Higher Neighborhood Crime Rates Don't Always Predict Early Initiation of Tobacco, Marijuana, and Alcohol
Charles R Drew University of Medicine and Science, Los Angeles, California, USA
Morgan State University, Baltimore, Maryland, USA
Abstract
Background:
The initiation of substance use during adolescence is a significant public health concern with long-term implications. Structural and environmental factors, such as community-level crime statistics related to drug offenses, are often assumed to influence the likelihood of substance use among youth. However, the relationship between these environmental crime indicators and early substance use initiation in adolescents is not well understood.
Objective:
This study aims to examine the association between environmental drug-related crime statistics—derived from Uniform Crime Reports, including drug abuse violations, drug sales, marijuana sales, drug possession, and driving under the influence (DUI)—and the use of tobacco, marijuana, and alcohol among adolescents aged 9-16 in the United States.
Methods:
Data from the Adolescent Brain Cognitive Development (ABCD) study, which follows a cohort of adolescents from ages 9-10 to 16, were analyzed. 11,878 participants entered our analysis. The primary environmental measures included total drug abuse violations, drug sale offenses, marijuana sale offenses, drug possession offenses, and DUI reports. Substance use outcomes of interest were the frequency and prevalence of tobacco, marijuana, and alcohol use across the observed age range.
Results:
Contrary to expectations, higher levels of drug-related crime in a community were not associated with increased use of tobacco, marijuana, or alcohol among adolescents. In fact, the study found a significant inverse relationship between the total number of drug-related crimes and tobacco use, suggesting lower tobacco use in areas with higher reported drug abuse violations, drug sales, marijuana sales, drug possession, and DUI incidents. No significant associations were observed between these crime indicators and the use of marijuana or alcohol.
Conclusions:
These findings challenge the prevailing assumption that higher environmental drug-related crime statistics necessarily predict greater substance use among adolescents. The observed inverse relationship between drug-related crime and tobacco use warrants further investigation to understand the underlying mechanisms and to inform targeted intervention strategies. Future research should explore the complex interplay between structural environmental factors and youth substance use to better inform public health policies.
Untitled section
Keywords: Adolescent Substance Use, Environmental Crime Statistics, Tobacco Use, Marijuana Use, Alcohol Use, Drug-Related Crime
Article notes
Untitled section
Issue date 2024.
1.Introduction
Substance use during adolescence poses a significant public health challenge, with enduring consequences for both individual health and broader societal well-being [1,2]. Understanding the factors that contribute to the early initiation and sustained use of substances such as tobacco, marijuana, and alcohol is crucial for developing effective prevention strategies [3,4]. Among the numerous influences on adolescent substance use, structural and environmental factors have garnered increasing attention [5]. These factors, which encompass community-level characteristics like crime rates, socioeconomic conditions, and the density of tobacco and liquor retail outlets, are believed to shape the environments in which young people are exposed to and initiate substance use [6].
The impact of structural and environmental influences on various health behaviors, including substance use, is well-documented [7-10]. For instance, higher crime rates, particularly those related to drug activities, are often presumed to create environments that elevate the likelihood of substance use among adolescents [11]. The underlying assumption is that areas with elevated drug-related crime may expose youth to greater opportunities for substance use, normalize drug-related behaviors, or reflect broader social and economic distress that predisposes young people to engage in substance use as a coping mechanism [12-14].
However, while the association between structural and environmental influences on substance use is well-established in adult populations, the extent to which these higher-level factors affect the early initiation of substance use among youth remains less clear [12-14]. Adolescents are at a critical and formative stage of development, and the pathways through which environmental risk factors influence their behaviors may differ substantially from those observed in adults2. For example, research has shown that for adults, drug-related offenses—such as drug abuse violations, drug sales, marijuana sales, drug possession, and driving under the influence (DUI)—are associated with higher rates of substance use [15-17]. However, the specific role of these drug-related crime statistics in predicting substance use among adolescents is not fully understood.
