Patterns and predictors of intimate partner violence among married women living in urban informal settlements of Bangladesh: A cross-sectional survey

Authors

  • Shafayatul Islam Shiblee Maternal and Child Health Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh https://orcid.org/0000-0001-6594-5441
  • Md. Harunor Rashid Health Systems and Population Studies Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh

DOI:

https://doi.org/10.3329/bsmmuj.v18i1.75888

Keywords

IPV, GBV, VAW, urban slums, violence

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Correspondence

Shafayatul Islam Shiblee
Email: shafayatul.shiblee@icddrb.org

Publication history

Received: 1 Sep 2024
Accepted: 11 Feb 2025
Published inline: 17 Feb 2025

Responsible editor

Reviewer

Funding

Funded by Ministry of Local Government, Rural Development and Cooperatives, the Government of the People’s Republic of Bangladesh through Urban Primary Health Care Services Delivery Project (GR-01927).

Ethical approval

Approved by IRB of International Centre for Diarrhoeal Disease Research, Bangladesh (No. PR- 15045, dated: 26 July 2015).

Trial registration number

Not applicable

Copyright

© The Author(s) 2025; all rights reserved
Published by Bangabandhu Sheikh
Mujib Medical University
Abstract
Background: Despite legal and policy proscriptions, intimate partner violence remains a pervasive issue worldwide, with particularly severe implications for marginalised and vulnerable women. In Bangladesh, women living in urban slums may face increased risks, but evidence remains limited. This study aimed to address this knowledge gap.

Methods: Cross-sectional survey was done among ever-married women resident (aged 18 years or older) in slums-dwelling in Dhaka North, Dhaka South and Gazipur. Data was collected using face-to-face interview on four types of intimate partner violence: physical, sexual, emotional and economic. Univariate and multivariate logistic regression analyses were done to explore the association between the violence outcome variables and sociodemographic characteristics.

Results: Six hundred seven women participated in the survey. The overall level of any form of life-time violence was 82%. Physical violence was most reported (66%), followed by economic (47%), sexual (44%), and emotional (38%). Uneducated women, and those whose husbands were uneducated, faced particularly high risks of violence. Gazipur stood out as an area with higher intimate partner violence than other slum areas. Working women also experienced more life-time violence than non-working women.

Conclusion: Urban slum women face high levels of violence in Dhaka and Gazipur. Policy level interventions, workplace-based actions and community-level measures should be taken to curb this epidemic. Specific steps should be taken to increase awareness related to intimate partner violence, and improve attitudes towards gender roles among women residing in urban slums. 
Key messages
Women living in urban slums face increased risks of intimate partner violence in Bangladesh. In our survey nested within existing health and demographic surveillance system, eighty-two percent women reported of any form of life-time violence and among four types of violence physical violence was most reported. High risk of violence is more common among uneducated women, and those whose husbands were uneducated. Working women experienced more life-time violence than non-working women. Urgent action is required to curb this epidemic. 
Introduction
Gender-based violence (GBV) is a pervasive issue affecting millions of women worldwide, with particularly severe implications for those living in marginalised and vulnerable communities [1]. Among different forms of GBV intimate partner violence (IPV) is prevalent in all countries at alarming rates, [2] and violence against women (VAW) is a worldwide concern [3]. Approximately one in three women and girls worldwide experience physical or sexual violence in her lifetime by their intimate partner [4]. Many women not only suffer from physical or sexual violence, but may also be deprived of their rights to access financial and other resources [5]. Husbands or other relatives are often the perpetrators of VAW, and the effects extend beyond the women themselves, to children, families and society as a whole [6]. Despite international commitments including the convention on the elimination of all forms of discrimination against women [7] and national-level legal protections against GAW in many countries, [8] in practice significant barriers inhibit women’s access to justice. Poor implementation of legal rights and policies, and inadequate knowledge among women of their rights are common [9, 10]. Moreover, relevant data and evidence related to GBV including IPV remains inadequate in low-and middle-income countries (LMICs), inhibiting evidence-based policies and interventions [11].

In LMICs, including Bangladesh, IPV is a matter of great concern which impacts public health and is a major burden on the society and economy [12]. Domestic violence against women is criminalised under the 2010 domestic violence act in Bangladesh. Despite this, implementation of measures to curb violence and prosecute offenders continues to face significant challenges, including the widespread perception that domestic violence is a private matter. The national VAW Survey 2015 revealed that 73% of ever-married women had ever experienced any type of violence by their current husband and 55% experienced violence during the past 12 months [13]. Available evidence suggests that the prevalence of IPV is higher in urban slums than non-slum and rural populations,[14] and may be particularly high among women with no or little education [15]. Despite the high prevalence and severe consequences of IPV in these settings, there is a significant gap in evidence. Research is needed to understand the levels and correlates of various forms of violence.

