A Public health wound: health and work among children engaged in worst forms of child labour in the informal sector in Dhaka, Bangladesh: a retrospective analysis of Médecins Sans Frontières Occupational health data from 2014 to 2023.

A Public health wound: health and work among children engaged in worst forms of child labour in the informal sector in Dhaka, Bangladesh: a retrospective analysis of Médecins Sans Frontières Occupational health data from 2014 to 2023. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Public health wound: health and work among children engaged in worst forms of child labour in the informal sector in Dhaka, Bangladesh: a retrospective analysis of Médecins Sans Frontières Occupational health data from 2014 to 2023. Grazia Caleo, Sohana Sadique, Didem Yuce, Martins Dada, Bianca Benvenuti, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5313328/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Apr, 2025 Read the published version in BMC Public Health → Version 1 posted 4 You are reading this latest preprint version Abstract Background: Bangladesh has the second highest burden of child labour in South Asia. The informal sector employs most of the children however, evidence and data on health including injuries and place of work for children are limited. As the deadline for the Sustainable Development Goals to end child labour by 2025 is approaching, it is paramount to document the impact of child labour on health. This study aims to contribute to this knowledge gap by presenting medical data from occupational health clinics set up by Médecins Sans Frontières (MSF) in an urban area of Dhaka, Bangladesh. Methods: We did a retrospective analysis of health care records of children attending MSF occupational health clinics between February 2014 and December 2023 in Dhaka. We stratified the analysis by gender and age (<14 years and ≥14-<18 years). We looked at morbidities according to type of factory, whether children reported working with machinery, and examined nutritional and mental health (2018-2023) status. Results: Over the study period, there were 10,200 occupational health consultations among children <18 years, of which 4945 were new/first time consultations. The average age of children attending their first consultation was 14.7 years, of which 61% were male. Fifteen percent reported living inside the factory. Musculoskeletal (26%) and dermatological (20%) were the most identified conditions, and 7.5% of consultations were for work-related injuries. Almost all children reported operating machinery. A higher proportion of male children had injuries (11% vs 2.5% in girls). Children working in metal factories accounted for most injuries (65%). Mood-related disorders accounted for 86% of the 51 mental health consultations. Half of all children were malnourished with higher levels in boys and those <14 years. Specific gender and age vulnerabilities were documented. Conclusions Findings suggest that children face hazardous realities, engaged in the worst form of labour, bearing important morbidity and injury burden. Weak enforcement of labour regulations in the informal sector ultimately enables child exploitation and suffering. Further research is essential to explore the intersectional dimensions of child labour, such as gender, age, and disability, to inform interventions aimed at eliminating child labour and its severe consequences in this and similar contexts. Child labour occupational health work-related injuries Bangladesh Introduction “ There are many factories where child workers have to do heavy work like the welding factory. They have to weld the iron during day or night. They have to work using hammer or sometimes in front of the fire. This scenario is extreme for those child workers. As we (factory owners) we treat them (children/adolescents) a bit rudely and as he is a child so it hurts him, otherwise if he would be adult it would not impact that much. ….But there are many (factory) owners who torture the child workers. Female workers face some harassment. You understood what type of harassment I meant. Some of them do not mention about the harassment thinking of they might lose their job so, it’s a fear for them’’. (Metal factory owner , Kamrangirchar a peri-urban area in Dhaka, Bangladesh )– ( 1 ) This is one of the common narratives Médecins Sans Frontières (MSF) teams heard about children working in Kamrangirchar, a peri-urban area in Dhaka, Bangladesh. Narratives corroborated by observational experience and occupational health medical services provided over the last 10 years by MSF in this area, where an unknown number of children under 18 years old are employed in the informal sector and involved in recycling, metal, plastic, leather and garment sector ( 2 ). Child labour is a violation of children’s rights, and despite the ambition to end child labour by 2025, the number of children working is increasing from 151·6 million in 2016 to 160 million in 2020 ( 3 ). Suboptimal statistics report that another 22,000 children are deemed to be killed at work every year ( 4 ). From a public health perspective, child labour and its consequences hinder the achievement of Sustainable Development Goal (SDG) 8 (Decent Work and Growth) and is considered a harmful practice. By inflicting physical, emotional and mental health ‘’wounds’’ on children, it is associated with negative health outcomes in the long and short term, creating social vulnerabilities that are perpetuated in vicious cycles over generations (Box 1) ( 5 – 8 ). Child labour has been previously recognised as a public health issue in the South Asia region where the International Labour Organization (ILO), estimates that child labour (aged 5–17 years old) varies from 5.8 million in India to 2 million in Nepal ( 9 , 10 ). With an estimated 1,776,097 children working (of whom 60.14% engaged in hazardous child work), Bangladesh has the second highest burden of child labour in the South Asia region (4.4%) ( 11 ). In recent years, the child labour burden and its devasting individual and social welfare consequences have influenced the Government of Bangladesh to align its legal framework with international standards by ratifying: i) the ILO Convention on the Worst Forms of Child Labour (1999, No. 182) ii) the ILO’s Minimum Age Convention (1973, No 138), iii) the Protocol to the Forced Labour Convention, and by revising the National Plan of Action to Eliminate Child Labour by 2025 ( 12 , 13 ). The Bangladesh Labour Act sets the minimum working age at 14 years old and restricts hazardous work for those below 18 years old; in the Act. hazardous work is defined as a work’ that is “likely to hamper child health, safety and moral development, this includes working with dangerous machines that are harmful to their physical and mental development” ( 14 , 15 ). Any form of work that encompass hazardous activities is considered the worst forms of child labour (Box1). The government also published a list of activities/processes (e.g. garment) that are officially prohibited for use among children under 18 years old. Currently, 95 percent of child labour in Bangladesh is estimated to occur in the informal sector, however evidence and health data on children employed in this sector are limited. As the deadline for the SDG to end child labour is approaching, it is paramount to document the impact of child labour on health and draw attention to this neglected public health issue. The present study aims to contribute to this knowledge gap by presenting a retrospective multiyear analysis (2014–2023) of medical data collected by MSF occupational health clinics among children working in the informal sector in an urban area of Dhaka and highlight this largely invisible workforce. Box 1. Key concepts on child labour ( 16 ) Child labour : Child labour is any work that deprives children of their childhood, their potential and their dignity. It interferes with children’s education and negatively affects their emotional, developmental and physical well-being. Worst forms of child labour (WFCL) : The WFCL are a subset of child labour, defined by the ILO Convention No. 182 as prohibited for all children under the age of 18 years and are to be eliminated as a matter of urgency . They include: • all forms of slavery or practices similar to slavery, such as the sale and trafficking of children, debt bondage and serfdom and forced or compulsory labour, including forced or compulsory recruitment of children for use in armed conflict; • the use, procuring or offering of a child for prostitution, for the production of pornography or for pornographic performances; • the use, procuring or offering of a child for illicit activities, in particular for the production and trafficking of drugs as defined in relevant international treaties; • work which, by its nature or the circumstances in which it is carried out, is likely to harm the health, development, safety or morals of a child (also called: “ hazardous work” ). Hazardous Work : is work which by its nature or the circumstances in which it is carried out, is likely to harm the health, development, safety or morals of children. It is one of the worst forms of child labour and is therefore prohibited for all children under the age of 18 years . ILO Recommendation No. 190 urges governments to consider the following hazardous work activities: • work which exposes children to physical, emotional or sexual abuse. • work underground, under water, at dangerous heights or in confined spaces. • work with dangerous machinery , equipment and tools, or which involves the manual handling or transport of heavy loads. • work in an unhealthy environment which may, for example, expose children to hazardous substances, agents or processes, or to temperatures, noise levels, or vibrations damaging to their health. • work under particularly difficult conditions such as work for long hours or during the night or work where the child is unreasonably confined to the premises of the employer. Methods Study setting. Kamrangirchar, located on the Buriganga River, is one of Dhaka’s largest peri-urban area, with a population of 440,000 concentrated in an area of four square kilometres. It is estimated that Kamrangirchar has over 1,000 informal factories, including those mainly in the metal, garment, and plastics sectors, along with tanneries that relocated to Savar in 2017. In Kamrangirchar, MSF runs two occupational health clinics to provide medical care and psychological support to workers from the informal factory sector. The MSF team set up agreements with factory owners/managers across tannery, garment, metal and plastics sectors allowing factory workers to attend health clinics during their working hours. Workers from these registered factories could access MSF occupational health services. However, workers from unregistered factories (no formal agreement with MSF) could also access emergency services and first aid for their injuries. Study design and participants. In this retrospective study, we used anonymised primary health care records from the MSF occupational health clinics database containing demographics, date of consultation, age, gender, visit status (new/follow-up), residence inside a factory, type of factory they worked in, if they were working with machines inside the factory, nutritional, and mental health status, type and dynamic of injury experience and diagnosis. Our population consisted of children and adolescents under 18 years presenting to MSF occupational health clinics between February 2014 to December 2023. The mental health dataset consisted of consultation data between September 2018 to December 2023. Variable definitions New consultations were defined as patients who came for the first time for a consultation. Follow-up consultations were defined as patients who came for subsequent appointments scheduled after the first consultation to reassess and manage the patient's ongoing occupational health condition. Nutritional status among workers less than 18 years was assessed using Body Mass Index (BMI) and those with a BMI for age more than two standard deviations less than the median for their age group were classified as malnourished. Work-related morbidity refers to any physical or psychological health conditions that are directly caused or exacerbated by the patient's job or working conditions. This includes illnesses, chronic conditions, injuries, and psychological distress that arise from factors such as repetitive strain, exposure to hazardous materials, workplace accidents, or stressful work environments. In our analysis, a "yes" for work-related morbidity indicates a decision made by an occupational health doctor that the individual has a health condition directly caused or exacerbated by their job or working conditions, while "no" indicates that the individual does not have any such work-related health conditions. Data analysis Descriptive analysis of the occupational health data among children under 18 years old was performed. The analysis was stratified