Perinatal Risk Factors and ADHD in Children and Adolescents: A Hierarchical Structure of Disorder Predictors

Abstract

Objective: The aim of the study was to hierarchically assess the predictive power of low and high birth weight, pre-term and post-term birth, and low Apgar score as the risk factors for ADHD. Method: The data of 132 boys diagnosed with ADHD and 146 boys from control group, aged 6 to 18 years, have been analyzed. The boys were categorized according to term of birth, birth weight, and Apgar score. CART method (Classification and Regression Trees) was used for assessment of the relationship between perinatal factors and the risk of ADHD. Results: Low Apgar score (21.97% vs. 13.01%) and post-term birth (12.12% vs. 0.68%) were more frequent in the sample than in the control group. CART method additionally indicated low birth weight as associated with the risk of ADHD. Among analyzed risk factors, Apgar score had the highest predictive value. Conclusion: The decreased Apgar score is the most important perinatal risk factor of ADHD. Research results also indicated a high significance of post-term birth in predicting the disorder.

Keywords

ADHD perinatal factors Apgar birth weight term of birth

Introduction

ADHD is a neuropsychological disorder manifesting in attention deficits, impulsivity, and increased motor activity (Milberger, Biederman, Faraone, Guite, & Tsuang, 1997). The worldwide prevalence of ADHD in childhood is approximately 5% (Polanczyk, de Lima, Horta, Biederman, & Rohde, 2007).

Genetic research demonstrated a relationship between ADHD and, among others, dopamine transporter gene (DAT) and human dopamine receptor D4 gene (DRD4; Biederman, 2005). However, irrespective of genetic factors, a higher risk of ADHD was also associated with harmful environmental conditions affecting the fetus, such as alcohol (Mick, Biederman, Faraone, Sayer, & Kleinman, 2002), nicotine (Sagiv, Epstein, Bellinger, & Korrick, 2013), toxins (Ribs-Fito et al., 2007), maternal stress (Motlagh et al., 2010) and glucocorticoids (French, Hagan, Evans, Mullan, & Newham, 2004), illicit drug use during pregnancy (Milberger et al., 1997; Sagiv et al., 2013), and maternal bleeding (Milberger et al., 1997). Many studies also assessed the relationship between ADHD and general indicators of sub-optimal in utero environment, such as a small size at birth (Heinonen et al., 2010), pre-term birth (Perricone, Morales, & Anzalone, 2013), or a low Apgar score at 5 min (Li, Olsen, Vestergaard, & Obel, 2011). The results suggest that a higher risk of ADHD is associated with pre-term birth (Lindström, Lindblad, & Hjern, 2011), being small for gestational age (SGA; Heinonen, Järvenpää, Eriksson, & Andersson, 2008), extremely low birth weight (Saigal, Pinelli, Hoult, Kim, & Boyle, 2004), very low birth weight (Indredavik et al., 2004), or low birth weight (Boulet, Schieve, & Boyle, 2011). However, some studies demonstrated no relationship between ADHD and pre-term birth (Harris et al., 2013; Heinonen et al., 2010; Sagiv et al., 2013), low birth weight (Halmøy, Klungsøyr, Skjaerven, & Haavik, 2012), or SGA (Indredavik et al., 2004).

Few studies took into account birth term, birth weight, and Apgar score (Halmøy et al., 2012; Li et al., 2011; Lindström et al., 2011). The results suggested that a low Apgar score, low birth weight, and pre-term birth increase the risk of ADHD in children independently from each other. Nevertheless, none of the conducted studies evaluated the internal hierarchy of importance of these factors in assessing the risk of ADHD in children and adolescents. In addition, to the best of our knowledge, a relationship between ADHD, post-term birth, and high birth weight has not been analyzed yet, whereas those two factors can result in perinatal complications and hypoxia in children (Joseph, 2011; Sjaarda et al., 2014).

The aim of the study is to assess the hierarchy of perinatal factors that can increase the risk of ADHD, taking into account high birth weight and post-term birth. We have assumed that both pre-term and post-term birth, low and high birth weight, and a low Apgar score increase the risk of ADHD, and the assessment of the newborn infant’s general condition according to Apgar scale is of the greatest predictive importance.

Materials and Method

Ethical Statements

The study has been approved by the Ethics Committee of the Poznan University of Medical Sciences and has, therefore, been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. The participants and their legal guardians were fully informed about research procedures and gave written consent to participate in the study.

Participants

The research was carried out in the years 2005 to 2008. The aim of the study was to assess the growth of children with ADHD. Boys aged 6 to 18 years were recruited. We have collected the data of boys with clinically diagnosed ADHD and of a group of boys selected according to age, without psychiatric symptoms and coming from a community-based sample (Hanć, Słopień, Wolańczyk, Dmitrzak-Węglarz, et al., 2015). The data from this study have been previously analyzed for the assessment of growth in a subsample of treatment-naive school-age children with ADHD (Hanć, Cieślik, Wolańczyk, & Gajdzik, 2012) and assessment of the relation between ADHD and obesity (Hanć, Słopień, Wolańczyk, Dmitrzak-Węglarz, et al., 2015; Hanć, Słopień, Wolańczyk, Szwed, et al., 2015).

