Cumulative Risk for Early SexualInitiation Among American Indian Youth:

A Discrete-Time Survival Analysis

Christina M. Mitchell, Nancy Rumbaugh Whitesell, PaulSpicer, Janette Beals, Carol E. Kaufman, and The Pathways of

Choice and Healthy Ways Project Team

American Indian and Alaska Native Programs, University of Colorado at Denverand Health Sciences Center

Approximately 3 million teens are diagnosed with a sexually transmitteddisease (STD) annually; STDs rates for American Indian young adults areamong the highest of any racial/ethnic group. An important risk factor forSTDs is early initiation of sex. In this study, we examined risk for earlyinitiation with 474 American Indian youth ages 14–18, using 11 risk factorsfrom three developmentally important microsystems (family, school, peers)along with several individual characteristics; a cumulative risk index wasalso calculated. Discrete-time survival analyses showed that predictors ofearly initiation differed by gender. For young men, younger initiation of sexwas predicted by lower grades, liking school less, having peers with lessprosocial attitudes, a greater likelihood of having used alcohol or drugs atfirst sex, and having higher sensation-seeking. For young women, earlierinitiation was related to having mothers who had their first child at an earlyage and who had less formal education, to having parents who had divorced,

JOURNAL OF RESEARCH ON ADOLESCENCE, 17(2), 387–412Copyright r 2007, Society for Research on Adolescence

In addition to the above people, the Project Team included Sonia Bauduy,Cathy A. E. Bell, Cecelia Big Crow, Dedra Buchwald, Nichole Cottier, AmyD. Dethlefsen, Ann Wilson Frederick,Ellen Keane, Shelly Hubing, NatalieMurphy, Angela Sam, Jennifer Settlemire, Jennifer Truel, and FrankeeWhite Dress.

Requests for reprints should be sent to Christina M. Mitchell, AIANP, MS F800, PO Box6508, Aurora, CO 80045. E-mail: christina.mitchell@UCHSC.edu

dropping out of high school, using alcohol/drugs at first sex, and highersensation-seeking. Higher cumulative risk was associated with elevated riskof sexual initiation, although the degree of added risk varied with age forwomen. Cumulative risk deserves broader attention in understanding theearly initiation of sexual intercourse.

Every year, an estimated 3 million teenagers in the United States are di-agnosed with a sexually transmitted disease (STD)—including, most crit-ically, HIV—making the risk of contracting an STD one of the most seriouspublic health issues for adolescents and young adults (Rosenthal et al.,2001). Young people account for approximately 25% of all newly diag-nosed STDs, and the number of youth infected with HIV has been dou-bling almost annually (Donenberg, Bryant, Emerson, Wilson, & Pasch,2003). Early initiation of sexual intercourse is an important risk factor forSTDs. The younger the age at first intercourse, the longer a youth is ex-posed over his or her lifetime to the risks of sexual transmission of disease(Graber, Brooks-gunn, & Galen, 1998). In addition to exposure over longerperiods of time, more frequent exposure is of concern as well: Those whoinitiate sex earlier generally have more partners, more frequent sex, andlower rates of contraceptive use, all of which increase the likelihood ofcontracting an STD (Graber et al., 1998; Mcbride, Paikoff, & Holmbeck,2003; Romer, Stanton, Galbraith, Feigelman, & Black, 1999). Furthermore,younger adolescents may be less able to recognize or personalize sexualrisks than are older teens because of their less advanced cognitive andsocial development; as a result, they may not be as responsive to typicalpreventive interventions—if they receive them at all (Graber et al., 1998;McBride et al., 2003).

Although STD rates for youth are high in all racial/ethnic groups,American Indian 20– to 24-year-olds have among the highest rates of anygroup in the United States, with gonorrhea and chlamydia rates more thanthree times those found among Whites (Centers for Disease Control andPrevention, 2002). In addition, diagnoses of AIDS have risen more rapidlyamong American Indians than in any other ethnic group, increasing morethan 10-fold from 1990 (233 cases) to 2002 (2,875 cases) (Centers for DiseaseControl and Prevention, 2003). Even more worrisome is the fact that re-ported rates of any diseases among American Indians are most likelyunderestimates, since substantial misclassification of American Indians asnon-Indian occurs in many surveillance systems (Thoroughman, Freder-ickson, Cameron, Shelby, & Cheek, 2002).

Here, we continue to seek to understand sexual risk among AmericanIndian youth. Compared with U.S.-All Races, the percentage of never-

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married female youth who reported having had sex by age 13 in thesample used in this study was similar (6.0% versus 5.7%, respectively).However, the percentage of males in this category was double: 7.9% forU.S.-All Races compared with 16.4% for this sample (U.S. Department ofHealth and Human Services, 2004). In ethnographic work, which includedin-depth interviews and small-group discussions with adolescents andyoung adults living on the reservation involved in this project (Kaufman etal., 2007), we found that youth often referred to sexual activity, peer pres-sure, and the challenges of synthesizing contemporary sexual attitudesand expectations with cultural values. Youth acknowledged that the tra-ditional culture of this tribe places value on monogamy and virginity untilmarriage. However, youth frequently receive mixed messages about sex-ual relationships, including pressure to initiate sex or to establish sexualprowess. Thus, they may grow up in families that inculcate strong tra-ditional values, which may protect against sexual risk-taking; yet life inreservation communities at times can mean ongoing, powerful pressuresto use substances or engage in sexual activity, even at a young age. Someyouth do not have the benefit of learning about cultural values in the firstplace, which can leave them even more vulnerable to compromised de-cision-making about sex. Moreover, the lack of employment, educational,and recreational opportunities may add even greater pressures to becomeinvolved in risky activities such as sexual intercourse at young ages, re-gardless of traditional values. Given such potential pressures and highrates of STDs, understanding sexual risk-taking among American Indianyouth is important in considering prevention approaches.

