Advances in Neurodevelopmental Disorders

, Volume 2, Issue 2, pp 190–198 | Cite as

Cesarean Section as a Predictor for Autism: a Case-Control Study in Valencia (Spain)

  • Alfredo Perales-Marín
  • Agustín Llópis-González
  • Isabel Peraita-Costa
  • Pablo Cervera-Boada
  • Montserrat Téllez de Meneses
  • Salvador Marí-Bauset
  • María Morales-Suárez-Varela


Growing interest has been shown in recent decades in the role of perinatal factors in relation to autistic spectrum disorders (ASD). Several studies have identified that cesarean sections (CS) could be a risk factor for ASD. The objective was to evaluate the relationship between CS and ASD in childhood as an early indicator for ASD diagnosis. This is a hospital-based nested case-control study in a retrospective cohort of births during 1996–2011. Cases were defined as children diagnosed with ASD at the Neuropediatric Unit of the La Fe Hospital in the last 10 years and controls as children without ASD. After pairing controls with cases for children’s date of birth at a 4:1 ratio, 251 mother-child pairs (53 cases, 198 controls) were studied, for whom information about perinatal risk factors, such as mode of delivery (vaginal vs. CS), and potential confounders was collected. Of the children identified, the control group was made up of 100 boys and 98 girls while the case group included 47 boys and 6 girls. A multivariable conditional logistic regression model was built (matched by children’s date of birth) to assess any potential association in relation with ASD diagnosis, where birth by CS presented a cOR = 3.37 (95% CI 1.57–7.25) of ASD. The adjusted model (for maternal age, child’s sex, gravidity, and gestation weeks) suggested a relation between CS and ASD (aOR = 3.36, 95% CI 1.44–7.85). The results suggest that the probability of ASD after a birth by CS is over three times that observed after unassisted vaginal delivery. Large prospective studies are needed to understand if this relationship is a causal pathway or consequence of ASD. The results suggest that using birth by CS as a predictor for ASD by pediatric health professionals in their patient follow-ups may be an appropriate tool that could improve early ASD detection.


Autism spectrum disorder Cesarean section Pregnancy Predictor Case-control studies 


Autism spectrum disorder (ASD) comprises a complex set of behaviorally defined neurodevelopmental abnormalities in two core areas: deficits in social communication and fixated or restricted, repetitive or stereotyped behaviors and interests which typically emerge in the first years of life (American Psychiatric Association 2013). The reported prevalence of ASD has increased markedly in recent decades, both in the USA and in other industrialized nations. ASD prevalence in the USA, as reported by parents in the 2014 National Health Interview Survey, was estimated at 2.24% (Zablotsky et al. 2015); however, a recent global review has described overall ASD prevalence to be around 17 cases per 10,000 inhabitants or 0.17% (range = 2.8–94.0) (Elsabbagh et al. 2012). The causes of the increase of ASD prevalence are not clear and include the following: improved diagnostic practices, more social awareness of this disease, changes in diagnostic protocols, a true change in the disease prevalence, or a combination of all these factors (Elsabbagh et al. 2012; Hansen et al. 2015; Keyes et al. 2012; King and Bearman 2009; Maenner et al. 2014). No evidence for increased syndromic ASD, whose prevalence has been reported as being stable at around 10%, has been found (Krumm et al. 2014). This could suggest that the epigenetic factors which could alter gene expression would be net contributors to the growing ASD trend (Dawson 2013). Although it is now widely accepted that ASD development involves a polygenic disorder, several environmental and obstetric co-factors from the pre- or perinatal period could play a key role to help our understanding of how the pathogenic pathways of ASD develop (Burstyn et al. 2010; Glasson et al. 2004; Zachor and Curatolo 2014).

Several studies have performed epidemiological research into this disorder in relation to obstetric factors which have been associated with ASD (Bilder et al. 2009; Dodds et al. 2011; Glasson et al. 2004; Guinchat et al. 2012; Schieve et al. 2014). These factors have been related to higher ASD prevalence given that the perinatal period is a critical time for neurodevelopment in babies (Schieve et al. 2011). Among the most well-studied factors, we find three distinct groups, those related (a) to the mother: obesity, diabetes, preeclampsia, hypertension, smoking, mother’s advanced age; (b) to the child: low or very low birth weight, premature or very premature birth, multiple pregnancy, breech birth; and (c) to the technique used: use of assisted reproduction techniques and cesarean section (CS) (Bilder et al. 2009; Guinchat et al. 2012; Tanne 2012). Current research seems to suggest that certain obstetric factors are more frequent in children with ASD than in neurotypical (NT) controls (Burstyn et al. 2010; Polo-Kantola et al. 2014), despite these results having been questioned by other authors (Bilder et al. 2009; Schieve et al. 2011). Such inconsistencies in the results may be due to methodological variations, which include sample size, diagnosis criteria, methods to evaluate exposure, or comparison groups.