Both in the clinical and control group, inclusion criteria comprised of the agreement of legal guardians to examination and the age of boys (between 6 and 18 years). Data on the boys’ health were collected during clinical assessment. For the purposes of the analyses for the present study, boys with ADHD-comorbid depression, anxiety disorders, anorexia, or bulimia were excluded from the clinical group. Boys with suspected psychiatric disorders were excluded from the control group. Individuals with endocrine disorders were excluded from both groups.

Information concerning the pregnancy, delivery, and newborn infants’ general condition was collected with the use of medical registries. Questionnaires filled in by parents provided, among others, demographic data and data concerning parents’ education and socioeconomic status (SES) of the family as well as children’s health.

Psychiatric Assessment

Boys with ADHD were recruited in psychiatric clinics and university outpatient clinics. Diagnoses were confirmed by a team including a psychiatrist and a psychologist on the basis of the Conners’ Parent Rating Scale and the Diagnostic Structured Interview for ADHD and Hyperkinetic Disorder according to International Classification of Diseases (ICD-10; World Health Organization, 1994) and Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.; DSM-IV-TR; American Psychiatric Association, 2000; Wolańczyk & Kołakowski, 2005). Diagnosis was assumed as confirmed if both methods indicated that a child fulfills the ADHD diagnostic criteria. The Diagnostic Structured Interview for ADHD and Hyperkinetic Disorder according to ICD-10 and DSM-IV-TR also contains questions concerning oppositional defiant disorder and conduct disorder (Wolańczyk & Kołakowski, 2005). Mood, anxiety, and eating disorders were diagnosed based on data from standard diagnostic psychiatric interview and mental state assessment, according to diagnostic criteria of ICD-10 (WHO, 1994) and DSM-IV-TR (American Psychiatric Association, 2000).

In the case of the control group, only boys with no suspicion of mental problems were enrolled. Assessment of mental health was based on parents’ responses to the following questions proposed and used with success in other research (Warring & Lapane, 2008): (a) “Has a doctor or health professional ever told you that your child has attention-deficit disorder, attention-deficit/hyperactive disorder, that is, Attention Deficit Disorder (ADD) or ADHD?” (b) “Has a doctor or health professional ever told you that your child has psychiatric disorders?” (c) “Has a doctor or health professional ever told you that your child has oppositional defiant disorder, conduct disorder, emotional problems, such as depression or anxiety disorder?” Only boys whose parents had answered “no” to the above questions were included in the control group.

Family Profile

For the purpose of this study, we had decided to exclude children from families with low SES (family income level lower than PLN (Polish New Złoty) 4,000 net, based on the data of the Central Statistical Office for the years 2005-2008; Główny Urząd Statystyczny, 2015) and children of parents with low education (at least one parent having only elementary education). Therefore, the compared groups were homogeneous in terms of factors influencing newborn infants’ condition, as have been confirmed in many studies (Kosińska, 2011; Lim, Park, Park, & Kim, 2012; Verropoulou & Basten, 2014; Young, Weinberg, Vieira, Aschengrau, & Webster, 2010). Similarly, strict methods of preselection were previously employed in large projects aimed at establishing international standards of development from the conception to the birth (INTERGROWTH-21st; Villar et al., 2013; Villar et al., 2014), and from birth to 5 years of age (WHO Multicentre Growth Reference Study [MGRS]; Garza & de Onis, 2004; WHO, 2006), where the sample was selected, among others, according to education level and SES.

Newborn Infant Health Evaluation

The participants were divided according to obtained Apgar score, term of birth, and birth weight. Newborn infants born before 37th week of pregnancy were classified as pre-term-born, and those born after 42nd week of pregnancy as post-term-born (WHO, 2004). Irrespective of term of birth, birth weight below 2,500 g was classified as low, and birth weight above 4,000 g as high (Centers for Disease Control and Prevention [CDC], 2009).

Similarly as in previous research (Halmøy et al., 2012; Li et al., 2011; Lindström et al., 2011), Apgar score at 5 min was used in the study. This indicator of newborns’ health had been proved to be a valid predictor of neonatal mortality, neurological health, and cognitive development (Almeida et al., 2008; Casey, McIntire, & Leveno, 2001; Ehrenstein, 2009; Odd, Rasmussen, Gunnell, Lewis, & Whitelew, 2008; Stark et al., 2006). For the purpose of hierarchical evaluation of risk factors, the following two categories of Apgar score were distinguished: score of 9 to 10, standing for a very good general condition of a newborn infant (the newborn infant does not require additional medical examination or observation), and score <9, which may stand for a necessary observation of the newborn infant. The second category includes newborn infants with a wide range of symptoms, which may cause difficulties in adaptation to extra-uterine life. The above way of categorization was adopted following Li, Olsen, Vestergaard, and Obel (2011).

