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The Impact of Episodic and Chronic Poverty on Child Cognitive Development
The Impact of Episodic and Chronic Poverty on Child Cognitive Development JAKE M. NAJMAN, PHD, FASSA, MOHAMMAD R. HAYATBAKHSH, MD, PHD, MICHELLE A. HERON, PHD, WILLIAM BOR, PHD, MICHAEL J. O’CALLAGHAN, MBBS, MSC, FRACP, AND GAIL M. WILLIAMS, PHD
Objective
To determine whether changes in family poverty between pregnancy, early childhood, and adolescence predict child cognitive development at 14 years of age. Study design We conducted a population-based prospective birth cohort study of 7223 mothers who gave birth to a live singleton baby, observed to 14 years of age. Family income was measured on 4 occasions from pregnancy to the 14-year follow-up. Child cognitive development was measured at the 14-year follow-up using the Raven’s Standard Progressive Matrices and Wide Range Achievement Test. Results Poverty experienced at any stage of the child’s development is associated with reduced cognitive outcomes. Exposure to poverty for a longer duration (birth to 14 years) is more detrimental to cognitive outcomes than experiencing poverty at only 1 period. For each additional exposure to poverty, the Raven’s Standard Progressive Matrices scores declined by 2.19 units and the Wide Range Achievement Test scores declined by 1.74 units. Conclusion Children experiencing family poverty at any developmental stage in their early life course have reduced levels of cognitive development, with the frequency that poverty is experienced predicting the extent of reduced cognitive scores. (J Pediatr 2009;154:284-9)
hildren from low-income families have lower cognitive test scores when compared with children from more affluent backgrounds. This has been reported for children from 2 years of age and to adolescence.1-7 The association remains even after statistical control for maternal cognitive skills, parent education,1,6,8,9 and family structure.9,10 It is estimated that children reared in poverty score between 15% and 40% of a SD lower on cognitive assessments than children from higher income backgrounds.11 Earlier investigations have also found that parent and grandparent socioeconomic status (SES) are independently associated with lower child cognitive development at 5 and 14 years of age.12 However, From the School of Population Health, Unithese findings leave unresolved the issue of whether poverty experienced at a particular versity of Queensland, Brisbane, Australia stage of the child’s developmental trajectories (in pregnancy, early childhood, or adoles(J.N., M.H., G.W.); School of Social Science, University of Queensland, Brisbane, Austracence) has an independent impact on cognitive development and whether those effects lia (J.N.); Qom University of Medical Scimight be particularly important or cumulative. ence, Qom, Iran (M.H.); Department of Some studies have investigated the effect of timing (childhood versus adolescence) Psychology, University of Sheffield, Sheffield, United Kingdom (M.H.); and Mater and duration of poverty on cognitive development. Duncan et al6 report that experiencing Misericordiae Children’s Hospital, Brisbane, poverty in early childhood (birth to 5 years) has a greater impact on cognitive achievement Australia (M.O.). than experiencing poverty in middle childhood (6-10 years) or adolescence (11-15 years). The core study was funded by the National Health and Medical Research Council of Lipman and Offord13 report that experiencing poverty earlier in the life course (between Australia, but the views in this paper are 4 and 12 years) is more detrimental than between 8 and 16 years. For duration of poverty, those of the authors and do not reflect in 1,3,4,7,10 any way the views of any funding body. The persistent poverty appears to be more detrimental than transient poverty. authors declare no conflicts of interest. Guo14 reports that exposure to poverty during childhood (birth to 6 years) appears Submitted for publication Jan 23, 2008; last to have a more detrimental effect on childhood cognitive ability than experiencing poverty revision received Jun 23, 2008; accepted Aug 4, 2008. during early adolescence. Poverty experienced in the 4 years before adolescence had no Reprint requests: Jake M. Najman, PhD, additional impact on child cognitive outcomes beyond the effects observed for poverty School of Population Health, University of experienced in early childhood. Guo’s study uses data taken from the National LongituQueensland, Herston Road, Herston, QLD
C
ANCOVA Analysis of covariance ANOVA Analysis of variance PPVT Peabody Picture Vocabulary Test
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SES SPM WRAT
Socioeconomic status Standard Progressive Matrices Wide Range Achievement Test
4006, Australia. E-mail: [email protected]. edu.au. 0022-3476/$ - see front matter Copyright © 2009 Mosby Inc. All rights reserved. 10.1016/j.jpeds.2008.08.052
dinal Survey of Youth (NLSY) and the children of the NLSY (NLSY-C). Income data is from early childhood and adolescence, with no data on income during pregnancy or very early childhood. Although experimental approaches offer evidence of how changed family circumstances enhance later cognitive outcomes, natural experiments involving changes of household income levels during childhood may provide important tests of how income change influences children’s cognitive development.15 Longitudinal studies that capture natural transitions in family income over time and child/adolescent cognitive development can be used to further test the impact of income change on cognitive abilities. We investigated the effects of changes in poverty among pregnancy, early childhood, and adolescence on cognitive ability in children aged 14 years, by using data from an Australian prospective birth cohort study.
years) and persistent poverty (birth to 14 years) on cognitive development at 14 years of age. The sample from this study derives from a public (free) hospital and is somewhat overrepresented by lower income earners. The overrepresentation of low income earners has the effect of increasing the numbers in this group, while reducing the numbers of very high income earners in the study. This “shrinking” of the income variability evident in the broader population has the effect of providing a conservative test of the income and cognitive development relationship.
We used data from the Mater-University of Queensland Study of Pregnancy, a prospective longitudinal study of a consecutive cohort of individuals born at a major public hospital (Mater Misericordiae Hospital) in Brisbane, Australia between 1981 and 1983. The hospital was 1 of only 2 major obstetrical hospitals in Brisbane and served the south side of the city. Recruitment procedures for the larger study have been detailed elsewhere.16,17 Baseline data were collected at the first antenatal visit (average 18 weeks of pregnancy) from 7223 consecutive women who subsequently gave birth to live singleton babies and were observed at 3 to 5 days, 6 months, 5 years and 14 years after the birth. At the 6-month follow-up visit, 6720 women (93%) responded to the questionnaire; this number dropped to 5234 at the 5-year follow-up visit and 5185 (72%) at the 14-year follow-up visit. At 14 years, a child assessment questionnaire was completed by 3799 children (53%).17 Approximately 3000 participants provided cognitive development scores at 14 years, and data were available on family income between pregnancy and the 14year follow-up for them. Written informed consent from the mother was obtained at all phases of data collection. Ethics committees at the Mater Hospital and the University of Queensland approved each phase of the study.
