Risks,Assets,and Negative Health Behaviors Among Arkansas’ Hispanic Adolescents

Abstract

This study examined the relationship between risk, assets, and negative health behaviors among a large sample of Hispanic adolescents. Data were collected from over 1,000 Hispanic youth in grades 6, 8, 10, and 12 attending school in a moderate size school district in Northwest Arkansas. Logistic regression models examined the variation in the odds of youth engaging in negative health behaviors (antisocial or drug use). Within the context of a risk-assets framework, depressive symptomatology, negative peer networks, and community disorganization were related to higher odds of engaging in negative behavior. Knowing the difference between right and wrong—having some sense of a moral order—and having parents who exerted some control (curfew, etc.) were important assets lowering the odds of engaging in negative behavior. Risks and assets help to explain negative health behaviors among Hispanic youth not unlike their non-Hispanic counterparts. The findings in this article reemphasize the need for continued focus on special populations and the framework for understanding risk behaviors.

Keywords

adolescents Hispanics risks assets health behaviors Arkansas depression

Introduction

A recent Pew Hispanic Center report underscores the importance of the present investigation. Representing one in five school children and nearly one in five newborns in the United States, the growth in this minority group over the last two decades has been explosive and certainly not without consequence. (Centers for Disease Control and Prevention, 2003; Pew Hispanic Center, 2009; U.S. Bureau of Census, 2001). With high school dropout rates nearly three times that of Caucasians or African Americans, and reported rates of fighting, threats by other persons, and weapon possession at or higher than those reported for other racial/ethnic groups, Hispanic adolescents are fast becoming the new negative health behavior focus for adolescent researchers (Barrera, Gonzales, Lopez, & Fernandez, 2004; De La Rosa, Holleran, Rugh, MacMaster, 2005; Pew Hispanic Center, 2009; Schwartz et al., 2009). In addition, several studies note increasing prevalence of substance abuse among Latino populations where rates are as high or higher than most racial/ethnic groups (De La Rosa et al., 2005; Schwartz et al., 2009; Wallace et al., 2002).

A well-established body of literature documents the salience of examining risks and assets to understand a variety of negative health behaviors in the context of adolescent development (e.g., Arthur, Hawkins, Pollard, Catalano, & Baglioni, 2002; Fitzpatrick, 1997; Fitzpatrick, Piko, Wright, & LaGory, 2005; Hawkins et al., 1998; Jessor, 1998). While the literature is not as developed as it is for other racial/ethnic groups, there appears to be growing concern that Hispanic adolescents may be exhibiting signs of increased probabilities for engaging in certain kinds of negative health behaviors—behaviors that clearly threaten one’s health and mortality (De La Rosa et al., 2005; Eaton et al., 2008; Gallegos-Castillo & Patino, 2006; Romero, Martinez, & Carvajal, 2007; Springer, Kelder, Orpinas, & Baumler, 2007). We acknowledge there are some important differences between Hispanic youth depending on their nativity, however, findings remain unclear and seem to indicate that Hispanic adolescents, relative to their Caucasian and African American counterparts, engage in earlier sexual activity (Brindis, Wolfe, McCarter, Ball, & Starbuck-Morales, 1995; Kaplan, Erickson & Jaurez-Reyes, 2002), substance abuse (De La Rosa et al., 2005; Ojeda, Patterson, & Strathdee, 2008), and gang activity/violence (Pew Hispanic Center, 2009). These differences highlight the importance of continuing to examine negative health behaviors among adolescents generally, and Hispanics specifically. Much like the work on negative health behavior among African American adolescents (Fitzpatrick et al., 2005; Fitzpatrick, Piko, & Miller, 2008; Tandon & Solomon, 2009), we seek to expand our current knowledge with a more careful analysis and examination of risk, assets, and negative health outcomes among a specific subpopulation of youth. Again, while we acknowledge the distinctiveness within subgroups across all Hispanics, our data do not allow for those types of distinctions to be made; this preliminary analysis combines all students of Hispanic origin and recognizes the implications of that in the final interpretations.

The current study, focusing only on Hispanic adolescents, is both timely and critical to further understanding and evaluating negative health behavior among these youth. Comparing Hispanic youth outcomes to other racial or ethnic groups is beyond the scope of the present article. Rather we ask the question, is there something, anything, unique about the interplay between risk, assets, and negative health behaviors across multiple social domains for Hispanic youth? Specifically, this article is designed to examine the following: (a) To what extent do Hispanic adolescents take risks and are there clear differences in these risks across sociodemographic subgroups?; and (b) What specific risk and assets in multiple social domains are related to negative health behaviors among Hispanic youth?; and (c) Is there any difference in how risks and assets help us understand different behavioral outcomes, that is, antisocial versus drug use behavior. Before developing new sets of risks and assets relevant to particular subgroups of youth, we must first examine, in some detail, their relationships to earlier specified risks and assets found to be important in other youth subgroup outcomes.

A Framework for Understanding Negative Health Behaviors

Risks and assets continue to play an important role in understanding adolescent problem behaviors and their potential solutions (Arthur et al., 2002; Atzaba-Poria, Pike, & Deater-Deckard, 2004; Hawkins et al., 1998; Hawkins, Catalano, & Miller, 1992). In addition, research clearly demonstrates that regardless of outcome, risk and assets are two very different factors and do not just operate in the absence of the other (Atzaba-Poria et al., 2004; Bond, Tombourou, Thomas, Catalano, & Patton, 2005; Fitzpatrick, Dulin, & Piko, 2009; Fitzpatrick et al., 2005; Tandon & Solomon, 2009). Thus, we argue the utility in examining the independent effects of risk and assets on two different types of negative health behaviors (antisocial and drug use behaviors).

