6** 18.9 (0.3) 18.0 (0.3)
0.2 0.1 0.1 44.8** 7.4** 0.7
3.4*** 7.5** 0.3 1.0 5.7* 7.4**
0.8 3.1** 3.1** 0.0 1.5 2.2*
2.0 6.0* 2.5 0.3 9.4** 5.7* 12.5** 7.1** 5.3* 1.9 27.7** 2.7 1.0 14.7** 3.2*** 1.9 19.3** 10.8** 1.1 6.4* 0.8 0.5 9.6** 4.0*
4.1** 0.5 0.5 0.7 3.3** 0.3 2.5* 1.3 3.0** 1.8*** 2.4* 0.3
2.5 14.0** 15.0** 7.7** *** 3.4 5.9* 22.9** 3.5*** *** 2.8 1.3 0.5 3.8***
2.5* 3.1** 0.2 0.4 0.7 2.0*
88.6** 57.3* ** 22.0 21.3** 2.7*** 0.2
1.3 1.3 17.6**
*** po0:1, *po0:05, **po0:01. WHOQOL-BREF ¼ World Health Organization Quality of Life Assessment Instrument, SRQ ¼ Self Reporting Questionnaire, YSR ¼ Youth Self-Report, BMI ¼ body mass index. a The outcome of comparison between genders in Non-Slum adolescents utilizing ANCOVA with age as the covariate. b The outcome of comparison between genders in Slum adolescents utilizing t-test.
ðpo0:05Þ, ‘‘YSR DSM-Oriented Anxiety Problems’’ ðpo0:01Þ, ‘‘YSR DSM-Oriented Somatic Complaints’’ ðpo0:01Þ and ‘‘YSR DSM-Oriented Attention Deficit/Hyperactivity Problems’’ ðpo0:05Þ were all higher in the Non-Slum group for females. On the other hand, the rate of having ‘‘YSR DSMOriented Conduct Problems’’ in males was significantly higher in the Slum group ðpo0:05Þ, and the rate of having ‘‘YSR Somatic Complaints’’ had a tendency to be higher in the Slum group ðpo0:1Þ,
while SRQ had a tendency to be worse in the NonSlum group ðpo0:1Þ. Comparing genders using the same 93rd percentile cutoff, male subjects in both sites had worse rates of being categorized as having problems in ‘‘YSR Rule-Breaking Behaviour’’ (Non-Slum; po0:01, Slum; po0:05), and ‘‘YSR Externalizing’’ (po0:01), ‘‘YSR DSM-Oriented Somatic Complaints’’ (po0:05), ‘‘YSR DSM-Oriented Conduct problems’’ (po0:05) in the Slum group, and females were worse in ‘‘YSR Somatic
1.3
2.5 3.8 5.1 4.5 3.2 3.8 9.6 5.1 3.8 7.0 3.8 3.2 3.8 6.4 5.1
3.8 10.2
4.3
4.8 3.2
1.6 4.8 8.6 5.9 9.6
6.4
3.7 7.5 5.3
3.7 5.3 3.2 4.8
5.3
3.7
Above 93rd percentile %
Above 93rd percentile %
*** po0:1, *po0:05, **po0:01. SRQ ¼ Self Reporting Questionnaire, YSR ¼ Youth Self-Report. w2 analysis except for a Fisher’s exact test.
SRQ YSR Syndrome scales Anxious/depressed Withdrawn/ depressed Somatic complaints Social problems Thought problems Attention problems Rule-breaking behaviour Aggressive behaviour Internalizing Externalizing Total score DSM-oriented scales Affective problems Anxiety problems Somatic complaints Attention deficit/ hyperactivity problems Oppositional defiant problems Conduct problems
Slum ðn ¼ 157Þ
Non-Slum ðn ¼ 187Þ
Male
6.6 2.2
5.7*
8.0 12.4 10.2 6.6
5.8 4.4 7.3
0.4
0.1 0.4 1.9 0.0
0.0 0.0 0.4
7.3
5.8 7.3 14.6 8.0 2.2
3.4*** 0.0 4.3* 0.8 0.0 0.3
9.5 5.8
4.4
Above 93rd percentile %
Non-Slum ðn ¼ 137Þ
1.2 0.1
2.8***
w2
Female
Table 3 Comparisons between Non-Slum and Slum groups of the ratio above/below the 93rd percentile of each scale score
3.3
4.1
6.6 0.8 1.7 1.7
3.3 1.7 3.3
1.7
1.7 3.3 5.0 2.5 2.5
5.8 5.8
2.5
Above 93rd percentile %
Slum ðn ¼ 121Þ
a
0.7
a*
a**
a**
0.2
a
a
a
a*
a
a*
a**
a
a***
1.2 0.0
a
w2
a
0.2
2.8 *** 5.2* 6.7* 0.5
0.8 1.3 0.5
0.1
a**
0.9 2.9*** 0.6
a*
2.7*** 1.3
0.0
w2
NonSlum
a*
0.0
a
a*
a
1.8
a
a*
a
a
a*
a
0.6
a
a
0.6
a
a
w2
Slum
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Complaints’’ ðpo0:05Þ, ‘‘YSR DSM-Oriented Anxiety Problems’’ ðpo0:05Þ and ‘‘YSR DSM-Oriented Somatic Complaints’’ ðpo0:05Þ in the Non-Slum group. Factors associated with the SRQ and YSR scores Tables 4–6 shows the outcomes of standard logistic regression analyses putting above/less than the cutoff point as the dependent variable, and age, gender, BMI, educational status, job status and income as independent variables. As to ‘‘YSR Social Problems’’, ‘‘YSR Internalizing’’ and ‘‘YSR DSMOriented Oppositional Defiant Problems’’, only older ages were associated with having the problems. For ‘‘YSR Attention Problems’’ and ‘‘YSR DSM-Oriented Attention Deficit/Hyperactivity Problems’’, higher age and school attendance were associated with having the problems. As to ‘‘YSR Rule-Breaking Behaviour’’ and ‘‘YSR Externalizing’’, higher age, being male, lower BMI, school going and working were associated, and for ‘‘YSR Total Score’’ all were the same and with no association with gender. ‘‘YSR Thought Problems’’ and ‘‘YSR Aggressive Problems’’ were associated with higher age, lower BMI and school attendance. Table 4 Predictors of SRQ, YSR internalizing, externalizing, and total scores
SRQ Age Working status YSR Internalizing Age Externalizing Sex Age BMI School enrollment Working status Total score Age BMI School enrollment Working status
Odds ratio
(95% CI)
1.5** 0.2***
(1.2–1.9) (0.0–1.3)
1.4**
(1.1–1.7)
0.4* 1.5** 0.9*** 3.1* 2.8*
(0.2–1.0) (1.2–1.8) (0.8–1.0) (1.0–9.3) (1.1–7.3)
1.4 0.9*** 4.9* 3.4*
(1.2–1.8) (0.8–1.0) (1.4–17.0) (1.3–8.8)
Logistic regression analysis ***po0:1, *po0:05, **po0:01. SRQ ¼ Self Reporting Questionnaire, YSR ¼ Youth Self-Report. Site (0 ¼ non-slum, 1 ¼ slum), gender (0 ¼ male, 1 ¼ female), school enrollment (0 ¼ not attending, 1 ¼ attending), working status (0 ¼ not working, 1 ¼ working).
