Adverse Life Events and Resilience

Adverse Life Events and Resilience Q W E N Q. TIET, PH.D., HECTOR R. BIRD, M.D., MARK DAVIES, M.P.H., CHRISTINA HOVEN, DR.P.H., PATRICIA COHEN, PH.D., PETER S. JENSEN, M.D., AND SHERRYL GOODMAN, PH.D.

ABSTRACT Objective: Adverse life events are well-documented risk factors of psychopathology and psychological dysfunction in children and adolescents.Youth with good adjustment despite high levels of adverse life events are considered resilient. This study identifies factors that characterize resilience. Method: Household probability samples of youth aged 9 through 17 years at four sites were used. Main and interaction effects of 11 factors were examined to assess their impact on youth adjustment. Results: Children at risk because of higher levels of adverse life events exhibited a greater degree of resilience when they had a higher IQ, better family functioning, closer parental monitoring, more adults in the household, and higher educational aspiration. The interaction between maternal psychopathology and adversity was significant, and the interaction between IQ and adversity approached significance. Conclusion: Resilient youth received more guidance and supervision by their parents and lived in higher-functioning families. Other adults in the family probably complemented the parents in providing guidance and support to the youth and in enhancing youth adjustment. Higher educational aspirations might have provided high-risk youth with a sense of direction and hope. Although IQ had no impact in youth at low risk, youth at high risk who had a higher IQ might have coped better. J. Am. Acad. ChildAdolesc.Psychiatry; 1998,37(11):1191-1200. Keywords: resilience, adverse life events, psychopathology, adjustment, risk factor, protective factor, resource factor, epidemiology.

Both Western and Eastern folklore seem to link extreme adverse life events, such as the sudden loss of a significant other, to the onset of psychiatric disorders. However, not until recent decades have empirical studies compiled mounting evidence for the association between adverse life events and psychiatric disorders among both adults (e.g., Dohrenwend and Dohrenwend, 1978; Lazarus and Folkman, 1984; Seyle, 1956) and youth (e.g., Coddington, 1972; Goodyer, 1990). Adverse life events have been associated with child and adolescent depression (Friedrich et al., 1982), anorexia nervosa (Gowers et al., 1996; Russell et al., 1990), substance use or abuse (Biafora et al., 1994; Duncan, 1977), and suicidal behavior (de Wilde et al., 1992). Notwithstanding these associations, researchers have consistently observed that certain individuals maintain Reviewed under and accepted by Michael S.Jellinek, M.D., Associate Editor. Accepted June 23, 199%. From Columbia Uniuersig, New York, and the New York State Psychiatric Institute (Drs. Eet, Bird Hoven, Cohen, and Mr. Dauies), Emory University, Atlanta (Dr. Goodman), and the N I M K Rockville, M D (Dr.Jensen). Reprint requests to Dr. Tiet, Department of Child and Adolescent Psychiany, Columbia University/NyPI, Unit 43, 722 West 168th Street, New York, N Y 10032. 0890-8567/98/3711-1191/$03.00/00 1998 by the American Academy of Child and Adolescent Psychiatry.

competence despite the exposure to risk (Garmezy, 1985), a phenomenon that has prompted research on resilience. Studies of resilience in the face of stressful life events have identified higher I Q (Garmezy et al., 1984; Masten et al., 1988), quality of parenting (Masten et al., 1988), connection to other competent adults (Garmezy et al., 1984), internal locus of control, and social skills (Luthar, 1991) as protective factors. Many of these studies have used school-based competence as the outcome (e.g., Garmezy et al., 1984; Luthar, 1993; Masten et al., 1990); however, these studies have also found that many school-based competent youth had other negative outcomes, including emotional problems (Luthar, 1993; Luthar et al., 1993). Others have examined the association between exposure to stress and various aspects of schoolbased competence and found that the relation of stress exposure to competence varied as a function of how the competence criterion was constructed (Masten et al., 1988). Conceptually, school-based competence alone does not appear to be sufficient as an indicator of resilience. Resilient youth are a subset of well-adjusted youth who have experienced adversity, and thus in this study resilient youth are those who have higher levels of adverse life events andare well-adjusted and free of 30 psychiatric disorders.

