Traumatic life events and development of post-traumatic stress disorder among female factory workers in a developing country



Post-traumatic stress disorder (PTSD) may be more prevalent and burdensome in developing countries. Aims: The goals of this study were to (1) determine the prevalence of PTSD, (2) identify types and number of traumas related to screening positive for PTSD, and (3) determine other sociodemographic risk factors and health/medical conditions that may be correlated to PTSD among garment-factory workers and a comparable working population in Bangladesh.


A survey was administered to a convenient sample of 607 lower socio-economic status (SES) working women in Bangladesh, 310 of who were garment workers. The primary outcome of PTSD was measured by the PTSD Checklist. The Life Events Checklist determined the number and type of traumatic events.


The prevalence of PTSD was found to be 17.79% – 7.25% in garment workers and 21.55% in the comparison worker group. In multivariate analysis, PTSD was found to be significantly associated with age, income, chronic pain, and a number of stressful events. Participants between 45–50 years of age had the greatest odds of reporting PTSD – 15.68 fold (95% confidence interval (CI) = 4.08, 60.29) compared with those younger than 24 years. PTSD was more common in those with lower income (2,000–4,000 taka) (odds ratio (OR) = 1.60; 95% CI = 0.79, 3.26), who had chronic pain (OR

= 2.48; 95% CI = 1.51, 4.07) and who experienced over three traumatic life events (OR = 11.25; 95% CI = 4.59, 27.59). The mean number of traumatic events experienced by this entire population was 4.9 with PTSD being more likely in those who experienced physical assault (OR = 6.35; 95% CI = 4.07, 9.90), who caused serious harm or death to someone else (OR = 4.80; 95% CI = 1.36, 16.87) and who had exposure to combat or war (OR = 4.76; 95% CI = 1.17, 19.34).


Undiagnosed and untreated PTSD impacts the quality of life and decreases worker productivity among working-age women in this developing country.


Factory workers, PTSD, developing country


More tools, data, and resources have become available to identify, report, and treat post-traumatic stress disorder (PTSD) as a health condition. Studies in the United States suggest that the prevalence of PTSD is at least 1% in the general population in a single year with the lifetime prevalence thought to be around 7.8% (Atwoli, Stein, Koenen, & Mclaughlin, 2015; Kessler, Sonnega, Bromet, Hughes, & Nelson, 1995). Despite the heightened attention on and emerging evidence of this health condition in developed countries, very limited studies in developing countries have been conducted on PTSD prevalence. The mental health status of the population is often ignored or under-diagnosed there, and awareness among healthcare providers on


1Department of Pediatrics, Emory University, Atlanta, GA, USA 2Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA

3Epidemiology and Biostatistics Program, Department of Environmental and Occupational Health, University of Nevada, Las Vegas, Las Vegas, NV, USA

4Inclusive Job Centre, CRP-Mirpur, Dhaka, Bangladesh 5Department of Health Policy and Management, New York Medical College, Valhalla, NY, USA

Corresponding author:

Hasanat Alamgir, Department of Health Policy and Management, New York Medical College, Valhalla, NY 10595, USA.



this issue is also very low. The very few studies conducted in South Asia and Southeast Asia on PTSD were undertaken to study consequences of a specific traumatic event, such as a natural disaster like a hurricane/tsunami or after a man-made disaster like a factory fire or collapse and the prevalence was found to range from 11.3% to 60.2% (Arnberg, Bergh Johannesson, & Michel, 2013; Fitch, Villanueva, Quadir, Sagiraju, & Alamgir, 2015).

