Neighborhood characteristics and prescription drug misuse among adolescents: The importance of social disorganization and social capital

International Journal of Drug Policy 46 (2017) 47–53

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International Journal of Drug Policy journal homepage: www.elsevier.com/locate/drugpo

Research paper

Neighborhood characteristics and prescription drug misuse among adolescents: The importance of social disorganization and social capital Jason A. Ford* , Sarah Ann Sacra, Alexis Yohros Department of Sociology, University of Central Florida, Orlando, FL 32816, United States

A R T I C L E I N F O

A B S T R A C T

Article history: Received 5 December 2016 Received in revised form 5 April 2017 Accepted 2 May 2017 Available online xxx

Background: Prior research on prescription drug misuse has focused on identifying individual risk factors. While a few studies examine differences in misuse based on geographic residence, there is a lack of research that examines the relevance of neighbourhood characteristics. Methods: The current research used data from the 2000 National Household Survey on Drug Abuse, a sample of respondents that was generalizable to the non-institutionalised population of the United States. Logistic regression models were estimated to examine the relationship between neighbourhood characteristics (e.g., social disorganisation, social capital, and social participation) and prescription drug misuse (e.g., any misuse, pain reliever misuse, sedative/tranquiliser misuse, and stimulant misuse) among adolescent respondents ages 12–17. Results: Findings show that neighbourhood characteristics were significantly associated with any prescription drug misuse and also the misuse of prescription opioids. Adolescents in socially disorganised neighbourhoods and also those in neighbourhoods with lower levels of social capital were more likely to report prescription drug misuse. Interestingly, adolescents with greater levels of social participation were more likely to report prescription drug misuse. Conclusion: These findings were largely consistent with prior research examining the significance of neighbourhood characteristics in relation to crime and deviance. In order to adequately address the ongoing prescription drug epidemic in the United States, policy makers must address the neighbourhood characteristics that are known to be associated with prescription drug misuse. © 2017 Elsevier B.V. All rights reserved.

Keywords: Prescription drug misuse Neighbourhood characteristics Social disorganisation Social capital

Introduction The United States is in the midst of a prescription drug epidemic. Epidemiological surveillance data shows that prescription drug misuse has the second highest prevalence rate for any illegal drug use, and this is primarily driven by the misuse of prescription opioids (Center for Behavioral Health Statistics and Quality, 2015). Data from the United States also shows a dramatic increase, 114% between 2014 and 2011, in visits to hospital emergency rooms that were related to prescription drug misuse (Center for Behavioral Health Statistics and Quality, 2013). Also, the incidence of neonatal abstinence syndrome, associated with prescription opioids, nearly tripled in the U.S. between 2000 and 2009 (Patrick et al., 2012). Drug overdose is now the leading cause of accidental death in the United States and this is largely due

* Corresponding author. E-mail address: [email protected] (J.A. Ford). http://dx.doi.org/10.1016/j.drugpo.2017.05.001 0955-3959/© 2017 Elsevier B.V. All rights reserved.

to prescription drug misuse (Centers for Disease Control and Prevention, 2016). For these reasons, much research attention has focused on prescription drug misuse among adolescents and young adults. The bulk of this research has identified primarily individual level risk factors associated with prescription drug misuse. To date, research has focused on demographic, social, psychological, and behavioural factors (Ford & Rigg, 2015; Rigg & Ford, 2014; Young, Glover, & Havens, 2012). Partly due to the lack of publicly available data with geographic identifiers and quality measures of neighbourhood characteristics, there is a noticeable lack of research that focuses on neighbourhood characteristics and prescription drug misuse. This limitation is troubling, given the fact that drug “epidemics” have exacted a devastating toll on certain types of neighbourhoods in the past (Acker, 2010; Golub & Brownstein, 2013; Reinarman & Levine, 2004). While existing research has not specifically examined the relationship between neighbourhood characteristics and prescription drug misuse, a few studies have examined the importance of

