Abstract
Background:
No Indian study has profiled adolescents with addictive disorders in rehabilitation.
Aim:
Hence, this study aimed to describe sociodemographic and clinical profiles of adolescent boys in short-term rehabilitation.
Methods:
A retrospective chart review was conducted, including the scores on Disruptive Behaviour Disorders (DBDs) Rating Scale, for 59 patients, over 16 months.
Results:
Mean age of first use of nicotine, alcohol, and cannabis was 12.1 ± 2.7, 13.6 ± 1.7, and 14.3 ± 1.4 years, respectively. Of the patients, 57.6% were from broken families, 59.3% had experienced childhood adversities, 49.2% had parental psychiatric history, 10.2% had only behavioural addictions, 84.7% had dual diagnosis, and 79.7% had comorbid DBD, the commonest being conduct-dissocial disorder (in 47.5%).
Conclusion:
Earlier onset of substance use was associated with lower socioeconomic status (SES) (P = 0.003) and poor parental supervision (P = 0.017). Clinicians should screen for dual diagnoses and target prevention for broken families, childhood adversities, poor parental supervision, and low SES.
Keywords: Adolescent, comorbidity, dual diagnosis, rehabilitation centers, substance-related disorders
INTRODUCTION
Half of all lifetime cases of mental disorders, including substance use disorders, begin by age 14, highlighting the critical need for early intervention.[1] Adolescents harbor more curiosity and are more prone to peer influence, predisposing them to substance use. In India, among those aged 10–17, the prevalence of use of opioids, alcohol, inhalants, and cannabis was 1.8%, 1.3%, 1.17%, and 0.9%.[2] Across India, most people who sought addiction treatment started the use in adolescence.[3]
In India, factors associated with adolescent addictive disorders have been investigated in community,[4] school,[5,6] street,[7] and tertiary care[8,9] samples. However, no study had systematically assessed dual diagnoses or covered a sample from a short-term rehabilitation center for adolescent boys. Hence, we aimed to describe the sociodemographic and clinical profiles of such a sample, as patients in rehabilitation are reported to have comorbid diagnoses, multiple behavior problems, and high rates of relapse[10] and as their profiles would help inform resource requirements, staff training, policy decisions, and preventive efforts.
MATERIALS AND METHODS
The Institutional Ethics Committee approved the study and waived the requirement for parental consent and patient assent, as the assessments were based on retrospective chart review.
The center, located in a village in central Kerala, is sponsored by the Ministry of Social Justice and Empowerment, which funds one center for adolescent boys in each state through its National Action Plan for Drug Demand Reduction (NAPDDR). Our sampling frame included all patients admitted between May 2024 and August 2025. We included files of all patients who stayed long enough for the assessments to be completed (1–2 weeks).
In the files, history is recorded using a structured pro forma. Diagnoses are based on ICD-11. We routinely confirm the diagnoses of disruptive behavior disorders (DBDs), which include attention deficit hyperactivity disorder (ADHD), oppositional defiant disorder, and conduct disorder, with a content-validated Malayalam version[11] of the DBDs Rating Scale (RS-DBD).[12] History of childhood adversities (poverty, early parental loss, parents’ extramarital affairs, violence, and sexual abuse) is part of the proforma. Age of first use of different substances and the number of friends who use them are routinely collected using a previously used questionnaire.[5] Parental supervision was considered poor if, based on history, the parent(s) were frequently away, had limited time for the adolescent, or were reported to pay little attention.
The second and third authors extracted information from the charts (the structured proforma, questionnaires, counselling notes, psychiatrist’s notes, and past records). The fourth author verified the information. The first author clarified ambiguities and confirmed the diagnoses.
Statistical analyses were conducted using R version 4.4.0 (R Foundation for Statistical Computing, Vienna, Austria). The packages summary tools, dplyr, and gmodels were used for data management and analysis. As Shapiro-Wilk tests indicated mild non-normality (P < 0.05), exploratory analyses used Mann-Whitney U tests with medians and interquartile ranges (IQRs) to compare the age of first use across relevant binary variables.
