Sage Journals: Discover world-class research

Abstract

Background:

Family members, peers, and significant others are part of a college student’s social network. This cross-sectional study aimed to

Assess substance use prevalence and patterns among college students,

Compare the social network characteristics of substance users (SUs) and non-users (NUs), and

Explore the association of social factors with substance use.

Methods:

The study involved 902 students from 11 Government and aided private degree colleges. Demographic and clinical data sheet, ASSIST, and Social Network Questionnaire were used to collect the data.

Results:

Prevalence of substance use was 26.9% and higher among males (21.5%). Alcohol (20%) and tobacco (15.5%) were the commonly used substances. SUs’ network was composed of unmarried persons (p<0.002), male members (p<0.001), and friends (p<0.001) with substance use. In contrast, the NUs’ network comprised parents (p<0.016) and siblings (p<0.001). NUs had a higher number of influential members in the network, whereas SUs had more closeness with members and received higher financial support (p<0.001). Participant’s age (OR 1.27), family history of substance use (OR 2.46), parents’ occupation (Business: OR 1.79, being employee in the government or industry: OR 1.76),and having three substance-using members in the network (OR .211) were found to be risk factors.

Conclusion:

Social network has an association with substance use among college students. Social-network-based interventions may benefit them.

Keywords

Addiction college students social network substance use young adults

Key Messages:

Among college students, 26.9% used substances. Alcohol (20%) and tobacco (15.5%) were the commonly used substances.

Social network of substance users comprised more males, unmarried members, and friends who use substances. In contrast, the non-users’ network comprised parents, siblings, and significant others.

Social network provide a new dimension to understanding the substance use problem among college students.

The period of transition from adolescence to young adulthood, known as emerging adulthood, is distinct in demography and identity exploration. In addition to physical and psychological developments, this stage also offers opportunities to explore the world in newer ways and through bolder experiences. Hence, this stage is also associated with risk-taking behaviour, including substance use (SU).¹ SU behaviour generally begins in adolescence and increases in young adulthood.^2,3 Peer influence, curiosity, and a sense of growing are significant reasons attributed to its initiation, and feeling good and socialization, to its perpetuation.⁴ Two factors consistently associated with SU initiation and maintenance are a lack of awareness of ill effects and peer influence. Other factors include enjoyment, reduced academic-related stress, love relationship, and parental and family problems.^5,6 Thus, family, friends, and other people in a close circle play a vital role in college-going young adults’ SU behaviour.

In India, the college years are between 18 and 25 years. During this period, the students like to be self-reliant as they encounter individual decision-making situations such as obtaining a job, selecting romantic partners, deciding on higher education, securing financial independence, and developing support systems, and such decisions are considerably influenced by peers.⁴ Students show certain necessary behaviours to obtain power, status, peer acceptance, and recognition within their networks.⁷ If the students get to choose members of their networks or get selected by friends who are likely to engage in similar behaviours, these reinforce their affiliation.⁸ Factors influencing SU are complex because that occurs in various social network (SN) contexts, such as meeting friends, family members, and others.⁹ Thus, exploring SN to understand the students’ SU problem is critical, and previous literature has also reported the necessity of this.¹⁰

Social Network Approach

As they generate and disseminate social influence, SN help study the social context of SU behaviour. A SN consists of an index individual and others with whom they are connected by interaction and behaviour of interest.¹¹ Such a SN has three components:

SN Integration, which includes relationship quality, frequency of contact, trust, and closeness among members,

Network Structure, which refers to the relationship between index individuals and members, and

Relationship Content, which is described in support domains such as emotional, financial, and instrumental support and social roles like friend, classmate, romantic partner, parent, spouse, and significant others.^12,13 The current study attempts to understand and compare the SN characteristics of substance users and non-users.

Materials and Methods

Design

This cross-sectional study aimed to

Assess substance use prevalence and patterns among college students,

Compare the SN characteristics of substance users (SUs) and non-users (NUs), and

Explore the social factors’ association with substance use.