Frequencies of trauma exposure in the lower-income country are usually higher compared with high-income countries because of widespread socioeconomic disparities, political unrest, poor law and order situation, less control over daily living, and ongoing conflicts in many of these areas (Atwoli et al., 2015). However, people in high- income countries living in urban low-income areas also suffer from frequent exposure to trauma. One study in Detroit reported that 69.7% of its population had exposure to some traumatic event (Keyes et al., 2013). It has also been well studied that these traumatic events, including the number of such events, lead to higher incidence rates of PTSD (Cardozo et al., 2004; Miller & Rasmussen, 2010). In addition, women in many of these settings are more likely to be exposed to traumas earlier in life and more frequently and they are at higher risk of developing PTSD (Axinn et al., 2013). Despite being a highly prevalent health issue, there have been few studies looking at the association between traumatic events and the development of PTSD in low-income countries (Axinn, Ghimire, Williams, & Scott, 2013; Cardozo et al., 2004), especially in a working population.

Bangladesh, one of the largest developing countries in the world, has 31.5% of its population living in poverty (The World Bank, 2011, 2014). Women in Bangladesh like many other developing countries suffer from a number of socioeconomic and health issues: few social protections, higher economic insecurity, low education and empowerment, early marriage, high rates of fertility, high infant mortality, frequent domestic abuse, and violence. All of these place these working-age women at a higher risk of developing PTSD (Begum, 2010; Kodali, 2015). Garment factories which provide jobs for millions of women with lower education and skills are now a key driver of this country’s economic growth. However, very little is known about these workers’ mental health, including the number of traumatic events sustained and possible PTSD development or diagnosis (Fitch et al., 2017). The impact of poor mental health on workers’ well-being, quality of life, morale, productivity, and absenteeism has not been investigated, understood, or appreciated in developing countries (Kessler, Chiu, Demler, Merikangas, & Walters, 2005; Lépine & Briley, 2011; Breslau & Anthony, 2007). Despite the tremendous amount of resources allocated recently to understand, protect and improve the mental health of workers in developed countries, the mental health status of workers in developing countries has largely been ignored even though

most Western companies and consumers continue to benefit immensely from this cheap workforce.

The goals of this study were to (1) determine the prevalence of PTSD, (2) identify types and number of traumas related to screening positive for PTSD, and (3) determine other sociodemographic risk factors and health/medical conditions that may be correlated to PTSD among garment-factory workers and a comparable working population in Bangladesh.



The study population is made up of working women of lower socioeconomic status SES. The population was approached via the Centre for the Rehabilitation of the Paralyzed (CRP) – a large local non-governmental organization (NGO). This organization provides preventive care as well as aid and treatment for those who have disabilities. The subjects were recruited at garment factories and others who were invited to come to CRP. The comparison group was made up of tailors, beauticians, housekeepers, shopkeepers, and secretaries from nearby facilities as well as certain employees from the CRP itself who were thought by the research team to have a comparable income and education as garment-factory workers. Once an individual participated in this study, she was requested to pass on study information to other working women she knew. This snowballing technique allowed to recruit a large sample size in a relatively short period of time.

The survey administered included components on demographic and health conditions, Chronic Pain Scale, Life Events Checklist (LEFC), Post-traumatic Stress Disorder Checklist-Specific (PCLS), and Patient Health Questionnaire 9 (PHQ-9). All surveys were translated into Bengali keeping the education level of the participants in mind. Before administered, the surveys were first piloted for 3 days on comparable lower SES women, and modifications were made accordingly. The study took place in February of 2016. The surveys were administered by a CRP research staff and a health science undergraduate student. They were both trained by this research team and they also helped in obtaining consent forms. Institutional review board (IRB) approval was obtained from both The University of Texas Health Science Center Medical School and CRP, Bangladesh.