RISK RESEARCH

One approach to STD prevention among adolescents focuses on helpingthem decide to delay sexual initiation. Effective delay interventions willdepend on the identification of factors related to early initiation. Riskfactors for early initiation have typically been addressed as distinct factors,with predictive models assessing the independent contribution of each.Such an approach carries the implicit assumption that each variable has aunique impact on an outcome, apart from other predictors in the model;the relative importance of each variable’s unique contribution is evaluatedby comparing the size of the coefficients. However, overlap or correlationsamong predictors deflate parameter estimates and can obscure the com-bined or cumulative effects of a set of risk factors (Burchinal, Roberts,Hooper, & Zeisel, 2000). Furthermore, as development is influenced by amultitude of typically interrelated factors, a focus on single risk factors

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does not reflect the reality of the lives of most youth (Evans, 2003; Gerard& Buehler, 2004; Kalil & Kunz, 1999; Sameroff, Guttman, & Peck, 2003).Consequently, it is unlikely that a single ‘‘magic bullet’’ for prevention orintervention will be uniformly effective (Sameroff et al., 2003). Preventionand intervention efforts may instead benefit from a better understandingof the combined effects of multiple risk factors.

Cumulative Risk

One of the ways in which multiple factors may influence children’s prob-lem behavior is through a cumulative process. For the understanding ofadverse outcomes, the accumulation of risk factors across a variety ofcontexts or systems often matters more than any single factor (Atzaba-poria, Pike, & Deater-deckard, 2004; Pungello, Kupersmidt, Burchinal, &Patterson, 1996; Sameroff et al., 2003). Cumulative risk has been found tobe important in predicting a number of different youth outcomes, such aseducational achievement (Prelow & Loukas, 2003), internalizing and ex-ternalizing behaviors (Atzaba-Poria et al., 2004), well-being (Meyers &Miller, 2004), substance use (Griffin, Scheier, Botvin, & Diaz, 2000), andnonmarital childbirth (Kalil & Kunz, 1999). This construct may also proveuseful for intervening with youth at risk for early initiation of sex; to date,though, researchers have not explored the relationship of cumulative riskwith age of sexual initiation. Indeed, it may be the accumulation of riskfactors that should trigger intervention, rather than the presence or ab-sence of particular individual risk factors. For example, parental divorceper se may not set a teen on a trajectory of early sex; but couple that divorcewith peers who support more antisocial behaviors and a tenuous con-nection to school, and a teen’s motivation to resist having sex may beseriously undermined. This study was designed to explore the timing ofsexual initiation among a group potentially at high risk for STDs—Amer-ican Indian adolescents and young adults—and to examine the utility of acumulative risk approach to explaining early sexual initiation.

Risk Factors for Early Sexual Initiation

Researchers have focused both on youth’s relationships in several inter-connected domains or ‘‘microsystems’’ (e.g., family, school, peers) that arecritical in adolescent development (Bronfenbrenner, 1979) and on indi-vidual-level characteristics of the youth themselves. Within the familymicrosystem, previous studies have found that mothers’ age at the birth oftheir first child was related to daughters’ age at initiation of sexual in-

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tercourse (Paul, Fitzjohn, Herbison, & Dickson, 2000). Higher levels ofmaternal education have been found to be associated with girls’ delayingintercourse (Rosenthal et al., 2001). Divorce and the physical presence ofonly one parent in the home have been related to early sexual initiation(Raine et al., 1999). Underlying each of these associations may be processesthrough which adults either model acceptable behavior or provide greatermonitoring of youth to help them avoid risky situations (Miller, Benson, &Galbraith, 2001). Family relationship factors found to be associated withdelayed sexual initiation include parental support, family cohesion andconnectedness, and feelings of parental warmth and caring (Donenberg etal., 2003; McBride et al., 2003; Miller et al., 2001; Rosenthal et al., 2001).

A second microsystem important for adolescents is school; severalschool-related factors have been associated with delay of initiation, in-cluding academic achievement (Donenberg et al., 2003; Raine et al., 1999),connectedness or attachment to school (Paul et al., 2000), and higher valueon achievement (Donenberg et al., 2003). High-achieving youth withstrong future educational goals may postpone or avoid risk-taking in or-der to pursue their long-term goals. Involvement with or attachment toschool during adolescence may also be an indicator of attachment to adultsociety rather than to a youth culture that is more prone to risk-taking.

Within a third microsystem—peers—evidence suggests that positivepeer influence (e.g., associating with peers who get good grades and avoidrisk-taking) has been related to delayed sexual initiation (Donenberg et al.,2003; Meschke, Zweig, Barber, & Eccles, 2000). By associating with moreprosocially oriented peers, teens may become part of social networks thatsupport safe and healthy behavior.

Finally, individual-level characteristics of the adolescent have been ex-amined in relation to sexual initiation. Impulsivity and sensation-seekinghave been associated with sex at an early age (Kalichman, Heckman, &Kelly, 1996), as has early substance use. Specifically, use of substancesbefore sexual activity may be another risk for earlier initiation, resulting in‘‘hazy’’ thinking and loss of inhibition. Alternatively, alcohol and drugsmay serve as a social cue for sexual activity rather than a cause for thedecision to engage in sexual behavior (Raine et al., 1999; Rosenthal et al.,2001).