Children with ASD are typically diagnosed between ages 2 and 6 and about 1% of children are recognized at school age to have ASD (Baird et al. 2006; Boyd et al. 2010; Dababnah et al. 2011). The median age of diagnosis ranges from 3 years old for children with the most severe cases of autistic disorder to 11 years old for high-functioning children (Howlin and Asgharian 1999; Shattuck et al. 2009). Some studies suggest that symptoms of neurodevelopmental abnormalities, such as atypical visual contact, tracking and engagement, orienting to name, imitation, social smiling, reactivity, social interest and affect, and sensory-oriented behaviors, can be seen starting during infancy (Deconinck et al. 2013). In one study, half of the parents of children with ASD interviewed reported having concerns due to their child’s behavior before 1 year of age and even more reported having concerns before their child’s second birthday (Asperger 1991). While retrospectively parents may easily identify concerns, the difficulty in the identification of the symptoms and the recognition of their transcendence by the parents can delay their query to the specialist and therefore delay diagnosis. However, if pediatric health professionals had an ASD indicator or predictor that would prompt specific questions to the parents during routine follow-ups about symptoms of neurodevelopmental abnormalities, this might aid in early detection and diagnosis of ASD.

Early diagnosis (before 3 years of age) and appropriate intervention are key factors for the outcome of children diagnosed with ASD and play an invaluable role in improving the future neurodevelopmental prognosis of children with ASD (Filipek et al. 2000; McTiernan et al. 2011). Given this, it is important to identify perinatal factors that may serve as predictors for ASD and more closely monitor the children that present these predictors. This would help improve the early detection of and intervention rates on ASD and, therefore, improve the overall outcome and quality of life of children with ASD.

One of these possible factors that might be used as a predictor is birth by CS. An increasing trend has been observed for CS worldwide, particularly in developed countries (Betrán et al. 2007; Dobson 2001), and there is also an increasing number of studies on its association with ASD (Curran et al. 2015a, b, 2016; Gregory et al. 2013; Schieve et al. 2014). Despite there being a feasible direct link between CS and ASD, it is worth considering that this association may emerge as a confounder by indication, and as an indirect reflection about the overall underlying cause; for example, oxytocinergic system dysregulation or underlying fetal distress, which could well mark a suboptimum perinatal setting as the genuine risk factor (Gialloreti et al. 2014; Schieve et al. 2014).

To our knowledge, no studies that considered the possibility or adequacy of using predictors for ASD have been performed. Therefore, there might be a lack of knowledge among pediatric health professionals about perinatal factors that may serve as early predictors of ASD. The presence of early predictors of ASD in children whose parents detect symptoms of neurodevelopmental abnormalities could improve early detection and intervention rates and therefore improve the outcome and quality of life of the ASD children and their families. The objective of this study is to identify if birth by CS could be used as a predictor for the development of ASD. This objective will be pursued by identifying if within the studied population the rate of birth by CS is statistically significantly higher for children with ASD than for the NT controls once possible confounders have been taken into account.



Potential cases were identified from the list of patients diagnosed with ASD by the Neuropediatric Unit of the La Fe Hospital (Health Department 7 (Valencia-La Fe) of the Valencian Community, Spain) within the last 10 years. Cases were included only if they met the following inclusion criteria: the mother was aged over 18 years at the time of birth, birth occurred at La Fe Hospital between 01/01/1996 and 12/31/2011, and the child was both diagnosed with ASD and followed up by the neuropediatric team of the same La Fe Hospital. Only those with a minimum follow-up of 3 years (considered sufficient for ASD diagnosis) were considered. We considered only the diagnosis of the disorder and not its severity. Cases diagnosed with ASD outside La Fe, and any cases or controls not followed up at La Fe, were excluded.

Once cases were identified, the identifying number of the medical history of the mother was obtained from the hospital’s patient record database. Controls were selected at a ratio of 4:1. The children of the births recorded immediately prior and following that of the case and without ASD diagnosis and with matching criteria were taken as controls. For both groups, the exclusion criteria were parents’ refusal to participate, unable to contact parents by phone or letter, and incomplete data records. Once all cases and controls were selected, the mode of delivery and the characteristics of the mother, clinical history, and the pregnancy of each child were obtained from the medical history records.

This study was approved by the hospital’s Ethics Committee of Clinical Research with no. 2014/0479. This study was performed according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement (Vandenbroucke et al. 2007).