The first portion of the survey (demographics and health conditions) collected data on age, employment, salary, hours worked, and medical history. The Chronic Pain Scale gathered information on the location of pain, severity, duration, and if the pain was secondary to a workplace injury. If the patient had pain lasting longer than 6 months, she was grouped as having chronic pain. The LEFC determined lifetime traumatic experiences; examples of traumatic events included toxic exposures, traffic/workplace accidents, physical/sexual assaults, and exposure to death


and dying (a sick family member, witnessing a violent event). The PCLS was administered only when LEFC sur-

Table 1. Characteristics of garment workers and non- garment workers in Bangladesh surveyed in this study.


vey revealed a traumatic event happened to a respondent. If the score was 45 or higher, the individual was deterred-

Garment workers

Non-garment workers



mined to screen positive for PTSD. The PCLS was selected                                            N = 310 (52%) N = 297 (48%)
as this tool has been used in other international studies,

including one in Bangladesh, with reported sensitivity and

Mean SD Mean SD
specificities ranging from 0.78 to 0.82 and 0.83 to 0.86, Age (years) 27.9 7.3 33.4 9.7 <.0001
respectively (Blanchard, Jones-alexander, Buckley, & Number of 3.7 1.5 3.5 1.8 .0378
Forneris, 1996; Fitch et al., 2015; Hoge, Riviere, Wilk, dependents
Herrell, & Weathers, 2014). Household size 4.1 1.5 3.9 1.5 .1343
The logistic regression model was applied to investigate Monthly income 4,049 3,997 6,374 3,884 <.0001
the influence of risk factors on PTSD. Odds ratios (taka)
Hours of work in

a day

8.1 3–13b 7.9 1–24b .2182
Duration of 4.0 3.6 6.8 5.9 <.0001
employment (years)
Working days/week 6.0 0.3 6.3 1.1 <.0001
N % N % p-valuec
Received any education                                                         <.0001
No 118 38.06 138 46.46
Yes 192 61.94 159 53.54


(ORs) were calculated from exponential estimated coeffi-

clients. A 95% confidence interval (CI) and a p-value were also calculated. The significance level was set at 5%. Data management and analysis were carried out by SAS v9.4 (SAS Institute, Cary, NC).



Among 607 subjects, 52% (N = 310) were garment work-

ers. Their characteristics are presented in Table 1. Garment

workers were younger, had more dependents, earned less,

Marital status Married  








were employed for shorter tenure, worked fewer days a Single 31 10.03 25 8.45
week, more likely to have an only primary school education, Divorced 9 2.91 18 6.08
were more likely to be married and were working full-time. In terms of traumatic events, participants experienced or witnessed (Table 2), the three most commonly reported Widowed

Current employment Full time















were natural disasters (71.83%), fire/explosions (43.33%) Part-time 2 0.65 86 28.96


and exposure to sudden accidental death (30.15%). Among

them, 26.52% admitted to being a victim of physical assault. The participants encountered on average about 3.20 traumatic events (standard deviation (SD) = 0.99).

If the participant admitted being exposed to a traumatic event (N = 108), the PCLS tool was then administered to screen for PTSD for the event that was deemed to be most traumatic by the subject. From the PCLS, 17.79% of the 108 participants screened positive for PTSD (Table 3). The data were further evaluated to determine if a certain type of traumatic event was more related to PTSD positive screening. Among participants with PTSD, most of them experienced and witnessed natural disasters (N = 81), fire/explosion (N

= 65) and physical assault (N = 65). On average, participants with PTSD suffered 4.90 traumatic events (SD = 2.27) – significantly higher – than participants without  PTSD. In the univariate analysis, participants experiencing or witnessing more traumatic events had significantly higher odds of having PTSD, except for natural disasters and sudden violent death. In the multivariate analysis, only transportation accident (OR = 1.96; p-value = .0122; 95% CI = 1.16, 3.32) and physical assault (OR = 3.33; p-value

<.0001; 95% CI = 1.99, 5.57) had significantly higher OR for PTSD. On average, participants with PTSD had 3.83 traumatic stressful events (SD = 0.50), while participants

SD: standard deviation.

at-test. range.

cChi-square test.

dTwo missing values.



without PTSD had only 3.06 traumatic events (SD = 0.05). The OR for PTSD was statistically significant by 3.91 (p-value <.0001; 95% CI = 2.55, 6.00) among participants incurring one traumatic event.

