The Relationship between maternal education and mental retardation in 10-year-old children

ELSEVIER

The Relatimskp between Maternal Education Retardation in IO-Year-Old Children PIERRE DECOUFLE,

ScD,

AND COLEEN

A. BOYLE,

and Men&

PHD

We conducted a case-contml study of mental retardation (MR) in which caz childten (aged 10 years) were identified jivm existing records at multiple sources, primarily the public school systems. Control children were drawn from a roster of public school students not receiving special education services. We found that maternal educational level at the time of delivery was strongly and inversely related to a form of MR not accompanied by other serious neurobgic conditions. For this isdared fmm of MR, matewd educational level was by far the most important predictor h among seven sociodemographic variables examined. There was a significant raceeduuaion interaction that indicated a steeper gradient in risk among white mothers than among black mothers. Relative to children of white mothers with 12 years ofe&cat&m, all children of black mothers, exczpt those whose m&ers had 16 or more years of education, were at increased risk. 7%~ results may be useful as a guide jot selecting high-risk poups L*Tu.n&kates for early childhood intervention programs. Ann Epidemiol 1995;5:347-353. KEY WORDS: Mental

retardation, socioeconomic factors.

maternal education, cognitive

INTRODUCTION Mental retardation (MR) is a serious developmental disability in children that can result in a lifetime of less than optimal functioning (1). Children in classes for the mentally handicapped constitute about 1.2% of all school-aged children and about 15% of all children in special education classes (2). Although subaverage intellectual functioning (as determined by the results from one or more individually administered standardized tests of cognitive ability) is considered only one component of the clinical definition of MR (l), it alone constitutes the most common case definition of MR for epidemiologic purposes (3-5). For such purposes, the upper limit for subaverage intellectual functioning is usually considered to be a test score of 70 (3-S). Mild MR (usually defined by a cognitive test score between 50 and 70) is a less functionally severe form of the condition, and accounts for about 80 to 90% of all children with MR (4). A consistent finding in the literature is that the prevalence of mild MR in children and young adults is inversely related to their families’ socioeconomic status (6-9). Further, strong positive associations have been noted between children’s mean cognitive test scores and their mothers’ level

From the Developmental Disabilities Branch, Division of Birth Defects and Developmental Disabilities, National Center for Environmental Health, Centers for Disease Control and Prevention (CDC), Public Health Service, US Department of Health and Human Services, Atlanta, GA. Address reprint requests to: Pierre Decoufl6, ScD, Centers for Disease Control (F-15), 4770 Buford Highway NE, Atlanta, GA 30341-3724. Received February 28, 1994; accepted November 8, 1994. Published 1995 by Elsevier Science Inc. 655 Avenue of the Amwicas. New York.

NY

10010

ability, race,

of education (10-14). Although these findings could be due, in part, to prenatal factors, such as an unfavorable intrauterine environment ( 15- 17), other literature describes how the infancy and toddler periods of development are important epochs during which future cognitive performance is determined (18-20). In a previous report, coauthored by one of us, maternal education at delivery was one of six sociodemographic variables examined for their relationship to MR in IO-year-old children living in the metropolitan Atlanta area in the mid1980s (21). In that article, maternal education (categorized as < 12 years, 12 years, and 13+ years) was found to be inversely associated with the prevalence& MR. The association was particularly strong for MR in the absence of severe neurologic conditions, regardless of the magnitude of the cognitive test score. In this article, we use the same data set to examine in more detail the nature and magnitude of the association between maternal educational level at delivery and the prevalence of various forms of MR. Here, we treat maternal education as a continuous as well as a categorical variable. In addition, we conduct exploratory anafyses of the possible modifying effects of each of six covariates on the association.

METHODS We used data from the Metropolitan Atlanta Developmental Disabilities Study (MADDS), a population-based study of the prevalence of and risk factors for each of five

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Decoufl~ and Boyle CHILDHOOD MENTAL RETARDATION

AND MATERNAL

developmental disabilities (MR, cerebral palsy, epilepsy, hearing impairment, and vision impairment) among IOyear-old children (22). The study area comprised the five contiguous Georgia counties of Clayton, Cobb, Dekalb, Fulton, and Gwinnett, an area that includes the city of Atlanta. Case children were those born from 1975 to 1977 and identified as having one or more of the five conditions while they were residing in the study area at the age of 10 years. Children with MR were those who were found at one or more existing sources (see below) to have an intelligence quotient (IQ) of 70 or less on their most recent standardized test of cognitive ability.

