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Objectives <p>To examine whether exposure to crime within individuals’ immediate social networks is associated with the risk of commercial sexual exploitation (CSE) victimization and assess whether this reflects an independent network effect rather than underlying individual or structural vulnerabilities.</p> Methods <p>A population-based dataset of minors and young adults in the Netherlands (2016–2023) with police-recorded CSE victimization was used and linked to large-scale ego-network data (household, family, school, neighborhood, and work ties). These unique data provided an innovative lens through which to study network exposure and victimization risk. Latent profile analysis identified patterns of network exposure to offending and victimization. Individuals were assigned to profiles using modal classification. Matching weights were applied to adjust for selection into the high-exposure profile based on individual and neighborhood covariates. Next, a set of weighted logistic regression models with doubly robust adjustment estimated associations across three comparisons: CSE victims versus (1) other sex crime victims, (2) other crime victims, and (3) the general population.</p> Results <p>Across all comparisons, individuals in the high-exposure network profile had a substantially higher likelihood of CSE victimization, also after adjusting for individual risk factors (e.g., prior victimization, youth care involvement) and neighborhood characteristics.</p> Conclusions <p>Exposure to crime within proximal social networks constitutes a distinct and persistent risk factor for CSE victimization. These findings extend lifestyle and routine activity theories by emphasizing relational, rather than purely situational, exposure to risk. Limitations include potential unmeasured confounding, temporal ambiguity, and classification uncertainty in latent profiles. Future research should further examine causal mechanisms and incorporate network-informed prevention strategies targeting relational environments.</p>

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Everyday Network Exposure to Crime and Victimization and its Association with Commercial Sexual Exploitation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月31日

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  • Ieke de Vries,
  • Miranda Sentse,
  • Arjan Blokland

摘要

Objectives

To examine whether exposure to crime within individuals’ immediate social networks is associated with the risk of commercial sexual exploitation (CSE) victimization and assess whether this reflects an independent network effect rather than underlying individual or structural vulnerabilities.

Methods

A population-based dataset of minors and young adults in the Netherlands (2016–2023) with police-recorded CSE victimization was used and linked to large-scale ego-network data (household, family, school, neighborhood, and work ties). These unique data provided an innovative lens through which to study network exposure and victimization risk. Latent profile analysis identified patterns of network exposure to offending and victimization. Individuals were assigned to profiles using modal classification. Matching weights were applied to adjust for selection into the high-exposure profile based on individual and neighborhood covariates. Next, a set of weighted logistic regression models with doubly robust adjustment estimated associations across three comparisons: CSE victims versus (1) other sex crime victims, (2) other crime victims, and (3) the general population.

Results

Across all comparisons, individuals in the high-exposure network profile had a substantially higher likelihood of CSE victimization, also after adjusting for individual risk factors (e.g., prior victimization, youth care involvement) and neighborhood characteristics.

Conclusions

Exposure to crime within proximal social networks constitutes a distinct and persistent risk factor for CSE victimization. These findings extend lifestyle and routine activity theories by emphasizing relational, rather than purely situational, exposure to risk. Limitations include potential unmeasured confounding, temporal ambiguity, and classification uncertainty in latent profiles. Future research should further examine causal mechanisms and incorporate network-informed prevention strategies targeting relational environments.