Table 4 shows that PTSD was more likely among participants of younger age, lower-income, no education, longer duration of chronic pain, no headaches, no heart diseases, and among those working in non-garment industries and experienced more traumatic events. The univariate analysis concluded that except for headache and heart disease, most other risk factors studied had a significant association with PTSD. In multivariate analysis, PTSD was found to be significantly associated with age, income, chronic pain, and a number of traumatic events. Among these significant risk factors, participants aged 45–50 years had the greatest odds of getting PTSD by 15.68-fold (95% CI = 4.08, 60.29) compared with those aged younger than 24-year-olds. Moreover, participants who earned more than 6,000 taka had a lower risk. Furthermore,


Table 2. Frequency and type of traumatic events experienced by garment workers and non-garment workers in Bangladesh.


Variable N % p-valuea
Natural disaster 436 71.83 <.0001
Fire/explosion 263 43.33 .001
Transportation accident 163 26.85 <.0001
Physical assault 161 26.52 <.0001
Life-threatening illness/injury 146 24.05 <.0001
Severe human suffering 102 16.8 <.0001
Sudden violent death 109 17.96 <.0001
Sudden accidental death 183 30.15 <.0001
Serious accident 87 14.33 <.0001
Exposure to a toxic substance 38 6.26 <.0001
Assaulted with a weapon 32 5.27 <.0001
Sexual assault 19 3.13 <.0001
Other unwanted or uncomfortable sex experience 18 2.97 <.0001
Combat/war exposure 8 1.32 <.0001
Captivity 19 3.13 <.0001
Serious injury harm or death you caused to someone else 10 1.65 <.0001
Any other stressful event 110 18.12 <.0001

aChi-square test.



participants with chronic pain more than 6 months and who experienced at least three traumatic events had a significantly 2.48-fold (95% CI = 1.51, 4.07) and 11.25-fold higher (95% CI = 4.59, 27.59) odds of having PTSD compared with those with chronic pain less than 6 months and less than three traumatic events, respectively.



This study reveals that PTSD is a highly prevalent health condition in working women in Bangladesh with about 17% screening positive out of those who reported experiencing a traumatic event in life, which the majority of participants reported, n = 563 (92%). To our knowledge, no studies of PTSD have been conducted in a generally healthy working-age female population (i.e. not those exposed to any immediate large-scale natural or industrial disasters or war or conflict) in Bangladesh. Studies have shown that the prevalence of PTSD in the general population of high-income countries, such as the United States ranges from 3.5% to 7% (Kessler et al., 2005; National Comorbidity Survey, 2005). A meta-analysis of US veterans – a high- risk group – included studies that report a point prevalence of 2%–17% (Richardson, Frueh, & Acierno, 2010). Our data demonstrate a prevalence that is similar to the high- risk population of US veterans. This similarity in the prevalence of PTSD among US veterans and Bangladeshi working women may likely be due to the number of traumatic events experienced by the study population as well as the psychosocial vulnerabilities (e.g. little or no social protection) of women in this country.

While our study found no significant difference between the garment-factory workers and the comparison group in terms of PTSD screening and the number of traumas, we did identify common associated factors with the development of PTSD in both groups. A higher number of traumatic event exposures, lower-income, chronic pain, and older age were all statistically linked with PTSD in multivariate regression models. This information is immensely valuable for healthcare providers, health advocates, worker groups, and employers to further examine the mental health issues of working-age women.

The World Health Organization World Mental Health Survey Initiative stated that 19.8% of respondents (n = 51,295) reported PTSD and PTSD was more likely to occur among those with multiple traumatic events (Karam et al., 2014). This is corroborated in our study as well,  where many women had experienced multiple traumatic events (average of 4.9 events per person), and the more such events they experienced, the more likely they were to have PTSD. Not only are rates of PTSD higher in those with more traumatic events, but it was more prevalent among respondents with certain health conditions as well. Other studies have shown increases in morbidity, dysfunction in daily living, and physical disabilities such as arthritis among people with PTSD. This is unrelated to whether a person develops PTSD from the trauma (Husarewycz, El-gabalawy, Logsetty, & Sareen, 2014; Karam et al., 2014; Keyes et al., 2013). These findings suggest better monitoring and health surveillance is warranted for people who suffer from traumatic events in developing countries.

