Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

General cognitive but not mathematic abilities predict very preterm and healthy term born adults’ wealth

  • Julia Jaekel ,

    Roles Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Visualization, Writing – original draft

    jjaekel@utk.edu

    Affiliations Department of Child and Family Studies, University of Tennessee, Knoxville, Tennessee, United States of America, Department of Psychology, University of Warwick, Coventry, United Kingdom

  • Nicole Baumann,

    Roles Formal analysis, Methodology, Project administration, Validation, Writing – review & editing

    Affiliation Department of Psychology, University of Warwick, Coventry, United Kingdom

  • Peter Bartmann,

    Roles Data curation, Funding acquisition, Project administration, Resources, Writing – review & editing

    Affiliation Neonatology, University Hospital Bonn, Bonn, Germany

  • Dieter Wolke

    Roles Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Resources, Writing – review & editing

    Affiliations Department of Psychology, University of Warwick, Coventry, United Kingdom, Division of Mental Health and Wellbeing, Warwick Medical School, University of Warwick, Coventry, United Kingdom

General cognitive but not mathematic abilities predict very preterm and healthy term born adults’ wealth

  • Julia Jaekel, 
  • Nicole Baumann, 
  • Peter Bartmann, 
  • Dieter Wolke
PLOS
x

Abstract

Objective

Very preterm (<32 weeks gestation; VP) and/or very low birth weight (<1500g; VLBW) children often have cognitive and mathematic difficulties. It is unknown whether VP/VLBW children’s frequent mathematic problems significantly add to the burden of negative life-course consequences over and above effects of more general cognitive deficits. Our aim was to determine whether negative consequences of VP/VLBW versus healthy term birth on adult wealth are mediated by mathematic abilities in childhood, or rather explained by more general cognitive abilities.

Methods

193 VP/VLBW and 217 healthy term comparison participants were studied prospectively from birth to adulthood as part of a geographically defined study in Bavaria (South Germany). Mathematic and general cognitive abilities were assessed at 8 years with standardized tests; wealth information was assessed at 26 years with a structured interview and summarized into a comprehensive index score. All scores were z-standardized.

Results

At 8 years, VP/VLBW (n = 193, 52.3% male) had lower mathematic and general cognitive abilities than healthy term comparison children (n = 217, 47.0% male). At 26 years, VP/VLBW had accumulated significantly lower overall wealth than term born comparison adults (-0.57 (1.08) versus -0.01 (1.00), mean difference 0.56 [0.36–0.77], p < .001). Structural equation modeling confirmed that VP/VLBW birth (β = -.13, p = .022) and childhood IQ (β = .24, p < .001) both directly predicted adult wealth, but math did not (β = .05, p = .413). Analyses were controlled for small-for-gestational-age (SGA) birth, child sex, and family socioeconomic status.

Conclusion

This longitudinal study from birth to adulthood shows that VP/VLBW survivors’ general cognitive rather than specific mathematic problems explain their diminished life-course success. These findings are important in order to design effective interventions at school age that reduce the burden of prematurity for those individuals who were born at highest neonatal risk.

Introduction

Very preterm (<32 weeks gestational age, VP) and very low birth weight (<1500 g, VLBW) children have a highly increased risk for academic underachievement, in particular in mathematics [14], which has partly been attributed to underlying deficits in attention and executive function [58]. It remains controversial whether these mathematic deficits are domain specific [9] or rather explained by global cognitive problems [10].

Population studies have shown that early arithmetic abilities predict long-term academic achievement [1113] and poor mathematical skills are associated with lower wages, more frequent periods of unemployment, reduced employment opportunities, and lower rates of promotion [1416]. A number of publications from large datasets such as the British Cohort Study (BCS70) and the National Child Development Study (NCDS) have documented the life-course consequences of basic mathematic skills [1719] and how these are influenced by cognitive development [20] and environmental risk over time [21]. All these studies have consistently shown that low mathematic skills have uniquely detrimental effects on adult economic success [19].

