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RESEARCH |
The authors are with the Developmental Epidemiology Program, Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC.
Correspondence: Requests for reprints should be sent to E. Jane Costello, PhD, Center for Developmental Epidemiology, Box 3454, Duke University Medical Center, Durham, NC 27710 (e-mail: jcostell{at}psych.mc.duke.edu).
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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Objectives. This study examined the effect of poverty on the prevalence of psychiatric disorder in rural Black and White children.
Methods. A representative sample of 541 Black children and 379 White children aged 9 to 17 was drawn from 4 predominantly rural counties. Structured interviews with parents and children collected information on psychiatric disorders, absolute and relative poverty, and risk factors for psychiatric disorder.
Results. Three-month prevalence of psychiatric disorder was similar to that found in other community samples (20%). Federal criteria for poverty were met by 18% of the White and 52% of the Black families. Black and White children were exposed to equal numbers of risk factors overall, but the association between poverty and psychopathology was stronger for White children (odds ratio [OR] = 2.1; 95% confidence interval [CI] = 1.1, 4.2) than for Black children (OR = 1.5; 95% CI = 0.9, 2.6). Family history of mental illness, poor parenting, and residential instability mediated this association in both groups.
Conclusions. In this rural sample, poverty was only weakly associated with child psychiatric disorders. Risk factors for both racial/ethnic groups were family mental illness, multiple moves, lack of parental warmth, lax supervision, and harsh punishment.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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The goals of this study were (1) to compare the prevalence of psychiatric disorders in nonurban poor and nonpoor Black and White children, (2) to compare the types and numbers of family risk factors for child psychopathology in poor and nonpoor Black and White families, and (3) to examine the effects of the interaction of poverty and minority status on child psychopathology.
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METHODS |
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Setting
The 4 contiguous counties in North Carolina chosen for this study are poor but have no large cities, so poverty and inner-city residence are not confounded. The 1990 census data, which are the latest available by race/ethnicity within county, indicate that 76% of the population live in rural areas, and 50% of the area's children (aged 0 to 17 years) are Black. The remainder are White, except for a very few Native American and Latino inhabitants. Median household incomes for the 4 counties ranged from $20 554 to $31 708, compared with $31 548 for North Carolina and $37 303 for the United States. In the United States as a whole, median household incomes for Black households are approximately 60% of White household incomes; in the 4 counties studied, the range was 56% to 63%. Between 18% and 36% of the children were living in poverty in 1989, compared with 17% in North Carolina and 20% in the United States. On the basis of recent census projections, we expect that household incomes and poverty rates have changed very little between 1989 and 1997 to 1998, when the study was conducted.
Sample
The sampling frame was the 17 117 names and addresses of children aged 9 through 17 years provided by the public schools information management system. A random sample of 4500 was selected, and 3942 (88%) of the children were traceable, still in the area, and at the correct age.
Parents of 3615 (92%) completed a brief telephone questionnaire, on the basis of which a subsample of children was selected for intensive assessment. The screening questionnaire consisted of the "externalizing" broadband scale items from the Child Behavior Checklist (CBCL).19 The externalizing subscale was used as a brief screen because it correlates highly with the internalizing subscale in high-risk children.20
Scores were ranked and divided into 10 equal-sized groups of lowest to highest scores. We used a decision rule based on earlier work on the sensitivity and specificity of the screen relative to a full psychiatric assessment21 and randomly selected subjects from the 10 groups with probabilities ranging from 11% (lowest scoring group) to 51% (highest scoring group). The goal was to optimize the 2-stage design to provide the narrowest variance estimates and maximum statistical power from a sample of fixed size (determined by the budget).21–23
Of the 1333 parent–child pairs selected for interview, 29 could not be reached for further recruitment. Of the remaining 1304, complete interviews were obtained in 71% (n = 920). No significant differences in response rate were found at any stage by screen score, race/ethnicity, age, or sex. Following informed consent from parents and assent from the children, interviews were conducted at home, concurrently in separate rooms to ensure privacy.
Measures
Psychiatric disorders.
The Child and Adolescent Psychiatric Assessment,24,25 a psychiatric interview for children aged 9 and older, elicits information about symptoms contributing to a wide range of computer-generated Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition,26 symptom scales and diagnoses, based on combining symptoms from parents and children.
Risk factors for psychiatric disorder.
The Child and Adolescent Psychiatric Assessment interview also collects information on a wide range of factors associated in several studies with increased risk for developing child psychiatric disorders; these are listed in Table 1
, together with their prevalence in this sample.