Measurement of Cognitive Development At the 14-year follow-up, assessments of cognitive development were based on youth scores on the Raven’s Standard Progressive Matrices (SPM)18 and the Wide Range Achievement Test (WRAT).19 The Raven’s SPM is a test of non-verbal reasoning ability that is widely used for psychological assessment in clinical and educational contexts.18,20 Although there is some debate about the specific cognitive attributes the Raven’s SPM measures, it has demonstrated a high correlation with full-length intelligence tests21 and is generally accepted as a measure of general intelligence. De Lemos22 re-standardized Raven’s SPM scores, on the basis of the mean and SD at each year level. However, in this study, child scores have been standardized in 6 monthly groupings rather than yearly levels. The WRAT is an age-normed referenced test that assesses reading and word decoding skills.19 It has been found to be a stable measure23 and to have high test-retest reliability24 and high internal consistency reliability coefficients.25 In this study, only the reading subscale of the WRAT was administered. WRAT scores were standardized (M ⫽ 100; SD ⫽ 15) for consistency with other measures. The WRAT is intended to measure basic reading skills and academic achievement. It is intended to identify children who have difficulties with reading.26,27 Although the Raven’s SPM and the WRAT both provide a measure of child IQ, the SPM is likely to reflect the child’s general ability whereas the WRAT reflects the degree that learning has taken place. The Pearson correlation between the Raven’s SPM and WRAT was 0.42 (P ⬍ .001; n ⫽ 3784).
Measurement of Economic Status Mothers were asked about gross annual household (family) income (including spouse’s income, child endowment, etc.) at the child’s birth and when the child was 6 months, 5 years, and 14 years old. Seven discrete income categories were given as response options (listed in weekly and annual amounts). Mothers with family income in the category closest to the bottom 20% of incomes were considered to be low income (poor). The low income figure selected at every phase of data collection is similar to the estimate of the proportion of the Australian population living at or below the poverty level. We examined the effects of poverty when it is limited to early childhood (birth to 5 years) or adolescence (14
Demographic Information Only mothers and their children were followed in this study, with limited (no self-report) paternal data available. Marital status when the child was 5 years old was divided in 2 categories: mothers who were married or living in a de facto relationship, and mothers with no partner (single, separated/ divorced or widowed). Maternal education at entry to the study was coded in 3 categories: high school not completed, completed high school, and studies beyond high school. Maternal age at entry to the study was included as a discrete original variable. We adjusted for marital status and maternal age because these factors are associated with both SES and cognitive development. Similarly, maternal education was
METHODS
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Table I. Exposure to poverty at different stages over the early life-course and child standardized Raven’s score at 14 years
Phase
Poverty status
Pregnancy
F (df) P Poor Not poor
6 months
F (df) P Poor Not poor
5 years
F (df) P Poor Not poor
14 years
F (df) P Poor Not poor
Unadjusted model
Adjusted for poverty at other stages of development
Adjusted for poverty at other stages of development and maternal educational age and marital status
22.30 (1,2924) (⬍.001) 98.41 101.29 20.18 (1,2901) (⬍.001) 98.21 101.20 50.84 (1,2920) (⬍.001) 96.76 101.51 37.34 (1,2908) (⬍.001) 96.78 101.25
4.98 (1,2921) (0.026) 99.40 100.91 1.39 (1,2898) (0.239) 99.86 100.74 20.92 (1,2917) (⬍.001) 97.90 101.21 17.84 (1,2905) (⬍.001) 97.84 101.03
2.35 (1,2918) (0.125) 99.74 100.77 2.03 (1,2895) (0.154) 99.67 100.79 17.13 (1,2914) (⬍.001) 97.92 101.20 18.30 (1,2902) (⬍.001) 97.97 101.01
F, df, P values, and standardized Raven’s score.
controlled to distinguish home environment effects supporting educational outcomes from the broader impact of poverty/ socioeconomic disadvantage.
Data Analysis We first examined univariate relations (1-way analysis of variance [ANOVA]) in family economic status at each phase of the study, measured between pregnancy and the 14-year follow-up, and child Raven’s SPM and WRAT scores at 14 years. Next, a series of 1-way analysis’ of covariance (ANCOVA) was conducted with the same variables controlling for maternal education and age at first clinic visit and maternal marital status assessed at the 5-year follow-up. To examine the association between family income at each follow-up and child cognitive development, we conducted a multivariate ANCOVA controlling for family income and potential confounders at other phases. In a further analysis, using family income at 4 phases of the study, we created a composite variable that measures the number of exposures to poverty during 14 years of life. This variable comprised 4 categories: never poor, once, twice, and 3 or more times poor. Finally, a 1-way ANOVA was run to test the relation between number of exposure to family poverty and child cognitive development measured with Raven’s SPM and WRAT.
RESULTS Raven’s Standard Progressive Matrices A 1-way between-subjects ANOVA was run with family income as the independent variable and cognitive development (Raven’s scores) at 14 years as the dependent variable (Table I). A significant difference between the means for each phase of data collection, with the low income group consis286
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Table II. Exposure to poverty and mean Raven’s score at 14 year follow-up* Times poor Never 1 2 3-4 times
n
Mean Raven’s Score
95% CI
1486 713 427 311
102.33 100.09 98.30 95.52
74.7-129.9 71.0-129.2 67.9-128.7 63.7-127.3
*Regression of times in poverty against Raven’s SPM indicates a reduction of 2.19 (95% CI, 1.67-2.71) for every additional instance of exposure to poverty.
tently experiencing lower scores, was revealed with the ANOVA. Next, a 1-way ANCOVA was run with the same variables, but with adjustment for poverty at each other stage. Poverty during pregnancy, at 5 years, and at 14 years remain independently associated with the Raven’s score, suggesting poverty additionally contributes to cognitive outcomes at each of these stages of the life course. Additional adjustment for some key socio-demographic variables has limited impact on the mean differences. For model 3, poverty in childhood and adolescence independently contribute to Raven’s scores, even after additional adjustment for some indicators of family socioeconomic circumstances. In Table II, we examine the frequency with which the child/adolescent experiences poverty by the child’s Raven’s SPM outcomes. There is a linear trend that shows a reduction in Raven’s SPM scores associated with increased frequency of exposure to poverty. By using a regression to estimate the linear trend of the association between frequency of poverty in the early life course and Raven’s SPM scores, we find that there is a reduction of 2.19 points for every additional experience of poverty. The Journal of Pediatrics • February 2009
Table III. Exposure to poverty at different stages over the early life-course and child standardized WRAT score at 14 years
Phase
Poverty status
Pregnancy
F (df) P Poor Not poor
6 months
F (df) P Poor Not poor
5 years
F (df) P Poor Not poor
14 years
F (df) P Poor Not poor
Unadjusted model
Adjusted for poverty at other stages of development
Adjusted for poverty at other stages of development and maternal education and age and marital status
27.32 (1,2918) (⬍.001) 97.89 101.05 22.07 (1,2896) (⬍.001) 97.82 100.90 13.74 (1,2914) (⬍.001) 98.23 100.70 20.88 (1,2902) (⬍.001) 97.38 100.71
10.97 (1,2915) (0.001) 98.57 100.79 4.14 (1,2893) (0.042) 99.06 100.55 1.46 (1,2911) (0.227) 99.49 100.36 11.20 (1,2899) (0.001) 98.05 100.57
6.43 (1,2912) (0.011) 98.94 100.65 2.64 (1,2890) (0.104) 99.23 100.50 5.00 (1,2908) (0.025) 98.78 100.55 15.75 (1,2896) (⬍.001) 97.38 100.71
F, df, P values, and standardized WRAT score.