2.Aims
This study utilizes data from the Adolescent Brain Cognitive Development (ABCD) study [18-22] to examine the influence of environmental drug-related crime statistics on the subsequent use of tobacco, marijuana, and alcohol among adolescents aged 9-10 to 16 in the United States. We hypothesized that adolescents residing in areas with higher levels of drug offenses—including drug sales, marijuana sales, drug possession, and DUI incidents—would exhibit higher rates of tobacco, marijuana, and alcohol use. This paper aims to explore these associations in detail, considering the broader implications for understanding the environmental and structural determinants of adolescent substance use and the potential for targeted policy interventions in areas with elevated substance use or crime rates.
3.Methods
3.1.Design and Setting
This study employed a longitudinal design using data from the Adolescent Brain Cognitive Development (ABCD) study [18-22], the largest long-term investigation of brain development and child health in the United States. The ABCD study follows a diverse cohort of children from ages 9-10 through early, mid, and late adolescence, with the goal of understanding various factors influencing brain development and related outcomes, particularly the early initiation of substance use behaviors such as tobacco, marijuana, and alcohol. The data for this analysis were collected every six months over several years, enabling early detection of substance use initiation. This frequent measurement schedule also allowed for the examination of predictors and changes in substance use patterns as participants aged from 9-10 to 16 years old.
3.2.Sample and Sampling
The ABCD study recruited a nationally representative sample of 11,878 children aged 9-10 years from 21 research sites across the United States. The sample was drawn using a probability sampling method designed to reflect the demographic diversity of the U.S. population. This approach ensured the inclusion of participants from various racial, ethnic, socioeconomic, and geographical backgrounds, providing a broad and generalizable understanding of adolescent development. For the present analysis, we included participants who had data on substance use behaviors and environmental crime statistics from the baseline assessment through the 16-year follow-up.
3.3.Process and Measures
Substance use behaviors (tobacco, marijuana, and alcohol) were assessed through self-report questionnaires administered to participants every six months. These assessments were conducted either during in-person visits at research sites or via remote surveys, depending on participant availability and logistical considerations. The self-report format was chosen to capture the participants' perceptions and experiences of substance use in their own words, providing a subjective account of their behaviors. To ensure the accuracy and reliability of self-reported data, participants were assured of the confidentiality of their responses. Standardized questionnaires, such as the iSay Sipping Inventory [23] and the web-based Timeline Follow-Back [24], were used. These measures were administered using standardized protocols across all study sites. We used three dichotomous variables to reflect the use of tobacco, marijuana, and alcohol between the ages of 9 and 16, indicating early initiation.
In addition to substance use, environmental crime statistics were derived from Uniform Crime Reports [25-27], capturing indicators such as total drug abuse violations, drug sales, marijuana sales, drug possession, and DUI incidents in the participants' residential areas. These crime statistics were linked to participants' data based on their residential zip codes, providing a measure of the environmental risk factors present in their communities. We obtained these data from the ABCD residential history dataset. The variables used included 1) Total Drug Abuse Violation, 2) Drug Sale Offenses, 3) Marijuana Offenses, 4) Drug Possession Offenses, and 5) Driving Under the Influence (DUI) [28-31].
3.4.Data Analysis
Data were analyzed using Stata to assess the relationship between baseline environmental crime statistics and substance use behaviors over the follow-up period. Pearson correlations were employed for bivariate analyses and were specifically used in Tables 1, 2, and 3. Some of the statistics reported in these tables may overlap. A significance level of p < 0.05 was set for all analyses.