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geographic residence, or urban, suburban, rural differences in misuse (Keyes, Cerda, Brady, Havens, & Galea, 2014). A number of studies have used data from the National Survey on Drug Use and Health, a sample of respondents aged 12 and older that is representative to the non-institutionalised population of the United States, to examine this difference. Focusing on adolescents, respondents aged 12–17, several studies found that prescription drug misuse overall (Ford, 2009; Havens, Young, & Havens, 2011), prescription opioid misuse (Ford, 2009; Monnat & Rigg, 2016; Wang, Becker, & Fiellin, 2013; Wu, Pilowsky, & Patkar, 2008), prescription sedative misuse (Ford, 2009), and prescription tranquiliser misuse (Ford & Rivera, 2008) was more likely in rural or non-metropolitan areas. Research focusing on adult respondents has shown those living in urban areas have higher rates of prescription opioid misuse than those living in rural areas (Rigg & Monnat, 2015). A number of studies published by researchers affiliated with the Center on Drug and Alcohol Research at the University of Kentucky have used regional samples to investigate urban/rural differences in prescription drug misuse. These studies show that rural users had an earlier age of onset for prescription drug misuse, higher rates of lifetime and current prescription drug misuse, and were also more likely to snort and inject prescription drugs (Young & Havens, 2012; Young, Havens, & Leukefeld, 2010). A study of respondents who were on felony probation found that those who lived in rural area were about five times more likely to report prescription opioid misuse compared to those who lived in urban areas (Havens et al., 2007). The lack of research on neighbourhood characteristics and prescription drug misuse is interesting given the importance that social scientists tend to place on neighbourhoods (Aneshensel & Sucoff, 1996; Diez Roux, 2001; Hill, Ross, & Angel, 2005; Sampson, 2012). Much of the research in this area has focused on the importance of neighbourhood disadvantage, sometimes referred to as social disorganisation. Social disorganisation focuses on the forces at work in large urban areas, primarily structural disadvantage and cultural norms to explain involvement in crime and deviance (Bursik & Grasmik, 1993; Sampson, 2012; Shaw & McKay, 1942). A number or researchers have examined the relationship between neighbourhood disadvantage and substance use. Neighbourhood disadvantage, generally characterised by poor housing conditions, high levels of school dropout and unemployment, fewer intact families, lower socioeconomic status, and a transient population has been shown to be an important correlate of drug use (Hayes-Smith & Whaley, 2009; Hays, Hays, & Mulhall, 2003; Hill & Angel, 2005; Winstanley et al., 2008). Among adolescents, neighbourhood disadvantage may play a role in the availability of substances, as well as acceptability of use, providing a context where drug use can be initiated, established, and maintained (Jang & Johnson, 2001). Neighbourhood disadvantage also lowers social cohesion in neighbourhoods, which is associated with higher rates of adolescent drug and alcohol related arrests (Duncan, Duncan, & Stryker, 2002). In addition to neighbourhood disadvantage several studies examine the relationship between social capital and substance use. Social capital, generally defined as having access to a network of pro-social relationship manifested by trust, reciprocity, and mutual cooperation, has become an important concept in the social sciences (Coleman, 1988; Putnam, 2001). Much of the research on social capital and drug use finds a significant association between social capital and decreased substance use among adolescents and young adults (Awgu, Magura, & Coryn, 2016; Curran, 2007; Reynoso-Vallejo, 2011; Weitzman, Byrd & Auinger, 1999; Winstanley et al., 2008). Social capital is likely associated with lower levels of drug use due to