Sampling and Participants

A two-stage sample technique was deployed. In the first stage, a total of 22 colleges situated in the Bangalore south zone were stratified as Government First Grade Degree Colleges (GFGC, n=4) and Government Aided Private Degree Colleges (GAPDC, n=18). 50% in each stratum (nine GAPDC and two GFGC) gave permission and were part of this study. In the second stage, cluster sampling was used. All students from the classes allotted to the researcher were included in the study. Previous studies reported a SU prevalence of 15-30%^14–16 among college students. Considering that and anticipating 25% of the study population to be using any substance, for a 95% confidence level and precision of ± 4%, the minimum sample size was observed to be 450. Considering a design effect of 2, the final sample size was calculated to be 900. Accordingly, 740 students (aged 18 to 25) from GAPDC and 160 (of the same age group) from GFGC were included. The study was conducted during the post-COVID-19 period (December 2020 to August 2021), and many colleges were busy completing their pending academic targets. Therefore, college authorities allotted entire classrooms to the researcher, and all students from those classrooms were part of the study (N=902). The cluster size ranged from 35 to 45, and a minimum of two clusters were allotted from each college.

Data Collection Procedure

The researcher explained the study to the college principal and students and obtained written consent for participation. Subsequently, proper seating arrangements were made to ensure privacy for students to fill in responses. A self-administered instrument kit was distributed to all students, the responding procedure was explained, and doubts were clarified. The Department of Collegiate Education, the Government of Karnataka, and the Institutional Ethics Committee permitted and approved the study.

Tools Used

Socio-Demographic and Clinical Data Sheet: This was used to collect data related to age, sex, education, monthly income of the family, part-time job status, residential status, parents’ occupation, and family history of SU.

The Alcohol Smoking and Substance Involvement Screening Test Version 3.1 (ASSIST): It consists of eight main items and 70 sub-items. It assesses the past three months’ intake of tobacco products, alcohol, cannabis, cocaine, amphetamine-type stimulants (ATS), sedatives and sleeping pills (benzodiazepines), hallucinogens, inhalants, opioids, and other drugs. It determines a severity score for each substance. The score obtained for each substance falls into lower, moderate, or high severity categories. This tool was developed for the World Health Organisation by an international group of researchers and clinicians.

Social Network Questionnaire (SNQ): The SNQ was developed from published studies to capture ego-centric social network (ESN) information.^13,17–19 ESN considers the index subject’s (IS) direct linkages with their network.²⁰ SNQ covers questions on network size and structure, relationship, closeness, influence on IS, social network members’ (SNMs) substance use details, and perceived support from SNMs. SNQ was face and content validated by six experts: two psychiatrists, two psychiatric social workers, one clinical psychologist, and a person from a non-governmental organization with experience in addiction medicine. Subsequently, the questionnaire was pilot tested on 160 students, and further modification was conducted by simplifying the questions.

SNQ consists of the following questions: First, the IS is asked to identify people they knew very well. Subsequently, the IS is asked to name five people (SNMs) they were in contact within the last three months through face-to-face, telephone, or on social media platforms. The SNMs demographic characteristics and relationship duration are collected. The level of closeness and influence is measured through responses as 1) not close/influential, 2) little, 3) moderate, and 4) very close/influential.

Additional questions probed whether the SNMs are SUs (Yes/No), whether the IS spend time with them during their use (Yes/No), number of times IS used substances in a week, whether the IS share or disclose important personal matters with the SNMs, whether the IS receive financial support from the SNMs, whether the IS receive emotional support from the SNMs in times of misery and sadness, and whether the IS get general health advice from the SNMs.

Statistical Analysis

Data was tabulated in Microsoft Excel and then analysed in IBM SPSS Statistics for Windows, version 28.0 (IBM Corp., Armonk, N.Y., USA). Descriptive statistics and χ² tests were used to describe the participants’ socio-demographic characteristics and the difference between SUs and NUs groups, respectively. As the SN characteristic variables were not normally distributed, the Mann-Whitney U test was performed to see the differences in SN characteristics between SUs and NUs. Multiple logistic regression was used to find the predictors of SU among college students. For all analyses, p<0.05 was considered significant.

Results

Of the 902 participants, 26.9% (n=243) reported using substances. Most SUs were male, slightly older, and had higher monthly family incomes than NUs. Most SUs and NUs were single, were from nuclear families, stayed with their families, and were not doing part-time jobs; so marital status, family type, residential status, or employment status did not differ between them. Most SUs hailed from urban areas. Most of the SUs’ parents’ occupation was business, whereas most of the NUs’ parents were daily wage workers. In both groups, the father was the main person who used substances, and alcoholic beverages and tobacco were the common substances used by family members (Table 1).