Data Analysis

Initial assessment of differences between children with ADHD and the control group included two-tailed Student’s t test and chi-square test. CART method (Classification and Regression Trees) was used to assess the influence of potential interactions between the examined variables on the risk of ADHD. Gini splitting criterion was adopted as a measure of goodness of fit. Gini index measures how often a randomly chosen element would be incorrectly labeled if it were randomly labeled according to the distribution of labels in the subset. As an impurity measure, Gini index reaches a value of zero if only one class is present at a node and reaches its maximum value when sizes of the class at the node are equal (StatSoft, 2013). Selection of a tree of the right size was made with the adoption of the minimal subset size of 10 and standard error rule of 1.0 as stopping parameters (Breiman, Friedman, Olshen, & Stone, 1998; Ripley, 1996). CART method also enabled putting the examined risk factors in order of their predictive value—from 0 that stood for a low predictive value to 100 that stood for a high predictive value.

Results

The examined sample consisted of 132 boys with ADHD, without comorbid mood and anxiety disorders, and 146 boys in the control group, without suspicion of mental problems, for whom complete information about Apgar score (5 min), birth term, and birth weight was obtained. All of them came from cities with more than 100,000 residents and were brought up in families characterized by at least average income level and parents’ level of education higher than elementary.

Age of the boys at the time of recruitment did not differ significantly between the groups (ADHD: 11.05 ± 2.63; Control group: 10.69 ± 1.48, t = 1.40, p = .16). ADHD combined subtype occurred in 90 (68.18%) boys, attention deficits disorder in 31 (23.48%) boys, and hyperactive/impulsive subtype in 11 (8.34%) boys. Comorbid diagnosis of oppositional defiant disorder was made in 64 (48.49%) boys, while conduct disorder was observed in 15 (11.36%) boys with ADHD.

Unadjusted analyses did not show statistically significant differences in parents’ age at the time of birth between boys with and without ADHD. The groups also did not differ in birth weight. Statistically significant differences were demonstrated for birth term (χ² = 16.40, p < .001) and Apgar score (χ² = 3.90, p = .04). Pre-term birth was more frequent in boys from the control group (14.38% vs. 9.85%), and post-term birth was more frequent in boys with ADHD (12.12% vs. 0.68%). In addition, boys with ADHD more often had a lower Apgar score (<9 scores: 21.97% vs. 13.01%) (Table 1).

Table 1.

Estimation of Differences in Analyzed Variables Between Boys With and Without ADHD.

Characteristics	Boys with ADHD(n = 132)M ± 1SD or n (%)	Boys without ADHD(n = 146)M ± 1SD or n (%)	Differences between groups of boys with and without ADHD(2-tailed t test or χ² and p)
Mother’s age at birth
<25 y	56 (42.42)	74 (50,68)	χ² = 1.92, p = .38
25-35 y	66 (50)	62 (42.47)
>25 y	10 (7.58)	10 (6.85)
Father’s age at birth
<25 y	36 (27.27)	42 (28.77)	χ² = 0.09, p = .96
25-35 y	76 (57.58)	83 (56.85)
>25 y	20 (15.15)	21 (14.38)
Birth term
At term	103 (78.03)	124 (84.93)	χ² = 16.40, p < .001
Pre-term	13 (9.85)	21 (14.38)
Post-term	16 (12.12)	1 (0.68)
Body weight at birth (g)	3378 ± 555.33	3488 ± 455.48	t = −1.82, p = .07
Low birth weight	7 (5.30)	3 (2.05)	χ² = 2.21, p = .33
Normal birth weight	110 (83.33)	124 (84.93)
High birth weight	15 (11.36)	19 (13.01)
Apgar score	9.11 ± 1.44	8.87 ± 2.21	t = 1.07, p = .28
<9	29 (21.97)	19 (13.01)	χ² = 3.90, p = .04
9-10	103 (78.03)	127 (86.99)

Note. y = years; n = numbers; % = percentage; SD = standard deviation; t = value of Student’s t test; χ² = value of χ² test; p = the level of significance; bold = differences significant at the level of p < .05.

CART method generated a classification tree including three divisions, which resulted in four terminal nodes. The root node (t1) comprises all the examined children (boys without ADHD: N₁ = 146, boys with ADHD: N₂ = 132). Other nodes show the number of a given node (N) and the number and percentage of boys in the control group and boys with ADHD (N₁ and N₂, respectively). Node t1 was divided based on the term of birth. Full-term- and pre-term-born children together formed node t2, while post-term-born children formed terminal node t3. A significant majority of individuals included in node t3 were boys with ADHD (12.12% of the sample vs. 0.69% of the control group). Division of node t2 was based on birth weight. Terminal node t4 consisted of children with low birth weight, and boys with ADHD are predominant in that node (5.31% of the sample vs. 2.05% of the control group). Node t5 included children with normal and high birth weight (97.26% of the control group and 82.57% of boys with ADHD) and was divided into two terminal nodes t6 and t7 due to Apgar score. Node t6 included children with Apgar score of 9 to 10. It accounted for 85.61% of boys from the control group and 66.67% of boys with ADHD. Node t7 were children with Apgar score below 9 (11.64% of the control group and 15.44% of boys with ADHD) (Figure 1).