Higher age and not working in ‘‘SRQ’’, and higher age and working in ‘‘YSR Somatic Complaints’’ were associated with each type of mental ill health, and ‘‘YSR DSM-Oriented Somatic Complaints’’ was associated with the same factors as ‘‘YSR Somatic Complaints’’ plus school attendance. For having ‘‘YSR Anxious/Depressed’’ problem, higher age, being female, school attendance and working were associated, while for ‘‘YSR Withdrawn/Depressed’’, as with ‘‘YSR Anxious/Depressed’’, except school attendance which had no association, and for ‘‘YSR DSM-Oriented Affective Problems’’, it was the same, except for school attendance and
Table 5 Predictors of YSR syndrome scales
Anxious/depressed Gender Age School enrollment Working status Withdrawn/depressed Gender Age Working status Somatic complaints Age School enrollment Working status Social problems Age Thought problems Age BMI School enrollment Attention problems Age BMI School enrollment Working status Rule-breaking behaviour Sex Age BMI School enrollment Working status Aggressive behaviour Age BMI School enrollment
Odds ratio
(95% CI)
2.1*** 1.2* 4.1* 2.9*
(1.0–4.3) (1.0–1.5) (1.2–13.6) (1.2–7.2)
2.5* 1.3* 2.2***
(1.1–6.0) (1.0–1.5) (0.9–5.5)
1.3*
(1.0–1.6)
2.5***
(1.0–6.3)
1.2*
(1.0–1.5)
1.3** 0.9*** 5.3**
(1.1–1.5) (0.8–1.0) (1.6–17.8)
1.2*
(1.0–1.4)
10.1*
(1.4–75.2)
0.2** 1.6** 0.9* 3.7* 3.4*
(0.1–0.6) (1.3–2.0) (0.8–1.0) (1.3–11.1) (1.3–8.6)
1.3* 0.9* 2.5***
(1.1–1.5) (0.8–1.0) (0.8–7.5)
Logistic regression analysis ***po0:1, *po0:05, **po0:01. YSR ¼ Youth Self-Report. Site (0 ¼ non-slum, 1 ¼ slum), gender (0 ¼ male, 1 ¼ female), School enrollment (0 ¼ not attending, 1 ¼ attending), Working status (0 ¼ not working, 1 ¼ working).
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Affective problems Gender Age Anxiety problems Site Somatic problems Age School enrollment Working status Attention deficit/hyperactibity Age School enrollment Oppositional defiant problems Age Conduct problems Site Gender Age
Odds ratio
(95% CI)
2.3* 1.4**
(1.1–5.0) (0.1–1.6)
0.3*
(0.1–0.7)
1.3** 3.5* 4.4**
(0.1–1.6) (1.2–10.4) (1.8–10.9)
1.2*** 2.8***
(1.0–1.4) (0.8–9.6)
1.2***
(1.0–1.4)
3.2** 0.4* 1.3**
(1.4–7.2) (0.2–0.9) (1.1–1.6)
Logistic regression analysis ***po0:1, *po0:05, **po0:01. YSR ¼ Youth Self-Report. Site (0 ¼ non-slum, 1 ¼ slum), gender (0 ¼ male, 1 ¼ female), School enrollment (0 ¼ not attending, 1 ¼ attending), Working status (0 ¼ not working, 1 ¼ working).
working which were not associated. Site was associated only in ‘‘YSR DSM-Oriented Anxiety Problems’’ and ‘‘YSR DSM-Oriented Conduct Problems’’. For the former, living in non-slum areas was the only significant factor and had an odds ratio of 3.45, while for the latter, living in slums (odds ratio ¼ 3:17), higher age and being male associated with having the problems (Table 6). Discussion The present study investigated Bangladeshi urban adolescents’ QOL and mental health status utilizing internationally recognized scales. At the same time, this study showed differences in living status, QOL, mental health and nutritional status between adolescents in urban slums and those living in non-slum areas. Slum adolescents had lower school enrolment rates, lower literacy rates, lower family incomes, lighter weights, lower BMI (in females only a tendency was observed), and higher rates of child labour; which taken together revealed several severe situations for Bangladeshi urban slum adolescents. Heights of female subjects were also significantly less in slums even after controlling for age
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differences between groups, and implied long term malnutrition in Slum group females. Also, QOL in the environmental domain for both genders, and the social relationships domain for males were worse in slum adolescents. As to the social relationships domain for females, as female social relationships are culturally limited in Bangladesh (United Nations Children’s Fund, 1999), though recently slowly changing, female adolescents’ QOL may be low irrespective of their living area. As to the mental health of male subjects, all significant differences of mean scores were worse in non-slum subjects except for the conduct problem, which had the opposite tendency of being worse in slum subjects. When analysed by dividing subjects into two groups; a 11–14 year old group and a 15–18 year old one, the differences in conduct problems score between the groups became significant (po0:05) for the higher age group while there was no significant difference between the younger group (data not shown). Comparison of the ratio of being above the cutoff point showed more than 10% of male subjects in slums were categorized as having a conduct problem while this was only about 3% for non-slum adolescents. This difference of ratio was significant. ‘‘YSR Somatic Complaints’’ also showed a tendency for slum subjects have a worse ratio. The finding that the higher the socioeconomic status the worse their mental status was consistent with a previous study of middle-class Dhaka city adults (Islam et al., 2003). This may be because more non-slum area boys go to school and live in a structured and rule-based society; they have to manage to live in a social framework and their attention and thought related problems are subjected to teachers’ attention and comparison with friends, so that they may come to be aware of these problems. As well, non-slum adolescents learn not to give vent to their indignations but to endure the experience of not being able to satisfy their desires in school and in a structured family environment, and so they come to develop their internal process such as anxiety and thinking about solutions as a coping strategy. Contrarily, adolescents in slums generally have less opportunity to face a social framework especially when they are young; which means they may have limited chances to learn these coping skills. Thus, non-slum adolescents may be able to feel anxiety when they face stress, whereas slum adolescents may not be able to learn or practice
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this highly cognitive procedure but rather vent their frustrations by acting out as they get older. Conduct disorder is known to associate with future anti-social personality disorder, substancerelated disorders and crimes (American Psychiatric Association, 2000). This may be one of the mechanisms for high risk behaviours and crime in slum populations (Flisher & Chalton, 2001); indicating the necessity of slum children and adolescents to have opportunities to learn how to internally process difficult situations so that they will not become maladaptive and act out when older. Thus, increasing school enrolment rates in slum areas appears crucial. Indeed, there have been many attempts in Bangladesh at governmental, international organizations, and NGO levels to increase the school attendance of children, and step by step these efforts are succeeding. However, because young children and adolescents are important breadwinners in slums, many practical obstacles remain. Therefore, to deploy short-time mobile teaching teams in slums for non schoolattending children/adolescents in the early morning or late evening may help to make it possible for working children/adolescents to receive education, and to teach/give essential experiences such as group activities, social rules, risk avoidance, and self-defence to these children. Because a high percentage of the slum male adolescents displayed conduct problems, specific risky behaviours such as drug use or sexually risky behaviour should be studied with regard to mental health for the construction of effective interventions. As to females, in many of the YSR subscales, significant differences were observed and non-slum adolescents showed a worse status in all differences. Non-slum area females in Bangladesh are exposed to stricter cultural structures/frameworks or social pressures than slum females and males, and this appeared to make differences in many scales. In the analysis of ratios, utilizing the cutoff, in addition to anxiety related problems, aggressive and somatic problems were observed to be higher in non-slum areas, which was contrary to the findings in males. Because the ratios of subjects who had affective and anxiety problems were as high as around 10%, females seemed to have the ability to internally process stressful situations. However, the situation may be too hard to process only internally and so aggressive and somatic symptoms were shown for some of the females in non-slum areas. The need for gender equity is now broadly recognized and female