In addition to life-course studies of normal population samples, recent research on VP/VLBW individuals followed from childhood into adulthood has found that neurodevelopmental and academic problems continue after adolescence, and decreased economic success has also been noted [2226]. Scandinavian registry-based studies have also shown decreased wealth [2729] and lower job-related success among preterm born adults [27]. One study has documented strong effects of moderately and late preterm (32–36 weeks gestation, MLP) children’s math achievement on wealth at 42 years, i.e. mathematic attainment uniquely predicted adult wealth after accounting for general intelligence, reading attainment, family socioeconomic status (SES), and various other confounders [30]. This suggests that the pathway from MLP birth to low adult economic success may be specifically mediated by low mathematic attainment in childhood. Compared with MLP, VP/VLBW individuals however are born at more severe neurodevelopmental risk and more often suffer from medical complications and subsequent neurocognitive problems [3134]. Thus, apart from specific math deficits, VP/VLBW often have multiple cognitive impairments [31, 34]. Research has yet to determine whether VP/VLBW children’s frequent mathematic problems significantly add to their burden of negative life-course consequences over and above the effects of more general cognitive deficits. The aim of this study was to investigate whether negative consequences of VP/VLBW versus healthy term birth on adult wealth are mediated by mathematic abilities in childhood, or rather explained by more general cognitive abilities.

Materials and methods

Data were collected as part of the prospective Bavarian Longitudinal Study (BLS) [35], a geographically defined whole-population sample of VP/VLBW and term control individuals in Germany. Participating parents were approached within 48 hours of the infant’s hospital admission and were included in the study once they had given written consent for their child to participate. Initial ethical approval was obtained from the University of Munich Children’s Hospital Ethics committee and again in 2009 from the Ethical Board of the University Hospital Bonn, Germany (reference #159/09). All adult participants gave fully informed written consent.

Participants

Of 70,600 children born in South Bavaria during a 15-month period in 1985/86, 682 were VP (<32 weeks gestation) or VLBW infants (<1,500g), or both. Of these, 411 were eligible for longitudinal follow-up, 14 had severe disability and were unable to be interviewed, and 193/397 (49%) participated at both 8 and 26 years and had complete datasets. The BLS VP/VLBW participants did not differ from adults who dropped out in terms of gestational age, birth weight, duration of hospitalization, sex, maternal age, parental marital status, and childhood cognitive scores, but they had fewer prenatal complications and were more often of higher SES [34].

Healthy term infants who were born in the same obstetric hospitals during the same period were recruited as comparisons. Of an initial sample of 916 children alive at 6 years, 350 were randomly selected within the stratification variables sex and family SES to be comparable to the VP/VLBW sample. Of these, 308 were eligible for longitudinal follow-up assessments, and 217 (70%) participated at both 8 and 26 years. The comparison participants assessed at the 26 year follow-up did not differ from those lost to follow-up in terms of neonatal characteristics but they more often had higher SES, older mothers and higher childhood cognitive scores [34].

Procedure

Details of peri- and neonatal data have been described elsewhere [35] and are only briefly outlined here. At both 8 and 26 years, participants were assessed for one whole day including cognitive testing and detailed interviews. All assessors and raters were blind to group membership.

Measures

Neonatal variables.

Gestational age (GA) was determined from maternal reports of the last menstrual period and serial ultrasounds during pregnancy. Birth weight was documented in the birth records. Infants were classified as small for gestational age (SGA) if they weighed less than the sex specific 10th percentile for their respective GA according to the national standard weight charts (1985–1986) [36].

Socioeconomic status (SES).

Information was obtained by standardized interviews within the first 10 days of life. Family SES was computed as a weighted composite score derived from the occupation of the self-identified head of each family together with the highest educational qualification held by either parent [37]. The sum of these three scores was divided by three and recoded into six predefined categories (1 = lowest to 6 = highest).

Assessment of mathematic and general cognitive abilities.

At age 8 years, children were administered a comprehensive mathematic test [10, 38]. Test tasks were presented in book form with 79 items assessing numerical estimations, calculation, reasoning, and mental rotation abilities. Item responses were scored for accuracy and subscale scores were then summed into a comprehensive total score representing general mathematic abilities. Cognitive abilities were assessed with the German version of the Kaufman Assessment Battery for Children, K-ABC [39]. In the K-ABC, intelligence is measured as a composite score (Mental Processing Component; MPC) based on eight subtests from the Sequential (3 subtests) and Simultaneous (5 subtests) Processing Scales, tapping general cognitive functioning (i.e., Number recall, Hand movements, Word order, Gestalt closure, Matrix analogies, Triangles, Spatial memory, and Photo series, respectively). Reliability is good (.83-.98, split-half method) and construct validity is high (e.g. correlation of .70 with the WISC-R total score).