Wide Range Achievement Test Univariate ANOVA was run with family income as the independent variable and cognitive development (WRAT scores) at 14 years as the dependent variable (Table III). Significant differences in WRAT scores were revealed with the ANOVA for those living in poverty at every phase of data collection from pregnancy to the 14 year follow-up. Next, a 1-way ANCOVA was run with adjustment for poverty at other stages of child development. Family poverty during pregnancy, at 6 months, and at 14 year follow-up independently predicted WRAT scores. Additional adjustment for maternal education, age, and marital status in model 3 reduces some of the differences in WRAT scores (poor versus not poor), but 3 of the 4 differences remain statistically significant. In Table IV, we have aggregated the number of times the child lived in a family that scored around or below the poverty line. The data here points to a cumulative linear trend, with frequency of exposure to poverty associated with decreasing WRAT scores. A trend test (regression) indicates that the WRAT scores decline by 1.74 points per every additional instance a child is exposed to family poverty. Modeling the Effects of Loss to Follow-up Of those for whom mother’s family income are recorded at entry to the study, 45.2% of lowest income group mothers and 57.4% of other income group mothers remained in the study at 14 years. To estimate the association between cognitive ability and loss to follow-up, we examined child Peabody Picture Vocabulary Test (PPVT) scores at 5 years as a predictor of loss to follow-up at 14 years. Of those who completed the PPVT at 5 years, 74.6% of those with normal
Table IV. Exposure to poverty and mean WRAT score at 14 year follow-up* Times poor Never 1 2 3-4
n
Mean WRAT score
95% CI
1483 713 425 310
101.53 100.37 97.82 96.40
75.0-128.0 70.6-130.2 66.4-129.2 64.8-128.0
*Regression of times in poverty against WRAT indicates a reduction of 1.74 points (95% CI, 1.22-2.25) for every additional exposure to poverty.
and higher scores remained in the study, compared with 65.6% of those with less than normal scores who remained in the study. Children subsequently lost to follow-up had a mean (at age 5 years) PPVT of 96.85, compared with a mean of 100.35 for children who remained in the study. The existing data suggest that children in low income groups and who have lower PPVT scores are more likely to be lost to follow-up. However, the magnitude of this association is modest, and loss to follow-up is about 10% to 15% higher in the most disadvantaged groups. The Appendix (available at www.jpeds.com) presents various estimates of the effect of loss to follow-up on the estimates of the association, on the basis of what is known about the characteristics of children lost to follow-up. On the assumption that approximately 60% of each group are lost to follow-up, but that the outcomes are 5%, 7%, and 10% worse (lower scores) in those lost to follow-up, then replacing these cases would generally slightly increase the differences in raven’s scores for increasing frequency of exposure to poverty. If it is estimated that the rate of loss to follow-up increases with increasing exposure to poverty (likely) and that Raven’s
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outcomes are worse for children lost to follow-up (also likely), then the Raven’s score differences in poverty groups become slightly larger than they were before the replacement of the missing cases. In every instance, it is clear that the actual findings are a conservative estimate of the association observed without any loss to follow-up, and that the differences between the observed and estimated associations are minor. None of the main findings of the study are in conflict with any of the projected associations on the basis of what is known about loss to follow-up, nor would any of the conclusions differ were any of the estimates to replace the observed findings.
DISCUSSION Poverty experienced in early childhood is more detrimental to adolescent cognitive outcomes than poverty experienced in adolescence alone.6,13 Our findings indicate that neither pregnancy, early childhood, nor the adolescent period can be characterized as specifically sensitive for the impact of poverty on cognitive development. Our findings suggest that the frequency of childhood exposure to poverty has a greater impact on child cognitive outcomes than the timing of that exposure. However, it appears that poverty experienced at the 14-year follow-up has the strongest and most consistent association with cognitive outcomes. Our results may differ from earlier studies for a number of reasons. First, our measures of cognitive outcomes included the Raven’s and the WRAT, and the studies by Lipman and Offords13 and Duncan6 used school outcomes (completion, placement in special class/remedial) and school performance as measures of cognitive outcomes. Lipman and Offord’s13 subjects had a range of ages (age at initial assessment ranged from 4-12 years), and the period of follow-up was comparatively short (4 years). The finding that more frequent experiences of family poverty, in a child’s early life course, is associated with lower levels of cognitive development, directs attention to the mechanisms that link family poverty and cognitive development. Children from more economically disadvantaged backgrounds are likely to experience many differences in their environmental circumstances when compared with their more economically advantaged counterparts. Aside from a greater home emphasis on learning and literacy, there is a better physical environment (better housing, more space per person), better nutrition, and generally of better educational facilities. There may be fewer family disruptions and a generally more stable home environment. Although the impact of any of these differences may be small, the cumulative impact in a lifetime is likely to be substantial. Flynn28 has emphasized the potential of the environmental impact on child cognitive development, noting that for a wide number of countries for which data are available, IQ scores have been increasing at between 0.3 and 0.5 units per year. For example, for the United States, with comparable tests, there has been an average population increase of 14 IQ points in the period from 1932 to 1978. Flynn argues that although IQ scores have been increasing with time, no single 288
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proposed cause can account for the observed changes.28 Improved economic conditions, better nutrition, and a better physical environment are all likely to contribute to increase in population IQ with time. There are 3 policy directions that are a consequence of our findings. First, the cumulative effect of poverty on a child’s cognitive development suggests that initiatives are required during the whole of the child’s early life course. Early childhood intervention programs will need to be reinforced by initiatives in later childhood and early adolescence, because poverty independently impacts on the child’s cognitive development, even when it occurs only in adolescence. Second, there is the possibility of targeted programs specifically directed to cognitive outcomes. These would include early childhood interventions such as Head Start,29 but continuing beyond early childhood. Third, there are policies that have the effect of reducing economic inequalities in a population. Such policies would seek to move the chronically poor out of poverty (eg, assist single mothers in re-entering the workforce) and improve the physical environment in which children are reared. In part, this may involve a process of advocacy on behalf of disadvantaged children. The authors thank the MUSP participants, the MUSP Research and data collection teams, and MUSP Data Manager Greg Shuttlewood for their support.