Table 1.

Difference in demographic characteristics of substance users and non-users.

Variable		Substance Users (n=243)	Non-Users (n=659)	p-value
Variable		n (%)		p-value
Sex*	Male	194 (80)	282 (43)	0.001
Sex*	Female	49 (20)	377 (57)	0.001
Education in years^# Median (IQR)		15 (14 -15)	15 (14 -15)	0.002
Age in years^# Mean ± SD		20.24 ± 1.23	19.92 ± 1.08	0.002
Family Monthly Income^# (₹) Median (IQR)		25000 (15000 – 50000)	20000 (10000 – 35000)	0.001
Marital status*	Married	1 (0.4)	16 (2.4)	0.08
Marital status*	Single	242 (99.6)	643 (97.6)	0.08
Part-time job*	Yes	45 (18.5)	121 (18.4)	0.95
Part-time job*	No	198 (81.5)	538 (81.6)	0.95
Domicile*	Rural	61 (25.1)	212 (32.2)	0.004
Domicile*	Urban	182 (74.9)	447 (67.8)	0.004
Family type*	Nuclear	206 (84.8)	560 (85)	0.94
Family type*	Joint	37 (15.2)	99 (15)	0.94
Parents’ occupation*	Daily Wage Worker	61 (25.1)	265 (40.2)	0.001
	Govt/Industry sector	51 (21)	111 (16.8)
	Business	104 (42.8)	196 (29.7)
	Other	27 (11.1)	87 (13.2)
Residential status*	Home	217 (89.3)	631 (93)	0.06
Residential status*	Other	26 (10.7)	46 (7)	0.06
Family history of substance use *	Yes	109 (44.9)	173 (26.3)	0.001
Family history of substance use *	No	134 (55.1)	486 (73.7)	0.001
Who uses in family*	Father	72 (66.05)	124 (71.67)	0.27
	Brother	12 (11)	23 (13.29)
	Grand Father	10 (9.17)	10 (5.78)
	Mother	6 (5.50)	5 (2.89)
	Grand Mother	5 (4.58)	2 (1.15)
	Others	4 (3.66)	9 (5.20)
Substance used by family members*	Tobacco	27 (23.9)	41 (24.6)	0.97
	Alcohol	46 (40.7)	68 (40.7)
	Tobacco & Alcohol	39 (33.9)	55 (32.9)
	Other	3 (2.7)	3 (1.8)

#: Mann Whitney U Test, *: Chi-Square test. p<0.05 is significant, IQR: Interquartile range.

Among the 243 SUs, 194 (21.5%) were male and 49 (5.4%) were female. More than half of the SUs used ≥2 substances. Alcohol and tobacco were the commonly used substances, followed by inhalants and illicit drugs. Most participants’ alcohol and tobacco use severity was at a moderate level. Those who used inhalants had moderate to high severity of use, while most of those who used other substances (cannabis, sleeping pills, hallucinogens, and cocaine) had low and moderate severity of use (Table 2).

Table 2.

Substance use profile of the substance-using group.

Variable		Frequency (N= 243)	Percent
Sex	Male	194	21.5
Sex	Female	49	5.4
Number of substances used	One	119	49.0
	Two	103	42.4
	More than two	21	8.6
Severity of substance use
Alcohol (n= 180, 20%)	Low	57	31.7
	Moderate	95	52.8
	High	28	15.6
Tobacco (n= 131, 15.5%)	Low	15	11.5
	Moderate	91	69.5
	High	25	19.1
Inhalant (n= 42, 4.7%)	Low	9	21.4
	Moderate	29	69.0
	High	4	9.5
Other (n=41, 4.54%) (Cannabis, sleeping pills, hallucinogens, and cocaine)	Low	20	48.8
	Moderate	20	48.8
	High	1	2.4

Table 3 explains the SN characteristics in four domains: 1) network structure, 2) closeness and influence, 3) substance use profile of SNMs, and 4) types of perceived support.

Table 3.

Social network characteristics of substance users and non-users.

