Schizophrenia Research 156 (2014) 133–134
Contents lists available at ScienceDirect
Schizophrenia Research journal homepage: www.elsevier.com/locate/schres
Letter to the Editor Mathematics achievement scores and early psychosis in school-aged children
Dear Editors, Although evidence suggests that the typical age of onset of psychosis is in late adolescence into the mid-twenties (Kessler et al., 2007), those with psychosis vulnerability can exhibit earlier symptoms at a sub-diagnostic level as well as premorbid characteristics including academic difficulties. Within the United States, standardized measures of math achievement are routinely given as part of annual academic assessments and can therefore serve as one potential tool for examining cognitive vulnerability in the premorbid period. Although studies have examined the relation between early symptoms and general academic achievement (Ang and Tan, 2004; Dickson et al., 2012), to date, no study has specifically examined the relationship between these symptoms and standardized math achievement. Given that working memory is integral for mathematics computations (Meyer et al., 2010) and that impairments in this domain have characterized psychosis risk and schizophrenia patients (Fusar-Poli et al., 2012; Lee and Park, 2005), this is a potentially important area of inquiry. In the present investigation, a large sample of 486 school-aged children (M age = 10.99, SD = 2.49, 50% male) from the Colorado Learning Disabilities Research Center was assessed to determine if math disorder was associated with psychosis items endorsed by parents and/or teachers (see Table 1). Recruiting and testing procedures for this study are described in detail in previous papers (see Willcutt et al., 2005). All participants had complete full-scale IQ (FSIQ), reading and math achievement, and behavioral data (Child Behavioral Checklist, see below). Math achievement was assessed by the math subtest of the Wide Range Achievement Test, Revised (WRAT-R; Jastak and Wilkinson, 1984; M = 97.20, SD = 17.66). Per current research-area standards (see Mazzocco, 2008), children with math scores below the 11th percentile were considered to be Math Learning Disabled (MLD). Based on these cutoffs, 88 of the children were classified as MLD, and 398 were classified as non-MLD. Psychosis symptoms were assessed using 4 items taken from the Child Behavioral Checklist (CBCL; Achenbach, 1991; Achenbach et al., 2003). The 4 items, chosen on the basis of high face validity, included: 1) hears sounds or voices that aren't there (M = 0.01, SD = 0.09), 2) sees things that aren't there (M = 0.01, SD = 0.10), 3) strange behaviors (M = 0.09, SD = 0.37), and 4) strange ideas (M = 0.05, SD = 0.22). With respect to categorical ratings, children were classified as “psychosis-positive” if any of the 4 aforementioned items were rated as “very true or often true” by both parent and teacher. The current sample yielded 15 psychosis-positive children (3.09%) out of the 486 total participants who had CBCL ratings. A continuous measure of overall symptom frequency was also calculated by summing the total scores of the 4 items (M = 0.16,
http://dx.doi.org/10.1016/j.schres.2014.03.027 0920-9964/© 2014 Elsevier B.V. All rights reserved.
