Adolescent Risk and Vulnerability Concepts and Measurement (2001) / Chapter Skim
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4. Modeling the Payoffs of Interventions to Reduce Adolescent Vulnerability
4. Modeling the Payoffs of Interventions to Reduce Adolescent Vulnerability
Pages 73-108
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From page 73... ...
The few existing treatments of the cost of adolescent risk behaviors have likewise focused on single behaviors (e.g., teen childbearing Burt, 1985, 1986; Burt and Levy, 1987) or narrowly defined patterns (e.g., being a career criminal Cohen, 19981.
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From page 74... ...
A strong argument can be made that we need all of our youth to develop into productive adults, with skills and attitudes ready to cope with twenty-first-century work, politics, and community and interpersonal relationships. The evidence suggests that for significant portions of our youth, seriously inadequate educational achievement, and life-threatening habits such as addictions, risky sexual behavior, involvement in crime and violence, and too-early childbearing foreclose the possibility that they will become contributing members of society.
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From page 75... ...
One particularly telling example was a small rural jurisdiction in a conservative state, where it was very difficult to get any resources either for pregnancy prevention or to help teen mothers stay in school. After making the calculations for the 20-year projection, the jurisdiction realized that it was spending more than $1 million in welfare benefitsfor each and every birth cohort, without even knowing it and without
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From page 76... ...
Furthermore, we will examine the payoffs of patterns of youth risk behavior, rather than of a single type of risk behavior. The reasons for this approach should be obvious from the results of the past decades of research on youth risk behaviors and evaluations of programs taking a single-focus versus a holistic approach to promoting positive youth outcomes.
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From page 77... ...
It will attempt to meet various challenges such as "payoffs of adolescent risk behaviors to/for whom? " and "compared to what?
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From page 78... ...
are only weakly related to adolescent risk-taking behaviors such as substance use, risky sexual activity, and violence. Additionally, Resnick, Blum, and others have found that processes such as family connectedness, school connectedness, and time spent in structured activity work to reduce the amount of risky behavior among youth.
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From page 79... ...
We attempted to capture the multidimensional nature of youth risk taking using Add Health data and cluster analysis. We found that youth participate in both health-enhancing lifestyles (Elliot, 1993)
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From page 80... ...
3.00 2.50 2.001.501.00 0.50 0.00 ~ ~ ~'~ ~ Risk Behavior Profile 1: Low risk I' Profile 2: Moderate risk, sexually active, substance use Profile 3: Moderate risk, high on marijuanna use and suicide ~ Profile 4: High risk FIGURE 4-2 Profiles of risk Males grades 9-12. SOURCE: Zwieg, )
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From page 81... ...
Teens in high-risk profiles and profiles distinguished by suicidal thoughts and behaviors are also similar teens in both groups consistently report higher levels of vulnerability factors and lower levels of protective factors. Teens who are suicidal but do not report participating in any other risk behaviors are as vulnerable and unprotected as those who participate in all types of risk behaviors.
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From page 82... ...
For example, recent trends in adolescent risk taking indicate decreases in some risk behaviors such as violence and sexual activity, and increases in others such as substance use (Boggess et al., 20001. Therefore, although we may be able to measure the same age groups, differences of cohort and time may make it difficult to compare results across data sets and tell a full story of the payoffs of adolescent risk (Baltes et al., 19771.
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From page 83... ...
Participants were asked about their background, educational history and aspirations, sexual activity, substance use, attitudes about contraception and gender roles, and knowledge about sexual activity, contraception, and AIDS. Like Add Health, NSAM would be an appropriate data set to identify adolescent health-compromising and health-enhancing lifestyles, but also like Add Health, the participants were only followed through young adulthood, allowing assessment of only shorter term outcomes related to risk.
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From page 84... ...
Participants were asked about background information, friends and family, neighborhood issues, education, employment, psychosocial adjustment, delinquency, substance use, sexual activity, pregnancy and abortion, use of mental health services, and violence. Like Add Health and NSAM, NYS would be an appropriate data set to identify adolescent lifestyles.
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From page 85... ...
Most important, it appears to be possible with existing data sets to begin the work of mapping complex health risk profiles onto equally complex multidimensional outcome sets. This is a matter of identifying multidimensional probability clistributions on both sicles, rather than the much simpler task of estimating the separate probabilities that one type of risk behavior will lead to various different undesirable outcomes, taken one at a time.