school enrolment and literacy rates are improving in Dhaka (United Nations Children’s Fund, 1999). However, as this study shows, their severe situation is persistent and urgent attention to assist this group is necessary, as the Bangladeshi gender development index (GDI) is 0.468 and ranks at 121 out of 146 countries (United Nations Development Programme, 2002). School teachers may need to study aspects of mental health, as it might be helpful if they had the ability to counsel female students. Logistic regression analysis revealed factors associated with the scales. As to site differences, for the ‘‘YSR DSM-Oriented Anxiety Problems’’, the odds ratio of living in non-slum areas was 3.44, and for the ‘‘YSR DSM-Oriented Conduct Problems’’, slum living adolescents’ odds ratio was 3.17 for having problems. This seems to accord with the view noted above. The odds ratio of being female in mood disorder related scales was high, while those of being male in rule-breaking and externalizing kinds of scales were likewise high, consistent with past studies (Achenbach & Rescorla, 2001). Though statistically significant, the magnitude of the odds ratio of age and BMI shown in many scales was not very large, and instead school enrolment and employment status had higher odds ratios. The outcome of all school-attending or working adolescents except for ‘‘SRQ’’ might be understandable using the same argument noted above for the social framework. As to employment status, however, the situation that they have to work even though they are too young may adversely affect children’s mental health; though from the data in this study, it is difficult to properly clarify this point. The mechanism for why only higher ‘‘SRQ’’ scores are associated with not working may be because the SRQ contains questions such as ‘‘Are you unable to play a useful part in life?’’ and ‘‘Do you feel that you are a worthless person?’’; working adolescents who are helping their family may thus would probably indicate a better outcome. Further research regarding working adolescents’ mental health is needed. Details of their working situation are necessary because many are exploited and abused in the work place, while others get good outcomes from employment; indeed, such differences would confound outcomes. The United Nations (UN) estimates that by 2025, two thirds of the world’s population of approximately 8.4 billion will live in cities, and by 2050 this will rise to more than 80% (United Nations, 1995a, 1995b). Especially, in slums, inferior living conditions
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with poor social support including health and welfare, lack of opportunities for education, and the menace of illicit drugs and crime oppress adolescents in addition to the difficulties presented by their role as important economic and practical resources for their families. With respect to slums, although there have been some studies on the mental health of adults (Chattopadhyay, Gill, Bali, & Wig, 1989; Ruiz & Saiger, 1972) and children (Kora, Kaur, & Gill, 1991), studies of the mental health of adolescents are lacking; although studies of nutritional status (Singh & Mishra, 2001), sexually transmitted diseases (Awasthi, Nichter, & Pande, 2000, Pandit, Angadi, Chvan, & Pai, 1995), and violence (WHO, 2001b) have shown the severe situations facing this adolescent group. This study has, for the first time, shown the urgent need for site and gender sensitive countermeasures to assist in remedying Bangladeshi adolescents’ poor mental health. In addition, the need for concrete provisions for non-slum females and working adolescents has been shown. There were some limitations to this study. First of all, participants were only from Dhaka city, and since there are totally different cultures, ethnicities and situations in the provinces of Bangladesh, the subjects of this study might not be truly representative of Bangladeshi adolescents as a whole. Studies of the situations in rural areas will thus be needed to obtain a more comprehensive picture. However, because this study adopted a randomized stratified sampling method, which included both non-slum and slum areas, and both school-attending and non school-attending adolescents, the outcome shown in this study suggests important characteristics of adolescents in urban areas of Bangladesh. In addition, the design of the present study is crosssectional; therefore, we could not infer any causeeffect relationships. Longitudinal study is required based on our results. Furthermore, cognition and understanding of the questions of scales may differ depending on literacy, and this too might influence outcomes. As well, given that mental health problems are highly stigmatized in Bangladesh, this might have skewed the answers to the modest side. In conclusion, living conditions and QOL of slum adolescents were worse than their non-slum counterparts, and this contrasts with most of the mental indexes, which were better in slums. For males, conduct problems were observed more in the slum population, and the need for attempts to educate them to internally process stress rather act out was
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implied. For females, the prevailing difficulties in urban female adolescents were revealed and a need for urgent care shown as essential. Gender and site sensitive countermeasures for Bangladeshi urban adolescents are thus imperative. Acknowledgements The authors would like to express their gratitude to National Institute of Mental Health, Bangladesh, and to the interviewers Abu Ala Mahmudul Hasan, Anjuman Tahmina Ferdous, Imran Al Amin, Jasmin Khan, Khadiza Begum, Md. Saiful Islam, Shormin Sultana, Zahangir Alam, and Md. Iqbal Hossain for their enormous contribution, and without whom the data collection would have been impossible. This study was supported by Mitsubishi Foundation grants for social welfare activities.
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Social Science & Medicine 63 (2006) 1477–1488 www.elsevier.com/locate/socscimed
Mental health, quality of life, and nutritional status of adolescents in Dhaka, Bangladesh: Comparison between an urban slum and a non-slum area Takashi Izutsua,, Atsuro Tsutsumib, Akramul Md. Islamc, Seika Katod, Susumu Wakaie, Hiroshi Kuritaf a
National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan b Hyogo Institute for Traumatic Stress, Japan c BRAC, Dhaka, Bangladesh d Faculty of Health Sciences, Tokyo Metropolitan University, Japan e Department of International Community Health, Graduate School of Medicine, The University of Tokyo, Japan f Department of Mental Health, Graduate School of Medicine, The University of Tokyo, Japan Available online 9 June 2006
Abstract This study aims to clarify the quality of life (QOL), mental health, and nutritional status of adolescents in Dhaka city, Bangladesh by comparing non-slum areas and slums, and to find the factors associated with their mental health problems. A sample of 187 boys and 137 girls from non-slum areas, and 157 boys and 121 girls from slums, between 11–18 years old were interviewed with a questionnaire consisting of a Bangla translation of the World Health Organization Quality of Life Assessment Instrument (WHOQOL-BREF), Self Reporting Questionnaire (SRQ), Youth Self-Report (YSR) and other questions. The height and weight of the respondents were measured. All significant differences in demographic characteristics, anthropometric measures, and WHOQOL-BREF were found to reflect worse conditions in slum than in non-slum areas. Contrarily, all differences in SRQ and YSR were worse in non-slum areas for both genders, except that the ‘‘conduct problems’’ score for YSR was worse for slum boys. Mental states were mainly associated with school enrolment and working status. Worse physical environment and QOL were found in slums, along with gender and area specific mental health difficulties. The results suggest gender specific needs and a requirement for area sensitive countermeasures. r 2006 Elsevier Ltd. All rights reserved. Keywords: Bangladesh; Developing countries; Mental health; Slum; Quality of life; Urbanization; Gender
Introduction According to the World Health Organization (WHO), about 450 million of the world’s population Corresponding author. Tel.: +81 90 9391 8888.