by gender and age group (comparing those under 14 years to those aged 14-under 18 years) and documented the type of factory/sector where children were employed and if children reported working with machines, an additional proxy of hazardous work as per the Bangladesh Labour Act. We choose those two age groups, since the Bangladesh Labour Act, 2006 (and Amendments) defines a child as anyone under the age of 14 years and prohibits their employment in any establishment (Section 34) and prohibits the employment of adolescents (aged 14–17) in hazardous work (Section 39). Chi-squared and fisher’s exact tests were used to check for differences in proportions across categorical variables. The analysis was performed using R Studio software ( 17 ) Results Demographics Over the study period, there were a total of 10,200 occupational health consultations among children aged under 18 years. Of the 10,200 consultations, there were 4945 new consultations, 3895 were follow-up consultations, and consultation status was missing for 1360 records (Table 1 ). There was an average of 494 new consultations among children every year with a peak of 967 in 2018. Among the new consultations, 38% were among children aged under 14 years (the youngest child was five years), the mean age was 14.7 years (standard deviation of 2.02 years) and 61% were male. Eighty-five per cent of the children reported living outside the factory (Table 1 ). Overall, male children were more likely to live inside the factory compared to female children (23% vs 1%, p < 0.001) (see additional file 1). Table 1 Demographic characteristics of occupational health patients < 18 years, MSF clinics, Dhaka, 2014–2023 Characteristic Overall, N = 4,945 1 =14–17, N = 3,063 1 p-value 2 Gender 0.004 Female 1,950 (39%) 694 (37%) 1,256 (41%) Male 2,995 (61%) 1,188 (63%) 1,807 (59%) Residence < 0.001 Lives outside factory 3,570 (85%) 1,417 (88%) 2,153 (84%) Lives inside factory 611 (15%) 189 (12%) 422 (16%) Unknown 764 276 488 1 n (%) 2 Pearson's Chi-squared test Place of work Almost a third (32%) of new consultations were among children who worked in garment factories, followed by plastics (30%) and metal (21%) factories (Table 2 ). Among female children attending new consultations with MSF, plastics and garment factories were their major employers, while garment and metal factories employed most male children (Table 2 ). Similar proportions of children aged < 14 and 14–17 years worked in the various factory types. Over 96% of children irrespective of age group or gender reported operating machinery as part of their work. There was little difference in this proportion across factory type. Table 2 Place of work among occupational health patients < 18 years by gender, MSF clinics, Dhaka, 2014–2023 Characteristic Overall, N = 4,945 1 Female, N = 1,950 1 Male, N = 2,995 1 Type of factory Garment 1,575 (32%) 724 (37%) 851 (29%) Plastics 1,483 (30%) 750 (39%) 733 (25%) Metal 1,033 (21%) 187 (9.6%) 846 (28%) Leather 195 (4.0%) 88 (4.5%) 107 (3.6%) Embroidery 181 (3.7%) 69 (3.6%) 112 (3.8%) Tannery 141 (2.9%) 16 (0.8%) 125 (4.2%) Rubber 148 (3.0%) 42 (2.2%) 106 (3.6%) Other 136 (2.8%) 65 (3.3%) 71 (2.4%) Chemical 26 (0.5%) 1 (< 0.1%) 25 (0.8%) Battery 1 (< 0.1%) 1 (< 0.1%) 0 (0%) Unknown 26 7 19 1 n (%) Morbidities Among new consultations, almost half of the children were diagnosed as having either musculoskeletal (26%) or dermatology (20%) (e.g., fungal infection, mainly scabies) conditions, with similar proportions across age group and gender (Table 3 ). In addition, no major differences were observed in conditions whether a child lived inside or outside a factory. Work related injuries accounted for 7.5% of all new consultations among children and with no difference by age group. However, male children had a higher proportion of injuries (11%) compared to female children (2.5%) (see additional file 1). The majority (83%) of the diagnoses irrespective of age group or gender were suspected as being work-related. Sixty-three per cent of under 14 years were found to be malnourished compared to 40% of 14–17 years and similarly a higher proportion of male children compared to female. Table 3 Primary diagnoses among occupational health patients < 18 years, MSF clinics, Dhaka, 2014–2023 Characteristic Overall, N = 4,945 1 =14–17, N = 3,063 1 p-value 2 Primary diagnosis Musculoskeletal 1,197 (26%) 455 (26%) 742 (26%) Dermatology 907 (20%) 302 (17%) 605 (22%) Respiratory 632 (14%) 281 (16%) 351 (12%) Gastro-intestinal (GI) 550 (12%) 184 (11%) 366 (13%) Injury 339 (7.5%) 134 (7.7%) 205 (7.3%) ENTDEH (Ear, Nose, Throat, Dental, Eyes and Head) 419 (9.2%) 163 (9.4%) 256 (9.1%) Others 167 (3.7%) 75 (4.3%) 92 (3.3%) Other Chronic Condition 128 (2.8%) 79 (4.5%) 49 (1.7%) SRH (Sexual & Reproductive Health) 131 (2.9%) 40 (2.3%) 91 (3.2%) Infectious Disease 17 (0.4%) 5 (0.3%) 12 (0.4%) Cardiovascular 26 (0.6%) 6 (0.3%) 20 (0.7%) Urinary 11 (0.2%) 4 (0.2%) 7 (0.2%) Hematology 7 (0.2%) 2 (0.1%) 5 (0.2%) Neurological Disorder 13 (0.3%) 6 (0.3%) 7 (0.2%) Mental Health 3 (< 0.1%) 0 (0%) 3 (0.1%) Non-Communicable Disease 2 (< 0.1%) 1 (< 0.1%) 1 (< 0.1%) Unknown 396 145 251 Suspected occupational condition 0.6 Work-related 2,971 (83%) 1,138 (84%) 1,833 (83%) Non-work related 588 (17%) 219 (16%) 369 (17%) Unknown 1,386 525 861 Nutrition status -2SD) 2,217 (51%) 607 (37%) 1,610 (60%) Malnourished (BMI for age <-2SD) 2,121 (49%) 1,050 (63%) 1,071 (40%) Unknown 607 225 382 1 n (%) 2 Pearson's Chi-squared test Among the 51 mental health outcomes recorded between September 2018 to December 2023, the primary mental health disorders diagnosed were mood-related (86%) and behavioural symptoms (10%), with most patients being male (55%) and aged 14–17 years (80%) (Table 4 ). Key precipitating events included family disruptions, domestic violence, and socio-economic issues, reported by half of the patients. Domestic violence was more prevalent among females (17%) compared to males (4%), (Table 4 ). Table 4 Mental health characteristics of occupational health patients 0.9 =14–17 41 (80%) 19 (83%) 22 (79%) Type of mental health disorder > 0.9 Mood related problems 43 (86%) 21 (91%) 22 (81%) Behavior related symptoms 5 (10%) 2 (9%) 3 (11%) Neuro-psychiatric related symptoms 1 (2%) 0 (0%) 1 (4%) Social Functioning 1 (2%) 0 (0%) 1 (4%) Unknown 1 0 1 Precipitating event 0.2 Disruption of family & relationships 12 (26%) 7 (30%) 5 (22%) Domestic discord & family violence 5 (11%) 4 (17%) 1 (4%) Socio-economic functioning 5 (11%) 3 (13%) 2 (9%) Events related to abuse during detention 2 (4%) 0 (0%) 2 (9%) Medical illness-related 2 (4%) 0 (0%) 2 (9%) Events related to violence 1 (2%) 1 (4%) 0 (0%) Sexual trauma or abuse 1 (2%) 1 (4%) 0 (0%) Neuro-psychiatric related 1 (2%) 1 (4%) 0 (0%) Others 17 (37%) 6 (26%) 11 (48%) Unknown 5 0 5 1 n (%) 2 Fisher's exact test Among the 182 injuries diagnosed in new consultations across the study period, the majority occurred in patients reporting working in metal factories (65%), followed by plastics (20%) and garments (7%) (Table 5 ). The proportions of injuries by factory type were similar across age groups. There were differences by gender such that there were higher proportions of male children with injuries overall and particularly working in metal factories and higher proportions of female children with injuries working in plastics factories (Table 5 ). Table 5 Injury characteristics among occupational health patients < 18 years by gender, MSF clinics, Dhaka, 2014–2023 Characteristic Overall, N = 182 1 Female, N = 26 1 Male, N = 156 1 p-value 2 Type of factory < 0.001 Metal 119 (65%) 7 (27%) 112 (72%) Plastics 36 (20%) 12 (46%) 24 (15%) Garment 13 (7.1%) 3 (12%) 10 (6.4%) Embroidery 7 (3.8%) 0 (0%) 7 (4.5%) Other 6 (3.3%) 4 (15%) 2 (1.3%) Leather 1 (0.5%) 0 (0%) 1 (0.6%) Rubber 0 (0%) 0 (0%) 0 (0%) Tannery 0 (0%) 0 (0%) 0 (0%) Type of injury 0.4 Cut/laceration 100 (61%) 14 (61%) 86 (61%) Other 29 (18%) 5 (22%) 24 (17%) Crushing injury 14 (8.6%) 4 (17%) 10 (7.1%) Burn 10 (6.1%) 0 (0%) 10 (7.1%) Abrasion 8 (4.9%) 0 (0%) 8 (5.7%) Amputation 2 (1.2%) 0 (0%) 2 (1.4%) Broken bone 0 (0%) 0 (0%) 0 (0%) Bruise 0 (0%) 0 (0%) 0 (0%) Concussion 0 (0%) 0 (0%) 0 (0%) Unknown 19 3 16 Mechanism of injury 0.3 Struck 130 (72%) 18 (69%) 112 (73%) Other 26 (14%) 3 (12%) 23 (15%) Fall 11 (6.1%) 4 (15%) 7 (4.5%) Burn 6 (3.3%) 0 (0%) 6 (3.9%) Caught in between 7 (3.9%) 1 (3.8%) 6 (3.9%) Unknown 2 0 2 1 n (%) 2 Fisher's exact test Discussion Globally, one in ten children are engaged in child labour, and in some countries one in four are engaged in labour that is considered harmful to their health and development ( 18 , 19 ). This study gives visibility to a neglected population and adds important evidence on health among child workers. Our findings reveal a young working population, employed in all categories forbidden by Bangladesh law, including garment, and informal steel-based work (metal factories), with 7.5% of new consultations resulting from work-related injuries ( 20 ). Any children engaged in these sectors/tasks are deemed to be engaged in hazardous child labour classified as the worst form of child labour. In our study, almost 15% of the children reported they were living inside the factory where they were employed. Living inside the factories was more frequent among male children across both age groups (under 14 and under 18 years old) compared to girls. Living and working in a hazardous context might add additional health burden and social alienation for children, and likely expose children to additional risks which include, physical, emotional or sexual abuse ( 7 , 21 ). We observed concerning patterns of morbidity with both age groups experiencing a high burden of musculoskeletal, and dermatological conditions (e.g. fungal infection; scabies). A similar high burden of these conditions was reported from studies in child workers in India, Pakistan, Bangladesh, and Brazil ( 7 , 22 – 24 ). In addition, almost 50% of children were malnourished and high levels of malnutrition have been previously reported in a study among 100 child workers (aged 5–17 years) in Dhaka ( 25 ). Current evidence suggests that hazardous child labour can have devastating consequences that directly endangers a child’s health, safety, and moral development ( 7 ). It can result in disability, ill health and psychological damage, which prevents children of all ages from going to school, takes them away from their families, uses up time for play and recreation in the company of their peers, and causes significant short- and long-term harm ( 16 , 26 ). Far from having a positive effect, it impedes children’s growth and development, as well as perpetuates a cycle of poverty for the children involved, their families and communities. Almost all children in this study reported working with machines in sectors forbidden by current Bangladesh legislation, with 7.5% of children experiencing injuries with cuts or laceration being the most frequently documented injury type. Previous surveys in Bangladesh reported 18.5% of children engaged in hazardous work, with a significant portion facing injuries and abuse ( 27 ). Another study conducted in the slums/suburbs of Dhaka that looked at the pattern of injuries among children of urban slum dwellers in Dhaka City, found that occupational injury was the third highest cause of injuries among surveyed children and second among 10–15 years age group ( 28 ). Children might be more vulnerable than adults to workplace hazards. Previous MSF consultations with parents and caregivers highlighted the profound sorrow experienced by parents witnessing their children being injured. "It is heartbreaking. There are times when my son suffers from cuts and injuries on his hands... Frequent cuts on their legs and injuries on their lips, along with abrasions on their chests are a common occurrence. Witnessing their pain and injuries fills us with sorrow, but unfortunately, we are helpless to prevent them." - Caregiver of a young factory worker ( 1 ). Mental health has also emerged as a concern in both age groups, with mood-related disorders and behavioural symptoms being most diagnosed. The prevalence of these mental health issues is consistent with findings from other studies on child labour, emphasizing the psychological impact of stressful and abusive work environments ( 29 ). In our study contributing factors include family disruptions, domestic violence, and socio-economic challenges, with domestic violence particularly affecting female children. This intersection of risks from both work and home environments underscores their combined influence on mental health outcomes among child workers. In our study we identified specific gender and age vulnerabilities. For instance, more male children compared to female reported living inside the factories, experiencing injuries, and presenting with poor nutritional status. On the other hand, females reported more musculoskeletal and dermatological conditions, and overall female children represented a lower proportion of