REFERENCES 1. Smith JR, Brooks-Gunn J, Klebanov PK. Consequences of living in poverty for young children’s cognitive and verbal ability and early school achievement. In: Duncan G, Brooks-Gunn J, editors. Consequences of growing up poor. New York: Russell Sage Foundation; 1997. p. 132-89. 2. Taylor BA, Dearing E, McCartney K. Incomes and outcomes in early childhood. J Hum Resources 2004;39:980-1007. 3. NICHD. Duration and developmental timing of poverty and children’s cognitive and social development from birth through third grade. Child Dev 2005;76:795-810. 4. Korenman S, Miller JE, Sjaastad JE. Long-term poverty and child development in the United States: results from the NLSY. Child Youth Serv Rev 1995;17:127-55. 5. Black MM, Hess C, Berenson-Howard J. Toddlers from low-income families have below normal mental, motor, and behavior scores on the Revised Bayley Scales. J Appl Dev Psychol 2000;21:655-66. 6. Duncan GJ, Brooks-Gunn J, Yeung WJ, Smith JR. How much does childhood poverty affect the life chances of children? Am Sociol Rev 1998;63:406-23. 7. Brooks-Gunn J, Duncan GJ. The effects of poverty on children. Future Child 1997;7:55-71. 8. Crane J. Effects of home environment, SES and maternal test scores on mathematics achievement. J Educ Res 1996;89:305-14. 9. Tong S, Baghurst P, Vimpani G, McMichael A. Socioeconomic position, maternal IQ, home environment, and cognitive development. J Pediatr 2007;151:284-8. 10. Duncan GJ, Brooks-Gunn J, Klebanov PK. Economic deprivation and early childhood development. Child Dev 1994;65:296-318. 11. Ryan RM, Fauth RC, Brooks-Gunn J. Childhood poverty: implications for school readiness and early childhood education. In: Spodek B, Saracho ON, editors. Handbook of research on the education of young children. Mahwah, New Jersey: Lawrence Erlbaum Associates Publishers; 2006. p. 323-46. 12. Najman JM, Aird R, Bor W, O’Callaghan M, Williams GM, Shuttlewood GJ. The generational transmission of socioeconomic inequalities in child cognitive development and emotional health. Soc Sci Med 2004;58:1147-58. 13. Lipman EL, Offord DR. Psychosocial morbidity among poor children in Ontario. In: Duncan G, Brooks-Gunn J, editors. Consequences of growing up poor. New York: Russell Sage Foundation; 1997. p. 239-87. 14. Guo G. The timing of the influences of cumulative poverty on children’s cognitive ability and achievement. Soc Forces 1998;77:257-88. 15. Rutter M. Poverty and child mental health: natural experiments and social causation. JAMA 2003;290:2063-4. 16. Keeping JD, Najman JM, Morrison J, Western JS, Andersen MJ, Williams GM.
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A prospective longitudinal study of social, psychological and obstetric factors in pregnancy: response rates and demographic characteristics of the 8556 respondents. Br J Obstet Gynaecol 1989;96:289-97. 17. Najman JM, Bor W, O’Callaghan M, Williams GM, Aird R, Shuttlewood GJ. Cohort profile: the Mater-University of Queensland Study of Pregnancy (MUSP). Int J Epidemiol 2005;34:992-7. 18. Raven J. The Raven progressive matrices: an overview of international norming studies. Psychol Test Bull 1989;2:7-16. 19. Wilkinson GS. The Wide Range Achievement Test: administration manual. Wilmington, Delaware: Wide Range; 1993. 20. de Lemos MM. Standard progressive matrices: Australian manual. Victoria: Australian Council for Educational Research; 1989. 21. Raven JC, Court JH, Raven J. A manual for Raven’s Progressive Matrices and Vocabulary Tests. San Antonio, Texas: The Psychological Corporation; 1987. 22. de Lemos MM. The Australian re-standardization of the standard progressive matrices. Psychol Test Bull 1989;2:17-24.
23. Dura JR, Myers EG, Freathy DT. Stability of the Wide Range Achievement Test in an adolescent psychiatric inpatient setting. Educ Psychol Meas 1989;49:253-6. 24. Woodward CA, Santa-Barbara J, Roberts R. Test-retest reliability of the Wide Range Achievement Test. J Clin Psychol 1975;31:81-4. 25. Mishra SP. Reliability and validity of the WRAT with Mexican-American children. Psychol Schools 1981;18:154-8. 26. Klimczak NC, Bradford KA, Burright RG, Donovick PJ. K-FAST and WRAT-3: are they really different? Clin Neuropsychol 2000;14:135-8. 27. Orme DR, Johnstone B, Hanks R, Novack R. The WRAT-3 reading subtest as a measure of premorbid intelligence among persons with brain injury. Rehabil Psychol 2004;49:250-3. 28. Flynn JR. Massive IQ gains in 14 nations: what IQ tests really measure. Psychol Bull 1987;101:171-91. 29. Currie J, Thomas D. Does Head Start make a difference. Am Econ Rev 1995;85:341-64.
50 Years Ago in The Journal of Pediatrics INTERSCORER
AGREEMENT FOR THE
GRAHAM BEHAVIOR TEST
FOR NEONATES
Rosenblith JF, Lipsitt LP. J Pediatr 1959;54:200-5
Today it is as important to observe babies’ behaviors as it was in the past, despite the many technological advances in the field of neonatology. Fifty years ago in The Journal, Rosenblith and Lipsitt reported satisfactory interscorer agreement across 4 domains of the Graham test, one of the first standardized behavioral examinations designed for newborn infants. The authors raised a number of important issues including the need for norms for each day of life and cut-points for determining “normal” versus “abnormal” behavior. They also highlighted that “traumatized” infants may perform differently from the “normal” babies. This raises the difficulty in defining the appropriate “norms” for different groups of infants, which remains an important issue for us today. How should we expect a baby of 23 weeks’ gestation to behave at 1 week of age, or, for that matter, upon reaching term? Should we compare preterm infant behavior with term behavior? As technology has advanced, so have the opportunities for greater understanding of the relationships between newborn behaviors and cerebral injury using neuro-imaging, not available in 1959. Similarly, the examination of newborn behaviors has progressed with the development of more complex tools such as the NBAS (Neonatal Behavioral Assessment Scale) and the APIB (Assessment of Preterm Infant Behavior). However, despite changing terminology, the themes remain the same. Like Graham, Rosenblith, Lipsitt and others, we remain interested in babies’ “muscle-tension” (muscle tone), their irritability and self-regulatory behaviors, their maturation, and their ability to engage and orientate to specific stimuli. We still have much to learn from our infants’ behavior—although the latest monitors can tell us about numerous vital signs, we can all do with a simple reminder to “observe the baby’s behavior.” Nisha C. Brown, PhD Victorian Infant Brain Studies Murdoch Childrens Research Institute Newborn Research The Royal Women’s Hospital Department of Obstetrics and Gynaecology University of Melbourne Melbourne, Australia Terrie E. Inder, MD Department of Pediatrics, Neurology, and Radiology St. Louis Children’s Hospital Washington University St. Louis, Missouri 10.1016/j.jpeds.2008.08.005
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Appendix. Estimated WRAT scores after adjustment for loss to follow-up: association between poverty and WRAT cognitive development
Never poor Poor 1 time Poor 2 times Poor 3-4 times Never poor Poor 1 time Poor 2 times Poor 3-4 times
Attrition (estimated)
Observed mean
Estimated mean (5%)*
Estimated mean (7%)†
Estimated mean (10%)‡
60% 60% 60% 60% 50% 55% 60% 65%
101.53 100.37 97.82 96.40 101.53 100.37 97.82 96.40
98.48 97.36 94.89 93.51 98.99 97.61 94.89 93.27
97.27 96.15 93.7 92.35 97.98 96.51 93.71 92.01
95.44 94.35 91.95 90.62 96.45 94.85 91.95 90.13
For estimated means, missing cases are replaced on the basis of known associations with projections for worst case scenarios. *Estimated that cases lost to follow-up have 5% lower scores on cognitive development. †Estimated that cases lost to follow-up have 7% lower scores on cognitive development. ‡Estimated that cases lost to follow-up have 10% lower scores on cognitive development.