Social Network Characteristics	Substance user		Non-user
Social Network Characteristics	Mean ± SD	Median (IQR)	Mean ± SD	Median (IQR)	p-Value
Network Structure
Total number of males SNMs	7.28 ± 4.19	6 (4 - 10)	5.79 ± 4.06	5 (3 - 8)	0.001
Total number of females SNMs	3.67 ± 2.95	3 (1 - 5)	4.84 ± 3.51	5 (2 - 6)	0.001
SNMs in contact last three months	4.77 ± 0.64	5 (5 - 5)	4.73 ± 0.74	5 (5 - 5)	0.084
Age of SNMs	22.25 ± 5.50	21 (20 – 21.5)	21.67 ± 4.42	20 (20 – 22)	0.148
Number of married SNMs	0.68 ± 1.09	0 (0 - 1)	0.83 ± 1.11	0 (0 - 2)	0.007
Number of unmarried SNMs	4.06 ± 1.18	5 (3 - 5)	3.86 ± 1.27	4 (3 - 5)	0.002
At least one parent in the network	0.44 ± .66	0 (0 - 1)	0.58 ± .84	0 (0 - 1)	0.016
At least one sibling in the network	0.57 ± .91	0 (0 - 1)	0.78 ± 1.07	0 (0 - 1)	0.001
Number of friends in the network	2.77 ± 1.84	3 (1 - 5)	2.39 ± 1.85	2 (1 - 4)	0.001
Number of classmates in the network	0.82 ± 1.66	0 (0 - 1)	0.73 ± 1.56	0 (0 - 1)	0.50
Number of others in the network	0.16 ± 0.44	0 (0 - 1)	0.26 ± 0.64	0 (0 - 1)	0.006
Relationship duration (in years)	8.46 ± 4.70	7.2 (5 - 11.8)	8.28 ± 5.18	7.2 (3.6 - 12.2)	0.23
Closeness and influence
Mean closeness with SNMs	3.58 ± 0.56	4 (3.2 - 4)	3.54 ± 0.62	3.8 (3.2 - 4)	0.47
Mean influence by SNMs	3.01± 0.81	3.2 (2.6 - 4)	3.19 ± 0.83	3.33 (2.8 - 4)	0.54
Total very close SNMs	3.25 ± 1.90	4 (2 - 5)	3.22 ± 1.8	3 (2 - 5)	0.61
Total very influencing SNMs	2.37 ± 2.08	2 (0 - 5)	2.46 ± 2.03	2 (0 - 5)	0.53
Substance use profile of SNMs
Total substance users	1.6 ± 1.92	0 (0 - 3)	0.32 ± 0.91	0 (0 - 0)	0.001
Total non-user	3.17 ± 1.92	4 (1 - 5)	4.41 ± 1.15	5 (4 - 5)	0.001
Giving company to SNMs	1.23 ± 1.83	0 (0 - 2)	0.12 ± 0.58	0 (0 - 1)	0.001
Perceived support from SNMs
Total people, IS shares things	3.2 ± 1.84	4 (2 - 5)	2.97 ± 1.94	3 (1 - 5)	0.12
Total people IS receives financial support from	3.03 ± 1.79	3 (2 - 5)	2.54 ± 1.87	3 (1 - 5)	0.001
Total people IS receives health advice from	3.46 ± 1.82	4 (2 - 5)	3.51 ± 1.81	5 (2 - 5)	0.65
Total people IS receives emotional support from	3.15 ± 1.9	3 (1 - 5)	2.98 ± 1.94	3 (1 - 5)	0.22

IS: Index Subject. SNMs: social network members, p<0.05 is significant. The Mann-Whitney U test has been used for the above variable to measure differences in social network characteristics between SUs and NUs.

Network Structure

The SUs’ network comprised more male members of a higher age than NUs’. The number of females, married SNMs, at least one parent, siblings, and significant other people in the close circle were higher in the NUs’ network. In contrast, the number of unmarried SNMs, friends, and classmates were higher in the SUs’ network. Further, the relationship duration between the IS and their SNMs was slightly longer among SUs than NUs.

Closeness and Influence

The closeness of the IS with SNMs and their influence on them were similar in both groups. However, SUs had a slightly higher closeness with SNMs, while in the case of NUs, the SNMs exerted a slightly greater influence on the IS.