This study aims to provide a comprehensive picture of levels and types of intimate partner violence experienced and identify factors associated with the risk of experiencing intimate partner violence for generating evidence for policymakers to formulate strategies and interventions to improve the lives of the urban poor.
Methods
Study area and data collection
The study was conducted in slums of Dhaka North, Dhaka South and Gazipur City Corporations during May to June, 2022. The study was carried out within the areas where International Centre for Diarrhoeal Disease Research, Bangladesh (icddr'b) was operating a high quality Health and Demographic Surveillance System, visiting around 34,000 households on a quarterly basis.  
Sample size

Earlier work had provided an estimate that 54.3% women had suffered from violence,[16] and we used this together with 95% confidence interval, 5% margin of error, 10% non-response and 1.5 design effect, to determine the required sample size of 637. We then distributed the sample across the three city corporation areas within the HDSS using probability proportional to size of each area to ensure representativeness. Eligible respondents were ever married women aged 18 years or over. For omitting biasness samples were drawn at random.
Data collection

After 3-day training on data collection tool, data collectors collected data for this survey using Android tablets. Prevalence estimates of lifetime violence by an intimate partner were obtained by asking direct, clearly worded questions about the respondent’s experience of specific acts [17]. For different type of violence, women were asked whether a current or former partner had ever done any of the acts listed in Table 1, and recorded as ‘yes’ if any one of these was reported. Women who reported experiencing life-time physical or sexual violence were asked to give the reasons for the violence as they understood it. Respondents could report multiple reasons. 
Table 1 Measuring different types intimate-partner violence among women in urban slums

Types of violence

Question

Physical violence

  • hit or hurt her with anything

  • slapped her, or thrown something at her that could hurt her;

  • hit her with a fist or something else that could hurt;

  • kicked, dragged or beaten her up;

Sexual violence

  • being physically forced to have sexual intercourse against her will;

  • being forced to do something sexual she found degrading or humiliating.

Emotional violence

  • insulted you or made you feel bad about yourself; 

  • intimidated you

Economic violence

  • ever taken earnings or savings against her will;

  • refuse to give you money for household expenses, even when he has money for other things; 

  • left job because of husbands’ disapproval

Table 1 Measuring different types intimate-partner violence among women in urban slums

Types of violence

Question

Physical violence

· hit or hurt her with anything

· slapped her, or thrown something at her that could hurt her;

· hit her with a fist or something else that could hurt;

· kicked, dragged or beaten her up;

Sexual violence

· being physically forced to have sexual intercourse against her will;

· being forced to do something sexual she found degrading or humiliating.

Emotional violence

· insulted you or made you feel bad about yourself; 

· intimidated you

Economic violence

· ever taken earnings or savings against her will;

· refuse to give you money for household expenses, even when he has money for other things; 

· left job because of husbands’ disapproval

Types of  violence

Question

Physical violence

  • hit or hurt her with anything
  • slapped her, or thrown something at her that could hurt her;
  • hit her with a fist or something else that could hurt;
  • kicked, dragged or beaten her up;

Sexual violence

  • being physically forced to have sexual intercourse against her will;
  • being forced to do something sexual she found degrading or humiliating.

Emotional violence

  • insulted you or made you feel bad about yourself;
  • intimidated you

Economic violence

  • ever taken earnings or savings against her will;
  • refuse to give you money for household expenses, even when he has money for other things;
  • left job because of husbands’ disapproval
Ethical consideration

Ethical approval and favourable scientific review of the study was approved by IRB of icddr'b. Furthermore, before enrolling in study, written informed consent from each of the participant preceding data collection were taken from each participant and maintained privacy during data collection.
Data analysis

Wealth index was calculated using principal component analysis based on the wealth profile for the whole HDSS population (i.e. household's ownership of selected assets, including televisions and bicycles; housing construction materials for roofs, wall and floor; and types of water access and facilities for sanitation) [18].

Univariate statistical analysis was performed to explore different types of violence and its association with sociodemographic characteristics. Subsequently, four multivariate logistic regression models were fitted to further examine the adjusted associations in order to explore the correlates of different types of violence. In the final multivariate logistic regression models, 78 cases with missing data in socio-demographic variables (husband education, household asset information) were excluded. The model included age, years of schooling, working status, marital status, parity, wealth index and husbands' schooling as covariates. Data analyses were performed using Stata v15.
Results
Sociodemographic characteristics
Our final study sample consisted of 607 ever-married women aged 18 years and over: 230 were from Dhaka North, 197 were from Dhaka South and 180 were from Gazipur city corporations.

The majority of the respondents were aged 30 years and above (61%), currently married (90%) and had had at least one child (95%). Approximately one quarter of the respondents had no education at all, 43% had been schooled for 1 to 5 years, while only 6% had more than 10 years of education. Almost 60% of the women were housewives. Among others, 18% were domestic workers, followed by 10% were garments worker. Among the participants’ who were garment workers, 58% of them are from Gazipur City Corporation.

In terms of husband’s characteristics, 30% had no formal education and 38% had 1-5 years of schooling. There were clear differentials in many of these indicators between respondents in the three city corporation areas with Gazipur residents standing out as disadvantaged compared to those living in Dhaka North and Dhaka South (Table 2).
Table 2 Sociodemographic characteristics of ever married women aged 18 years old or above and their husbands/ partners by slum locations, number (%)

Socio-demographic characteristics

Dhaka North

(n=230)

Dhaka South

(n=197)

Gazipur

(n=180)

Total

(n=607)

Age (years)

 

 

 

 

     18‒29

101 (43.9)

74 (37.6)

64 (35.6)

239 (39.4)

     30‒34

54 (23.5)

42 (21.3)

28 (15.6)

124 (20.4)

     35‒39

33 (14.3)

38 (19.3)

33 (18.3)

104 (17.1)

      ≥40

42 (18.3)

43 (21.8)

55 (30.6)

140 (23.1)

Mean (SD)*

31.1 (8.2)

32.7 (8.5)

35.0 (10.2)

32.8 (9.0)

Schooling (years)

 

 

 

 

   No education

28 (12.2)

61 (31.0)

68 (37.8)