SD = 0.52). As the items showed weak internal consistency (Cronbach's α = .30), items were also evaluated independently in continuous analyses. The demographic and control/target variable differences between MLD/non-MLD as well as psychosis-positive/non-positive groups are presented in Table 1. The odds ratio of being in the psychosis-positive group if one met criteria for MLD relative to one who was non-MLD was 3.16 (95% CI = 1.10–9.13, z = 2.13, p = .03). In other words, a child with MLD was 3.16 times more likely to have exhibited a psychosis symptom relative to a child who was non-MLD. From a continuous perspective, Spearman correlations indicated that low math achievement was linked with elevated symptoms for the entire sample (r = − .13, p ≤ .01) and the magnitude of the effect increased in the psychosis-positive group alone (r = − .22, p = .22; Note: the low power for the supplementary analysis may have contributed to this null result). The strange behaviors (r = − .13, p ≤ .01) and strange ideas (r = − .10, p ≤ .05) items were most strongly correlated with math achievement whereas the other two items were not significant. Our results indicate that children who are low achieving in mathematics (i.e., MLD) exhibit a significantly greater likelihood of being rated positively for psychosis behaviors relative to children who are typically achieving in math, perhaps due to the working memory deficits that underlie both disorders. Given that children are routinely being screened for math achievement, this may be one means of examining premorbid functioning in a broader population. In a related point, continued development in this area may allow for teachers and parents to more easily identify children who may be good candidates for additional in-depth screening (e.g., screening for a prodromal syndrome). Future studies would benefit from the use of empirically validated psychosis screeners (e.g., Prodromal-Questionnaire, Loewy et al., 2012; Community Assessment for Psychic Experiences, Stefanis et al., 2002) and larger sample sizes. In addition, it would also be beneficial to determine whether other common developmental disabilities that are frequently assessed for in school settings may also be tied to elevated risk for psychosis symptomatology.
Role of funding This work was supported by National Institute of Child Health and Human Development Grant # P50 HD 27802. Dr. Richard K. Olson is the Director of the Center and Dr. Erik Willcutt is the Primary Investigator.
Contributors Ms. Wu and Dr. Mittal prepared and analyzed the data, conceptualized the study, and drafted the manuscript. Dr. Willcutt attained funding and assisted with the manuscript. Dr. Pennington also assisted with the manuscript.
Conflict of interest There are no conflicts of interest to report.
Acknowledgments None.
134
Letter to the Editor
Table 1 Demographics of the MLD versus non-MLD and psychosis-positive versus non-positive participants. Math learning disabled (MLD)
Gender (% male) Age FSIQ WRAT math achievement PIAT-R reading achievement CBCL DSM-oriented ADHD subscale CBCL hears sounds CBCL sees things CBCL strange behavior CBCL strange ideas CBCL 4 psychosis item total
MLD (n = 88) M (SD)
Non-MLD (n = 398) M (SD)
42% 11.27 (2.78) 95.90 (12.99) 72.63 (8.82) 90.13 (12.10) 5.63 (3.89) 0.02 (0.15) 0.01 (0.11) 0.20 (0.55) 0.11 (0.35) 0.35 (0.80)
50% 10.93 (2.42) 110.44 (12.16) 102.65 (14.15) 106.51 (11.11) 3.12 (3.28) 0.01 (0.07) 0.01 (0.10) 0.07 (0.32) 0.03 (0.18) 0.11 (0.42)
Psychosis ta
1.94 −1.17 10.03⁎⁎⁎ 19.09⁎⁎⁎ 12.32⁎⁎⁎ −6.26⁎⁎⁎ −1.67 −0.11 −3.19⁎⁎ −3.98⁎⁎⁎ −3.12⁎⁎
df
Positive (n = 15) M (SD)
Non-positive (n = 471) M (SD)
1 484 484 484 484 484 484 484 484 484 484
48% 10.89 (1.95) 105.93 (13.11) 91.80 (17.20) 98.80 (15.24) 5.67 (3.87) .000 (.000) .000 (.000) 1.89 (0.52) 0.27 (0.59) 2.13 (0.83)
73% 10.99 (2.51) 107.87 (13.54) 97.38 (17.66) 103.69 (12.84) 3.51 (3.50) 0.009 (0.09) 0.01 (0.10) 0.03 (0.18) 0.04 (0.20) 0.09 (0.36)
ta
3.94 0.15 0.55 1.21 1.44 −2.34⁎ 0.36 0.40 −35.09⁎⁎⁎ −3.94⁎⁎⁎ −20.51⁎⁎⁎
df
1 484 484 484 484 484 484 484 484 484 484
Note: Analyses of covariance (ANCOVA) were used to control for FSIQ, reading achievement, and ADHD symptom severity when examining MLD and non-MLD group differences in psychosis symptom severity. Analyses indicated the MLD and non-MLD group differences were still significant after controlling for FSIQ, reading achievement, and ADHD symptom severity. a For gender, statistic is a χ2 instead of t-test. ⁎ p b .05. ⁎⁎ p b .01. ⁎⁎⁎ p b .001.