Wealth at 26 years.

Information on occupational success and wealth was obtained using a range of interviews and questionnaires. Critical items were selected from these assessments including no own income, unemployment, social benefits, part time work, frequent job changes, not living independently, low educational qualifications, failure to honor financial obligations, impoverishment, as well as restrictions in occupation and economic self-sufficiency. Items were summarized into a comprehensive wealth index score (S1 Table) that was reverse coded for further analysis (i.e., higher scores represent more wealth) analogue to previous summary scores [40, 41].

Statistical analyses

Data was analyzed with SPSS 24 and Amos 24. We used structural equation modeling (SEM) to answer our research question, whether negative consequences of VP/VLBW versus healthy term birth on adult wealth were mediated by mathematic abilities in childhood or rather explained by more general cognitive abilities. All analyses were adjusted for child sex, SGA birth, and family socio-economic status at birth.

Results

Table 1 shows that, by definition, VP/VLBW participants were born at lower gestational age and birth weight and more often small for gestational age (SGA) than healthy term born comparisons. There were no group differences with regard to sex, but VP/VLBW adult participants had been born into families of lower SES than term comparisons. At age 8 years, VP/VLBW children had lower mathematic and general cognitive abilities (see Table 1 for details). At age 26 years, VP/VLBW had accumulated significantly lower overall wealth than term born comparison adults.

thumbnail
Table 1. Descriptive characteristics of the VP/VLBW and term participants from birth to age 26 years.

https://doi.org/10.1371/journal.pone.0212789.t001

Structural equation modeling (Fig 1) showed that VP/VLBW birth and childhood IQ both predicted adult wealth, but math did not. Overall, the model explained 16% of the variation in adult wealth. Model fit was excellent with χ2 = 7.55, p = .374, CFI = .999, and RMSEA = .014. Fig 2 shows the total (combined direct and indirect) effects for all the predictors in the model on adult wealth. These effects are unique (i.e., corrected for each other and confounders). With standardized total effect sizes above 0.2, IQ at 8 years (0.24) and VP/VLBW birth (-0.21) were most predictive of wealth at 26 years. The indirect effect of VP/VLBW birth on adult wealth (-0.08; via childhood math and IQ) was not significant, thus mediation was not confirmed.

thumbnail
Fig 1. Structural equation model showing effects of VP/VLBW birth, intelligence, and math on adult wealth (N = 410).

https://doi.org/10.1371/journal.pone.0212789.g001

thumbnail
Fig 2. Standardized total (direct + indirect) effects of predictors in the model on VP/VLBW and term adults’ wealth at 26 years (N = 410).

Please note: Effects are corrected for each other and confounders; for comparison purposes, the strength of each total effect size is shown, irrespective of its positive or negative direction in the model.

https://doi.org/10.1371/journal.pone.0212789.g002

Discussion

This prospective 26 year longitudinal study shows that VP/VLBW birth and childhood IQ both independently and directly predict adult wealth, but childhood math does not seem to add to the predictive effects of these two major influences. Previous findings have documented the specific importance of childhood math for adult wealth in moderately and late preterm individuals [30]. In contrast, our results suggest that general cognitive rather than mathematic problems may put VP/VLBW children on a trajectory of economic underachievement and diminished life-course success. Thus, specific math problems may have less of an impact on later wealth considering VP/VLBW individuals’ multiple neurocognitive difficulties. This finding adds to emerging evidence of life-long wide-spread changes in VP/VLBW’s brain networks that are associated with cognitive deficits in adulthood [4245], and supports previous findings of multiple cognitive rather than specific problems after VP/VLBW birth [34].

Recently, studies have started exploring the neural mechanisms underlying math performance in young adults born preterm and reported altered fronto-parietal activity [43, 46] whereas frontal brain activation is most relevant for general cognitive function [47, 48]. There is additional new evidence that cholinergic basal forebrain integrity may be reduced in those born VP/VLBW and related to low general IQ [49]. This recent finding may open the opportunity for intervention, either by choline supplementation perinatally, dietary or pharmacologically with procholinergic drugs to improve brain development and cognitive development in VP/VLBW children [50], but this is still in the far away future.