Also important to this framework and to the understanding of negative health behaviors is that risk and assets tend not to act in isolation of one another but rather in complex and cumulative ways. A number of studies clearly demonstrate that multiple factors in different social domains intersect in such a way as to create differing impacts on negative health behaviors (Atzaba-Poria et al., 2004; Deater-Deckard, Dodge, Bates, & Pettit, 1998; Fitzpatrick, Dulin, & Piko 2009; Hawkins, Catalano, & Miller, 1992). While there are a number of domains that have been identified as important to understanding negative health behaviors, the current study examines risk and assets in four primary domains: individual, peer, family, and neighborhood/community.

A considerable body of work has indicated that negative health behaviors vary considerably across gender and age categories; we expect that to be the case in examining Hispanic youth’s negative health behaviors (Atzaba-Poria et al., 2004; Fitzpatrick, 1997; Sen, 2004; Stueve & O’Donnell, 2005). Thus, we hypothesize that Hispanic boys will engage in more negative health behavior than girls, and older adolescents (high school) will report more negative health behavior than younger ones (junior/middle school).

Risk Factor Hypotheses

Besides controlling for the above sociodemographics, we examine several specific risk hypotheses including the individual domain where we hypothesize that depressive symptoms will be related to negative health behaviors; adolescents reporting mental health problems routinely engage in more problem behaviors than those reporting little or no symptomatology (Eaton et al., 2006; Fitzpatrick, 1999; Tandon & Solomon 2009). Mental health problems, including but not limited to depression, among Hispanic youth continues to be of concern; some studies suggest higher symptom reporting among Hispanic youth compared to most other racial/ethnic minority youth (Joiner, Perez, & Wagner, 2001; Umana-Taylor & Updegraff, 2007).

In the peer domain, we posit that homophilic relationships are particularly important to determining negative behaviors among Hispanic youth. Adolescents reporting more friends engaging in risk behaviors are likewise going to report engaging in similar types of behaviors (Barrera et al., 2002; Loukas, Prelow, Suizzo, & Allua, 2008; Roosa et al., 2005). At a particularly vulnerable time in their development, middle, junior, and high school youth are moving beyond their parental control and looking for a new group to advise them, while providing support.

In the family domain, we expect that adolescents who come from unsupportive families are more likely to report more negative health behaviors than adolescents whose parents are strict but supportive (Loukas et al., 2008; Repetti, Taylor, & Seeman, 2002). Permissive family structure and attitudes toward drugs, alcohol, and sexual activity has been noted as a risk factor in related research concerning negative health behavior among adolescents (Arbina & Power, 2003; Eamon, 2001; Eamon & Mulder 2005; Foley, Altman, Durant, & Wolfson, 2004; Nash, McQueen, & Bray, 2005; Schwartz, Pantin, Prado, Sullivan, & Szapocznik, 2005).

Finally, we hypothesize that community disorganization will be related to negative health behaviors among youth—unhealthy neighborhoods are a context for unhealthy behaviors (Frank, Cerda, & Rendon, 2007; Mather & Adams, 2006; Sampson, Sharkey, & Raudenbush 2008; Shetgiri et al., 2009). An expanding literature is repeatedly finding that place matters; understanding the mechanisms behind negative health behaviors are particularly important for specific subgroups that have limited mobility and are place-bound like youth (Fitzpatrick & LaGory 2003, 2010; Kawachi & Berkman 2003). We acknowledge that risks, like the ones hypothesized here, can actually demonstrate reverse causality with these negative health behaviors. While certainly this limits our analytical strategy, we nevertheless are able to identify an important set of relationships regardless of their causal direction.

Social Assets Hypotheses

Equally important to understanding negative health behaviors among youth generally and Hispanic youth specifically, is the role that social assets play in minimizing or reducing the negative effect of risks on health behavior outcomes. Specific asset hypotheses include the individual domain where we hypothesize that youth who understand the difference between right and wrong are less likely to engage in negative health behavior. The importance of socialization is highlighted repeatedly when examining negative behaviors; (Hawkins, Smith, & Catalano 2002; Loukas et al., 2008) being able to understand the existence of a moral order and it’s important to guiding behavior can be a critical individual-level asset.

In the peer domain, we hypothesize a similar relationship to the aforementioned peer-risk hypothesis. However, in this case, we propose that adolescents who report spending more time with friends engaging in prosocial behaviors are less likely to report negative health behaviors (Barrera et al., 2002). Thus, the same principles derived from social interaction theory that supports deviant peer association would be hypothesized to support prosocial peer association.

In the family domain, we hypothesize that those who feel a greater attachment to family and have more rules and boundaries set by their family are less likely to engage in negative health behaviors than their counterparts (Almodover, Tomaka, Thompson, McKinnon, & O’Rourke, 2006; Gorman-Smith, Henry, & Tolan, 2004; Marshal & Cassin, 2000). Youth who are supported by their parents, feel comfortable talking with them about their problems, and follow rules and regulations set down by family members, are less likely to be in trouble with their peers, at school, or in the community.

Finally, youth who feel connected/bonded to their community and are engaged in their community are less likely than those who are not to report negative health behaviors—good neighborhoods are a context for good behavior for adults as well as for youth (Vanderbilt-Adriance & Shaw, 2008; Wolfe, Jaffe, & Crooks, 2006).

