Original Article
School Performance After Preterm Birth Fredrik Ahlsson,a Magnus Kaijser,b,c Johanna Adami,b Maria Lundgren,a and Mårten Palmed Background: An increased risk of poor school performance for children born preterm has been shown in many studies, but whether this increase is attributable to preterm birth per se or to other factors associated with preterm birth has not been resolved. Methods: We used data from the Swedish Medical Birth Register, the Longitudinal Integration Database for Sickness Insurance and Labor Market Study, the Swedish Multigeneration Register, and the National School Register to link records comprising the Swedish birth cohorts from 1974 through 1991. Linear regression was used to assess the association between gestational duration and school performance, both with and without controlling for parental and socioeconomic factors. In a restricted analysis, we compared siblings only with each other. Results: Preterm birth was strongly and negatively correlated with school performance. The distribution of school grades for children born at 31–33 weeks was on average 3.85 (95% confidence interval = −4.36 to −3.35) centiles lower than for children born at 40 weeks. For births at 22–24 weeks, the corresponding figure was −23.15 (−30.32 to −15.97). When taking confounders into account, the association remained. When restricting the analysis to siblings, however, the association between school performance and preterm birth after week 30 vanished completely, whereas it remained, less pronounced, for preterm birth before 30 weeks of gestation. Conclusions: Our study suggests that the association between school performance and preterm birth after 30 gestational weeks is attributable to factors other than preterm birth per se. (Epidemiology 2015;26: 106–111)
compared with their peers born at term.1–5 The increased risk for poor academic performance is not confined to those born very preterm; however, several studies have found negative associations between academic performance and moderately preterm birth (33–36 weeks).6–8 Even among children born at term (37–41 weeks), a positive association between gestational duration and cognitive skills has been reported.9 Although the increased risk for impaired cognitive skills among preterm infants can be attributed, in part, to adverse events in the perinatal period such as intraventricular hemorrhage, hypoglycemia, and sepsis,3,10 these events explain only a small proportion of the increase. It is therefore hypothesized that the risk for cognitive impairment is primarily due to the immature axon being more susceptible to stress imposed by the preterm birth.11–13 Another possible explanation is that mothers of infants born preterm differ from mothers who give birth at term—most notably in age, socioeconomic status, and education level.14,15 Some studies have shown that the association between moderately preterm birth and low academic skills was reduced when factors such as maternal age and income level were controlled for.9,14 To further assess the mechanism behind the increased risk of low cognitive function among children born preterm, we conducted a registerbased study of school performance among 1,647,000 children born in Sweden during the period 1974–1991.
METHODS
I
t is well known that children and young adults with a history of very preterm birth, before 32 completed weeks of gestation, have an increased risk of poor academic performance Submitted 5 November 2013; accepted 16 June 2014; posted 11 September 2014. From the aDepartment of Women’s and Children’s Health, Uppsala University, Uppsala, Sweden; bClincial Epidemiology Unit, Department of Medicine, Karolinska Institutet, Stockholm, Sweden; cDepartment of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden; and dDepartment of Economics, Stockholm University, Stockholm, Sweden. This work was supported by the Gillberg Foundation, the Swedish Medical Society, the Swedish Society for Medical Research, the Institute for Evaluation of Labour Market and Education Policy, and through the regional agreement on medical training and clinical research (ALF) between Stockholm County Council and Karolinska Institutet. Correspondence: Magnus Kaijser, Department on Neuroradiology, R3:00, Karolinska University Hospital, 171 76, Stockholm, Sweden. E-mail: [email protected]. Copyright © 2014 by Lippincott Williams & Wilkins ISSN: 1044-3983/15/2601-0106 DOI: 10.1097/EDE.0000000000000171
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Setting Since 1947, residents of Sweden have been given a unique 10-digit personal identifier. That includes information on year, month, and day of birth, as well as sex of the person. This national registration number is used in all official registers and documents, making it possible to link information between registries. The Swedish Medical Birth Register, started in 1973, contains prospectively collected data on maternal demographic factors, reproductive history, complications during pregnancy, and information on the delivery and neonatal period. Approximately 99% of all births in Sweden are included.16 The National School Register has recorded information on each person’s final grades in compulsory school since 1988. During the study period, there were 2 systems of assigning final grades. Initially, a system with a maximum of 5 points in each topic was used. Later, a system with a total maximum of 320, the sum of all individual grades, was introduced. Epidemiology • Volume 26, Number 1, January 2015
Epidemiology • Volume 26, Number 1, January 2015
School Performance of Preterm Children
Recalculating the sum of grades into percentiles for each year (ie, assigning each individual a rank from 1 to 100 within his/ her birth cohort) may allow for comparisons over time. The Longitudinal Integration Database for Sick Insurance and Labor Market Study contains data from 1990 onward, including demographic variables, education, occupation, and family on every person older than 16 years. The database is updated annually and provides the ability to follow people over time in labor market and health insurance.17 The Swedish Multi-generation register includes every person born in Sweden from 1932 onward, as well as persons who have at some time been registered as residents in Sweden after 1961. The register contains information on the persons and their biological parents, enabling linkages among parents, children, and their siblings.18
Cohort The study consists of all subjects included in the Medical Birth Registry from 1974 through 1991 with a
Birth cohort n=1,783,777
Missing value gestational age (n= 5,525)
n = 1,777,252 Unrealistic gestational age (n= 4,609)
correctly registered national registration number, who had information in the register on birth weight, birth length, and gestational age, and who reached the age of 17 years. We extracted information on birth weight and length, gestational age and maternal age, and parity from the register. Where available, we used gestational age according to ultrasound examinations in the second trimester. For subjects born before the introduction of ultrasound dating of pregnancies or whose information was missing, we based our gestational age estimate on last menstrual period instead. Because last menstrual period may underestimate gestational duration, and ultrasound dating may underestimate gestational age for growth-restricted children and overestimate it for those large for gestational age, we excluded subjects with a birth weight more than 4 standard deviations above the average birth weight for the highest gestational age or less than 4 standard deviations below the average birth weight for each gestational week according to Swedish reference curves for estimated intrauterine growth.19 By using the National Registration number, we linked the data from the Medical Birth Register to the Multi-generation register, thus identifying all siblings. Outcomes were retrieved through linkage to the national register containing the grades in the final year of compulsory school. Information about parental education and income was gathered from the Longitudinal Integration Database for Sick Insurance and Labor Market Study register.