157 (25.9)

     1‒5

136 (59.1)

65 (33.0)

60 (33.3)

261 (43.0)

     ≥6

66 (28.7)

71 (36.0)

52 (28.9)

189 (31.1)

Working status

 

 

 

 

   Not working

157 (68.3)

113 (57.4)

91 (50.6)

361 (59.5)

   Working

73 (31.7)

84 (42.6)

89 (49.4)

246 (40.5)

Marital status

 

 

 

 

   Currently married

217 (94.3)

180 (91.4)

152 (84.4)

549 (90.4)

   Widowed

7 (3.0)

15 (7.6)

15 (8.3)

37 (6.1)

   Divorced/ separated

6 (2.6)

2 (1.0)

13 (7.2)

21 (3.5)

Parity

 

 

 

 

     0‒1

87 (37.8)

84 (42.6)

69 (38.3)

240 (39.5)

     2

73 (31.7)

70 (35.5)

61 (33.9)

204 (33.6)

     ≥3

70 (30.4)

43 (21.8)

50 (27.8)

163 (26.9)

Wealth index

n=213

n=173

n=167

n=553

   Lowest

77 (36.2)

24 (13.9)

38 (22.8)

139 (25.1)

   Second

43 (20.2)

31 (17.9)

46 (27.5)

120 (21.7)

   Third

45 (21.1)

52 (30.1)

64 (38.3)

161 (29.1)

   Highest

48 (22.5)

66 (38.2)

19 (11.4)

133 (24.1)

Husbands’ schooling (years)

n=229

n=175

n=179

n=583

   No education

46 (20.1)

60 (34.3)

71 (39.7)

177 (30.4)

     1‒5

107 (46.7)

63 (36.0)

52 (29.1)

222 (38.1)

     ≥6

76 (33.2)

52 (29.7)

56 (31.3)

184 (31.6)

*SD indicates standard deviation.

Experiences of violence
The proportion of women who reported that they had experienced any type of violence perpetrated by an intimate partner at any time during their lifetime was 83%. Physical violence was most commonly reported (66%), followed by economic violence (47%), sexual violence (44%), and emotional violence (38%).

Among those reported experiencing lifetime physical and/or sexual violence by an intimate partner, 60% could not identify a specific reason. Financial reasons were stated by 44% of respondents, followed by problems with partner’s family members (27%), partner’s unemployment (16%), disruption in work (15%), wife’s disobedience (11%).
Patterns and predictors of violence
Across all four types of violence, currently working women were more likely to report experiencing life-time violence than women who were not currently working. Considering all forms of violence combined, over 90% of respondents without education reported experiencing some form in their lifetime compared to 69% of those with 6 or more years of education. The husband's education level also showed the same pattern, and an even more pronounced difference in prevalence of any lifetime violence. Women who were divorced, separated, and widowed at the time of the survey were more likely to report violence than those who were currently married. Bivariate results suggested that economic violence varied significantly between the wealth quintiles, with the prevalence being the highest among the lowest quintile (50%) and the lowest among the highest wealth quintile (23%). Bivariate results also showed differences across the three city corporation areas in the levels of all types of violence, except physical violence, with Gazipur residents being most likely to report violence. Parity and age groups showed no differences across any of the forms of violence (Table 3).
Table 3 Lifetime experience of various types of violence by socio-demographic characteristics

Characteristics  

Physical violence 

(n=485)

Sexual violence

(n=268)

Emotional violence 

(n=233)

Economic violence 

(n=284)

Any violence 

(n

Age (years)

 

 

 

 

 

     18‒29

59.4

43.9

37.2

40.2

82.4

     30‒34

59.7

41.1

42.7

52.4

81.5

     35‒39

68.3

43.3

35.6

51.9

79.8

     ≥40

70.0

47.9

38.6

49.3

85.7

Schooling (years)

 

 

 

 

 

     No education

73.3b

60.0b

47.8b

65.0b

92.4b

     1‒5

66.7

45.6

39.1

46.4

86.6

     ≥6

50.8

30.7

29.6

32.3

68.8

Working status

 

 

 

 

 

   Not working

59.8a

40.0b

29.4b

42.4b

79.8a

   Working

68.7

53.3

51.6

53.3

86.6

Marital Status

 

 

 

 

 

   Married

62.7b

44.3

36.6b

45.9b

82.3

   Widowed

56.8

37.8

32.4

37.8

75.7

   Divorced/separated

95.2

52.4

95.2

85.7

100

Parity

 

 

 

 

 

      0‒1

60.8a

43.3

39.2

42.5

81.3

      2

61.8

44.6

40.7

50.0

81.4

      ≥3

69.3

44.8

34.4

49.1

85.9

Wealth Index

 

 

 

 

 

      Lowest

64.8

48.9b

46.8

49.6

84.2

      Second

60.0

46.7

34.2

44.2

79.2

   Third

65.8

49.1

36.0

49.1

85.7

   Highest

60.2

28.6

33.8

42.1

77.4

Husband’s schooling (years)                   

     No education

77.4b

54.2b

46.3b

61.6b

95.5b

     1‒5

64.0

46.9

40.1

46.0

83.3

     ≥6

51.6

35.3

27.2

37.0

72.3

City corporation

 

 

 

 

 

   Dhaka North

64.4

39.6b

30.4b

33.9b

79.6b

   Dhaka South

60.4

23.9

37.1

52.3

79.2

   Gazipur

65.6

72.2

50.0

57.2

90.0

a<0.05, b<0,01

Table 3 Lifetime experience of different types of violence by socio-economic characteristics

Characteristics   

Physical violence

Sexual violence

Emotional violence

Economic violence

Any violence

(n = ?)