Figure 1.

Classification tree of ADHD risk factors.

CART method indicated Apgar score as the most important predictive factor (predictor importance = 100). The factor of the lowest importance was birth weight (predictor importance = 55). Birth term held an intermediate place on the scale of importance (predictor importance = 80) (Figure 2).

Figure 2.

Validity ranking of the analyzed factors significantly increase the risk of ADHD (CART method).

Discussion

The aim of the study was to assess the hierarchy of perinatal risk factors of ADHD. For this purpose, a low Apgar score, prevalence of pre-term and post-term birth, and low and high birth weight were assessed in a group of boys with ADHD and in the control group. The adopted exclusion criteria eliminated potential influence of SES, parents’ education, place of residence, and some ADHD-comorbid disorders on the results.

The majority of available papers demonstrated an increased risk of ADHD in children with subnormal birth weight (Anderson et al., 2011; Botting, Powls, Cooke, & Marlow, 1997; Indredavik et al., 2004; Saigal et al., 2004) and in pre-term-born children (Anderson et al., 2011; Lindström et al., 2011; Perricone et al., 2013). Our adjusted analysis also revealed a relationship between low birth weight and an increased risk of ADHD. However, the percentage of premature infants was higher in the control group than among boys with ADHD. The lack of a relationship between ADHD and pre-term birth has also been demonstrated in several papers published in the recent years (Halmøy et al., 2012; Harris et al., 2013; Heinonen et al., 2010). Differences in the obtained results can be due to different methodology and selection criteria. In our study, we excluded the influence of low SES and low parents’ education, which can be factors associating ADHD with pre-term birth and low birth weight. Both ADHD (Claycomb, Ryan, Miller, & Schnakenberg-Ott, 2004; Sagiv et al., 2013) and the mentioned perinatal disorders (Kosińska, 2011; Lim et al., 2012; Verropoulou & Basten, 2014; Young et al., 2010) can be related to parents’ worse living conditions.

We observed a distinct relationship between an increased risk of ADHD and post-term birth, which, according to our knowledge, was not analyzed in earlier studies. The prevalence of post-term birth is 8.1% of all live births in Europe (Zeitlin, Blondel, Alexander, Breart, & Group, 2007) and 5.6% in the United States (Martin et al., 2009). It is related to perinatal mortality and morbidity, such as operative delivery, low Apgar score, and macrosomia (Olesen, Westergaard, & Olsen, 2003; Schierding, O’Sullivan, Derraik, & Cutfield, 2014), as well as adverse neurodevelopmental outcomes in pre-school children (Lindström, Fernell, & Westgren, 2005). Post-term birth can also be related to perinatal hypoxia, which is an important risk factor in later attention deficits, hyperactivity, and impulsivity (Getahun et al., 2013; Golubnitschaja, Yeghiazaryan, Cebioglu, Morelli, & Herrera-Marschitz, 2011). Therefore, the relationship between post-term delivery and an increased risk of ADHD seems valid; however, it needs to be confirmed.

Only few earlier studies analyzing the background of ADHD assessed the combined effect of birth weight, birth term, and Apgar score. The results suggest that these factors are associated with ADHD independently from each other (Halmøy et al., 2012; Li et al., 2011; Lindström et al., 2011). However, the structure of the relationships between predictive variables and predictive importance of individual factors has not been examined so far. Using the CART method, we confirmed the results of unadjusted analysis, which showed that both post-term birth and a low Apgar score are factors increasing the risk of ADHD. The analysis also indicated low birth weight as a risk factor of ADHD. The relationship between low birth weight and ADHD was revealed in a group of pre-term- and full-term-born children, whereas the relationship between low Apgar score was revealed even after previously excluding fractions of post-term-born boys and boys with low birth weight from the compared groups.

Predictive importance of a low Apgar score is confirmed by the ranking of importance of the assessed predictors, where that variable was ranked the highest. The considerable predictive importance of Apgar score presumably results from its general nature. The score assesses also such nonspecific indicators of newborn infant health as appearance, pulse rate, reflex irritability, activity and muscle tone, and respiratory effort (Finster & Wood, 2005). Hence, a lower Apgar score can be related to many complications with pregnancy and delivery, to both pre-term and post-term birth (Kitlinski, Källén, Marsál, & Olofsson, 2003), and it can also be given to a full-term-born newborn infant with normal birth weight but with other complications. Therefore, it can only generally be assumed that a higher risk of ADHD can be observed in newborn infants after difficult delivery and/or in a worse general condition, and having difficulties in adaptation to extra-uterine environment. Nevertheless, similar to earlier studies, our research confirms that Apgar score is an important predictor of neurodevelopmental disorders (Larson et al., 2005; Li et al., 2011; Odd et al., 2008).