‘‘AGE-TO-EVENT’’ ANALYSES

Dichotomizing Age

One challenge when analyzing the timing of a specific event such as firstintercourse is the reality that some may not experience this event during

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the data collection period. Thus, estimates of the average age of initiationrepresent downwardly biased estimates based only on respondents whoinitiate no later than the end of the study period. These estimates ignorerespondents who delay initiation beyond this point, whose observationsare referred to as ‘‘censored’’ (Singer & Willett, 1991). One way of handlingcensored data is to dichotomize ‘‘age at event’’ information, comparingthose who have had sex by a particular point in time to those who have not(Chewning et al., 2001; Donenberg et al., 2003; Kinsman, Romer, Fursten-berg, & Schwarz, 1998; McBride et al., 2003; Paul et al., 2000; Romer et al.,1999). Unfortunately such dichotomization deliberately destroys mean-ingful continuous age data to create the new grouping variable, eliminat-ing potentially important variation by clustering together everyone whoseonset was before a particular cutoff age. Moreover, such an approach runsthe risk of collapsing very different people into the same group. For ex-ample, Romer et al. (1999) inclusion of youth who had had sex as young asage 9 in the same category as those who first had sex at age 17 could easilyhave masked important developmental differences between such dispa-rate ages. In addition, the choice of an age cutoff is often rather arbitrary—for instance, Paul et al. (2000) used 16 as the cutoff age, while Donenberg etal. (2003) used 14, with little or no empirical, theoretical, or developmentaljustification offered for either choice. Selection of different age cutoffscould easily result in inconsistent or even contradictory conclusions.

Survival Analysis

In contrast to dichotomizing strategies, survival analysis can answer notonly whether but also when critical events such as sexual initiation occur(Singer & Willett, 1991) and can examine a wide variety of predictors ofevent age. Reflecting its early roots of disease progression and mortality,survival analysis uses terms such as the ‘‘hazard’’ or ‘‘risk’’ of an event’soccurrence and an individual’s ‘‘survival’’—i.e., not having experiencedthe event—to a certain point in time. Unlike other approaches, survivalmethods are designed explicitly to deal with otherwise problematic cen-sored cases, rather than ignoring them completely or collapsing them withother, possibly quite different, individuals. The oldest and now mostwidely used survival models are continuous-time models based on Cox’sregression (Willett & Singer, 1993). However, for research looking at age atsexual initiation, two major drawbacks to such models exist. First, con-tinuous-time models assume, not surprisingly, that the time to an event ismeasured using a continuous metric (e.g., seconds, hours, days). Thehazard of an event’s occurrence then is an instantaneous rate of change,

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given that the event has not already occurred up to the immediately priorinstant (Willett & Singer, 1993). Yet data collected about the timing ofsexual initiation are typically measured in a much more discrete metric,most typically age in years (e.g., ‘‘how old were you the first time you hadsex’’), thereby immediately violating a basic assumption of continuous-time survival models. Second, Cox’s regression generally ignores theshape of the hazard profile across time, utilizing a restrictive assumptionof ‘‘proportional hazards’’ that requires the influence of a predictor onthe hazard to be constant over time. Some statistical packages allowthe examination of the proportional hazards assumption of Cox’sregression (e.g., SPSS’s (2003) time-dependent covariates); however,such practices have not been widely used. Yet even when the proportion-al hazards assumption is not contra-indicated, an examination of anonproportional hazards model can often detect additional age-relatedvariation in hazards that might inform intervention design (Muthen& Masyn, 2005).

Despite such strengths, surprisingly few studies of age at sexual ini-tiation have used any type of survival analysis; those that have, with fewexceptions (Capaldi, Crosby, & Stoolmiller, 1996), have relied on Cox’sregression (Lammers, Ireland, Resnick, & Blum, 2000; Mcneely et al., 2002;Resnick et al., 1997), despite the fact that time was measured in years ormonths, thereby violating a basic assumption of Cox’s regression. Fur-thermore, none of these studies explicitly tested the tenability of the pro-portional hazards assumption.

As an alternative to continuous-time survival analysis, discrete-timesurvival analysis (DTSA) assumes that such ‘‘event history’’ data as age atfirst sex are not measured on a continuous metric, but rather in morediscrete units such as years or months. It analyzes a hazard that is definedas the conditional probability that a randomly selected individual in thepopulation will experience the target event in a specific time period, giventhat he or she has not experienced the event before that period (Willett &Singer, 1993). In DTSA, the fundamental quantity that represents the riskof an event’s occurring in each time period is called the hazard probability.In each time period, the group of people still at risk of first experiencingthe event (the risk set) is identified; once someone experiences the event(or is censored) at one age period, that individual is no longer a member ofthe risk set in any subsequent age periods. The proportion of the risk setthat experiences the event during a specific time period is the hazardprobability for that time period. The plot of the hazard probabilities acrossall time points yields the hazard function, a chronological summary of therisk of event occurrence (Willett & Singer, 1993). Under DTSA, the pro-portionality assumption is easily checked; either for exploratory purposes

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or if proportionality is untenable, nonproportional models can readilybe fit.

The hazard function can be used to estimate the points of the survivorfunction, which describes the cumulative, rather than age-specific, prob-ability that an event will occur at each successive occasion (Singer &Willett, 1991). For any time period, the survival probability is 1 minus thehazard probability for that period multiplied by the survival probabilityfrom the previous period. Plotted across time, the survival probabilitiesform the survivor function. Unlike the hazard function, which can takeany number of shapes, all survivor functions have a similar shape, namelya monotonically nonincreasing function of time.

Predictors of Heterogeneity

In most studies of age at sexual initiation, researchers are interested notonly in describing the hazard and survivor functions but also in identi-fying predictors of heterogeneity in the hazard function across levels ofvariables or subgroups—that is, in examining whether the hazard func-tion differs in systematic ways for different types of people (Singer &Willett, 1991). The hazard function thus becomes the outcome; variablessuch as gender, parental divorce, or educational attachment serve as pre-dictors of heterogeneity in that outcome across individuals (Willett &Singer, 1993). Most DTSAs begin, as Cox’s regression does, with a pro-portional hazard model of predictors, where variation in a predictor isassumed to vertically shift the entire baseline hazard function, increasingor decreasing the hazard of an event’s occurrence uniformly across time.Thus, a hazard model is described by a baseline hazard function, chartinga profile of risk across time, and this ‘‘shift’’ parameter, which summarizesthe overall effect of the predictor on the baseline profile (Willett & Singer,1993). Contrary to Cox’s regression, though, the proportionality assump-tion can easily be tested in DTSA and nonproportional hazard models canbe charted. Thus, DTSA permits the estimation of a broader class of sta-tistical models, allowing researchers to develop models of event historyvariables such as age at initiation in a more comprehensive and flexiblefashion (Willett & Singer, 1993). An additional strength of nonproportionalmodels, even if a proportional model may suffice overall, is the ability tohighlight potential ages of special vulnerability that may be useful indesigning and targeting interventions. Thus, the ability of DTSA to focuson nonproportional models can be especially important for those inter-ested in such age-specific differences.