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Since high-risk populations may be exposed to multiple risk factors (e.g., adverse life events, low socioeconomic status [SES], maternal psychopathology), distinguishing the effect of specific risk factors is crucial to understand resilience in the face of adverse life events. One of the major drawbacks of studies of resilience despite adverse life events is that they have not controlled for other risk factors as covariates. Dohrenwend and colleagues (Dohrenwend et al., 1995) noted that a similar problem exists with studies of the association between life events and psychiatric disorders. Moreover, resilience studies have seldom examined a large number of protective factors in the same study, and therefore they have been unable to examine confounds among these factors. An important advancement in the study of resilience is the distinction between a resource factor and a protective factor. Although the term protective fdctor has been used to denote a main effect (see Luthar, 1993, for a review), it is limited to one that has a buffering effect at high risk but no effect at low risk and therefore involves an interaction effect (Cowan et al., 1996; Garmezy, 1987; Rutter, 1979). When a factor always has a beneficial effect whether at low or high risk (i.e., a main effect), it is referred to as a resource factor (Conrad and Hammen, 1993; Garmezy, 1987). A resource factor has also been termed an asset or compensatory factor (Garmezy, 1987). The opposite of a resource factor is a risk factor, which also has a main effect on outcome, whereas the opposite of a protective factor is a vulnerability factor, which has little or no effect at low risk but magnifies a detrimental effect at high risk. To understand resilience, it is essential to identify protective factors that buffer the detrimental effects of risk factors. However, it is also important to identify resource factors because they predict good adjustment at both high and low risk and therefore become critical in the design of preventive efforts. Composite measures of adverse life events have been used in most studies of the association between youth adjustment and adverse life events (Johnson, 1986). Distinctions have also been made between types of events, including favorable versus adverse, chronic versus acute, and events that are controllable by the child to some extent (e.g., breakup with boyfriend/girlfriend) versus those that are uncontrollable (e.g., death of a relative). There are also events that by definition can be confounded with other risk factors (e.g., parental divorce as an adverse life event and poor family function1192

ing as a risk factor). Events that are to some extent controllable by the child can be confounded with functioning of the child (e.g., school failure). Researchers of resilience have consistently used only the life events that are adverse to and uncontrollable by the child to measure adversity (e.g., Garmezy et d., 1984; Luthar, 1991; Masten et al., 1988). This approach has been adopted by the current study Based on a multisite cross-sectional data set, this study simultaneously examines multiple factors in the youth, their family, and their social environment to identify the unique contribution which adverse life events and each of the predictors make to youth adjustment. Main effects and interaction effects between the predictors of youth adjustment and adverse life events are examined. The identification of main and interaction effects is used as the basis to classify the predictors as resource, protective, risk, or vulnerability factors of youth adjustment or resilience in the face of adverse life events. METHOD

Sample Data were obtained from the National Institute of Mental Health (NIMH) Methods for the Epidemiology of Child and Adolescent Mental Disorders (MECA) Study, a collaborativestudy conducted to develop methods for surveys of mental disorder and service utilization in population samples of children and adolescents. Details of the study design and sampling procedures appeared in Lahey et al. (1996). The sample was obtained at four geographic sites in (1) Hamden, East Haven, and West Haven, Connecticut; (2) Dekalb, Rockdale, and Henry counties, Georgia; (3) Westchester county, New York; and (4) metropolitan San Juan, Puerto Rico. Approximately 7,500 households across the four study sites were enumerated. Probability samples of children and adolescents aged 9 through 17 years residing in each geographic area were selected, and a total of 1,285 dyads of youth and their caretakers were interviewed. Each year of age contributed 10% to 12% of the total sample at every site. Forty-seven percent of the sample were female. Fifty-one percent were non-Hispanic white, 28% Latino, 15% African-American, and 6% other. Ninety percent of the adult informants were biological mothers.

Measures Dependent Kiriable. Psychiatric disorders and functional impairment were used to classify youth adjustment. Psychiatricdisorder was measured by the NIMH Diagnostic Interview Schedule for Children Version 2.3 (DISC-2.3) (Shaffer et al., 1996) based on DSM-III-R criteria. Thirty psychiatric disorders were assessed (major depression, dysthymia, mania, hypomania, attention-deficit hyperactivity disorder, oppositional defiant disorder, conduct disorder, agoraphobia, overanxious disorder, obsessive-compulsive disorder, avoidant disorder, panic disorder, separation anxiety disorder, social phobia, generalized anxiety disorder, alcohol abuseldependence, marijuana