| 1 | 2 | 3 | 4 | 5 | ||
|---|---|---|---|---|---|---|
| 1 Total Drug Abuse Violation | r | 1 | ||||
| P | ||||||
| 2 Drug Sale Offenses | r | 0.987 | 1 | |||
| P | <0.001 | |||||
| 3 Marijuana Offenses | r | 0.965 | 0.9755 | 1 | ||
| P | <0.001 | <0.001 | ||||
| 4 Drug Possession Offenses | r | 0.9991 | 0.9803 | 0.9589 | 1 | |
| P | <0.001 | <0.001 | <0.001 | |||
| 5 Driving Under the Influence (DUI) | r | 0.977 | 0.9674 | 0.9759 | 0.9759 | 1 |
| P | <0.001 | <0.001 | <0.001 | <0.001 |
| 1 | 2 | 3 | ||
|---|---|---|---|---|
| 1 Tobacco Initiation | r | 1 | ||
| P | ||||
| 2 Marijuana Initiation | r | 0.422 | 1 | |
| P | <0.001 | |||
| 3 Alcohol Initiation | r | 0.1826 | 0.1414 | 1 |
| P | <0.001 | <0.001 |
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | ||
|---|---|---|---|---|---|---|---|---|---|
| 1 Tobacco Initiation | r | 1.000 | |||||||
| P | |||||||||
| 2 Marijuana Initiation | r | 0.422 | 1.000 | ||||||
| P | < 0.001 | ||||||||
| 3 Alcohol Initiation | r | 0.183 | 0.141 | 1.000 | |||||
| P | < 0.001 | < 0.001 | |||||||
| 4 Total Drug Abuse Violation | r | −0.029 | 0.007 | −0.011 | 1.000 | ||||
| P | 0.002 | 0.443 | 0.249 | ||||||
| 5 Drug Sale Offenses | r | −0.027 | 0.007 | −0.012 | 0.987 | 1.000 | |||
| P | 0.005 | 0.480 | 0.206 | < 0.001 | |||||
| 6 Marijuana Offenses | r | −0.023 | 0.007 | 0.003 | 0.965 | 0.976 | 1.000 | ||
| P | 0.015 | 0.455 | 0.747 | < 0.001 | < 0.001 | ||||
| 7 Drug Possession Offenses | r | 0.029 | 0.007 | −0.010 | 0.999 | 0.980 | 0.959 | 1.000 | |
| P | 0.002 | 0.437 | 0.277 | < 0.001 | < 0.001 | < 0.001 | |||
| 8 Driving Under the Influence (DUI) | r | −0.027 | 0.008 | −0.004 | 0.977 | 0.967 | 0.976 | 0.976 | 1.000 |
| P | 0.004 | 0.405 | 0.702 | 0.000 | < 0.001 | < 0.001 | < 0.001 |
3.5.Ethics
The ABCD study received ethical approval from the Institutional Review Boards (IRBs) of the University of California, San Diego (UCSD), as well as from the IRBs of the 20 other participating research sites. Informed consent was obtained from the parents or legal guardians of all youth participants, and assent was obtained from the youth participants themselves. The ethical considerations of this study were aligned with the principles outlined in the Declaration of Helsinki and the U.S. Department of Health and Human Services regulations for the protection of human research subjects. Data were kept fully confidential, and all participants were informed of their right to withdraw from the study at any time without penalty. The confidentiality and anonymity of all collected data were strictly maintained, with identifying information removed and data securely stored in accordance with federal regulations and IRB guidelines.
4.Results
4.1.Correlations between Crime Data
As shown in Table 1, there were strong positive correlations between the different types of crime data reported in the ABCD study. Specifically, total drug abuse violations were highly correlated with drug sale offenses (r = 0.987; p < 0.001), marijuana offenses (r = 0.965; p < 0.001), drug possession offenses (r = 0.999; p < 0.001), and DUI incidents (r = 0.977; p < 0.001). Similarly, drug sale offenses were strongly correlated with marijuana offenses (r = 0.976; p < 0.001), drug possession offenses (r = 0.980; p < 0.001), and DUI incidents (r = 0.967; p < 0.001). All other crime categories showed significant positive correlations with each other, indicating a high level of overlap among these crime statistics.
4.2.Correlations between Substance Use Initiation in Youth
Table 2 presents the correlations between the initiation of different substances among youth. Early tobacco initiation showed a significant positive correlation with early initiation of both marijuana (r = 0.422, p < 0.001) and alcohol (r = 0.183, p < 0.001). Similarly, early marijuana initiation was positively correlated with early alcohol initiation (r = 0.141, p < 0.001). These findings suggest a clustering of early substance use, indicating that the initiation of one substance is associated with the initiation of others. Notably, the strongest association was observed between early tobacco and marijuana initiation.