This study included 251 mother-child pairs (53 cases and 198 controls) for an overall participation rate of 91.3% which was 96.4% for cases and 90% for controls. There were 100 boys and 98 girls in the control group, and 47 boys and 6 girls in the cases group. Table 1 shows the main sample characteristics for all the considered variables and their gross association with ASD diagnosis.
Table 1

Maternal and child characteristics grouped according to presence of ASD and multivariable conditional logistic regression results for ASD


Controls (n = 198)

Cases (n = 53)

p valuea

cORb (95% CI)

aORc (95% CI)

n (%)

n (%)

Mode of delivery

Unassisted VD

103 (52.0)

16 (30.2)

< 0.01

1.00 (Reference)

1.00 (Reference)

Assisted VD

46 (23.2)

14 (26.4)


1.98 (0.89–4.39)

1.50 (0.61–3.68)


49 (24.8)

23 (43.4)


3.37 (1.57–7.25)

3.36 (1.44–7.85)

Mother’s age (at delivery)

< 25 years

30 (15.5)

4 (7.7)


1.00 (Reference)

1.00 (Reference)

25–29 years

47 (24.2)

13 (25.0)


1.73 (0.76–3.97)

2.08 (0.79–5.51)

30–34 years

73 (37.6)

22 (42.3)


0.72 (0.35–1.47)

0.64 (0.29–1.41)

> 35 years

44 (22.7)

13 (25.0)


1.13 (0.63–2.03)

1.07 (0.55–2.09)

Previous pregnancies


77 (38.9)

29 (54.7)


1.00 (Reference)

1.00 (Reference)


121 (61.1)

24 (45.3)


0.52 (0.28–0.96)

0.45 (0.21–0.96)




123 (62.1)

43 (81.1)

< 0.01

1.00 (Reference)



75 (37.9)

10 (18.9)


0.38 (0.18–0.81)


Previous abortion


141 (71.2)

39 (73.6)


1.00 (Reference)


≥ 1

57 (28.8)

14 (26.4)


0.84 (0.42–1.68)


Previous CS


171 (86.4)

44 (83.0)


1.00 (Reference)


≥ 1

20 (10.1)

6 (11.3)


1.13 (0.43–3.00)



7 (3.5)

3 (5.7)



Without pathologies

15 (7.6)

4 (7.5)


1.00 (Reference)


With pathologies

176 (88.9)

48 (90.6)


1.02 (0.32–3.22)


Not related to pregnancy

19 (10.8)

3 (6.2)


0.59 (0.11–3.06)


Related to pregnancy

101 (57.4)

23 (47.9)


0.85 (0.26–2.81)



56 (31.8)

22 (45.8)


1.47 (0.44–4.93)


Mother’s current illnesses

21 (11.9)

3 (6.2)


0.54 (0.10–2.75)


Fetal-placental problems

25 (14.2)

4 (8.3)


0.60 (0.13–2.76)



21 (11.9)

7 (14.6)


1.25 (0.31–5.05)


Failed induction

12 (6.8)



Abnormal uterine contraction

1 (0.6)



Obstetric trauma

6 (3.4)



Puerperal complications


2 (4.2)


More than one pathologye

90 (51.1)

32 (66.7)


1.33 (0.41–4.31)



7 (3.5)

1 (1.9)


Multiple birth


189 (95.4)

50 (94.3)


1.00 (Reference)



9 (4.6)

3 (5.7)


1.26 (0.33–4.85)


Gestational age

≥ 35 weeks

187 (94.4)

48 (90.6)


1.00 (Reference)

1.00 (Reference)

< 35 weeks

11 (5.6)

5 (9.4)


1.92 (0.63–5.81)

1.17 (0.30–4.56)

Type of onset labor


133 (67.2)

29 (54.7)


1.00 (Reference)



44 (22.2)

14 (26.4)


1.76 (0.93–3.31)


Reason for CS

Fetal stress

14 (28.6)

10 (43.5)


1.00 (Reference)


Other causes

35 (71.4)

13 (56.5)


1.92 (0.69–5.39)


pH in umbilical artery

Mean (SD)

7.23 (0.07)

7.26 (0.07)



Missing, n (%)

156 (0.79)

34 (64.2)


Infant sex


98 (49.5)

6 (11.3)

< 0.01

1.00 (Reference)

1.00 (Reference)


100 (50.5)

47 (88.7)


7.58 (3.10–18.54)

9.35 (3.46–25.23)

Birth weight

Normal (2500–4000 g)

170 (85.9)

40 (75.5)


1.00 (Reference)


Low (< 2500 g)