Although recent studies have shown that delivery at any gestation other than full term may represent a risk for adverse neurocognitive outcomes [2, 51], the trajectories from neonatal risk through childhood cognitive and academic functioning to life-course success may be different depending on the timing and severity of gestational insults. While VP/VLBW children are at high risk for adverse outcomes, this relationship is not linear across the whole range of gestation: We have previously shown that preterm birth has significant adverse effects on basic mathematic processing following birth at all gestations <36 weeks and on IQ and mathematic attainment <34 weeks of gestational age [4], while adverse effects are generally stronger with lower gestation at birth (i.e., non-linear effect). Moreover, while studies consistently show that VP/VLBW individuals are at high risk for learning disabilities, these are usually comorbid with intellectual impairments [52]. Altogether, the verdict is still open whether VP/VLBW birth puts children at specifically increased risk for mathematic impairments and dyscalculia [1, 3, 53] or whether this risk is explained by their global cognitive difficulties [6, 10]. Our new results add to our understanding of the ‘very preterm phenotype’, i.e. long-term outcomes after preterm birth, suggesting that specific math problems may have less of an impact on later economic success than other factors among those born at highest risk, while there may be different profiles among those born MLP [54]. Future studies may consider VP/VLBW individuals’ multiple problems that extend beyond general cognition [34], such as attention [55], social [56], and emotional difficulties [5759], in order to investigate the interplay of mechanisms that determine life-course success. We would like to specifically point out that the aim of these current analyses was to assess the competing effects of math versus general IQ in predicting adult economic success, thus we did not include potential additional developmental influences such as attention regulation and executive functions that may further contribute to life-course outcomes after VP/VLBW birth.

No matter whether their functional deficits are global or specific, many VP/VLBW individuals require continuous educational support across various areas of development in order to succeed in school and in life. Most VP/VLBW children have multiple and complex learning difficulties [52] and attend mainstream schools, thus teachers are responsible for supporting them in class. However, teachers may lack formal training about how to appropriately facilitate preterm children’s learning progress [60] and it may be challenging to alleviate the consequences of general cognitive impairments in children’s daily lives. In the last decade, tentative evidence started emerging that an adaptive computerized training program may improve the working memory capacity of extremely LBW preschool children [61] and adolescents [62]. However, transfer effects of cognitive training on long-term academic achievement were not evaluated until two years ago, when new findings from a large population-based Australian RCT showed that academic outcomes of children with low working memory were not improved by working memory training [63]. To date, it is not clear to what extent VP/VLBW children’s long-term developmental trajectories may be influenced with training [64, 65], but life-course studies such as the current one can provide important pointers for the development of effective interventions. Interdisciplinary collaboration and multi-method frameworks will be needed to address different functions that underlie preterm individuals’ complex problems.

This is the first longitudinal study testing independent effects of childhood IQ and math on adults’ wealth using a large, prospectively defined, whole-population sample of VP/VLBW and term comparison individuals born in the same obstetric hospitals at the same time. In total, 58% of the eligible VP/VLBW and term comparisons recruited at birth had complete data at both age 8 and 26 years, however, the dropout was not random as low SES families were less likely to continue participation. As a result, our findings may not generalize to cohorts of preterm children from high social risk backgrounds. However, social factors are a major reason for dropout in most longitudinal studies [66, 67], and our analyses were controlled for family SES at birth. Structural equation model fit values indicated that the pathways shown here very accurately reflect the true developmental mechanisms in our population, and 16% of the total variation in adult wealth was explained, a substantial amount considering the time passed between assessments as well as the ecological validity and comprehensiveness of this outcome variable. Different from previous evidence suggesting the specific importance of childhood math skills for adult success [18, 30], our results show that IQ is a highly significant predictor of adult wealth, as is VP/VLBW birth. In our model, we combined healthy term and VP/VLBW individuals into one sample, nevertheless SEM fit values indicate its validity for both groups. Thus, although our findings may differ from previous studies, they offer new empirical evidence to inform scientific discourse.

In conclusion, our results show that VP/VLBW survivors’ general cognitive problems rather than specific mathematic deficits explain their diminished life-course success. These findings can help inform the design of follow-up and intervention services to reduce the burden of prematurity for those individuals that were born at highest neonatal risk.