On the basis of the classification tree, it can be concluded that perinatal risk factors had been observed in as much as 33.33% of boys with ADHD. It proves a strong relationship between the course of pregnancy and delivery and the risk of ADHD. However, one should note that terminal node t6 still includes a large group of children with ADHD, that is, 66.67%. It proves the importance of other factors (genetic or environmental) increasing the risk of ADHD, which were not addressed in this study, and which do not necessarily result in worse general condition of a newborn infant.

The limitation of the study is its retrospective nature; therefore, it was not possible to assess the accuracy of measurement of birth weight and Apgar score. In the study, we did not control for many psychiatric disorders, which can be comorbid with ADHD. Exclusion criteria comprised of depression, anxiety disorders, anorexia, or bulimia; however, they are not a complete list of ADHD comorbidities. Some doubts can also be aroused by the method of selecting the control group. For this purpose, we used questions previously asked by other researchers in screening examinations (Finster & Wood, 2005; Getahun et al., 2013; Golubnitschaja et al., 2011), which cannot replace clinical diagnosis of the disorder. The initial study focused on multifactor determinants of growth and obesity, and the procedure consisted of many questionnaires and inventories. On the other hand, we have tried to achieve the greatest possible sample size. We assumed that detailed assessment of psychiatric disorders in control group might discourage parents and their children to participate in the study. Therefore, we decided to use the method that was less detailed but more suitable for population study. Although we cannot exclude that the control group also contains individuals with undiagnosed emotional disorders, we are sure that the employed procedure limited the possibility as much as possible.

It is possible that mode of delivery may also be involved with the risk of ADHD. One recent study found higher levels of symptoms of ADHD in children born at term by Caesarean section, but only if preceded by induced labor (Talge, Allswede, & Holzman, 2016). Most of other studies have not shown, however, the relationship between Caesarean section and an increased risk of ADHD (Curran et al., 2016; Curran et al., 2015). Because in our study we gathered data on the type of delivery only for children with ADHD, it was therefore not possible to carry out analysis of relationship of Caesarean section with the risk of the disorder. Our records indicate that 20% of children with ADHD in our sample (children born in 1987-2002) were born by Caesarean section. For comparison, the rate of births by Caesarean section in Poland was 18.5% in 1991 (Słupczyński, Jezierska, & Ratoń, 1996) and 29.2% in 2001-2002 (Stasiełuk, Langowicz, Kosińska-Kaczyńska, Pietrzak, & Wielgoś, 2012). The comparison of the data suggests that Caesarean section is not more common in children with ADHD and, therefore, does not increase the risk of the disorder.

Conclusion

A reduced Apgar score was determined as the most important among the assessed risk factors of ADHD in the research aided by hierarchical analysis with simultaneous control of many disturbing factors. The obtained results also indicated the necessity of control of post-term birth as an important predictor of ADHD, not assessed in earlier studies.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The research was partially supported by the Polish Ministry of Science and Higher Education (Grant N N303 0175 33).

Author Biographies

Tomasz Hanć is an adjunct in the Department of Human Biological Development, Institute of Anthropology, Faculty of Biology, Adam Mickiewicz University in Poznań, an auxologist, psychologist, and a cognitive-behavioral therapist.

Anita Szwed is an assistant professor in the Department of Human Biological Development, Institute of Anthropology, Faculty of Biology, Adam Mickiewicz University in Poznań, a physical anthropologist and auxologist. Her research examines the environmental factors affecting optimal human development across the life span.

Agnieszka Słopień is head of the Department of Child and Adolescent Psychiatry of Poznan University of Medical Sciences, Poland, since 2015. She is a specialist in psychiatry, child and adolescent psychiatry, and is a certified psychotherapist. She is author of 130 scientific publications.

Tomasz Wolańczyk is head of the Department of Child Psychiatry of Warsaw Medical University, Poland, since 2002. He is a specialist in neurology, child psychiatry, and cognitive-behavioral therapist. He is author of 130 scientific publications.

Monika Dmitrzak-Węglarz is an assistant professor in the Department of Psychiatric Genetics (Poznan University of Medical Sciences), a specialist in medical laboratory genetics. Her current research interests include genetics background of psychiatric disorders especially with disturbances of circadian rhythms and neurodevelopmental components. She is author of more than 70 scientific publications.

Joanna Ratajczak is a doctoral student in the Department of Human Biological Development, Institute of Anthropology, Faculty of Biology, Adam Mickiewicz University in Poznań. Her current research interests include the impact of stress and emotional reactivity on biological development of children at early school age.

References

Almeida

M. F.

Guinsburg

Martinez

F. E.

Procianoy

R. S.

Leone

C. R.

Marba

S. T.

. . . Lopes

J. M.

(2008). Perinatal factors associated with early deaths of preterm infants born in Brazilian Network on Neonatal Research centers. Jornal de Pediatria, 84, 300-307.

American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4th ed., text rev.). Washington, DC: Author.

Anderson

P. J.

De Luca

C. R.

Hutchinson

Spencer-Smith

M. M.

Roberts

Doyle

L. W.

(2011). Attention problems in a representative sample of extremely preterm/extremely low birth weight children. Developmental Neuropsychology, 36, 57-73.