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STUDY AIMS

This study looked at age of sexual initiation among a group at high risk forSTDs—American Indian adolescents and young adults. Using DTSA, weexplored a variety of risk factors concerning relationships with three keymicrosystems and individual characteristics. We also utilized a cumula-tive risk index, summing across risk factors, to examine the effects ofcumulative risk on age at sexual initiation. All analyses were stratified bygender to explore possible differences in risk factors for men and women.We examined both proportional and nonproportional hazard models ofcumulative risk to explore meaningful age differences that might informintervention design.

METHODS

We surveyed a cohort of youth (ages 14–18) from one Northern Plainscommunity annually over 7 years, under two linked projects: The Voices ofIndian Teens (VOICES, 1993–1995; Waves 1–3) and Pathways of Choices(CHOICES, 1996–1999; Waves 4–7). (In work with American Indiangroups, maintenance of community confidentiality can be as important asthat of individual confidentiality (Norton & Manson, 1996). Therefore, thegeneral cultural descriptor of Northern Plains is used here, rather than aspecific tribal name.)

Sample

All students (n 5 637) listed on the school rosters of two community highschools in fall, 1993 were eligible to participate in the project; 522 (81.9%)youth filled out a Wave 1 (W1) survey. (We attempted to locate those whowere on the roster but not in school on the day of data collection either inschool one week later or in the community during a 3-month period offollow-up.) Comparing the youth who did not take part in this initial datacollection to those who did on the only information available from theschool rosters (gender), a slightly higher percentage of females (84%)completed the survey than did males (79%). Across all seven waves, 474respondents reported an age at sexual initiation or that they had not hadsex at all by Wave 7 (W7).1 More than three quarters of the sample (77%)

1 Because our interest was in nonabuse sexual initiation, we eliminated those who reportedsexual initiation younger than age 12 (n 5 36). In accordance with local and state laws, wereported to authorities all identifiable statutory rape, rape, or molestation cases. However, given

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participated in five or more waves of data collection; the average numberof waves of data was 5.5. We compared those with all seven waves(n 5 134) to those who were missing at least one wave (n 5 340) on 23demographic and psychosocial variables from the W1 data set. The twogroups differed on only one variable: Those who were missing at least onewave of data were about 1/2 year older. Thus, selective attrition did notappear to be of major concern in this longitudinal data set.

In all, 55% (n 5 274) of the W1 sample was female; average age was 16.2years. Average age at sexual initiation was 15.1, with a range of 12–20; 6%(n 5 29) had not had sex by the end of W7 data collection.

Procedures

Data were collected annually by trained local Field Office research staffwho were tribal members. Data collection for W1–W3 was primarilyschool-based; however, youth who were on the 1993 high school roster butwere not found in school were contacted in the community at eachwave. W4–W7 data collection was completely community-based. Eachself-administered survey took most participants less than one hour to fillout; participants received compensation upon completion of the survey. Allparticipants provided informed consent at each wave of data collection; forminors, parental permission was obtained before seeking youth assent.

Measures

The surveys are available at http://aianp.uchsc.edu/aianp/ncaianmhr/research/voit.htm (VOICES) and http://aianp.uchsc.edu/ncaianmhr/research/choics.htm (CHOICES). The majority of the measures were thesame for VOICES and CHOICES, although some constructs were addedfor CHOICES and a few measures were made more age-appropriate, pri-marily by including additional items. Before data collection for bothprojects, all measures were reviewed for understandability and culturalrelevance by Field Office staff, community focus groups, and/orkey community members. All items were recoded as necessary sothat larger numbers reflected higher scores on that construct. Compositevariables were calculated as average-item unit-weighted scale scores.

the age of the sample at W1 (none younger than 14), the retrospective nature of the questions,and the manner in which questions were asked, we typically had no information about who therespondents’ partners were if they reported having had sex before age 12. Thus, we were unableto inform the authorities of most incidences of possible sexual abuse reported in surveys.

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Table 1 shows descriptive statistics, by gender, of the variables used insubsequent analyses.

Because this was a longitudinal design, we had repeated measures onmost variables. Of interest for some variables was information simplyabout whether a situation had, in the respondent’s lifetime to date, everoccurred (e.g., parental divorce, dropped out of school) or about some-thing invariant (e.g., mother’s age at first birth). For these variables, weselected the information provided in the latest survey we had for eachindividual, in order to cover the longest period possible. For example, forthose who provided information about a particular variable in W7, weused that data. If W7 data were missing, we used the W6 data, and so forth.However, for some variables that were more psychosocial in nature andlikely to fluctuate with development (e.g., educational goals, prosocialpeers), this latest information had been gathered for most respondentsseveral years after they had first had sex. So, for these variables, we usedW1 data. We note from which wave each measure was drawn.