RESULTS
Charts of four patients discharged before completion of the assessment were excluded.
Mean ± SD age of the patients (N = 59) was 15.3 ± 1.4 years (range: 11–17). The mean age of first use was 12.1 ± 2.7 years for nicotine (n = 53), 13.6 ± 1.7 years for alcohol (n = 44), and 14.3 ± 1.4 years for cannabis (n = 34). Mean age of first use of any substance was 11.9 ± 2.7 years (n = 53). The median (IQR) number of friends using nicotine (n = 50), alcohol (n = 48), and cannabis (n = 42) was 11 (31), 8 (19), and 6 (21).
Most (n = 50, 84.7%) patients were from villages, 34 (57.6%) were from broken families, and one-third had dropped out of school. Of the 27 patients who lived with one parent, four lived with their fathers and the remaining 23 with their mothers. Parental supervision was poor in 24 (40.7%), and 35 (59.3%) had a history of childhood adversities [Table 1]. A parent had a history of substance use or another psychiatric disorder in 29 (49.2%) boys. The commonest dual diagnosis was conduct-dissocial disorder [n = 28, 47.5%, Table 2]. At least one DBD was present in 47 (79.7%), and any dual diagnosis, in 50 (84.7%). Additional clinical profiles are in Table S1.
Table 1.
Sociodemographic details of the patients (N=59)
| Variables | n (%) | |
|---|---|---|
| Habitat | Village | 50 (84.7%) |
| Town | 7 (11.9%) | |
| City | 2 (3.4%) | |
| Religion | Hindu | 39 (66.1%) |
| Christian | 15 (25.4%) | |
| Muslim | 5 (8.5%) | |
| Socioeconomic status | Below Poverty Line | 41 (69.5%) |
| Above Poverty Line | 18 (30.5%) | |
| Living with | One parent | 27 (45.8%) |
| Both parents | 24 (40.7%) | |
| In an institution | 6 (10.2%) | |
| Relatives or others | 2 (3.4%) | |
| Status of parents’ marriage* | Intact | 25 (42.4%) |
| Separated | 17 (28.8%) | |
| Widowed | 8 (13.6%) | |
| Divorced | 9 (15.3%) | |
| Level of parental supervision | Sufficient | 35 (59.3%) |
| Poor | 24 (40.7%) | |
| Currently studying | Yes | 39 (66.1%) |
| No | 20 (33.9%) | |
| History of disciplinary actions at school | Absent | 47 (79.7%) |
| Present | 12 (20.3%) | |
| History of childhood adversities | Absent | 24 (40.7%) |
| Present | 35 (59.3%) | |
*When separated, widowed, and divorced were combined to a category of broken families, 34 (57.6%) belonged to it
Table 2.