The types of traumas reported most commonly in this study were natural disasters, fire/explosions, and witnessing a sudden or accidental death. However, positive screening of PTSD was more common who reported combat or who witnessed or caused serious injury or harm to another individual. The highest OR was observed in those who experienced physical assault. These results corroborate findings from other studies that have examined PTSD rates for certain types of traumas (Fitch et al., 2015; Richardson et al., 2010). The reported sexual assault was only 3% in this population. This report is likely to be an underestimate; women in this society and culture are very unlikely to discuss sexual trauma. This again greatly highlights the importance of building awareness among healthcare provides around the impact of traumas on young women of lower SES in developing countries. These respondents were mostly young women and this is most likely to be underestimated as women in this society and culture are very unlikely to talk or share this information. This again greatly highlights the requirement of awareness building among healthcare providers who treat young women of lower SES in developing countries.

Our study also identified that people with lower income were more likely to screen positive for PTSD. Other studies also support the association of low SES with PTSD as well as increased risk for people quitting schools earlier in



Table 3. Association of traumatic events with and without PTSD: garment workers and non-garment workers in Bangladesh.


Variables                                                              With PTSD

(N = 108;


Without PTSD (N = 499;


Univariate analysis                             Multivariate analysisa



N % N % OR 95% CI p-value OR 95% CI p-value
Natural disaster 81 18.58 355 81.42 1.22 (0.76, 1.96) .4795 1.05 (0.59, 1.85) .8682
Fire/explosion 65 24.71 198 75.29 2.30 (1.50, 3.51) .0001 1.82 (1.09, 3.02) .0217
Transportation accident 49 30.06 114 69.94 2.80 (1.82, 4.32) <.0001 2.49 (1.50, 4.14) .0004
Serious accident 24 27.59 63 72.41 1.98 (1.17, 3.34) .0147 0.84 (0.43, 1.64) .6053
Exposure to a toxic substance 17 44.74 21 55.26 4.25 (2.16, 8.37) <.0001 2.27 (1.00, 5.16) .0495
Physical assault 65 40.37 96 59.63 6.35 (4.07, 9.90) <.0001 4.00 (2.41, 6.64) <.0001
Assault with a weapon 13 40.63 19 59.38 3.46 (1.65, 7.24) .0015 2.06 (0.82, 5.21) .1264
Sexual assault 8 42.11 11 57.89 3.55 (1.39, 9.05) .0105 1.64 (0.43, 6.20) .4677
Other unwanted or uncomfortable sex experience 7 38.89 11 61.11 3.07 (1.16, 8.12) .0268 1.04 (0.25, 4.24) .9608
Combat/war exposure 4 50.00 4 50.00 4.76 (1.17, 19.34) .0372 4.01 (0.72, 22.47) .1139
Captivity 8 42.11 11 57.89 3.55 (1.39, 9.05) .0105 0.97 (0.29, 3.26) .9617
Life-threatening illness/injury 36 24.66 110 75.34 1.77 (1.12, 2.78) .0179 0.94 (0.51, 1.71) .8274
Severe human suffering 34 33.33 68 66.67 2.91 (1.80, 4.71) <.0001 2.01 (1.08, 3.75) .0288
Sudden violent death 26 23.85 83 76.15 1.58 (0.96, 2.62) .0026 0.71 (0.37, 1.39) .3223
Sudden accidental death 46 25.14 137 74.86 1.96 (1.28, 3.01) .0026 1.12 (0.65, 1.93) .6762
Serious injury harm or death you caused to someone else 5 50.00 5 50.00 4.80 (1.36, 16.87) .0194 2.91 (0.72, 11.76) .1342
Any other stressful event 41 37.27 69 62.73 3.81 (2.40, 6.07) <.0001 3.17 (1.82, 5.54) <.0001

PTSD: post-traumatic stress disorder; OR: odds ratio; CI: confidence interval.

all variables are included in the multivariate analysis.

be reference level is not experiencing or witnessing in a corresponding traumatic event.