Acknowledgments

We thank all current and former members of the Bavarian Longitudinal Study Group who contributed to general study organization, recruitment, and data collection, management and subsequent analyses. Most importantly, we thank all our study participants for their efforts to take part in this study.

This study was supported by the German Research Foundation (DFG JA 1913 to J. J.) and the German Federal Ministry of Education and Science (BMBF; PKE24, JUG14). D. W. was supported by EU Horizon 2020 (733280; RECAP). Funding for open access to this research was provided by University of Tennessee’s Open Publishing Support Fund. The authors declare no conflicts of interest. The contents are solely the responsibility of the authors and do not necessarily represent the official view of the DFG, BMBF, EU, or UT. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

  1. 1. Johnson S, Wolke D, Hennessy E, Marlow N. Educational outcomes in extremely preterm children: Neuropsychological correlates and predictors of attainment. Developmental neuropsychology. 2011;36(1):74–95. pmid:21253992
  2. 2. MacKay DF, Smith GCS, Dobbie R, Pell JP. Gestational age at delivery and special educational need: retrospective cohort study of 407,503 schoolchildren. PLoS Med. 2010;7(6):e1000289. pmid:20543995
  3. 3. Simms V, Cragg L, Gilmore C, Marlow N, Johnson S. Mathematics difficulties in children born very preterm: current research and future directions. Archives of Disease in Childhood—Fetal and Neonatal Edition. 2013;98(5):F457–F63. pmid:23759519
  4. 4. Wolke D, Strauss VY-C, Johnson S, Gilmore C, Marlow N, Jaekel J. Universal gestational age effects on cognitive and basic mathematic processing: 2 cohorts in 2 countries. The Journal of Pediatrics. 2015;166(6):1410–6. e2. pmid:25842966
  5. 5. Aarnoudse-Moens CSH, Weisglas-Kuperus N, Duivenvoorden HJ, van Goudoever JB, Oosterlaan J. Executive function and IQ predict mathematical and attention problems in very preterm children. PLoS ONE. 2013;8(2):e55994. pmid:23390558
  6. 6. Simms V, Gilmore C, Cragg L, Clayton S, Marlow N, Johnson S. Nature and origins of mathematics difficulties in very preterm children: a different etiology than developmental dyscalculia. Pediatr Res. 2015;77(2):389–95. pmid:25406898
  7. 7. Van der Ven SHG, Kroesbergen EH, Boom J, Leseman PPM. The development of executive functions and early mathematics: A dynamic relationship. British Journal of Educational Psychology. 2012;82(1):100–19.
  8. 8. Espy KA, McDiarmid MM, Cwik MF, Stalets MM, Hamby A, Senn TE. The contribution of executive functions to emergent mathematic skills in preschool children. Developmental neuropsychology. 2004;26(1):465–86. pmid:15276905
  9. 9. Simms V, Gilmore CK, Cragg L, Marlow N, Wolke D, Johnson S. Mathematics difficulties in extremely preterm children: Evidence of a specific deficit in basic mathematics processing. Pediatric Research. 2013;73(2):236–44. pmid:23165451
  10. 10. Jaekel J, Wolke D. Preterm birth and dyscalculia. The Journal of Pediatrics. 2014;164(6):1327–32. pmid:24630355
  11. 11. Duncan GJ, Claessens A, Huston AC, Pagani LS, Engel M, Sexton H, et al. School readiness and later achievement. Developmental Psychology. 2007;43(6):1428–46. pmid:18020822
  12. 12. Lubinski D, Benbow CP. Study of Mathematically Precocious Youth after 35 years: Uncovering antecedents for the development of math-science expertise. Perspectives on Psychological Science. 2006;1:316–45. pmid:26151798
  13. 13. Webb RM, Lubinski D, Benbow CP. Mathematically facile adolescents with math-science aspirations: New perspectives on their educational and vocational development. Journal of Educational Psychology. 2002;94:785–94.
  14. 14. Rivera-Batiz FL. Quantitative literacy and the likelihood of employment among young adults in the United States. Journal of Human Resources. 1992;27(2):313–28.