the strong social bonds that access to social capital makes possible. The current study seeks to address an important gap in the literature on prescription drug misuse. While prescription drug misuse has been widely identified as a major public health issue, there is a glaring lack of research that focuses on identifying neighbourhood characteristics that are significantly associated with prescription drug misuse. Given that drug use has been shown to isolate itself to certain types of neighbourhoods, the crack epidemic for example, it is important to fully understand how both neighbourhood and individual levels characteristics influence prescription drug misuse. Thus, the current research examines the relationship between important neighbourhood characteristics, social disorganisation and social capital, and prescription drug misuse. Methods Data The data for the current study was the 2000 National Household Survey on Drug Abuse (NHSDA), an ongoing study sponsored by the U.S. Substance Abuse and Mental Health Services Administration that dates back to the 1970s and examines the prevalence of substance use and mental illness in a sample that is generalisable to the civilian noninstitutionalised population of the United States aged 12 and older. More recent data from the same survey is available, but the questions we used to measure neighbourhood disadvantage were discontinued from the survey. A sample of 71,764 persons aged 12 and older was generated using a state-based sampling plan, including all 50 states and Washington, D.C. Each state were geographically divided into equal sized field interviewer (FI) regions. These FI regions were then split into smaller areas composed of adjacent census blocks or segments. These segments served as the primary sampling unit. Dwelling units (e.g., housing units or group quarters) were then selected within the primary sampling unit. The sampling design required roughly the same number of respondents in three age groups: 12–17, 18–25, and 26 and older. The weighted screening response rate was 93% and the weighted interview response rate was 74%. The NHSDA implemented many strategies to improve the validity of the survey. Given that several survey items cover sensitive or illegal behaviours, respondent privacy was enhanced by the interview procedures. Respondents were surveyed in the privacy of their own homes, and a combination of computerassisted personal interviewing (CAPI) and audio computer-assisted self-interviewing (ACASI) were used to collect the data (Office of Applied Studies, 2001). This data collection strategy allowed survey respondents to enter responses directly into a computer, providing respondents with a highly private and confidential means of responding to questions, thereby increasing the level of honest reporting of illicit drug use and other sensitive behaviours (Aquilino, Wright, & Supple, 2000; Newman et al., 2002; Perlis, Des Jarlais, Friedman, Arasteh, & Turner, 2004). The current research used data from the public use version of the NHSDA (N = 56,680), which was created using a subsampling step to control the risk of disclosing the identity of any respondent. The current research focuses on only adolescent respondents, ages 12–17, in the NHSDA (N = 19,430). We used listwise deletion to handle observations with missing data. With missing cases removed, about 8% of the respondents, we had a total of 17,856 respondents in our analytical models. Analysis showed that respondents with missing data, primarily on the neighbourhood characteristic measures, had a lower prevalence of prescription drug misuse than respondents with no missing data.

J.A. Ford et al. / International Journal of Drug Policy 46 (2017) 47–53

Measures We included four different measures of prescription drug misuse in the past 12 months as dependent variables, all of which were coded 0 = No use, 1 = use. Prescription drug misuse was based on the following survey item . . . use prescription medication that was not prescribed for you or that you took only for the experience or feeling that it caused. We included measures of (1) any prescription drug misuse, (2) prescription opioid misuse, (3) sedatives or tranquilisers misuse, and (4) stimulant misuse. We included three separate measures to examine neighbourhood characteristics. First, we created a scale to measure social disorganisation. This scale included six items that described the neighbourhood where the respondent currently lived: there is a lot of crime, a lot of drug selling, there are lots of street fights, there are many empty or abandoned buildings, there is a lot of graffiti, and people move in and out often. These items were coded (1) strongly disagree, (2) somewhat disagree, (3) agree, and (4) strongly agree, so that a higher score reflected a greater perception of social disorganisation. Second, we created a scale to measure social capital. This scale included two items: people often help each other out and people often visit each other’s homes. These items were coded (1) strongly disagree, (2) somewhat disagree, (3) agree, and (4) strongly agree, so that a higher score reflected a greater perception of social capital. Finally, we created an index of social participation that captured involvement in different activities: (1) peer mentoring or tutoring program, (2) community centers, (3) boy or girl scouts, (4) team sports, (5) 4-H club, (6) volunteer or community work, (7) church, and (8) private lessons. These items were all coded 0 = no and 1 = yes and then summed. We included a number of covariates in our multivariate models. These included a number of demographic characteristics: age (coded 12–17), gender (0 = female, 1 = male), race (0 = nonwhite, 1 = white), and respondent enrolled in at least one government assistance program (0 = no, 1 = yes). We also measured geographic residence (1 = urban, 2 = suburban, 3 = rural), whether a respondent lived with biological parents (1 = lived with neither parent, 2 = lived with only one parent, 3 = lived with both parents), and a measure of school enrolment (0 = not currently enrolled, 1 = currently enrolled). We also included a scale measuring respondent delinquency that included the following items: gotten into serious fight, taken part in a group/gang fight, carried a handgun, sold illegal drugs, stolen or tried to steal something worth mot than fifty dollars, and attacked someone with the intent to seriously hurt them. This scale was coded with a higher score reflecting greater involvement in delinquent behaviour. Finally, we included a number of measures of substance use, all coded 0 = no and 1 = yes. Tobacco use included the use of any of the following: cigarettes, cigars, pipes, smokeless tobacco, chewing tobacco, or snuff. Binge drinking was defined as drinking five or more drinks on the same occasion on at least one day in the past thirty days. Marijuana use was measured separately from other drugs. Finally, other illicit drug use included any of the following drugs: cocaine, crack, heroin, hallucinogens, LSD, pcp, inhalants, or methamphetamines. Analytic strategy To examine the relationship between neighbourhood characteristics and prescription drug misuse a number of logistic regression models were estimated. To begin we examined the bivariate relationship between neighbourhood characteristics and prescription drug misuse by estimating a total of twelve separate bivariate logistic regression models. Next we estimated four (any prescription drug misuse, opioid misuse, sedative or tranquiliser misuse, and stimulant misuse) separate multivariate logistic regression models to examine the impact of all neighbourhood