consultations. However, these differences could be explained as a disparity in access or by a differential mobility across the two genders and by the type of factory with whom MSF set up an agreement. In term of age disparities, we documented that prevalence of malnutrition was higher among children under 14 years compared to children aged 14–17 years old. The ILO, Decent Work Country Program (DWCP) 2022–2026 framework for Bangladesh, delineated some aspects to improve health and safety for children working in the informal sector ( 30 ). However, our data shows that children across different age groups experience different vulnerabilities that will require a dedicated framework. Current gaps contribute to continued exploitation of children and delay elimination of child labour and its worst form manifestations. Specific short-term harm reduction initiatives for older children (above 14 years old) could mitigate several types of consequences of worst forms of work (e.g. tetanus immunization, protective equipment, clear safety instruction, shorter hours, light tasks which are less exposed to chemicals, heavy machinery, noise etc.). However, besides the immediate short-term interventions, there is a clear need for initiatives to cooperate with child welfare bodies and civil society organizations towards removing children who are too young to work legally and protect older children from hazardous conditions in the long term ( 31 ). This will require adaptation of existing child labour law to encompass a public health multi-sector approach including social-case management, integration into learning and economic assistance for parents and caregivers, along with awareness campaigns related to child labour. Similarly, referral to specialised mental health services and counselling for children experiencing exploitation; rehabilitation services for work-related impairments and to essential services such as education, and child protection. Currently lack of occupational health care and nutrition programmes in places where child labour occurs further harms children’s wellbeing, and limits opportunities for referral. This lack of services also presents a barrier to generating solid evidence on the impact of child labour and the worst forms of child labour on a child’s health to inform targeted and effective interventions along with public health awareness campaigns. Overall, greater attention needs to be paid to the tens of thousands of small businesses which exist in the shadow economy, as the informal sector is where the worst forms of child labour are found and to the economic drivers that push children into a context of exploitation ( 32 ). Strengths and limitations There are several limitations and strengths of this study, Firstly, there is a lack of qualitative data to explore children’s perspectives and the impact of labour on general and mental health wellbeing. A lack of data on variables of work exposure that could be used as predictor of morbidities, nutritional status or injuries and lack of a control group. The data is limited by its treatment-focused approach, which may not fully capture the breadth of occupational hazards, particularly those related to machinery use. A limited exploration of varied machinery types restricts understanding of diverse occupational hazards. Furthermore, the study lacked data on education, hours of work, salary received by children that could have helped to understand the severity of exploitation. Data are related to children who had access to the occupational clinic but is likely that other severe and mild morbidities have not been captured. However, only a surveillance system inside the factories could monitor and capture the extent of morbidities. In addition, this was a retrospective analysis of occupational health service data and therefore may not fully capture the experiences of child workers who did not seek medical care or who were employed in factories not registered with MSF. Another limitation is the absence of data capturing the severity of injuries, limiting our understanding of immediate and long-term health consequences. Mental health data were limited; thus, we might have incomplete information on the extent of the impact of hazardous work on children. However, important strengths of this study are that data have been collected from trained staff on occupational health and have been consistent over time. Morbidities observed are largely corroborated by existing literature on health among child labourers. Additional strengths include the large number of diverse patients. The study provides insights into the diagnosis of mental health issues among child workers, shedding light on the often-overlooked psychological challenges stemming from their exposure to stressful and abusive work environments. Finally, the study provides reliable data on children under 14 years, data rarely available that are essential to design targeted interventions. Conclusion As the deadline for the Sustainable Development Goal to end child labour by 2025 is approaching, it is paramount to document the impact of child labour on health and formulate public health solutions. Findings suggest that children face hazardous realities, engaged in the worst form of labour, bearing important morbidity and injury burden. Poor nutritional status further compounds an already fragile health status. Children are engaged in all the categories of the informal sector, however despite their substantial contribution to the economy, they remain a largely invisible workforce, at the edge of society, excluded from essential social and legal protection framework thus hampering children’s rights and health. Weak enforcements of labour regulations for the informal sector ultimately enables child exploitation, suffering, and fuelling a society of poverty and exclusion. Further research is essential to explore the intersectional dimensions of child labour—such as gender, age, disability, and migration status— to identify specific vulnerabilities. Engaging in lived experience research, including community-based approaches and arts-based methods, in collaboration with communities and children, can provide deeper contextual insights into these dimensions. Such approaches will help inform targeted interventions aimed at eliminating child labour and mitigating its severe health and social consequences in this and similar contexts. Declarations Ethics approval and consent to participate This study fulfilled the exemption criteria set by the MSF Ethics Review Board for a posteriori analysis of routinely-collected clinical data and thus did not require MSF ERB review. It was conducted with permission from the Research Committee, Operational Centre, Amsterdam, MSF. The study also fulfilled the exemption criteria of the Centre of Injury Prevention and Research ERB. All procedures were conducted in accordance with the Declaration of Helsinki and its amendments. Consent to participate declaration is not applicable as this study relied on routinely-collected clinical data. Competing interests The authors declare that they have no competing interests. Funding Funded by MSF. No external funding was received for this study. Médecins Sans Frontières. Corresponding author Correspondence to Patrick Keating ( [email protected] ) Additional information Additional file 1 Includes two supporting tables : ( 1 ) Residence of occupational health patients < 18 years by gender, MSF clinics, Dhaka, 2014–2023 and ( 2 ) Primary diagnoses among occupational health patients < 18 years by gender, MSF clinics, Dhaka, 2014–2023. Author Contribution GC, SS, DY, MD, BB, JJ, DM, KV, SMC, CM, TS, GS, RRM, MS, HA and PK contributed to the conceptualization of this study. SS, DR and PK managed and analysed the data. GC, SS, PK and DY wrote the main manuscript text and SS prepared all tables. All authors reviewed the manuscript. Acknowledgement Special thanks to Martins Dada for his critical reflections on an earlier draft of the manuscript. We would also like to acknowledge all MSF staff who contributed to the running of the occupational health project in Dhaka over the past 10 years Data Availability The datasets supporting the conclusions of this article are available on request in accordance with MSF’s data sharing policy. Requests for access to data should be made to [email protected] References Medecins Sans Frontieres. Strengthening the protection and support services for children and adolescents in Kamrangirchar: internal report. 2023. Caleo G, Islam S, Freidl G, O’Connor L, Imran Talukder M, Gray N et al. Improving health and restoring dignity among slum factory workers in Bangladesh: internal report. 2018. The Lancet. Childhood as a commodity: ending child labour. Lancet (London, England) [Internet]. 2023 May 27 [cited 2024 Aug 1];401(10390):1745. https://pubmed.ncbi.nlm.nih.gov/37244680/ International Labour Organization. Occupational safety and health (OSH) | International Labour Organization [Internet]. 2013 [cited 2024 Aug 21]. https://www.ilo.org/resource/occupational-safety-and-health-osh Sturrock S, Hodes M. Child labour in low- and middle-income countries and its consequences for mental health: a systematic literature review of epidemiologic studies. Eur Child Adolesc Psychiatry [Internet]. 2016 Dec 1 [cited 2024 Aug 1];25(12):1273–86. https://pubmed.ncbi.nlm.nih.gov/27217156/ Srivastava RN. Children at Work, Child Labor and Modern Slavery in India: An Overview. INDIAN Pediatr. 2019;633. Ibrahim A, Abdalla SM, Jafer M, Abdelgadir J, De Vries N. Child labor and health: a systematic literature review of the impacts of child labor on child’s health in low- and middle-income countries. J Public Health (Oxf) [Internet]. 2019 Mar 1 [cited 2024 Aug 1];41(1):18–26. https://pubmed.ncbi.nlm.nih.gov/29409061/ Abusaleh K, Islam MR, Ali MM, Khan MA, Shahinuzzaman M, Haque MI. Prevalence of Economic Exploitations and Their Determinants Among Child Labourers in Dhaka City, Bangladesh: A Mixed-Method Study. Child Indic Res [Internet]. 2022 Feb 1 [cited 2024 Aug 1];15(1):87–106. https://ideas.repec.org/a/spr/chinre/v15y 2022i1d10.1007_s12187-021-09862-9.html. Gulzar Aziz Saleema VSPL. Child labour: A public health issue. J Pak Med Assoc [Internet]. 2009 [cited 2024 Aug 1];59(11). https://www.archive.jpma.org.pk/article-details/1853 International Labour Organization. Child labour in South Asia | International Labour Organization [Internet]. [cited 2024 Aug 1]. https://www.ilo.org/resource/child-labour-south-asia Bangladesh Bureau of Statistics, International Labour Organization. National Child Labour Survey (NCLS) 2022 (provisional report). Dhaka; 2023. Hoque MM. A Critical Review of Bangladesh’s Child Labor Regulations and Policies. World Dev Sustain [Internet]. 2024 Dec 1 [cited 2024 Aug 8];5:100177. https://linkinghub.elsevier.com/retrieve/pii/S2772655X24000557 International Labour Organization. Bangladesh ratifies fundamental ILO Convention on child labour | International Labour Organization [Internet]. [cited 2024 Aug 8]. https://www.ilo.org/resource/news/bangladesh-ratifies-fundamental-ilo-convention-child-labour Ministry of Labour and Employment of Bangladesh. The Bangladesh Labour Act, 2006 [Internet]. Bangladesh. 2006. https://mccibd.org/wp-content/uploads/2021/09/Bangladesh-Labour-Act-2006_English-Upto-2018.pdf Hoque MM. Hazardous Child Labor in Bangladesh: A Critical Evaluation of The Legal and Policy Framework Vis A Vis Practical Challenges. Proc World Conf Child Youth [Internet]. 2022 Nov 3 [cited 2024 Aug 5];3(01):34–55. https://tiikmpublishing.com/proceedings/index.php/ccy/article/view/1036 Alliance CHPA. Inter-Agency Toolkit | Preventing and Responding to Child Labour in Humanitarian Action | Alliance CHPA [Internet]. [cited 2024 Aug 2]. https://alliancecpha.org/en/cltf R Studio Team, Studio R: Integrated Development for R [Internet]. Boston, Studio MAR. PBC; 2020. http://www.rstudio.com/ International Labour Organization. Child Labour: Global estimates 2020, trends and the road forward | International Labour Organization [Internet]. [cited 2024 Aug 2]. https://www.ilo.org/publications/major-publications/child-labour-global-estimates-2020-trends-and-road-forward UNICEF. Child Labor Statistics - UNICEF DATA [Internet]. [cited 2024 Aug 2]. https://data.unicef.org/topic/child-protection/child-labour/ US Department of Labor. Findings on the Worst Forms of Child Labor - Bangladesh | U.S. Department of Labor [Internet]. [cited 2024 Aug 2]. https://www.dol.gov/agencies/ilab/resources/reports/child-labor/bangladesh Das B. Health hazards and risks for musculoskeletal problems among child labourers in the brickfield sector of West Bengal, India. Int Health [Internet]. 2019 Jul 1 [cited 2024 Aug 5];11(4):250–7. https://pubmed.ncbi.nlm.nih.gov/30329072/ Tiwari RR, Saha A. Morbidity Profile of Child Labor at Gem Polishing Units of Jaipur, India. Int J Occup Environ Med [Internet]. 