The Multiple Imputation procedures (PROC MI and PROC MIANA-LYZE) available in SAS (SAS Institute Inc., 2001) were used to estimatemissing values. In addition to all independent and dependent variablesdescribed here, we also included six W1 variables as predictors of miss-ingness in a school-based sample (Collins, Schafer, & Kam, 2001): gender,age, parental drinking, drugs used in lifetime and past month, and self-

TABLE 1

Descriptive Statistics

Variable

Men Women

% Mean SD Range % Mean SD Range

Age at sexual initiation 14.4 1.7 12–20 15.6 1.5 12–20

Mother’s age at first birth 19.8 3.7 11–49 19.9 3.6 14–45

Mother’s higher education 13.7 22.1

parental divorce 51.7 58.2

Family support 2.2 1.1 1–5 2.2 1.2 1–5

High school dropout 36.5 41.4

Educational goals 3.1 1.2 1–6 2.8 1.4 2–6

Self-reported grades 2.6 .6 1–4 2.3 .7 1–4

School attachment 2.3 .8 1–4 2.3 .9 1–5

Peer values 2.9 .7 1–4.7 2.8 .6 1–5

Alcohol/drugs at first sex 32.7 39.5

Sensation-seeking 2.7 .9 1–4.7 2.2 .8 1–4.4

Note. All variables coded so that a high score represents greater risk.

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rated health. Following suggestions by Schafer and Olsen (1998), we cre-ated an imputed data set by storing the estimates and standard errors from10 imputed data sets; we then combined the results using Rubin’s (1987)rules into an overall data set. This multiple-imputation process results inmeans and variances that are less biased than are those using alternativeapproaches to dealing with missing data, such as pairwise or listwisedeletion or mean substitution (Allison, 2002). It should be noted that onlyfour respondents had only one wave of data; exclusion of these respon-dents did not change the results, so we included the full sample in thefollowing analyses.

Age at sexual initiation. Age at sexual initiation was defined as theyoungest age (in years) at first sex reported by participants in any wave ofdata collection. We converted this single question into a series ofdichotomous variables—e.g., first had sex at age 12 (yes/no), first hadsex at age 13, and so forth. Each respondent was coded 0 for all agevariables before age at first sex; that age variable was coded 1 and allsubsequent age variables were set to missing (Muthen & Masyn, 2005).For example, data from someone who initiated sex at age 15 were coded 0for age variables of 14 and younger; the age 15 variable was coded 1; agevariables for 16 and older were coded as missing. The age variables forthose who had not initiated sex by W7 were coded 0 through their age atW7; all subsequent age variables were coded missing.

Family microsystem. All risk factor measures were recoded asnecessary so that high scores represented greater risk. Mother’s age atbirth of first child (W7) was assessed from the question ‘‘How old was yourmother when she had her first child?’’ Mother’s higher education (W7) wasassessed by ‘‘Did your mother or the woman who raised you ever attend a4-year college or university?’’ and ‘‘Did your mother or the woman whoraised you ever attend graduate or professional school?’’; an answer of‘‘no’’ to both of these was coded 1; ‘‘yes’’ to either or both was coded 0.Parental divorce (W7) was assessed by the question ‘‘Your parents everdivorced or stopped living together’’ (1 5 yes; 0 5 no). One other measureof family context, family support (W1), was based on the item ‘‘I can talkabout my problems with my family’’; response options ranged from 1(‘‘agree’’) to 5 (‘‘disagree’’).

School and peers microsystems. High school dropout (W7) was assessedby ‘‘Did you ever drop out of high school (1 5 yes; 0 5 no). Educationalgoals (W1) were coded from the question ‘‘My highest educational goal isto . . . ’’ (1 5 ‘‘get Master’s, MD, or PhD degree’’ to 6 5 ‘‘not finish high

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school’’). Self-reported grades (W1) were assessed by ‘‘In general, howwell do you do in school? (1 5 ‘‘mostly As’’ to 4 5 ‘‘mostly Ds’’).School attachment (W1) asked youth how much they liked school(1 5 ‘‘like school very much’’ to 5 5 ‘‘hate school’’). Peer values (W1)were assessed with a six-item scale (a5 .70) adapted from Seidmanand his colleagues (Seidman, 1991). Sample items included ‘‘My friendsthink it’s okay to skip school’’ and ‘‘My friends like a kid who alwaysobeys parents’ rules.’’ Responses ranged from 1 (‘‘agree’’) to 5(‘‘disagree’’); items were recoded so that higher values signified lessprosocial peers.

Individual characteristics. Any alcohol/drugs at first sex (W7) wasbased on two questions: ‘‘The first time you had sexual intercourse, wereyou using alcohol or drugs?’’ and ‘‘The first time you had sexualintercourse, was your partner using alcohol or drugs?’’ A ‘‘yes’’ answer toeither or both questions was coded 1; those answering ‘‘no’’ to both werecoded 0. Sensation-seeking (W1) was adapted from Huba, Wingard, andBentler (1981) and included six items such as ‘‘I like wild parties’’ and ‘‘Iwould like to try parachute jumping’’ (a5 .81). Responses ranged from 1(‘‘disagree’’) to 5 (‘‘agree’’).

Analytic Approach

In these analyses, we used Mplus (version 3.11; Muthen & Muthen,1998–2004). The hazard function is a set of conditional probabilities, witheach point bounded by 0 and 1. When dealing with a weightedlinear combination of predictors of such a bounded dependent variable(DV), it is customary to transform the data so that the DV is unbounded;thus, all data were log-transformed by Mplus for DTSA analyses. We be-gan with bivariate models—one DTSA for each risk factor; any predictorthat was significant in its bivariate model was then included in thecumulative risk index. We also tested the cumulative risk index bothwith and without the proportional hazards constraint—the former, byregressing the full hazard function on the risk index; the latter, by re-gressing each ‘‘age of initiation’’ variable on the risk index. No hard andfast rules for determining the best-fitting models currently exist. However,we used the Bayesian Information Criterion (BIC) as a guide to the ad-equacy of a model, where a much smaller BIC represents a more parsi-monious, and therefore more desirable, model. We conducted theseanalyses separately for men and women, to determine whether risk factorsvaried by gender.

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RESULTS

Men

Figure 1A presents the fitted survivor function of age at initiation for men,with no predictors. This figure shows, for example, that just under 90% ofthe young men in this sample had not had sex at age 12; the median age atinitiation (the age at which 50% of the sample had had sex) was approx-imately 14.