  15. 15. Stern E. The development of mathematical competencies: Sources of individual differences and their developmental trajectories. In: Schneider W., Bullock M, editors. Human development from early childhood to early adulthood: Evidence from the Munich Longitudinal Study on the Genesis of Individual Competencies (LOGIC). Mahwah, NJ: Erlbaum; 2009. p. 221–37.
  16. 16. Geary DC. An evolutionary perspective on learning disability in mathematics. Developmental neuropsychology. 2007;32(1):471–519. pmid:17650991
  17. 17. Crawford C, Cribb J. Reading and maths skills at age 10 and earnings in later life: a brief analysis using the British Cohort Study. London: Institue for Fiscal Studies and Centre for Analysis of Youth Transitions (CAYT); 2013.
  18. 18. Bynner J, Parsons S. It doesn’t get any better. The impact of poor basic skills on the lives of 37 year olds. London: The Basic Skills Agency; 1997.
  19. 19. Parsons S, Bynner J. Does numeracy matter more? London: National Research and Development Centre for adult literacy and numeracy (NRDC); 2006.
  20. 20. Feinstein L, Bynner J. The importance of cognitive development in middle childhood for adulthood socioeconomic status, mental health, and problem behavior. Child Development. 2004;75(5):1329–39. pmid:15369517
  21. 21. Schoon I, Bynner J, Joshi H, Parsons S, Wiggins RD, Sacker A. The influence of context, timing, and duration of risk experiences for the passage from childhood to midadulthood. Child Development. 2002;73(5):1486–504. pmid:12361314
  22. 22. Hack M. Adult outcomes of preterm children. Journal of Developmental & Behavioral Pediatrics. 2009;30(5):460–70.
  23. 23. Darlow BA, Horwood LJ, Pere-Bracken HM, Woodward LJ. Psychosocial outcomes of young adults born very low birth weight. Pediatrics. 2013;132(6):E1521–E8. pmid:24249818
  24. 24. Doyle LW, Anderson PJ. Adult outcome of extremely preterm infants. Pediatrics. 2010;126(2):342–51. pmid:20679313
  25. 25. Lohaugen GCC, Ostgard HF, Andreassen S, Jacobsen GW, Vik T, Brubakk AM, et al. Small for gestational age and intrauterine growth restriction decreases cognitive function in young adults. Journal of Pediatrics. 2013;163(2):447-+. pmid:23453550
  26. 26. Saigal S, Day KL, Van Lieshout RJ, Schmidt LA, Morrison KM, Boyle MH. Health, wealth, social integration, and sexuality of extremely low-birth-weight prematurely born adults in the fourth decade of life. JAMA Pediatrics. 2016;170(7):678–86. pmid:27213291
  27. 27. Moster D, Lie RT, Markestad T. Long-term medical and social consequences of preterm birth. The New England journal of medicine. 2008;359(3):262–73. pmid:18635431
  28. 28. Heinonen K, Eriksson JG, Kajantie E, Pesonen AK, Barker DJ, Osmond C, et al. Late-preterm birth and lifetime socioeconomic attainments: the Helsinki birth cohort study. Pediatrics. 2013;132(4):647–55. pmid:24082003
  29. 29. Lindstrom K, Winbladh B, Haglund B, Hjern A. Preterm infants as young adults: a Swedish national cohort study. Pediatrics. 2007;120(1):70–7. pmid:17606563
  30. 30. Basten M, Jaekel J, Johnson S, Gilmore C, Wolke D. Preterm birth and adult wealth: mathematics skills count. Psychological Science. 2015;26(10):1608–19. pmid:26324513
  31. 31. Jaekel J, Baumann N, Wolke D. Effects of gestational age at birth on cognitive performance: a function of cognitive workload demands. PLoS One. 2013;8(5):e65219. pmid:23717694
  32. 32. Poulsen G, Wolke D, Kurinczuk JJ, Boyle EM, Field D, Alfirevic Z, et al. Gestational age and cognitive ability in early childhood: a population-based cohort study. Paediatric and Perinatal Epidemiology. 2013;27(4):371–9. pmid:23772939
  33. 33. Breeman LD, Jaekel J, Baumann N, Bartmann P, Wolke D. Neonatal predictors of cognitive ability in adults born very preterm: a prospective cohort study. Developmental medicine and child neurology. 2017;59(5):477–83. pmid:28111747