Data Categorization and Analysis
n = 1,772,643
Outcome variables in the study were school grades in the last year of compulsory schooling at age 16. School grades where categorized in centiles per school year—ie, all subjects graduating in one year were ranked according to school grades from 1 to 99. This allowed comparison between birth cohorts without interference from changes in grading system or possible shifts in average school grades over time. In addition to the centiles, we analyzed whether or not the subject qualified to high school—which required the level of “pass” in the core subjects English, Swedish, and Mathematics, according to the new school system, introduced in 1991.
Dead before age 17 (n=17,529)
n = 1,755,114 No school records (n=111,156) Final sample: n=1,643,958
FIGURE 1. Study population. TABLE 1. Descriptive Statisticsa by Gestational Duration
Gestational Duration (weeks)
Mother’s age at birth (years) Mother’s weight (kg) Mother’s education levelb Father’s education levelb Subject’s average school performancec
All
22–24
25–27
28–30
31–33
34–36
37–39
40
41
42
≥43
27.5 66.9 3.5 3.4 51.1
27.9 66.5 2.9 3.0 28.6
27.8 66.0 3.3 3.4 41.9
27.6 67.7 3.3 3.2 44.5
27.6 66.4 3.3 3.3 48.0
27.6 66.5 3.4 3.3 49.1
27.7 66.8 3.5 3.4 51.0
27.4 67.6 3.5 3.4 51.8
27.2 68.4 3.5 3.4 51.6
26.9 69.6 3.5 3.4 50.6
26.3 70.4 3.4 3.3 48.5
a
Mean, unless otherwise specified. Education level measured as 1 for pre-reform compulsory level; 2 for post-reform compulsory level/junior secondary school; 3 for vocational schooling; 4 for secondary schooling academic track; 5 for secondary schooling + 1 or 2 years of additional schooling; 6 for graduated from college or university education; 7 for PhD. c Centile. b
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TABLE 2. Association Between Gestational Duration and School Performance: Difference in the Percentile Distribution of School Grades in Last Year of Compulsory Schooling, at Age 16 Years, Compared with the Reference Group
22 wk to 24 wk 6 days 25 wk to 27 wk 6 days 28 wk to 30 wk 6 days 31 wk to 33 wk 6 days 34 wk to 36 wk 6 days 37 wk to 39 wk 6 days 40 wk to 40 wk 6 daysa 41 wk to 41 wk 6 days 42 wk to 42 wk 6 days 43 wk or more R2
(1)
(2)
(3)
(4)
No Confounders (n = 1,643,958) Difference (95% CI)
Including Confounders on Parental Characteristics (n = 1,011,356) Difference (95% CI)
Siblings Correlations (n = 1,360,259) Difference (95% CI)
−23.15 (−30.32 to −15.97) −10.058 (−11.84 to −8.28) −7.39 (−8.30 to −6.48) −3.85 (−4.36 to −3.35) −2.67 (−2.90 to −2.44) −0.75 (−0.86 to −0.64) 0 −0.13 (−0.26 to −0.00) −1.10 (−1.28 to −0.92) −3.10 (−3.46 to −2.73) 0.002
−24.47 (−34.14 to −14.80) −10.42 (−12.79 to −8.06) −6.26 (−7.38 to −5.13) −2.60 (−3.22 to −1.99) −1.48 (−1.76 to −1.21) −0.45 (−0.58 to −0.32) 0 −0.37 (−0.52 to −0.22) −0.89 (−1.09 to −0.69) −1.98 (−2.35 to −1.61) 0.178
−10.30 (−21.45 to −0.85) −8.74 (−11.93 to −5.55) −3.89 (−5.48 to −2.30) −0.41 (−1.29 to 0.47) 0.68 (0.29 to 1.08) 0.23 (0.05 to 0.40) 0 −0.35 (−0.55 to −0.15) −0.45 (−0.74 to −0.17) −0.10 (−0.68 to −0.48) 0.755
Siblings Correlations with 1 Sibling Born in Week 40 (n = 694,155) Difference (95% CI) −12.94 (−43.22 to 17.34) −10.69 (−16.76 to −4.62) −2.92 (−5.82 to −0.03) −0.74 (−2.45 to 0.96) 0.63 (−0.03 to 1.29) 0.28 (0.09 to 0.47) 0 −0.43 (−0.67 to −0.187) −0.47 (−0.86 to −0.08) −0.07 (−0.98 to 0.849) 0.737
CI indicates confidence interval a Reference category
Exposure variables were gestational age (categorized in 3-week intervals from 22 to 39 completed weeks of gestation, 1-week intervals from 40 to 42 completed weeks, and more than 42 completed weeks), maternal age at childbirth (in years), maternal and paternal education (compulsory/junior secondary, vocational, upper secondary, and collage/university). We used linear regression to analyze the continuous outcomes of school grades and probit regression for the binary outcome of qualification to secondary education. Data were analyzed in four steps. First, we did a crude analysis with school grades as the dependent variable and gestational age and maternal age as the independent variables. Second, we added information on maternal and paternal education to the model. In the third model, we restricted the analysis to families with more than one child and stratified the analysis on mothers, so that each mother constituted a separate stratum and siblings were compared only with each other. In this analysis, we also included a variable for birth order, because birth order is associated with school performance in other studies.20,21 In addition, we analyzed each education level separately to assess whether the association between gestational age and school performance varied among groups of 108 | www.epidem.com
maternal education. In the last step, we continued the analysis stratified on siblings, but we restricted it to pairs/groups of siblings in which at least one of the siblings had a gestational age of 40 weeks. The study was approved by the Ethics Committee of the Medical Faculty of Uppsala University.
RESULTS During the study period, 1,765,327 children were recorded in the Medical Birth Registry with information on national registration number and birth weight. After exclusion of 5525 subjects due to missing information on gestational age, 4609 subjects with unrealistic gestational age, and 17,529 who died before age 17, 1,755,114 subjects remained in the cohort. Of these, 111,156 lacked information on school performance, yielding 1,643,958 subjects (841,447 were boys and 802,511 girls) in the final cohort (Figure 1). In Table 1, we present descriptive statistics of key variables used in the subsequent analysis, by gestation duration of the child. Table 2 shows the results from regression analysis on the effect of gestational duration relative to 40 weeks’ gestation on © 2014 Lippincott Williams & Wilkins
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0
10
-5
0
-10
-10
-15
-20
-20
-30 25
30
35
40
Gestation Length (Weeks)
45
FIGURE 2. Relation between gestation length and percentile score in grades obtained at age 16. Difference in averages as compared with children born at 40 weeks. Diamonds indicate point estimates; dashed lines, upper and lower 95% CI bounds. 5
0
-5
-10
-15
-20 25
30
35
40
Gestation Length (Weeks)
45
FIGURE 3. Relation between gestation length and percentile score in grades obtained at age 16, including fixed effect for mothers. Difference in averages as compared with children born at 40 weeks. Diamonds indicate point estimates; dashed lines, upper and lower 95% CI bounds.