Risk factors. Among the 11 risk factors investigated, five weresignificantly related to age at sexual initiation in bivariate models: lowergrades, lower school attachment, peers with less prosocial values, use ofalcohol or drugs at first sex, and higher sensation-seeking. Table 2 presentsthe predictor shift parameters for males and Figure 1B displays the hazardfunction for the variable with the largest shift parameter (self-reportedgrades—.43) Figure 1B shows that 30% of those boys who had not yet had

B Hazard function: Age at Sexual Initiation by Self-reported Grades

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surv

ival

pro

bab

ility

FIGURE 1 Survivor and hazard functions, men.

400 MITCHELL ET AL.

sex and who had lower grades initiated at age 15; only 20% of those withhigher grades and who had not yet had sex first initiated at age 15. Theother four variables had similar patterns, with slightly smaller verticalshift upward for those with that particular risk factor.

Cumulative risk index. Except for alcohol/drug use at first sex, thesignificant predictors for men were ordinal variables. To create acumulative risk index, we dichotomized each variable, as a number ofother researchers have, by setting the highest quartile score to 1 and allother responses to 0 (Evans, 2003; Forehand, Biggar, & Kotchick, 1998;Gerard & Buehler, 2004; Prelow & Loukas, 2003; Sameroff et al., 2003). Wesummed across these five variables to create a cumulative risk index for

TABLE 2

Significant Predictors and Cumulative Risk Index

Shift Parameters Men Women

Self-reported grades .43n .11

School attachment .22n � .053

Peer values .35n � .074

Alcohol/drugs at first sex .30n .51n

Sensation-seeking .17n .25n

Mother’s age at first birth � .06 .36n

Mother’s higher education .05 .31n

Parental divorce � .08 .55n

High school Dropout .19 .82n

Cumulative risk index—proportional

hazard (men only)

.17n

Cumulative risk index—nonproportional

hazard (women only)

Age Baseline

Shift

parameter

12 5.57 .37

13 3.36 .38n

14 3.09 .35n

15 1.58 .24n

16 1.40 .51n

17 .98 .42n

18 1.86 .78n

19 1.67 .19

20 2.24 � .34

np � .05.

CUMULATIVE RISK FOR EARLY SEXUAL INITIATION 401

men (mean 5 1.8), which we then entered as a predictor in the survivalanalyses. This risk index was significantly related to age at initiationfor men (Table 2). We tested this model with and without the proportionalhazard assumption. The BIC of the model with the proportionalityconstraint was considerably smaller than that of the model withoutthe proportionality constraint (the BIC of the nonproportional modelminus the BIC of the proportional model 5 36.5% or 4.2% of theproportional hazard model’s BIC), suggesting that the proportionalhazard constraint was not inappropriate for this model—that is, that theinfluence of the risk index could be considered to be consistent across theages. Figure 1C displays the hazard function for the proportional modelfor youth with low (0), moderate (2), and high (4) risk indices.For example, among youth with no risk factors, only 25% of those whohad not had sex before age 14 initiated sex that year; among those withtwo risk factors, just over 30%; and among those with four risk factors,almost 40%.

Women

Figure 2A presents the fitted survivor function of age at initiation forwomen, with no predictors. This figure shows, for example, that almost allof the young women in the sample had not had sex at age 12; the medianage at initiation was just under 15.5.

Risk factors. Six of the 11 risk factors were significantly related to ageat sexual initiation in bivariate models: having dropped out of high school,having a mother who had a child before age 18, having a mother who didnot attend college or graduate school, parental divorce, any use ofalcohol/drugs at first sex, and greater sensation-seeking. Table 2 presentsthe predictor shift parameters for the women; Figure 2B displays thehazard function for the risk factor with the largest shift parameter (highschool dropout—.82). As shown there, approximately 40% of women whohad not yet had sex and who eventually dropped out of high schoolinitiated sex at age 15, while just over 20% who did not drop out of schoolfirst initiated sex at 15.

Cumulative risk index. All of the significant risk factors for womenwere dichotomous except for sensation-seeking; we dichotomizedsensation-seeking at the highest quartile, as was done with the men’sscores. We then counted the number of risk factors that were present tocreate a cumulative risk index score for women (mean 5 2.7). As with the

402 MITCHELL ET AL.

men, this index was significantly related to age at initiation. We tested thismodel with and without the proportional hazard assumption. The BIC ofthe model with the proportionality constraint was only slightly smallerthan that of the model without the proportionality constraint (the BIC ofthe nonproportional model minus the BIC of the proportionalmodel 5 14.4% or 1.4% of the proportional hazard model’s BIC),suggesting that the proportional hazard constraint was not clearlysupported for this model. Thus the influence of the risk index may nothave been strictly uniform across ages. To explore possible differences inrisk by age, we examined the nonproportional model, where we foundthat the cumulative risk index was not significant at ages 12, 19, or 20;however, it significantly increased risk, although to varying degrees, at allother ages. Figure 2C displays the hazard function for the nonproportionalmodel for youth with low (0), moderate (2), and high (4) risk indices. Forinstance, among girls with no risk factors, only 20% of those who had nothad sex before age 16 initiated sex that year; among those with moderaterisk, over 40%; and among those with highest risk, around 65%.

B Hazard Function: Age at Sexual Initiationby High School Dropout

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

12 13 14 15 16 17 18 19 20age

haz

ard

pro

bab

ility

dropped outfinished high

C Hazard Function: Age at Sexual Initiaion by Cumulative Risk Index

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

12 13 14 15 16 17 18 19 20age

haz

ard

pro

bab

ility

A Fitted Survivor Function

0.000.100.200.300.400.500.600.700.800.901.00

12 13 14 15 16 17 18 19 20age

surv

ival

pro

bab

ility

FIGURE 2 Survivor and hazard functions, women.