Substance Use Profile of SNMs

Both groups had substance-using SNMs. However, in the SUs group, the number of substance users in the network and IS giving company or spending time with SNMs was higher.

Perceived Types of Support

The SUs reported receiving higher financial and emotional support from and sharing things with SNMs. However, the NUs obtained more health advice from their network (Table 3 and Figure S1).

The variables considered for regression analysis were age, family type, parents’ occupation, residential background, family history of SU, number of close SNMs, and company to SNMs. The result showed that the risk of SU among students was higher if they were older in age or had a family history of SU or if the parents had business as occupation or were employees in government or industry. Further, the IS social networking with >3 substance-using members and engaging weekly once in substance use with SNMs predicted a significant risk for substance use (Table 4).

Table 4.

Multiple logistic regression outcomes predicting substance use.

Dependent variable: Substance use (Yes/No)
Variables	OR (95% CI)	P - Value
Age in years	1.27 (1.080 – 1.495)	0.004
Parents’ occupation (Business)	1.79 (0.996 – 3.217)	0.003
Parents’ occupation (Employee in government and industry sector)	1.76 (0.920 – 3.392)	0.003
Family substance use history	2.46 (1.697 – 3.580)	0.001
Three or more SUs in SN	.221 (0.118 – 0.376)	0.001
SU with ≥3 SNMs in a week	.24 (0.003 – 0.220)	0.001
R2 = .291, Sensitivity = 96.3 Specificity = 34.1 and Accuracy = 79.9 H - L X2 P = .881

OR: Odds ratio, CI: Class interval, SUs: Substance users, SU: substance use, SN: Social network, SNMs: Social network members, p < 0.05 considered significant.

Discussion

Our study focused on young adults’ substance use in association with their SN and found the prevalence of SU to be 26.9%. Studies from Kerala and Dehradun on college students had reported a prevalence of 31.8% and 31.3% for SU, while a study in Chandigarh among only male students reported 57.2%.^21–23 Studies from Kerala and Dehradun that measured lifetime and one-month SU prevalence had findings similar to ours. Our results indicate that magnitude of SU among college students is substantial. Studies in developing countries like Nepal (42.8%),²⁴ Ethiopia (73.7%),²⁵ Sudan (31%),²⁶ and Kenya (25%)²⁷ had reported a slightly higher prevalence compared to the current study. We observed that alcohol (20%) and tobacco (15.5%) were the most used substances. A similar observation was also made by a Kerala-based study on alcohol (21.4%)¹⁴ and tobacco (15.1%)²⁸ use among college students.

We compared SUs’ and NUs’ SN characteristics. SUs’ network comprised a higher number of substance-using male SNMs and slightly older friends, with longer relationship duration, and fewer parents and siblings. They also received more financial support from SNMs. It could be because a higher number of male SNMs with substance use background, longer duration of relationships, intimate friendships, and similar interests among members possibly leading to frequent contact, sharing of emotions, and helping financially or instrumentally increased their ties and influence relating to SU. In addition, the IS and members within the network were generally involved in group activities for peer and social recognition. SU could be one of the means for college students to make more friends and become leaders in group activities.²⁹

Our study observed that parents and siblings were higher in the NUs’ network. A study on family relationships and adolescent health attitudes reported that family relationships are an important predictor of adolescent/young adults’ behaviour and health attitudes.³⁰ Authoritative parenting, demandingness, and closeness between parents and young adults increase connectedness and bonding.^31,32 Also, siblings’ influence plays a significant role in individual attitudes and behaviour. Relationship quality, such as closeness and warmth between siblings, improves understanding and affection. In contrast, rivalry leads to delinquency, depression, and SU.³³ Therefore, quality relationships with siblings and parents lead to better social support, self-confidence, and self-regulation and appear to protect against stressors such as poor peer relationships, academic difficulties, family conflict, and substance use.^34,35 A few studies among students from the US and Hong Kong on the relationship of peer influence and SN characteristics with alcohol and alcohol-related consequences and other addictive behaviours reported a significant effect of peer relationships, intimacy, and relationship duration on the students’ SU.^36–38