  34. 34. Eryigit Madzwamuse S, Baumann N, Jaekel J, Bartmann P, Wolke D. Neuro-cognitive performance of very preterm or very low birth weight adults at 26 years. Journal of Child Psychology and Psychiatry. 2015;56(8):857–64. pmid:25382451
  35. 35. Wolke D, Meyer R. Cognitive status, language attainment, and prereading skills of 6-year-old very preterm children and their peers: the Bavarian Longitudinal Study. Developmental Medicine & Child Neurology. 1999;41:94–109.
  36. 36. Riegel K, Ohrt B, Wolke D, Österlund K. Die entwicklung gefährdet geborener kinder bis zum fünften lebensjahr. [The development of children born at risk until their fifth year of life.]. Stuttgart: Ferdinand Enke Verlag; 1995.
  37. 37. Bauer A. Ein Verfahren zur Messung des fuer das Bildungsverhalten relevanten Sozial Status (BRSS)—ueberarbeitete Fassung. [A measure assessing SES in Germany, revised version]. Frankfurt: Deutsches Institut fuer Internationale Paedagogische Forschung; 1988.
  38. 38. Wolke D, Leon-Villagra J. Mathematiktest für Grundschulkinder. Munich: Bavarian Longitudinal Study; 1993.
  39. 39. Melchers P, Preuss U. K-ABC: Kaufman Battery for Children: Deutschsprachige Fassung. Frankfurt, AM: Swets & Zeitlinger; 1991.
  40. 40. Moffitt TE, Arseneault L, Belsky D, Dickson N, Hancox RJ, Harrington H, et al. A gradient of childhood self-control predicts health, wealth, and public safety. Proceedings of the National Academy of Sciences. 2011;108(7):2693–8.
  41. 41. Wolke D, Copeland WE, Angold A, Costello EJ. Impact of bullying in childhood on adult health, wealth, crime, and social outcomes. Psychological Science. 2013.
  42. 42. Bäuml JG, Daamen M, Meng C, Neitzel J, Scheef L, Jaekel J, et al. Correspondence between aberrant intrinsic network connectivity and gray matter volume in the ventral brain of preterm born adults. Cerebral Cortex. 2014;25(11):4135–45.
  43. 43. Bäuml JG, Meng C, Daamen M, Baumann N, Busch B, Bartmann P, et al. The association of children’s mathematic abilities with both adults’ cognitive abilities and intrinsic fronto-parietal networks is altered in preterm-born individuals. Brain Structure and Function. 2016:1–14.
  44. 44. Finke K, Neitzel J, Bäuml JG, Redel P, Müller HJ, Meng C, et al. Visual attention in preterm born adults: Specifically impaired attentional sub-mechanisms that link with altered intrinsic brain networks in a compensation-like mode. NeuroImage. 2015;107(0):95–106.
  45. 45. Meng C, Bauml JG, Daamen M, Jaekel J, Neitzel J, Scheef L, et al. Extensive and interrelated subcortical white and gray matter alterations in preterm-born adults. Brain Struct Funct. 2016;221(4):2109–21. pmid:25820473
  46. 46. Clark CAC, Liu Y, Wright NLA, Bedrick A, Edgin JO. Functional neural bases of numerosity judgments in healthy adults born preterm. Brain and Cognition. 2017;118:90–9. pmid:28802184
  47. 47. Cox SR, MacPherson SE, Ferguson KJ, Nissan J, Royle NA, MacLullich AMJ, et al. Correlational structure of ‘frontal’ tests and intelligence tests indicates two components with asymmetrical neurostructural correlates in old age. Intelligence. 2014;46:94–106. pmid:25278641
  48. 48. Duncan J, Seitz RJ, Kolodny J, Bor D, Herzog H, Ahmed A, et al. A neural basis for general intelligence. Science. 2000;289(5478):457–60. pmid:10903207
  49. 49. Grothe MJ, Scheef L, Bauml J, Meng C, Daamen M, Baumann N, et al. Reduced cholinergic basal forebrain integrity links neonatal complications and adult cognitive deficits after premature birth. Biological psychiatry. 2017;82(2):119–26. pmid:28129944
  50. 50. Balu DT. Cognitive deficits in prematurely born adults are associated with reduced basal forebrain integrity. Biological psychiatry. 2017;82(2):e15–e6. pmid:28645360