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From page 86... ...
These programs usually aim to prevent bad outcomes, intervene after youth behavior has already reached the "risky" level, and have relatively little focus on promoting good outcomes. The second approach incorporates the latest thinking about positive youth development, including the desire to help large segments of the most disadvantaged youth in this country to move toward healthy and productive adulthood, not just avoid negative outcomes.
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From page 87... ...
GED = General Education Development Tests; AFDC = Aid to Families with Dependent Children; SSI = Supplemental Security Income; YLL = Years of Life Lost.
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From page 88... ...
However, we also want to be able to model the payoffs of programs and activities based on a positive youth development approach. Such programs are more likely than prevention programs to serve a broader array of youth, to start younger and stay longer, and perhaps to take as their focus families, whole communities, neighborhoods, or schools.
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From page 89... ...
What the final column of Table 4-1 does suggest, however, is that enough knowledge exists to warrant attempts to model the payoffs of adolescent vulnerability, once we can establish sets of outcomes we want to "price." To pursue our example of payoffs associated with a program designed to prevent criminal behavior in adolescents (Cohen, 2000) , a list of negative payoffs might yield the following: .
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From page 90... ...
But obtaining that information is only half the battle. We also need information about the ability of programs to change the probabilities that certain outcomes will happen reducing negative outcomes and their associated costs, and/or increasing positive outcomes and their associated benefits.
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From page 91... ...
In addition, we could easily calculate the benefits to be expected from a reduction of X percent in the proportion of youth exhibiting a particularly hazardous health risk profile, or an increase of Y percent in the proportion exhibiting profiles of very low risk. For the purpose of articulating the probable benefits of intervention, calculations of this type might be enough to win an argument about how .
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From page 92... ...
Intangibles "Intangibles" are those things about which we all care passionately but on which we cannot put a price. "Public burden" analyses omit these payoffs entirely, while "social welfare" analyses struggle with how to place value on valuable but priceless things.
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From page 93... ...
Suppose one had a classic prevention approach that was closely targeted on the worst youth, did not do anything for most youth, and succeeded in preventing several of those "worst youth" from fulfilling the worst, most costly, expectations for the outcomes of their behavior. And suppose another program, taking a positive youth development approach with all the youth in a particular neighborhood, succeeded in helping most of them graduate from high school, go on to college or into the labor market, and lead productive lives.
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From page 94... ...
94 o To Cal .
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From page 95... ...
95 XX X XX X X X XXX X X X XX XX X X X x X X X X Con a O O .Y ~ ~ ~ ~ 5 C O ~ ~ ~ ~ X O =0 -0 ._ 11 X o .
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From page 96... ...
. In addition, this model treats resiliency factors as exogenous, and as moderators of the effects of health risk profiles on negative and positive outcomes (paths E)
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From page 97... ...
97 ~ ~ ~ O ~ ~ cn u)
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From page 100... ...
With respect to the associations of health risk profiles with negative outcomes, space and layout on the page did not let us show in Figure 4-4 the moderating effects of resiliency factors (paths E in Figure 4-3) , because we would have had to draw arrows from resiliency factors to every arrow in the figure.
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From page 101... ...
Embedded in Figure 4-6 are two hypothetical "designs" for estimating payoffs. We spoke earlier of the traditional prevention approach and of the positive youth development approach, and specified in Table 4-2 how we expected payoffs to be distributed among the various recipients youth, their community, the public sector, and the rest of society.
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From page 102... ...
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From page 105... ...
Typical efforts of these programs start early and try to affect resiliency factors, behaviors, attitudes, relationships, and competencies leading to positive outcomes as well as reducing negative ones. The paths labeled "B" symbolize the goals of these programs to increase payoffs for youth, communities, and the rest of society through creation of more positive outcomes, as well as to reduce public costs by reducing negative outcomes.
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From page 106... ...
. The effects of race/ethnicity, income, and family structure on adolescent risk behaviors.
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From page 107... ...
. Risk behaviors in adolescence: A psychosocial framework for understanding and action.
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From page 108... ...
. Adolescent health risk profiles: The co-occurrence of health risks among females and males.
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