Method

Data come from the Arkansas Prevention Needs Assessment Survey (APNA) administered to schools throughout the state in the fall of 2007. The survey was given to students in English for grades 6, 8, 10, and 12 and was conducted by a private research company with a reported response rate of approximately 69% (Arkansas Prevention Needs Assessment Survey, 2007). Arkansas, like many states, use the Risk and Protective Factors framework to guide its prevention efforts; the majority of these factors and the specific questions/scales used to operationalize them are found in the Communities That Care Survey instrument which has been both theoretically and empirically vetted (Hawkins, Smith, & Catalano, 2002; Hawkins, Catalano & Arthur, 2003; Hawkins & Catalano, 2002).

The data used for the analysis are from a mid-sized school system in Northwest Arkansas with nearly 17,000 students. This particular school district has 28% of students in the targeted grades of which 36% were Hispanic. Nearly 50% of the students in this school district were on free/reduced lunch. Of particular interest to the present research is the significant change in the student population as a whole that has taken place between 1994 and 2007. By the study year, 2007, the Hispanic population in the selected school district peaked with more than 40% of Hispanic students and the school district now predominantly non-White (with the addition of African Americans, Asians, Pacific Islanders). The total number of 6, 8, 10, and 12 grade students available to take the survey were 4,695 and Pride Surveys received 2,990 completed (64% response rate). The majority of students who did not respond were absent the day of the survey; some students either refused to participate or their parents/guardians sent notifications to the school for them not to participate. We examined the general demographics of the group that responded to the survey relative to the entire grades 6, 8, 10, 12 population and observed no significant differences between these two groups—leading us to suspect that there were few if any differences between the nonresponding group and the current sample used in the analysis.

In addition to using data from a single-school district, because the study focuses only on Hispanic negative health behavior, the total sample is reduced to 1,030 Hispanic junior and high school students. This study uses the term Hispanic and Latino interchangeably because of the wording used throughout the survey. Surveys were mailed to the participating school in October and administered a month later in 2007. Teachers were given a script to read in the classroom that reminded students their responses were anonymous; they were not required to fill out any of the questions on the survey if they did not want to. The participating school’s IRB reviewed and approved the final protocol for recruiting students and administering the survey. The university IRB approved the use of this data as secondary data with all delimiters, both personal and geographic, removed from the analysis.

The Hispanic students used in the analysis included 52% male with a median age of 14 years. The breakdowns across grades consisted of 381 (38%) 6th graders, 340 (33%) 8th graders, 185 (18%) 10th graders, and 114 (11%) 12th graders.

Measurement

The dependent variables are both binary (0 = did not engage in negative health behavior; 1= did engage in negative health behavior) in the present analysis. Our interest was in examining two different types of negative health behaviors (antisocial and drug use). The focus was not examining the extent of the behavior but whether there were important risk and asset differences between those students that engaged in these behaviors versus those who did not.

Operationalizing antisocial behavior, students were asked questions about a series of behaviors they engaged in over the past 12 months including: whether they had carried a handgun, sold illegal drugs, stolen/tried to steal a motorcycle or car, attacked someone with the intention of seriously hurting them, been drunk/high at school, or taken a handgun to school. All of these behaviors have the potential for compromising ones’ health and ultimately ones’ mortality. These questions have been found to be both reliable and valid indicators for antisocial behaviors among adolescents in both the Communities That Care research (Hawkins & Catalano, 2002) and the Youth Risk Behavior Surveillance Study (YRBSS; Centers for Disease Control and Prevention, 2009). Student responses were recoded to coincide with the binary dependent variable (0 = none; 1 = one or more). Nonbinary analysis (OLS) revealed no difference in the reported results; we decided to present the binary analysis for its straightforward examination of negative health risk comparisons. Nearly 15% of the sample engaged in at least one antisocial behavior in the year prior to the interview (Cronbach’s α = 0.72)

Likewise, in operationalizing drug use among students in the last 30 days, students were asked about drug use behavior 30 days prior to the interview and whether or not they had used 13 different drugs including: smokeless tobacco, cigarettes, alcohol, binged with alcohol, marijuana, LSD, cocaine/crack, inhalants (glue, aerosols, etc.), stimulants (Ritalin, Dexidrine, etc.), sedatives, methamphetamines, heroin, and MDMA (ecstasy). Approximately 12% of the sample said they had engaged in at least one drug use behavior in the 30 days prior to the interview (Cronbach’s α = 0.71).

Risk Factors

In addition to the sociodemographic variables, we included variables from four separate domains. These variables and their connection to earlier studies are noted in the previous section of this article. In addition to individual risk, these social domains represent important elements of the social environment that youth are exposed to and influenced by. The degree to which youth are embedded in these environments is an important part of the larger question being addressed in this article—domain influence among Hispanic youth. In an effort to maintain some parsimony, we focused on single indicators of risk and assets in the models that follow.

The individual risk variable was an index that assessed depressive symptoms/sadness among students. Students were asked to rank responses to a set of questions: whether they felt sad most days, thought they were a failure, thought they were no good at all, and thought that life “just isn’t worth it.” The four-item index was reliable (Cronbach’s α = 0.79). Student responses were coded as Likert-type responses ranging from definitely no = 1 to definitely yes = 4 with a Mean = 8.6 and SD = 3.1.

The peer-risk variable was an index that assessed the negative behavior of the respondent’s four closest friends. Students were asked how many of their four closest friends had been suspended from school, carried a handgun, sold illegal drugs, stolen/tried to steal a car or motorcycle, been arrested, dropped out of school, smoked cigarettes, drank alcohol, used marijuana, been in a gang, and/or used other hard drugs. The index was reliable (Cronbach’s α = 0.89). Student responses ranged from zero to four for all 11 questions with a Mean = 16.3 and SD = 7.6.