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The Journal of Pediatrics • February 2009
Objective
To determine whether changes in family poverty between pregnancy, early childhood, and adolescence predict child cognitive development at 14 years of age. Study design We conducted a population-based prospective birth cohort study of 7223 mothers who gave birth to a live singleton baby, observed to 14 years of age. Family income was measured on 4 occasions from pregnancy to the 14-year follow-up. Child cognitive development was measured at the 14-year follow-up using the Raven’s Standard Progressive Matrices and Wide Range Achievement Test. Results Poverty experienced at any stage of the child’s development is associated with reduced cognitive outcomes. Exposure to poverty for a longer duration (birth to 14 years) is more detrimental to cognitive outcomes than experiencing poverty at only 1 period. For each additional exposure to poverty, the Raven’s Standard Progressive Matrices scores declined by 2.19 units and the Wide Range Achievement Test scores declined by 1.74 units. Conclusion Children experiencing family poverty at any developmental stage in their early life course have reduced levels of cognitive development, with the frequency that poverty is experienced predicting the extent of reduced cognitive scores. (J Pediatr 2009;154:284-9)
hildren from low-income families have lower cognitive test scores when compared with children from more affluent backgrounds. This has been reported for children from 2 years of age and to adolescence.1-7 The association remains even after statistical control for maternal cognitive skills, parent education,1,6,8,9 and family structure.9,10 It is estimated that children reared in poverty score between 15% and 40% of a SD lower on cognitive assessments than children from higher income backgrounds.11 Earlier investigations have also found that parent and grandparent socioeconomic status (SES) are independently associated with lower child cognitive development at 5 and 14 years of age.12 However, From the School of Population Health, Unithese findings leave unresolved the issue of whether poverty experienced at a particular versity of Queensland, Brisbane, Australia stage of the child’s developmental trajectories (in pregnancy, early childhood, or adoles(J.N., M.H., G.W.); School of Social Science, University of Queensland, Brisbane, Austracence) has an independent impact on cognitive development and whether those effects lia (J.N.); Qom University of Medical Scimight be particularly important or cumulative. ence, Qom, Iran (M.H.); Department of Some studies have investigated the effect of timing (childhood versus adolescence) Psychology, University of Sheffield, Sheffield, United Kingdom (M.H.); and Mater and duration of poverty on cognitive development. Duncan et al6 report that experiencing Misericordiae Children’s Hospital, Brisbane, poverty in early childhood (birth to 5 years) has a greater impact on cognitive achievement Australia (M.O.). than experiencing poverty in middle childhood (6-10 years) or adolescence (11-15 years). The core study was funded by the National Health and Medical Research Council of Lipman and Offord13 report that experiencing poverty earlier in the life course (between Australia, but the views in this paper are 4 and 12 years) is more detrimental than between 8 and 16 years. For duration of poverty, those of the authors and do not reflect in 1,3,4,7,10 any way the views of any funding body. The persistent poverty appears to be more detrimental than transient poverty. authors declare no conflicts of interest. Guo14 reports that exposure to poverty during childhood (birth to 6 years) appears Submitted for publication Jan 23, 2008; last to have a more detrimental effect on childhood cognitive ability than experiencing poverty revision received Jun 23, 2008; accepted Aug 4, 2008. during early adolescence. Poverty experienced in the 4 years before adolescence had no Reprint requests: Jake M. Najman, PhD, additional impact on child cognitive outcomes beyond the effects observed for poverty School of Population Health, University of experienced in early childhood. Guo’s study uses data taken from the National LongituQueensland, Herston Road, Herston, QLD
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ANCOVA Analysis of covariance ANOVA Analysis of variance PPVT Peabody Picture Vocabulary Test
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SES SPM WRAT
Socioeconomic status Standard Progressive Matrices Wide Range Achievement Test
4006, Australia. E-mail: [email protected]. edu.au. 0022-3476/$ - see front matter Copyright © 2009 Mosby Inc. All rights reserved. 10.1016/j.jpeds.2008.08.052
dinal Survey of Youth (NLSY) and the children of the NLSY (NLSY-C). Income data is from early childhood and adolescence, with no data on income during pregnancy or very early childhood. Although experimental approaches offer evidence of how changed family circumstances enhance later cognitive outcomes, natural experiments involving changes of household income levels during childhood may provide important tests of how income change influences children’s cognitive development.15 Longitudinal studies that capture natural transitions in family income over time and child/adolescent cognitive development can be used to further test the impact of income change on cognitive abilities. We investigated the effects of changes in poverty among pregnancy, early childhood, and adolescence on cognitive ability in children aged 14 years, by using data from an Australian prospective birth cohort study.
years) and persistent poverty (birth to 14 years) on cognitive development at 14 years of age. The sample from this study derives from a public (free) hospital and is somewhat overrepresented by lower income earners. The overrepresentation of low income earners has the effect of increasing the numbers in this group, while reducing the numbers of very high income earners in the study. This “shrinking” of the income variability evident in the broader population has the effect of providing a conservative test of the income and cognitive development relationship.
We used data from the Mater-University of Queensland Study of Pregnancy, a prospective longitudinal study of a consecutive cohort of individuals born at a major public hospital (Mater Misericordiae Hospital) in Brisbane, Australia between 1981 and 1983. The hospital was 1 of only 2 major obstetrical hospitals in Brisbane and served the south side of the city. Recruitment procedures for the larger study have been detailed elsewhere.16,17 Baseline data were collected at the first antenatal visit (average 18 weeks of pregnancy) from 7223 consecutive women who subsequently gave birth to live singleton babies and were observed at 3 to 5 days, 6 months, 5 years and 14 years after the birth. At the 6-month follow-up visit, 6720 women (93%) responded to the questionnaire; this number dropped to 5234 at the 5-year follow-up visit and 5185 (72%) at the 14-year follow-up visit. At 14 years, a child assessment questionnaire was completed by 3799 children (53%).17 Approximately 3000 participants provided cognitive development scores at 14 years, and data were available on family income between pregnancy and the 14year follow-up for them. Written informed consent from the mother was obtained at all phases of data collection. Ethics committees at the Mater Hospital and the University of Queensland approved each phase of the study.
Measurement of Cognitive Development At the 14-year follow-up, assessments of cognitive development were based on youth scores on the Raven’s Standard Progressive Matrices (SPM)18 and the Wide Range Achievement Test (WRAT).19 The Raven’s SPM is a test of non-verbal reasoning ability that is widely used for psychological assessment in clinical and educational contexts.18,20 Although there is some debate about the specific cognitive attributes the Raven’s SPM measures, it has demonstrated a high correlation with full-length intelligence tests21 and is generally accepted as a measure of general intelligence. De Lemos22 re-standardized Raven’s SPM scores, on the basis of the mean and SD at each year level. However, in this study, child scores have been standardized in 6 monthly groupings rather than yearly levels. The WRAT is an age-normed referenced test that assesses reading and word decoding skills.19 It has been found to be a stable measure23 and to have high test-retest reliability24 and high internal consistency reliability coefficients.25 In this study, only the reading subscale of the WRAT was administered. WRAT scores were standardized (M ⫽ 100; SD ⫽ 15) for consistency with other measures. The WRAT is intended to measure basic reading skills and academic achievement. It is intended to identify children who have difficulties with reading.26,27 Although the Raven’s SPM and the WRAT both provide a measure of child IQ, the SPM is likely to reflect the child’s general ability whereas the WRAT reflects the degree that learning has taken place. The Pearson correlation between the Raven’s SPM and WRAT was 0.42 (P ⬍ .001; n ⫽ 3784).