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youth adjustment existed at any level of adversity, and the relationship did not show a natural cutoff point. Moreover, the results obtained were similar whether life events were used as a dichotomous or a continuous variable. For these reasons, we elected to dichotomize into higher- and lower-risk groups to make the interpretation of the regression analyses more coherent. The median was chosen to dichotomize to increase the power of the analyses. Based on this decision, youth with two or more adverse life events (43.7%) were considered at higher risk whereas those who had one (24.7%) or no adverse life event (31.6%) were considered at lower risk. Multivariate logistic regressions were conducted with data from each of the two subsamples. All predictors were included in each of the logistic regressions to examine separately their effects in youth at lower risk and in youth at higher risk. Finally, interaction effects between each of the 11 predictors and adverse life events were examined. Given the large number of analyses conducted in this study, there is an increased chance of type I error. However, because the study is exploratory in nature, we present all of the results to avoid missing important trends, instead of adjusting the a.The results at lower levels of significance are therefore interpreted with caution.

abuse/dependence, other substance abuse/dependence, anorexia, bulimia, encopresis, diurnal enuresis, nocturnal enuresis, chronic motor tic, Tourette’s disorder, transient tic disorder, and chronic vocal tic). The Children’s Global Assessment Scale (CGAS) (Shaffer et al., 1983) was used as a measure of functional impairment. Good adjustment was operationalized as having no psychiatric diagnosis and a score greater than 70 on the CGAS ( n = 697; 54.24%). Children who had either any diagnosis or a score of 70 or less on the CGAS were classified as maladjusted (n = 588; 45.75%). Adverse Life Events. The measure of adversity was based on 25 possible events occurring in the previous year over which the youth had no control. The youth both reported the events and indicated that they were negative or adverse events. Predictors. The predictors consisted of four dichotomous variables and seven continuous variables. The dichotomous variables were (1) gender; (2) maternal psychopathology, as measured by the caretaker‘s report on the Family History-Epidemiologic Measure (Lish et al., 1995), dichotomized to separate those with no history of maternal psychiatric disorder versus any disorder; (3) family structure, which distinguished between children living with two biological parents and those in any other family structure, including step, foster, adoptive, or single parents; and (4) parental marital relationship, based on caretaker‘s rating of the marital relationship, dichotomized between excellent or good and fair or poor. The seven scaled variables were (1) SES, measured by Hollingshead’s Two Factor Index of Social Position (Hollingshead, 1971; Hollingshead and Redlich, 1958); (2) IQ, measured by the Peabody Picture Vocabulary Test-Revised (Dunn and Dunn, 1981) and standardized for use in all analyses; (3) parental monitoring, based on 13 items reported by the caretakers on a scale derived from instruments by Cohen and Brook (1987), Dishion et al. (1991), and Kandel (1990); (4) family functioning, based on 5 items reported by the caretakers about their satisfaction with the family environment and communication patterns of the family (Good et al., 1979); (5) educational aspiration, as reported by youth, ranging from “Less than high school graduate” to “Graduate or professional school”; (6) physical health, based on the caretaker‘s rating of the child‘s health; and (7) the number of other adults living in the family excluding the biological, step, foster, or adoptive parents.

RESULTS

Table 1 shows the number of youth who had good versus poor adjustment across levels of adverse life events. The number of adverse life events ranged from 0 through 14. Table 2 shows the distributions of adverse life events for the full sample and among the higher-risk and lower-risk youth. Among the higher-risk youth, more than one third had experienced a death in the family or a serious injury of a family member or had witnessed a crime or an accident. More than 20% of them had lost a friend, had a close friend who was seriously sick or injured, experienced a change in the family’s financial situation, or faced a drug or alcohol problem in a family member. Fewer than 10% of the lower-risk youth had experienced any of these adverse life events.

Statistical Analysis First, univariate analyses were conducted to examine the relationships between adjustment and adverse life events and between adjustment and all predictors. Second, multivariate logistic regressions were conducted to test the unique contribution of adverse life events and the main effects of each of the predictors. Third, multivariate logistic regressions were conducted on the high-risk and the low-risk groups. The measure of adverse life events was dichotomized at the sample median. The relationship between adverse life events and

Univariate Analyses

Table 3 shows the univariate associations between adjustment and adverse life events as well as between adjustment and the 11 predictors. Good adjustment was related to lower levels of adverse life events, absence of

TABLE 1 Number of Youth Who Had Good Versus Bad Adjustment Based on the Number of Adverse Life Events No. of Adverse Life Events