References Achenbach, T.M., 1991. Integrative Guide for the 1991 CBCL/4-18, YSR, and TRF profiles. Department of Psychiatry, University of Vermont Burlington. Achenbach, T.M., Rescorla, L.A., McConaughey, S., Pecora, P., Wetherbee, K., Ruffle, T., 2003. The Achenbach system of empirically based assessment. Handbook of psychological and educational assessment of children: Personality, behavior, and context. , 2, pp. 406–432. Ang, Y.G., Tan, H.Y., 2004. Academic deterioration prior to first episode schizophrenia in young Singaporean males. Psychiatry Res. 121 (3), 303–307. Dickson, H., Laurens, K.R., Cullen, A.E., Hodgins, S., 2012. Meta-analyses of cognitive and motor function in youth aged 16 years and younger who subsequently develop schizophrenia. Psychol. Med. 42, 743–1498. Fusar-Poli, P., Deste, G., Smieskova, R., Barlati, S., Yung, A.R., Howes, O., & Borgwardt, S., 2012. Cognitive functioning in prodromal psychosis: a meta-analysis. Arch. Gen. Psychiatry 69 (6), 562–571. Jastak, S., Wilkinson, G.S., 1984. Wide Range Achievement Test. Revised: Administration Manual, Wilmington, DE. Kessler, R.C., Amminger, G.P., Aguilar‐Gaxiola, S., Alonso, J., Lee, S., Ustun, T.B., 2007. Age of onset of mental disorders: a review of recent literature. Curr. Opin. Psychiatry 20 (4), 359. Lee, J., Park, S., 2005. Working memory impairments in schizophrenia: a meta-analysis. J. Abnorm. Psychol. 114 (4), 599. Loewy, R.L., Therman, S., Manninen, M., Huttunen, M.O., Cannon, T.D., 2012. Prodromal psychosis screening in adolescent psychiatry clinics. Early Interv. Psychiatry 6 (1), 69–75. Mazzocco, M., 2008. Introduction: Mathematics ability, performance, and achievement. Dev. Neuropsychol. 33 (3), 197–204. Meyer, M.L., Salimpoor, V.N., Wu, S.S., Geary, D.C., Menon, V., 2010. Differential contribution of specific working memory components to mathematics achievement in 2nd and 3rd graders. Learn. Individ. Differ. 20 (2), 101–109. Stefanis, N., Hanssen, M., Smirnis, N., Avramopoulos, D., Evdokimidis, I., Stefanis, C., Verdoux, H., Van Os, J., 2002. Evidence that three dimensions of psychosis have a distribution in the general population. Psychol. Med. 32 (2), 347–358. Willcutt, E.G., Pennington, B.F., Olson, R.K., Chhabildas, N., Hulslander, J., 2005. Neuropsychological analyses of comorbidity between reading disability and attention deficit hyperactivity disorder: in search of the common deficit. Dev. Neuropsychol. 27 (1), 35–78.