Measurement of Economic Status Mothers were asked about gross annual household (family) income (including spouse’s income, child endowment, etc.) at the child’s birth and when the child was 6 months, 5 years, and 14 years old. Seven discrete income categories were given as response options (listed in weekly and annual amounts). Mothers with family income in the category closest to the bottom 20% of incomes were considered to be low income (poor). The low income figure selected at every phase of data collection is similar to the estimate of the proportion of the Australian population living at or below the poverty level. We examined the effects of poverty when it is limited to early childhood (birth to 5 years) or adolescence (14
Demographic Information Only mothers and their children were followed in this study, with limited (no self-report) paternal data available. Marital status when the child was 5 years old was divided in 2 categories: mothers who were married or living in a de facto relationship, and mothers with no partner (single, separated/ divorced or widowed). Maternal education at entry to the study was coded in 3 categories: high school not completed, completed high school, and studies beyond high school. Maternal age at entry to the study was included as a discrete original variable. We adjusted for marital status and maternal age because these factors are associated with both SES and cognitive development. Similarly, maternal education was
METHODS
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Table I. Exposure to poverty at different stages over the early life-course and child standardized Raven’s score at 14 years
Phase
Poverty status
Pregnancy
F (df) P Poor Not poor
6 months
F (df) P Poor Not poor
5 years
F (df) P Poor Not poor
14 years
F (df) P Poor Not poor
Unadjusted model
Adjusted for poverty at other stages of development
Adjusted for poverty at other stages of development and maternal educational age and marital status
22.30 (1,2924) (⬍.001) 98.41 101.29 20.18 (1,2901) (⬍.001) 98.21 101.20 50.84 (1,2920) (⬍.001) 96.76 101.51 37.34 (1,2908) (⬍.001) 96.78 101.25
4.98 (1,2921) (0.026) 99.40 100.91 1.39 (1,2898) (0.239) 99.86 100.74 20.92 (1,2917) (⬍.001) 97.90 101.21 17.84 (1,2905) (⬍.001) 97.84 101.03
2.35 (1,2918) (0.125) 99.74 100.77 2.03 (1,2895) (0.154) 99.67 100.79 17.13 (1,2914) (⬍.001) 97.92 101.20 18.30 (1,2902) (⬍.001) 97.97 101.01
F, df, P values, and standardized Raven’s score.
controlled to distinguish home environment effects supporting educational outcomes from the broader impact of poverty/ socioeconomic disadvantage.
Data Analysis We first examined univariate relations (1-way analysis of variance [ANOVA]) in family economic status at each phase of the study, measured between pregnancy and the 14-year follow-up, and child Raven’s SPM and WRAT scores at 14 years. Next, a series of 1-way analysis’ of covariance (ANCOVA) was conducted with the same variables controlling for maternal education and age at first clinic visit and maternal marital status assessed at the 5-year follow-up. To examine the association between family income at each follow-up and child cognitive development, we conducted a multivariate ANCOVA controlling for family income and potential confounders at other phases. In a further analysis, using family income at 4 phases of the study, we created a composite variable that measures the number of exposures to poverty during 14 years of life. This variable comprised 4 categories: never poor, once, twice, and 3 or more times poor. Finally, a 1-way ANOVA was run to test the relation between number of exposure to family poverty and child cognitive development measured with Raven’s SPM and WRAT.
RESULTS Raven’s Standard Progressive Matrices A 1-way between-subjects ANOVA was run with family income as the independent variable and cognitive development (Raven’s scores) at 14 years as the dependent variable (Table I). A significant difference between the means for each phase of data collection, with the low income group consis286
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Table II. Exposure to poverty and mean Raven’s score at 14 year follow-up* Times poor Never 1 2 3-4 times
n
Mean Raven’s Score
95% CI
1486 713 427 311
102.33 100.09 98.30 95.52
74.7-129.9 71.0-129.2 67.9-128.7 63.7-127.3
*Regression of times in poverty against Raven’s SPM indicates a reduction of 2.19 (95% CI, 1.67-2.71) for every additional instance of exposure to poverty.
tently experiencing lower scores, was revealed with the ANOVA. Next, a 1-way ANCOVA was run with the same variables, but with adjustment for poverty at each other stage. Poverty during pregnancy, at 5 years, and at 14 years remain independently associated with the Raven’s score, suggesting poverty additionally contributes to cognitive outcomes at each of these stages of the life course. Additional adjustment for some key socio-demographic variables has limited impact on the mean differences. For model 3, poverty in childhood and adolescence independently contribute to Raven’s scores, even after additional adjustment for some indicators of family socioeconomic circumstances. In Table II, we examine the frequency with which the child/adolescent experiences poverty by the child’s Raven’s SPM outcomes. There is a linear trend that shows a reduction in Raven’s SPM scores associated with increased frequency of exposure to poverty. By using a regression to estimate the linear trend of the association between frequency of poverty in the early life course and Raven’s SPM scores, we find that there is a reduction of 2.19 points for every additional experience of poverty. The Journal of Pediatrics • February 2009
Table III. Exposure to poverty at different stages over the early life-course and child standardized WRAT score at 14 years
Phase
Poverty status
Pregnancy
F (df) P Poor Not poor
6 months
F (df) P Poor Not poor
5 years
F (df) P Poor Not poor
14 years
F (df) P Poor Not poor
Unadjusted model
Adjusted for poverty at other stages of development
Adjusted for poverty at other stages of development and maternal education and age and marital status
27.32 (1,2918) (⬍.001) 97.89 101.05 22.07 (1,2896) (⬍.001) 97.82 100.90 13.74 (1,2914) (⬍.001) 98.23 100.70 20.88 (1,2902) (⬍.001) 97.38 100.71
10.97 (1,2915) (0.001) 98.57 100.79 4.14 (1,2893) (0.042) 99.06 100.55 1.46 (1,2911) (0.227) 99.49 100.36 11.20 (1,2899) (0.001) 98.05 100.57
6.43 (1,2912) (0.011) 98.94 100.65 2.64 (1,2890) (0.104) 99.23 100.50 5.00 (1,2908) (0.025) 98.78 100.55 15.75 (1,2896) (⬍.001) 97.38 100.71
F, df, P values, and standardized WRAT score.