the percentile distribution of grades in the last year of compulsory schooling at age 16. The first column shows the results when we included only indicator variables for year of birth of the child along with the indicator variables for gestational age. In the second specification, we included indicator variables for maternal age, as well as indicators for 7 levels of each mother’s and father’s education. In the third column, we included indicator variables for each mother, restricting the comparison to between siblings. In the last column, we repeated the analysis from the third column, restricted to those persons who had a mother who had given birth to at least 1 child born in week 40. Column 1 shows that there was a strongly negative effect of being born both before and after week 40. For example, © 2014 Lippincott Williams & Wilkins
25
30
35
40
Gestation Length (Weeks)
45
FIGURE 4. Relation between gestation length and percentile score in grades obtained at age 16 in families with one sibling born in week 40, including fixed effect for mothers. Differences in averages are compared with children born at 40 weeks. Diamonds indicate point estimates; dashed lines, upper and lower 95% CI bounds.
being born week 25–27 was associated with about a 10 percentage point reduction in the percentile distribution of school grades. Similarly, if birth was delayed until after week 43, there was an average reduction of about 3 percentage points in the percentile distribution. Figure 2 show the results from similar regression analyses with indicator variables for each week of gestation. For children born before or after 40 weeks of gestation, school grades were considerably lower. Column 2 shows that the effects were somewhat smaller when we included controls for confounders in the model. However, the estimates were still different from zero for all gestation lengths other than 40 weeks. Column 3 shows that the results shrank substantially when we compared siblings with the same mother; there was no longer any meaningful difference in school performance for children born at 31–33 weeks of gestation and onward, compared with children born after 40 weeks (Figure 3). When the analysis was restricted to sets of siblings in which at least one person was born after 40 weeks, the results were similar; we found no association between gestational age and school performance when birth occurred at 31 weeks of gestation or later (Figure 4). Similarly, although we found that a birth after 43 weeks of gestation was negatively associated with school performance in the analysis where no confounders were taken into account, the association disappeared in the sibling analysis. Because families with only 1 child (n = 283,699) were excluded from the sibling analysis, we assessed whether any differences between families with 1 child and families with multiple children could have influenced our results by repeating the analysis from column 1 with 1-child families excluded. Results from this analysis were virtually identical to those in column 1 (data available on request). www.epidem.com | 109
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TABLE 3. Association Between Gestational Duration and Probability of Qualifying for Secondary Education at Age 16 Years
22 wk to 24 wk 6 days 25 wk to 27 wk 6 days 28 wk to 30 wk 6 days 31 wk to 33 wk 6 days 34 wk to 36 wk 6 days 37 wk to 39 wk 6 days 40 wk to 40 wk 6 daysa 41 wk to 41 wk 6 days 42 wk to 42 wk 6 days 43 wk or more Pseudo R2/R2
(1)
(2)
(3)
(4)
No Confounders (n = 1,643,958) OR (95% CI)
Including Confounders on Parental Characteristics (n = 1,011,356) OR (95% CI)
Siblings Correlations (n = 1,360,259) OR (95% CI)
Siblings Correlations with 1 Sibling Born in Week 40 (n = 694,155) OR (95% CI)
0.86 (0.80–0.92) 0.94 (0.92–0.95) 0.95 (0.94–0.96) 0.98 (0.97–0.98) 0.99 (0.98–0.99) 1.00 (0.99–1.00) 1.00 1.00 (1.00–1.00) 1.00 (0.99–1.00) 0.99 (0.98–0.99) 0.026
0.89 (0.82–0.964) 0.95 (0.94–0.97) 0.97 (0.96–0.98) 0.99 (0.98–0.994) 0.99 (0.99–1.00) 1.00 (1.00–1.00) 1.00 1.00 (1.00–1.00) 1.00 (1.00–1.00) 1.00 (0.99–1.00) 0.087
0.88 (0.70–1.11) 0.93 (0.89–0.97) 0.95 (0.93–0.98) 0.99 (0.98–1.00) 1.00 (1.00–1.01) 1.00 (1.00–1.00) 1.00 1.00 (1.00–1.00) 1.00 (1.00–1.00) 1.00 (1.00–1.01) 0.589
1.00 (0.67–1.50) 0.92 (0.84–1.00) 0.95 (0.91–0.99) 1.00 (0.98–1.02) 1.00 (1.00–1.01) 1.00 (1.00–1.00) 1.00 1.00 (1.00–1.00) 1.00 (0.99–1.00) 1.00 (0.99–1.01) 0.557
OR indicates odds ratio. a Reference category.
Table 3 shows linear probability model estimates for the probability of qualifying for secondary education for various groups of gestation lengths. The results were similar to those in Table 2, with a strong negative association between gestational duration and probability of qualifying for secondary school when confounders were not included in the model, but a negative effect only for those born before 31 weeks of gestation when the analysis was restricted to siblings. To validate our results from the sibling analysis, we also analyzed data with birth weight, rather than gestational age, as the independent variable and percentiles in the grade distribution in the final year of compulsory schooling as the dependent variable. In this analysis, the pattern was the same: birth weight was strongly correlated with school grades, but much less so when the analysis was restricted to siblings (data available on request).
DISCUSSION We found a strong negative association of school performance with preterm birth after 31 weeks of gestation, but this association vanishes when the preterm children are compared with their siblings. Thus, our study suggests that, for birth after 31 weeks of gestation, it is the factors leading to 110 | www.epidem.com
preterm birth, rather than preterm birth per se, that has a negative impact on school performance.
