Social Networks and Smoking: Exploring the Effects of Peer Influence and Smoker Popularity Through Simulations
Open accessAbstract
Adolescent smoking and friendship networks are related in many ways that can amplify smoking prevalence. Understanding and developing interventions within such a complex system requires new analytic approaches. We draw on recent advances in dynamic network modeling to develop a technique that explores the implications of various intervention strategies targeted toward micro-level processes. Our approach begins by estimating a stochastic actor-based model using data from one school in the National Longitudinal Study of Adolescent Health. The model provides estimates of several factors predicting friendship ties and smoking behavior. We then use estimated model parameters to simulate the coevolution of friendship and smoking behavior under potential intervention scenarios. Namely, we manipulate the strength of peer influence on smoking and the popularity of smokers relative to nonsmokers. We measure how these manipulations affect smoking prevalence, smoking initiation, and smoking cessation. Results indicate that both peer influence and smoking-based popularity affect smoking behavior and that their joint effects are nonlinear. This study demonstrates how a simulation-based approach can be used to explore alternative scenarios that may be achievable through intervention efforts and offers new hypotheses about the association between friendship and smoking.
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The interventions reported across the review varied widely and included a few targeting behavioural change at the individual patient level, such as weight reduction and smoking cessation programs, or at the level of healthcare professionals, such as interventions targeting general practitioners to reduce inappropriate prescribing behaviours.
“The interventions reported across the review varied widely and included a few targeting behavioural change at the individual patient level, such as weight reduction [27] and smoking cessation programs [10, 44, 49], or at the level of healthcare professionals, such as interventions targeting general practitioners to reduce inappropriate prescribing behaviours [26, 45, 46, 48].”
From Optimisation of complex health interventions prior to a randomised controlled trial: a scoping review of strategies used (2016) · cites this paper as [49]
The remaining included studies did not specify the guidelines or frameworks adopted to develop the intervention of interest.
“The remaining studies did not specify the guidelines or frameworks adopted to develop the intervention of interest [48–50].”
From Optimisation of complex health interventions prior to a randomised controlled trial: a scoping review of strategies used (2016) · cites this paper as [49]
Seven included studies were a preliminary investigation of the effect of the intervention or a combination of its components on proxy or final outcomes and intervention long-term impact.
“Seven studies were a preliminary investigation of the effect of the intervention or a combination of its components on proxy or final outcomes and intervention long-term impact [10, 24, 44–46, 49, 50].”
From Optimisation of complex health interventions prior to a randomised controlled trial: a scoping review of strategies used (2016) · cites this paper as [49]
Factorial experiments, uncontrolled pilot studies and probability models tended to be used to test specific components and to identify an intervention's potential effect.
“For example, factorial experiments, uncontrolled pilot studies and probability models tended to be used to test specific components and to identify an intervention’s potential effect [10, 25, 29, 38, 44, 49, 50].”
From Optimisation of complex health interventions prior to a randomised controlled trial: a scoping review of strategies used (2016) · cites this paper as [49]
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- Authors
- Schaefer, D. R., adams, J., & Haas, S. A.
- Affiliations
- Arizona State University (US)
- American University (US)
- Pennsylvania State University (US)
- Venue
- Health Education & Behavior
- Publisher
- SAGE Publications
- Published
- 2013-10
- Volume
- 40
- Issue
- 1_suppl
- Pages
- 24S-32S
- ISSN
- 1090-1981 (ISSN-L)
- 1552-6127
- PMC
- PMC4587387
- OpenAlex
- W2033380302
- Cited by
- 102
- References
- 38
- Citations by year
- 2026: 7 · 2025: 8 · 2024: 6 · 2023: 10 · 2022: 10 · 2021: 9 · 2020: 8 · 2019: 7 · 2018: 9 · 2017: 5 · 2016: 7 · 2015: 11 · 2014: 3 · 2013: 1 · 2012: 1
- FWCI
- 5.11
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Health Education & Behavior (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institute on Minority Health and Health Disparities (L60 MD003181)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R21HD060927)
- Topics
- Health Sciences › Medicine › Physiology › Smoking Behavior and Cessation
- Health Sciences › Medicine › Epidemiology › Substance Abuse Treatment and Outcomes
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- adolescent smoking, friendship networks, peer influence, smoking initiation, smoking cessation, smoking prevalence, stochastic actor-oriented models, dynamic network modeling, school-based intervention
- MeSH terms
- Adolescent, Adolescent Behavior, Computer Simulation, Female, Humans, Male, Peer Group, Smoking, Social Support, Systems Theory, Prevalence, Smoking Cessation, Friends, National Longitudinal Study of Adolescent Health
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