Wide Range Achievement Test Univariate ANOVA was run with family income as the independent variable and cognitive development (WRAT scores) at 14 years as the dependent variable (Table III). Significant differences in WRAT scores were revealed with the ANOVA for those living in poverty at every phase of data collection from pregnancy to the 14 year follow-up. Next, a 1-way ANCOVA was run with adjustment for poverty at other stages of child development. Family poverty during pregnancy, at 6 months, and at 14 year follow-up independently predicted WRAT scores. Additional adjustment for maternal education, age, and marital status in model 3 reduces some of the differences in WRAT scores (poor versus not poor), but 3 of the 4 differences remain statistically significant. In Table IV, we have aggregated the number of times the child lived in a family that scored around or below the poverty line. The data here points to a cumulative linear trend, with frequency of exposure to poverty associated with decreasing WRAT scores. A trend test (regression) indicates that the WRAT scores decline by 1.74 points per every additional instance a child is exposed to family poverty. Modeling the Effects of Loss to Follow-up Of those for whom mother’s family income are recorded at entry to the study, 45.2% of lowest income group mothers and 57.4% of other income group mothers remained in the study at 14 years. To estimate the association between cognitive ability and loss to follow-up, we examined child Peabody Picture Vocabulary Test (PPVT) scores at 5 years as a predictor of loss to follow-up at 14 years. Of those who completed the PPVT at 5 years, 74.6% of those with normal
Table IV. Exposure to poverty and mean WRAT score at 14 year follow-up* Times poor Never 1 2 3-4
n
Mean WRAT score
95% CI
1483 713 425 310
101.53 100.37 97.82 96.40
75.0-128.0 70.6-130.2 66.4-129.2 64.8-128.0
*Regression of times in poverty against WRAT indicates a reduction of 1.74 points (95% CI, 1.22-2.25) for every additional exposure to poverty.
and higher scores remained in the study, compared with 65.6% of those with less than normal scores who remained in the study. Children subsequently lost to follow-up had a mean (at age 5 years) PPVT of 96.85, compared with a mean of 100.35 for children who remained in the study. The existing data suggest that children in low income groups and who have lower PPVT scores are more likely to be lost to follow-up. However, the magnitude of this association is modest, and loss to follow-up is about 10% to 15% higher in the most disadvantaged groups. The Appendix (available at www.jpeds.com) presents various estimates of the effect of loss to follow-up on the estimates of the association, on the basis of what is known about the characteristics of children lost to follow-up. On the assumption that approximately 60% of each group are lost to follow-up, but that the outcomes are 5%, 7%, and 10% worse (lower scores) in those lost to follow-up, then replacing these cases would generally slightly increase the differences in raven’s scores for increasing frequency of exposure to poverty. If it is estimated that the rate of loss to follow-up increases with increasing exposure to poverty (likely) and that Raven’s
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outcomes are worse for children lost to follow-up (also likely), then the Raven’s score differences in poverty groups become slightly larger than they were before the replacement of the missing cases. In every instance, it is clear that the actual findings are a conservative estimate of the association observed without any loss to follow-up, and that the differences between the observed and estimated associations are minor. None of the main findings of the study are in conflict with any of the projected associations on the basis of what is known about loss to follow-up, nor would any of the conclusions differ were any of the estimates to replace the observed findings.
DISCUSSION Poverty experienced in early childhood is more detrimental to adolescent cognitive outcomes than poverty experienced in adolescence alone.6,13 Our findings indicate that neither pregnancy, early childhood, nor the adolescent period can be characterized as specifically sensitive for the impact of poverty on cognitive development. Our findings suggest that the frequency of childhood exposure to poverty has a greater impact on child cognitive outcomes than the timing of that exposure. However, it appears that poverty experienced at the 14-year follow-up has the strongest and most consistent association with cognitive outcomes. Our results may differ from earlier studies for a number of reasons. First, our measures of cognitive outcomes included the Raven’s and the WRAT, and the studies by Lipman and Offords13 and Duncan6 used school outcomes (completion, placement in special class/remedial) and school performance as measures of cognitive outcomes. Lipman and Offord’s13 subjects had a range of ages (age at initial assessment ranged from 4-12 years), and the period of follow-up was comparatively short (4 years). The finding that more frequent experiences of family poverty, in a child’s early life course, is associated with lower levels of cognitive development, directs attention to the mechanisms that link family poverty and cognitive development. Children from more economically disadvantaged backgrounds are likely to experience many differences in their environmental circumstances when compared with their more economically advantaged counterparts. Aside from a greater home emphasis on learning and literacy, there is a better physical environment (better housing, more space per person), better nutrition, and generally of better educational facilities. There may be fewer family disruptions and a generally more stable home environment. Although the impact of any of these differences may be small, the cumulative impact in a lifetime is likely to be substantial. Flynn28 has emphasized the potential of the environmental impact on child cognitive development, noting that for a wide number of countries for which data are available, IQ scores have been increasing at between 0.3 and 0.5 units per year. For example, for the United States, with comparable tests, there has been an average population increase of 14 IQ points in the period from 1932 to 1978. Flynn argues that although IQ scores have been increasing with time, no single 288
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proposed cause can account for the observed changes.28 Improved economic conditions, better nutrition, and a better physical environment are all likely to contribute to increase in population IQ with time. There are 3 policy directions that are a consequence of our findings. First, the cumulative effect of poverty on a child’s cognitive development suggests that initiatives are required during the whole of the child’s early life course. Early childhood intervention programs will need to be reinforced by initiatives in later childhood and early adolescence, because poverty independently impacts on the child’s cognitive development, even when it occurs only in adolescence. Second, there is the possibility of targeted programs specifically directed to cognitive outcomes. These would include early childhood interventions such as Head Start,29 but continuing beyond early childhood. Third, there are policies that have the effect of reducing economic inequalities in a population. Such policies would seek to move the chronically poor out of poverty (eg, assist single mothers in re-entering the workforce) and improve the physical environment in which children are reared. In part, this may involve a process of advocacy on behalf of disadvantaged children. The authors thank the MUSP participants, the MUSP Research and data collection teams, and MUSP Data Manager Greg Shuttlewood for their support.