Selection of Case and Control

Children

The details of case ascertainment have been given elsewhere (22) and are summarized here. The MADDS investigators used multiple sources within the local community and the state government to identify children meeting the study definition of MR, starting with the special education departments of the nine public school systems in the area. Individual school records of children in selected special education classes as well as all records of psychological testing done in the schools were reviewed to identify case children. The study team also reviewed records at various public and private health, mental health, and social service agencies to find additional children meeting the case definition. For this report, we restricted the case group to the 526 children with MR who were born to residents of the study area in 1975 or 1976 and who were living in the area when they were 10 years old. We subdivided these children according to each of two factors, IQ and the presence or absence of selected neurologic conditions. Three subgroups based on IQ were used to denote various gradations of MR (i.e., IQ < 50, IQ of 50 to 59, and IQ of 60 to 70). The selected neurologic conditions included the other four developmental disabilities ascertained in the MADDS as well as structural, chromosomal, and metabolic birth defects affecting the central nervous system. The birth defects were identified through review of all available medical records for case children, including data from the Metropolitan Atlanta Congenital Defects Program, which is an active birth defects surveillance system covering the study area (23). We limited our choice to the above-mentioned conditions as they were the only ones for which there was systematic ascertainment in the MADDS. We defined “isolated MR” as MR not accompanied by any of the selected neurologic conditions. For comparison with the case children, a control group was chosen at random (with no matching of any kind) from a roster of all IO-year-old children enrolled in regular education classes (including classes for high achievers) in the public schools serving the study area in 1985 to 1987. All children previously identified as having any of the five study

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disabilities were excluded. Of 1200 potential control children selected from this roster, 650 were born to a resident of the study area in 1975 or 1976. None of the 650 control children had any of the selected birth defects.

Covariates and Exclusions Data on maternal education (number of years of school completed) and six other characteristics of the study children and their birth mothers were abstracted from Georgia birth certificates. We used as categorical covariates maternal race (black, white) and age at delivery (< 25, 25 to 34,35 + years) and the child’s gender and birth weight (< 2500, 2500+ g). The child’s birth order was treated as a continuous variable (first, second, . . ., eighth or later) in some analyses (see Tables 2 and 3) and as a categorical variable (first or second, third or fourth, fifth or later) in all other analyses. We attempted to link each mother’s address from the birth certificate to its 1980 “census block group,” which is a subdivision of a census tract (24). We used the 1979 median income (from 1980 census data) of the census block group in which the mother resided at the time of delivery as a proxy for the family’s economic status. The income data were divided into deciles and treated as a continuous variable in some analyses (see Tables 2 and 3) and then grouped into terciles for use as a categorical covariate in all other analyses. Of the 526 study children ascertained with MR and the 650 control children, we excluded 60 case children (13%) and 88 control children (14%) because of missing information on the birth certificates or because the birth mother’s raceiethnicity was neither black nor white. Most (81%) of the records with missing data lacked information on maternal education. Thus, we were left with 456 children with MR and 562 control children who formed the final study group for this report. About 18% of the birth certificates for the final study group did not specify the children’s birth order. We assumed these children were all first-born, since 112 (95%) of 118 birth mothers whose children’s birth certificates did not specify birth order and who were interviewed face to face in a separate component to the MADDS (25) reported that the study child was their first live-born child.