Strengths and Limitations The study comprises the entire live-born Swedish population between 1974 and 1991. The large sample size in the study provides highly precise effect estimates and also independent effects of gestational age and birth weight. The large nationwide databases allow us to identify individuals from the same family, enabling family-based analyses that separate within-family and between-family associations. The data on birth weight and gestational age were not obtained by maternal report but from the prospectively collected national birth registry. Thus, our results are not subject to recall bias. The quality of data from the Swedish Medical Birth Registry has been validated, and the reliability on the parameters gestational age, birth weight, maternal age, and parity is high.16 Some of the persons in our study were born before routine ultrasound dating, and thus their gestational age was based on maternal report of last menstrual period. Still, as this information was obtained before completion of pregnancy, measurement errors should be nondifferential. Nonetheless, © 2014 Lippincott Williams & Wilkins
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because mothers have a tendency to repeat the gestational age of their offspring, nondifferential misclassification of gestational age would have a stronger impact on the sibling analysis, suggesting that the lack of an association between gestational duration and school outcome in the sibling analysis could be due at least in part to measurement errors of gestational duration. However, when we used birth weight as the independent variable, rather than gestational duration, a similar pattern appeared—with birth weight being strongly correlated with school grades, but much less so in the analysis restricted to siblings. We therefore find it unlikely that our results could be explained by misclassification of gestational duration. Another potential limitation is that we cannot account for the possibility that parents compensate for disadvantages of children born preterm or post-term compared with their siblings with normal gestational duration. Such compensations would bias the estimates in the sibling analyses toward the null. However, another possibility is that parents encourage individual differences, which would imply that they devote more resources to help the more able siblings in their school work, which would bias our results toward overestimating differences between siblings. We cannot determine the effects of such parental behavior on our results. Family-based approaches to examining the association between fetal growth and cognitive ability have reported inconsistent results. The earliest study, by Record et al,21 found that the association between birth weight and cognitive ability was attributable to differences in family factors, a finding supported by more recent studies.22,23 In contrast, other studies have found within-sibling differences in cognitive ability by birth weight.22,24 The main finding in our study is that the negative effects of preterm gestation ages between week 31 and 40, as well as post-term delivery, on average school grades are wiped out when fixed effects are added to the specification. As shown in Table 2, the confidence intervals for the gestation age categories are covered by one percentile around the excluded category “born at term.” The estimated negative effects of being born preterm before week 30 are also much smaller within families than between families. These results are confirmed by the analysis of the probability of qualifying for secondary education shown in Table 3, which are qualitatively the same as those shown in Table 2. Our interpretation of these findings is that the negative effects on school results associated with moderately preterm and post-term birth discovered in previous epidemiologic studies1,6,9 are attributable to other exposures (eg, sociodemographic and genetic factors), rather than to gestational age per se. Furthermore, our study suggests that these factors contribute to the effects seen in children born before week 30, as well. References 1. Bhutta AT, Cleves MA, Casey PH, Cradock MM, Anand KJ. Cognitive and behavioral outcomes of school-aged children who were born preterm: a meta-analysis. JAMA. 2002;288:728–737.
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School Performance of Preterm Children
2. Gross SJ, Mettelman BB, Dye TD, Slagle TA. Impact of family structure and stability on academic outcome in preterm children at 10 years of age. J Pediatr. 2001;138:169–175. 3. Downie AL, Frisk V, Jakobson LS. The impact of periventricular brain injury on reading and spelling abilities in the late elementary and adolescent years. Child Neuropsychol. 2005;11:479–495. 4. Grunau RE, Whitfield MF, Davis C. Pattern of learning disabilities in children with extremely low birth weight and broadly average intelligence. Arch Pediatr Adolesc Med. 2002;156:615–620. 5. Saigal S, Hoult LA, Streiner DL, Stoskopf BL, Rosenbaum PL. School difficulties at adolescence in a regional cohort of children who were extremely low birth weight. Pediatrics. 2000;105:325–331. 6. Huddy CL, Johnson A, Hope PL. Educational and behavioural problems in babies of 32-35 weeks gestation. Arch Dis Child Fetal Neonatal Ed. 2001;85:F23–F28. 7. Nomura Y, Halperin JM, Newcorn JH, et al. The risk for impaired learning-related abilities in childhood and educational attainment among adults born near-term. J Pediatr Psychol. 2009;34:406–418. 8. Chyi LJ, Lee HC, Hintz SR, Gould JB, Sutcliffe TL. School outcomes of late preterm infants: special needs and challenges for infants born at 32 to 36 weeks gestation. J Pediatr. 2008;153:25–31. 9. Yang S, Bergvall N, Cnattingius S, Kramer MS. Gestational age differences in health and development among young Swedish men born at term. Int J Epidemiol. 2010;39:1240–1249. 10. Tam EW, Haeusslein LA, Bonifacio SL, et al. Hypoglycemia is associated with increased risk for brain injury and adverse neurodevelopmental outcome in neonates at risk for encephalopathy. J Pediatr. 2012; 161:88–93. 11. Bhutta AT, Anand KJ. Abnormal cognition and behavior in preterm neonates linked to smaller brain volumes. Trends Neurosci. 2001;24:129– 130; discussion 31–32. 12. Bhutta AT, Anand KJ. Vulnerability of the developing brain. Neuronal mechanisms. Clin Perinatol. 2002;29:357–372. 13. de Jong M, Verhoeven M, van Baar AL. School outcome, cognitive functioning, and behaviour problems in moderate and late preterm children and adults: a review. Semin Fetal Neonatal Med. 2012;17: 163–169. 14. Ekeus C, Lindström K, Lindblad F, Rasmussen F, Hjern A. Preterm birth, social disadvantage, and cognitive competence in Swedish 18- to 19-yearold men. Pediatrics. 2010;125:e67–e73. 15. Svensson AC, Sandin S, Cnattingius S, et al. Maternal effects for preterm birth: a genetic epidemiologic study of 630,000 families. Am J Epidemiol. 2009;170:1365–1372. 16. Cnattingius S, Ericson A, Gunnarskog J, Källén B. A quality study of a medical birth registry. Scand J Soc Med. 1990;18:143–148. 17. Statistics Sweden. Longitudinal integration database for health insurance and labour market studies (Longitudinal Integration Database for Sick Insurance and Labor Market Study). Available at http://www.scb.se/LISA. Accessed October 1, 2011. 18. Ekbom A. The Swedish Multi-generation Register. Methods Mol Biol. 2011;675:215–220. 19. Marsál K, Persson PH, Larsen T, Lilja H, Selbing A, Sultan B. Intrauterine growth curves based on ultrasonically estimated foetal weights. Acta Paediatr. 1996;85:843–848. 20. Record RG, McKeown T, Edwards JH. The relation of measured intelligence to birth order and maternal age. Ann Hum Genet. 1969;33: 61–69. 21. Record RG, McKeown T, Edwards JH. The relation of measured intelligence to birth weight and duration of gestation. Ann Hum Genet. 1969;33:71–79. 22. Bergvall N, Iliadou A, Tuvemo T, Cnattingius S. Birth characteristics and risk of low intellectual performance in early adulthood: are the associations confounded by socioeconomic factors in adolescence or familial effects? Pediatrics. 2006;117:714–721. 23. Lawlor DA, Clark H, Smith GD, Leon DA. Intrauterine growth and intelligence within sibling pairs: findings from the Aberdeen children of the 1950s cohort. Pediatrics. 2006;117:e894–e902. 24. Lawlor DA, Bor W, O’Callaghan MJ, Williams GM, Najman JM. Intrauterine growth and intelligence within sibling pairs: findings from the Mater-University study of pregnancy and its outcomes. J Epidemiol Community Health. 2005;59:279–282.
