Abstract
Introduction.
Young adulthood, typically conceptualized as stretching from the late teens to the mid-twenties, is a period of elevated risk for residential mobility (i.e., moving or changing residences frequently) and drug involvement. However, our understanding of the trends and drug-related correlates of residential mobility among young adults remains limited.
Methods.
We analyzed national trend data from the National Survey on Drug Use and Health (2003–2016) on residential mobility and drug involvement among young adults (N = 230,790) in the United States. For tests of trend, we conducted logistic regression analyses with survey year specified as a continuous independent variable and residential mobility as the dependent variable (no/yes), controlling for sociodemographic factors.
Results.
The prevalence of residential mobility was stable among females, but decreased significantly— a 20 percent reduction in the relative proportion of respondents—among males during the study period (AOR = 0.98, 95% CI = 0.97–0.99). Male and female young adults reporting residential mobility were significantly more likely to report involvement in all drug- related outcomes examined, but effects were larger among females for drug selling and drug- related arrests.
Discussion.
Study findings show that a substantial minority of young adults experience residential mobility and that, while rates are declining among young men, the experience of mobility is connected with risk for drug involvement, particularly among females. Mobility may be an important target for drug prevention/intervention efforts, but further research is needed to provide insight into how mobility and drug involvement are connected in the lives of young adults.
Keywords: Residential mobility, drug use, substance use, housing, trends, young adults
1. Introduction
Young adulthood is a critically important developmental stage in the life course. Situated between adolescence and full-fledged adulthood, the young adult years, typically conceptualized as stretching from the late teens to the mid-twenties, constitute a particularly elevated period of peril as young people encounter greater opportunities for experimentation (often without or with little parental oversight) but are yet to reach full maturity in decision-making and risk assessment (Steinberg, 2014). Indeed, neuroimaging research has revealed that the brain’s frontal cortex, vital for executive function, is not fully developed until the mid-to-late twenties (Johnson, Blum, & Giedd, 2009; Lenroot & Giedd, 2006). We also know that the young adult years are a time of peak risk for marijuana and other illicit drug use and substance-related addictive disorders, and a period when drug involvement in general (e.g., drug offers, drug access) reaches its apex (Salas-Wright et al., 2016).
Young adulthood is also a period of elevated residential mobility and instability. We know that rates of residential mobility—that is, moving or changing residences multiple times over the course of a year—tend to decline from early childhood into adolescence before increasing markedly in the late teens and early twenties (Jelleyman & Spencer, 2008). Importantly, residential mobility is not only stressful but, according to prior research, is also related in key ways to risk of substance use among young people (Buu et al., 2009; DeWit, 1998; Dong et al., 2005; Gasper, DeLuca, & Estacion, 2010; Glasheen, Forman-Hoffman, & Williams, 2017).
There are a number of theoretical explanations why residential mobility is related to drug use and may be related to drug involvement more broadly. The first relates to the relationship between stress and drug use risk. More precisely, moving frequently is stressful, and it is often driven by challenging social (e.g., socioeconomic deprivation, unstable work) and interpersonal (e.g., family conflict, problems with roommates) factors. It is well documented that stressful experiences, and cumulative exposure to stress over time, can increase risk of substance use initiation and misuse (Sinha, 2008). Additionally, it likely that residential mobility may expose young people to environments in which substance use risk is elevated, particularly among those moving due to economic hardship and/or unstable housing. We know that factors such as housing quality (Galea, Ahern, & Vlahok, 2003) and drug use exposure (Wagner & Anthony, 2002) are closely related to substance use risk, and that young people without a stable residence are often exposed to substandard living environments and contexts in which drug use is common (McLoughlin, 2013). As such, it is intuitive that youth who experience mobility would be more likely to encounter peer networks or individuals who will offer drugs, help them to obtain drugs, or encourage engagement in drug selling or other criminal behaviors.