Maternal

Education

We treated the number of completed years of maternal education from the child’s birth certificate in several ways, Initially, we used it as a continuous variable (< 7, 8, 9, . . ., 16,17 + years) to estimate its average overall effect on various forms of MR (see Tables 2 and 3), as previous work suggested a more or less linear relationship between maternal education and children’s mean cognitive test score (10, 14). In subsequent analyses, we used it as a categorical variable to estimate the magnitude of the effect of specific levels of maternal education on MR. For the analyses in which ma-

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CHILDHOOD

ternal education was treated as a categorical variable, we used as the referent group those children whose mothers had 12 years of education at delivery, assuming that that level meant completion of high school, Such a choice seemed to us a natural one (although still arbitrary), since completion of high school is widely portrayed as an important goal of adolescents. Analytic

Methods

We computed exposure odds ratios (ORs) to estimate the strength of the association between maternal education and MR, and to compare the prevalence of MR (at age 10) among children of women with a given level of education with that among children of women with 12 years of,education (26). Using multiple logistic regression modeling, we adjusted the ORs for the possible competing effects of the other six birth certificate variables mentioned above (27). All seven independent variables described above were included in all models. No backward or forward variable selection methods were used. We also assessedthe possibility of a two-way interaction between maternal education and each of the six covariates (while controlling for the other five), using a = 0.10 as the statistical criterion. Finally, we performed trend tests to assessthe linear relation between maternal education and MR, while controlling for the six birth certificate covariates (28).

MENTAL RETARDATION

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TABLE 1. Selectedcharacteristics of caseand control children and their birth mothers-Metropolitan Atlanta Developmental Disabilities Study, I985 to 1986” Controls

CiWS

Characteristic

(n = 562)

Race of mother White Black

296 (52.7) 266 (47.!!

146 (32.0)

1 L8 (21.0) 146 (26.0) 274 (48.8) 24 (4.3)

112 (24.6) 138 (30.3) 172 (37.7) 34 (7.5)

Birth order of study child First born Second born Third born or later

231 (41.1) 212 (37.7) 119 (21.21

155 (34.0) 136 (29.8) 165 (36.2)

Median block group income (terciles) Low Middle High

196 (34.9) 186 (33.1) 180 (32.0)

249 (54.6) 126 (27.6) 81 (17.8)

Birth weight (g) < 2500 2500+

47 (8.4) 515 (91.6)

99 (21.7) 357 (78.3)

266 (47.3) 296 (52.7)

274 (60.1) I82 (39.9)

Age of mother at birth of study child (y) < 20 20-24 25-34 35+

(n =

456)

3 10 (68.0)

Gender BOY

Girl

L?Data are number of subjects,with percentagesin parentheses

RESULTS Table 1 depicts the distributions of caseand control children according to the six birth certificate covariates. Case children tended to be of higher birth order, lower birth weight, and male more often than control children. Mothers of case children tended to be of black race and come from less affluent neighborhoods more often than mothers of control children. There was slightly higher frequency of younger and older case mothers than control mothers, but the average ages of the two groups were similar (about 24.5 years). We analyzed the average overall effect of maternal educational level on various forms of MR after controlling for the effects of the six covariates (Table 2). The ORs given here represent the average decrement in prevalence of MR for each additional year of maternal education holding the six covariates constant. Each OR can be thought of as the factor by which the MR prevalence rate among children of mothers with any given level of education can be multiplied to obtain an estimate of the MR prevalence rate for children of mothers with the next highest educational level. Inclusion of all case children in the analysis produced an overall adjusted OR of 0.81. There was almost no effect of maternal educational level on MR when it was accompanied by other neurologic conditions (adjusted OR = 0.98).

The strongest negative associations existed for the two subcategories of isolated MR with IQ higher than 49 (adjusted ORs = 0.71 or 0.72). At this point we decided to pursue the remaining analy ses using as the primary case group the 314 children with isolated MR, regardless of IQ, since this case group had the most children and the smallest adjusted OR of all the groups in Table 2. The relative impact of maternal educational level and each of the six covariates on the prevalence of isolated MR is shown in Table 3. It can be seen from the partial R values that maternal educational level is by far the strongest predictor of isolated MR. The next most important risk factors are maternal race and the child’s birth weight. Median block group income and maternal age did not appear to be independent predictors of isolated MR. Our next step in the analyses was to estimate the effect of specific levels of maternal educational level on the prevalence of isolated MR (Table 4). With the notable exception of the subgroup of children whose morhers had exactly 10 years of education, there was a fairly consistent downward trend in the ORs associated with increasing levels of maternal education (P value for linear trend < O.OOI). Relative