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School Performance After Preterm Birth Fredrik Ahlsson,a Magnus Kaijser,b,c Johanna Adami,b Maria Lundgren,a and Mårten Palmed Background: An increased risk of poor school performance for children born preterm has been shown in many studies, but whether this increase is attributable to preterm birth per se or to other factors associated with preterm birth has not been resolved. Methods: We used data from the Swedish Medical Birth Register, the Longitudinal Integration Database for Sickness Insurance and Labor Market Study, the Swedish Multigeneration Register, and the National School Register to link records comprising the Swedish birth cohorts from 1974 through 1991. Linear regression was used to assess the association between gestational duration and school performance, both with and without controlling for parental and socioeconomic factors. In a restricted analysis, we compared siblings only with each other. Results: Preterm birth was strongly and negatively correlated with school performance. The distribution of school grades for children born at 31–33 weeks was on average 3.85 (95% confidence interval = −4.36 to −3.35) centiles lower than for children born at 40 weeks. For births at 22–24 weeks, the corresponding figure was −23.15 (−30.32 to −15.97). When taking confounders into account, the association remained. When restricting the analysis to siblings, however, the association between school performance and preterm birth after week 30 vanished completely, whereas it remained, less pronounced, for preterm birth before 30 weeks of gestation. Conclusions: Our study suggests that the association between school performance and preterm birth after 30 gestational weeks is attributable to factors other than preterm birth per se. (Epidemiology 2015;26: 106–111)
compared with their peers born at term.1–5 The increased risk for poor academic performance is not confined to those born very preterm; however, several studies have found negative associations between academic performance and moderately preterm birth (33–36 weeks).6–8 Even among children born at term (37–41 weeks), a positive association between gestational duration and cognitive skills has been reported.9 Although the increased risk for impaired cognitive skills among preterm infants can be attributed, in part, to adverse events in the perinatal period such as intraventricular hemorrhage, hypoglycemia, and sepsis,3,10 these events explain only a small proportion of the increase. It is therefore hypothesized that the risk for cognitive impairment is primarily due to the immature axon being more susceptible to stress imposed by the preterm birth.11–13 Another possible explanation is that mothers of infants born preterm differ from mothers who give birth at term—most notably in age, socioeconomic status, and education level.14,15 Some studies have shown that the association between moderately preterm birth and low academic skills was reduced when factors such as maternal age and income level were controlled for.9,14 To further assess the mechanism behind the increased risk of low cognitive function among children born preterm, we conducted a registerbased study of school performance among 1,647,000 children born in Sweden during the period 1974–1991.
METHODS
I
t is well known that children and young adults with a history of very preterm birth, before 32 completed weeks of gestation, have an increased risk of poor academic performance Submitted 5 November 2013; accepted 16 June 2014; posted 11 September 2014. From the aDepartment of Women’s and Children’s Health, Uppsala University, Uppsala, Sweden; bClincial Epidemiology Unit, Department of Medicine, Karolinska Institutet, Stockholm, Sweden; cDepartment of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden; and dDepartment of Economics, Stockholm University, Stockholm, Sweden. This work was supported by the Gillberg Foundation, the Swedish Medical Society, the Swedish Society for Medical Research, the Institute for Evaluation of Labour Market and Education Policy, and through the regional agreement on medical training and clinical research (ALF) between Stockholm County Council and Karolinska Institutet. Correspondence: Magnus Kaijser, Department on Neuroradiology, R3:00, Karolinska University Hospital, 171 76, Stockholm, Sweden. E-mail: [email protected]. Copyright © 2014 by Lippincott Williams & Wilkins ISSN: 1044-3983/15/2601-0106 DOI: 10.1097/EDE.0000000000000171
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Setting Since 1947, residents of Sweden have been given a unique 10-digit personal identifier. That includes information on year, month, and day of birth, as well as sex of the person. This national registration number is used in all official registers and documents, making it possible to link information between registries. The Swedish Medical Birth Register, started in 1973, contains prospectively collected data on maternal demographic factors, reproductive history, complications during pregnancy, and information on the delivery and neonatal period. Approximately 99% of all births in Sweden are included.16 The National School Register has recorded information on each person’s final grades in compulsory school since 1988. During the study period, there were 2 systems of assigning final grades. Initially, a system with a maximum of 5 points in each topic was used. Later, a system with a total maximum of 320, the sum of all individual grades, was introduced. Epidemiology • Volume 26, Number 1, January 2015
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Recalculating the sum of grades into percentiles for each year (ie, assigning each individual a rank from 1 to 100 within his/ her birth cohort) may allow for comparisons over time. The Longitudinal Integration Database for Sick Insurance and Labor Market Study contains data from 1990 onward, including demographic variables, education, occupation, and family on every person older than 16 years. The database is updated annually and provides the ability to follow people over time in labor market and health insurance.17 The Swedish Multi-generation register includes every person born in Sweden from 1932 onward, as well as persons who have at some time been registered as residents in Sweden after 1961. The register contains information on the persons and their biological parents, enabling linkages among parents, children, and their siblings.18
Cohort The study consists of all subjects included in the Medical Birth Registry from 1974 through 1991 with a
Birth cohort n=1,783,777
Missing value gestational age (n= 5,525)
n = 1,777,252 Unrealistic gestational age (n= 4,609)
correctly registered national registration number, who had information in the register on birth weight, birth length, and gestational age, and who reached the age of 17 years. We extracted information on birth weight and length, gestational age and maternal age, and parity from the register. Where available, we used gestational age according to ultrasound examinations in the second trimester. For subjects born before the introduction of ultrasound dating of pregnancies or whose information was missing, we based our gestational age estimate on last menstrual period instead. Because last menstrual period may underestimate gestational duration, and ultrasound dating may underestimate gestational age for growth-restricted children and overestimate it for those large for gestational age, we excluded subjects with a birth weight more than 4 standard deviations above the average birth weight for the highest gestational age or less than 4 standard deviations below the average birth weight for each gestational week according to Swedish reference curves for estimated intrauterine growth.19 By using the National Registration number, we linked the data from the Medical Birth Register to the Multi-generation register, thus identifying all siblings. Outcomes were retrieved through linkage to the national register containing the grades in the final year of compulsory school. Information about parental education and income was gathered from the Longitudinal Integration Database for Sick Insurance and Labor Market Study register.
Data Categorization and Analysis
n = 1,772,643
Outcome variables in the study were school grades in the last year of compulsory schooling at age 16. School grades where categorized in centiles per school year—ie, all subjects graduating in one year were ranked according to school grades from 1 to 99. This allowed comparison between birth cohorts without interference from changes in grading system or possible shifts in average school grades over time. In addition to the centiles, we analyzed whether or not the subject qualified to high school—which required the level of “pass” in the core subjects English, Swedish, and Mathematics, according to the new school system, introduced in 1991.