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Relative deprivation is defined in terms of an implicit comparison group.17 We defined the comparison group as families of one's own self-defined race/ethnicity. Each family was ranked by income within its racial/ethnic group, and the range was divided into thirds. Relative affluence was defined as being in the top third of the White or Black group, respectively, and relative poverty was defined as being in the bottom two thirds.
Data Analysis
Unbiased general population prevalence estimates and group comparisons were calculated with the empirical option of the SAS program GENMOD to provide appropriately weighted parameter estimates and standard errors corrected for the study's sampling design.27,28
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RESULTS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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shows the differences between the 2 racial/ethnic groups on a range of measures of poverty. The Black families bore a much greater burden of poverty than did the White families. The mean income of the Black families was less than two thirds that of the White families (62%). Adjusted for family size, the mean annual income of the poorest third of the Black families was 44% that of the poorest White families. In the middle third of each racial/ethnic group, the Black family incomes were on average 51% of the White family incomes, and in the top third, they were 68% of the White family incomes. Thus, the racial/ethnic disparity in income increased as income decreased.
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shows the 3-month prevalence of the major psychiatric disorders, by race/ethnicity and poverty level. Comparisons between the racial/ethnic groups overall (see Table 3 superscript b, columns 1 vs 6) showed only 1 significant difference: the prevalence of depression was higher in the White children. The next set of comparisons (superscripts a and e, columns 2 vs 7 and 4 vs 9) tested for racial/ethnic differences at different levels of poverty. All the significant differences were confined to comparisons between poor children; there were no racial/ethnic differences by diagnosis in nonpoor children. Poor White children had more emotional disorders, particularly depression, and more oppositional defiant and conduct disorders when poverty was defined with federal criteria. When the relative poverty criterion was used, poorer White children had more emotional disorders, especially depression, and more oppositional disorders.
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Table 4, Model I, shows the results of logistic regression analysis, with poverty as the predictor and with age and sex included as covariates. The White children in federal poverty were 80% more likely than the nonpoor White children to have a diagnosis, but the excess risk for poor compared with nonpoor Black children was only 40%. In a test of the effect of the interaction of race/ethnicity and poverty on psychopathology, the White children in poverty were 59% more likely than Black children in poverty to have a psychiatric diagnosis (interaction odds ratio [OR] = 0.12; 95% confidence interval [CI] = 0.01, 1.00, P = .049).
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Poverty, Race/Ethnicity, Risk Factors, and Child Psychiatric Disorder
The second model in Table 4
adds number of risk factors to poverty, age, and sex. A large improvement in model fit occurred (
21 deviance change > 12, P < .001 in every case). Once the number of risk factors was added, absolute poverty ceased to contribute significantly to the model for psychiatric disorder for either racial/ethnic group. In the case of relative poverty, however, risk was still significantly higher for poorer than for nonpoor White (but not Black) children.
We examined racial/ethnic differences in the relation between relative poverty and psychopathology at different levels of risk. Although the prevalence of psychiatric disorder steadily increased with increasing risk, the poor White children were especially vulnerable to the highest level of risk. Post hoc analysis showed that poorer White children with 5 or more risk factors were much more likely than equivalently disadvantaged Black children to have a psychiatric disorder (56% vs 34%, OR = 2.5; 95% CI = 1.2, 5.2, P = .015).
To examine which specific risk factors mediated the relation between poverty and psychopathology, we included all the variables in Table 1, along with sex, age, and poverty, in multivariate models for each racial/ethnic group. Five risk factors contributed to the model for both racial/ethnic groups and both definitions of poverty: (1) family history of psychiatric disorder, (2) multiple (4 or more) moves of home in the past 5 years, (3) lack of warmth in the parent–child relationship, (4) poor parental supervision of the child, and (5) harsh parental disciplinary style.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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The relations among poverty, race/ethnicity, and risk for child psychiatric disorder described here replicate the associations found in our study of White and American Indian children carried out in another part of the same state.29 In that case also, the minority children were much more likely to be living in poverty than were the White children (66% vs 21%), but again poverty predicted child psychiatric disorder only in the White sample.