CUMULATIVE RISK FOR EARLY SEXUAL INITIATION 403

DISCUSSION

Given indications of risk for HIV/AIDS in Native populations, there is aneed to explore the determinants of early sexual behavior in AmericanIndian youth. While the analyses presented here were limited to one tribe,our goal was to identify potential points of intervention, informed byculturally appropriate measures, that may be used to influence the con-stellation of risk for HIV, especially as this may be manifested in youngerinitiation of sexual behavior. In this sample, as other researchers havefound (e.g., Kalichman et al., 1996; Raine et al., 1999; Rosenthal et al., 2001),two risk factors were significant for both genders: use of alcohol/drugs atfirst sex and sensation-seeking. For men, the school and peer microsystemsappeared to be more important predictors of earlier initiation than forwomen, replicating findings of several other researchers (Chewning et al.,2001; Paul et al., 2000; Raine et al., 1999). Young men who felt more attachedto school and who reported higher grades were more likely to postponesexual initiation. Several explanations are possible for these findings. First,it may be that, for these youth, positive experiences with school had a directeffect on early sexual initiation. Alternatively, as pointed out by Raine et al.(1999), the factors that predispose boys to poor school performance mayalso predispose them to sexual activity. Finally, high levels of involvementin risk behaviors such as sexual activity may lead to poor school perfor-mance. Young men who reported having peers with more prosocial atti-tudes were also more likely to postpone sexual initiation. A potentialexplanation for this finding emerges from our earlier ethnographic workin this community. As boys mature, they typically move into activities thattake place within larger groups of male peers, while girls move moretoward dyadic, romantic relationships. Thus, young men may be moreattuned to their peers and, more specifically, to the less prosocial attitudesof their friends toward sexual abstinence (O’Nell & Mitchell, 1996).

In contrast, again as others have reported (Miller et al., 2001; Rosenthalet al., 2001), the influence of mothers for young women was clearly im-portant, as evidenced by associations of delayed initiation with mothers’intact marriages, postponed childbirth, and higher education. In general,same-sex role models (e.g., mothers, for girls) have been found to wieldmore power in vicarious learning situations than do models of the op-posite gender (e.g., mothers, for boys; Bandura, 1986, 1997). Although thedata set used here did not include information about father’s age when hisfirst child was born, it is possible that this might have been a significantpredictor for early initiation among young men. Further, it may be thatparental and family influences are more likely mediated through schooland peer factors for young men than for young women.

404 MITCHELL ET AL.

Without ‘‘accepting the null hypothesis,’’ risk factors that were not sig-nificant may also reveal important insights into the configurations of riskin this cultural context. Family support was measured here by a singleitem; it is possible that this simple measure limited its predictive abilities.We cannot rule out the possibility that support from families for youth inthis culture may be evidenced in other ways. In the school microsystemvariables, youth’s educational goals were not related to delayed initiation.This may reflect the nature of life in this and many other reservationcommunities. Youth would likely need to travel a considerable distance,even moving far from home, in order to pursue higher educational goals.Given strong connections to home communities, coupled with a seriouslack of employment opportunities on the reservation—especially thosethat require advanced educational degrees—the importance of highereducation to daily life in general and, specifically, as motivation to post-pone the initiation of sex may be different for many American Indianyouth than for others.

Most importantly, though, we found considerable support for the im-portance of cumulative risk in predicting age at initiation for both genders.The concept of cumulative risk suggests a move beyond an emphasis onindividual risk factors for earlier sexual initiation, focusing instead onyouth who may be experiencing heightened stress through multiple riskfactors. Here, we found that those at high or moderate risk were morelikely to initiate sex 1 year earlier than were those at low risk; also, anumber of different risk factors contributed to the cumulative risk set formen than for women, and vice versa. In addition, for young women, thenonproportional model highlighted that this risk was not necessarilyevenly spread across ages 12–20; instead, cumulative risk seemed to bemost detrimental during high school years, ages 16–18. Thus, preventiveintervention before these ages is indicated. Especially for younger teens(e.g., middle-school-aged youth), schools are often selected as the point ofpreventive intervention, in large part because youth have not yet droppedout of school in high numbers. Yet the nature of the risk factors that weresignificant, especially for young men—e.g., lower grades, peers who wereless prosocial, alcohol and drug use in connection with sexual activity,higher sensation-seeking—also correspond with youth who are less con-nected to traditional institutions such as schools even at young ages. Thus,interventions that are community-based are likely to be necessary sup-plements to school-based programs.

It is interesting to note that the median age for sexual initiation acrossthe sample was not extremely young, although still younger than manyadults would likely prefer. One might have expected that, given the lack ofwide-ranging opportunities for youth on this reservation, such risk-taking

CUMULATIVE RISK FOR EARLY SEXUAL INITIATION 405

would have been much higher. It is possible that cultural beliefs andpractices may be protective, supporting youth in delaying sexual initiationdespite the presence of risk. Gender and culture are likely to be inter-twined. For example, while personal educational goals were not associ-ated with delay of first sex for young women, mother’s educationalachievement did matter. Thus, the value of accomplishment at the familylevel may resonate more sharply than accomplishment at the individuallevel. For young men, the focus on achievement again may not be onthemselves, but rather on demonstrating same-sex peer-connectedness or‘‘brotherhood’’ in displays of strength and courage, as described in earlierethnographic work (O’Nell & Mitchell, 1996). Understanding the inter-play between risk behaviors and cultural value systems may prove to bekey in identifying strategic methods of intervention and supporting youthin making healthy choices.

The findings around cumulative risk also generate a variety of sug-gestions for interventionists. Substantively, multiple microsystemsmust be taken into account when attempting to understand and changeadolescent sexual behavior (Miller et al., 2001; Sameroff et al., 2003).Moreover, the specific microsystems to be considered in any interventionmay need to vary by gender for maximum impact. Implications forprogram evaluation arise, as well. Analysis of overall program effectsmay erroneously indicate that a program did not work when in factit was effective for some subgroups but not for others. For instance, aprogram that universally targets all youth in a middle school mightevidence no overall impact on sexual initiation; however, it might befound that those who were at moderate or high risk for such behaviordid lower their levels of risk behavior, while those at lowest risk madeno change in their already low levels of risk behavior. In this way, anunderstanding of individual or cumulative risk factors can provideinsight into components of an intervention that may be functioningas moderators of program effectiveness (Dawson-McClure, Sandler,Wolchik, & Millsap, 2004).