Dead before age 17 (n=17,529)
n = 1,755,114 No school records (n=111,156) Final sample: n=1,643,958
FIGURE 1. Study population. TABLE 1. Descriptive Statisticsa by Gestational Duration
Gestational Duration (weeks)
Mother’s age at birth (years) Mother’s weight (kg) Mother’s education levelb Father’s education levelb Subject’s average school performancec
All
22–24
25–27
28–30
31–33
34–36
37–39
40
41
42
≥43
27.5 66.9 3.5 3.4 51.1
27.9 66.5 2.9 3.0 28.6
27.8 66.0 3.3 3.4 41.9
27.6 67.7 3.3 3.2 44.5
27.6 66.4 3.3 3.3 48.0
27.6 66.5 3.4 3.3 49.1
27.7 66.8 3.5 3.4 51.0
27.4 67.6 3.5 3.4 51.8
27.2 68.4 3.5 3.4 51.6
26.9 69.6 3.5 3.4 50.6
26.3 70.4 3.4 3.3 48.5
a
Mean, unless otherwise specified. Education level measured as 1 for pre-reform compulsory level; 2 for post-reform compulsory level/junior secondary school; 3 for vocational schooling; 4 for secondary schooling academic track; 5 for secondary schooling + 1 or 2 years of additional schooling; 6 for graduated from college or university education; 7 for PhD. c Centile. b
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TABLE 2. Association Between Gestational Duration and School Performance: Difference in the Percentile Distribution of School Grades in Last Year of Compulsory Schooling, at Age 16 Years, Compared with the Reference Group
22 wk to 24 wk 6 days 25 wk to 27 wk 6 days 28 wk to 30 wk 6 days 31 wk to 33 wk 6 days 34 wk to 36 wk 6 days 37 wk to 39 wk 6 days 40 wk to 40 wk 6 daysa 41 wk to 41 wk 6 days 42 wk to 42 wk 6 days 43 wk or more R2
(1)
(2)
(3)
(4)
No Confounders (n = 1,643,958) Difference (95% CI)
Including Confounders on Parental Characteristics (n = 1,011,356) Difference (95% CI)
Siblings Correlations (n = 1,360,259) Difference (95% CI)
−23.15 (−30.32 to −15.97) −10.058 (−11.84 to −8.28) −7.39 (−8.30 to −6.48) −3.85 (−4.36 to −3.35) −2.67 (−2.90 to −2.44) −0.75 (−0.86 to −0.64) 0 −0.13 (−0.26 to −0.00) −1.10 (−1.28 to −0.92) −3.10 (−3.46 to −2.73) 0.002
−24.47 (−34.14 to −14.80) −10.42 (−12.79 to −8.06) −6.26 (−7.38 to −5.13) −2.60 (−3.22 to −1.99) −1.48 (−1.76 to −1.21) −0.45 (−0.58 to −0.32) 0 −0.37 (−0.52 to −0.22) −0.89 (−1.09 to −0.69) −1.98 (−2.35 to −1.61) 0.178
−10.30 (−21.45 to −0.85) −8.74 (−11.93 to −5.55) −3.89 (−5.48 to −2.30) −0.41 (−1.29 to 0.47) 0.68 (0.29 to 1.08) 0.23 (0.05 to 0.40) 0 −0.35 (−0.55 to −0.15) −0.45 (−0.74 to −0.17) −0.10 (−0.68 to −0.48) 0.755
Siblings Correlations with 1 Sibling Born in Week 40 (n = 694,155) Difference (95% CI) −12.94 (−43.22 to 17.34) −10.69 (−16.76 to −4.62) −2.92 (−5.82 to −0.03) −0.74 (−2.45 to 0.96) 0.63 (−0.03 to 1.29) 0.28 (0.09 to 0.47) 0 −0.43 (−0.67 to −0.187) −0.47 (−0.86 to −0.08) −0.07 (−0.98 to 0.849) 0.737
CI indicates confidence interval a Reference category
Exposure variables were gestational age (categorized in 3-week intervals from 22 to 39 completed weeks of gestation, 1-week intervals from 40 to 42 completed weeks, and more than 42 completed weeks), maternal age at childbirth (in years), maternal and paternal education (compulsory/junior secondary, vocational, upper secondary, and collage/university). We used linear regression to analyze the continuous outcomes of school grades and probit regression for the binary outcome of qualification to secondary education. Data were analyzed in four steps. First, we did a crude analysis with school grades as the dependent variable and gestational age and maternal age as the independent variables. Second, we added information on maternal and paternal education to the model. In the third model, we restricted the analysis to families with more than one child and stratified the analysis on mothers, so that each mother constituted a separate stratum and siblings were compared only with each other. In this analysis, we also included a variable for birth order, because birth order is associated with school performance in other studies.20,21 In addition, we analyzed each education level separately to assess whether the association between gestational age and school performance varied among groups of 108 | www.epidem.com
maternal education. In the last step, we continued the analysis stratified on siblings, but we restricted it to pairs/groups of siblings in which at least one of the siblings had a gestational age of 40 weeks. The study was approved by the Ethics Committee of the Medical Faculty of Uppsala University.
RESULTS During the study period, 1,765,327 children were recorded in the Medical Birth Registry with information on national registration number and birth weight. After exclusion of 5525 subjects due to missing information on gestational age, 4609 subjects with unrealistic gestational age, and 17,529 who died before age 17, 1,755,114 subjects remained in the cohort. Of these, 111,156 lacked information on school performance, yielding 1,643,958 subjects (841,447 were boys and 802,511 girls) in the final cohort (Figure 1). In Table 1, we present descriptive statistics of key variables used in the subsequent analysis, by gestation duration of the child. Table 2 shows the results from regression analysis on the effect of gestational duration relative to 40 weeks’ gestation on © 2014 Lippincott Williams & Wilkins
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0
10
-5
0
-10
-10
-15
-20
-20
-30 25
30
35
40
Gestation Length (Weeks)
45
FIGURE 2. Relation between gestation length and percentile score in grades obtained at age 16. Difference in averages as compared with children born at 40 weeks. Diamonds indicate point estimates; dashed lines, upper and lower 95% CI bounds. 5
0
-5
-10
-15
-20 25
30
35
40
Gestation Length (Weeks)
45
FIGURE 3. Relation between gestation length and percentile score in grades obtained at age 16, including fixed effect for mothers. Difference in averages as compared with children born at 40 weeks. Diamonds indicate point estimates; dashed lines, upper and lower 95% CI bounds.