Biederman

(2005). Attention-deficit/hyperactivity disorder: A selective overview. Biological Psychiatry, 57, 1215-1220.

Botting

Powls

Cooke

Marlow

(1997). Attention deficit hyperactivity disorder and other psychiatric outcomes in very low birth weight children at 12 years of age. Journal of Child Psychology and Psychiatry, 38, 931-941.

Boulet

S. I.

Schieve

L. A.

Boyle

C. A.

(2011). Birth weight and health and developmental outcomes in US children, 1997-2005. Maternal and Child Health Journal, 15, 836-844.

Breiman

Friedman

J. H.

Olshen

R. A.

Stone

C. J.

(1998). Classification and regression trees. Boca Raton, FL: Chapman & Hall/CRC.

Casey

B. M. C.

McIntire

D. D.

Leveno

A. K.

(2001). The continuing value of the Apgar score for the assessment of newborn infants. The New England Journal Medicine, 344, 467-471.

Centers for Disease Control and Prevention. (2009). PedNSS health indicators. Retrieved from http://www.cdc.gov/pednss/what_is/pednss_health_indicators.htm

10.

Claycomb

C. D.

Ryan

J. J.

Miller

L. J.

Schnakenberg-Ott

S. D.

(2004). Relationships among attention deficit hyperactivity disorder, induced labor, and selected physiological and demographic variables. Journal of Clinical Psychology, 60, 689-693.

11.

Curran

E. A.

Cryan

J. F.

Kenny

L. C.

Dinan

T. G.

Kearney

P. M.

Khashan

A. S.

(2016). Obstetrical mode of delivery and childhood behavior and psychological development in a British cohort. Journal of Autism Developmental Disorder, 46, 603-614.

12.

Curran

E. A.

O’Neill

S. M.

Cryan

J. F.

Kenny

L. C.

Dinan

T. G.

Khashan

A. S.

Kearney

P. M.

(2015). Research Review: Birth by caesarean section and development of autism spectrum disorder and attention-deficit/hyperactivity disorder: A systematic review and meta-analysis. Journal of Child Psychology and Psychiatry, 56, 500-508.

13.

Ehrenstein

(2009). Association of Apgar scores with death and neurologic disability. Clinical Epidemiology, 1, 45-53.

14.

Finster

Wood

(2005). The Apgar score has survived the test of time. Anesthesiology, 102, 855-857.

15.

French

N. P.

Hagan

Evans

S. F.

Mullan

Newham

J. P.

(2004). Repeated antenatal corticosteroids: Effects on cerebral palsy and childhood behavior. American Journal of Obstetrics & Gynecology, 190, 588-595.

16.

Garza

de Onis

(2004). Rationale for developing a new international growth reference. Food and Nutrition Bulletin, 25, 5-14.

17.

Getahun

Rhoads

G. G.

Demissie

S. E.

Quinn

V. P.

Fassett

M. J.

. . . Jacobsen

J. S.

(2013). In utero exposure to ischemic-hypoxic conditions and attention-deficit/hyperactivity disorder. Pediatrics, 131, e53-e61.

18.

Główny Urząd Statystyczny. (2015). Przeciętne miesięczne wynagrodzenie w gospodarce narodowej w latach 1950-2015 [Average monthly gross wage and salary in national economy (1950-2015)]. Retrieved from http://stat.gov.pl/en/topics/labour-salaries/working-employed-wages-and-salaries-cost-of-labour/average-monthly-gross-wage-and-salary-in-national-economy-1950-2015,2,1.html

19.

Golubnitschaja

Yeghiazaryan

Cebioglu

Morelli

Herrera-Marschitz

(2011). Birth asphyxia as the major complication in newborns: Moving towards improved individual outcomes by prediction, targeted prevention and tailored medical care. EPMA Journal, 2, 197-210.

20.

Halmøy

Klungsøyr

Skjaerven

Haavik

(2012). Pre- and perinatal risk factors in adults with attention-deficit/hyperactivity disorder. Biological Psychiatry, 71, 474-481.

21.

Hanć

Cieślik

Wolańczyk

Gajdzik

(2012). Assessment of growth in pharmacological treatment-naïve Polish boys with attention-deficit/hyperactivity disorder. Journal of Child and Adolescent Psychopharmacology, 22, 300-306.

22.

Hanć

Słopień

Wolańczyk

Dmitrzak-Węglarz

Szwed

. . . Cieślik

(2015). ADHD and overweight in boys: Cross-sectional study with birth weight as a controlled factor. European Child & Adolescent Psychiatry, 24, 41-53.

23.

Hanć

Słopień

Wolańczyk

Szwed

Czapla

Durda

. . . Ratajczak

(2015). Attention-deficit/hyperactive disorder is related to decreased weight in preschool period and increased rate of overweight in school age boys. Journal of Child and Adolescent Psychopharmacology, 25, 691-700.

24.

Harris

M. N.

Voigt

R. G.

Barbaresi

W. J.

Voge

G. A.

Killian

J. M.

Weaver

A. L.

. . . Katusic

S. K.

(2013). ADHD and learning disabilities in former late preterm infants: A population-based birth cohort. Pediatrics, 132, e630-e636.