(n = ?)

(n = ?)

(n = ?)

(n = ?)

Age (years)

 

 

 

 

 

18‒29

59.4

43.9

37.2

40.2

82.4

30‒34

59.7

41.1

42.7

52.4

81.5

35‒39

68.3

43.3

35.6

51.9

79.8

≥=40

70.0

47.9

38.6

49.3

85.7

Schooling (years)

 

 

 

 

 

No education

73.3b

60.0b

47.8b

65.0b

92.4b

1‒5

66.7

45.6

39.1

46.4

86.6

≥=6

50.8

30.7

29.6

32.3

68.8

Working status

 

 

 

 

 

Not working

59.8a

40.0b

29.4b

42.4b

79.8a

Working

68.7

53.3

51.6

53.3

86.6

Marital Status

 

 

 

 

 

Married

62.7b

44.3

36.6b

45.9b

82.3

Widowed

56.8

37.8

32.4

37.8

75.7

Divorced/separated

95.2

52.4

95.2

85.7

100

Parity

 

 

 

 

 

0‒1

60.8a

43.3

39.2

42.5

81.3

2

61.8

44.6

40.7

50.0

81.4

≥=3

69.3

44.8

34.4

49.1

85.9

Wealth Index

 

 

 

 

 

Lowest

64.8

48.9b

46.8

49.6

84.2

Second

60.0

46.7

34.2

44.2

79.2

Third

65.8

49.1

36.0

49.1

85.7

Highest

60.2

28.6

33.8

42.1

77.4

Husband’s schooling (years)                   

No education

77.4b

54.2b

46.3b

61.6b

95.5b

1‒5

64.0

46.9

40.1

46.0

83.3

≥=6

51.6

35.3

27.2

37.0

72.3

City corporation

 

 

 

 

 

Dhaka north

64.4

39.6b

30.4b

33.9b

79.6b

Dhaka south

60.4

23.9

37.1

52.3

79.2

Gazipur

65.6

72.2

50.0

57.2

90.0

a<0.05, b<0,01

Table 3 Lifetime experience of different types of violence by socio-economic characteristics

Characteristics   

Physical violence

Sexual violence

Emotional violence

Economic violence

Any violence

(n = ?)

(n = ?)

(n = ?)

(n = ?)

(n = ?)

Age (years)

 

 

 

 

 

18‒29

59.4

43.9

37.2

40.2

82.4

30‒34

59.7

41.1

42.7

52.4

81.5

35‒39

68.3

43.3

35.6

51.9

79.8

≥=40

70.0

47.9

38.6

49.3

85.7

Schooling (years)

 

 

 

 

 

No education

73.3b

60.0b

47.8b

65.0b

92.4b

1‒5

66.7

45.6

39.1

46.4

86.6

≥=6

50.8

30.7

29.6

32.3

68.8

Working status

 

 

 

 

 

Not working

59.8a

40.0b

29.4b

42.4b

79.8a

Working

68.7

53.3

51.6

53.3

86.6

Marital Status

 

 

 

 

 

Married

62.7b

44.3

36.6b

45.9b

82.3

Widowed

56.8

37.8

32.4

37.8

75.7

Divorced/separated

95.2

52.4

95.2

85.7

100

Parity

 

 

 

 

 

0‒1

60.8a

43.3

39.2

42.5

81.3

2

61.8

44.6

40.7

50.0

81.4

≥=3

69.3

44.8

34.4

49.1

85.9

Wealth Index

 

 

 

 

 

Lowest

64.8

48.9b

46.8

49.6

84.2

Second

60.0

46.7

34.2

44.2

79.2

Third

65.8

49.1

36.0

49.1

85.7

Highest

60.2

28.6

33.8

42.1

77.4

Husband’s schooling (years)                   

No education

77.4b

54.2b

46.3b

61.6b

95.5b

1‒5

64.0

46.9

40.1

46.0

83.3

≥=6

51.6

35.3

27.2

37.0

72.3

City corporation

 

 

 

 

 

Dhaka north

64.4

39.6b

30.4b

33.9b

79.6b

Dhaka south

60.4

23.9

37.1

52.3

79.2

Gazipur

65.6

72.2

50.0

57.2

90.0

a<0.05, b<0,01

Women married to husbands with no education had higher odds of experiencing physical violence than women in all other categories, even those whose husbands had just 1-5 years of schooling. Women who were currently working had odds of ever experiencing lifetime sexual and emotional violence twice those of non-working women and women resident in Gazipur had higher odds than that of those in Dhaka North.