the percentile distribution of grades in the last year of compulsory schooling at age 16. The first column shows the results when we included only indicator variables for year of birth of the child along with the indicator variables for gestational age. In the second specification, we included indicator variables for maternal age, as well as indicators for 7 levels of each mother’s and father’s education. In the third column, we included indicator variables for each mother, restricting the comparison to between siblings. In the last column, we repeated the analysis from the third column, restricted to those persons who had a mother who had given birth to at least 1 child born in week 40. Column 1 shows that there was a strongly negative effect of being born both before and after week 40. For example, © 2014 Lippincott Williams & Wilkins
25
30
35
40
Gestation Length (Weeks)
45
FIGURE 4. Relation between gestation length and percentile score in grades obtained at age 16 in families with one sibling born in week 40, including fixed effect for mothers. Differences in averages are compared with children born at 40 weeks. Diamonds indicate point estimates; dashed lines, upper and lower 95% CI bounds.
being born week 25–27 was associated with about a 10 percentage point reduction in the percentile distribution of school grades. Similarly, if birth was delayed until after week 43, there was an average reduction of about 3 percentage points in the percentile distribution. Figure 2 show the results from similar regression analyses with indicator variables for each week of gestation. For children born before or after 40 weeks of gestation, school grades were considerably lower. Column 2 shows that the effects were somewhat smaller when we included controls for confounders in the model. However, the estimates were still different from zero for all gestation lengths other than 40 weeks. Column 3 shows that the results shrank substantially when we compared siblings with the same mother; there was no longer any meaningful difference in school performance for children born at 31–33 weeks of gestation and onward, compared with children born after 40 weeks (Figure 3). When the analysis was restricted to sets of siblings in which at least one person was born after 40 weeks, the results were similar; we found no association between gestational age and school performance when birth occurred at 31 weeks of gestation or later (Figure 4). Similarly, although we found that a birth after 43 weeks of gestation was negatively associated with school performance in the analysis where no confounders were taken into account, the association disappeared in the sibling analysis. Because families with only 1 child (n = 283,699) were excluded from the sibling analysis, we assessed whether any differences between families with 1 child and families with multiple children could have influenced our results by repeating the analysis from column 1 with 1-child families excluded. Results from this analysis were virtually identical to those in column 1 (data available on request). www.epidem.com | 109
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TABLE 3. Association Between Gestational Duration and Probability of Qualifying for Secondary Education at Age 16 Years
22 wk to 24 wk 6 days 25 wk to 27 wk 6 days 28 wk to 30 wk 6 days 31 wk to 33 wk 6 days 34 wk to 36 wk 6 days 37 wk to 39 wk 6 days 40 wk to 40 wk 6 daysa 41 wk to 41 wk 6 days 42 wk to 42 wk 6 days 43 wk or more Pseudo R2/R2
(1)
(2)
(3)
(4)
No Confounders (n = 1,643,958) OR (95% CI)
Including Confounders on Parental Characteristics (n = 1,011,356) OR (95% CI)
Siblings Correlations (n = 1,360,259) OR (95% CI)
Siblings Correlations with 1 Sibling Born in Week 40 (n = 694,155) OR (95% CI)
0.86 (0.80–0.92) 0.94 (0.92–0.95) 0.95 (0.94–0.96) 0.98 (0.97–0.98) 0.99 (0.98–0.99) 1.00 (0.99–1.00) 1.00 1.00 (1.00–1.00) 1.00 (0.99–1.00) 0.99 (0.98–0.99) 0.026
0.89 (0.82–0.964) 0.95 (0.94–0.97) 0.97 (0.96–0.98) 0.99 (0.98–0.994) 0.99 (0.99–1.00) 1.00 (1.00–1.00) 1.00 1.00 (1.00–1.00) 1.00 (1.00–1.00) 1.00 (0.99–1.00) 0.087
0.88 (0.70–1.11) 0.93 (0.89–0.97) 0.95 (0.93–0.98) 0.99 (0.98–1.00) 1.00 (1.00–1.01) 1.00 (1.00–1.00) 1.00 1.00 (1.00–1.00) 1.00 (1.00–1.00) 1.00 (1.00–1.01) 0.589
1.00 (0.67–1.50) 0.92 (0.84–1.00) 0.95 (0.91–0.99) 1.00 (0.98–1.02) 1.00 (1.00–1.01) 1.00 (1.00–1.00) 1.00 1.00 (1.00–1.00) 1.00 (0.99–1.00) 1.00 (0.99–1.01) 0.557
OR indicates odds ratio. a Reference category.
Table 3 shows linear probability model estimates for the probability of qualifying for secondary education for various groups of gestation lengths. The results were similar to those in Table 2, with a strong negative association between gestational duration and probability of qualifying for secondary school when confounders were not included in the model, but a negative effect only for those born before 31 weeks of gestation when the analysis was restricted to siblings. To validate our results from the sibling analysis, we also analyzed data with birth weight, rather than gestational age, as the independent variable and percentiles in the grade distribution in the final year of compulsory schooling as the dependent variable. In this analysis, the pattern was the same: birth weight was strongly correlated with school grades, but much less so when the analysis was restricted to siblings (data available on request).
DISCUSSION We found a strong negative association of school performance with preterm birth after 31 weeks of gestation, but this association vanishes when the preterm children are compared with their siblings. Thus, our study suggests that, for birth after 31 weeks of gestation, it is the factors leading to 110 | www.epidem.com
preterm birth, rather than preterm birth per se, that has a negative impact on school performance.