Good adjustment Bad adjustment Odds

0

1

2

257 148

187 131

106 89

1.74

1.43

1.19

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3 73 71 1.03

4 34 46 0.74

5 23 41 0.56

26 17 62 0.27

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TABLE 2 Distributions of Adverse Life Events in the Full Sample and the Higher-Risk and Lower-Risk Subsamples Adverse Life Events in Previous Year That Youth Endorsed as a Negative Experience Someone in family died Family member was seriously injured Saw crime/accident Lost a close friend Close friend was seriously sicklinjured Negative change in parent's financial situation Family member had drug/alcohol problem Got seriously sick or injured Parents argued more than previously Motherlfather figure lost job One parent was away from home more often Someone in the family was arrested Close friend died Family member had mental/emotional problem Brother or sister left home Being a victim of crime/violence/assault Parents separated in last 12 months Parent(s) got into trouble with the law Attended a new school Family moved Parents got divorced One of the parents went to jail Got new stepmother or stepfather Parent got a new job Got new brother or sister

Note: High-risk

=

Full Sample (n = 1,285)

Low-Risk Youth (n = 723)

Frequency

YO

Frequency

YO

Frequency

YO

302 239 219 188 157 143 131 123 106 99 88 83 83 80 67 50 42 30 29 29 25 16 14 14 5

23.3 18.5 16.9 14.5 12.1 11.1 10.1 9.5 8.2 7.7 6.8 6.4 6.4 6.2 5.2 3.9 3.2 2.3 2.2 2.2 1.9 1.2 1.1 1.1 0.4

234 20 1 190 158 136 123 114 97 92 90 79 77 77 75 57 47 41 29 28 27 25 16 13 12 4

41.4 35.6 33.6 28.0 24.1 21.8 20.2 17.2 16.3 15.9 14.0 13.6 13.6 13.3 10.1 8.3 7.3 5.1 5.0 4.8 4.4 2.8 2.3 2.1 0.7

68 38 29 30 21 20 17 26 14 9 9 6 6 5 10 3 1 1 1 2 0 0 1 2 1

9.3 5.2 4.0 4.1 2.9 2.7 2.3 3.6 1.9 1.2 1.2 0.8 0.8 0.7 1.4 0.4 0.1 0.1 0.1 0.3 0.0 0.0 0.1 0.3 0.1

two or more adverse events in the previous year; low-risk = one or no adverse event in the previous year.

maternal psychopathology, living with two biological parents, good parental marital relationship, higher SES, higher IQ, closer parental monitoring, higher family functioning, better physical health, and higher educational aspiration (Table 3). The number of other adults in the family and gender of the youth were not related to adjustment in the univariate analyses. Multivariate Logistic Regressions

Adjustment was regressed on all predictors simultaneously. The measure of parental marital relationship was not applicable to some youth who lived in singleparent families; therefore, two logistic regressions were conducted: one on the full sample excluding the measure of parental marital relationship (Table 3 ) , and another that included the measure of parental marital relationship with single-parent families excluded (not shown). In the full sample analysis (Table 3), good adjustment in the youth was predicted ( p <.05)by a lower 1194

High-Risk Youth (n = 562)

level of adverse life events, absence of maternal psychopathology, living with two biological parents, higher IQ, closer parental monitoring, higher family functioning, better physical health, higher educational aspiration, and larger number of other adults in the household. When parental marital relationship was included in the model (293 single-parent families were excluded; n = 992), significance levels of the variables did not change and parental marital relationship did not predict adjustment ( p = .43). Because the number of adults in the family was associated with adjustment in the multivariate analysis but not in the univariate analysis, a series of univariate analyses were conducted on the associations between the number of other adults in the family and other predictors in the current study. A larger number of other adults in the family was related to lower SES, lower IQ, lower levels of parental monitoring, lower educational aspirations, poor physical health, and not living with two biological parents.

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Predictors of Youth Adjustment at Lower Versus Higher Risk

As previously discussed, the sample was divided into two subsamples at the median of the measure of adverse life events (51 and 22). Multivariate logistic regressions were conducted on each of the two subsamples. All predictors were included in each of the regressions simultaneously. As previously described, the regressions were carried out twice (for a total of four) to take into account single-parent families. Results of the Analyses on the Full Sample

The variable of parental marital relationship was excluded in these analyses because this variable was not applicable to single-parent families. Two logistic regressions were conducted separately, based on the higher-risk versus lower-risk subsamples. Adjustment was regressed upon all 10 predictors simultaneously in both analyses. In analyses based on the higher-risk subsample (Table 4), resilience in youth was predicted by higher IQ, better family functioning, and higher educational aspiration. Parental monitoring and the number of other adults in the family were marginally significant in this analysis ( p <.lo). In analyses based on the lower-risk subsample

(Table 4), good adjustment was predicted ( p <.05) by the absence of maternal psychopathology, closer parental monitoring, better family functioning, better physical health, and higher educational aspirations. Family structure and the number of other adults in the family were marginally significant ( p <. 10). Results of the Analyses Excluding Single-Parent Families