The family-risk variable was an index that assessed parent’s attitudes toward student’s negative behaviors. Students were asked how their parents would feel if they drank alcohol, smoked cigarettes, smoked marijuana, stole anything worth more than five dollars, drew graffiti/destroyed private property, and picked a fight with someone. The index was reliable (Cronbach’s α = 0.81). Student responses for each of these six questions ranged from 1 = Very wrong, 2 = Wrong, 3 = A little wrong, and 4 = Not wrong at all (Mean = 7.6; SD = 2.6).

The final-risk variable assessed community disorganization as perceived by students. Students were asked a series of questions about their neighborhood including: How much do the following statements describe your neighborhood: crime and/or drug selling, fights, lots of empty and abandoned buildings, and/or lots of graffiti. The four-item index was reliable (Cronbach’s α = 0.82), and student responses for each of these questions ranged from 1 = definitely no to 4 = definitely yes (Mean = 5.8; SD = 2.5). All of these risk variables (indices) have been previously discussed in earlier sections of this article, they are reliable, and have been vetted as important assessments of risk among adolescents.

Social Assets

The first asset from the individual domain assessed student’s understanding of a moral order—knowing the difference between right and wrong. Long argued to be a critical individual asset, students were asked if they thought it was OK to take something without asking and get away with it, if it was OK to cheat in school, if it was OK to beat up someone if he/she starts a fight, and if it was important to be honest with your parents, regardless of the consequences. The response categories in the first three questions were recoded to 1 = definitely yes to 4 = definitely no to match with the responses in the last question on parental honesty. The index was reliable (Cronbach’s α = 0.76) with a Mean = 12.6 and SD = 2.6.

The next asset was from the peer domain and assessed the positive behavior of the student’s four closest friends. Students were asked how many of their closest friends participated in clubs in school, activities in school, made a commitment to stay drug-free, tried to do well in school, liked school, and regularly attended religious services. The six-item index was marginally reliable (Cronbach’s α = 0.68). Student responses ranged from zero to four with a Mean = 16.8 and SD = 4.8.

The family-asset variable assessed the extent to which students felt connected to and monitored by their family. Respondents were asked if their parents knew about their homework, would know if they missed a curfew, would have someone to stay with them when they were not at home, state family rules clearly, would catch them if they drank, skipped school, and/or carried a handgun. The student’s responses to the eight items were coded as 1 = definitely no to 4 = definitely yes. The index was reliable (Cronbach’s α = 0.85) with a Mean = 26.2 and SD = 5.2.

The final-asset variable assessed the student’s connectedness to his/her community. Students responded to a series of eight questions that included: If I had to move, I would miss the neighborhood I live in now; my neighbors notice when I am doing a good job; I like my neighborhood; there are a lot of adults in my neighborhood that I can talk to about something important; there are people in my neighborhood that are proud of me when I do something well; there are people in my neighborhood that encourage me; and I feel safe in my neighborhood. The student responses to the items ranged from 1 = definitely no to 4 = definitely yes. The index was reliable (Cronbach’s α = 0.85) with a Mean = 17.1 and SD = 5.1. Again all these social assets included in the present analysis have been discussed earlier and have been shown in numerous studies to be both reliable and valid indicators of assets among adolescents.

Results

The major focus of the analysis is to examine the cross-sectional relationships between selected risk factors, social assets, and antisocial and drug use behaviors among Hispanic adolescents. Logistic regression estimates the odds of engaging in one of the negative health behaviors, taking into account multiple factors contributing to these behaviors. The analysis provides odds ratios (OR) and estimated effects for each of the predictor variables, a measure of statistical significance for the entire model, a summary measure of the model’s fit (Naglekerke R²), and some assessment of statistical significance for each of the variables.

The logistic regression results for antisocial behavior are presented in Table 1. By examining the OR, those that are less than one indicate lower odds of engaging in antisocial behavior compared to OR greater than one, where odds increase chances of engaging in antisocial behavior.

Table 1.

Estimated effects and odds ratios (OR) for sociodemographic, risk, and assets on antisocial behavior among Hispanic adolescents.

Predictors	OR (B)
Gender (male = 1)	.528* (–.639)
Age	.867* (–.142)
Depressive symptomatology	1.13** (.122)
Negative friendships	1.07** (.064)
Parents’ unsupportive views	1.01 (.001)
Community disorganization	1.16* (.147)
Moral order: right vs. wrong	.838** (–.177)
Prosocial friendships	.980 (–.020)
Parent attachment and control	.946 (–.056)
Community connectedness	1.08 (.084)
χ²	91.56**
df	10
Nagelkerke R²	29.50%

p < .05; **p < .01.

The results in the logistic regression show that both sociodemographic variables are significant in determining the odds of engaging in negative health behavior. Quite unexpected, girls are more likely (0.528) than boys to engage in antisocial behaviors. Also, contrary to what we originally hypothesized, it appears as though younger adolescents (middle and junior high school students) are more likely to engage in antisocial behaviors than their older counterparts (high school students).

All of the risk factors were operating in the expected direction (positive); the only nonsignificant risk factor was students’ perception of their parents’ attitudes toward risk behaviors as being acceptable. Depressive symptoms, negative peer friendships, and community disorganization/dysfunction were all statistically significant (p < .05). As hypothesized, risk factors like these increased the odds of adolescents reporting more antisocial behaviors. Thus, those students reporting more depressive symptoms, with more friends that engaged in risk behaviors and, lived in communities that were unsafe and deteriorating, had increased odds in reporting at least some antisocial behaviors compared to those that did not report those risks.