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characteristics together along with a number of covariates. In order to take into account the complex multistage sampling design of the NHSDA, analyses were conducted using the SVYSET and SVY commands in STATA. These commands allowed STATA to consider survey design effects, including stratification and weight variables and the primary sampling unit, when estimating test statistics. Findings Sample characteristics for all measures used in the current research are shown in Table 1. Slightly more than 7% of the adolescents in the survey reported prescription drug misuse in the past 12 months. This included 5.4% for opioid misuse, 1.8% for sedative or tranquiliser misuse, and 2.4% for stimulant misuse. The mean scores for the neighbourhood characteristics were 10.02 for social disorganisation, 6.01 for social capital, and 2.79 for social participation. Regarding demographic characteristics, the average age was 14 years old, 51% were males, 66% were white, 15% received some form of government assistance, and 71% were currently living with both parents. The results for several bivariate logistic regression models are shown in Table 2. The social disorganisation measure was significantly related to all types of prescription drug misuse, as adolescents in neighbourhoods with higher levels of perceived social disorganisation were more likely to report prescription drug misuse. Social capital was also significantly related to all forms of prescription drug misuse. Adolescents who lived in neighbourhoods with higher levels of perceived social capital were less likely to report prescription drug misuse. Finally, social participation was significantly related to the misuse of any prescription drug and also the misuse of sedatives/tranquilisers or stimulants, but not opioids. These findings showed that adolescents more involved in social activities were less likely to report prescription drug misuse. Finally, we estimated several multivariate logistic regression models, results shown in Table 3. These models included all three measures related to neighbourhood characteristics and also covariates. The first model included the misuse of any prescription drug as the dependent variable. In this model, all three measures of neighbourhood characteristics were significantly related with

Table 1 Sample characteristics (N = 17,856). Any PDM (yes = 1) Opioids Sedative/Tranquiliser Stimulants Social disorganisation (scale range 6–24) Social capital (scale range 2–8) Social participation (scale range 0–13) Age (range 12–17) Gender (male = 1) Race (white = 1) Geographic residence Urban Suburban Rural Government assistance program (yes = 1) Live with parents Neither Only one Both Currently enrolled in school (yes = 1) Delinquency (scale range 6–30) Tobacco use (yes = 1) Binge drinking (yes = 1) Marijuana use (yes = 1) Other drug use (yes = 1)

7.11% 5.42% 1.82% 2.42% 10.02 (mean) 6.01 (mean) 2.79 (mean) 14.53 (mean) 51.40% 65.79% 43.31% 33.32% 23.37% 14.75% 4.67% 24.16% 71.17% 72.31% 6.75 (mean) 24.89% 10.84% 13.62% 7.14%

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Table 2 Neighbourhood characteristics and prescription drug misuse (bivariate analysis).

Social disorg. Social capital Social part.

Any PDM

Opioids

Sed/Tranq

Stimulant

1.06*** (1.04, 1.08) 0.88*** (0.84, 0.92) 0.96* (0.93, 0.99)

1.07*** (1.05, 1.09) 0.87*** (0.83, 0.91) 0.97 (0.93, 1.00)

1.06*** (1.03, 1.309) 0.83*** (0.76, 0.90) 0.90** (0.83, 0.97)

1.04** (1.01, 1.07) 0.87** (0.80, 0.94) 0.91* (0.85, 0.98)

Table shows the results of 12 separate bivariate logistic regression models, with odds ratios and 95% confidence intervals shown in the table. * p < .05. **p < .01. *** p < .001.