Important clinical details of the patients (N=59)*
| Variables | n (%) | |
|---|---|---|
| Diagnoses of addictive disorders according to ICD-11† | Harmful pattern of use of nicotine | 33 (55.9%) |
| Nicotine dependence | 19 (32.2%) | |
| Harmful pattern of use of cannabis | 18 (30.5%) | |
| Harmful pattern of use of alcohol | 15 (25.4%) | |
| Gaming disorder | 8 (13.6%) | |
| Harmful pattern of use of volatile inhalants | 4 (6.8%) | |
| Cannabis dependence | 4 (6.8%) | |
| Sedative, hypnotic, or anxiolytic dependence | 1 (1.7%) | |
| Other specified disorder due to addictive behaviors | 1 (1.7%) | |
| Dual diagnoses† | Conduct-dissocial disorder | 28 (47.5%) |
| ADHD | 23 (39.0%) | |
| ODD | 14 (23.7%) | |
| Developmental learning disorder | 12 (20.3%) | |
| Disorder of intellectual development | 9 (15.3%) | |
| Dysthymic disorder | 2 (3.4%) | |
| Anxiety or fear-related disorder, unspecified | 3 (5.1%) | |
| Mixed depressive and anxiety disorder | 2 (3.4%) | |
| Intermittent explosive disorder | 1 (1.7%) | |
| Single episode depressive disorder | 1 (1.7%) | |
| Bipolar disorder | 1 (1.7%) | |
| Cyclothymia | 1 (1.7%) | |
| History of receiving psychiatric medications | Present | 20 (33.9%) |
| Absent | 39 (66.1%) | |
| History of inpatient treatment for addictive disorders | Present | 22 (37.3%) |
| Absent | 37 (62.7%) | |
| History of substance use or other psychiatric disorder in a parent‡ | Present | 29 (49.2%) |
| Absent | 25 (42.4%) | |
| No information | 5 (8.5%) | |
| History of aggression or violence | Present | 37 (62.7%) |
| Absent | 22 (37.3%) | |
| History of self-harm | Present | 14 (23.7%) |
| Absent | 45 (76.3%) | |
| History of sexual abuse | Present | 5 (8.5%) |
| Absent | 54 (91.5%) | |
| History of conflict with law§ | Present | 19 (32.2%) |
| Absent | 40 (67.8%) | |
ADHD=attention deficit hyperactivity disorder; ODD=oppositional defiant disorder. *For variables exclusive for substance use, n is 53, as six patients had screen-related addictive disorders only. †For ICD-11 diagnoses and dual diagnoses, percentages are of the total sample (n=59); multiple responses allowed. ‡Alcohol dependence in 20; nicotine dependence in two; both alcohol and nicotine dependence in two; and alcohol dependence and psychosis, and depression, in one each. §Theft in 4, physical assault in 2, no details available in others
Table S1.
Additional clinical details of the patients (N=59)*
| Variables | n (%) | |
|---|---|---|
| History of substance use or other psychiatric disorder in a sibling† | Present | 6 (10.2%) |
| Absent | 47 (79.7%) | |
| No information | 6 (10.2%) | |
| Referred by | Child Welfare Committee | 32 (54.2%) |
| Self | 12 (20.3%) | |
| Juvenile Justice Board | 8 (13.6%) | |
| Kaaval Plus‡ | 4 (6.8%) | |
| Excise department | 3 (5.1%) | |
| Reason for seeking admission | External pressure | 55 (93.2%) |
| Own interest | 4 (6.8%) | |
| Pattern of use* | Social | 46 (86.8%) |
| Alone | 7 (13.2%) | |
| Source of substances* | Friends | 44 (83.0%) |
| Dealers | 4 (7.5%) | |
| Family | 3 (5.7%) | |
| Other | 2 (3.8%) | |
| Source of money to buy substances* | Friends | 21 (39.6%) |
| Pocket money | 13 (24.5%) | |
| Personal income sources | 13 (24.5%) | |
| Other | 6 (11.3%) | |
| Reason behind initial use* | Curiosity | 19 (35.8%) |
| Peer pressure | 25 (47.2%) | |
| Availability | 5 (9.4%) | |
| Coping with stress | 2 (3.8%) | |
| Places of use* | Public places | 44 (83.0%) |
| Home | 5 (9.4%) | |
| School | 4 (7.5%) | |
*For variables exclusive for substance use, n is 53, as six patients had screen-related addictive disorders only.†Harmful use of nicotine, harmful use of cannabis, nicotine dependence, alcohol dependence, and obsessive compulsive personality disorder in one each. ‡A programme implemented by the Department of Women and Child Development in Kerala, targeting children in need of care and protection
Exploratory inferential analyses examined the associations of age of first use of any substance and the presence of a DBD diagnosis with some clinically relevant subgroups that had sufficient sample size. Patients from families in the below-poverty-line category (P = 0.003) or with poor parental supervision (P = 0.017) were significantly more likely to initiate substance use at a younger age [Table 3]. An analysis of whether the presence of a DBD was associated with different relevant variables revealed no statistically significant findings [Table S2].