E-mail addresses: [email protected] (T. Izutsu), [email protected] (A. Tsutsumi). 0277-9536/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.socscimed.2006.04.013
suffer from mental or neurological disorders or from psychosocial problems; while one in every four persons is affected by a mental disorder at some stage in their life (WHO, 2001a). In particular, major depression, with a life time prevalence of 10–25% for females and 5–12% for males (American Psychiatric Association, 2000), is ranked fourth in terms of the global burden of disease, and is projected to be the
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second cause of the global disease burden within 20 years (WHO, 2001a). However, mental health issues tend to be overtaken by other health problems, especially in developing countries. Particularly, such situations in the ever increasing slum areas is a global concern with rapid urbanization taking place in developing countries, where slum inhabitants today are estimated to constitute 60% of the inhabitants in big cities (Hussain, Ali, & Kvale, 1999). Urbanization has been shown to be associated with higher incidents of injuries from traffic accidents (United Nations Environment Programme, & WHO, 1994), respiratory illness (Lovik, Dybing & Smith, 1996; SIDRIA, 1997), cancer (Schouten, Meijer, Huveneers, & Kiemeney, 1996), cardiovascular diseases and hypertension (Barnett, Strogatz, Armstrong, & Wing, 1996; Kaufman, Owoaje, James, Rotimi, & Cooper, 1996; Mufundu, Sigola, Chifamba, & Vengesa, 1994), deterioration in infant and child mortality (Backmann, London, & Barron, 1996; Kuate Defo, 1996; Sastry, 1997), and mental health problems (Cheng, 1989; Gillis, Welman Kock, & Joyi, 1991; Mueller, 1981; Rahim & Cederblad, 1986; Shepherd, 1984; Sijuwade, 1995; Varma et al., 1997). As for children’s and adolescents’ mental health, Takano, Nakamura, and Watanabe (1996) indicated that in young people urbanization increased risks such as drug, alcohol, tobacco, and risky sexual behaviours, and that these behaviours were linked to the rise of psychological problems. Also, Harpham (1994) pointed out that urbanization initiated rapid social changes, disintegration, dissolution of social relations, and decreased social control, and that these factors contributed to mental diseases particularly in young people. However, research regarding the mental health status of slumliving adolescents is lacking. In Bangladesh, there has been no official data on mental health. As to mental health research in Bangladesh, Islam, Ali, Ferroni, Underwood and Alam (2003) conducted a community survey in urban middle-class adult population in Dhaka, and presented prevalence of psychiatric disorders as 28%. A study of a married rural population utilizing MOS-Short Form 36 (SF-36) by Ahmed, Rana, Chowdhury and Bhuiya (2002) showed consistent negative self-evaluation of mental health status, and its deterioration with the advancement of age. Khan (2002) indicated suicides in the countries of the Indian subcontinent including Bangladesh had differences from those in Western
countries, showing higher organophosphate insecticides use, a higher ratio of married women, fewer elder subjects, and more problems in interpersonal relationship and life events as causative factors. As to children, Rabbani and Hossain (1999), using teachers’ reports, showed that 13.4% of urban primary school children in Dhaka had emotional, conduct or undifferentiated disorders. As to adolescents, though the reliability and validity of the Strengths and Difficulties Questionnaire (SDQ) has been assessed utilizing adolescent subjects (Goodman, Renfrew, & Mullick, 2000; Mullick & Goodman, 2001), the mental health status of such subjects remains unclear. To add to the prevailing poverty and severe health status; 33% of the total population of approximately 129 million is malnourished and the infant mortality rate in 2000 was 54 in 1000 births (United Nations Development Programme, 2002; World Bank & Bangladesh Centre for Advanced Studies, 1998), the urban growth rate in Bangladesh is projected to be 5.4% in the period 1999–2010 (Bangladesh Bureau of Statistics, 2002), and Bangladeshi urban population in 2020 is estimated to be nearly half the national total; and that of its capital Dhaka will be doubled by 2010 (WHO, 1997). A good percentage of this population increase will reside in slums due to lack of job opportunities, income and housing. Thus the mental health status of children and adolescents in urban slum population in Bangladesh needs to be evaluated. Therefore, this study aimed at (A) clarifying the QOL, general mental health, behavioural, emotional and social problems and nutritional status of adolescents in Dhaka city, Bangladesh in comparisons between non-slum area adolescents and slum area adolescents using internationally comparable scales; and (B) finding factors (age, sex, nutritional status, educational status, job status, and family income) associated with mental health problems. Methods Subjects General population subjects This study was conducted in Dhaka, the capital of Bangladesh. To obtain a representative cross sample of Dhaka city, six wards were randomly selected, with one non-slum area and one slum area being selected from each ward as study sites. A UN expert group established an operational definition
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of slums as being an area that to various extents combines the following characteristics: inadequate access to safe water; inadequate access to sanitation and other infrastructure; poor structural quality of housing; overcrowding; and insecure residential status (United Nations Human Settlements Programme, 2003). The slum areas we selected were consistent with this definition, whereas the nonslum areas referred to here are general urban residential areas. We conducted a household survey by visiting the houses clockwise from a post office as the starting point in non-slum areas, and from the entrance of slums. All adolescents from 11–18 years old were asked to participate in the study. Where there was more than one adolescents of this age range in a family, just one adolescent was selected. Before the interview, verbal consent was obtained from the adolescent and his/her parent. Interviews with respondents were performed in separate rooms or places away from other people for privacy and confidentiality. From a total of 653 adolescents who were identified as potential subjects, 602 questionnaires were collected (response rate 92.19%). The mean ages of the subjects’ were 14.61 years ðSD ¼ 2:10Þ for 187 boys and 15.16 (2.02) for 137 girls in non-slum areas, and 13.76 (2.07) for 157 boys and 13.44 (2.09) for 121 girls in slum areas. They were, respectively, grouped as the Non-Slum group and the Slum group. The mean age was significantly higher in the Non-Slum group than in the Slum group for both genders (po0:01 each). Instruments The World Health Organization Quality of Life Assessment Instrument (WHOQOL-BREF) was used to assess respondents’ Quality of Life; QOL. The Bangla version of WHOQOL-BREF was shown to have reliability and validity (Izutsu et al., 2005b). It has 26 items, all with 1–5 response sets. In addition to total scores, there are four domains each of which has scores on; physical, psychological, social relationships and environment domains, with higher score indicating better QOL. Permission for use and translation of WHOQOLBREF was obtained from the WHO. The translation process and examination of reliability and validity have been noted elsewhere (Izutsu et al., 2005b). Additionally, the Self Reporting Questionnaire (SRQ) was administered. This is a scale for screening psychiatric disturbances, especially in develop-
1479
ing countries. It includes 20 items covering the major components of general mental health. Each item has a 0 (no) or 1 (yes) response set. Its composite score ranges from 0 to 20 with higher scores indicating a worse general mental health status. Like the original English version, the Bangla version showed good reliability and validity. Furthermore, the Achenbach system of empirically based assessment (ASEBA) form, the Youth Self-Report (YSR) was employed. The YSR is internationally one of the most used self-rating questionnaires (translated into more than 60 languages) for assessing competence or adaptive functioning and emotional and behavioural problems of adolescents. In this study, only the problem scale was employed since competence or adaptive functioning is difficult to assess in different cultures. For the scale, respondents rated each problem item as 0 (not true), 1 (somewhat or sometimes true), and 2 (very true or often true). By summing 1s and 2s on all items, eight syndromes (anxious/depressed, withdrawn/depressed, somatic complaints, social problems, thought problems, attention problems, rule-breaking behaviour, and aggressive behaviour) as well as internalizing, externalizing and total problems scores can be calculated. Also DSM-Oriented Scales comprising affective problems, anxiety problems, somatic problems, attention deficit/hyperactivity problems, oppositional defiant problems, and conduct problems are computed to infer respondents’ diagnoses. There has been research using ASEBA scales across different countries and cultures, and for these cross cultural validity and reliability have been shown in many countries including non-western countries, including for the YSR in Bangladesh (Achenbach & Rescorla, 2001; Izutsu et al., 2005a). Furthermore, demographic and other information, which is not included in the scales above; about each adolescent’s religion and ethnicity, history of education, literacy, occupational status, current disease, annual family income, and the number of family members who lived together was requested. Further, adolescent weights and heights were assessed to the nearest 0.1 kg/cm. Participants were required to take off shoes and socks but not all clothes when measuring weight and height. One and half kg was subtracted from their weight as the weight of clothing. The body mass index (BMI) (weight (kg)Cheight (m)Cheight (m)) was calculated to infer nutritional status.