While a number of studies have examined the relationship between residential mobility and substance use, important gaps in our understanding remain. One is that our knowledge of secular trends in residential mobility—and the links between such trends and substance use risk—is limited. This is noteworthy as it is quite possible, given the improvements in the economy/unemployment rate in the United States (US) since the economic collapse of 2008, that rates of residential mobility may have decreased in recent years. Second, much of the prior research on residential mobility and substance use has neglected to examine how mobility may affect men and women differently. Ample research points to distinct etiological pathways for drug use and risky behavior among male and female youth (see Byrnes, Miller, & Schafter, 1999; Nolen-Hoeksema, 2004), suggesting that women on average are less likely than men to use drugs or take part in illegal behavior, but that—among the women who do partake in such behaviors—exposure to adverse or stressful environmental factors is particularly salient (Raine, 2005). As such, it may be that residential mobility, often an indicator of adversity and cause of substantial stress among young people (Oishi, 2010), is a more robust predictor of drug involvement among young adult women than among their male counterparts. Third, although prior studies of residential mobility have examined drug use, our understanding of a broader constellation of factors related to drug involvement is incomplete. For instance, the links between residential mobility and key prevention targets, such as receipt of drug offers and perceived drug access, has yet to be examined.
In the present study, we address these shortcomings by examining national data on more than 230,000 young adults collected between 2003 and 2016 as part of the National Survey on Drug Use and Health (NSDUH). We examine the year-by-year prevalence of residential mobility and test for secular trends among young adults (ages 18–25) in general, and among male and female young adults specifically. We also systematically examine—among young adults in general and across gender—the association between residential mobility and key drug involvement variables, including receipt of drug offers, access to and use of marijuana and other illicit drugs, and drug selling and drug related arrests. This research is critically important as it promises to provide new evidence on the trends and drug involvement correlates of residential mobility among young adults, a subgroup at heightened risk for both frequent changes in residence and drug use initiation and misuse.
3. Method
3.1. Data and Procedures
The present study used data from the NSDUH (2003–2016). The NSDUH provides crosssectional estimates of an array of variables related to drug use and drug involvement among noninstitutionalized civilians aged 12 and older in the US. The present study’s analytic sample included 230,790 young adults aged 18–25 (109,826 men and 120,964 women). A detailed description of the NSDUH is available elsewhere (SAMHSA, 2017).
3.2. Key Measures
Residential mobility.
All participants were asked: “How many times in the past 12 months have you moved?” Those who reported moving 2 or more times were classified as having experienced residential mobility/instability and were coded as 1 and all others coded as 0.
Drug involvement.
We examined an array of binary measures of drug involvement across multiple domains such as drug exposure/access, drug use, and criminal involvement. These include: receipt of drug offers (0 = no, 1 = yes), perceived access to marijuana or other illicit drugs (examined independently; 0 = difficult/impossible to obtain, 1 = easy/very easy to obtain), past year marijuana or other illicit drug use (examined independently; 0 = no use, 1 = one or more instances of use), past year drug selling (0 = no, 1 =yes), and past year arrest for possession or sale of drugs (0 = no, 1 = yes).
3.3. Statistical Analysis
Statistical analyses were conducted in three sequential steps. First, we examined the annual prevalence estimates of residential mobility (i.e., 2+ moves in past year) and tested for linear trends in residential mobility. Trend tests were conducted using survey adjusted binomial logistic regression models in which residential mobility was specified as the dependent variable and survey year was specified as an independent variable along with the sociodemographic factors listed above (CDC, 2016). Next, we used logistic regression analyses to examine the associations between residential mobility (specified as an independent variable) and drug involvement (specified as dependent variables using a multivariable approach) while controlling for sociodemographic factors and survey year. All estimates were obtained using Stata 15 and conducted in accordance with SAMHDA (2014) guidelines.
4. Results
4.1. Trends in Residential Mobility among Young Adult Americans
Figure 1 displays the year-by-year prevalence estimates for residential mobility among young adults in the US. Overall, the prevalence of moving two or more times in the past year decreased from 21.1% (2005/2006) to 17.6% (2015/2016), indicating a 17% reduction over the study period (AOR = 0.98, 95% CI = 0.98–0.99). However, further examination of trends across gender showed that the declines in mobility were significant only among males. For males, the prevalence of residential mobility fell from 19.6% in 2003/2004 to 15.6% in 2015/2016, indicating a 20% reduction during the study period (AOR = 0.98, 95% CI = 0.97–0.99).