Economic violence was negatively associated with women’s education and husband’s education. The odds of reporting economic violence were again higher among women in Gazipur and Dhaka South than those in Dhaka North (Table 4). 
Table 4 Factors associated with lifetime different types of violence experience among married women in urban slums: adjusted odds ratios (95% confidence intervals) from logistic regression* (n=607)

Characteristics

Physical

Sexual

Emotional

Economic

Age (years)

     18‒29

1

1

1

1

     30‒34

0.8 (0.5‒1.5)

0.9 (0.5‒1.6)

1.0 (0.6‒1.8)

1.7 (1.0‒3.0)

     35‒39

1.3 (0.7‒2.5)

0.7 (0.4‒1.3)

0.7 (0.4‒1.4)

1.2 (0.7‒2.2)

     ≥40

1.3 (0.7‒2.4)

0.9 (0.5‒1.7)

0.9 (0.5‒1.7)

0.9 (0.5‒1.7)

Schooling (years)

No education

1

1

1

1

     1‒5

1.2 (0.7‒2.2)

0.7 (0.4‒1.2)

1.0 (0.6‒1.7)

0.6 (0.3‒1.0)

      ≥6

0.8 (0.4‒1.6)

0.4 (0.2‒0.8)

0.6 (0.3‒1.2)

0.4 (0.2‒0.8)

Working status

Not working

1

1

1

1

Working

1.5 (1.0‒2.2)

2.1 (1.4‒3.2)

2.4 (1.6‒3.6)

1.3 (0.9‒1.9)

Marital status

Currently married

1

1

1

1

Widowed

0.4 (0.1‒0.8)

0.3 (0.1‒0.8)

0.6 (0.2 ‒1.3)

0.3 (0.1‒0.8)

Divorced/ separated

5.4 (0.7‒43.2)

0.3 (0.1‒0.9)

14.4 (1.8‒114.6)

5.6 (1.2‒26.5)

Parity

     0‒1

1

1

1

1

2

1.1 (0.7‒1.7)

1.0 (0.6‒1.6)

1.2 (0.7‒2.0)

1.2 (0.7‒1.9)

     ≥3

1.3 (0.8‒2.4)

0.8 (0.5‒1.5)

1.0 (0.6‒1.7)

1.0 (0.6‒1.8)

Wealth index

Lowest

1

1

1

1

Second

1.0 (0.6‒1.7)

0.8 (0.5‒1.5)

0.5 (0.3‒0.9)

0.8 (0.5‒1.4)

Third

1.1 (0.6‒2.0)

0.7 (0.4‒1.2)

0.6 (0.4‒1.1)

0.8 (0.5‒1.4)

Highest

1.0 (0.6‒1.7)

0.8 (0.5‒1.5)

0.5 (0.3‒0.9)

0.8 (0.5‒1.4)

Husbands’ schooling (years)

No education

1

1

 

 

     1‒5

0.5 (0.3‒0.9)

0.9 (0.5‒1.6)

1.3 (0.8‒2.3)

0.8 (0.5‒1.4)

     ≥6

0.4 (0.2‒0.6)

0.7 (0.4‒1.2)

0.7 (0.4‒1.4)

0.5 (0.3‒0.9)

City corporation

Dhaka North

1

1

 

 

Dhaka South

0.9 (0.6‒1.5)

0.5 (0.3‒0.9)

1.3 (0.8‒2.2)

2.8 (1.8‒4.6)

Gazipur

0.8 (0.5‒1.3)

4.0 (2.5‒6.6)

2.1 (1.3‒3.5)

2.3 (1.4‒3.7)

*Multivariate model included age, years of schooling, working status, marital status, parity, wealth index and husbands' schooling as covariates.

Table 4Factors associated with lifetime different types of violence experience among married women in urban slums: adjusted odds ratios (95% confidence intervals) from logistic regression* (n = 607)

Characteristics

Physical

Sexual

Emotional

Economic

Age (years)

18‒29

1

1

1

1

30‒34

0.8 (0.5‒1.5)

0.9 (0.5‒1.6)

1.0 (0.6‒1.8)

1.7 (1.0‒3.0)

35‒39

1.3 (0.7‒2.5)

0.7 (0.4‒1.3)

0.7 (0.4‒1.4)

1.2 (0.7‒2.2)

≥40

1.3 (0.7‒2.4)

0.9 (0.5‒1.7)

0.9 (0.5‒1.7)

0.9 (0.5‒1.7)

Schooling (years)

No education

1

1

1

1

1‒5

1.2 (0.7‒2.2)

0.7 (0.4‒1.2)

1.0 (0.6 ‒1.7)

0.6 (0.3‒1.0)

≥6

0.8 (0.4‒1.6)

0.4 (0.2‒0.8)

0.6 (0.3 ‒1.2)

0.4 (0.2‒0.8)

Working status

Not working

1

1

1

1

Working

1.5 (1.0‒2.2)

2.1 (1.4‒3.2)

2.4 (1.6‒3.6)

1.3 (0.9‒1.9)

Marital status

   Currently married

1

1

1

1

   Widowed

0.4 (0.1‒0.8)

0.3 (0.1‒0.8)

0.6 (0.2 ‒1.3)

0.3 (0.1‒0.8)

 Divorced/separated

5.4 (0.7‒43.2)

0.3 (0.1‒0.9)

14.4 (1.8‒114.6)

5.6 (1.2‒26.5)

Parity

0‒1

1

1

1

1

2

1.1 (0.7‒1.7)

1.0 (0.6‒1.6)

1.2 (0.7‒2.0)

1.2 (0.7‒1.9)

≥3

1.3 (0.8‒2.4)

0.8 (0.5‒1.5)

1.0 (0.6‒1.7)

1.0 (0.6‒1.8)

Wealth index

Lowest

1

1

1

1

Second

1.0 (0.6‒1.7)

0.8 (0.5‒1.5)

0.5 (0.3‒0.9)

0.8 (0.5‒1.4)

Third

1.1 (0.6‒2.0)

0.7 (0.4‒1.2)

0.6 (0.4‒1.1)

0.8 (0.5‒1.4)

Highest

1.0 (0.6‒1.7)

0.8 (0.5‒1.5)