4.3.Correlations between Crime Data and Substance Use Initiation in Youth
As indicated in Table 3, the correlations between crime data and substance use initiation were generally weak. Total Drug Abuse Violations were negatively correlated with Tobacco initiation (r = −0.029; p = 0.002) and Alcohol initiation (r = −0.011; p = 0.249), but showed no significant correlation with Marijuana initiation (r = 0.007; p = 0.443). Similarly, Drug Sale Offenses were negatively correlated with Tobacco initiation (r = −0.027; p = 0.005) and Alcohol initiation (r = −0.012; p = 0.206), with no significant correlation with Marijuana initiation (r = 0.007; p = 0.480). Marijuana Offenses were also negatively correlated with Tobacco initiation (r = −0.023; p = 0.015) and had no significant correlation with Marijuana (r = 0.007; p = 0.455) or Alcohol initiation (r = 0.003; p = 0.747). Drug Possession Offenses and DUI incidents followed similar patterns, showing negative correlations with Tobacco initiation and no significant correlations with Marijuana or Alcohol initiation.
5.Discussion
In this study, we aimed to explore the influence of baseline environmental drug-related crime statistics on the early initiation of tobacco, marijuana, and alcohol use among adolescents aged 9-16 in the United States. Specifically, we sought to determine whether indicators derived from Uniform Crime Reports—such as drug abuse violations, drug sales, marijuana sales, drug possession, and DUI at baseline—were predictive of higher subsequent substance use within this population. The results are intended to contribute to our understanding of how structural and environmental factors, particularly those related to crime, influence the early initiation and continued use of substances in youth.
Contrary to our expectations, the results did not indicate a positive association between higher baseline drug-related crime rates and increased initiation of any substances among US adolescents. No significant correlations were observed between the crime statistics and the use of marijuana or alcohol. Surprisingly, we found lower rates of tobacco use in areas reporting higher total drug offenses, drug sales, marijuana sales, drug possession, and DUI incidents. This inverse, weak, yet statistically significant relationship between these environmental crime indicators and tobacco use among ABCD adolescents was unexpected.
The literature on substance use has long suggested that ecological and structural factors, such as community crime rates, socioeconomic status, and neighborhood disorganization, play a significant role in shaping substance use behaviors [7,32]. Environments with higher crime rates, particularly drug-related offenses, are typically associated with increased exposure to substance use opportunities, normalization of risky behaviors, and overall higher substance use among adolescents. These findings have led to the widely held belief that individuals in high-crime areas are more likely to engage in substance use due to environmental stressors and increased accessibility to substances.
However, our study’s findings did not align with this prevailing understanding. Rather than observing higher youth substance use in areas with elevated drug-related crime33,34, we found no significant association for marijuana or alcohol use and an unexpected inverse relationship with tobacco use. This discrepancy raises questions about the applicability of existing theories to the specific age group and demographic context examined in the ABCD study. It suggests that the relationship between environmental crime factors and adolescent substance use may be more complex and potentially mediated by other variables not captured in our study [35-37].
Several potential explanations could account for the divergence between our findings and the existing literature. One possibility is that adolescents in high-crime areas may be subject to stricter parental or community supervision, reducing opportunities for substance use [32,38]. Additionally, these areas might have a more robust law enforcement presence or community programs aimed at substance use prevention, which could contribute to lower use rates. Moreover, as previous research by Luthar [39-48] has shown, substance use is increasingly prevalent in affluent areas, indicating that environmental influences may differ based on socioeconomic status. Another possibility is that our measures of drug-related crime did not adequately capture the nuances of the environments these adolescents live in, such as informal social controls or protective community factors that mitigate the risk of substance use. Finally, resilience factors, such as individual-level psychological or social support, might buffer the impact of adverse environments on substance use behaviors.
5.1.Future Research
Future research should aim to explore more deeply the mechanisms behind the lack of association between environmental crime data and adolescent substance use initiation. Studies could benefit from incorporating a broader range of ecological and individual-level variables, such as neighborhood cohesion, family dynamics, and access to substance use prevention resources, to better understand the complex interplay of factors. Longitudinal research designs that follow adolescents over an extended period may also provide more insight into how the influence of environmental crime evolves as youth age. Additionally, qualitative studies could investigate the lived experiences of adolescents in high-crime areas to uncover contextual factors that might not be captured through quantitative measures.