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The questionnaire consisting of the above scales was answered through interviews because the literacy rate in Bangladesh is low (57% for males and 29.9% for females) (United Nations Development Programme, 2002). Nine highly experienced interviewers (5 males and 4 females) were recruited and undertook one week’s training, and were under the working supervision of experienced public health doctors. Female interviewers were principally in charge of interviewing female respondents. All procedures were accepted by the ethical committee of the School of Medicine, The University of Tokyo. Data analysis Analysis of covariance (ANCOVA), with age as the covariate between the Non-Slum and Slum groups was conducted for each gender. Also, ANCOVA with age being the covariate was employed for comparison between genders in the Non-Slum group, and a t-test was used to compare genders in the Slum group. Then, for each scale, subjects were divided into two groups, above or below the 93 percentile of all subjects (above indicates the possibility of having a problem, while being below indicates the possibility of being in a normal range), and the ratio between Non-Slum and Slum groups was compared for each scale using w2 analysis. Next, to reveal factors associated with SRQ and YSR scores, standard logistic regression analysis using above and below the cutoff of the borderline clinical range (93 percentile) of each scale as dependent variables and factors such as site (non-slum or slum); age; gender; BMI; educational status (school-attending or not); literacy (can both read and write or not); job status (working or not) and income as independent factors, were employed. SPSS version 11.0J was used in the analyses. Statistical significance was set at 0.05. Results Demographic characteristics Demographic data are shown in Table 1. Since there was a significant age difference between the Non-Slum and Slum groups for both genders ðpo0:01Þ; parametric analyses between the groups were performed using age as the covariate. Almost all of the respondents in this research were Muslim
and of Bangla ethnicity. Slum adolescents had significantly lower school enrolment, literacy, family income and higher employment rates than non-slum area adolescents ðpo0:01Þ. There was no significant difference between the groups in the ratio of currently having a disease and the number of family members who lived together. Comparison between the Non-Slum and Slum groups Comparisons between non-slum and slum adolescents on the WHOQOL-BREF, SRQ, YSR and anthropometric measures are presented in Table 2. As to the WHOQOL-BREF, scores of ‘‘WHOQOLBREF Domain 4 (environmental)’’ and ‘‘WHOQOL-BREF Total’’ were significantly worse for slum adolescents of both genders, and the ‘‘WHOQOL-BREF Domain 3 (social relationships)’’ score was significantly worse for male slum adolescents (po0:01 for all). On the other hand, with respect to mental health status, there were no significant differences in ‘‘SRQ total score’’. Contrarily, in the ‘‘YSR total score’’ (male; po0:05, female; po0:01), subscales such as ‘‘YSR Thought Problems’’ (male; po0:01, female; po0:05), ‘‘YSR Attention Problems’’ (po0:01 for both genders), ‘‘YSR DSM-Oriented Anxiety Problems’’ (po0:01 for both genders), ‘‘YSR DSMOriented Attention Deficit/Hyperactivity Problems’’ (male; po0:05, female; po0:01) for both genders, and ‘‘YSR Somatic Complaints’’ (po0:05) and ‘‘YSR Social Problems’’ ðpo0:01Þ for females, the Non-Slum group presented worse (higher) scores than did the Slum group. Only ‘‘YSR DSMOriented Conduct Problems’’ in males had a tendency for slum adolescents to score worse ðpo0:1Þ. As for nutritional status, slum adolescents of both genders had a significantly lower weight ðpo0:01Þ. Female height was also significantly lower in the Slum than the Non-Slum group ðpo0:01Þ. Male BMI ðpo0:01Þ was significantly lower in the Slum than the Non-Slum group, and the same tendency was observed for females ðpo0:1Þ. Outcomes of comparisons of rates above the cutoff at the 93rd percentile in the whole study population are shown in Table 3. The ratios of having ‘‘YSR Thought Problems’’ were higher in the Non-Slum group for both genders (male; po0:05, female; po0:01). The ratios of people who were assumed to have ‘‘YSR Attention Problems’’ ðpo0:05Þ, ‘‘YSR Aggressive Problems’’
(2.1)
(97.9) (2.1)
(100.0) (0.0)
(95.2) (4.8)
(0.0)
(100.0)
(0.0) (0.0)
(95.1) (4.9)
(6.0) (94.0) (96 000–240 000)
(2.2)
14.6
183 4
187 0
178 9
0
187
0 0
173 9
11 173 146 000
4.6
4.5
7 150 4 8000
79 73
26 22
106
15
82 60
157 0
156 1
13.8
(2.1)
(4.5) (95.5) (36 000–60 000)
(52.0) (48.0)
(18.0) (14.1)
(67.9)
(9.6)
(52.2) (38.2)
(100.0) (0.0)
(99.4) (0.6)
(2.1)
SDc/%d/1st– 3rd precentilee
0.4
1924.5**
0.4
83.0
**
70.2
**
86.2**
–
3.8** 1.3
tc/w2d/Ue
4.7
14 122 144 000
124 9
0 0
137
1
133 9
137 0
134 3
15.2
Meanc/ Frequencyd/ Mediane
Non-Slum ðn ¼ 137Þ
Female
(2.8)
(10.3) (89.7) (100 000–240 000)
(93.2) (6.8)
(0.0) (0.0)
(100.0)
(0.7)
(97.1) (2.2)
(100.0) (0.0)
(97.8) (2.2)
(2.0)
SDc/%d/1st– 3rd precentilee
4.7
19 101 36 000
94 25
17 13
91
15
75 31
114 6
115 6
13.4
Meanc/ Frequencyd/ Mediane
Slum ðn ¼ 121Þ
1$ ¼ 56 Tk. *** po0:1, *po0:05, **po0:01. a The outcome of comparison between genders in the Non-Slum group utilizing t-tests, w2-test, and Mann–Whitney U-test. b The outcome of comparison between genders in the Slum group utilizing t-tests, w2-test, and Mann–Whitney U-test. c t-test. d 2 w analysis. e Mann–Whitney U-test.