The asset variables were mostly operating as originally hypothesized; the only significant asset variable in the model was the student’s ability to recognize wrong from right. Two other asset variables were nonsignificant—having prosocial peer relationships and attachment to their community/neighborhood. This model accounted for nearly 30% of the variation in differences between the odds of engaging or not engaging in antisocial behavior among this sample of Hispanic adolescents.

The logistic regression results for 30-day drug use are presented in Table 2. Unlike the results in Table 1, it appears as though males engage in drug use behavior nearly twice as much (OR = 1.76) as females; as expected older youth reported more drug use behavior within 30 days of their interview. All of the risk variables were significant and in the expected direction. Similar to antisocial behavior, this type of negative health behavior was related to depressive symptoms, delinquent peer friendships, unsupportive parents, and community disorganization. The intersection of these environments underscores the importance of examining multiple social domains of risk.

Table 2.

Estimated effects and odds ratios (OR) for sociodemographic, risk, and assets on 30-day drug use behavior among Hispanic adolescents.

Predictors	OR (B)
Gender (male = 1)	1.76* (.568)
Age	1.15* (.141)
Depressive symptomatology	1.13** (.122)
Negative friendships	1.04* (.034)
Parents unsupportive views	1.09** (.087)
Community disorganization	1.11* (.105)
Moral order: right vs. wrong	.783** (–.244)
Prosocial friendships	1.01 (.012)
Parent attachment and control	.903** (–.102)
Community connectedness	.946 (–.055)
χ²	177.49**
df	10
Nagelkerke R²	39.50%

p < .05; **p < .01.

The asset variables, as noted earlier, were generally operating in the hypothesized direction. Two asset variables were significant. Youth who understood the difference between right and wrong, and those that had parents they were attached to but also expected them to follow certain rules, were less likely to report engaging in any substance abuse 30 days prior to the interview compared to their counterparts. Individual and family domains were more important to determining drug use outcomes—individual circumstances about understanding moral order was the only significant asset variable related to lower odds in engaging in antisocial behavior.

Discussion

Overall, we believe this study makes an important contribution to the literature on risk, assets, and negative health behaviors (i.e., antisocial and drug use) generally and specifically as it applies to Hispanic adolescents. First, the study is part of a small number of studies that examine risk and assets among a sample of Hispanic students only. We believe it is important to continue to understand risk and assets among groups like these and not necessarily in the context of how they compare to Caucasian samples. In addition, the results of this study suggest that while risk may be the universal experience across racial and ethnic subgroups, assets may be different. There may be some important cultural mechanisms operating here, which protects Hispanic adolescents in a way that we were unable to tap in the current study. Clearly, risk and assets need further exploration when examining unique racial and ethnic subgroups of adolescents and their negative health behaviors.

The relationship between gender and negative drug use was significant; boys engage in more reported drug use than girls. However, when examining the gender relationship with antisocial behavior, it appears as though girls reported more antisocial behavior than boys. Interestingly, there appears to be some developing anecdotal evidence that among African American and to somewhat lesser extent Hispanic adolescents, girls are engaging in antisocial behaviors particularly in the lower grades. The age-antisocial behavior relationship was significant but in the opposite direction as originally hypothesized. At least among Hispanic youth, it appears as though younger (middle/junior high) students reported engaging in more antisocial behaviors than their older (senior high) counterparts. What generally has held up in a majority of studies, particularly among Whites, may need some rethinking when it comes to specific ethnic and racial subgroups. As expected, the gender and age relationships held up when looking at the drug use outcome. Boys and older youth had higher odds of engaging in drug use than girls or younger adolescents.

Not surprisingly, risk factors operated in the expected direction, and all but one of them was statistically significant. Youth reporting depressive symptoms and a general malaise were more likely to engage in negative health behaviors. This finding reported elsewhere (Fitzpatrick, Piko, & Miller, 2008; Fitzpatrick, 1999) underscores the importance of both recognizing and treating mental health problems in youth because of its potential consequences that can lead to destructive behaviors—both during adolescence and later in adulthood.

Peer networks continue to be important navigators to adolescent behavior. Despite the efforts of parents to provide protection, having close friends that engage in negative health behaviors (antisocial and/or drug use) significantly increases one’s odds of engaging in risky behaviors. The juvenile delinquency literature over the last 50 years confirms this, but other recent work on negative health behaviors among Hispanic adolescents does as well (Loukas et al., 2008).

Place matters. It particularly matters in this analysis as a risk factor where adolescents who are exposed to community disorganization and dysfunction are more likely to engage in negative health behavior than students who are not exposed (Fitzpatrick & LaGory, 2010; Leventhal & Brooks-Gunn, 2000; Sampson, Sharkey, & Raudenbush, 2008). While the index is built upon student’s perceptions, the reality of where adolescents spend their time and the uncertainty of those environments can be important to determining both negative and positive outcomes. Despite the mounting evidence of the place–health relationship for adolescents, little has been done in the last several decades to impact the millions of youth who are at risk.

As mentioned earlier, the results related to assets are suggestive of the need to reexamine and rethink what works best for Hispanic adolescents and why. Is there something unique about the cultural traditions and norms among this particular ethnic group that needs further exploration? Is the church potentially playing an important role in helping to develop this sense of moral order or is it grounded in family tradition? Certainly we are not in a position to definitively talk about why this relationship was so important, but it again points to the potential uniqueness of these adolescents that still will require us to examine assets more carefully in future analyses.