Table 4. Association of personal characteristics and health conditions of participants with PTSD and without PTSD: garment workers and non-garment workers in Bangladesh.


Variable                 Level                 With PTSD (N = 108;


Without PTSD (N = 499; 82.21%)

Univariate analysis                           Multivariate analysisa



N % N % OR 95% CI p-value OR 95% CI p-value
Age (years) >50 6 46.15 7 53.85 9.86 (2.91, 33.37) <.0001 5.20 (1.25, 21.63) .0031
45–50 11 68.75 5 31.25 25.3 (7.7, 83.13) 15.68 (4.08, 60.29)
40–44 8 28.57 20 71.43 4.6 (1.72, 12.32) 2.88 (0.93, 8.94)
35–40 13 18.31 58 81.69 2.58 (1.14, 5.81) 2.63 (1.04, 6.63)
31–34 16 17.58 75 82.42 2.45 (1.14, 5.29) 2.56 (1.09, 6.01)
25–30 40 18.78 173 81.22 2.66 (1.4, 5.07) 3.05 (1.49, 6.23)
<24b 14 8.00 161 92.00 1.00 1.00
Income (taka) >8,000 15 9.93 136 90.07 0.40 (0.21, 0.76) .0012 0.26 (0.13, 0.54) <.0001
6,001–8,000 16 14.41 95 85.59 0.62 (0.33, 1.16) 0.40 (0.19, 0.82)
4,001–6,000 12 15.38 66 84.62 0.67 (0.33, 1.35) 0.56 (0.25, 1.26)
2,000–4,000 24 31.58 52 68.42 1.69 (0.93, 3.06) 1.60 (0.79, 3.26)
<2,000b 41 21.47 150 78.53 1.00 1.00
Received any Yes 52 14.81 299 85.19 0.62 (0.41, 0.94) .0254 1.05 (0.62, 1.80) .8472
education Nob 56 21.88 200 78.13 1.00 1.00
Chronic Pain >6 months 83 28.62 207 71.38 2.97 (1.93, 4.57) <.0001 2.48 (1.51, 4.07) .0003
£6 months 25 7.89 292 92.11 1.00 1.00
Occupation Garment 44 14.19 266 85.81 0.60 (0.4, 0.92) .0186 0.15 (0.36, 0.55) .5495
Non-garments 64 21.55 233 78.45 1.00 1.00



Table 4. (Continued)

Variable                 Level                With PTSD (N = 108;



Without PTSD (N = 499; 82.21%)


Univariate analysis                           Multivariate analysis



N % N % OR 95% CI p-value OR 95% CI p-value
# of stressful ³3 96 29.45 230 70.55 10.92 (4.67, 25.53) <.0001 11.25 (4.59, 27.59) <.0001
events 2 6 5.08 112 94.92 1.40 (0.44, 4.46) 1.55 (0.47, 5.14)
0–1b 6 3.68 157 96.32 1.00 1.00
Headache Yes 2 40.00 3 60.00 3.12 (0.52, 18.9) .2178 4.29 (0.59, 31.01) .1486
Nob 106 17.61 496 82.39 1.00 1.00
Heart disease Yes 3 27.27 8 72.73 1.75 (0.46, 6.72) .4126 1.12 (0.21, 5.93) .8925
Nob 105 17.62 491 82.38 1.00 1.00

PTSD: post-traumatic stress disorder; OR: odds ratio; CI: confidence interval.

all variables are included in the multivariate analysis.

preference level.



life, which limits social mobility (Kessler, Sonnega, et al., 1995; Norris et al., 2003). In addition, other studies have shown an association of unemployment with a higher number of traumatic events (Karam et al., 2014). As our study focused on the employed population, it is possible that women in Bangladesh who are unemployed, who we did not survey, are exposed to an even higher number of traumatic events and PTSD prevalence might be higher among them.