Agec Religiond Islam Hindu Ethnicityd Bangla Others Schoold Going to school Stopped going to school Never entered school Literacyd Both read and write Only read or write No literacy Job statusd Not working Working Current diseased Yes No Yearly family income (Tk)e Number of families who live togetherc
Meanc/ Frequencyd/ Mediane
Meanc/ Frequencyd/ Mediane
SDc/%d/1st– 3rd precentilee
Slum ðn ¼ 157Þ
Non-Slum ðn ¼ 187Þ
Male
Table 1 Demographic data for Non-Slum and Slum groups
(2.0)
(15.8) (84.2) (24 000–48 000)
(79.0) (21.0)
(14.0) (10.7)
(75.2)
(12.4)
(62.0) (25.6)
(95.0) (5.0)
(95.0) (5.0)
(2.1)
SDc/%d/1st– 3rd precentilee
0.1
458.0**
1.7
10.9**
38.4**
50.7**
7.0**
6.7** 1.5
tc/w2d/Ue
0.5
12384.5
2.0
0.5
–
2.9
–
2.4* 0.0
tc/w2d/Ue
NonSluma
0.8
6485.5**
10.3**
21.1**
4.1
5.0***
8.0**
1.3 5.2*
tc/w2d/Ue
Slumb
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Table 2 Comparison between Non-Slum and Slum adolescents on WHOQOL-BREF, SRQ, YSR and anthropometric measures by ANCOVA with age as the covariate Male Non-Slum ðn ¼ 187Þ
Female Slum ðn ¼ 157Þ
Mean (SE) Mean (SE)
F
Non-Slum ðn ¼ 137Þ
Slum ðn ¼ 121Þ
F
Non-Sluma
Slumb
F
t
Mean (SE) Mean (SE)
WHOQOL-BREF Domain 1 (Physical) 15.7 (0.1) 15.5 (0.2) 1.3 15.2 (0.2) 15.3 (0.2) Domain 2 (Psychological) 14.3 (0.1) 14.3 (0.2) 0.0 13.6 (0.2) 13.4 (0.2) Domain 3 (Social relationships) 15.4 (0.2) 14.3 (0.2) 14.8** 15.2 (0.2) 15.2 (0.2) Domain 4 (Environment) 14.0 (0.1) 11.8 (0.2) 107.9** 13.7 (0.2) 11.8 (0.2) Total 93.5 (0.8) 87.9 (0.8) 23.2** 90.0 (0.9) 86.2 (1.0) SRQ 4.4 (0.3) 4.2 (0.3) 0.3 5.7 (0.3) 5.3 (0.4) YSR Syndrome scales Anxious/depressed 8.9 (0.3) 7.4 (0.3) 15.5*** 10.1 (0.4) 9.3 (0.4) Withdrawn/depressed 5.6 (0.2) 5.9 (0.2) 1.2 5.7 (0.2) 6.3 (0.3) Somatic complaints 4.3 (0.2) 4.3 (0.3) 0.0 5.6 (0.3) 4.5 (0.3) Social problems 6.3 (0.2) 6.0 (0.3) 1.1 7.6 (0.3) 6.1 (0.3) Thought problems 3.6 (0.2) 2.3 (0.3) 14.4** 4.4 (0.3) 3.4 (0.3) Attention problems 7.3 (0.3) 5.9 (0.3) 12.4** 8.1 (0.3) 5.9 (0.3) Rule-breaking behaviour 4.5 (0.3) 4.9 (0.3) 1.4 3.4 (0.3) 3.7 (0.3) Aggressive behaviour 9.2 (0.4) 8.5 (0.4) 1.6 10.2 (0.4) 9.1 (0.5) Other problems 4.4 (0.2) 3.2 (0.2) 15.3** 5.5 (0.2) 4.1 (0.2) Internalizing 18.7 (0.6) 17.5 (0.6) 2.1 21.2 (0.7) 20.1 (0.8) Externalizing 13.7 (0.6) 13.4 (0.7) 0.1 13.6 (0.6) 12.8 (0.6) Total score 54.1 (1.6) 48.4 (1.7) 5.6* 60.5 (1.7) 52.4 (1.8) DSM-oriented scales Affective problems 5.9 (0.3) 5.4 (0.3) 0.8 7.8 (0.4) 6.9 (0.4) Anxiety problems 4.1 (0.2) 3.1 (0.2) 17.1** 4.9 (0.2) 3.8 (0.2) Somatic complaints 2.6 (0.2) 2.7 (0.2) 0.3 3.3 (0.2) 2.7 (0.2) 6.0 (0.2) 4.2 (0.3) Attention deficit/hyperactivity problems 5.2 (0.2) 4.4 (0.2) 5.1* Oppositional defiant problems 2.8 (0.1) 2.7 (0.2) 0.3 3.2 (0.2) 2.7 (0.2) Conduct problems 4.8 (0.3) 5.7 (0.3) 3.6*** 4.2 (0.3) 4.5 (0.3) Anthropometric measures Height 158.3 (6.0) 159.5 (6.5) 0.0 151.9 (0.5) 145.8 (0.6) Weight 46.8 (0.6) 38.5 (0.7) 77.2** 43.2 (0.7) 38.5 (0.7) BMI 18.4 (0.2) 16.8 (0.2) 24.6** 18.9 (0.3) 18.0 (0.3)
0.2 0.1 0.1 44.8** 7.4** 0.7
3.4*** 7.5** 0.3 1.0 5.7* 7.4**
0.8 3.1** 3.1** 0.0 1.5 2.2*
2.0 6.0* 2.5 0.3 9.4** 5.7* 12.5** 7.1** 5.3* 1.9 27.7** 2.7 1.0 14.7** 3.2*** 1.9 19.3** 10.8** 1.1 6.4* 0.8 0.5 9.6** 4.0*
4.1** 0.5 0.5 0.7 3.3** 0.3 2.5* 1.3 3.0** 1.8*** 2.4* 0.3
2.5 14.0** 15.0** 7.7** *** 3.4 5.9* 22.9** 3.5*** *** 2.8 1.3 0.5 3.8***
2.5* 3.1** 0.2 0.4 0.7 2.0*
88.6** 57.3* ** 22.0 21.3** 2.7*** 0.2
1.3 1.3 17.6**
*** po0:1, *po0:05, **po0:01. WHOQOL-BREF ¼ World Health Organization Quality of Life Assessment Instrument, SRQ ¼ Self Reporting Questionnaire, YSR ¼ Youth Self-Report, BMI ¼ body mass index. a The outcome of comparison between genders in Non-Slum adolescents utilizing ANCOVA with age as the covariate. b The outcome of comparison between genders in Slum adolescents utilizing t-test.
ðpo0:05Þ, ‘‘YSR DSM-Oriented Anxiety Problems’’ ðpo0:01Þ, ‘‘YSR DSM-Oriented Somatic Complaints’’ ðpo0:01Þ and ‘‘YSR DSM-Oriented Attention Deficit/Hyperactivity Problems’’ ðpo0:05Þ were all higher in the Non-Slum group for females. On the other hand, the rate of having ‘‘YSR DSMOriented Conduct Problems’’ in males was significantly higher in the Slum group ðpo0:05Þ, and the rate of having ‘‘YSR Somatic Complaints’’ had a tendency to be higher in the Slum group ðpo0:1Þ,
while SRQ had a tendency to be worse in the NonSlum group ðpo0:1Þ. Comparing genders using the same 93rd percentile cutoff, male subjects in both sites had worse rates of being categorized as having problems in ‘‘YSR Rule-Breaking Behaviour’’ (Non-Slum; po0:01, Slum; po0:05), and ‘‘YSR Externalizing’’ (po0:01), ‘‘YSR DSM-Oriented Somatic Complaints’’ (po0:05), ‘‘YSR DSM-Oriented Conduct problems’’ (po0:05) in the Slum group, and females were worse in ‘‘YSR Somatic
1.3
2.5 3.8 5.1 4.5 3.2 3.8 9.6 5.1 3.8 7.0 3.8 3.2 3.8 6.4 5.1
3.8 10.2
4.3
4.8 3.2
1.6 4.8 8.6 5.9 9.6
6.4
3.7 7.5 5.3
3.7 5.3 3.2 4.8
5.3
3.7
Above 93rd percentile %
Above 93rd percentile %
*** po0:1, *po0:05, **po0:01. SRQ ¼ Self Reporting Questionnaire, YSR ¼ Youth Self-Report. w2 analysis except for a Fisher’s exact test.