5.2.Limitations
This study has several limitations that should be acknowledged. First, relying solely on baseline Uniform Crime Reports does not fully capture the complexities of the environments in which adolescents live, as these reports are limited to recorded offenses and do not account for unreported crimes or the subjective experiences of residents. Additionally, the same environmental conditions may have different effects across groups, with environmental impacts potentially being buffered or modified by other factors at the family or individual level. Moreover, the short follow-up period of the study limits our ability to generalize our conclusions about the lack of a relationship between environmental crime and substance use. The generalizability of our findings may also be constrained by the non-random sample design of the ABCD study. While the ABCD demographic data reflect the U.S. population, the study included larger samples from certain geographic areas, such as California, which may limit the representativeness of our results for all U.S. adolescents. In addition, we only investigated overall associations. The effects of social context may differ for males, females, low-income, and high-income groups. Finally, this study only investigated the effect of crime data, without considering other contextual factors such as retail density, the availability of substances in neighborhoods, schools, and families, or substance use by parents. As a result, not all relevant contextual factors were exhaustively studied here.
6.Conclusions
In conclusion, our study challenges the assumption that higher environmental drug-related crime statistics predict increased substance use among adolescents. The unexpected finding of a weak yet statistically significant association between higher crime rates and lower tobacco use may be influenced by the large sample size. These results suggest that the relationship between structural factors and adolescent behavior is complex and warrants further investigation. Our findings highlight the need for a nuanced understanding of ecological influences on youth substance use and underscore the importance of considering a wide range of factors, including potential protective elements, in future research and intervention efforts.
Acknowledgment
Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive Development (ABCD) Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children age 9–10 and follow them over 10 years into early adulthood. The opinions, findings, and conclusions herein are those of the authors and not necessarily represent The Regents of the University of California, or any of its programs. The ABCD Study® is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/consortium_members/. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators.
Authors’ Funding
Payam Sheikhattari received funding from the NIMHD RCMI award (U54MD013376). Shervin Assari research is partially supported by Funds provided by The Regents of the University of California, Tobacco-Related Diseases Research Program, Grant Number no T32IR5355. The funders were not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.
References
Untitled section
References
- [1]Lewinsohn PM, Rohde P, Brown RA. Level of current and past adolescent cigarette smoking as predictors of future substance use disorders in young adulthood. Addiction. 1999;94(6):913–921.
- [2]Chassin L, Hussong A, Barrera M Jr, Molina BS, Trim R, Ritter J. Adolescent substance use. Handbook of adolescent psychology. 2004:665–696.
- [3]Graham JW, Marks G, Hansen WB. Social influence processes affecting adolescent substance use. Journal of applied psychology. 1991;76(2):291.
- [4]Chassin L, Ritter J, Trim RS, King KM. Adolescent substance use disorders. Child psychopathology. 2003;2:199–230.
- [5]Kenney A, Dennis CB. Environmental paths that inform adolescent substance use prevention. Journal of Human Behavior in the Social Environment. 2019;29(7):897–908.
- [6]Murray DM, Perry CL. The prevention of adolescent drug abuse: Implications of etiological, developmental, behavioral, and environmental models. Etiology of drug abuse: implications for prevention National Institute on Drug Abuse Research Monograph. 1985;56:236–256.
- [7]Amaro H, Sanchez M, Bautista T, Cox R. Social vulnerabilities for substance use: Stressors, socially toxic environments, and discrimination and racism. Neuropharmacology. 2021;188:108518.
- [8]Snedker KA, Herting JR, Walton E. Contextual effects and adolescent substance use: Exploring the role of neighborhoods. Social Science Quarterly. 2009;90(5):1272–1297.
- [9]Pinter-Wollman N, Jelić A, Wells NM. The impact of the built environment on health behaviours and disease transmission in social systems. Philosophical Transactions of the Royal Society B: Biological Sciences. 2018;373(1753):20170245.