Limitations

A consideration of the limitations of this study may help to put thesefindings in proper perspective. For instance, participants were membersof only one American Indian tribe; as a result, generalizations to otherethnic groups—or even to other tribes—can be made only cautiously. Thesample began as a school-based one, drawn from one parochial school andone public school run by the Bureau of Indian Affairs. Although this

406 MITCHELL ET AL.

sample was not technically representative of all youth in this tribe, it islikely to have captured broad variation in the communities from which theschools drew their students. In addition, active community-based follow-up to find those who could not be located in schools helped guard againstserious bias in the sample due to factors that may have kept youth fromcompleting their education.

Analytically, alternative approaches exist to creating a cumulative riskindex. Although widely used, a cutpoint of the upper quartile may tie thecalculation of the cumulative risk index to the sample under investigation.Also, a few researchers have employed differentially weighted risk factorsrather than unit-weighted—and therefore equally weighted—risk factors(Burchinal et al., 2000). Even moreso than using an extreme quartile as acutpoint, such an approach can restrict results very specifically to a par-ticular sample, especially if the weightings are determined empiricallyfrom the sample (e.g., using exploratory factor analysis to determine factorloadings for weights; see Burchinal et al., 2000). Measurement limitationsexist as well. Although we drew on longitudinal data, some variables werestill reliant on memory. For instance, age at first sex was gathered retro-spectively for those who were older and who had initiated sex at youngerages. In addition, the measures selected here provide only partial assess-ment of the complex microsystems of school, peers, and family; futureresearch may want to include wider operationalizations of these domains.Further, self-reported measures of peers can be influenced by the respon-dent’s own sexual activities, and correlates thereof, rather than beingthe cause of subsequent sexual activities. Similarly, the psychosocialcharacteristics, which are likely to fluctuate more with development thando demographic variables such as mother’s education, might best be se-lected from the data collection period closest to the age at which therespondents first had sex. However, even at the more molar level of mea-surement used here, significant relationships were found for many of therisk factors.

Finally, in many survival analyses, one may expect that the majority ofthe sample will ‘‘survive’’—e.g., avoid a relapse of cancer or a diagnosis ofalcohol dependence. In this analysis, as with any analysis that focuses on arelatively normative event, the event is expected to be experienced by thevast majority of the sample during the period of interest. Indeed, nationalstatistics show that, in 2002, over half of all 15– to 19-year-olds had had sexby age 18; over two thirds, by age 19 (U.S. Department of Health andHuman Services, 2004). Once the majority of youth have initiated sex, it islikely that predictors of initiation become less important. Thus, the em-phasis throughout this paper has been on early initiation of sex rather thaninitiation per se.

CUMULATIVE RISK FOR EARLY SEXUAL INITIATION 407

CONCLUSIONS

This study has demonstrated the utility of DTSA in examining issuesaround risk factors for early sexual initiation. As a unique contribution toan understanding of risk and sexual initiation, the construct of cumulativerisk deserves broader attention. Youth may be resilient to specific pres-sures and risks; yet the cumulative effect of these pressures for both boysand girls can work to undermine their resilience. Thus, interventiondesigners and youth service providers need to consider not only specifictypes of risk for early sexual initiation but also the accumulation of factorsin key microsystems of an adolescent’s life that might not necessarily besignificant on their own.

ACKNOWLEDGMENTS

The Pathways of Choice/Healthy Ways Projects would not have been pos-sible without the significant contributions of many people. The followinginterviewers, computer/data management and administrative staff sup-plied energy and enthusiasm for an often difficult job: Phyllis Brewer, AnnaClifford, Laticia Decory, Marvine Douville, Karen DuBray, Danny Ecoffy,Mary I’Atala, Kim Jack, Alvin Johnson, Mary Little Bear, Mary Lame, De-nise Lee, Anthony Long Soldier, Alberta McCrary, Frank Means, Lisa Mer-rival, Robert Moran, Sandra Pettigrew, Rhiannon Shangreau, Tina StandingSoldier, Michelle Spotted Elk, Herman Tall, Jessica Tobacco, Tricia Tyon,Helen Wilson, Intriga Wounded Head, Michelle Yankton, and Sheila Young.We would also like to acknowledge the contributions of the Healthy WaysNational Advisory Committee: Sevgi O. Aral, Paul D. Bouey, Dedra Bu-chwald, Terry Friend, Pamina M. Gorbach, Randy Her Many Horses, KingK. Holmes, Bonnie Holy Rock, Alberta Iron Cloud Miller, Andrew Catt-IronShell, Robert J. Magnani, Rachel Pacheco, John J. Potterat, Karen S. Red Star,Dorothy A. Rhoades, Marion Sorace, Tim Ryschon, Gayla J. Twiss, Nancy L.Vande Brake, Edith Welty, and Tom Welty. Finally, we want to thank theparticipants who so generously answered all the questions asked of them.

Data collection for the study was supported by the National Institute onAlcohol Abuse and Alcoholism grant R01 AA08474 (Manson, PI), the Na-tional Institute of Mental Health grant R01 MH59017 (Mitchell, Beals, andBuchwald, PIs), the National Institute of Child Health and HumanDevelopment grant R01 HD33275 (Mitchell and Beals, PIs), and a sup-plement to HD33275 from the National Institute of General Medical Sci-ences. Production of this manuscript was supported in part by theNational Institute of Mental Health K02 MH02049 (Mitchell, PI).

408 MITCHELL ET AL.

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