Age of the IS, a family history of SU, parent’s occupation (business, employee in government and industry sector) having >3 substance-using SNMs, and engaging weekly once in SU with SNMs significantly emerged as a predictor of regular SU. The reason could be that as age increases, the students’ social world widens and they could see themselves as adults. Using tobacco and alcohol could be considered symbolic of adulthood. In addition, early exposure and initiation, curiosity, peer influence and pressure, the industry’s advertisement and promotional strategies to target youth,^39,40 easy availability, and SU within the household may provide an impetus for students to use substances.⁴¹

Further, students’ parents’ occupations and income were seen to be associated with SU. Business and information-technology-related professions demand more human presence and involvement. Therefore, parents may not spend quality time with their children. Parents from higher socioeconomic backgrounds also seem to show greater tolerance towards substances than parents of lower socioeconomic strata.⁴² Children of affluent families are at a greater risk of anxiety and depression due to pressure from parents to achieve higher in academics. Poor emotional attachment, permissible and pampering parenting, and easy availability of money might also increase the risk of SU⁴³ among college students.

Strengths and Limitations

This study describes the SN characteristics and risk factors for SU among college students. Understanding students’ SN is essential as it can possibly steer SN based interventions to reduce SU risk in colleges. Limitations of this study are:

This was a cross-sectional survey based on self-reported data and SNMs were restricted to only five people per IS, which might have led to social desirability and recall biases.

Despite all assurances of confidentiality, under-reporting related to SU may have occurred.

No biochemical validation was undertaken of self-reported SU over the three-month span for which responses were sought.

Conclusion

Among college students, SN has an association with SU. Peer networks, quality of peer relationships, closeness with peers, perceived support from peers and parental pressure are associated with SU in them. Future studies should assess the effectiveness of social-network-based intervention studies in this population.

Supplemental Material

Supplemental material for this article is available online.

Footnotes

Acknowledgements

We thank the Department of collegiate education, Government of Karnataka and Principals of the colleges who have supported us to conduct this study.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

References

Tucker

, Cheong

, Chandler

, Crawford

, and Simpson

. Social networks and substance use among at-risk emerging adults living in disadvantaged urban areas in the southern United States: A cross-sectional naturalistic study. Addiction, 2015 Sep 1; 110(9): 1524–1532.

Ahmad

, Khalique

, Khan

, and Amir

Prevalence of psychosocial problems among school going male adolescents. Indian J Community Med, 2007; 32(3): 219, https://www.ijcm.org.in/article.asp?issn=0970-0218;year=2007;volume=32;issue=3;spage=219;epage=221;aulast=Ahmad (accessed Jul 8, 2021).

Tripodi

, Bender

, Litschge

, and Vaughn

. Interventions for reducing adolescent alcohol abuse: A meta-analytic review [Internet]. Arch Pediatr Adolesc Med, 2010; 164: 85–91, https://pubmed.ncbi.nlm.nih.gov/20048247/ (accessed Jul 8, 2021).

Gopiram

and Kishore

. Psychosocial attributes of substance abuse among adolescents and young adults: A comparative study of users and non-users. Indian J Psychol Med, 2014 Jan; 36(1): 58–61, http://pmc/articles/PMC3959021/ (accessed Jul 5, 2021).

Kangule

, Darbastwar

, and Kokiwar

A cross sectional study of prevalence of substance use and its determinants among male tribal youths. Int J Pharm Biomed Sci, 2011; 2(3): 61–64.

Kelly

, O’Flaherty

, Toumbourou

, Connor

, Hemphill

, and Catalano

. Gender differences in the impact of families on alcohol use: A lagged longitudinal study of early adolescents. Addiction, 2011 Aug; 106(8): 1427–1436, http://pmc/articles/PMC3135699/ (accessed Jul 8, 2021).

Rice

, Milburn

, Rotheram-Borus

, Mallett

, and Rosenthal

The effects of peer group network properties on drug use among homeless youth. Am Behav Sci, 2005 Apr; 48(8): 1102–1123, http://pmc/articles/PMC2882629/ (accessed Jul 6, 2021).

Cruz

, Emery

, and Turkheimer

Peer network drinking predicts increased alcohol use from adolescence to early adulthood after controlling for genetic and shared environmental selection. Dev Psychol, 2012 Sep; 48(5): 1390–1402, http://pmc/articles/PMC3616641/ (accessed Jul 6, 2021).