- [10]Galea S, Rudenstine S, Vlahov D. Drug use, misuse, and the urban environment. Drug and alcohol review. 2005;24(2):127–136.
- [11]Cooley-Strickland M, Quille TJ, Griffin RS, Stuart EA, Bradshaw CP, Furr-Holden D. Community violence and youth: Affect, behavior, substance use, and academics. Clinical child and family psychology review. 2009;12:127–156.
- [12]Dick DM, Pagan JL, Viken R, et al. Changing environmental influences on substance use across development. Twin Research and Human Genetics. 2007;10(2):315–326.
- [13]Rhee SH, Hewitt JK, Young SE, Corley RP, Crowley TJ, Stallings MC. Genetic and environmental influences on substance initiation, use, and problem use in adolescents. Archives of general psychiatry. 2003;60(12):1256–1264.
- [14]Kirby J, Van der Sluijs W, Inchley J. Young people and substance use: the influence of personal, social and environmental factors on substance use among adolescents in Scotland. Edinburgh: NHS Health Scotland. 2008;
- [15]Boardman JD, Finch BK, Ellison CG, Williams DR, Jackson JS. Neighborhood disadvantage, stress, and drug use among adults. Journal of health and social behavior. 2001:151–165.
- [16]Fagan AA, Wright EM, Pinchevsky GM. Exposure to violence, substance use, and neighborhood context. Social science research. 2015;49:314–326.
- [17]Linton SL, Haley DF, Hunter-Jones J, Ross Z, Cooper HL. Social causation and neighborhood selection underlie associations of neighborhood factors with illicit drug-using social networks and illicit drug use among adults relocated from public housing. Social science & medicine. 2017;185:81–90.
- [18]Alcohol Research: Current Reviews Editorial S. NIH's Adolescent Brain Cognitive Development (ABCD) Study. Alcohol Res. 2018;39(1):97.
- [19]Garavan H, Bartsch H, Conway K, et al. Recruiting the ABCD sample: Design considerations and procedures. Developmental cognitive neuroscience. 2018;32:16–22.
- [20]Kennedy JT, Harms MP, Korucuoglu O, et al. Reliability and stability challenges in ABCD task fMRI data. Neuroimage. 2022;252:119046.
- [21]Hoffman EA, Clark DB, Orendain N, Hudziak J, Squeglia LM, Dowling GJ. Stress exposures, neurodevelopment and health measures in the ABCD study. Neurobiol Stress. Feb 2019;10:100157. doi: 10.1016/j.ynstr.2019.100157
- [22]Wade NE, Tapert SF, Lisdahl KM, Huestis MA, Haist F. Substance use onset in high-risk 9-13 year-olds in the ABCD study. Neurotoxicol Teratol. May-Jun 2022;91:107090. doi: 10.1016/j.ntt.2022.107090
- [23]Murphy MA, Dufour SC, Gray JC. The association between child alcohol sipping and alcohol expectancies in the ABCD study. Drug and alcohol dependence. 2021;221:108624.
- [24]Sullivan RM, Wade NE, Wallace AL, et al. Substance use patterns in 9 to 13-year-olds: Longitudinal findings from the Adolescent Brain Cognitive Development (ABCD) study. Drug and alcohol dependence reports. 2022;5:100120.
- [25]Boxerman R. The Effect upon State Crime Rates of the Legalization of Recreational Marijuana in California. 2020;
- [26]Caulkins JP, Reuter P. How drug enforcement affects drug prices. Crime and Justice. 2010;39(1):213–271.
- [27]Kozlowski M, Glazener E, Mitchell JA, Lynch JP, Smith J. Decriminalization and depenalization of marijuana possession: A case study of enforcement outcomes in Prince George's County. Criminology, Crim Just L & Soc’y. 2019;20:109.
- [28]Schwartz J, & Rookey BD (2008). The narrowing gender gap in arrests: Assessing competing explanations using self-report, traffic fatality, and official data on drunk driving, 1980–2004. Criminology, 46(3), 637–671.
- [29]Carpenter C., 2007. Heavy alcohol use and crime: Evidence from underage drunk-driving laws. The Journal of Law and Economics, 50(3), pp.539–557.