REFERENCES 1. Smith JR, Brooks-Gunn J, Klebanov PK. Consequences of living in poverty for young children’s cognitive and verbal ability and early school achievement. In: Duncan G, Brooks-Gunn J, editors. Consequences of growing up poor. New York: Russell Sage Foundation; 1997. p. 132-89. 2. Taylor BA, Dearing E, McCartney K. Incomes and outcomes in early childhood. J Hum Resources 2004;39:980-1007. 3. NICHD. Duration and developmental timing of poverty and children’s cognitive and social development from birth through third grade. Child Dev 2005;76:795-810. 4. Korenman S, Miller JE, Sjaastad JE. Long-term poverty and child development in the United States: results from the NLSY. Child Youth Serv Rev 1995;17:127-55. 5. Black MM, Hess C, Berenson-Howard J. Toddlers from low-income families have below normal mental, motor, and behavior scores on the Revised Bayley Scales. J Appl Dev Psychol 2000;21:655-66. 6. Duncan GJ, Brooks-Gunn J, Yeung WJ, Smith JR. How much does childhood poverty affect the life chances of children? Am Sociol Rev 1998;63:406-23. 7. Brooks-Gunn J, Duncan GJ. The effects of poverty on children. Future Child 1997;7:55-71. 8. Crane J. Effects of home environment, SES and maternal test scores on mathematics achievement. J Educ Res 1996;89:305-14. 9. Tong S, Baghurst P, Vimpani G, McMichael A. Socioeconomic position, maternal IQ, home environment, and cognitive development. J Pediatr 2007;151:284-8. 10. Duncan GJ, Brooks-Gunn J, Klebanov PK. Economic deprivation and early childhood development. Child Dev 1994;65:296-318. 11. Ryan RM, Fauth RC, Brooks-Gunn J. Childhood poverty: implications for school readiness and early childhood education. In: Spodek B, Saracho ON, editors. Handbook of research on the education of young children. Mahwah, New Jersey: Lawrence Erlbaum Associates Publishers; 2006. p. 323-46. 12. Najman JM, Aird R, Bor W, O’Callaghan M, Williams GM, Shuttlewood GJ. The generational transmission of socioeconomic inequalities in child cognitive development and emotional health. Soc Sci Med 2004;58:1147-58. 13. Lipman EL, Offord DR. Psychosocial morbidity among poor children in Ontario. In: Duncan G, Brooks-Gunn J, editors. Consequences of growing up poor. New York: Russell Sage Foundation; 1997. p. 239-87. 14. Guo G. The timing of the influences of cumulative poverty on children’s cognitive ability and achievement. Soc Forces 1998;77:257-88. 15. Rutter M. Poverty and child mental health: natural experiments and social causation. JAMA 2003;290:2063-4. 16. Keeping JD, Najman JM, Morrison J, Western JS, Andersen MJ, Williams GM.
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A prospective longitudinal study of social, psychological and obstetric factors in pregnancy: response rates and demographic characteristics of the 8556 respondents. Br J Obstet Gynaecol 1989;96:289-97. 17. Najman JM, Bor W, O’Callaghan M, Williams GM, Aird R, Shuttlewood GJ. Cohort profile: the Mater-University of Queensland Study of Pregnancy (MUSP). Int J Epidemiol 2005;34:992-7. 18. Raven J. The Raven progressive matrices: an overview of international norming studies. Psychol Test Bull 1989;2:7-16. 19. Wilkinson GS. The Wide Range Achievement Test: administration manual. Wilmington, Delaware: Wide Range; 1993. 20. de Lemos MM. Standard progressive matrices: Australian manual. Victoria: Australian Council for Educational Research; 1989. 21. Raven JC, Court JH, Raven J. A manual for Raven’s Progressive Matrices and Vocabulary Tests. San Antonio, Texas: The Psychological Corporation; 1987. 22. de Lemos MM. The Australian re-standardization of the standard progressive matrices. Psychol Test Bull 1989;2:17-24.
23. Dura JR, Myers EG, Freathy DT. Stability of the Wide Range Achievement Test in an adolescent psychiatric inpatient setting. Educ Psychol Meas 1989;49:253-6. 24. Woodward CA, Santa-Barbara J, Roberts R. Test-retest reliability of the Wide Range Achievement Test. J Clin Psychol 1975;31:81-4. 25. Mishra SP. Reliability and validity of the WRAT with Mexican-American children. Psychol Schools 1981;18:154-8. 26. Klimczak NC, Bradford KA, Burright RG, Donovick PJ. K-FAST and WRAT-3: are they really different? Clin Neuropsychol 2000;14:135-8. 27. Orme DR, Johnstone B, Hanks R, Novack R. The WRAT-3 reading subtest as a measure of premorbid intelligence among persons with brain injury. Rehabil Psychol 2004;49:250-3. 28. Flynn JR. Massive IQ gains in 14 nations: what IQ tests really measure. Psychol Bull 1987;101:171-91. 29. Currie J, Thomas D. Does Head Start make a difference. Am Econ Rev 1995;85:341-64.
50 Years Ago in The Journal of Pediatrics INTERSCORER
AGREEMENT FOR THE
GRAHAM BEHAVIOR TEST
FOR NEONATES
Rosenblith JF, Lipsitt LP. J Pediatr 1959;54:200-5
Today it is as important to observe babies’ behaviors as it was in the past, despite the many technological advances in the field of neonatology. Fifty years ago in The Journal, Rosenblith and Lipsitt reported satisfactory interscorer agreement across 4 domains of the Graham test, one of the first standardized behavioral examinations designed for newborn infants. The authors raised a number of important issues including the need for norms for each day of life and cut-points for determining “normal” versus “abnormal” behavior. They also highlighted that “traumatized” infants may perform differently from the “normal” babies. This raises the difficulty in defining the appropriate “norms” for different groups of infants, which remains an important issue for us today. How should we expect a baby of 23 weeks’ gestation to behave at 1 week of age, or, for that matter, upon reaching term? Should we compare preterm infant behavior with term behavior? As technology has advanced, so have the opportunities for greater understanding of the relationships between newborn behaviors and cerebral injury using neuro-imaging, not available in 1959. Similarly, the examination of newborn behaviors has progressed with the development of more complex tools such as the NBAS (Neonatal Behavioral Assessment Scale) and the APIB (Assessment of Preterm Infant Behavior). However, despite changing terminology, the themes remain the same. Like Graham, Rosenblith, Lipsitt and others, we remain interested in babies’ “muscle-tension” (muscle tone), their irritability and self-regulatory behaviors, their maturation, and their ability to engage and orientate to specific stimuli. We still have much to learn from our infants’ behavior—although the latest monitors can tell us about numerous vital signs, we can all do with a simple reminder to “observe the baby’s behavior.” Nisha C. Brown, PhD Victorian Infant Brain Studies Murdoch Childrens Research Institute Newborn Research The Royal Women’s Hospital Department of Obstetrics and Gynaecology University of Melbourne Melbourne, Australia Terrie E. Inder, MD Department of Pediatrics, Neurology, and Radiology St. Louis Children’s Hospital Washington University St. Louis, Missouri 10.1016/j.jpeds.2008.08.005
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Appendix. Estimated WRAT scores after adjustment for loss to follow-up: association between poverty and WRAT cognitive development
Never poor Poor 1 time Poor 2 times Poor 3-4 times Never poor Poor 1 time Poor 2 times Poor 3-4 times
Attrition (estimated)
Observed mean
Estimated mean (5%)*
Estimated mean (7%)†
Estimated mean (10%)‡
60% 60% 60% 60% 50% 55% 60% 65%
101.53 100.37 97.82 96.40 101.53 100.37 97.82 96.40
98.48 97.36 94.89 93.51 98.99 97.61 94.89 93.27
97.27 96.15 93.7 92.35 97.98 96.51 93.71 92.01
95.44 94.35 91.95 90.62 96.45 94.85 91.95 90.13
For estimated means, missing cases are replaced on the basis of known associations with projections for worst case scenarios. *Estimated that cases lost to follow-up have 5% lower scores on cognitive development. †Estimated that cases lost to follow-up have 7% lower scores on cognitive development. ‡Estimated that cases lost to follow-up have 10% lower scores on cognitive development.
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