0.5 (0.3‒0.9)

0.8 (0.5‒1.4)

Husbands’ schooling (years)

No education

1

1

 

 

1‒5

0.5 (0.3‒0.9)

0.9 (0.5‒1.6)

1.3 (0.8‒2.3)

0.8 (0.5‒1.4)

≥6

0.4 (0.2‒0.6)

0.7 (0.4‒1.2)

0.7 (0.4‒1.4)

0.5 (0.3‒0.9)

City corporation

Dhaka North

1

1

 

 

Dhaka South

0.9 (0.6‒1.5)

0.5 (0.3‒0.9)

1.3 (0.8‒2.2)

2.8 (1.8‒4.6)

Gazipur

0.8 (0.5‒1.3)

4.0 (2.5‒6.6)

2.1 (1.3‒3.5)

2.3 (1.4‒3.7)

*Multivariate model included age, years of schooling, working status, marital status, parity, wealth index and husbands' schooling as covariates.

Table 4 Factors associated with lifetime different types of violence experience among married women in urban slums: adjusted odds ratios (95% confidence intervals) from logistic regression

Characteristics

Physical

Sexual

Emotional

Economic

Age (years)

18‒29

1

1

1

1

30‒34

0.8 (0.5‒1.5)

0.9 (0.5‒1.6)

1.0 (0.6‒1.8)

1.7 (1.0‒3.0)

35‒39

1.3 (0.7‒2.5)

0.7 (0.4‒1.3)

0.7 (0.4‒1.4)

1.2 (0.7‒2.2)

≥=40

1.3 (0.7‒2.4)

0.9 (0.5‒1.7)

0.9 (0.5‒1.7)

0.9 (0.5‒1.7)

Schooling (years)

No education

1

1

1

1

1‒5

1.2 (0.7‒2.2)

0.7 (0.4‒1.2)

1.0 (0.6 ‒1.7)

0.6 (0.3‒1)

≥=6

0.8 (0.4‒1.6)

0.4 (0.2‒0.8)

0.6 (0.3 ‒1.2)

0.4 (0.2‒0.8)

Working status

Not working

1

1

1

1

Working

1.5 (1.0‒2.2)

2.1 (1.4‒3.2)

2.4 (1.6‒3.6)

1.3 (0.9‒1.9)

Marital status

Currently married

1

1

1

1

Widowed

0.4 (0.1‒0.8)

0.3 (0.1‒0.8)

0.6 (0.2 ‒1.3)

0.3 (0.1‒0.8)

Divorced/separated

5.4 (0.7‒43.2)

0.3 (0.1‒0.9)

14.4 (1.8‒114.6)

5.6 (1.2‒26.5)

Parity

0‒1

1

1

1

1

2

1.1 (0.7‒1.7)

1.0 (0.6‒1.6)

1.2 (0.7‒2.0)

1.2 (0.7‒1.9)

≥=3

1.3 (0.8‒2.4)

0.8 (0.5‒1.5)

1.0 (0.6‒1.7)

1.0 (0.6‒1.8)

Wealth index

Lowest

1

1

1

1

Second

1.0 (0.6‒1.7)

0.8 (0.5‒1.5)

0.5 (0.3‒0.9)

0.8 (0.5‒1.4)

Third

1.1 (0.6‒2.0)

0.7 (0.4‒1.2)

0.6 (0.4‒1.1)

0.8 (0.5‒1.4)

Highest

1.0 (0.6‒1.7)

0.8 (0.5‒1.5)

0.5 (0.3‒0.9)

0.8 (0.5‒1.4)

Husbands’ schooling (years)

No education

1

1

 

 

1‒5

0.5 (0.3‒0.9)

0.9 (0.5‒1.6)

1.3 (0.8‒2.3)

0.8 (0.5‒1.4)

≥=6

0.4 (0.2‒0.6)

0.7 (0.4‒1.2)

0.7 (0.4‒1.4)

0.5 (0.3‒0.9)

City corporation

Dhaka North

1

1

 

 

Dhaka South

0.9 (0.6‒1.5)

0.5 (0.3‒0.9)

1.3 (0.8‒2.2)

2.8 (1.8‒4.6)

Gazipur

0.8 (0.5‒1.3)

4.0 (2.5‒6.6)

2.1 (1.3‒3.5)

2.3 (1.4‒3.7)

Multivariate model included...

Categories

Number (%)

Sex

 

   Male

36 (60.0)

   Female

24 (40.0)

Age in yearsa

8.8 (4.2)

   Education

 

   Pre-school

20 (33.3)

   Elementary school

24 (40.0)

   Junior high school

16 (26.7)

Cancer diagnoses

 

   Acute lymphoblastic leukemia

33 (55)

   Retinoblastoma

5 (8.3)

   Acute myeloid leukemia

4 (6.7)

   Non-Hodgkins lymphoma

4 (6.7)

   Osteosarcoma

3 (5)

   Hepatoblastoma

2 (3.3)

   Lymphoma

2 (3.3)

   Neuroblastoma

2 (3.3)

   Medulloblastoma

1 (1.7)

   Neurofibroma

1 (1.7)

   Ovarian tumour

1 (1.7)

   Pancreatic cancer

1 (1.7)

   Rhabdomyosarcoma

1 (1.7)

aMean (standard deviation)

Categories

Number (%)

Sex

 

   Male

36 (60.0)

   Female

24 (40.0)

Age in yearsa

8.8 (4.2)

Education

 

   Pre-school

20 (33.3)

