Baseline characteristics
A total of 381 garment factory workers were included in the analysis. The sample was predominantly female (84.0%). The age of participants ranged from 18 to 68 years with a mean of 32.9 (SD = 10.5) years. All the workers were from the Sinhala ethnicity and almost all (99.2%) identified themselves as Buddhists, with the remainder being Catholic. More than half of the workers were married at the time, while 34.4% were neither currently nor previously married (See Table 1).
Sewing Machine Operators (57.7%) followed by Helpers (20.7%) and Quality Controllers (10.2%) constituted the main job categories covered among the factory workers. Over 95% of the workers were monthly or weekly wage earners. A small percentage of the Machine Operators and Helpers were either paid daily or according to the amount of work done (See Table 1). Almost three-quarters (74.3%) of the respondents were working at a factory in their own hometown. Over 86% of respondents own a permanent job contract.
In response to health-related questions in the study, 10.0% of the sample identified themselves as having chronic physical illnesses, while 5.0% have had a history of depression. Almost eight per cent of the sample (7.9%) revealed that they have previously attempted suicide. Family history of mental illness was noted by 5.8% of the responding garment factory workers.
Prevalence of psychological morbidity
As the screening tool for psychological morbidity, GHQ-12 scores of the garment factory workers showed the full range of values from 0 to 12, with a mean of 1.75 (SD = 2.55). Of the 381 participants, 132 had a score of over 1. Therefore, based on the categorisation of GHQ-12 scores above 1 as being positive for psychological morbidity, the estimated prevalence of psychological morbidity in the study was 34.65% (95% CI: 21.04–48.25%).
Predictors of psychological morbidity
Before fitting a logistic regression model for psychological morbidity (as screened by the GHQ-12), potential predictors were assessed using bivariate analysis. The predictors considered in this regard were: age, gender, highest education level, marital status, number of dependents, accommodation type, travel time from residence to factory, whether factory is in their hometown or not, job title, income category, salary interval, level of work experience, whether or not engaging in extra duty work, employed full-time or part-time, being a permanent or casual worker, amount of time spent daily on watching television, amount of time spent daily on social media, presence of substance use, currently being pregnant or not, presence of a chronic physical illness, history of depression, history of attempted suicide and family history of mental illness. Out of which, only six variables i.e., marital status, salary interval, presence of a chronic physical illness, history of depression, history of attempted suicide and family history of mental illness showed individual statistically significant relationships with psychological morbidity (See Table 2). When a multiple logistic regression analysis was attempted using these six predictor variables, two (marital status and family history of mental illness) became non-significant. Therefore, only four of the factors assessed in the study were found to be independent predictors of psychological morbidity (See Table 3).
After controlling for the other three variables in the model, garment factory workers that get paid daily or by the number of pieces they finish have 6.75 (95% C.I: 2.05 to 22.24) times higher odds of having a psychological morbidity compared to those earning monthly or weekly salaries. In comparison to individuals that did not report having attempted suicide in the past, those with such a history were 6.14 (95% C.I: 2.45 to 15.39) times more likely to be positive for psychological morbidity as assessed by GHQ-12. Similarly, having a history of depression accounted for about 5 times higher odds, and having a chronic illness lead to almost 3 times higher odds of showing psychological morbidity in garment factory workers (Table 4).
Prevalence of depression
All of the 132 individuals suspected of having psychological morbidity were screened for depression using the BDI-II. Out of the possible score range of 0 to 63, the GHQ-12 positive participants in the study had BDI-II scores from 0 to 46 with a mean score of 17.18 (SD = 10.37). Out of the 132 screened workers, 64 (48.48%) had a score equal to or above the BDI-II cut-off value of 16. Assuming that all the participants that were negative for psychological morbidity were also negative for depression, the estimated prevalence of depression in the study was 16.80% (95% CI: 13.04–20.55%).
Predictors of depression
The identification of independent predictors of depression was done using the same process as that used with psychological morbidity. In bivariate analysis, six out of the before mentioned candidate predictor variables showed a significant association with presence of depression. These variables were: marital status, whether or not engaging in extra duty work, presence of a chronic physical illness, history of depression, history of attempted suicide and family history of mental illness (See Table 4). When performing logistic regression analysis using these six variables presence of a chronic physical illness, history of attempted suicide and family history of mental illness ended up in the final fitted model. According to the results, garment workers with a suicide attempt history had 10.79 (95% C.I: 4.68 to 24.89) times higher odds (compared to those who had not reported such a history) of currently having depression, after adjusting for the effects of chronic physical illness and family history of mental illness. Having a chronic medical condition leads to 3.51 (95% C.I: 1.61 to 7.67) higher odds of depression, while those with mental illness in their family resulted in three times higher odds of depression when controlling for the other two variables (See Table 5).