350

Decoufle and Boyle CHILDHOOD MENTAL RETARDATION

TABLE 2. Relative importance

of mothers’

AND MATERNAL

education

TABLE 4. Association between mothers’ education at delivery and isolated mental retardation (IQ ( 70)

at

delivery as a predictor of various forms of mental retardation in their children Mental retardation

No. of Crude cases odds ratio

in their children

Adjusted odds ratio”

95% CI

All case children

456

0.75

0.81

0.75-0.87

Case children with other neurologic conditions*

142

0.91

0.98

0.88-1.09

Case children with isolated MR* IQ = 60-70 IQ = 50-59 IQ< 50

314 188 83 43

0.68 0.67 0.69 0.73

0.71 0.71 0.72 0.79

0.65-0.79 0.64-0.80 0.62-0.83 0.65-O.%

CI, confidence interval; IQ, intelhience quotient. ’ Adjusted for race, maternal ageat delivety, birth o&t, bitth weight, gender, and median block gtoup income. Sameconttol group used in each analysis. * Other neurologic conditions include the other four developmental dissbikties ascertainedin the study and sttuctutal, chromosomsl, and metabolic birth defects affecting the cennsl nervous system.Children with isolated MR ate those with MR not accompaniedby any of the foregoing conditions.

to children whose mothers had 12 years of education, children of mothers in the lowest educational category (< 8 years) had an OR of 6.1 and children of mothers in the highest category (16+ years) had an OR of 0.2. When we examined the “fit” of both the previous model, in which education was used as a continuous variable (see Table 2), and the present model, in which education was used as a categorical variable (seeTable 4), we found that both models explained about the same proportion of log likelihood

TABLE 3. Relationship between each of seven independent variables and isolated mental retardation (IQ < 70)

Variable Continuous variables Maternal education at delivery (y) Birth order Median block group income (deciles) Categorical variables Birth weight (g) (< 2500 vs. 2500+) Matetnal race (black vs. white) Gender of child (boy vs. girl) Maternal age at delivery (y) < 25 vs. 25-34 35+ vs. 25-34

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Crude odds ratio

Adjusted odds ratid

95% CI

Partial R value

0.68 1.28

0.71 1.19

0.65-0.79 1.04-1.37

-0.20 0.06

0.81

0.98

0.91-1.05

< 0.01

2.59

2.38

1.51-3.76

0.10

3.37

2.33

1.59-3.43

0.12

1.67

1.72

1.26-2.35

0.09

1.77 1.99

1.16 1.00

0.79-1.70 0.44-2.29

< 0.01 < 0.01

CI, confidence interval. ’ All odds ratios derived from a single logistic model containing the sevenindicated variables.

Maternal education (y) <8 8 9 10 11 12 13 14 15 16+

Case children 17 29 37 37 56 114 8 10 2 4

Control children

Odds ratio”

95% CI

6 13 18 66 53 245 24 42 12 83

6.1 4.2 4.4 1.1 1.9 1.0 1.2 0.7 0.5 0.2

2.2-16.6 2.0-8.8 2.3-8.5 0.7-1.9 1.2-3.0 Referent 0.5-2.9 0.3-1.5 0.1-2.2 0.1-0.5

Cl, confidence intetvsl. nAdjusted for race, mate& ageat delivery, bitth order, birth weight, gender. and median block group income.

(about 10% in the univariate models and 15% in the multivariate models). When we conducted tests for interaction between maternal education and each of the six covariates, the only statistically significant one we found was that involving race (P = 0.04). The average overall adjusted OR for maternal education among black children was 0.77 and for white children, 0.64, indicating a steeper gradient in prevalence among whites. The anomalous result for 10 years of maternal education seen in Table 4 was present among both white and black children (ORs = 1.3 and 1.1 respectively). Race-specific ORs for five categories of maternal education (derived from two separate logistic models) are shown in Table 5 (under the “Two models” subheading) to illustrate the nature of the interaction. Almost all of the difference in the education gradient is accounted for by the divergent results for children whose mothers had less than 10 years of education (ORs = 9.1 and 2.9 for whites and blacks, respectively). It is interesting to note that ORs for both black and white children whose mothers had 13 to 15 years of education are little different from 1.0. Referring back to Table 4, most of this “noneffect” may be due to the mothers who reported exactly 13 years of education. The race-education interaction finding led us to examine further the joint effect of these factors on isolated MR. Using children whose mothers were white and who had 12 years of education as the arbitrary referent group, we obtained a second set of ORs from a single logistic model (see Table 5). Roth white and black children whose mothers had less than 10 years of education now appear to be at almost the same increased risk (both ORs about 9.0). All black children appear to be at some increased risk, except for those whose mothers had at least 16 years of education.