   Elementary school

24 (40.0)

   Junior high school

16 (26.7)

Cancer diagnoses

 

Acute lymphoblastic leukemia

33 (55)

Retinoblastoma

5 (8.3)

Acute myeloid leukemia

4 (6.7)

Non-Hodgkins lymphoma

4 (6.7)

Osteosarcoma

3 (5)

Hepatoblastoma

2 (3.3)

Lymphoma

2 (3.3)

Neuroblastoma

2 (3.3)

Medulloblastoma

1 (1.7)

Neurofibroma

1 (1.7)

Ovarian tumour

1 (1.7)

Pancreatic cancer

1 (1.7)

Rhabdomyosarcoma

1 (1.7)

aMean (standard deviation)

Discussion
This study sought to contribute to the body of evidence on IPV by describing the levels of different types of violence among a relatively under-researched group - women living in urban slums in Bangladesh - and to explore some of the predictors.
The strengths of the study include being nested within an established HDSS which meant high levels of familiarity and trust among respondents leading to a high response rate and skilled data collectors, giving confidence in the quality of the data. The cross-sectional nature of the study inevitably means there will be some recall error and causal relationships cannot be confidently determined (for instance between working status and experience of violence). Furthermore, this study did not consider other factors like cultural norms and access to social services in the models which could have an influence on the risk of violence and the predictors examined.

Levels of lifetime violence were extremely high, with 82% of respondents reporting experiencing at least one form of violence by their husband in their marital life, higher than the national estimates for urban and rural areas, of a little over 70% [13].

More working women in the sample reported lifetime experiences of all four types of violence than those who were not working at the time of the survey. Also, women with lower education, and those whose husbands had lower education, were more likely to report violence than those with higher education. We also found important differences across the three city corporations, with Gazipur standing out as a place where women experienced higher levels of all forms of lifetime violence, except physical.

Findings across the different types of violence were complex, but this in part may reflect low power to detect some differences. In common with previous research, [15] our study provided evidence that education, of both women and their husbands, may be a protective factor. Husband’s education was found to be independently negatively associated with physical violence and with economic violence, while women’s own education was negatively associated with economic violence.

Women’s working status was also a relevant factor, showing independent associations with both sexual violence and emotional violence. Since we asked about lifetime violence and used a cross-sectional survey, it is not possible to determine the direction of causality, though it seems plausible, and other research has suggested, that working women are exposed to greater risk of violence from their husbands because their employment threatens the gendered hierarchy within the household [19, 20, 21].

Even having controlled for measures of education, marital status, working status and wealth, models indicated independent associations between being resident in Gazipur and emotional violence, economic violence, sexual violence, and any type of violence, compared to Dhaka North. These findings suggest that there may be differences in local area sub-cultures relating to the legitimacy and tolerance of violence against women.