Figure 1.
Proportion of Young Adult Americans (ages 18 to 25) with Two or More Past-year Residential Moves, National Survey of Drug Use and Health (NSDUH) 2003–2016
4.2. Drug Involvement Correlates of Residential Mobility
Table 1 displays the association between residential mobility and drug involvement. Across gender, both male and female young adults reporting residential mobility were significantly more likely to report involvement in all drug-related outcomes examined. Sensitivity analyses (available upon request) examining data from three 4-year windows—20032006, 2007–2011, 2012–2016—revealed that these associations were stable over time. Supplementary analyses also revealed a significant association between mobility and risk of past- month marijuana use (past year is show in Table 1) and the past year use of cocaine/crack, heroin, and LSD (“any other drug” is shown in Table 1). Overall, the size of the odds ratios for mobility-drug involvement were larger among females than among males. In testing for the moderating role of gender (e.g., drug selling*gender) we found that gender significantly moderated the mobility-involvement link for drug selling (AOR=1.24, 95% CI=1.09–1.40) and drug-related arrests (AOR=1.52, 95% CI=1.20–1.91).
Table 1.
Sociodemographic Characteristics and Drug-relatedInvolvement among Young Adults (Aged 18–25) in the US by Residential Mobility Status and Gender
| Male | Female | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Moved 2+ Times |
Adjusted Odds Ratio |
Moved 2+ Times |
Adjusted Odds Ratio |
|||||||||
| No (n=88,372; 81.6%) |
Yes (n = 21,455; 18.4%) |
No (n=94,415; 79.2%) |
Yes (n = 26,549; 20.8%) |
|||||||||
| % | 95% CI | % | 95% CI | AOR | 95% CI | % | 95% CI | % | 95% CI | AOR | 95% CI | |
| Drug Involvement | ||||||||||||
| Drug Offers | ||||||||||||
| No | 77.9 | 77.5–78.3 | 72.7 | 71.8–73.5 | 1.00 | - | 88.1 | 87.8–88.4 | 82.8 | 82.2–83.5 | 1.00 | - |
| Yes | 22.1 | 21.7–22.5 | 27.3 | 26.5–28.2 | 1.35 | 1.28–1.42 | 11.9 | 11.6–12.2 | 17.2 | 16.5–17.8 | 1.49 | 1.42–1.57 |
| Marijuana Access | ||||||||||||
| No (Difficult/Impossible) | 24.0 | 23.6–24.4 | 22.6 | 21.8–23.5 | 1.00 | - | 25.9 | 25.5–26.3 | 22.6 | 21.9–23.2 | 1.00 | - |
| Yes (Easy) | 76.0 | 75.6–76.4 | 77.4 | 76.5–78.2 | 1.14 | 1.08–1.21 | 74.1 | 73.7–74.5 | 77.4 | 76.8–78.1 | 1.25 | 1.20–1.30 |
| Other Drug Access | ||||||||||||
| No (Difficult/Impossible) | 58.9 | 58.5–59.4 | 55.0 | 54.2–55.9 | 1.00 | - | 57.2 | 56.8–57.6 | 53.1 | 52.2–53.9 | 1.00 | - |
| Yes (Easy) | 41.1 | 40.6–41.5 | 45.0 | 44.1–45.8 | 1.17 | 1.12–1.22 | 42.8 | 42.4–43.3 | 46.9 | 46.1–47.8 | 1.19 | 1.15–1.24 |
| Marijuana Use | ||||||||||||
| No | 66.4 | 65.9–66.9 | 59.5 | 58.5–60.4 | 1.00 | - | 75.5 | 75.2–75.9 | 67.8 | 67.0–68.6 | 1.00 | - |
| Yes | 33.6 | 33.2–34.1 | 40.5 | 39.6–41.5 | 1.35 | 1.29–1.42 | 24.5 | 24.2–24.8 | 32.2 | 31.4–33.0 | 1.47 | 1.41–1.53 |