Friends matter, but it appears from the present analysis that only if they are engaging in risky behaviors. The analysis seems to indicate that close friends are not as important to preventing negative health behaviors as originally hypothesized. Family assets were found to be significant in predicting odds of drug use among this particular group of Hispanic adolescents. Research has found that family acts as an important mediator to risk in predicting negative outcomes among youth (Livaudais, Napoles-Springer, Stewart, & Kaplan, 2007; Loukas et al., 2008); the parent control variable was in the expected direction for antisocial behavior but was not statistically significant. We suggest, again, a more careful analysis of the role of family in traditional and nontraditional racial and ethnic settings.

While it is important to broaden our understanding of risk and assets among Hispanic adolescents, this study is not without limitations. First the data are cross-sectional and limit our ability to predict negative health behavior. This study establishes important relationships, but is not a causal and the authors recognize the tenuous nature of these relationships. In addition, the nonresponse rate while not overly excessive could lead to bias. Our results do not include those students who were absent, unwilling to participate, or did not receive permission from their parents/guardians to participate in the study. It is possible that by including these students in the final analysis our responses would be different. We acknowledge this absence of data as important to our final discussion of the results. It is also important to recognize the limited generalizability of these findings—a sample of Hispanic adolescents, living in a mid-sized Arkansas metropolitan area, in grades 6, 8, 10, and 12. Additional studies are necessary to confirm or refute the present findings; the goal of this study was to highlight the importance of examining risk, assets, and negative health behaviors in a Hispanic only sample. The study not only highlights the importance of subgroup specific analyses but also the need to replicate them in other communities and/or other school districts.

Despite these limitations, this study makes several contributions. Our findings reemphasize the importance of examining negative health behaviors (antisocial and drug use) in the context of risk and assets. Hispanic adolescents may be unique compared to other adolescents, and this study continues that work emphasizing the importance of not comparing specific subgroup behaviors to the behaviors of a Caucasian majority. Rather in order to better understand Hispanic adolescent negative health behavior, we must continue examining the role of familia, the church, and the neighborhood/community and their complex roles in determining adolescent behavior.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Author Biographies

Kevin M. Fitzpatrick is professor of sociology and the Jones Chair in Community at the University of Arkansas. His research examines the interrelationship between health and place. In addition, his work explores the physical and mental health consequences of risk and environmental stressors on children and adolescents, particularly for low-income racial and ethnic minorities.

Wendie Choudary received a bachelor’s degree in sociology and anthropology from Hendrix College, and a master’s degree in sociology from the University of Arkansas. Her research interests are in urban sociology, demography, health, and public policy. She is currently working on her PhD in sociology at the University at Albany-SUNY.

Anne Kearney graduated from the University of Arkansas with a Bachelor of Science in Elementary Education and a Master of Arts in sociology. She resides in the Washington, DC Metropolitan Area where she is involved in early childhood education and active in addressing issues of social justice for underrepresented individuals.

Bettina F. Piko holds both MD and PhD degrees and is an associate professor in the Department of Behavioral Sciences at University of Szeged, Hungary. Currently, she teaches courses for medical students (medical anthropology, medical ethics, epidemiology, positive psychology, and addiction medicine). Her research interests include the contextual and relational correlates of adolescents’ substance use, problems behavior, mental health, and well being.

References

Almodover

Tomaka

Thompson

McKinnon

O’Rourke

. (2006). Risk and protective factors among high school students on the US/Mexico border. American Journal of Health Behavior, 30, 745-752.

Arbina

Power

T. G.

(2003). Parental attachment, self-esteem, and antisocial behaviors among African American, European American, and Mexican American adolescents. Journal of Counseling Psychology, 50, 40-51.

Arkansas Prevention Needs Assessment Survey. (2007). Arkansas State Report 2007. Bowling Green, KY: International Survey Associates, dba Pride Surveys.

Arthur

M. W.

Hawkins

J. D.

Pollard

J. A.

Catalano

R. F.

Baglioni

A. J.

(2002). Measuring risk and protective factors for substance use, delinquency, and other adolescent problem behaviors: The communities that care youth survey. Evaluation Review, 26, 575-601.

Atzaba-Poria

Pike

Deater-Deckard

(2004). Do risk factors for problem behaviour act in a cumulative manner? An examination of ethnic minority and majority children through an ecological perspective. Journal of Child Psychology and Psychiatry, 45, 707-718.

Barrera

Jr. Prelow

H. M.

Dumka

L. E.

Gonzales

N. A.

Knight

G. P.

Michaels

M. L.

Tein

. (2002). Pathways from economic conditions to adolescents’ distress: Supportive parenting, stressors outside the family, and deviant peers. Journal of Community Psychology, 30, 135-152.

Barrera

Jr. Gonzales

N. A.

Lopez

Fernandez

A. C

. (2004). Problem behaviors of Chicana/o and Latina/o adolescents: An analysis of prevalence, risk, and protective factors. In Valasquez

Arellano

L. M.

McNeill

B. W.

(Eds.), The handbook of Chicana/o psychology and mental health (pp. 83-109). Mahwah, NJ: Erlbaum.

Bond

Tombourou

J. W.

Thomas

Catalano

R. F.

Patton

(2005). Individual, family, school and community risk and protective factors for depressive symptoms in adolescents. Prevention Science, 6, 73-88.