25.

Heinonen

Järvenpää

A. L.

Eriksson

J. G.

Andersson

(2008). Very low birth weight and behavioral symptoms of attention deficit hyperactivity disorder in young adulthood: The Helsinki study of very-low-birth-weight adults. American Journal of Psychiatry, 165, 1345-1353.

26.

Heinonen

Räikkönen

Pesonen

A. K.

Andersson

Kajantie

Eriksson

J. G.

. . . Lano

(2010). Behavioral symptoms of attention deficit/hyperactivity disorder in preterm and term children born small and appropriate for gestation age: A longitudinal study. BMC Pediatrics, 10, Article 91. doi:10.1186/1471-2431-10-91

27.

Indredavik

M. S.

Vik

Heyerdahl

Kulseng

Fayers

Brubbak

A. M.

(2004). Psychiatric symptoms and disorders in adolescents with low birth weight. Archives of Disease in Childhood: Fetal & Neonatal Edition, 89, F445-F450.

28.

Joseph

K. S.

(2011). The natural history of pregnancy: Diseases of early and late gestation. BJOG: An International Journal of Obstetrics and Gynaecology, 118, 1617-1629.

29.

Kitlinski

M. L.

Källén

Marsál

Olofsson

(2003). Gestational age-dependent reference values for pH in umbilical cord arterial blood at term. Obstetrics & Gynecology, 102, 338-345.

30.

Kosińska

(2011). Stan okołourodzeniowy potomstwa jako efekt interakcji układu płód-matka-środowisko [Perinatal outcome of offspring as the effects of interactions of the fetus-mother-environment system factors]. Poznań, Poland: Wydawnictwo Naukowe UAM.

31.

Larson

H. J.

Eaton

W. W.

Madsen

K. M.

Vestergaard

Olesen

A. V.

Agerbo

. . . Mortensen

P. B.

(2005). Risk factors for autism: Perinatal factors, parental psychiatric history, and socioeconomic status. American Journal of Epidemiology, 161, 916-925.

32.

Olsen

Vestergaard

Obel

(2011). Low Apgar scores and risk of childhood attention deficit hyperactivity disorder. Journal of Pediatrics, 158, 775-779.

33.

Lim

H. T.

Park

H. Y.

Park

S. M.

Kim

H. S.

(2012). The associations of parental under-education and unemployment on the risk of preterm birth: 2003 Korean National Birth Registration database. International Journal of Public Health, 57, 253-260.

34.

Lindström

Fernell

Westgren

(2005). Developmental data in preschool children born after prolonged pregnancy. Acta Paediatrica, 94, 1192-1197.

35.

Lindström

Lindblad

Hjern

(2011). Preterm birth and attention deficit/hyperactivity disorder in schoolchildren. Pediatrics, 127, 858-865.

36.

Martin

Hamilton

Sutton

Ventura

S. J.

Menacker

Kirmeyer

Mathews

M. S.

(2009). Births: Final data for 2006. National Vital Statistics Reports, 57, 1-104.

37.

Mick

Biederman

Faraone

S. V.

Sayer

Kleinman

(2002). Case-control study of attention-deficit hyperactivity disorder and maternal smoking, alcohol use, and drug use during pregnancy. Journal of the American Academy of Child & Adolescent Psychiatry, 41, 378-385.

38.

Milberger

Biederman

Faraone

S. V.

Guite

Tsuang

M. T.

(1997). Pregnancy, delivery and infancy complications and attention deficit hyperactivity disorder: Issues of gene-environment interaction. Biological Psychiatry, 41, 65-75.

39.

Motlagh

M. G.

Katsovich

Thompson

Lin

Kim

Y. S.

. . . Leckman

J. F.

(2010). Severe psychosocial stress and heavy cigarette smoking during pregnancy: An examination of the pre- and perinatal risk factors associated with ADHD and Tourette syndrome. European Child & Adolescent Psychiatry, 19, 755-764.

40.

Odd

D. E.

Rasmussen

Gunnell

Lewis

Whitelew

(2008). A cohort study of low Apgar scores and cognitive outcomes. Archives of Disease in Childhood: Fetal & Neonatal Edition, 93, F115-F120.

41.

Olesen

A. W.

Westergaard

J. G.

Olsen

(2003). Perinatal and maternal complications related to postterm delivery: A national register-based study, 1978-1993. American Journal of Obstetrics & Gynecology, 189, 222-227.

42.

Perricone

Morales

M. R.

Anzalone

(2013). Neurodevelopmental outcomes of moderately preterm birth: Precursors of attention deficit hyperactivity disorder at preschool age. SpringerPlus, 2, Article 221. doi:10.1186/2193-1801-2-221

43.

Polanczyk

de Lima

M. S.

Horta

B. L.

Biederman

Rohde

L. A.

(2007). The worldwide prevalence of ADHD: A systematic review and metaregression analysis. American Journal of Psychiatry, 164, 942-948.

44.