Fujimoto

and Valente

. Social network influences on adolescent substance use: Disentangling structural equivalence from cohesion. Soc Sci Med, 2012 Jun; 74(12): 1952–1960.

10.

Valente

, Gallaher

, and Mouttapa

Using social networks to understand and prevent substance use: A transdisciplinary perspective [Internet]. Subst Use Misuse, 2004; 39: 1685–1712, https://pubmed.ncbi.nlm.nih.gov/15587948/ (accessed Jul 7, 2021).

11.

Buchanan

and Latkin

. Drug use in the social networks of heroin and cocaine users before and after drug cessation. Drug Alcohol Depend, 2008; 96(3): 286–289.

12.

Shen

, Assanangkornchai

, Liu

, . Influence of social network on drug use among clients of methadone maintenance treatment centers in Kunming, China. PLoS One, 2018 Jul 3; 13(7): e0200105, 10.1371/journal.pone.0200105 (accessed Jan 4, 2023).

13.

Latkin

, Mandell

, Oziemkowska

, . Using social network analysis to study patterns of drug use among urban drug users at high risk for HIV/AIDS. Drug Alcohol Depend, 1995; 38(1): 1–9, http://www.sciencedirect.com/science/article/pii/037687169401082V (accessed Jan 4, 2023).

14.

Raveendranathan

, Jaisoorya

, Nair

, . Gender-specific correlates of alcohol use among college students in Kerala, India. Indian J Psychol Med, 2020; 42(4): 341–345.

15.

Mir

, Mahesh

, Rajanna

, Ashok

, and Singh

Substance abuse pattern among medical college students in Tumkur, Karnataka, India: a cross sectional study. Int J Community Med Public Health 2016; 4(1): 238.

16.

Kumar

. Substance use disorder in medical graduates in tertiary medical college. Int J Indian Psyhcol, 2020; 8(3): 876–883.

17.

Shen

, Assanangkornchai

, Liu

, . Influence of social network on drug use among clients of methadone maintenance treatment centers in Kunming, China. PLoS One, 2018 Jul 1; 13(7), http://pmc/articles/PMC6029801/ (accessed Jun 3, 2021).

18.

Meisel

and Goodie

. Addictive behaviors predicting prescription drug misuse in college students’ social networks. Addict Behav [Internet] 2015; 45: 110–112.

19.

Tucker

, Cheong

, Chandler

, Crawford

, and Simpson

20.

Friedman

and Aral

Social networks, risk-potential networks, health, and disease. J Urban Health, 2001;78: 411–418.

21.

Raphael

, Raveendran

, Sajna

M V

, and Raphael

Prevalence and determinants of substance abuse among youth in Central Kerala, India. Int J Community Med Public Health, 2017; 4(3): 747–751.

22.

Juyal

, Bansal

, Kishore

, Negi

, Chandra

, and Semwal

Substance use among intercollege students in district Dehradun. Indian J Community Med, 2006; 31(4): 14–16.

23.

Sorab

Sarpal SS

, Kumar

, Kaur

, and Arora

. Prevalence, pattern and familial effects of substance use among the male college students-a north Indian study. J Clin Diagnostic Res [Internet] 2013 Aug 1; 7(8): 1632–1636, http://pmc/articles/PMC3782917/ (accessed Jul 5, 2021).

24.

Panthee

, Panthee

, Gyawali

, and Kawakami

Prevalence and correlates of substance use among health care students in Nepal: A cross sectional study. BMC Public Health, 2017; 17(1): 1–10.

25.

Shegute

and Wasihun

Prevalence of substance use in university students, Ethiopia. Subst Abus Res Treat, 2021; 15: 11782218211003558.

26.

Osman

, Victor

, Abdulmoneim

, . Epidemiology of substance use among university students in Sudan. J Addict, 2016; 2016: 1–8.

27.

Musyoka

, Mbwayo

, Donovan

, and Mathai

Alcohol and substance use among first-year students at the University of Nairobi, Kenya: Prevalence and patterns. PLoS One, 2020 Aug 1; 15(8): e0238170, https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0238170 (accessed Nov 14, 2021).