Brindis

Wolfe

A. L.

McCarter

Ball

Starbuck-Morales

(1995). The associations between immigrant status and risk-behaviors patterns in Latino adolescents. Journal of Adolescent Health, 17, 99-105.

10.

Centers for Disease Control and Prevention. (2003). Youth risk behavior surveillance, United States 2003. (MMWR) Morbidity Mortality Weekly Report, 53, 1-96.

11.

Centers for Disease Control and Prevention (2009). Youth violence: Facts at a glance. Atlanta, GA: Center for Disease Control and Prevention.

12.

Deater-Deckard

Dodge

K. A.

Bates

J. E.

Pettit

G. S.

(1998). Multiple risk factors in the development of externalizing behavior problems: Group and individual differences. Developmental Psychopathology, 10, 469-493.

13.

De La Rosa

M. R.

Holleran

L. K.

Rugh

MacMaster

S. A.

(2005). Substance abuse among U.S. Latinos: A review of the literature. Journal of Social Work Practice and Addiction, 5, 1-20.

14.

Eamon

(2001). Poverty, parenting, peer and neighborhood influences on young adolescent antisocial behavior. Journal of Social Service Research, 28, 1-23.

15.

Eamon

M. K.

Mulder

(2005). Predicting antisocial behavior among Latino youth adolescent: An ecological systems analysis. American Journal of Orthopsychiatry, 75, 117-127.

16.

Eaton

D. K.

Kann

Kinchen

Shanklin

Ross

Hawkins

Wechsler

(2006). Youth risk behavior surveillance—United States 2005. (MMWR) Morbidity Mortality Weekly Report, 5, 145-160.

17.

Eaton

D. K.

Kann

Kinchen

Shanklin

Ross

Hawkins

Weschler

(2008). Youth risk behavior surveillance—United States, 2007. (MMWR) Morbidity Mortality Weekly Report, 57, 1-131.

18.

Fitzpatrick

K. M.

(1997). Fighting among America’s youth: A risk and protective factors approach. Journal of Health and Social Behavior, 38, 131-48.

19.

Fitzpatrick

K. M.

(1999). Violent victimization among America’s school children. Journal of Interpersonal Violence, 14, 1055-1069.

20.

Fitzpatrick

K. M.

LaGory

(2003). Placing health in an urban sociology: Cities as mosaics of risk and assets. City and Community, 2, 33-46.

21.

Fitzpatrick

K. M.

LaGory

(2010). Unhealthy cities: Race, class and place in America. New York, NY: Routledge.

22.

Fitzpatrick

K. M.

Piko

B. F.

Wright

LaGory

(2005). Depressive symptomatology, exposure to violence and the role of social capital among African American adolescents. American Journal of Orthopsychiatry, 75, 262-274.

23.

Fitzpatrick

K. M.

Piko

B. F.

Miller

(2008). Suicide ideation and attempts among low-income African American adolescents. Suicide and Life Threatening Behavior, 38, 552-563.

24.

Fitzpatrick

K. M.

Dulin

Piko

B. F.

(2009). Bullying and depressive symptomatology among low-income, African American youth. Journal of Youth and Adolescence, 39, 634-645.

25.

Foley

K. L.

Altman

Durant

R. H.

Wolfson

(2004). Adults’ approval and adolescents’ alcohol use. Journal of Adolescent Health, 34, 17-26.

26.

Frank

Cerda

Rendon

(2007). Barrios and burbs: Residential context and health risk behaviors among Angeleno adolescents. Journal of Health and Social Behavior, 48, 283-300.

27.

Gallegos-Castillo

Patino

(2006). Bridging community, research, and action: An emerging center on Latino youth development. Oakland, CA: National Council on Crime and Delinquency.

28.

Gorman-Smith

Henry

D. B.

Tolan

P. H.

(2004). Exposure to community violence perpetration: The protective effects of family functioning. Journal of Clinical Child Adolescent Psychology, 33, 439-449.

29.

Hawkins

Herrenkohl

Farrington

D. P.

Brewer

Catalano

R. F.

Harachi

T. W.

(1998). A review of predictors of youth violence. In Loeber

Farrington

(Eds.), Serious and violent juvenile offenders: Risk factors and successful interventions (pp. 106-146). Thousand Oaks, CA: SAGE.

30.

Hawkins

J. D.

Catalano

R. F.

(2002). Investing in your community’s youth: An introduction to the communities that care system. South Deerfield, MA: Channing Bete.

31.

Hawkins

J. D.

Catalano

R. F.

Arthur

M. W.

(2003). Promoting science-based prevention in communities. Addictive Behaviors, 27, 951-976.

32.

Hawkins

J. D.

Catalano

R. F.

Miller

J. Y.

(1992). Risk and protective factors for alcohol and other drug problems in adolescence and early childhood: Implications for substance abuse prevention. Psychological Bulletin, 112, 64-105.

33.

Hawkins

J. D.

Smith

B. H.

Catalano

R. F.

(2002). Delinquent behavior. Pediatric Review, 23, 387-392.

34.

Jessor

(1998). New perspectives on adolescent risk behavior. New York, NY: Cambridge Press.

35.

Johnston

L. D.

O’Malley

P. M.

Bachman

J. G.

Schulenberg

J. E.

(2007). Monitoring the future national results on adolescent drug use: Overview of key finding, 2006. Bethesda, MD: National Institute on Drug Abuse.

36.

Joiner

T. E.

Perez

Wagner

K. D.

(2001). On fatalism, pessimism and depressive symptoms among Mexican American and other adolescents attending an obstetrics-gynecology clinic. Behavioral Research and Therapy, 39, 887-896.