Results of these analyses were comparable with those of the full sample. Again, two logistic regressions were conducted separately based on the higher-risk and lowerrisk subsamples, and youth adjustment was regressed upon all 11 predictors simultaneously in both analyses. Single-parent families ( n = 293) were excluded in these analyses so that the measure of parental marital relationship could be included. In analyses based on the higherrisk subsample (table not shown), resilience was predicted by higher IQ, closer parental monitoring, and better family functioning. There were two differences between this analysis and the full sample analysis. First, parental monitoring was only marginally significant ( p = .07) in the full sample analysis but significant, though barely, in this analysis ( p = .042; adjusted odds ratio = 1.60). Second, educational aspiration ceased to be significant in this analysis ( p = .44).

TABLE 3 Good Adjustment Predicted by Low Levels of Adverse Life Events and 11 Predictors: Univariate and Multivariate Logistic Regressions Univariate Logistic Regression

Multivariate Logistic Regression"

Variable

Parameter Estimate

Standard Error

Unadjusted Odds Ratio

Parameter Estimate

Standard Error

Adjusted Odds Ratio

Adverse life events Maternal psychopathology Living with 2 biological parents Gender (female) Good parental marital relationship Higher socioeconomic status Higher I Q Closer parental monitoring Higher family functioning Better physical health Higher educational aspiration No. of other adults in the family

-0.665*** -0.648*** 0.617*** 0.093 0.738*** 0.208*** 0.019*** 0.8 14*** 0.241*** 0.476*** 0.466*** 0.077

0.114 0.125 0.116 0.112 0.195 0.059 0.003 0.133 0.030 0.098 0.069 0.064

0.51' 0.52' 1.85' 1.10' 2.09' 1.23' 1.77d 2.08' 2.72' 1.74' 2.54f 1.08g

-0.440*** -0.416** 0.261* 0.093

0.123 0.136 0.128 0.123

0.61' 0.66' 1.30' 1.10'

-

-

-

-0.025 0.012** 0.439** 0.175*** 0.222* 0.358*** 0.175*

0.070 0.004 0.146 0.033 0.109 0.077 0.070

0.97' 1.43d 1.48' 2.07' 1.29' 2.04f 1.19g

" Full sample was used in this multivariate logistic regression. Model: N = 1,285;x2 = 179.27;df= 1l;p = ,0001.

'Dichotomized variable.

Odds ratio (OR) for one class difference on the Hollingshead scale. O R between I Q = 85 and I Q = 115. Between 2 SD (+1versus -1 SD). f O R between finishing college and finishing high school. g O R for each additional other adult in the family.

*p < .05; **p< .01; ***p< .001.

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M

nificant ( p = .06), similar to the analysis based on the full sample analysis ( p = .09). There were two differences in this analysis compared with the full sample analysis: Parental monitoring ceased to be significant in this analysis ( p = .11), and living with two biological parents was significant in this analysis ( p = .003; adjusted odds ratio = 2.03) but not in the full-sample analysis.

40

r:

.d

2 30

Interaction Effects Between Adverse Life Events and the 11 Predictors

s

3

20

i

m PI

0 or 1 event

2+

Adverse Life Events

events

Fig. 1 Interaction effects between I Q and adverse life events. *Predicted value based on the full sample ( N = 1,285).

In analyses based on the lower-risk subsample (table not shown), good adjustment was predicted ( p < .05) by absence of maternal psychopathology, living with two biological parents, better family functioning, better physical health, and higher educational aspirations. A larger number of other adults in the family was marginally sig-

Eleven interaction terms were created (product of adversity and each of the 11 factors) to test the interactions between adversity and the predictors. The interaction terms were tested individually while all other factors were controlled for simultaneously. Analyses were conducted both including and excluding the variable of parental marital relationship (excluding and including single-parent families, respectively). The interaction between adverse life events and I Q approached significance ( p = .07) in both analyses including or excluding the single-parent families. I Q had more predictive value in youth at higher risk than in those at lower risk (Fig. 1 and Table 4). For youth at higher risk, but not those at lower risk, higher I Q predicted good adjustment.