| Other Drug Use | ||||||||||||
| No | 92.3 | 92.0–92.5 | 88.0 | 87.4–88.6 | 1.00 | - | 95.7 | 95.5–95.8 | 92.0 | 91.6–92.5 | 1.00 | - |
| Yes | 7.7 | 7.5–8.0 | 12.0 | 11.4–12.6 | 1.59 | 1.48–1.71 | 4.4 | 4.2–4.5 | 8.0 | 7.5–8.5 | 1.82 | 1.67–1.98 |
| Drug Selling | ||||||||||||
| No | 93.1 | 92.8–93.3 | 88.8 | 88.1–89.4 | 1.00 | - | 97.6 | 97.5–97.8 | 95.0 | 94.6–95.4 | 1.00 | - |
| Yes | 7.0 | 6.7–7.2 | 11.2 | 10.6–11.9 | 1.69 | 1.57–1.82 | 2.4 | 2.2–2.5 | 5.0 | 4.7–5.4 | 2.07* | 1.87–2.28 |
| Drug-Related Arrest | ||||||||||||
| No | 98.1 | 98.0–98.2 | 96.8 | 96.5–97.1 | 1.00 | - | 99.6 | 99.6–99.7 | 99.0 | 98.8–99.1 | 1.00 | - |
| Yes | 1.9 | 1.8–2.0 | 3.2 | 2.9–3.6 | 1.62 | 1.44–1.82 | 0.4 | 0.4–0.5 | 1.0 | 0.9–1.2 | 2.42* | 1.95–2.99 |
| Demographic Controls | ||||||||||||
| Age | - | - | - | - | 1.01 | 0.99–1.04 | - | - | - | - | 0.89 | 0.87–0.91 |
| Race/Ethnicity | ||||||||||||
| Black | 13.0 | 12.6–13.4 | 13.2 | 12.5–13.9 | 0.88 | 0.83–0.94 | 15.0 | 14.7–15.4 | 13.9 | 13.2–14.5 | 0.72 | 0.68–0.76 |
| Hispanic | 20.1 | 19.6–20.6 | 18.2 | 17.4–19.0 | 0.81 | 0.76–0.86 | 19.0 | 18.5–19.4 | 16.4 | 15.7–17.1 | 0.74 | 0.70–0.78 |
| Non-Hispanic White | 59.4 | 58.9–59.9 | 61.3 | 60.3–62.3 | 1.00 | - | 58.2 | 57.6–58.7 | 62.2 | 61.4–63.0 | 1.00 | - |
| College Enrollment | ||||||||||||
| No | 70.1 | 69.5–70.6 | 75.8 | 74.7–76.8 | 1.00 | - | 66.3 | 65.8–66.9 | 71.3 | 70.3–72.2 | 1.00 | - |
| Yes | 29.9 | 29.4–30.5 | 24.2 | 23.2–25.3 | 0.82 | 0.77–0.87 | 33.7 | 33.1–34.3 | 28.7 | 27.8–29.7 | 0.75 | 0.71–0.80 |
| Household Income | ||||||||||||
| Less than $20,000 | 25.7 | 25.2–26.3 | 40.5 | 39.4–41.7 | 3.01 | 2.80–3.24 | 30.5 | 30.0–31.0 | 46.6 | 45.6–47.6 | 2.82 | 2.61–3.05 |
| $20,000 - $39,999 | 23.0 | 22.5–23.4 | 26.1 | 25.3–27.1 | 1.94 | 1.81–2.08 | 23.9 | 23.5–24.3 | 24.6 | 23.7–25.4 | 1.79 | 1.66–1.94 |
| $40,000 - $74,999 | 26.0 | 25.5–26.4 | 19.5 | 18.8–20.2 | 1.31 | 1.21–1.41 | 25.4 | 25.0–25.8 | 17.5 | 16.8–18.1 | 1.20 | 1.10–1.29 |
| $75,000 or higher | 25.3 | 24.9–25.8 | 13.8 | 13.1–14.6 | 1.00 | - | 20.2 | 19.8–20.6 | 11.4 | 10.7–12.0 | 1.00 | - |
| Marital status | ||||||||||||
| Married | 8.0 | 7.8–8.3 | 9.8 | 9.3–10.3 | 1.00 | - | 14.9 | 14.6–15.2 | 15.1 | 14.6–15.6 | 1.00 | - |
| Divorced/Separated | 0.9 | 0.9–1.0 | 2.4 | 2.1–2.7 | 2.21 | 1.83–2.67 | 2.0 | 1.8–2.1 | 4.4 | 4.1–4.7 | 1.92 | 1.72–2.14 |
| Never married | 91.0 | 90.8–91.3 | 87.8 | 87.2–88.4 | 0.96 | 0.89–1.04 | 83.2 | 82.9–83.5 | 80.5 | 79.9–81.1 | 0.93 | 0.88–0.98 |
Note. Adjusted odds ratios are estimates obtained while controlling for age, race/ethnicity, employment, marital status, educational attainment, current college enrollment, household income, presence of minor child in household, urbanicity, and survey year. Odds ratios in bold are statistically significant.