SRQ YSR Syndrome scales Anxious/depressed Withdrawn/ depressed Somatic complaints Social problems Thought problems Attention problems Rule-breaking behaviour Aggressive behaviour Internalizing Externalizing Total score DSM-oriented scales Affective problems Anxiety problems Somatic complaints Attention deficit/ hyperactivity problems Oppositional defiant problems Conduct problems
Slum ðn ¼ 157Þ
Non-Slum ðn ¼ 187Þ
Male
6.6 2.2
5.7*
8.0 12.4 10.2 6.6
5.8 4.4 7.3
0.4
0.1 0.4 1.9 0.0
0.0 0.0 0.4
7.3
5.8 7.3 14.6 8.0 2.2
3.4*** 0.0 4.3* 0.8 0.0 0.3
9.5 5.8
4.4
Above 93rd percentile %
Non-Slum ðn ¼ 137Þ
1.2 0.1
2.8***
w2
Female
Table 3 Comparisons between Non-Slum and Slum groups of the ratio above/below the 93rd percentile of each scale score
3.3
4.1
6.6 0.8 1.7 1.7
3.3 1.7 3.3
1.7
1.7 3.3 5.0 2.5 2.5
5.8 5.8
2.5
Above 93rd percentile %
Slum ðn ¼ 121Þ
a
0.7
a*
a**
a**
0.2
a
a
a
a*
a
a*
a**
a
a***
1.2 0.0
a
w2
a
0.2
2.8 *** 5.2* 6.7* 0.5
0.8 1.3 0.5
0.1
a**
0.9 2.9*** 0.6
a*
2.7*** 1.3
0.0
w2
NonSlum
a*
0.0
a
a*
a
1.8
a
a*
a
a
a*
a
0.6
a
a
0.6
a
a
w2
Slum
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Complaints’’ ðpo0:05Þ, ‘‘YSR DSM-Oriented Anxiety Problems’’ ðpo0:05Þ and ‘‘YSR DSM-Oriented Somatic Complaints’’ ðpo0:05Þ in the Non-Slum group. Factors associated with the SRQ and YSR scores Tables 4–6 shows the outcomes of standard logistic regression analyses putting above/less than the cutoff point as the dependent variable, and age, gender, BMI, educational status, job status and income as independent variables. As to ‘‘YSR Social Problems’’, ‘‘YSR Internalizing’’ and ‘‘YSR DSMOriented Oppositional Defiant Problems’’, only older ages were associated with having the problems. For ‘‘YSR Attention Problems’’ and ‘‘YSR DSM-Oriented Attention Deficit/Hyperactivity Problems’’, higher age and school attendance were associated with having the problems. As to ‘‘YSR Rule-Breaking Behaviour’’ and ‘‘YSR Externalizing’’, higher age, being male, lower BMI, school going and working were associated, and for ‘‘YSR Total Score’’ all were the same and with no association with gender. ‘‘YSR Thought Problems’’ and ‘‘YSR Aggressive Problems’’ were associated with higher age, lower BMI and school attendance. Table 4 Predictors of SRQ, YSR internalizing, externalizing, and total scores
SRQ Age Working status YSR Internalizing Age Externalizing Sex Age BMI School enrollment Working status Total score Age BMI School enrollment Working status
Odds ratio
(95% CI)
1.5** 0.2***
(1.2–1.9) (0.0–1.3)
1.4**
(1.1–1.7)
0.4* 1.5** 0.9*** 3.1* 2.8*
(0.2–1.0) (1.2–1.8) (0.8–1.0) (1.0–9.3) (1.1–7.3)
1.4 0.9*** 4.9* 3.4*
(1.2–1.8) (0.8–1.0) (1.4–17.0) (1.3–8.8)
Logistic regression analysis ***po0:1, *po0:05, **po0:01. SRQ ¼ Self Reporting Questionnaire, YSR ¼ Youth Self-Report. Site (0 ¼ non-slum, 1 ¼ slum), gender (0 ¼ male, 1 ¼ female), school enrollment (0 ¼ not attending, 1 ¼ attending), working status (0 ¼ not working, 1 ¼ working).
Higher age and not working in ‘‘SRQ’’, and higher age and working in ‘‘YSR Somatic Complaints’’ were associated with each type of mental ill health, and ‘‘YSR DSM-Oriented Somatic Complaints’’ was associated with the same factors as ‘‘YSR Somatic Complaints’’ plus school attendance. For having ‘‘YSR Anxious/Depressed’’ problem, higher age, being female, school attendance and working were associated, while for ‘‘YSR Withdrawn/Depressed’’, as with ‘‘YSR Anxious/Depressed’’, except school attendance which had no association, and for ‘‘YSR DSM-Oriented Affective Problems’’, it was the same, except for school attendance and
Table 5 Predictors of YSR syndrome scales
Anxious/depressed Gender Age School enrollment Working status Withdrawn/depressed Gender Age Working status Somatic complaints Age School enrollment Working status Social problems Age Thought problems Age BMI School enrollment Attention problems Age BMI School enrollment Working status Rule-breaking behaviour Sex Age BMI School enrollment Working status Aggressive behaviour Age BMI School enrollment
Odds ratio
(95% CI)
2.1*** 1.2* 4.1* 2.9*
(1.0–4.3) (1.0–1.5) (1.2–13.6) (1.2–7.2)
2.5* 1.3* 2.2***
(1.1–6.0) (1.0–1.5) (0.9–5.5)
1.3*
(1.0–1.6)
2.5***
(1.0–6.3)
1.2*
(1.0–1.5)
1.3** 0.9*** 5.3**
(1.1–1.5) (0.8–1.0) (1.6–17.8)
1.2*
(1.0–1.4)
10.1*
(1.4–75.2)
0.2** 1.6** 0.9* 3.7* 3.4*
(0.1–0.6) (1.3–2.0) (0.8–1.0) (1.3–11.1) (1.3–8.6)
1.3* 0.9* 2.5***
(1.1–1.5) (0.8–1.0) (0.8–7.5)
Logistic regression analysis ***po0:1, *po0:05, **po0:01. YSR ¼ Youth Self-Report. Site (0 ¼ non-slum, 1 ¼ slum), gender (0 ¼ male, 1 ¼ female), School enrollment (0 ¼ not attending, 1 ¼ attending), Working status (0 ¼ not working, 1 ¼ working).
ARTICLE IN PRESS T. Izutsu et al. / Social Science & Medicine 63 (2006) 1477–1488 Table 6 Predictors of YSR DSM-Oriented Scales
Affective problems Gender Age Anxiety problems Site Somatic problems Age School enrollment Working status Attention deficit/hyperactibity Age School enrollment Oppositional defiant problems Age Conduct problems Site Gender Age
Odds ratio
(95% CI)
2.3* 1.4**
(1.1–5.0) (0.1–1.6)
0.3*
(0.1–0.7)
1.3** 3.5* 4.4**
(0.1–1.6) (1.2–10.4) (1.8–10.9)
1.2*** 2.8***
(1.0–1.4) (0.8–9.6)
1.2***
(1.0–1.4)
3.2** 0.4* 1.3**
(1.4–7.2) (0.2–0.9) (1.1–1.6)
Logistic regression analysis ***po0:1, *po0:05, **po0:01. YSR ¼ Youth Self-Report. Site (0 ¼ non-slum, 1 ¼ slum), gender (0 ¼ male, 1 ¼ female), School enrollment (0 ¼ not attending, 1 ¼ attending), Working status (0 ¼ not working, 1 ¼ working).