TABLE 4 Good Adjustment Predicted by 11 Predictors Simultaneously: Multivariate Logistic Regressions Based on Two Subsamples Divided at the Median of Adverse Life Events (51 versus 22) Higher-Risk Subsample"

Lower-Risk Subsampleb

Variable

Parameter Estimate

Standard Error

Adjusted Odds Ratio

Parameter Estimate

Standard Error

Adjusted Odds Ratio

Maternal psychopathology Living with 2 biological parents Gender (female) Higher socioeconomic status Higher I Q Closer parental monitoring Better family functioning Better physical health Higher educational aspiration No. of other adults in the family

-0.160 0.195 -0.016 -0.073 0.021*** 0.393" 0.222*** 0.172 0.255* 0.206t

0.203 0.187 0.187 0.109 0.006 0.214 0.048 0.161 0.118 0.106

0.85' 1.22' 0.98' 0.93d 1.87' 1.42f 2.51f 1.22f 1.67g 1.23'

-0.633*** 0.328" 0.193 0.021 0.004 0.470* 0.123** 0.336* 0.431*** 0.162"

0.185 0.179 0.166 0.094 0.005 0.204 0.046 0.151 0.105 0.096

0.53' 1.39' 1.21' 1.02d 1.14' 1.53f 1.67f 1.48f 2.37g 1.18'

Note: Single-parent families were included in these analyses (N= 1,285);therefore, parental marital relationship was excluded. Higher-risk = two or more adverse events in the previous year; lower-risk = one or no adverse event in the previous year. a Model: n = 562; = 75.71; 1O;p < ,0001. Model: n = 723; = 84.68; df = 10;p < .0001. Dichotomized variable. Odds ratio (OR) for one class difference on the Hollingshead scale. 'OR between I Q = 85 and I Q = 115. f Between 2 SD (+1versus -1 SD). g O R between finishing college and finishing high school. O R for each additional other adult in the family. t p < .lo; * p < .05; * * p< .01; ***p< .001.

x2 x2

1196

df=

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The interaction between adverse life events and maternal psychopathology was significant when single-parent families were excluded ( p = .01); ( p = .10 when singleparent h i l i e s were included). Figure 2 shows that either higher levels of adverse life events or the presence of maternal psychopathology predicted maladjustment; however, the presence of both risk factors did not increase the odds any further.These results were similar when the regressions were repeated using life events as a continuous measure. Multivariate logistic analyses were conducted for youth who were exposed both to higher levels of adverse life events and to maternal psychopatholog. When singleparent families were included (n = 179), higher I Q ( p < .0001), closer parental monitoring ( p < .05), and better family functioning ( p < .05) were related to resilience. Living with two biological parents and being a girl approached significance ( p < .lo). When single-parent families were excluded (n = 127), higher I Q ( p < .002) and being a girl ( p < .04) were related to resilience. Closer parental monitoring, better family functioning, and a larger number of other adults in the family approached significance ( p < .lo). DISCUSSION

This study shows that the relationship between adverse life events and maladjustment is robust. Adverse life events contribute uniquely to predicting youth adjustment above and beyond the effects of 11 covariates investigated in this study. The finding is consistent with previous studies among adults (Dohrenwend and Dohrenwend, 1978; Lazarus and Folkman, 1984; Seyle, 1956) as well as among children and adolescents (Coddington, 1972; Goodyer, 1990). Our findings are consistent with the idea that child I Q is a protective factor (Garmezy et al., 1984, Masten et al., 1988). IQwas found to be significant in predicting adjustment in youth at high risk but not at low risk, meeting one of the necessary criteria of a protective factor. However, the test of interaction between IQand adverse life events was only suggestive ( p < .07), and a higher significance level would be necessary to allow us to reach this conclusion. Nonetheless, the fact that tests of interaction fail to reach significance could be explained by lack of power. Tests of interaction require more power (larger number of subjects or larger effect sizes) than tests of main effects (Cohen, 1988). The interaction between maternal psychopathology and adverse life events also warrants further investiga-

*

5

event

Adverse Life Events

2+ events

Fig. 2 Interaction effects between maternal psychopathology (MP) and adverse life events. *Predicted value based on the two-parentlguardian subsample (n = 992).

tion. This study shows that either experiencing higher levels of adverse life events or having a mother who has had any psychiatric disorder are both related to an increased chance of having a psychiatric disorder or psychological impairment in youth. Being exposed to both risk factors, however, did not produce an incremental effect in the level of risk. Two possible explanations, a ceiling effect and multicollinearity are considered. A ceiling effect is unlikely inasmuch as approximately 40% of the 127 youth who were exposed to both of the risk factors were well adjusted. Similarly, multicollinearity between adverse life events and maternal psychopathology is also unlikely because both factors are significant in predicting youth adjustment in a multivariate logistic regression (Table 3). Moreover, a substantial number of youth were exposed to one but not both of the risk factors (283 were exposed to higher IeveIs of adverse life events and 140 were children of mothers with psychopathology). It is therefore unclear why exposure to the two risk factors did not further increase the odds of youth maladjustment. It is possible that other factors that are not consider may further interact with maternal psychopathology and adverse life events and affect the level of risk. Clinical Implications

