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Legal and social consequences of substance use: Results from a nationwide study in Bangladesh.

Mohammad Delwer Hossain HawladerMohammad Hayatun NabiAmir HussainSaad Ullah Al AminSanjana ZamanIqbal Masud
Published in: Journal of ethnicity in substance abuse (2020)
Substance use is a major public health concern and its consequences can destroy someone's life. This study aimed to explore the legal and social consequences of substance use in Bangladesh. We conducted a nationwide descriptive cross-sectional study among relapse cases of substance use from January to December 2018. We visited 138 drug rehabilitation centers countrywide and were able to recruit 939 relapse cases, from where 28 cases were excluded due to incomplete data. Finally, data from 911 cases were analyzed. The majority (89.3%) of the study participants were 19-45 years old. Most commonly used drugs were amphetamine (76.1%), cannabis (75.0%), alcohol (54.3%), cough sirup (54.2%), heroin (47.0%) and sleeping pills (21.6%). Almost half (49.5%) of the substance users were arrested for drug use and among arrested cases, 52.1% were sent to jail. About 75% of the substance users experienced a lack of family interaction, 70% experienced destroyed family relationships, and 71.4% faced social stigma. Our study also found 60% of the participants were bullied, 50% were deprived or unwilling to have social interactions. Moreover, 13.8% of the participants left home, while 8% got divorced. Our data represented the significant impact of substance use on the legal aspect and social life of individuals. However, with a multi-dimensional treatment, rehabilitation, and social intervention approach, it is not impossible to overcome. Therefore, we believe it is imperative to focus on social awareness and to create a robust platform for health promotion and improve quality of life.
Keyphrases
  • mental health
  • healthcare
  • public health
  • randomized controlled trial
  • electronic health record
  • emergency department
  • big data
  • cross sectional
  • machine learning
  • mental illness
  • deep learning