working which were not associated. Site was associated only in ‘‘YSR DSM-Oriented Anxiety Problems’’ and ‘‘YSR DSM-Oriented Conduct Problems’’. For the former, living in non-slum areas was the only significant factor and had an odds ratio of 3.45, while for the latter, living in slums (odds ratio ¼ 3:17), higher age and being male associated with having the problems (Table 6). Discussion The present study investigated Bangladeshi urban adolescents’ QOL and mental health status utilizing internationally recognized scales. At the same time, this study showed differences in living status, QOL, mental health and nutritional status between adolescents in urban slums and those living in non-slum areas. Slum adolescents had lower school enrolment rates, lower literacy rates, lower family incomes, lighter weights, lower BMI (in females only a tendency was observed), and higher rates of child labour; which taken together revealed several severe situations for Bangladeshi urban slum adolescents. Heights of female subjects were also significantly less in slums even after controlling for age
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differences between groups, and implied long term malnutrition in Slum group females. Also, QOL in the environmental domain for both genders, and the social relationships domain for males were worse in slum adolescents. As to the social relationships domain for females, as female social relationships are culturally limited in Bangladesh (United Nations Children’s Fund, 1999), though recently slowly changing, female adolescents’ QOL may be low irrespective of their living area. As to the mental health of male subjects, all significant differences of mean scores were worse in non-slum subjects except for the conduct problem, which had the opposite tendency of being worse in slum subjects. When analysed by dividing subjects into two groups; a 11–14 year old group and a 15–18 year old one, the differences in conduct problems score between the groups became significant (po0:05) for the higher age group while there was no significant difference between the younger group (data not shown). Comparison of the ratio of being above the cutoff point showed more than 10% of male subjects in slums were categorized as having a conduct problem while this was only about 3% for non-slum adolescents. This difference of ratio was significant. ‘‘YSR Somatic Complaints’’ also showed a tendency for slum subjects have a worse ratio. The finding that the higher the socioeconomic status the worse their mental status was consistent with a previous study of middle-class Dhaka city adults (Islam et al., 2003). This may be because more non-slum area boys go to school and live in a structured and rule-based society; they have to manage to live in a social framework and their attention and thought related problems are subjected to teachers’ attention and comparison with friends, so that they may come to be aware of these problems. As well, non-slum adolescents learn not to give vent to their indignations but to endure the experience of not being able to satisfy their desires in school and in a structured family environment, and so they come to develop their internal process such as anxiety and thinking about solutions as a coping strategy. Contrarily, adolescents in slums generally have less opportunity to face a social framework especially when they are young; which means they may have limited chances to learn these coping skills. Thus, non-slum adolescents may be able to feel anxiety when they face stress, whereas slum adolescents may not be able to learn or practice
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this highly cognitive procedure but rather vent their frustrations by acting out as they get older. Conduct disorder is known to associate with future anti-social personality disorder, substancerelated disorders and crimes (American Psychiatric Association, 2000). This may be one of the mechanisms for high risk behaviours and crime in slum populations (Flisher & Chalton, 2001); indicating the necessity of slum children and adolescents to have opportunities to learn how to internally process difficult situations so that they will not become maladaptive and act out when older. Thus, increasing school enrolment rates in slum areas appears crucial. Indeed, there have been many attempts in Bangladesh at governmental, international organizations, and NGO levels to increase the school attendance of children, and step by step these efforts are succeeding. However, because young children and adolescents are important breadwinners in slums, many practical obstacles remain. Therefore, to deploy short-time mobile teaching teams in slums for non schoolattending children/adolescents in the early morning or late evening may help to make it possible for working children/adolescents to receive education, and to teach/give essential experiences such as group activities, social rules, risk avoidance, and self-defence to these children. Because a high percentage of the slum male adolescents displayed conduct problems, specific risky behaviours such as drug use or sexually risky behaviour should be studied with regard to mental health for the construction of effective interventions. As to females, in many of the YSR subscales, significant differences were observed and non-slum adolescents showed a worse status in all differences. Non-slum area females in Bangladesh are exposed to stricter cultural structures/frameworks or social pressures than slum females and males, and this appeared to make differences in many scales. In the analysis of ratios, utilizing the cutoff, in addition to anxiety related problems, aggressive and somatic problems were observed to be higher in non-slum areas, which was contrary to the findings in males. Because the ratios of subjects who had affective and anxiety problems were as high as around 10%, females seemed to have the ability to internally process stressful situations. However, the situation may be too hard to process only internally and so aggressive and somatic symptoms were shown for some of the females in non-slum areas. The need for gender equity is now broadly recognized and female
school enrolment and literacy rates are improving in Dhaka (United Nations Children’s Fund, 1999). However, as this study shows, their severe situation is persistent and urgent attention to assist this group is necessary, as the Bangladeshi gender development index (GDI) is 0.468 and ranks at 121 out of 146 countries (United Nations Development Programme, 2002). School teachers may need to study aspects of mental health, as it might be helpful if they had the ability to counsel female students. Logistic regression analysis revealed factors associated with the scales. As to site differences, for the ‘‘YSR DSM-Oriented Anxiety Problems’’, the odds ratio of living in non-slum areas was 3.44, and for the ‘‘YSR DSM-Oriented Conduct Problems’’, slum living adolescents’ odds ratio was 3.17 for having problems. This seems to accord with the view noted above. The odds ratio of being female in mood disorder related scales was high, while those of being male in rule-breaking and externalizing kinds of scales were likewise high, consistent with past studies (Achenbach & Rescorla, 2001). Though statistically significant, the magnitude of the odds ratio of age and BMI shown in many scales was not very large, and instead school enrolment and employment status had higher odds ratios. The outcome of all school-attending or working adolescents except for ‘‘SRQ’’ might be understandable using the same argument noted above for the social framework. As to employment status, however, the situation that they have to work even though they are too young may adversely affect children’s mental health; though from the data in this study, it is difficult to properly clarify this point. The mechanism for why only higher ‘‘SRQ’’ scores are associated with not working may be because the SRQ contains questions such as ‘‘Are you unable to play a useful part in life?’’ and ‘‘Do you feel that you are a worthless person?’’; working adolescents who are helping their family may thus would probably indicate a better outcome. Further research regarding working adolescents’ mental health is needed. Details of their working situation are necessary because many are exploited and abused in the work place, while others get good outcomes from employment; indeed, such differences would confound outcomes. The United Nations (UN) estimates that by 2025, two thirds of the world’s population of approximately 8.4 billion will live in cities, and by 2050 this will rise to more than 80% (United Nations, 1995a, 1995b). Especially, in slums, inferior living conditions
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with poor social support including health and welfare, lack of opportunities for education, and the menace of illicit drugs and crime oppress adolescents in addition to the difficulties presented by their role as important economic and practical resources for their families. With respect to slums, although there have been some studies on the mental health of adults (Chattopadhyay, Gill, Bali, & Wig, 1989; Ruiz & Saiger, 1972) and children (Kora, Kaur, & Gill, 1991), studies of the mental health of adolescents are lacking; although studies of nutritional status (Singh & Mishra, 2001), sexually transmitted diseases (Awasthi, Nichter, & Pande, 2000, Pandit, Angadi, Chvan, & Pai, 1995), and violence (WHO, 2001b) have shown the severe situations facing this adolescent group. This study has, for the first time, shown the urgent need for site and gender sensitive countermeasures to assist in remedying Bangladeshi adolescents’ poor mental health. In addition, the need for concrete provisions for non-slum females and working adolescents has been shown. There were some limitations to this study. First of all, participants were only from Dhaka city, and since there are totally different cultures, ethnicities and situations in the provinces of Bangladesh, the subjects of this study might not be truly representative of Bangladeshi adolescents as a whole. Studies of the situations in rural areas will thus be needed to obtain a more comprehensive picture. However, because this study adopted a randomized stratified sampling method, which included both non-slum and slum areas, and both school-attending and non school-attending adolescents, the outcome shown in this study suggests important characteristics of adolescents in urban areas of Bangladesh. In addition, the design of the present study is crosssectional; therefore, we could not infer any causeeffect relationships. Longitudinal study is required based on our results. Furthermore, cognition and understanding of the questions of scales may differ depending on literacy, and this too might influence outcomes. As well, given that mental health problems are highly stigmatized in Bangladesh, this might have skewed the answers to the modest side. In conclusion, living conditions and QOL of slum adolescents were worse than their non-slum counterparts, and this contrasts with most of the mental indexes, which were better in slums. For males, conduct problems were observed more in the slum population, and the need for attempts to educate them to internally process stress rather act out was
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implied. For females, the prevailing difficulties in urban female adolescents were revealed and a need for urgent care shown as essential. Gender and site sensitive countermeasures for Bangladeshi urban adolescents are thus imperative. Acknowledgements The authors would like to express their gratitude to National Institute of Mental Health, Bangladesh, and to the interviewers Abu Ala Mahmudul Hasan, Anjuman Tahmina Ferdous, Imran Al Amin, Jasmin Khan, Khadiza Begum, Md. Saiful Islam, Shormin Sultana, Zahangir Alam, and Md. Iqbal Hossain for their enormous contribution, and without whom the data collection would have been impossible. This study was supported by Mitsubishi Foundation grants for social welfare activities.
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