Good adjustment in the youth was predicted by several resource factors (those that predict good adjustment

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at both high and low risk): Better family functioning, higher educational aspiration, and, to a lesser extent, parental monitoring and larger number of other adults in the family. The effect of family functioning is consistent with a previous study that examined the protective and resource effects of family functioning (Burt et al., 1988). Burt et al. (1988) found that better family functioning had a resource effect, but no protective effect, on youth psychological functioning. Higher educational aspiration is associated with good adjustment in youth at both high and low levels of adverse life events. Higher educational aspiration might have served as a goal and motivator for some of these youth, and it might have provided them with a sense of meaning and purpose in their lives. However, one competing hypothesis for this relationship could be explained by academic achievement. Namely, youth who are symptom-free are more likely to do well in school, and their higher educational aspiration is simply the result of their academic achievement. To test this hypothesis, an additional analysis was conducted with grade point average (GPA) as a measure of academic achievement included in the multivariate logistic regression, similar to that shown in Table 3. In this analysis, both GPA and educational aspiration were significantly associated with adjustment. Therefore, the relationship between educational aspiration and youth adjustment is robust and cannot be explained completely by academic achievement. A larger number of other adults in the family is also predictive of better youth adjustment. However, a larger number of other adults in the family is also related to lower SES, lower youth IQ, less parental monitoring, lower educational aspirations of the youth, worse physical health, and not living with two biological parents. Consequently, the resource effect of additional adults in the family is canceled out by these associated factors. Only when these other factors are held constant do additional adults in the family predict better adjustment in children and adolescents. Additional adults in the family probably complement the parents in providing emotional support, guidance, informational resources, mentoring, or role-modeling to the youth. Poor parental monitoring has consistently been found to be a predictor of youth delinquency or antisocial behavior (e.g., Reid and Patterson, 1989; Steinberg, 1987). However, the current study did not find a strong relationship between parental monitoring and overall youth 1198

adjustment. It may be that parental monitoring plays a lesser role in child and adolescent overall adjustment than in the disruptive disorders. Alternatively, the effect of parental monitoring might have been obscured by the social desirability bias in which parents with low levels of monitoring avoided presenting themselves negatively and therefore overreported their levels of monitoring. However, this explanation is unlikely given that the literature cited above suggests that such a bias does not exist, even among parents whose children exhibit the most deviant and socially undesirable types of disruptive behaviors. The literature on gender effects is inconsistent. Some studies have found that being a female was a resource or a protective factor (Earls, 1987; Masten et al., 1988; Rutter, 1979, 1990). Other studies (e.g., Werner and Smith, 1992) found that being a girl was a resource factor from birth to 10 years, but that the trend reversed during the second decade, when problems in boys decreased and behavior problems in girls increased. Furthermore, gender effects were found to vary depending on the outcome measure used (Luthar et al., 1993). As the criteria of youth outcome get stricter, the effects of being female seem to change or disappear (Luthar et al., 1993). Luthar and colleagues (1993) found a significantly higher proportion of girls who were competent in one or more domains of social competence, compared with boys (a main effect of gender). However, when the criteria used were more stringent (i.e., children who were competent in one or more domains andwere not at the lowest third of any other domain), being a girl was a protective factor (an interaction effect of gender). Furthermore, when the criteria became even more stringent (i.e., absence of any self-reported symptoms in addition to the previous criteria), no main or interaction effects were detected (Luthar et al., 1993). The resource effect of being a female is detected in the current study only among the youth who were exposed to both higher levels of adverse life events and maternal psychopathology. The inconsistent results in the current study may have resulted from the use of stringent criteria in the definition of good adjustment (absence of any of the 30 psychiatric disorders and a CGAS score greater than 70). In conclusion, resilient youth tend to live in higherfunctioning families and receive more guidance and supervision by their parents and other adults in the family, Other adults in the family may complement the parents in providing guidance and support to the youth and in

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enhancing youth adjustment. Higher educational aspiration may also provide high-risk youth with a sense of direction and hope. Although I Q had no impact in youth at low risk, youth at high risk who have a higher I Q may cope better and therefore avert the harmful effects of adverse life events. Limitations