Sarah S. Wu Department of Psychology and Neuroscience, University of Colorado at Boulder, 345 UCB, Boulder, CO 80309-0345, United States Corresponding author. E-mail address: [email protected]. Vijay Mittal Department of Psychology and Neuroscience, University of Colorado at Boulder, 345 UCB, Boulder, CO 80309-0345, United States Center for Neuroscience, University of Colorado at Boulder, 345 UCB, Boulder, CO 80309-0345, United States Bruce Pennington Department of Psychology, University of Denver, 2155 S. Race St., Denver, CO 80210-4638, United States Erik G. Willcutt Department of Psychology and Neuroscience, University of Colorado at Boulder, 345 UCB, Boulder, CO 80309-0345, United States
7 November 2013
Contents lists available at ScienceDirect
Schizophrenia Research journal homepage: www.elsevier.com/locate/schres
Letter to the Editor Mathematics achievement scores and early psychosis in school-aged children
Dear Editors, Although evidence suggests that the typical age of onset of psychosis is in late adolescence into the mid-twenties (Kessler et al., 2007), those with psychosis vulnerability can exhibit earlier symptoms at a sub-diagnostic level as well as premorbid characteristics including academic difficulties. Within the United States, standardized measures of math achievement are routinely given as part of annual academic assessments and can therefore serve as one potential tool for examining cognitive vulnerability in the premorbid period. Although studies have examined the relation between early symptoms and general academic achievement (Ang and Tan, 2004; Dickson et al., 2012), to date, no study has specifically examined the relationship between these symptoms and standardized math achievement. Given that working memory is integral for mathematics computations (Meyer et al., 2010) and that impairments in this domain have characterized psychosis risk and schizophrenia patients (Fusar-Poli et al., 2012; Lee and Park, 2005), this is a potentially important area of inquiry. In the present investigation, a large sample of 486 school-aged children (M age = 10.99, SD = 2.49, 50% male) from the Colorado Learning Disabilities Research Center was assessed to determine if math disorder was associated with psychosis items endorsed by parents and/or teachers (see Table 1). Recruiting and testing procedures for this study are described in detail in previous papers (see Willcutt et al., 2005). All participants had complete full-scale IQ (FSIQ), reading and math achievement, and behavioral data (Child Behavioral Checklist, see below). Math achievement was assessed by the math subtest of the Wide Range Achievement Test, Revised (WRAT-R; Jastak and Wilkinson, 1984; M = 97.20, SD = 17.66). Per current research-area standards (see Mazzocco, 2008), children with math scores below the 11th percentile were considered to be Math Learning Disabled (MLD). Based on these cutoffs, 88 of the children were classified as MLD, and 398 were classified as non-MLD. Psychosis symptoms were assessed using 4 items taken from the Child Behavioral Checklist (CBCL; Achenbach, 1991; Achenbach et al., 2003). The 4 items, chosen on the basis of high face validity, included: 1) hears sounds or voices that aren't there (M = 0.01, SD = 0.09), 2) sees things that aren't there (M = 0.01, SD = 0.10), 3) strange behaviors (M = 0.09, SD = 0.37), and 4) strange ideas (M = 0.05, SD = 0.22). With respect to categorical ratings, children were classified as “psychosis-positive” if any of the 4 aforementioned items were rated as “very true or often true” by both parent and teacher. The current sample yielded 15 psychosis-positive children (3.09%) out of the 486 total participants who had CBCL ratings. A continuous measure of overall symptom frequency was also calculated by summing the total scores of the 4 items (M = 0.16,
http://dx.doi.org/10.1016/j.schres.2014.03.027 0920-9964/© 2014 Elsevier B.V. All rights reserved.
SD = 0.52). As the items showed weak internal consistency (Cronbach's α = .30), items were also evaluated independently in continuous analyses. The demographic and control/target variable differences between MLD/non-MLD as well as psychosis-positive/non-positive groups are presented in Table 1. The odds ratio of being in the psychosis-positive group if one met criteria for MLD relative to one who was non-MLD was 3.16 (95% CI = 1.10–9.13, z = 2.13, p = .03). In other words, a child with MLD was 3.16 times more likely to have exhibited a psychosis symptom relative to a child who was non-MLD. From a continuous perspective, Spearman correlations indicated that low math achievement was linked with elevated symptoms for the entire sample (r = − .13, p ≤ .01) and the magnitude of the effect increased in the psychosis-positive group alone (r = − .22, p = .22; Note: the low power for the supplementary analysis may have contributed to this null result). The strange behaviors (r = − .13, p ≤ .01) and strange ideas (r = − .10, p ≤ .05) items were most strongly correlated with math achievement whereas the other two items were not significant. Our results indicate that children who are low achieving in mathematics (i.e., MLD) exhibit a significantly greater likelihood of being rated positively for psychosis behaviors relative to children who are typically achieving in math, perhaps due to the working memory deficits that underlie both disorders. Given that children are routinely being screened for math achievement, this may be one means of examining premorbid functioning in a broader population. In a related point, continued development in this area may allow for teachers and parents to more easily identify children who may be good candidates for additional in-depth screening (e.g., screening for a prodromal syndrome). Future studies would benefit from the use of empirically validated psychosis screeners (e.g., Prodromal-Questionnaire, Loewy et al., 2012; Community Assessment for Psychic Experiences, Stefanis et al., 2002) and larger sample sizes. In addition, it would also be beneficial to determine whether other common developmental disabilities that are frequently assessed for in school settings may also be tied to elevated risk for psychosis symptomatology.