2014 [cited 2024 Aug 2];5(3):125. /pmc/articles/PMC7767600/ Health and Social Assessment of Child Laborers. in and Around Karachi An Area of Urgent Unmet Need for Action. Ahad MA, Parry YK, Willis E. The prevalence and impact of maltreatment of child laborers in the context of four South Asian countries: A scoping review. Child Abuse Negl [Internet]. 2021 Jul 1 [cited 2024 Aug 2];117. https://pubmed.ncbi.nlm.nih.gov/33831788/ Rahman MN, Mistry SK, Hossain MI. Nutritional Status of Child labourers in Dhaka city of Bangladesh: Findings from a Cross Sectional Study. Bangladesh J Child Heal. 2015;38(3):130–6. International Labour Organization. Assessing psychosocial hazards and impact of child labour | International Labour Organization [Internet]. [cited 2024 Aug 2]. https://www.ilo.org/publications/assessing-psychosocial-hazards-and-impact-child-labour Rahman SM. Occupational injuries among children in Bangladesh. Int Res J Soc Sci [Internet]. 2018;7(10):17–20. https://www.isca.me/IJSS/Archive/v7/i10/4.ISCA-IRJSS-2018-039.php Alamgir M, Mahboob S, Ahmed KS, Islam MS, Gazi S, Ahmed A. Pattern of Injuries Among Children of Urban Slum Dwellers in Dhaka City. J Dhaka Natl Med Coll Hosp [Internet]. 2012 Oct 18 [cited 2024 Aug 2];18(1):24–8. https://www.banglajol.info/index.php/JDNMCH/article/view/12235 Reza MH, Bromfield NF. Human Rights Violations Against Street Children Working in the Informal Economy in Bangladesh: Findings from a Qualitative Study. J Hum Rights Soc Work 2019 43 [Internet]. 2019 Apr 29 [cited 2024 Aug 5];4(3):201–12. https://link.springer.com/article/10.1007/s41134-019-00098-w International Labour Organization. Decent Work Country Programme for Bangladesh 2022–2026 [Internet]. Dhaka. 2023 [cited 2024 Aug 5]. Powerful lobby against child. labour paves the way to a safe future. - FNV [Internet]. [cited 2024 Aug 16]. https://www.fnv.nl/mondiaal-fnv/nieuws-mondiaal-fnv/nieuws-nieuws/powerful-lobby-against-child-labour-paves-the-way Maksud AKM, Reaz Hossain K, Sayed S, Arulanantham A. Mapping of Children Engaged in the Worst Forms of Child Labour in the Supply Chain of the Leather Industry in Bangladesh. 2021 Jul 15 [cited 2024 Aug 8]; https://www.ids.ac.uk/publications/mapping-of-children-engaged-in-the-worst-forms-of-child-labour-in-the-supply-chain-of-the-leather-industry-in-bangladesh/ Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.docx Additional information Additional file 1 Includes two supporting tables : (1) Residence of occupational health patients <18 years by gender, MSF clinics, Dhaka, 2014-2023 and (2) Primary diagnoses among occupational health patients <18 years by gender, MSF clinics, Dhaka, 2014-2023. Cite Share Download PDF Status: Published Journal Publication published 15 Apr, 2025 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Revision requested 28 Oct, 2024 Editor assigned by journal 28 Oct, 2024 Submission checks completed at journal 25 Oct, 2024 First submitted to journal 22 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Keating","email":"data:image/png;base64,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","orcid":"","institution":"Médecins Sans Frontières‎","correspondingAuthor":true,"prefix":"","firstName":"Patrick","middleName":"","lastName":"Keating","suffix":""}],"badges":[],"createdAt":"2024-10-22 16:09:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5313328/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5313328/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-025-22483-z","type":"published","date":"2025-04-15T15:57:47+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81050907,"identity":"99924035-0ffc-4447-93f7-8eeec0098c87","added_by":"auto","created_at":"2025-04-21 16:06:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1277480,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5313328/v1/afaff02e-4d0f-4fe6-8b40-9132c7330755.pdf"},{"id":68363284,"identity":"f75549d6-1220-4ad0-b703-b2f98e93f5a2","added_by":"auto","created_at":"2024-11-06 12:39:25","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":32166,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional file 1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIncludes two supporting tables : (1) Residence of occupational health patients \u0026lt;18 years by gender, MSF clinics, Dhaka, 2014-2023 and (2) Primary diagnoses among occupational health patients \u0026lt;18 years by gender, MSF clinics, Dhaka, 2014-2023.\u003c/p\u003e","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5313328/v1/a89f898c4c5b04a613f53804.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Public health wound: health and work among children engaged in worst forms of child labour in the informal sector in Dhaka, Bangladesh: a retrospective analysis of Médecins Sans Frontières Occupational health data from 2014 to 2023.","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003cem\u003e\u0026ldquo; There are many factories where child workers have to do heavy work like the welding factory. They have to weld the iron during day or night. They have to work using hammer or sometimes in front of the fire. This scenario is extreme for those child workers.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eAs we (factory owners) we treat them (children/adolescents) a bit rudely and as he is a child so it hurts him, otherwise if he would be adult it would not impact that much. \u0026hellip;.But there are many (factory) owners who torture the child workers.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eFemale workers face some harassment. You understood what type of harassment I meant. Some of them do not mention about the harassment thinking of they might lose their job so, it\u0026rsquo;s a fear for them\u0026rsquo;\u0026rsquo;. (Metal factory owner\u003c/em\u003e, Kamrangirchar a peri-urban area in Dhaka, Bangladesh\u003cem\u003e)\u0026ndash;\u003c/em\u003e (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThis is one of the common narratives M\u0026eacute;decins Sans Fronti\u0026egrave;res (MSF) teams heard about children working in Kamrangirchar, a peri-urban area in Dhaka, Bangladesh. Narratives corroborated by observational experience and occupational health medical services provided over the last 10 years by MSF in this area, where an unknown number of children under 18 years old are employed in the informal sector and involved in recycling, metal, plastic, leather and garment sector (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eChild labour is a violation of children\u0026rsquo;s rights, and despite the ambition to end child labour by 2025, the number of children working is increasing from 151\u0026middot;6\u0026nbsp;million in 2016 to 160\u0026nbsp;million in 2020 (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Suboptimal statistics report that another 22,000 children are deemed to be killed at work every year (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFrom a public health perspective, child labour and its consequences hinder the achievement of Sustainable Development Goal (SDG) 8 (Decent Work and Growth) and is considered a harmful practice. By inflicting physical, emotional and mental health \u0026lsquo;\u0026rsquo;wounds\u0026rsquo;\u0026rsquo; on children, it is associated with negative health outcomes in the long and short term, creating social vulnerabilities that are perpetuated in vicious cycles over generations (Box 1) (\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eChild labour has been previously recognised as a public health issue in the South Asia region where the International Labour Organization (ILO), estimates that child labour (aged 5\u0026ndash;17 years old) varies from 5.8\u0026nbsp;million in India to 2\u0026nbsp;million in Nepal (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWith an estimated 1,776,097 children working (of whom 60.14% engaged in hazardous child work), Bangladesh has the second highest burden of child labour in the South Asia region (4.4%) (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). In recent years, the child labour burden and its devasting individual and social welfare consequences have influenced the Government of Bangladesh to align its legal framework with international standards by ratifying: i) the ILO Convention on the Worst Forms of Child Labour (1999, No. 182) ii) the ILO\u0026rsquo;s Minimum Age Convention (1973, No 138), iii) the Protocol to the Forced Labour Convention, and by revising the National Plan of Action to Eliminate Child Labour by 2025 (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Bangladesh Labour Act sets the minimum working age at 14 years old and restricts hazardous work for those below 18 years old; in the Act. hazardous work is defined as a work\u0026rsquo; that is \u0026ldquo;likely to hamper child health, safety and moral development, this includes working with dangerous machines that are harmful to their physical and mental development\u0026rdquo; (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Any form of work that encompass hazardous activities is considered the worst forms of child labour (Box1).\u003c/p\u003e \u003cp\u003eThe government also published a list of activities/processes (e.g. garment) that are officially prohibited for use among children under 18 years old. Currently, 95 percent of child labour in Bangladesh is estimated to occur in the informal sector, however evidence and health data on children employed in this sector are limited. As the deadline for the SDG to end child labour is approaching, it is paramount to document the impact of child labour on health and draw attention to this neglected public health issue. The present study aims to contribute to this knowledge gap by presenting a retrospective multiyear analysis (2014\u0026ndash;2023) of medical data collected by MSF occupational health clinics among children working in the informal sector in an urban area of Dhaka and highlight this largely invisible workforce.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"1\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBox 1. Key concepts on child labour (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChild labour\u003c/b\u003e: Child labour is any work that deprives children of their childhood, their potential and their dignity. It interferes with children\u0026rsquo;s education and negatively affects their emotional, developmental and physical well-being.\u003c/p\u003e \u003cp\u003e\u003cb\u003eWorst forms of child labour (WFCL)\u003c/b\u003e: The WFCL are a subset of child labour, defined by the ILO Convention No. 182 as \u003cb\u003eprohibited for all children under the age of 18 years and are to be eliminated as a matter of urgency\u003c/b\u003e. They include:\u003c/p\u003e \u003cp\u003e\u0026bull; all forms of slavery or practices similar to slavery, such as the sale and trafficking of children, debt bondage and serfdom and forced or compulsory labour, including forced or compulsory recruitment of children for use in armed conflict;\u003c/p\u003e \u003cp\u003e\u0026bull; the use, procuring or offering of a child for prostitution, for the production of pornography or for pornographic performances;\u003c/p\u003e \u003cp\u003e\u0026bull; the use, procuring or offering of a child for illicit activities, in particular for the production and trafficking of drugs as defined in relevant international treaties;\u003c/p\u003e \u003cp\u003e\u0026bull; work which, by its nature or the circumstances in which it is carried out, is likely to harm the health, development, safety or morals of a child (also called: \u0026ldquo;\u003cb\u003ehazardous work\u0026rdquo;\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e\u003cb\u003eHazardous Work\u003c/b\u003e: is work which by its nature or the circumstances in which it is carried out, is likely to harm the health, development, safety or morals of children. \u003cb\u003eIt is one of the worst forms of child labour and is therefore prohibited for all children under the age of 18 years\u003c/b\u003e. ILO Recommendation No. 190 urges governments to consider the following hazardous work activities:\u003c/p\u003e \u003cp\u003e\u0026bull; work which exposes children to physical, emotional or sexual abuse.\u003c/p\u003e \u003cp\u003e\u0026bull; work underground, under water, at dangerous heights or in confined spaces.\u003c/p\u003e \u003cp\u003e\u0026bull; \u003cb\u003ework with dangerous machinery\u003c/b\u003e, equipment and tools, or which involves the manual handling or transport of heavy loads.\u003c/p\u003e \u003cp\u003e\u0026bull; work in an unhealthy environment which may, for example, expose children to hazardous substances, agents or processes, or to temperatures, noise levels, or vibrations damaging to their health.\u003c/p\u003e \u003cp\u003e\u0026bull; work under particularly difficult conditions such as work for long hours or during the night or work where the child is unreasonably confined to the premises of the employer.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e \u003cb\u003eStudy setting.