37.

Kawachi

Berkman

L. F.

(2003). Neighborhoods and health. Oxford, UK: Oxford University Press.

38.

Leventhal

Brooks-Gunn

(2000). The neighborhoods they live in: The effects of neighborhood residence on child and adolescent outcomes. Psychological Bulletin, 126, 309-337.

39.

Livaudais

Napoles-Springer

Stewart

Kaplan

C. P.

(2007). Understanding Latino adolescent risk behaviors: Parental and peer influences. Ethnicity and Disease, 7, 298-304.

40.

Loukas

Prelow

H. M.

Suizzo

M. A.

Allua

(2008). Mothering and peer associations mediate cumulative risk effects for Latino youth. Journal of Marriage and the Family, 70, 76-85.

41.

Kaplan

C. P.

Erickson

P. I.

Juarez-Reyes

(2002). Acculturation, gender role orientation, and reproductive risk-taking behavior among Latina adolescent family planning clients. Journal of Adolescent Research, 7, 103-121.

42.

Marshal

M. P.

Cassin

(2000). Peer influence on adolescent alcohol use: The moderating role of parental support and discipline. Applied Developmental Science, 4, 80-88.

43.

Mather

Adams

(2006). A KIDS COUNT/PRB Report on Census 20′00: The risk of negative child outcomes in low-income families. Washington, DC: KIDS COUNT & Population Reference Bureau.

44.

Nash

S. G.

McQueen

Bray

J. H.

(2005). Pathways to adolescent alcohol use: Family environment, peer influence, and parental expectations. Journal of Adolescent Health, 37, 19-28.

45.

Ojeda

V. D.

Patterson

T. L.

Strathdee

(2008). The influence of perceived risk to health and immigration-related characteristics on substance use among Latino and other immigrants. American Journal of Public Health, 98, 862-868.

46.

Pew Hispanic Center. (2009). Between two worlds: How young Latinos come of age in America. Washington, DC: Pew Hispanic Center.

47.

Repetti

R. L.

Taylor

S. E.

Seeman

T. E.

(2002). Risky families: Family social environments and the mental and physical health of offspring. Psychological Bulletin, 128, 330-366.

48.

Romero

A. J.

Martinez

Carvajal

(2007). Bicultural stress and adolescent risk behaviors in community sample of Latinos and non-Latino European Americans. Ethnicity and Health, 12, 443-463.

49.

Roosa

M. W.

Deng

Ryu

Burrell

J. L.

Tein

Jones

Crowder

(2005). Family and child characteristics linking neighborhood context and child externalizing behavior. Journal of Marriage and the Family, 67, 515-529.

50.

Sampson

R. J.

Sharkey

Raudenbush

S. W.

(2008). Durable effects of concentrated disadvantage on verbal ability among African American children. Proceedings of the National Academy of Science USA, 105, 845-852.

51.

Sen

(2004). Adolescent propensity for depressed mood and helpseeking: Race and gender differences. Journal of Mental Health Policy and Economics, 7, 133-145.

52.

Schwartz

S. J.

Pantin

Prado

Sullivan

Szapocznik

(2005). Family functioning, identity, and problem behavior in Hispanic immigrant early adolescents. Journal of Early Adolescence, 25, 392-420.

53.

Schwartz

S. J.

Mason

C. A.

Pantin

Wang

Brown

Campo

Szapocznik

(2009). Relationship of social context and identity to problem behavior among high-risk Hispanic adolescents. Youth and Society, 40, 541-570.

54.

Shetgiri

Kataoka

S. H.

Ryan

G. W.

Askew

L. M.

Chung

P. J.

Schuster

M. A.

(2009). Risk and resilience in Latinos: A community-based participatory research study. American Journal of Preventative Medicine, 37, 217-224.

55.

Stueve

O’Donnell

L. N.

(2005). Early alcohol initiation and subsequent sexual and alcohol risk behaviors among urban youth. American Journal of Public Health, 95, 887-893.

56.

Springer

Kelder

Orpinas

Baumler

(2007). A cross-national comparison of youth risk behaviors in Latino secondary school students living in El Salvador and the USA. Ethnicity and Health, 12, 69-88.

57.

Tandon

D. S.

Solomon

(2009). Risk and protective factors for depressive symptoms in urban African American adolescents. Youth and Society, 41, 80-99.

58.

Umana-Taylor

A. J.

Updegraff

K. A.

(2007). Latino adolescents’ mental health: Exploring the interrelations among discrimination, ethnic identity, cultural orientation, self-esteem, and depressive symptoms. Journal of Adolescence, 30, 549-567.

59.

U.S. Bureau of the Census. (2001). Census 2000 (Summary File 1). Washington, DC: U.S. Census Bureau.

60.

Vanderbilt-Adriance

Shaw

D. S.

(2008). Protective factors and the development of resilience in the context of neighborhood disadvantage. Journal of Abnormal Child Psychology, 36, 887-901.

61.

Wallace

J. M.

Jr. Bachman

J. G.

O’Malley

P. M.

Johnston

L. D.

Schulenberg

J. E.

Cooper

S. M

. (2002). Tobacco, alcohol, and illicit drug use: Racial and ethnic differences among U.S. high school seniors, 1976-2000. Public Health Reports, 117, S67-S75.

62.

Wolfe

D. A.

Jaffe

P. G.

Crooks

C. V.

(2006). Adolescent risk behaviors: Why teens experiment and strategies to keep them safe. New Haven, CT: Yale University Press.