Table 3 Neighbourhood characteristics and prescription drug misuse (multivariate analysis).

Social disorg. Social capital Social part. Age Male White Geographic Urban Suburban Rural Gov. program Live w parents Neither Only one Both In school Delinquency Tobacco Binge drinking Marijuana Other drugs

Any PDM

Opioids

Sed/Tranq

Stimulant

1.03 (1.00, 1.05)* 0.92 (0.87, 0.97)** 1.05 (1.01, 1.09)** 1.11 (1.05, 1.17)*** 0.70 (0.59, 0.83)*** 1.33 (1.10, 1.61)**

1.04 (1.01, 1.06)** 0.92 (0.87, 0.97)** 1.05 (1.01, 1.10)* 1.11 (1.05, 1.17)*** 0.81 (0.67, 0.97)* 1.23 (1.2, 1.48)*

0.99 (0.95, 1.02) 0.88 (0.80, 0.97)** 1.01 (0.93, 1.10) 1.10 (1.00, 1.21)* 0.45 (0.33, 0.62)*** 1.77 (1.24, 2.52)**

0.99 (0.95, 1.02) 0.92 (0.83, 1.03) 1.04 (0.97, 1.11) 1.06 (0.95, 1.18) 0.49 (0.36, 0.66)*** 2.09 (1.36, 3.21)**

– 1.19 (0.98, 1.43) 1.21 (0.97, 1.49) 0.90 (0.70, 1.17)

– 1.26 (1.02, 1.55)* 1.31 (1.04, 1.65)* 0.84 (0.63, 1.13)

– 1.26 (0.91, 1.74) 1.07 (0.75, 1.54) 0.99 (0.66, 1.50)

– 1.10 (0.83, 1.47) 1.25 (0.85, 1.83) 0.92 (0.60, 1.43)

– 1.02 (0.74, 1.39) 1.04 (0.75, 1.44) 0.84 (0.69, 1.02) 1.11 (1.08, 1.15)*** 2.01 (1.64, 2.47)*** 1.52 (1.20, 1.93)** 2.19 (1.74, 2.75)*** 4.54 (3.67, 5.62)***

– 1.16 (0.85, 1.58) 1.17 (0.85, 1.60) 0.88 (0.72, 1.07) 1.10 (1.07, 1.14)*** 1.68 (1.33, 2.10)*** 1.52 (1.16, 1.98)** 2.29 (1.82, 2.88)*** 3.68 (2.92, 4.64)***

– 0.75 (0.45, 1.24) 0.77 (0.45, 1.33) 1.05 (0.75, 1.47) 1.13 (1.08, 1.17)*** 1.97 (1.25, 3.10)** 1.40 (0.95, 2.07) 2.87 (1.83, 4.49)*** 7.59 (5.43, 10.63)***

– 0.68 (0.39, 1.18) 0.82 (0.49, 1.36) 0.83 (0.60, 1.15) 1.11 (1.07, 1.16)*** 2.75 (1.89, 3.99)*** 1.35 (0.98, 1.87) 2.51 (1.78, 3.55)*** 6.73 (4.86, 9.32)***

This table includes the results of four separate multivariate logistic regression models, with adjusted odds ratios and 95% confidence intervals shown in the table. * p < .05. ** p < .01. *** p < .001.