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eKamrangirchar, located on the Buriganga River, is one of Dhaka\u0026rsquo;s largest peri-urban area, with a population of 440,000 concentrated in an area of four square kilometres. It is estimated that Kamrangirchar has over 1,000 informal factories, including those mainly in the metal, garment, and plastics sectors, along with tanneries that relocated to Savar in 2017. In Kamrangirchar, MSF runs two occupational health clinics to provide medical care and psychological support to workers from the informal factory sector. The MSF team set up agreements with factory owners/managers across tannery, garment, metal and plastics sectors allowing factory workers to attend health clinics during their working hours. Workers from these registered factories could access MSF occupational health services. However, workers from unregistered factories (no formal agreement with MSF) could also access emergency services and first aid for their injuries.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStudy design and participants.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn this retrospective study, we used anonymised primary health care records from the MSF occupational health clinics database containing demographics, date of consultation, age, gender, visit status (new/follow-up), residence inside a factory, type of factory they worked in, if they were working with machines inside the factory, nutritional, and mental health status, type and dynamic of injury experience and diagnosis. Our population consisted of children and adolescents under 18 years presenting to MSF occupational health clinics between February 2014 to December 2023. The mental health dataset consisted of consultation data between September 2018 to December 2023.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eVariable definitions\u003c/h2\u003e \u003cp\u003eNew consultations were defined as patients who came for the first time for a consultation. Follow-up consultations were defined as patients who came for subsequent appointments scheduled after the first consultation to reassess and manage the patient's ongoing occupational health condition.\u003c/p\u003e \u003cp\u003eNutritional status among workers less than 18 years was assessed using Body Mass Index (BMI) and those with a BMI for age more than two standard deviations less than the median for their age group were classified as malnourished.\u003c/p\u003e \u003cp\u003eWork-related morbidity refers to any physical or psychological health conditions that are directly caused or exacerbated by the patient's job or working conditions. This includes illnesses, chronic conditions, injuries, and psychological distress that arise from factors such as repetitive strain, exposure to hazardous materials, workplace accidents, or stressful work environments. In our analysis, a \"yes\" for work-related morbidity indicates a decision made by an occupational health doctor that the individual has a health condition directly caused or exacerbated by their job or working conditions, while \"no\" indicates that the individual does not have any such work-related health conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eDescriptive analysis of the occupational health data among children under 18 years old was performed. The analysis was stratified by gender and age group (comparing those under 14 years to those aged 14-under 18 years) and documented the type of factory/sector where children were employed and if children reported working with machines, an additional proxy of hazardous work as per the Bangladesh Labour Act. We choose those two age groups, since the Bangladesh Labour Act, 2006 (and Amendments) defines a child as anyone under the age of 14 years and prohibits their employment in any establishment (Section 34) and prohibits the employment of adolescents (aged 14\u0026ndash;17) in hazardous work (Section 39). Chi-squared and fisher\u0026rsquo;s exact tests were used to check for differences in proportions across categorical variables. The analysis was performed using R Studio software (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eDemographics\u003c/h2\u003e \u003cp\u003eOver the study period, there were a total of 10,200 occupational health consultations among children aged under 18 years. Of the 10,200 consultations, there were 4945 new consultations, 3895 were follow-up consultations, and consultation status was missing for 1360 records (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). There was an average of 494 new consultations among children every year with a peak of 967 in 2018.\u003c/p\u003e \u003cp\u003eAmong the new consultations, 38% were among children aged under 14 years (the youngest child was five years), the mean age was 14.7 years (standard deviation of 2.02 years) and 61% were male. Eighty-five per cent of the children reported living outside the factory (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Overall, male children were more likely to live inside the factory compared to female children (23% vs 1%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (see additional file 1).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic characteristics of occupational health patients\u0026thinsp;\u0026lt;\u0026thinsp;18 years, MSF clinics, Dhaka, 2014\u0026ndash;2023\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall, N\u0026thinsp;=\u0026thinsp;4,945\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;14, N\u0026thinsp;=\u0026thinsp;1,882\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;=14\u0026ndash;17, N\u0026thinsp;=\u0026thinsp;3,063\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,950 (39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e694 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,256 (41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,995 (61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,188 (63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,807 (59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLives outside factory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,570 (85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,417 (88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,153 (84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLives inside factory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e611 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e189 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e422 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e1\u003c/sup\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e2\u003c/sup\u003ePearson's Chi-squared test\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePlace of work\u003c/h3\u003e\n\u003cp\u003eAlmost a third (32%) of new consultations were among children who worked in garment factories, followed by plastics (30%) and metal (21%) factories (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among female children attending new consultations with MSF, plastics and garment factories were their major employers, while garment and metal factories employed most male children (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Similar proportions of children aged\u0026thinsp;\u0026lt;\u0026thinsp;14 and 14\u0026ndash;17 years worked in the various factory types. Over 96% of children irrespective of age group or gender reported operating machinery as part of their work. There was little difference in this proportion across factory type.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePlace of work among occupational health patients\u0026thinsp;\u0026lt;\u0026thinsp;18 years by gender, MSF clinics, Dhaka, 2014\u0026ndash;2023\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall, N\u0026thinsp;=\u0026thinsp;4,945\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale, N\u0026thinsp;=\u0026thinsp;1,950\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMale, N\u0026thinsp;=\u0026thinsp;2,995\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of factory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGarment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,575 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e724 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e851 (29%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlastics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,483 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e750 (39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e733 (25%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,033 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e187 (9.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e846 (28%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeather\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e195 (4.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88 (4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e107 (3.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmbroidery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e181 (3.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69 (3.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e112 (3.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTannery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e141 (2.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (0.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e125 (4.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRubber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e148 (3.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106 (3.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e136 (2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (\u0026lt;\u0026thinsp;0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (0.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBattery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (\u0026lt;\u0026thinsp;0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (\u0026lt;\u0026thinsp;0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003e1\u003c/sup\u003en (%)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMorbidities\u003c/h2\u003e \u003cp\u003eAmong new consultations, almost half of the children were diagnosed as having either musculoskeletal (26%) or dermatology (20%) (e.g., fungal infection, mainly scabies) conditions, with similar proportions across age group and gender (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In addition, no major differences were observed in conditions whether a child lived inside or outside a factory. Work related injuries accounted for 7.5% of all new consultations among children and with no difference by age group. However, male children had a higher proportion of injuries (11%) compared to female children (2.5%) (see additional file 1). The majority (83%) of the diagnoses irrespective of age group or gender were suspected as being work-related. Sixty-three per cent of under 14 years were found to be malnourished compared to 40% of 14\u0026ndash;17 years and similarly a higher proportion of male children compared to female.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrimary diagnoses among occupational health patients\u0026thinsp;\u0026lt;\u0026thinsp;18 years, MSF clinics, Dhaka, 2014\u0026ndash;2023\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall, N\u0026thinsp;=\u0026thinsp;4,945\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;14, N\u0026thinsp;=\u0026thinsp;1,882\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;=14\u0026ndash;17, N\u0026thinsp;=\u0026thinsp;3,063\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMusculoskeletal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,197 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e455 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e742 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDermatology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e907 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e302 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e605 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e632 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e281 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e351 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastro-intestinal (GI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e550 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e184 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e366 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInjury\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e339 (7.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e134 (7.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e205 (7.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENTDEH (Ear, Nose, Throat, Dental, Eyes and Head)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e419 (9.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e163 (9.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e256 (9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e167 (3.