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TABLE 5. Association between mothers’ education at delivery and isolated mental retardation (IQ G 70) in their children, by mothers’ race Two models Maternal

One model

education (y)

CaWZ children

Control children

Odds ratio”

95% Cl

Odds ratio”

95% CI

Black mothers < 10 10-I 1 12 13-15 16+

55 77 90 11 3

22 71 127 26 20

2.9 1.4 1.0 0.7 0.2

1.6-5.4 0.9-2.2 Referent 0.3-1.5 0.1-0.9

9.1 4.3 3.0 2.1 0.8

4.3-19.2 2.3-8.1 1.7-5.4 0.9-4.9 0.2-3.0

White mothers < 10 10-I 1 12 13-15 16+

28 16 24 9 1

15 48 118 52 63

9.1 3.9-21.3 1.6 0.7-3.7 1.O Referent 1.0 0.4-2.5 0.1 0.0-0.8

9.0 1.6 1.0 1.0 0.1

4.1-20.1 0.8-3.4 Referent 0.4-2.4 0.0-0.7

Ci, confidence interval. ’ Adjusted for maternal age at delivery, birth order, birth weight, gender, and median block group income.

DIXUSSION Our finding of a strong inverse relationship between maternal education and isolated MR and almost no association between maternal education and MR accompanied by other neurologic conditions parallels results from two earlier studies in which paternal occupational status or a composite measure of socioeconomic status were the operative factors (6, 7). Viewed collectively, these findings suggest that the cognitive status of children without severe neurologic impairments is strongly influenced by family and social factors, and thus, potentially amenable to postnatal prevention efforts. In contrast, the more neurologically involved cases seem to imply a degree of biologic insult that is unrelated to those same social characteristics. About 86% of our children with isolated MR had IQs in the traditional “mild” range (i.e., 50 to 70), although the scores did reach a low of 19 (four children). About 79% of the children with isolated MR were first “diagnosed” with a low IQ after the age of 5 years, suggesting that academic challenges met in school serve as a natural screening mechanism for this condition. With regard to the race-education interaction (see Table 5), the narrower range of ORs among blacks could be due to a greater degree of nondifferential misclassification in educational level in that racial group, or might indicate a lack of discriminating power (vis-a-vis isolated MR) among the lowest educational levels for this racial group. On the other hand, these results parallel those from other investigations that showed a narrower range in mean cognitive test scores across parental education or socioeconomic levels among black children than among whites (29, 30).