Role of funding This work was supported by National Institute of Child Health and Human Development Grant # P50 HD 27802. Dr. Richard K. Olson is the Director of the Center and Dr. Erik Willcutt is the Primary Investigator.
Contributors Ms. Wu and Dr. Mittal prepared and analyzed the data, conceptualized the study, and drafted the manuscript. Dr. Willcutt attained funding and assisted with the manuscript. Dr. Pennington also assisted with the manuscript.
Conflict of interest There are no conflicts of interest to report.
Acknowledgments None.
134
Letter to the Editor
Table 1 Demographics of the MLD versus non-MLD and psychosis-positive versus non-positive participants. Math learning disabled (MLD)
Gender (% male) Age FSIQ WRAT math achievement PIAT-R reading achievement CBCL DSM-oriented ADHD subscale CBCL hears sounds CBCL sees things CBCL strange behavior CBCL strange ideas CBCL 4 psychosis item total
MLD (n = 88) M (SD)
Non-MLD (n = 398) M (SD)
42% 11.27 (2.78) 95.90 (12.99) 72.63 (8.82) 90.13 (12.10) 5.63 (3.89) 0.02 (0.15) 0.01 (0.11) 0.20 (0.55) 0.11 (0.35) 0.35 (0.80)
50% 10.93 (2.42) 110.44 (12.16) 102.65 (14.15) 106.51 (11.11) 3.12 (3.28) 0.01 (0.07) 0.01 (0.10) 0.07 (0.32) 0.03 (0.18) 0.11 (0.42)
Psychosis ta
1.94 −1.17 10.03⁎⁎⁎ 19.09⁎⁎⁎ 12.32⁎⁎⁎ −6.26⁎⁎⁎ −1.67 −0.11 −3.19⁎⁎ −3.98⁎⁎⁎ −3.12⁎⁎
df
Positive (n = 15) M (SD)
Non-positive (n = 471) M (SD)
1 484 484 484 484 484 484 484 484 484 484
48% 10.89 (1.95) 105.93 (13.11) 91.80 (17.20) 98.80 (15.24) 5.67 (3.87) .000 (.000) .000 (.000) 1.89 (0.52) 0.27 (0.59) 2.13 (0.83)
73% 10.99 (2.51) 107.87 (13.54) 97.38 (17.66) 103.69 (12.84) 3.51 (3.50) 0.009 (0.09) 0.01 (0.10) 0.03 (0.18) 0.04 (0.20) 0.09 (0.36)
ta
3.94 0.15 0.55 1.21 1.44 −2.34⁎ 0.36 0.40 −35.09⁎⁎⁎ −3.94⁎⁎⁎ −20.51⁎⁎⁎
df
1 484 484 484 484 484 484 484 484 484 484
Note: Analyses of covariance (ANCOVA) were used to control for FSIQ, reading achievement, and ADHD symptom severity when examining MLD and non-MLD group differences in psychosis symptom severity. Analyses indicated the MLD and non-MLD group differences were still significant after controlling for FSIQ, reading achievement, and ADHD symptom severity. a For gender, statistic is a χ2 instead of t-test. ⁎ p b .05. ⁎⁎ p b .01. ⁎⁎⁎ p b .001.