Strengths and Limitations The study comprises the entire live-born Swedish population between 1974 and 1991. The large sample size in the study provides highly precise effect estimates and also independent effects of gestational age and birth weight. The large nationwide databases allow us to identify individuals from the same family, enabling family-based analyses that separate within-family and between-family associations. The data on birth weight and gestational age were not obtained by maternal report but from the prospectively collected national birth registry. Thus, our results are not subject to recall bias. The quality of data from the Swedish Medical Birth Registry has been validated, and the reliability on the parameters gestational age, birth weight, maternal age, and parity is high.16 Some of the persons in our study were born before routine ultrasound dating, and thus their gestational age was based on maternal report of last menstrual period. Still, as this information was obtained before completion of pregnancy, measurement errors should be nondifferential. Nonetheless, © 2014 Lippincott Williams & Wilkins
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because mothers have a tendency to repeat the gestational age of their offspring, nondifferential misclassification of gestational age would have a stronger impact on the sibling analysis, suggesting that the lack of an association between gestational duration and school outcome in the sibling analysis could be due at least in part to measurement errors of gestational duration. However, when we used birth weight as the independent variable, rather than gestational duration, a similar pattern appeared—with birth weight being strongly correlated with school grades, but much less so in the analysis restricted to siblings. We therefore find it unlikely that our results could be explained by misclassification of gestational duration. Another potential limitation is that we cannot account for the possibility that parents compensate for disadvantages of children born preterm or post-term compared with their siblings with normal gestational duration. Such compensations would bias the estimates in the sibling analyses toward the null. However, another possibility is that parents encourage individual differences, which would imply that they devote more resources to help the more able siblings in their school work, which would bias our results toward overestimating differences between siblings. We cannot determine the effects of such parental behavior on our results. Family-based approaches to examining the association between fetal growth and cognitive ability have reported inconsistent results. The earliest study, by Record et al,21 found that the association between birth weight and cognitive ability was attributable to differences in family factors, a finding supported by more recent studies.22,23 In contrast, other studies have found within-sibling differences in cognitive ability by birth weight.22,24 The main finding in our study is that the negative effects of preterm gestation ages between week 31 and 40, as well as post-term delivery, on average school grades are wiped out when fixed effects are added to the specification. As shown in Table 2, the confidence intervals for the gestation age categories are covered by one percentile around the excluded category “born at term.” The estimated negative effects of being born preterm before week 30 are also much smaller within families than between families. These results are confirmed by the analysis of the probability of qualifying for secondary education shown in Table 3, which are qualitatively the same as those shown in Table 2. Our interpretation of these findings is that the negative effects on school results associated with moderately preterm and post-term birth discovered in previous epidemiologic studies1,6,9 are attributable to other exposures (eg, sociodemographic and genetic factors), rather than to gestational age per se. Furthermore, our study suggests that these factors contribute to the effects seen in children born before week 30, as well. References 1. Bhutta AT, Cleves MA, Casey PH, Cradock MM, Anand KJ. Cognitive and behavioral outcomes of school-aged children who were born preterm: a meta-analysis. JAMA. 2002;288:728–737.
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School Performance of Preterm Children
2. Gross SJ, Mettelman BB, Dye TD, Slagle TA. Impact of family structure and stability on academic outcome in preterm children at 10 years of age. J Pediatr. 2001;138:169–175. 3. Downie AL, Frisk V, Jakobson LS. The impact of periventricular brain injury on reading and spelling abilities in the late elementary and adolescent years. Child Neuropsychol. 2005;11:479–495. 4. Grunau RE, Whitfield MF, Davis C. Pattern of learning disabilities in children with extremely low birth weight and broadly average intelligence. Arch Pediatr Adolesc Med. 2002;156:615–620. 5. Saigal S, Hoult LA, Streiner DL, Stoskopf BL, Rosenbaum PL. School difficulties at adolescence in a regional cohort of children who were extremely low birth weight. Pediatrics. 2000;105:325–331. 6. Huddy CL, Johnson A, Hope PL. Educational and behavioural problems in babies of 32-35 weeks gestation. Arch Dis Child Fetal Neonatal Ed. 2001;85:F23–F28. 7. Nomura Y, Halperin JM, Newcorn JH, et al. The risk for impaired learning-related abilities in childhood and educational attainment among adults born near-term. J Pediatr Psychol. 2009;34:406–418. 8. Chyi LJ, Lee HC, Hintz SR, Gould JB, Sutcliffe TL. School outcomes of late preterm infants: special needs and challenges for infants born at 32 to 36 weeks gestation. J Pediatr. 2008;153:25–31. 9. Yang S, Bergvall N, Cnattingius S, Kramer MS. Gestational age differences in health and development among young Swedish men born at term. Int J Epidemiol. 2010;39:1240–1249. 10. Tam EW, Haeusslein LA, Bonifacio SL, et al. Hypoglycemia is associated with increased risk for brain injury and adverse neurodevelopmental outcome in neonates at risk for encephalopathy. J Pediatr. 2012; 161:88–93. 11. Bhutta AT, Anand KJ. Abnormal cognition and behavior in preterm neonates linked to smaller brain volumes. Trends Neurosci. 2001;24:129– 130; discussion 31–32. 12. Bhutta AT, Anand KJ. Vulnerability of the developing brain. Neuronal mechanisms. Clin Perinatol. 2002;29:357–372. 13. de Jong M, Verhoeven M, van Baar AL. School outcome, cognitive functioning, and behaviour problems in moderate and late preterm children and adults: a review. Semin Fetal Neonatal Med. 2012;17: 163–169. 14. Ekeus C, Lindström K, Lindblad F, Rasmussen F, Hjern A. Preterm birth, social disadvantage, and cognitive competence in Swedish 18- to 19-yearold men. Pediatrics. 2010;125:e67–e73. 15. Svensson AC, Sandin S, Cnattingius S, et al. Maternal effects for preterm birth: a genetic epidemiologic study of 630,000 families. Am J Epidemiol. 2009;170:1365–1372. 16. Cnattingius S, Ericson A, Gunnarskog J, Källén B. A quality study of a medical birth registry. Scand J Soc Med. 1990;18:143–148. 17. Statistics Sweden. Longitudinal integration database for health insurance and labour market studies (Longitudinal Integration Database for Sick Insurance and Labor Market Study). Available at http://www.scb.se/LISA. Accessed October 1, 2011. 18. Ekbom A. The Swedish Multi-generation Register. Methods Mol Biol. 2011;675:215–220. 19. Marsál K, Persson PH, Larsen T, Lilja H, Selbing A, Sultan B. Intrauterine growth curves based on ultrasonically estimated foetal weights. Acta Paediatr. 1996;85:843–848. 20. Record RG, McKeown T, Edwards JH. The relation of measured intelligence to birth order and maternal age. Ann Hum Genet. 1969;33: 61–69. 21. Record RG, McKeown T, Edwards JH. The relation of measured intelligence to birth weight and duration of gestation. Ann Hum Genet. 1969;33:71–79. 22. Bergvall N, Iliadou A, Tuvemo T, Cnattingius S. Birth characteristics and risk of low intellectual performance in early adulthood: are the associations confounded by socioeconomic factors in adolescence or familial effects? Pediatrics. 2006;117:714–721. 23. Lawlor DA, Clark H, Smith GD, Leon DA. Intrauterine growth and intelligence within sibling pairs: findings from the Aberdeen children of the 1950s cohort. Pediatrics. 2006;117:e894–e902. 24. Lawlor DA, Bor W, O’Callaghan MJ, Williams GM, Najman JM. Intrauterine growth and intelligence within sibling pairs: findings from the Mater-University study of pregnancy and its outcomes. J Epidemiol Community Health. 2005;59:279–282.
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