Table 3.
Association of the age of first use of any substance with selected sociodemographic variables (N=53)*
| Variable | Group | n | Median (IQR) | W† | P |
|---|---|---|---|---|---|
| Broken family | Present | 31 | 12 (4.00) | 435.5 | 0.087 |
| Absent | 22 | 13 (2.75) | |||
| Childhood adversities | Present | 31 | 12 (3.50) | 436 | 0.086 |
| Absent | 22 | 13 (3.75) | |||
| Socioeconomic status | BPL | 37 | 11 (3.0) | 447 | 0.003 |
| APL | 16 | 14 (1.5) | |||
| Currently studying | Yes | 34 | 12 (3.75) | 387.5 | 0.232 |
| No | 19 | 13 (4.00) | |||
| Parental supervision & involvement | Poor | 21 | 11 (3) | 205.5 | 0.017 |
| Sufficient | 32 | 13 (3) |
*Sample size is 53 because six patients had screen-related addictive disorders only. †W is the Wilcoxon rank-sum statistic from R’s Wilcox test, equivalent to the Mann-Whitney U test statistic for determining P values. IQR=Interquartile range, BPL=Below poverty line, APL=Above poverty line
Table S2.
Association of presence of any disruptive behaviour disorder with selected sociodemographic and clinical variables (N=59)*
| Variable | Disruptive behaviour disorder n (%)† |
χ2/W‡ | P | ||
|---|---|---|---|---|---|
| Absent (n=12) | Present (n=47) | ||||
| Family | Broken | 7 (58.3%) | 27 (57.4%) | 0.003 | 0.956 |
| Intact | 5 (41.7%) | 20 (42.6%) | |||
| History of childhood adversities | Present | 7 (58.3%) | 28 (59.6%) | 0.006 | 0.938 |
| Absent | 5 (41.7%) | 19 (40.4%) | |||
| Socioeconomic status | Below Poverty Line | 7 (58.3%) | 34 (72.3%) | 0.89 | 0.347 |
| Above Poverty Line | 5 (41.7%) | 13 (27.7%) | |||
| Currently studying | Yes | 9 (75.0%) | 30 (63.8%) | 0.53 | 0.466 |
| No | 3 (25.0%) | 17 (36.2%) | |||
| Parental supervision | Sufficient | 7 (58.3%) | 28 (59.6%) | 0.006 | 0.938 |
| Poor | 5 (41.7%) | 19 (40.4%) | |||
| Aggression/violence | Present | 5 (41.7%) | 32 (68.1%) | 2.85 | 0.091 |
| Absent | 7 (58.3%) | 15 (31.9%) | |||
| History of receiving psychiatric medications | Present | 2 (16.7%) | 18 (38.3%) | 2.00 | 0.158 |
| Absent | 10 (83.3%) | 29 (61.7%) | |||
| History of inpatient treatment for addictive disorders | Present | 4 (33.3%) | 18 (38.3%) | 0.10 | 0.751 |
| Absent | 8 (66.7%) | 29 (61.7%) | |||
| Age of first use of any substance (median [IQR]) | 13.5 (4) | 12 (4) | 332.5 | 0.065 | |
*For age of first use of any substance, n is 53, because six patients had screen-related addictive disorders only. †This is column-wise percentage. ‡W is the Wilcoxon rank-sum statistic from R’s wilcox.test, equivalent to the Mann-Whitney U test statistic for determining P values
DISCUSSION
In 59 patients admitted to a free rehabilitation center for adolescent boys with addictive disorders over 16 months, about 60% were from broken families, 85% had at least one dual diagnosis, 80% had a comorbid DBD, and the age of first substance use was significantly lower in those from families with lower socioeconomic status (SES) or poor parental supervision.