This study has a number of limitations. First, it must be emphasized that we relied on cross-sectional data, and it is unclear whether the findings would replicate with a longitudinal design. Cross-sectional data can provide information about the associations that exist between different variables but cannot provide information on the stability of either the risk factors or outcomes, or of the direction of the associations. Longitudinal data are necessary to determine the direction of the associations and to begin to hypothesize causality. Second, only a limited number of factors were used in this study. Other factors not ascertained in this study (e.g., internal locus of control, temperament) might also predict resilience and might change the effects of the factors used here. Third, although the findings are robust with significance levels of less than .O1 or .OO1, the effect sizes, on the other hand, as expressed in the odds ratios or adjusted odds ratios of the variables studied, were modest (odds ratios of less than 3). Therefore, the level of clinical significance of these factors might be limited. Finally, similar to most studies of resilience, this study did not go beyond identifying protective and resource factors. Therefore, the mechanisms through which the protective and resource factors have an impact were not explored. Future investigations should examine the nature of the mechanisms or processes through which higher I Q and other resource factors operate in protecting high-risk youth. The MECA Program is an epidemiologicalmethodologystudy pe$ormed by four independent research teams in collaboration with staffof the Division of Clinical Research, which was reorganized in 1992 with components now in the Division of Epidemiology and Services Research and the Division of Clinical and Treatment Research, of the NIMH, Rockville, Maryland. The NIMH Principal Collaborators are DarrelA. Reper, M.D., M.I?H., Ben Z Locke, M.S.I?H., Peter S.Jensen, M.D., William E. Narrow, M.D., M.I?H., DonaldS. Rae, M.A., John E. Richters, Ph.D., Karen H. Bourdon, M.A., and Margaret 7: Roper, M.S. The NIMH Project Oficer was WilliamJ. Huber. The Principal Investigatorsand coinvestigatorsfrom the four sites are asfollows: Emory Universiv,Atlanta, UOI MH46725: Mina K Dulcan, M.D., Benjamin B. Lahey, Ph.D., Donnaj. Brogan, Ph.D., Shenyl Goodman, Ph.D., and Elaine W Flag, Ph.D.; Research Foundztionfor Mental Hygiene at New York State Psychiatric Institute (Columbia

University), New York, UOI MH46718: Hector R. Bird, M.D., David Shaffer, M.D., Myrna Weissman, Ph.D., Patricia Coben, Pb.D., Denise Kandel, Ph.D., Christina Hoven, D d H . , Mark Davies, M.I?H., Madelyn S. Gould, Ph.D., andAgnes Whitaker,M.D.; Yale University,New Haven, Connecticut, UOI MH46717: Mary Schwab-Stone, M.D., Philip J. Leaf: Ph.D., Sarah Horwitz, Ph.D., and Judith H. Lichtman, M.I?H.; Universiy ofPuerto Rico, San Juan, Puerto Rim, UOI MH46732: Glorisa Canino, Ph.D., Maritza Rubio-Stipec, M.A., Milagros Bravo, Pb.D., Margarita Alegria, Ph. D., Julio Ribera, Ph.D., Sara Huertas, M. D., Michael Woodbury,M.D., and Jose Bauermeister, Ph.D.

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The Preparedness of Students to Discuss End-of-Life Issues with Patients. Mary K. Buss, M D , Eric S. Marx, MTS, Daniel I? Sulmasy M D , PhD

Putpose: To explore how well medical schools prepare students to address end-of-life issues with their patients. Method. In 1997, the authors surveyed 226 fourth-year students at Georgetown University School of Medicine and Mayo Medical School, assessing relevant knowledge, experiences, and attitudes, and the students’ sense of preparedness to address end-of-life issues. Results: Seventy-two percent (162) of the eligible students responded. Almost all (99%) recognized the importance of advance directives and anticipated discussing end-of-life issues with patients in their practices (84%). However, only 41% thought their education regarding end-of-life issues had been adequate, only 27% had ever discussed end-of-life issues with a patient themselves, and only 35% thought they had had adequate exposure and education regarding advance directives. Eighty percent favored more education about end-of-life issues. Educational exposure to end-of-life issues and to role models, ability to correctly define an advance directive, number of end-of-life discussions witnessed, and age all were associated with students’ sense of preparedness to discuss advance directives with patients. ConcLusionr: Most of the students felt unprepared to discuss end-of-life issues with their patients, but wanted to learn more. The factors associated with a sense of preparedness suggest several possible, easily made, educational interventions, but further research is required to understand the scope of the problem and to implement curricular modifications. Acad Med 1998;73:418-422

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