  51. 51. Quigley M, Poulsen G, Boyle EM, Wolke D, Field D, Alfirevic Z, et al. Early term and late preterm birth is associated with poorer school performance at age 5 years: a cohort study. Archives of Disease in Childhood—Fetal and Neonatal Edition. 2012.
  52. 52. Johnson S, Strauss V, Gilmore C, Jaekel J, Marlow N, Wolke D. Learning disabilities among extremely preterm children without neurosensory impairment: Comorbidity, neuropsychological profiles and scholastic outcomes. Early human development. 2016;103:69–75. pmid:27517525
  53. 53. Litt JS, Gerry Taylor H, Margevicius S, Schluchter M, Andreias L, Hack M. Academic achievement of adolescents born with extremely low birth weight. Acta paediatrica (Oslo, Norway: 1992). 2012;101(12):1240–5.
  54. 54. Johnson S, Waheed G, Manktelow BN, Field DJ, Marlow N, Draper ES, et al. Differentiating the preterm phenotype: distinct profiles of cognitive and behavioral development following late and moderately preterm birth. J Pediatr. 2018;193:85–92.e1. pmid:29254758
  55. 55. Breeman LD, Jaekel J, Baumann N, Bartmann P, Wolke D. Attention problems in very preterm children from childhood to adulthood: the Bavarian Longitudinal Study. Journal of Child Psychology and Psychiatry. 2016;57(2):132–40. pmid:26287264
  56. 56. Ritchie K, Bora S, Woodward LJ. Social development of children born very preterm: a systematic review. Developmental medicine and child neurology. 2015;57(10):899–918. pmid:25914112
  57. 57. Pyhälä R, Wolford E, Kautiainen H, Andersson S, Bartmann P, Baumann N, et al. Self-reported mental health problems among adults born preterm: a meta-analysis. Pediatrics. 2017.
  58. 58. Mathewson KJ, Chow CH, Dobson KG, Pope EI, Schmidt LA, Van Lieshout RJ. Mental health of extremely low birth weight survivors: a systematic review and meta-analysis. Psychological bulletin. 2017.
  59. 59. Jaekel J, Baumann N, Bartmann P, Wolke D. Mood and anxiety disorders in very preterm/very low–birth weight individuals from 6 to 26 years. Journal of Child Psychology and Psychiatry. 2018;59(1):88–95. pmid:28748557
  60. 60. Johnson S, Gilmore C, Gallimore I, Jaekel J, Wolke D. The long-term consequences of preterm birth: what do teachers know? Developmental Medicine & Child Neurology. 2015;57(6):571–7.
  61. 61. Grunewaldt KH, Lohaugen GC, Austeng D, Brubakk AM, Skranes J. Working memory training improves cognitive function in VLBW preschoolers. Pediatrics. 2013;131(3):e747–54. pmid:23400616
  62. 62. Lohaugen GCC, Antonsen I, Haberg A, Gramstad A, Vik T, Brubakk AM, et al. Computerized working memory training improves function in adolescents born at extremely low birth weight. Journal of Pediatrics. 2011;158(4):555–U56. pmid:21130467
  63. 63. Roberts G, Quach J, Spencer-Smith M, et al. Academic outcomes 2 years after working memory training for children with low working memory: A randomized clinical trial. JAMA Pediatrics. 2016;170(5):e154568. pmid:26954779
  64. 64. Jolles D, Crone EA. Training the developing brain: a neurocognitive perspective. Frontiers in Human Neuroscience. 2012;6.
  65. 65. Pascoe L, Roberts G, Doyle LW, Lee KJ, Thompson DK, Seal ML, et al. Preventing academic difficulties in preterm children: a randomised controlled trial of an adaptive working memory training intervention—IMPRINT study. Bmc Pediatrics. 2013;13.
  66. 66. Wolke D, Schmid G, Schreier A, Meyer R. Crying and feeding problems in infancy and cognitive outcome in preschool children born at risk: A prospective population study. Journal of Developmental and Behavioral Pediatrics. 2009;30:226–38. pmid:19433987
  67. 67. Hille ETM, Elbertse L, Gravenhorst JB, Brand R, Verloove-Vanhorick SP. Nonresponse bias in a follow-up study of 19-year-old adolescents born as preterm infants. Pediatrics. 2005;116:e662–e6. pmid:16263980