Women’s direct reports of the reasons for violence suggested that financial hardship, and related reasons (unemployment, lack of food, disruption in work) are often a factor at play. However, surprisingly, our logistic regression models did not provide evidence to support this, except in relation to sexual violence, where the highest wealth quintile had lower odds compared to the lowest wealth quintile. 
Conclusion
The study findings highlight worryingly high levels of all types of violence among women living in Bangladesh urban slums. Urgent action is clearly needed to curb this epidemic. Specifically, women who are themselves uneducated or are married to men who are uneducated require particular attention. Specific measures like educational interventions or support services for working women should be taken. Some community-level measures also need to be taken to increase awareness related to IPV, and improve attitudes towards gender roles, among women residing in urban slum areas. So too does the existence of geographic areas where IPV is particularly prevalent so that area-based interventions can be designed.  
Acknowledgements
We acknowledge the contribution of Dr. Sabrina Rashid and Rumayan Ahmed for reviewing the manuscript and helping in designing the study. We are thankful to our respondents in consenting to participate in the research. We also acknowledge the hard work of the field team in completing the study data collection. We are thankful to the Government of Bangladesh and Canada for their core/unrestricted support to icddr,b research.
Author contributions
Conception or design of the work; or the acquisition, analysis, or interpretation of data for the work: SIS, HR. Drafting the work or reviewing it critically for important intellectual content: SIS, HR. Final approval of the version to be published: SIS, HR. Accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved: SIS, HR.
Conflict of interest
We do not have any conflict of interest.
Data availability statement
We confirm that the data supporting the findings of the study will be shared upon reasonable request. 
Supplementary file
None
    References
    1. Division of International Protection, UNHCR. UNHCR Policy on the Prevention of, Risk Mitigation, and Response to Gender-Based Violence (GBV). International Journal of Refugee Law. 2021;33(3):506-527. doi: https://doi.org/10.1093/ijrl/eeac006
    [Google Scholar]
    2. Meshkat N, Landes M. Gender-Based Violence: A Call for Action. Public Health in the 21st Century. 2010:323. URL: http://repository.stikim.ac.id/file/22-02-2523.pdf#page=339
    [Google Scholar]
    3. Guedes A, Bott S, Garcia-Moreno C, Colombini M. Bridging the gaps: a global review of intersections of violence against women and violence against children. Glob Health Action. 2016 Jun 20;9:31516. doi: https://doi.org/10.3402/gha.v9.31516
    [PubMed]     [Google Scholar]
    4. García-Moreno C, Pallitto C, Devries K, Stöckl H, Watts C, Abrahams N. Global and regional estimates of violence against women: prevalence and health effects of intimate partner violence and non-partner sexual violence: World Health Organization; 2013. URL: https://www.who.int/publications/i/item/9789241564625
    [Google Scholar]
    5. Kaur R, Garg S. Addressing domestic violence against women: an unfinished agenda. Indian J Community Med. 2008 Apr;33(2):73-76. doi: https://doi.org/10.4103/0970-0218.40871
    [PubMed]     [Google Scholar]
    6. Wahed T, Bhuiya A. Battered bodies & shattered minds: violence against women in Bangladesh. Indian J Med Res. 2007 Oct;126(4):341-354. PMID: 18032809
    [PubMed]     [Google Scholar]
    7. Hillock RL. Establishing the rights of women globally: Has the United Nations Convention on the Elimination of All Forms of Discrimination against Women made a difference. Tulsa J Comp & Int'l L. 2004;12:481. URL: https://digitalcommons.law.utulsa.edu/tjcil/vol12/iss2/6
    [Google Scholar]
    8. Watts C, Zimmerman C. Violence against women: global scope and magnitude. Lancet. 2002 Apr 6;359(9313):1232-1237. doi: https://doi.org/10.1016/S0140-6736(02)08221-1
    [PubMed]     [Google Scholar]
    9. Chandra-Mouli V, Lane C, Wong S. What Does Not Work in Adolescent Sexual and Reproductive Health: A Review of Evidence on Interventions Commonly Accepted as Best Practices. Glob Health Sci Pract. 2015 Aug 31;3(3):333-340. doi: https://doi.org/10.9745/GHSP-D-15-00126
    [PubMed]     [Google Scholar]
    10. Heise, L., Ellsberg, M. and Gottemoeller, M. Ending Violence Against Women. Population Reports, Issues in World Health. 1999;11:1-44. PMID: 11056940
    [PubMed]     [Google Scholar]
    11. McGranahan M, Nakyeyune J, Baguma C, Musisi NN, Nsibirwa D, Sekalala S, Oyebode O. Rights based approaches to sexual and reproductive health in low and middle-income countries: A systematic review. PLoS One. 2021 Apr 29;16(4):e0250976. doi: https://doi.org/10.1371/journal.pone.0250976
    [PubMed]     [Google Scholar]
    12. Bhuiya A, Sharmin T, Hanifi SM. Nature of domestic violence against women in a rural area of Bangladesh: implication for preventive interventions. J Health Popul Nutr. 2003 Mar;21(1):48-54. PMID: 12751674
    [PubMed]     [Google Scholar]
     13. Report on the Violence Against Women (VAW) survey 2015 in Bangladesh. Bangladesh Bureau of Statistics (BBS), Ministry of Planning: Dhaka, Bangladesh. 2016. URL: https://asiapacific.unfpa.org/sites/default/files/pub-pdf/Bangladesh_VAW_survey_report_2015_compressed.pdf
    [Google Scholar]
    14. Parvin K, Sultana N, Naved RT. Disclosure and help seeking behavior of women exposed to physical spousal violence in Dhaka slums. BMC Public Health. 2016 May 10;16:383. doi: https://doi.org/10.1186/s12889-016-3060-7
    [PubMed]     [Google Scholar]
    15. Salam A, Alim A, Noguchi T. Spousal abuse against women and its consequences on reproductive health: a study in the urban slums in Bangladesh. Matern Child Health J. 2006 Jan;10(1):83-94. doi: https://doi.org/10.1007/s10995-005-0030-6
    [PubMed]     [Google Scholar]
    16. Mia MN, Mahmood SS, Chowdhury R, Mustafa AG, Razzaque A, Iqbal M. Women’s and Children’s Health and Well-being in Urban Slums. Chapter 7 in Slum Health in Bangladesh: insights from Health and demographic Surveillance. icddr,b. 2019; 142-180. [Accessed 20 Aug 2024] URL: http://dspace.icddrb.org/jspui/bitstream/123456789/9298/1/icddrb-SP154.pdf
    [Google Scholar]
    17. García-Moreno C, Jansen HA, Ellsberg M, Heise L, Watts C. WHO multi-country study on women’s health and domestic violence against women. Geneva: World health organization. 2005 Nov;204(1):18. URL: https://www.who.int/publications/i/item/924159358X
    [Google Scholar]
    18. Topa AR, Shiblee SI, Rashid MH. Knowledge, attitude and practice regarding sexual and reproductive health rights among married adolescents in urban slums of Bangladesh: A cross-sectional survey. Bangabandhu Sheikh Mujib Medical University Journal. 2024 Nov 6;17(4):e75623. doi: https://doi.org/10.3329/bsmmuj.v17i4.75623
    [Google Scholar]
    19. Heise LL, Kotsadam A. Cross-national and multilevel correlates of partner violence: an analysis of data from population-based surveys. Lancet Glob Health. 2015 Jun;3(6):e332-e340. doi: https://doi.org/10.1016/S2214-109X(15)00013-3
    [PubMed]     [Google Scholar]
    20. Naved RT, Persson LA. Dowry and spousal physical violence against women in Bangladesh. Journal of family issues. 2010;31(6):830-856. doi: https://doi.org/10.1177/0192513X09357554
    [Google Scholar]
    21. Kagy G. Female labor market opportunities, household decision-making power, and domestic violence: Evidence from the Bangladesh garment industry. Center for Economic Analysis, Department of Economics, University of Colorado at Boulder; 2014. URL: https://www.colorado.edu/economics/sites/default/files/attached-files/wp14-09.pdf
    [Google Scholar]