A small set of family and community risk factors more prevalent in poor families explains most of the effects of poverty on psychiatric disorders. This small set of variables, however, did not support any one causal pathway to the exclusion of all others. Among the adversities attendant on poverty, one that children were especially vulnerable to was the effect of having to move household (and probably school) multiple times. Among the social selection factors, a family history of mental illness was very powerful. This variable may reflect a genetic predisposition in the children, the problems of living with a mentally ill parent, or both. Child psychiatric disorder was strongly associated, in both Black and White children, with the "end-of-one's-rope" syndrome of poor parenting that may be either cause or consequence, or both, of the child's behavior.6
Some potential risk factors proved not to be important, however. Of note was the lack of significance of a single-parent household and of current maternal depression, once family psychiatric history was entered into the model. In general, the findings support the argument made by Rutter and Sandberg30 and others that it is important to take into account the total burden of risk.
Overall, the prevalence of child psychiatric disorder was the same as that found in almost every study conducted in the United States in the past 2 decades,31,32 indicating that compared with living in urban areas such as Boston, Mass,33 and Pittsburgh, Pa,34 living in this nonurban area did not necessarily protect children. In this rural area, it was not the Black but the White children who emerged as most vulnerable. Two studies of adults carried out in the same geographic area found low rates of psychiatric disorders in the rural Black participants, compared with the White and urban Black participants (apart from cognitive deficits associated with aging).9,35 One suggested explanation is migration9; this area has undergone steady outmigration since World War II, particularly of Black residents. However, the hypothesis of racial/ethnic differences in selection for outmigration needs further study.
Limitations of the Study
In addition to the fact that the survey was cross sectional, and therefore suited only to correlational analysis, some methodological aspects of the study could have affected the results. The analyses were based on the reports of parents and children alone and were limited to a single geographic area. Income data came from the interviewed parent, and some were not sure how much money their partners earned. For this reason, and to reduce noncompliance, we used broad ($5000) bands to categorize the family income, which inevitably reduced the accuracy of the estimates. However, the racial/ethnic differences and effect of poverty on other risk factors were sufficiently marked to survive a broad classification. Poverty was measured only in terms of the past year's income, whereas several researchers have associated poor outcome, especially for Black children, with persistent poverty.3,36 It seems highly likely, though, that in this rural area with few job opportunities, the poverty we observed was relatively chronic. Another limitation was the lack of better measures of "social capital,"13 such as job opportunities, community organizations, and mental health services. It seems unlikely, however, that social capital of this kind would prove to be much more readily available to Black than to White families.
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Acknowledgments |
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The authors wish to express their gratitude to the 4 school districts whose boards generously provided access to their student information management system database as a sampling frame for the study and to all study families who welcomed our interviewers so courteously. We also would like to thank Alaattin Erkanli, PhD, for designing the sampling plan and advising on statistical methods for the data analysis.
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Footnotes |
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References |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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17. Runciman WG. Relative Deprivation and Social Justice: A Study of Attitudes to Social Inequality in Twentieth Century England. Berkeley: University of California Press; 1966.
18. Angold A, Erkanli A, Farmer EMZ, Fairbank JA, Burns BJ, Hohmann A. Caring for children in the community: a study of psychiatric disorder, impairment and service use in rural African American and white youth. Arch Gen Psychiatry. In press.
19. Achenbach TM. Manual for the Child Behavior Checklist 4–18 and 1991 Profile. Burlington: University of Vermont, Department of Psychiatry; 1991.
20.
Costello EJ, Angold A, Burns BJ, et al. The Great Smoky Mountains Study of Youth: goals, designs, methods, and the prevalence of DSM-III-R disorders. Arch Gen Psychiatry. 1996;53:1129–1136.
21. Erkanli A, Soyer R, Angold A. Optimal Bayesian two-phase designs for prevalence estimation. J Stat Plann Inference. 1998;66:175–191.
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Reilly M. Optimal sampling strategies for two-stage studies. Am J Epidemiol. 1996;143:92–100.
24. Angold A, Prendergast M, Cox A, Harrington R, Simonoff E, Rutter M. The Child and Adolescent Psychiatric Assessment (CAPA). Psychol Med. 1995;25:739–753.[Medline]
25. Angold A, Costello EJ. A test-retest reliability study of child-reported psychiatric symptoms and diagnoses using the Child and Adolescent Psychiatric Assessment (CAPA-C). Psychol Med. 1995;25:755–762.[Medline]
26. Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition. Washington, DC: American Psychiatric Association; 1994.
27. SAS/STAT Software: Changes and Enhancements Through Release 6.11. Cary, NC: SAS Institute Inc; 1996.
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30. Rutter M, Sandberg S. Psychosocial stressors: concepts, causes, and effects. Eur Child Adolesc Psychiatry. 1992;1:3–13.
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