Employers of this working population (e.g. factory owners) should be made aware of this health condition and its consequences. One study reported that 23.2% of individuals with PTSD reported severe impairment in work, 24.2% in home maintenance, 26.8% in relationships, and 28.9% in social settings. They are at risk for other mental health disorders at an earlier age. That study also found that if a subject had experienced four or more traumatic events, they were more likely to have worse daily living (Karam et al., 2014). This should be of particular interest for the garment industry as the working women in this study experienced on an average of four or more traumatic events, meaning it is likely that many of these workers might have compromised working ability for their mental health conditions and this would reduce this industry’s productivity making it difficult to stay competitive in this highly manual and labor-intensive sector.

PTSD is a treatable health condition; the associated comorbid conditions (heart disease, chronic pain, and arthritis) can be managed. Many of the external exposure and strain related to PTSD appear to be modifiable through improving economic determinants of health, creating social stability and social safety nets, and improving law and order (Herrman & Swartz, 2007; Kessler et al., 2009; Ormel et al., 2007). Two mainstays of PTSD treatment are cognitive behavioral therapy and prolonged exposure therapy. Both involve a trained professional talking through the traumatic experience, with prolonged exposure needing the participant reporting in vivid detail about the traumatic

experience. Studies have shown that 64% of subjects with cognitive behavioral therapy and 68% with prolonged exposure achieve good functioning after 9 months (Resick, Nishith, Weaver, Astin, & Feuer, 2002). The regulators and policymakers in Bangladesh and other developing countries need to allocate resources and create training opportunities for health professionals for diagnosing and treating mental health conditions. Primary care physicians, hospitals, and health NGOs should preferentially screen working-age younger women who are inherently at higher risk. This study is likely underestimating the prevalence of certain traumatic events such as sexual assaults and abuse as well as PTSD rates due to difficulties in endorsing and reporting the symptoms to our translators. This was demonstrated by one participant asking at the end of the session not to share the results with her employers for fear of losing her job. Like translating any health survey questionnaires, translating the survey tools to Bengali for this study might have some inaccuracies. However, this should have been minimized as the team leader was a native Bengali speaker, the team had other Bengali researchers and data collectors as well, and there was extensive piloting of the translated surveys prior to administering it. In addition, the sampling method was not randomized; however, our researchers do not believe that this would significantly alter the results. Furthermore, the recent heightened level of international attention and scrutiny around garment- factory safety and work conditions in Bangladesh have made many factory owners and employers become critical of researchers in general. For this reason, a convenience sampling methodology was used for reasons of sensitivity

and practicality.

Based on the study findings, we conclude that mental health conditions are highly prevalent among `ES working women in developing countries and these may have serious consequences on their well-being, quality of life, and working lives. These conditions including PTSD are likely


under-recognized in many countries where few resources are allocated for health surveillance and healthcare services and less awareness exists among healthcare professionals and care providers. Millions of factory workers are earning a barely living wage from Western outsourced industries. It would be in the best interest of the Western companies, donor and aid agencies, and consumer groups to provide more resources and evidence support to improve the mental health along with the physical health of these workers. This will ultimately benefit all stakeholders economically (in terms of productivity and profitability) by preserving a healthy workforce.


Ethical Approval

Ethical approval for this study was obtained from the School of Medicine at UT Health Science Center San Antonio, Texas, and from the Centre for the Rehabilitation of the Paralyzed (CRP) in Bangladesh.



The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by funding from the UT Health Science Center San Antonio’s Global Health Program and Friends of CRP, Canada.



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