prescription drug misuse. Adolescents who lived in neighbourhoods with higher levels of social disorganisation (AOR = 1.03, 95% CI = 1.00, 1.05) and who were involved in more social activities (AOR = 1.05, 95% CI = 1.01, 1.09) were more likely to report prescription drug misuse. Additionally, adolescents who lived in neighbourhoods with higher levels of social capital (AOR = 0.92, 95% CI = 0.87, 0.97) were less likely to report prescription drug misuse. The next three models examined separate types of prescription drug misuse. The findings for prescription opioids were nearly identical to those of any prescription drug misuse. All three measures of neighbourhood characteristics were significantly related with prescription opioid misuse: social disorganisation (AOR = 1.04, 95% CI = 1.01, 1.06), social capital (AOR = 0.92, 95% CI = 0.87, 0.97), and social participation (AOR = 1.05, 95% CI = 1.01, 1.10). Next we examined the misuse of prescription sedatives or tranquilisers. Only one of the neighbourhood characteristic items was significant. Adolescents who lived in neighbourhoods with higher levels of social capital (AOR = 0.88, 95% CI = 0.80, 0.97) were less likely to report misuse. Finally, we examined the relationship between neighbourhood characteristics and the misuse of prescription stimulants. In this model, none of the neighbourhood measures were significantly related to the misuse of prescription stimulants. A number of the covariates we included in these multivariate models were also significantly associated with prescription drug misuse. The analysis showed that older adolescents, females, and white respondents were more likely to report prescription drug misuse. Adolescents living in suburban and rural areas were also

more likely to report the misuse of prescription opioids compared to adolescents living in urban areas. There was also a significant relationship between involvement in delinquency and prescription drug misuse. Finally, other types of drug used were generally significantly related to prescription drug misuse in the expected direction. Discussion The current research was important as it addressed a gap in the literature by examining the relationship between neighbourhood characteristics and prescription drug misuse. Analyzing data that was representative to the non-institutionalised population of the United States we found mixed results. Consistent with other research that examined the importance of neighbourhood characteristics we found social disorganisation and social capital were both significantly related to prescription drug misuse, especially the misuse of prescription opioids. However, our findings regarding social participation revealed a different story. The current research found that adolescents who lived in neighbourhoods with higher levels of social disorganisation were more likely to report prescription drug misuse. This finding is not surprising as social disorganisation is one of the most enduring and empirically validated theories in the study of crime and deviance (Sampson, 2012). The current research also adds to a list of studies that show rates of drug use are higher in neighbourhoods with high levels of social disorganisation (Hayes-Smith & Whaley, 2009; Hays et al., 2003; Hill & Angel, 2005; Winstanley et al., 2008). Future research in this area should seek to identify the likely causal

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mechanism that connect neighbourhood disadvantage and prescription drug misuse. For example, much research has focused on the important role of collective efficacy. This important mediating variable, defined as the ability of a community to control the behaviour of its residents, has been linked to a reduction in criminal and deviant behaviour (Sampson, 1997). The current research also showed that adolescents who lived in neighbourhoods with higher levels of social capital were less likely to report prescription drug misuse. Various studies have found a similar relationship between social capital and less use of illicit drugs (Curran, 2007; Reynoso-Vallejo, 2011) yet the relationship between social capital and prescription drug misuse remained unexplored prior to this research. Social capital is crucial in preventing crime and deviance as it promotes the development of strong social bonds (Sampson, 2012). Social capital is important in the neighbourhood context as it allows residents to develop strong ties to institutional forces of social control. Inconsistent with prior findings, the current research showed that adolescents who were more involved in various conventional activities were more likely to report prescription drug misuse. It is important to point out that this relationship was as expected in the binary analysis, respondents with more involvement in activities were less likely to report prescription drug misuse. In looking at this analysis in greater detail we saw that this relationship became insignificant once the age, gender, and race measures were added. It was not until the marijuana and drug use measures were finally added to the model that participation became significant and had an odds ratio above 1.00. Furthermore, the odds ratio for participation is only significant and above 1.00 for respondents who had not used marijuana or other illegal drugs. The social acceptance in using prescription medications may explain why greater levels of social participation were related to prescription drug misuse. Research on prescription drug misuse shows that individuals tend to think of prescription drugs differently than traditional street drugs. There is the belief that the misuse of prescription drugs is socially acceptable as they are easier to obtain, less likely to lead to an arrest, and are viewed as a safer alternative (Cicero, Inciardi, & Munoz, 2005; Mui, Sales, & Murphy, 2014; Quintero, 2009; Twombly & Holtz, 2008). Also, involvement in community activities may also mean an increase in interaction with other peers. This should be looked at more closely, as Unlu, Sahin, and Wan (2014) found peer influence as the strongest predictor of substance use when looking at measures of social capital. Also, much research on prescription drug misuse has identified friends and family members as the primary sources of diversion among adolescents (Ford & Watkins, 2012). In addition to looking at any type of prescription drug misuse, we also looked at different types of prescription drug misuse. Our multivariate models show that while neighbourhood characteristics were significantly related to the misuse of prescription opioids, these factors were largely unrelated to the misuse of prescription sedatives, tranquilisers, or stimulants. This is likely due to the notion that opioid misuse is considered the most deviant type of prescription drug misuse. The misuse of prescription opioids is strongly related to a number of negative health related outcomes (Center for Behavioral Health Statistics and Quality, 2013; Centers for Disease Control and Prevention, 2016; Patrick et al. 2012), and is associated with arrest (Ford, 2008) and transition to more traditional street drugs like heroin (Compton, Jones, & Baldwin, 2016; Jones, 2013; Kolodny et al., 2015). Meanwhile, prescription stimulant misuse is often associated with high school and college students whose main motive is studying (Benson, Flory, Humphreys, & Lee, 2015). Thus it makes sense that variables generally used to predict health outcomes or criminal behaviour are more strongly related to prescription opioid misuse.