- [30]Rosen L., 1995. The creation of the uniform crime report: The role of social science. Social Science History, 19(2), pp.215–238.
- [31]Shulman HM, 1966. The measurement of crime in the United States. The Journal of Criminal Law, Criminology, and Police Science, 57(4), pp.483–492.
- [32]Tillyer MS, Ray JV, Hinton ME. Protecting high-risk youth in high-risk contexts: Neighborhoods, parenting, and victimization. Journal of Youth and Adolescence. 2018;47:2027–2040.
- [33]Dembo R, Wareham J, Schmeidler J. Drug use and delinquent behavior: A growth model of parallel processes among high-risk youths. Criminal justice and behavior. 2007;34(5):680–696.
- [34]Carter PM, Cranford JA, Buu A, et al. Daily patterns of substance use and violence among a high-risk urban emerging adult sample: Results from the Flint Youth Injury Study. Addictive behaviors. 2020;101:106127.
- [35]Farrell C, Zimmerman GM. Does offending intensify as exposure to violence aggregates? Reconsidering the effects of repeat victimization, types of exposure to violence, and poly-victimization on property crime, violent offending, and substance use. Journal of Criminal Justice. 2017;53:25–33.
- [36]Brenner AB, Bauermeister JA, Zimmerman MA. Neighborhood variation in adolescent alcohol use: Examination of socioecological and social disorganization theories. Journal of studies on alcohol and drugs. 2011;72(4):651–659.
- [37]Zimmerman GM, Vasquez BE. Decomposing the peer effect on adolescent substance use: Mediation, nonlinearity, and differential nonlinearity. Criminology. 2011;49(4):1235–1273.
- [38]Wasserman GA, Miller LS, Pinner E, Jaramillo B. Parenting predictors of early conduct problems in urban, high-risk boys. Journal of the American Academy of Child & Adolescent Psychiatry. 1996;35(9):1227–1236.
- [39]Luthar SS, Small PJ, Ciciolla L. Adolescents from upper middle class communities: Substance misuse and addiction across early adulthood. Development and psychopathology. 2018;30(1):315–335.
- [40]Luthar SS, D'AVANZO K. Contextual factors in substance use: A study of suburban and inner-city adolescents. Development and Psychopathology. 1999;11(4):845–867.
- [41]Luthar SS. The culture of affluence: Psychological costs of material wealth. Child development. Nov-Dec 2003;74(6):1581–1593. doi: 10.1046/j.1467-8624.2003.00625.x
- [42]Sabri O, Becker GA, Meyer PM, et al. First-in-human PET quantification study of cerebral alpha4beta2* nicotinic acetylcholine receptors using the novel specific radioligand (−)−[(18)F]Flubatine. Neuroimage. Sep 2015;118:199–208. doi: 10.1016/j.neuroimage.2015.05.065
- [43]Luthar SS, Sexton CC. The high price of affluence. Advances in child development and behavior. Elsevier; 2004:125–162.
- [44]Luthar SS, Kumar NL, Zillmer N. High-Achieving Schools Connote Risks for Adolescents: Problems Documented, Processes Implicated, and Directions for Interventions. Am Psychol. Oct 2019;75(7):983–995. doi: 10.1037/amp0000556
- [45]Luthar SS, Barkin SH, Crossman EJ. “I can, therefore I must”: Fragility in the upper-middle classes. Development and Psychopathology. 2013;25(4 0 2):1529.
- [46]Suh B, Luthar SS. Parental aggravation may tell more about a child's mental/behavioral health than Adverse Childhood Experiences: Using the 2016 National Survey of Children's Health. Child Abuse Negl.> Mar 2020;101:104330. doi: 10.1016/j.chiabu.2019.104330
- [47]McMahon TJ, Luthar SS. Patterns and correlates of substance use among affluent, suburban high school students. Journal of Clinical Child and Adolescent Psychology. 2006;35(1):72–89.
- [48]Luthar SS, Becker BE. Privileged but pressured? A study of affluent youth. Child development. 2002;73(5):1593–1610.