= Significant differences in odds ratios across gender.
4. Discussion
The present study, conducted with data from a large national survey collected between 2003 and 2016, shows that roughly one in five young adults in the US experience residential mobility (i.e., two or more moves in the past 12 months). We also found that rates of residential mobility were consistently higher among females, and declined significantly among male but not female young adults from the early 2000’s to present. This is noteworthy as our findings also indicate that residential mobility is related to increased risk of involvement in an array of drug- related outcomes, including receipt of drug offers, marijuana and other illicit drug use, and drug selling and drug-related arrests. While significant associations were identified for all drug-related outcomes among both male and female participants, effects of residential mobility were consistently larger among females with particularly large effects observed for drug selling and drug arrests.
The differential findings across gender raise questions as to why rates of residential mobility are higher among women, and why the association between residential mobility and drug involvement was found to be greater among women as well. Several possibilities exist. For one, it may be that the experience of frequent moves or unstable housing opens up young adult women to drug-related risks in a way that is more tempered among men. That is, the experience of residential mobility may be more stressful for young adult women, perhaps due to associated risks for partner violence or victimization that often accompany residential instability (Pavao, Alvarez, Baumrind, Induni, & Kimerling, 2007). Alternatively, it is possible that drug involvement—particularly the more severe forms such as drug selling—is a more salient driver of residential mobility among young women than among young men. Indeed, prior research has shown that, among young adults reporting drug selling, young women are more likely to report co-occurring polydrug use and elevated rates of residential instability (Vaughn, Salas-Wright, DeLisi, Shook, & Terzis, 2015). Critically, these findings point to the importance of prevention and intervention efforts for young adults experiencing residential mobility, and perhaps suggest a targeted effort for young adult females.
Limitations
Findings from the present study should be interpreted in light of several limitations. First, all data examined are based on respondent self-report and, therefore, susceptible to social desirability or other reporting biases. Second, while we examined data from multiple survey years, the NSDUH is a cross-sectional study. Consequently, it is not possible to determine causal direction of observed associations. Finally, while we control for a number of sociodemographic factors, we do not have information on genetic, family, and broader contextual factors that might help explain the observed associations.
Conclusions
Overall, study findings make clear that a substantial minority of young adults experience residential mobility and that, while rates are declining among young men, the experience of mobility is connected with risk for drug involvement, particularly among females. This suggests that residential mobility and instability may be important targets for drug prevention and intervention efforts, but further research is also needed to provide insight into the ways in which mobility and drug involvement are connected in the lives of young adults.
Highlights.
One in five young adults report changing residences two or more times in past year.
Mobility was stable among women, but dropped significantly for men from 2003–2016.
Residential mobility is associated with an increased risk of drug involvement.
The mobility-drug involvement link was more robust for women than for men.
Acknowledgements:
None.
Role of Funding Source:
This research was supported in part by grant number R25 DA030310 from the National Institute on Drug Abuse at the National Institutes of Health and by the National Center for Advancing Translational Sciences, National Institutes of Health, through BU-CTSI Grant Number 1KL2TR001411. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.
Footnotes
Author Note: Research reported in this publication was supported by the National Institute on Alcohol Abuse and Alcoholism of the National Institutes of Health under Award Number K01AA026645. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors have no conflicts to be disclosed.
Conflict of Interest:
No conflict declared.
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