MENTAL RETARDATION

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351

The findings from the analysis of joint race-education effects in Table 5 suggest that what might appear to be equivalent educational levels among white and black women did not confer equivalent risks on their children. Differences in the mothers’ educational experiences or their children’s environments may account for some of the discrepancy. An even more dramatic example of this pattern is seen in national raceand education-specific infant mortality data, where there is very little overlap in the educational level distributions of mortality among black and white children (31). We were somewhat surprised not to find an independent association between economic status and the prevalence of isolated MR. Recent reports focused on “poverty” or “economic disadvantage” as a detrimental factor for child development (32). Among our control children, maternal education and median block group income were only moderately correlated (r = 0.44), thus permitting the identification of an independent effect of each variable. However, we may have been unable to detect a separate economic effect because of the ecologic nature of our family income measure. It is interesting to note that even though we identified our case children solely through review of existing records at multiple sources (primarily the public school systems), the general nature of the maternal education associations we found largely parallel those seen in studies in which all children in a well-defined population were tested with the same psychometric instrument at the same age (6,7). Thus, a possible bias arising from the action of various selective factors that cause a child to be referred for cognitive testing may be minimized in this study. With regard to the use of the public school systems as the source of our control subjects, we believe that any selection bias is minimal since only about 6% of children in the study area attended private schools in 1986 (33). The seemingly lower than expected OK for the lo-year educational level among both black and white mothers could be due to some form of misclassification, but why this one category would be affected so noticeably is unclear. Among mothers of the control children, however, the percentages with either 8, 9, 10, or 11 years of education at delivery were 2.3,3.2, 11.7, and 9.4, respecrively, suggesting a “piling on” for the lo-year category. Further, other study mothers with 10 years of education may be among the 106 subjects with missing education data, since the OR comparing those 106 subjects with the 12-year referent group was somewhat elevated (OR = 1.5). With respect to the apparent blurring of risks between the 12-year and 1i-year educational levels seen in Table 4, the National Center for Health Statistics found that the largest reporting discrepancy in years of maternal education occurred between these two levels (34). Thus, misclassification may be especially prevalent in these categories. It is likely that a multitude of factors (genetic and environmental) influence the risk of a child developing isolated MR

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(35,36). In OUTdata, at least some of these factors appear to be captured by a single maternal characteristic-educational level at delivery. Maternal education begins to exert its influence on children’s cognitive functioning by at least the age of 2 years (IO, 14, 37). Examples of p~stt~tal pathways through which maternal education may be associated with isolated MR include the quality of the home environment (38), the adequacy of maternal social support (39), maternal depression (39), maternal self-esteem (40), the quality of mother-child interactions (41-44), and the mother’s literacy level (45). Paternal factors include the presence of the father in the home and the father’s occupational and educational level (30, 46). With regard to prevention, it has been shown that early and intensive intervention programs for socioeconomically disadvantaged children can have a positive effect on their cognitive development (47-51). These programs appear to confer a beneficial effect on the mothers of such children as well, including the enhancement of maternal educational and occupational status (52). Our results may be helpful as a guide in selecting high-risk populations for these interventions.

This work was supported, in part, by funds from the Comprehensive Environmental Response, Compensation, and Liability Act trust fund through an interagency agreement with the Agency for Toxic Substances and Disease Registry, US Public Health Service.

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MENTAL RETARDATION

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42. Spiker D, Ferguson J, Brooks-Gunn J. Enhancing maternal interactive behavior and child social competence in low birth weight, premature infants, Child Dev. 1993;64:754-768. and family: The impact of 43. Laosa LM. School, occupation, CdNre, parental schooling on the parent-child relationship, J Educ Psycho]. 1982;74:791-827. 44. Richman AL, Miller PM, Levine RA. Cultural and educational variations in maternal responsiveness, Dev Psychol. 1992;28:614-621. 45. National Center for Education Statistics. Adult Literacy rn America. Washington, DC: US Government Printing C&e; 1993:25-29. 46. Wilson RS. The Louisville Twin Study: Developmental in behavior, Child Dev. 1983;54:298-316.

synchronies

47. Weikart DP, Bond JT, McNeil JT. The Ypsilanti Perry Preschool Project: Preschool Years and Longitudinal Resuks through Fourth Grade. Ypsilanti, MI: High/Scope Educational Research Foundation; 1978. 48. Zigler E, Abelson WD, Trickett PK, Seitz V. Is an intervention program necessary to improve economicatiy disadvantaged children’s IQ scores?, Child Dev. 1982;53:340-348. 49. McKay H, Sinisterra L, McKay A, Comes H, Uoreda P. Improving cognitive ability in chronically deprived children, Science. 1978;2OO: 270-278. 50. Wasik BH, Ramey CT, Bryant DM, Sparling JJ. A longitudinal study of two early intervention strategies: Project CARE, Child Dev. 1990; 61~1682-1696. 51. Ramey CT, Campbell FA. Preventive education for high-risk children: Cognitive consequences of the Carolina Abecedarian Project, Am J Ment Defic. 1984;88:515-523. 52. Benasich AA, Brooks-Gunn J, Clewell BC. How do mothers benefit from early intervention programs?, J Appi Dev Psychoi, 1992;13:31 l362.