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75 (4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Chronic Condition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e128 (2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79 (4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRH (Sexual \u0026amp; Reproductive Health)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e131 (2.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (2.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfectious Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrinary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeurological Disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMental Health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (\u0026lt;\u0026thinsp;0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Communicable Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (\u0026lt;\u0026thinsp;0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (\u0026lt;\u0026thinsp;0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (\u0026lt;\u0026thinsp;0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuspected occupational condition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWork-related\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,971 (83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,138 (84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,833 (83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-work related\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e588 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e219 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e369 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNutrition status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal (BMI for age \u0026gt;-2SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,217 (51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e607 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,610 (60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalnourished (BMI for age \u0026lt;-2SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,121 (49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,050 (63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,071 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e1\u003c/sup\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e2\u003c/sup\u003ePearson's Chi-squared test\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAmong the 51 mental health outcomes recorded between September 2018 to December 2023, the primary mental health disorders diagnosed were mood-related (86%) and behavioural symptoms (10%), with most patients being male (55%) and aged 14\u0026ndash;17 years (80%) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Key precipitating events included family disruptions, domestic violence, and socio-economic issues, reported by half of the patients. Domestic violence was more prevalent among females (17%) compared to males (4%), (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMental health characteristics of occupational health patients\u0026thinsp;\u0026lt;\u0026thinsp;18 years by gender, MSF clinics, Dhaka, 2014\u0026ndash;2023\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall, N\u0026thinsp;=\u0026thinsp;51\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale, N\u0026thinsp;=\u0026thinsp;23\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMale, N\u0026thinsp;=\u0026thinsp;28\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;=14\u0026ndash;17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType of mental health disorder\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMood related problems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBehavior related symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeuro-psychiatric related symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial Functioning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrecipitating event\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisruption of family \u0026amp; relationships\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDomestic discord \u0026amp; family violence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocio-economic functioning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvents related to abuse during detention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical illness-related\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvents related to violence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSexual trauma or abuse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeuro-psychiatric related\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e1\u003c/sup\u003en (%)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e2\u003c/sup\u003eFisher's exact test\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAmong the 182 injuries diagnosed in new consultations across the study period, the majority occurred in patients reporting working in metal factories (65%), followed by plastics (20%) and garments (7%) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The proportions of injuries by factory type were similar across age groups. There were differences by gender such that there were higher proportions of male children with injuries overall and particularly working in metal factories and higher proportions of female children with injuries working in plastics factories (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInjury characteristics among occupational health patients\u0026thinsp;\u0026lt;\u0026thinsp;18 years by gender, MSF clinics, Dhaka, 2014\u0026ndash;2023\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall, N\u0026thinsp;=\u0026thinsp;182\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale,\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;26\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMale,\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;156\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of factory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119 (65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e112 (72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlastics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGarment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (6.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmbroidery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (3.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (1.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeather\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRubber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTannery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of injury\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCut/laceration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100 (61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86 (61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrushing injury\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (8.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBurn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbrasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (4.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (5.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmputation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBroken bone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBruise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConcussion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanism of injury\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStruck\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e130 (72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e112 (73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBurn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (3.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaught in between\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (3.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (3.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (3.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e1\u003c/sup\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e2\u003c/sup\u003eFisher's exact test\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eGlobally, one in ten children are engaged in child labour, and in some countries one in four are engaged in labour that is considered harmful to their health and development (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). This study gives visibility to a neglected population and adds important evidence on health among child workers. Our findings reveal a young working population, employed in all categories forbidden by Bangladesh law, including garment, and informal steel-based work (metal factories), with 7.5% of new consultations resulting from work-related injuries (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Any children engaged in these sectors/tasks are deemed to be engaged in hazardous child labour classified as the worst form of child labour.\u003c/p\u003e \u003cp\u003eIn our study, almost 15% of the children reported they were living inside the factory where they were employed. Living inside the factories was more frequent among male children across both age groups (under 14 and under 18 years old) compared to girls. Living and working in a hazardous context might add additional health burden and social alienation for children, and likely expose children to additional risks which include, physical, emotional or sexual abuse (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe observed concerning patterns of morbidity with both age groups experiencing a high burden of musculoskeletal, and dermatological conditions (e.g. fungal infection; scabies). A similar high burden of these conditions was reported from studies in child workers in India, Pakistan, Bangladesh, and Brazil (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). In addition, almost 50% of children were malnourished and high levels of malnutrition have been previously reported in a study among 100 child workers (aged 5\u0026ndash;17 years) in Dhaka (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Current evidence suggests that hazardous child labour can have devastating consequences that directly endangers a child\u0026rsquo;s health, safety, and moral development (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). It can result in disability, ill health and psychological damage, which prevents children of all ages from going to school, takes them away from their families, uses up time for play and recreation in the company of their peers, and causes significant short- and long-term harm (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Far from having a positive effect, it impedes children\u0026rsquo;s growth and development, as well as perpetuates a cycle of poverty for the children involved, their families and communities.\u003c/p\u003e \u003cp\u003eAlmost all children in this study reported working with machines in sectors forbidden by current Bangladesh legislation, with 7.5% of children experiencing injuries with cuts or laceration being the most frequently documented injury type. Previous surveys in Bangladesh reported 18.5% of children engaged in hazardous work, with a significant portion facing injuries and abuse (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Another study conducted in the slums/suburbs of Dhaka that looked at the pattern of injuries among children of urban slum dwellers in Dhaka City, found that occupational injury was the third highest cause of injuries among surveyed children and second among 10\u0026ndash;15 years age group (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Children might be more vulnerable than adults to workplace hazards. Previous MSF consultations with parents and caregivers highlighted the profound sorrow experienced by parents witnessing their children being injured. \u003cem\u003e\"It is heartbreaking. There are times when my son suffers from cuts and injuries on his hands... Frequent cuts on their legs and injuries on their lips, along with abrasions on their chests are a common occurrence. Witnessing their pain and injuries fills us with sorrow, but unfortunately, we are helpless to prevent them.\"\u003c/em\u003e - Caregiver of a young factory worker (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Mental health has also emerged as a concern in both age groups, with mood-related disorders and behavioural symptoms being most diagnosed. The prevalence of these mental health issues is consistent with findings from other studies on child labour, emphasizing the psychological impact of stressful and abusive work environments (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). In our study contributing factors include family disruptions, domestic violence, and socio-economic challenges, with domestic violence particularly affecting female children. This intersection of risks from both work and home environments underscores their combined influence on mental health outcomes among child workers.