References Achenbach, T.M., 1991. Integrative Guide for the 1991 CBCL/4-18, YSR, and TRF profiles. Department of Psychiatry, University of Vermont Burlington. Achenbach, T.M., Rescorla, L.A., McConaughey, S., Pecora, P., Wetherbee, K., Ruffle, T., 2003. The Achenbach system of empirically based assessment. Handbook of psychological and educational assessment of children: Personality, behavior, and context. , 2, pp. 406–432. Ang, Y.G., Tan, H.Y., 2004. Academic deterioration prior to first episode schizophrenia in young Singaporean males. Psychiatry Res. 121 (3), 303–307. Dickson, H., Laurens, K.R., Cullen, A.E., Hodgins, S., 2012. Meta-analyses of cognitive and motor function in youth aged 16 years and younger who subsequently develop schizophrenia. Psychol. Med. 42, 743–1498. Fusar-Poli, P., Deste, G., Smieskova, R., Barlati, S., Yung, A.R., Howes, O., & Borgwardt, S., 2012. Cognitive functioning in prodromal psychosis: a meta-analysis. Arch. Gen. Psychiatry 69 (6), 562–571. Jastak, S., Wilkinson, G.S., 1984. Wide Range Achievement Test. Revised: Administration Manual, Wilmington, DE. Kessler, R.C., Amminger, G.P., Aguilar‐Gaxiola, S., Alonso, J., Lee, S., Ustun, T.B., 2007. Age of onset of mental disorders: a review of recent literature. Curr. Opin. Psychiatry 20 (4), 359. Lee, J., Park, S., 2005. Working memory impairments in schizophrenia: a meta-analysis. J. Abnorm. Psychol. 114 (4), 599. Loewy, R.L., Therman, S., Manninen, M., Huttunen, M.O., Cannon, T.D., 2012. Prodromal psychosis screening in adolescent psychiatry clinics. Early Interv. Psychiatry 6 (1), 69–75. Mazzocco, M., 2008. Introduction: Mathematics ability, performance, and achievement. Dev. Neuropsychol. 33 (3), 197–204. Meyer, M.L., Salimpoor, V.N., Wu, S.S., Geary, D.C., Menon, V., 2010. Differential contribution of specific working memory components to mathematics achievement in 2nd and 3rd graders. Learn. Individ. Differ. 20 (2), 101–109. Stefanis, N., Hanssen, M., Smirnis, N., Avramopoulos, D., Evdokimidis, I., Stefanis, C., Verdoux, H., Van Os, J., 2002. Evidence that three dimensions of psychosis have a distribution in the general population. Psychol. Med. 32 (2), 347–358. Willcutt, E.G., Pennington, B.F., Olson, R.K., Chhabildas, N., Hulslander, J., 2005. Neuropsychological analyses of comorbidity between reading disability and attention deficit hyperactivity disorder: in search of the common deficit. Dev. Neuropsychol. 27 (1), 35–78.
Sarah S. Wu Department of Psychology and Neuroscience, University of Colorado at Boulder, 345 UCB, Boulder, CO 80309-0345, United States Corresponding author. E-mail address: [email protected]. Vijay Mittal Department of Psychology and Neuroscience, University of Colorado at Boulder, 345 UCB, Boulder, CO 80309-0345, United States Center for Neuroscience, University of Colorado at Boulder, 345 UCB, Boulder, CO 80309-0345, United States Bruce Pennington Department of Psychology, University of Denver, 2155 S. Race St., Denver, CO 80210-4638, United States Erik G. Willcutt Department of Psychology and Neuroscience, University of Colorado at Boulder, 345 UCB, Boulder, CO 80309-0345, United States
7 November 2013