In a school-based study among Kerala adolescents, the mean age at onset of tobacco[6] and alcohol[5] use was 14 ± 2 and 13.6 ± 2.4 years. While the age for alcohol in our sample was similar, it was lower for tobacco.
In a tertiary care institute of Assam, the mean age of adolescent boys seeking treatment for substance use was 16.8 ± 0.75, and the number of patients who had received prior treatment or hospitalization was 16 (versus 25) and 7 (versus 34), respectively. The diagnoses in that sample were mental and behavioral disorders due to the use of multiple drugs (n = 18), opioids (n = 12), cannabinoids (n = 6), alcohol (n = 4), and tobacco (n = 1).[8] The mean age of our patients was lower, probably because our center is exclusively for adolescents. A higher proportion of our patients had a history of hospitalization, probably due to the better health infrastructure of Kerala.[13] Our patients did not have opioid use, probably reflecting the lower overall prevalence of opioid use in Kerala.[14]
Parental separation has been consistently linked to adolescent substance use. In Serbia, adolescent substance use was associated with living in a broken home.[15] In a meta-analysis, adolescents from non-intact families had greater odds of alcohol use.[16]
According to a systematic review, the most common dual diagnoses were affective and conduct disorders, followed by anxiety and psychotic disorders and ADHD.[17] A school-based study from Kerala found lifetime alcohol[5] and current tobacco[6] use to be associated with ADHD symptom scores.
Dual diagnoses often cause relationship problems, educational or vocational underachievement and legal issues and hence need prompt diagnosis and treatment.[18] The aetiology of dual diagnoses is hypothesized to be shared etiological factors, bidirectional feedback, the mental illness being secondary to substance use, or the addictive behavior being self-medication for the mental illness.[18]
The prevalence we found of history of aggression or violence (62.7%), self-harm (23.7%), and conflict with law (32.2%) is vital from assessment and treatment perspectives.
Our finding of a lower age of first use of substances in those from families with poor parental supervision has been previously reported. In the US, higher parental monitoring delayed the age of onset of alcohol or cannabis use in boys.[19] A longitudinal US study found that parental involvement reduced the likelihood of substance use initiation during the transition from eighth to ninth grade.[20] One mechanism could be that low parental monitoring encourages affiliation with substance-using peers.[21]
However, the literature is inconsistent regarding the association we found between lower age of first use and lower SES. In the UK, though children from lower working-class households were at greater risk of cannabis initiation, parental SES did not predict the age of initiation of drinking or smoking.[22] The literature reviewed in that paper also highlighted mixed and inconclusive findings regarding the relationship between SES and substance initiation. According to the normalization thesis, no relationship is expected between SES and substance use because the decision to use substances is “just another lifestyle choice” open to anyone.[23]
This study is limited by its small sample size and single-center design; the findings may not generalize to girls, other Indian states, or non-residential populations. This was a retrospective chart review, and information regarding some variables was unavailable for some patients and recall bias cannot be ruled out. Assessment of parental supervision was subjective and included parameters such as “limited time” that were not operationally defined, and inter-rater reliability was not ensured. RS-DBD is DSM-IV-based; no similar tool based on DSM-5 criteria is available. The detected associations are exploratory and cross-sectional and should not be interpreted as causal.
A combined analysis of data from other NAPDDR-funded or similar centers and qualitative studies focusing on how broken families and SES affect addictive disorders would help. Effectiveness studies should evaluate management strategies for addictive disorders and dual diagnoses.
CONCLUSION
Adolescent inpatients with addictive disorders should be screened for dual diagnoses, especially conduct-dissocial disorder. Prevention programmes can target boys from disrupted families and those with parental psychiatric history, poor parental supervision, low socioeconomic status, or childhood adversities.
Conflicts of interest
There are no conflicts of interest.
Acknowledgements
We thank Dr Raj Kiran Donthu, Associate Professor, NRI Academy of Medical Sciences, Guntur, for conducting the statistical analysis.
Funding Statement
Nil.
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