\u003c/p\u003e \u003cp\u003eIn our study we identified specific gender and age vulnerabilities. For instance, more male children compared to female reported living inside the factories, experiencing injuries, and presenting with poor nutritional status. On the other hand, females reported more musculoskeletal and dermatological conditions, and overall female children represented a lower proportion of consultations. However, these differences could be explained as a disparity in access or by a differential mobility across the two genders and by the type of factory with whom MSF set up an agreement. In term of age disparities, we documented that prevalence of malnutrition was higher among children under 14 years compared to children aged 14\u0026ndash;17 years old.\u003c/p\u003e \u003cp\u003eThe ILO, Decent Work Country Program (DWCP) 2022\u0026ndash;2026 framework for Bangladesh, delineated some aspects to improve health and safety for children working in the informal sector (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). However, our data shows that children across different age groups experience different vulnerabilities that will require a dedicated framework. Current gaps contribute to continued exploitation of children and delay elimination of child labour and its worst form manifestations.\u003c/p\u003e \u003cp\u003eSpecific short-term harm reduction initiatives for older children (above 14 years old) could mitigate several types of consequences of worst forms of work (e.g. tetanus immunization, protective equipment, clear safety instruction, shorter hours, light tasks which are less exposed to chemicals, heavy machinery, noise etc.). However, besides the immediate short-term interventions, there is a clear need for initiatives to cooperate with child welfare bodies and civil society organizations towards removing children who are too young to work legally and protect older children from hazardous conditions in the long term (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis will require adaptation of existing child labour law to encompass a public health multi-sector approach including social-case management, integration into learning and economic assistance for parents and caregivers, along with awareness campaigns related to child labour. Similarly, referral to specialised mental health services and counselling for children experiencing exploitation; rehabilitation services for work-related impairments and to essential services such as education, and child protection.\u003c/p\u003e \u003cp\u003eCurrently lack of occupational health care and nutrition programmes in places where child labour occurs further harms children\u0026rsquo;s wellbeing, and limits opportunities for referral. This lack of services also presents a barrier to generating solid evidence on the impact of child labour and the worst forms of child labour on a child\u0026rsquo;s health to inform targeted and effective interventions along with public health awareness campaigns.\u003c/p\u003e \u003cp\u003eOverall, greater attention needs to be paid to the tens of thousands of small businesses which exist in the shadow economy, as the informal sector is where the worst forms of child labour are found and to the economic drivers that push children into a context of exploitation (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStrengths and limitations\u003c/p\u003e \u003cp\u003eThere are several limitations and strengths of this study, Firstly, there is a lack of qualitative data to explore children\u0026rsquo;s perspectives and the impact of labour on general and mental health wellbeing. A lack of data on variables of work exposure that could be used as predictor of morbidities, nutritional status or injuries and lack of a control group. The data is limited by its treatment-focused approach, which may not fully capture the breadth of occupational hazards, particularly those related to machinery use. A limited exploration of varied machinery types restricts understanding of diverse occupational hazards. Furthermore, the study lacked data on education, hours of work, salary received by children that could have helped to understand the severity of exploitation. Data are related to children who had access to the occupational clinic but is likely that other severe and mild morbidities have not been captured. However, only a surveillance system inside the factories could monitor and capture the extent of morbidities. In addition, this was a retrospective analysis of occupational health service data and therefore may not fully capture the experiences of child workers who did not seek medical care or who were employed in factories not registered with MSF. Another limitation is the absence of data capturing the severity of injuries, limiting our understanding of immediate and long-term health consequences. Mental health data were limited; thus, we might have incomplete information on the extent of the impact of hazardous work on children.\u003c/p\u003e \u003cp\u003eHowever, important strengths of this study are that data have been collected from trained staff on occupational health and have been consistent over time. Morbidities observed are largely corroborated by existing literature on health among child labourers. Additional strengths include the large number of diverse patients. The study provides insights into the diagnosis of mental health issues among child workers, shedding light on the often-overlooked psychological challenges stemming from their exposure to stressful and abusive work environments. Finally, the study provides reliable data on children under 14 years, data rarely available that are essential to design targeted interventions.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAs the deadline for the Sustainable Development Goal to end child labour by 2025 is approaching, it is paramount to document the impact of child labour on health and formulate public health solutions. Findings suggest that children face hazardous realities, engaged in the worst form of labour, bearing important morbidity and injury burden. Poor nutritional status further compounds an already fragile health status.\u003c/p\u003e \u003cp\u003eChildren are engaged in all the categories of the informal sector, however despite their substantial contribution to the economy, they remain a largely invisible workforce, at the edge of society, excluded from essential social and legal protection framework thus hampering children\u0026rsquo;s rights and health. Weak enforcements of labour regulations for the informal sector ultimately enables child exploitation, suffering, and fuelling a society of poverty and exclusion.\u003c/p\u003e \u003cp\u003eFurther research is essential to explore the intersectional dimensions of child labour\u0026mdash;such as gender, age, disability, and migration status\u0026mdash; to identify specific vulnerabilities. Engaging in lived experience research, including community-based approaches and arts-based methods, in collaboration with communities and children, can provide deeper contextual insights into these dimensions. Such approaches will help inform targeted interventions aimed at eliminating child labour and mitigating its severe health and social consequences in this and similar contexts.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eThis study fulfilled the exemption criteria set by the MSF Ethics Review Board for a posteriori analysis of routinely-collected clinical data and thus did not require MSF ERB review. It was conducted with permission from the Research Committee, Operational Centre, Amsterdam, MSF. The study also fulfilled the exemption criteria of the Centre of Injury Prevention and Research ERB. All procedures were conducted in accordance with the Declaration of Helsinki and its amendments. Consent to participate declaration is not applicable as this study relied on routinely-collected clinical data.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eFunded by MSF. No external funding was received for this study.\u003c/p\u003e\u003cp\u003eM\u0026eacute;decins Sans Fronti\u0026egrave;res.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCorresponding author\u003c/h2\u003e \u003cp\u003eCorrespondence to Patrick Keating ([email protected])\u003c/p\u003e \u003c/p\u003e\u003ch2\u003e \u003cb\u003eAdditional information\u003c/b\u003e \u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eAdditional file 1\u003c/strong\u003e \u003cp\u003eIncludes two supporting tables : (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Residence of occupational health patients\u0026thinsp;\u0026lt;\u0026thinsp;18 years by gender, MSF clinics, Dhaka, 2014\u0026ndash;2023 and (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Primary diagnoses among occupational health patients\u0026thinsp;\u0026lt;\u0026thinsp;18 years by gender, MSF clinics, Dhaka, 2014\u0026ndash;2023.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eGC, SS, DY, MD, BB, JJ, DM, KV, SMC, CM, TS, GS, RRM, MS, HA and PK contributed to the conceptualization of this study. SS, DR and PK managed and analysed the data. GC, SS, PK and DY wrote the main manuscript text and SS prepared all tables. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eSpecial thanks to Martins Dada for his critical reflections on an earlier draft of the manuscript. We would also like to acknowledge all MSF staff who contributed to the running of the occupational health project in Dhaka over the past 10 years\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets supporting the conclusions of this article are available on request in accordance with MSF\u0026rsquo;s data sharing policy. Requests for access to data should be made to [email protected]\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMedecins Sans Frontieres. Strengthening the protection and support services for children and adolescents in Kamrangirchar: internal report. 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCaleo G, Islam S, Freidl G, O\u0026rsquo;Connor L, Imran Talukder M, Gray N et al. Improving health and restoring dignity among slum factory workers in Bangladesh: internal report. 2018.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThe Lancet. Childhood as a commodity: ending child labour. Lancet (London, England) [Internet]. 2023 May 27 [cited 2024 Aug 1];401(10390):1745. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/37244680/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/37244680/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInternational Labour Organization. Occupational safety and health (OSH) | International Labour Organization [Internet]. 2013 [cited 2024 Aug 21]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ilo.org/resource/occupational-safety-and-health-osh\u003c/span\u003e\u003cspan address=\"https://www.ilo.org/resource/occupational-safety-and-health-osh\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSturrock S, Hodes M. Child labour in low- and middle-income countries and its consequences for mental health: a systematic literature review of epidemiologic studies. Eur Child Adolesc Psychiatry [Internet]. 2016 Dec 1 [cited 2024 Aug 1];25(12):1273\u0026ndash;86. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/27217156/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/27217156/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSrivastava RN. Children at Work, Child Labor and Modern Slavery in India: An Overview. INDIAN Pediatr. 2019;633.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIbrahim A, Abdalla SM, Jafer M, Abdelgadir J, De Vries N. Child labor and health: a systematic literature review of the impacts of child labor on child\u0026rsquo;s health in low- and middle-income countries. J Public Health (Oxf) [Internet]. 2019 Mar 1 [cited 2024 Aug 1];41(1):18\u0026ndash;26. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/29409061/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/29409061/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbusaleh K, Islam MR, Ali MM, Khan MA, Shahinuzzaman M, Haque MI. Prevalence of Economic Exploitations and Their Determinants Among Child Labourers in Dhaka City, Bangladesh: A Mixed-Method Study. Child Indic Res [Internet]. 2022 Feb 1 [cited 2024 Aug 1];15(1):87\u0026ndash;106. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ideas.repec.org/a/spr/chinre/v15y\u003c/span\u003e\u003cspan address=\"https://ideas.repec.org/a/spr/chinre/v15y\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e2022i1d10.1007_s12187-021-09862-9.html.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGulzar Aziz Saleema VSPL. Child labour: A public health issue. J Pak Med Assoc [Internet]. 2009 [cited 2024 Aug 1];59(11). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.archive.jpma.org.pk/article-details/1853\u003c/span\u003e\u003cspan address=\"https://www.archive.jpma.org.pk/article-details/1853\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInternational Labour Organization. Child labour in South Asia | International Labour Organization [Internet]. [cited 2024 Aug 1]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ilo.org/resource/child-labour-south-asia\u003c/span\u003e\u003cspan address=\"https://www.ilo.org/resource/child-labour-south-asia\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBangladesh Bureau of Statistics, International Labour Organization. National Child Labour Survey (NCLS) 2022 (provisional report). Dhaka; 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoque MM. A Critical Review of Bangladesh\u0026rsquo;s Child Labor Regulations and Policies. World Dev Sustain [Internet]. 2024 Dec 1 [cited 2024 Aug 8];5:100177. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://linkinghub.elsevier.com/retrieve/pii/S2772655X24000557\u003c/span\u003e\u003cspan address=\"https://linkinghub.elsevier.com/retrieve/pii/S2772655X24000557\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInternational Labour Organization. Bangladesh ratifies fundamental ILO Convention on child labour | International Labour Organization [Internet]. [cited 2024 Aug 8]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ilo.org/resource/news/bangladesh-ratifies-fundamental-ilo-convention-child-labour\u003c/span\u003e\u003cspan address=\"https://www.ilo.org/resource/news/bangladesh-ratifies-fundamental-ilo-convention-child-labour\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinistry of Labour and Employment of Bangladesh. The Bangladesh Labour Act, 2006 [Internet]. Bangladesh. 2006. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mccibd.org/wp-content/uploads/2021/09/Bangladesh-Labour-Act-2006_English-Upto-2018.pdf\u003c/span\u003e\u003cspan address=\"https://mccibd.org/wp-content/uploads/2021/09/Bangladesh-Labour-Act-2006_English-Upto-2018.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoque MM. Hazardous Child Labor in Bangladesh: A Critical Evaluation of The Legal and Policy Framework Vis A Vis Practical Challenges. Proc World Conf Child Youth [Internet]. 2022 Nov 3 [cited 2024 Aug 5];3(01):34\u0026ndash;55. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tiikmpublishing.com/proceedings/index.php/ccy/article/view/1036\u003c/span\u003e\u003cspan address=\"https://tiikmpublishing.com/proceedings/index.php/ccy/article/view/1036\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlliance CHPA. Inter-Agency Toolkit | Preventing and Responding to Child Labour in Humanitarian Action | Alliance CHPA [Internet]. [cited 2024 Aug 2]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://alliancecpha.org/en/cltf\u003c/span\u003e\u003cspan address=\"https://alliancecpha.org/en/cltf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR Studio Team, Studio R: Integrated Development for R [Internet]. Boston, Studio MAR. PBC; 2020. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.rstudio.com/\u003c/span\u003e\u003cspan address=\"http://www.rstudio.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInternational Labour Organization. Child Labour: Global estimates 2020, trends and the road forward | International Labour Organization [Internet]. [cited 2024 Aug 2]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ilo.org/publications/major-publications/child-labour-global-estimates-2020-trends-and-road-forward\u003c/span\u003e\u003cspan address=\"https://www.ilo.org/publications/major-publications/child-labour-global-estimates-2020-trends-and-road-forward\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUNICEF. Child Labor Statistics - UNICEF DATA [Internet]. [cited 2024 Aug 2]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.unicef.org/topic/child-protection/child-labour/\u003c/span\u003e\u003cspan address=\"https://data.unicef.org/topic/child-protection/child-labour/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUS Department of Labor. Findings on the Worst Forms of Child Labor - Bangladesh | U.S. Department of Labor [Internet]. [cited 2024 Aug 2]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.dol.gov/agencies/ilab/resources/reports/child-labor/bangladesh\u003c/span\u003e\u003cspan address=\"https://www.dol.gov/agencies/ilab/resources/reports/child-labor/bangladesh\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDas B. Health hazards and risks for musculoskeletal problems among child labourers in the brickfield sector of West Bengal, India. Int Health [Internet]. 2019 Jul 1 [cited 2024 Aug 5];11(4):250\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/30329072/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/30329072/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTiwari RR, Saha A. Morbidity Profile of Child Labor at Gem Polishing Units of Jaipur, India. Int J Occup Environ Med [Internet]. 2014 [cited 2024 Aug 2];5(3):125. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e/pmc/articles/PMC7767600/\u003c/span\u003e\u003cspan address=\"http:///pmc/articles/PMC7767600/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHealth and Social Assessment of Child Laborers. in and Around Karachi An Area of Urgent Unmet Need for Action.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhad MA, Parry YK, Willis E. The prevalence and impact of maltreatment of child laborers in the context of four South Asian countries: A scoping review. Child Abuse Negl [Internet]. 2021 Jul 1 [cited 2024 Aug 2];117. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/33831788/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/33831788/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRahman MN, Mistry SK, Hossain MI. Nutritional Status of Child labourers in Dhaka city of Bangladesh: Findings from a Cross Sectional Study. Bangladesh J Child Heal. 2015;38(3):130\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInternational Labour Organization. Assessing psychosocial hazards and impact of child labour | International Labour Organization [Internet]. [cited 2024 Aug 2]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ilo.org/publications/assessing-psychosocial-hazards-and-impact-child-labour\u003c/span\u003e\u003cspan address=\"https://www.ilo.org/publications/assessing-psychosocial-hazards-and-impact-child-labour\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRahman SM. Occupational injuries among children in Bangladesh. Int Res J Soc Sci [Internet]. 2018;7(10):17\u0026ndash;20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.isca.me/IJSS/Archive/v7/i10/4.ISCA-IRJSS-2018-039.php\u003c/span\u003e\u003cspan address=\"https://www.isca.me/IJSS/Archive/v7/i10/4.ISCA-IRJSS-2018-039.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlamgir M, Mahboob S, Ahmed KS, Islam MS, Gazi S, Ahmed A. Pattern of Injuries Among Children of Urban Slum Dwellers in Dhaka City. J Dhaka Natl Med Coll Hosp [Internet]. 2012 Oct 18 [cited 2024 Aug 2];18(1):24\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.banglajol.info/index.php/JDNMCH/article/view/12235\u003c/span\u003e\u003cspan address=\"https://www.banglajol.info/index.php/JDNMCH/article/view/12235\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReza MH, Bromfield NF. Human Rights Violations Against Street Children Working in the Informal Economy in Bangladesh: Findings from a Qualitative Study. J Hum Rights Soc Work 2019 43 [Internet]. 2019 Apr 29 [cited 2024 Aug 5];4(3):201\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://link.springer.com/article/10.1007/s41134-019-00098-w\u003c/span\u003e\u003cspan address=\"https://link.springer.com/article/10.1007/s41134-019-00098-w\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInternational Labour Organization. Decent Work Country Programme for Bangladesh 2022\u0026ndash;2026 [Internet]. Dhaka. 2023 [cited 2024 Aug 5]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003c/span\u003e\u003cspan address=\"http://www.ilo.org/publns\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePowerful lobby against child. labour paves the way to a safe future. - FNV [Internet]. [cited 2024 Aug 16]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fnv.nl/mondiaal-fnv/nieuws-mondiaal-fnv/nieuws-nieuws/powerful-lobby-against-child-labour-paves-the-way\u003c/span\u003e\u003cspan address=\"https://www.fnv.nl/mondiaal-fnv/nieuws-mondiaal-fnv/nieuws-nieuws/powerful-lobby-against-child-labour-paves-the-way\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaksud AKM, Reaz Hossain K, Sayed S, Arulanantham A. Mapping of Children Engaged in the Worst Forms of Child Labour in the Supply Chain of the Leather Industry in Bangladesh. 2021 Jul 15 [cited 2024 Aug 8]; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ids.ac.uk/publications/mapping-of-children-engaged-in-the-worst-forms-of-child-labour-in-the-supply-chain-of-the-leather-industry-in-bangladesh/\u003c/span\u003e\u003cspan address=\"https://www.ids.ac.uk/publications/mapping-of-children-engaged-in-the-worst-forms-of-child-labour-in-the-supply-chain-of-the-leather-industry-in-bangladesh/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Child labour, occupational health, work-related injuries, Bangladesh","lastPublishedDoi":"10.21203/rs.3.rs-5313328/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5313328/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eBangladesh has the second highest burden of child labour in South Asia. The informal sector employs most of the children however, evidence and data on health including injuries and place of work for children are limited. \u0026nbsp;As the deadline for the Sustainable Development Goals to end child labour by 2025 is approaching, it is paramount to document the impact of child labour on health. This study aims to contribute to this knowledge gap by presenting medical data from occupational health clinics set up by Médecins Sans Frontières (MSF) in an urban area of Dhaka, Bangladesh.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We did a retrospective analysis of health care records of children attending MSF occupational health clinics between February 2014 and December 2023 in Dhaka. We stratified the analysis by gender and age (\u0026lt;14 years and ≥14-\u0026lt;18 years). We looked at morbidities according to type of factory, whether children reported working with machinery, and examined nutritional and mental health (2018-2023) status.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eOver the study period, there were 10,200 occupational health consultations among children \u0026lt;18 years, of which 4945 were new/first time consultations. The average age of children attending their first consultation was 14.7 years, of which 61% were male. Fifteen percent reported living inside the factory. Musculoskeletal (26%) and dermatological (20%) were the most identified conditions, and 7.5% of consultations were for work-related injuries. Almost all children reported operating machinery. A higher proportion of male children had injuries (11% vs 2.5% in girls). \u0026nbsp;Children working in metal factories accounted for most injuries (65%). \u0026nbsp;Mood-related disorders accounted for 86% of the 51 mental health consultations. Half of all children were malnourished with higher levels in boys and those \u0026lt;14 years. Specific gender and age vulnerabilities were documented.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFindings suggest that children face hazardous realities, engaged in the worst form of labour, bearing important morbidity and injury burden. Weak enforcement of labour regulations in the informal sector ultimately enables child exploitation and suffering. \u0026nbsp;Further research is essential to explore the intersectional dimensions of child labour, such as gender, age, and disability, to inform interventions aimed at eliminating child labour and its severe consequences in this and similar contexts.\u003c/p\u003e","manuscriptTitle":"A Public health wound: health and work among children engaged in worst forms of child labour in the informal sector in Dhaka, Bangladesh: a retrospective analysis of Médecins Sans Frontières Occupational health data from 2014 to 2023.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-06 12:39:21","doi":"10.21203/rs.3.rs-5313328/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-28T14:00:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-28T10:32:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-26T00:04:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-10-22T16:06:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"155f23a3-87b3-4159-8be1-9122185d069a","owner":[],"postedDate":"November 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-04-21T16:01:37+00:00","versionOfRecord":{"articleIdentity":"rs-5313328","link":"https://doi.org/10.1186/s12889-025-22483-z","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2025-04-15 15:57:47","publishedOnDateReadable":"April 15th, 2025"},"versionCreatedAt":"2024-11-06 12:39:21","video":"","vorDoi":"10.1186/s12889-025-22483-z","vorDoiUrl":"https://doi.org/10.1186/s12889-025-22483-z","workflowStages":[]},"version":"v1","identity":"rs-5313328","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5313328","identity":"rs-5313328","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}