Ribs-Fito

Torrent

Carrizo

Julvez

Grimalt

J. O.

Sunyer

(2007). Exposure to hexachlorobenzene during pregnancy and children’s social behavior at 4 years of age. Environmental Health Perspectives, 115, 447-450.

45.

Ripley

B. D.

(1996). Pattern recognition and neural networks. Cambridge, UK: Cambridge University Press.

46.

Sagiv

S. K.

Epstein

J. N.

Bellinger

D. C.

Korrick

S. A.

(2013). Pre- and postnatal risk factors for ADHD in a nonclinical pediatric population. Journal of Attention Disorders, 17, 47-57.

47.

Saigal

Pinelli

Hoult

Kim

M. M.

Boyle

(2004). Psychopathology and social competencies of adolescents who were extremely low birth weight. Pediatrics, 111, 969-975.

48.

Schierding

O’Sullivan

J. M.

Derraik

J. G. B.

Cutfield

W. S.

(2014). Genes and post-term birth: Late for delivery. BMC Research Notes, 7, Article 720. doi:10.1186/1756-0500-7-720

49.

Sjaarda

L. A.

Albert

P. S.

Mumford

S. L.

Hinkle

S. N.

Mendola

Laughon

S. K.

(2014). Customized large-for-gestational-age birthweight at term and the association with adverse perinatal outcomes. American Journal of Obstetrics & Gynecology, 210, 63.e1-63.e11. doi:10.1016/j.ajog.2013.09.006

50.

Słupczyński

Jezierska

Ratoń

(1996). Cesarean section performed in a country hospital in Zamość in the years 1980-1992. Ginekologia Polska, 67, 176-180.

51.

Stark

A. R.

Adamkin

D. H.

Batton

D. G.

Bell

E. F.

Bhutani

V. K.

Denson

S. E.

. . . Hawks

(2006). The Apgar score. Pediatrics, 177, 1444-1447.

52.

Stasiełuk

Langowicz

Kosińska-Kaczyńska

Pietrzak

Wielgoś

(2012). Is the epidemic of cesarean sections the result of more liberal indicators? Ginekologia Polska, 8, 604-608.

53.

StatSoft. (2013). Electronic statistics textbook. Tulsa, OK: Author. Retrieved from http://www.statsoft.com/textbook/

54.

Talge

N. M.

Allswede

D. M.

Holzman

(2016). Gestational age at term, delivery circumstance, and their association with childhood attention deficit hyperactivity disorder symptoms. Paediatric and Perinatal Epidemiology, 30, 171-180.

55.

Verropoulou

Basten

(2014). Very low, low and heavy weight births in Hong Kong SAR: How important is socioeconomic and migrant status? Journal of Biosocial Science, 46, 316-331.

56.

Villar

Altman

D. G.

Purwar

Noble

J. A.

Knight

H. E.

Ruyan

. . . Kennedy

S. H.

(2013). The objectives, design and implementation of the INTERGROWTH-21st Project. BJOG: An International Journal of Obstetrics and Gynaecology, 120, 9-26.

57.

Villar

Papageorghiou

A. T.

Pang

Ohuma

E. O.

Cheikh Ismail

Barros

F. C.

. . . Kennedy

S. H.

(2014). The likeness of fetal growth and newborn size across non-isolated populations in the INTERGROWTH-21st Project: The Fetal Growth Longitudinal Study and Newborn Cross-Sectional Study. The Lancet: Diabetes & Endocrinology, 2, 781-792.

58.

Warring

M. E.

Lapane

K. L.

(2008). Overweight in children and adolescents in relation to attention-deficit/hyperactivity disorder: Results from a national sample. Pediatrics, 122, e1-e6. doi:10.1542/peds.2007-1955

59.

Wolańczyk

Kołakowski

(2005). Kwestionariusze do diagnozy ADHD i zaburzeń zachowania [The Diagnostic Structured Interview for ADHD and Hyperkinetic Disorder]. Warsaw, Poland: Janssen-Cilag.

60.

World Health Organization. (1994). International classification of diseases (ICD) (10th revision). Geneva, Switzerland: Author.

61.

World Health Organization. (2004). International statistical classification of disease and related health problems, 10th revision (2nd ed., Vol. 2). Geneva, Switzerland. Retrieved from http://www.who.int/classifications/icd/ICD-10_2nd_ed_volume2.pdf

62.

World Health Organization. (2006). WHO Multicentre Growth Reference Study Group. Assessment of differences in linear growth among populations in the WHO Multicentre Growth Reference Study. Acta Paediatrica, 95(Suppl. 450), 56-65.

63.

Young

R. L.

Weinberg

Vieira

Aschengrau

Webster

T. F.

(2010). A multilevel non-hierarchical study of birth weight and socioeconomic status. International Journal of Health Geographics, 9, Article 36. doi:10.1186/1476-072X-9-36

64.

Zeitlin

Blondel

Alexander

Breart

Group

(2007). Variation in rates of postterm birth in Europe: Reality or artefact? British Journal of Obstetrics and Gynaecology, 114, 1097-1103.