28.

Jaisoorya

, Beena

, . Prevalence & correlates of tobacco use among adolescents in Kerala, India. Indian J Med Res, 2016 Nov; 144(5): 704, http://www.ncbi.nlm.nih.gov/pubmed/28361823 (accessed May 7, 2019).

29.

Quiroga

, Pinto-Carral

, García

, Molina

, Fernández-Villa

, and Martín

. The influence of adolescents’ social networks on alcohol consumption: A descriptive study of Spanish adolescents using social network analysis. Int J Environ Res Public Health, 2018 Sep 1; 15(9), https://pubmed.ncbi.nlm.nih.gov/30134531/ (accessed Nov 28, 2021).

30.

Senguttuvan

, Whiteman

, and Jensen

. Family relationships and adolescents’ health attitudes and weight: The understudied role of sibling relationships. Fam Relat, 2014; 63(3): 384–396.

31.

Youngblade

, Theokas

, Schulenberg

, Curry

, Huang

I-C

, and Novak

Risk and promotive factors in families, schools, and communities: a contextual model of positive youth development in adolescence. Pediatrics, 2007 Feb; 119 Suppl: S47–S53.

32.

Schmitz

, Lytle

, Phillips

, Murray

, Birnbaum

, and Kubik

. Psychosocial correlates of physical activity and sedentary leisure habits in young adolescents: The teens eating for energy and nutrition at school study. Prev Med (Baltim), 2002 Feb; 34(2): 266–278.

33.

Stocker

, Burwell

, and Briggs

. Sibling conflict in middle childhood predicts children’s adjustment in early adolescence. J Fam Psychol, 2002 Mar; 16(1): 50–57.

34.

Padilla-Walker

, Harper

, and Jensen

. Self-regulation as a mediator between sibling relationship quality and early adolescents’ positive and negative outcomes. J Fam Psychol, 2010 Aug; 24(4): 419–428.

35.

Milevsky

and Levitt

. Sibling support in early adolescence: Buffering and compensation across relationships. Eur J Dev Psychol, 2005; 2(3): 299–320.

36.

DiGuiseppi

, Meisel

, Balestrieri

, Ott

, Clark

, and Barnett

. Relationships between social network characteristics, alcohol use, and alcohol-related consequences in a large network of first-year college students: how do peer drinking norms fit in? Psychol Addict Behav, 2018; 32: 914–924, http://pmc/articles/PMC6296870/ (accesses May 13, 2021).

37.

Loke

and Mak

Family process and peer influences on substance use by adolescents. Int J Environ Res Public Health, 2013; 10: 3868–3885.

38.

Fujimoto

and Valente

. Decomposing the components of friendship and friends’ influence on adolescent drinking and smoking. J Adolesc Health, 2012 Aug; 51(2): 136–143, http://pmc/articles/PMC3404406/ (accessed Jul 12, 2021).

39.

Reddy

, Perry

, Stigler

, and Arora

Differences in tobacco use among young people in urban India by sex, socioeconomic status, age, and school grade: Assessment of baseline survey data. Lancet, 2006; 367(9510): 589–594.

40.

Jodalli

and Panchmal

. Tobacco use patterns and predictors among college students in Mangalore, South India: Findings from the Global Adult Tobacco Survey. Tob Prev Cessat, 2020; 6(October): 1–9.

41.

Srivastava

, Kumar

, Rashmi

Paul R

, and Dhillon

Does substance use by family members and community affect the substance use among adolescent boys? Evidence from UDAYA study, India. BMC Public Health, 2021; 21(1): 1–10.

42.

Luthar

and Goldstein

. Substance use and related behaviors among suburban late adolescents: The importance of perceived parent containment. Dev Psychopathol, 2008 Mar; 20(2): 591–614, https://pubmed.ncbi.nlm.nih.gov/18423096/ (accessed Feb 17, 2022).

43.

Patrick

, Wightman

, Schoeni

, and Schulenberg

. Socioeconomic status and substance use among young adults: A comparison across constructs and drugs. J Stud Alcohol Drugs, 2012; 73(5): 772–782.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.06 MB

Association of Social Network Characteristics with Substance Use among College-Going Young Adults: A Cross-Sectional Study