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The covariates included in the multivariate models were significantly associated with prescription drug misuse in a manner consistent with prior research (Ford & Watkins, 2012). The current research showed that prescription drug misuse was more likely among older adolescents, females, whites, and adolescents who had greater levels of delinquent involvement, and used alcohol and other types of drugs. Also, the misuse of prescription opioids was more common in suburban and rural areas than in urban areas (Ford, 2009; Havens et al., 2011; Monnat & Rigg, 2016; Wang et al., 2013; Wu et al., 2008), A few important limitations are worth noting. First, the data is cross-sectional which makes establishing an exact causal order difficult. In fact, it is possible that prescription drug misuse may also have a negative impact on social disorganisation and social capital. Second, the data we examine is somewhat old, but was used because very little publicly available data that is nationally representative includes these types of measures. This may be problematic given the recency of the prescription drug misuse epidemic in the United States. While the data we use continues to be collected, it no longer includes the questions we used to measure neighbourhood characteristics. It is important to note however that rates of prescription drug misuse among adolescents were higher in 2000 than today (Center for Behavioral Health Statistics and Quality, 2015). Third, we do not have any objective measures of neighbourhood characteristics or geographic identifiers that are commonly used when examining social disorganisation theory. The addition of measures such as African American population, female headed households, poverty, income inequality (Gini Index), educational attainment, residential in/stability, and unemployment are common in criminological inquiries and may reveal additional dimensions of risk of prescription drug misuse (Sampson, 2012). The findings of the current research are important given the huge toll prescription drug misuse is taking on public health. So far, the research in this area has primarily focused on individual factors associated with prescription drug misuse. The current research shows that in order to deal with the prescription drug epidemic in the United States, policy must be developed that focuses on neighbourhood characteristics. Going back to the extensively tested theory of social disorganisation, most policy efforts to reduce drug use and crime in neighbourhoods focus on collective efficacy (Sampson, 2012). Collective efficacy, generally defined as social cohesion combined with shared expectations for social control, is important as prior research suggests that it mediates the relationship between structural disadvantage and violent crime at the neighbourhood level (Sampson, 1997). Increasing collective efficacy in socially disorganised neighbourhoods is important because it increases the likelihood that residents will intervene when they identify social problems, as well as the capacity of agents of informal social control to be effective (Ohmer, 2016). Collective efficacy can be improved by implementing programmes that target social cohesion among residents in a community (O’Brien & Kauffman, 2012). This includes promoting high levels of home ownership, investing in local schools, and increasing access to local amenities such as parks, recreation centers, libraries, and churches (Higgins & Hunt, 2016). Research also shows the importance of socially disorganised neighbourhoods being connected to individuals and organizations that are located outside of the neighbourhood (Payne & Williams, 2008; Sabol, Coulton, & Korbin, 2004). Often referred to as “bridging social capital” the connexion to resources outside of the neighbourhood are vital in the process of reducing crime in disorganised neighbourhoods. In sum, any neighbourhoodlevel policy interventions to reduce prescription drug misuse should focus of collective efficacy and social capital. In closing the current research examines the relationship between neighbourhood characteristics and prescription drug

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