24 (12.1)

9 (16)


1.56 (0.67–3.63)


High (> 4000 g)

4 (2)

4 (7.5)


3.52 (0.79–15.76)


Size for gestational age


180 (90.1)

44 (83)


1.00 (Reference)



6 (3)

3 (5.7)


1.68 (0.41–6.99)



12 (6.1)

6 (11.3)


1.91 (0.68–5.41)


1 min Apgar ≤ 7


183 (92.4)

45 (84.9)


1.00 (Reference)



15 (7.6)

8 (15.1)


2.12 (0.84–5.38)


5 min Apgar ≤ 7


193 (97.5)

52 (98.1)


1.00 (Reference)



5 (2.5)

1 (1.9)


0.70 (0.08–6.11)


aOR adjusted odds ratio, CI confidence interval, CS cesarean section, cOR crude odds ratio, PROM premature rupture of membranes, VD vaginal delivery

aChi-square test for proportions (Fisher’s exact test when the expected count was ≤ 5 in more than 20% of cells), or t test (assuming equal variances) for continuous variables

bOR conditioned to children’s date of birth

cAdjusted for mother age, child’s sex, gravidity, and gestation weeks

dOR = 1.81 (95% CI 0.95–3.47) for two pathologies or more compared to only one pathology, related or not related to pregnancy. OR = 1.77 (95% CI 0.94–3.33) for two pathologies or more compared to only one pathology, related or not to pregnancy, and in relation with the “without pathologies” group

eOR = 1.91 (95% CI 0.98–3.73) for more than one pathology compared to the “only one pathology” groups. OR = 1.80 (95% CI 0.96–3.36) for “more than one pathology” compared to the “only one pathology” groups and with the “without pathologies” group


A nested case-control study designed on a retrospective cohort of births at the La Fe University Polytechnic Hospital (La Fe) in Valencia, Spain. ASD cases were diagnosed by the hospital’s team of specialists in psychiatry according to the DSM-IV-TR criteria (American Psychiatric Association 2000). Three modes of delivery categories were contemplated: unassisted vaginal delivery (VD), assisted VD, and CS. The medical histories that reported VD assisted with forceps, assisted vacuum extraction, assisted breach, or other assisted or induced deliveries were considered assisted VD. Given the nature of our hospital, which considers CS as an emergency measure per its healthcare protocol, elective CS was not considered. It must be noted that while CS are considered an emergency measure, there exist two distinct groups. First, those that have been planned due to previously known risk such as unchangeable transverse or podalic presentation, previous CS, intrauterine growth restriction, and/or severe preeclampsia. Secondly, those that are performed as a response to an emergency situation such as bleeding, loss of fetal well-being, pathological pH, alterations in fetal heart rate, non-progression of labor, failed induction, and/or pelvic fetal disproportion. In this study, no distinction has been made between the two groups.


To conduct this study, a number of other risk factors found in the literature were taken as potential confounders in the relation between CS and ASD. Information was collected on mother’s age at delivery (< 25, 25–29, 30–34, or > 35 years), previous pregnancies (no/yes), parity (no/yes), previous spontaneous abortions (no/yes), previous CS (no/yes), if the mother had any disease (no/yes, grouping pathologies), multiple birth (no/yes), gestation weeks (number), how birth started (spontaneous/induced), reason for CS (fetal stress/other causes), pH in umbilical artery, weight at birth (< 2500 g, 2500–4000 g, > 4000 g), newborn’s sex (girl or boy), size for gestational age (small, adequate, big), and Apgar score at 1 and 5 min(s) (> 7 or ≤ 7). Those variables that were significantly different (p < 0.05) between cases and controls were identified as potential confounders (i.e., previous pregnancy and infant sex). These potential confounders, along with mother’s age and gestational age due to the numerous previous studies identifying these factors as confounders, were taken into account in the multivariable analysis adjusting first for each one individually and since the results were virtually identical, we proceeded to adjust for them all together. In all of the multivariable analysis, the matching of cases and control was made using the data of birth.

Mothers’ pathologies were grouped into 13 categories according to the ICD 9-MC diagnoses (Centers for Disease Control and Prevention 2013): mother’s existing diseases, fetal-placenta-type problems, waters breaking early, failed induced birth, multiple pregnancy with advanced maternal age, abnormal fetal heartbeat, abnormal uterine contractions, first pregnancy with advanced maternal age, abnormal organs-soft pelvic tissue, obstetric trauma, puerperal complications, pregnancy classified as high risk, and overweight-obese mother.

Data Analyses

A bivariate comparison was made between cases and controls for demographic and clinical characteristics of the mother, perinatal variables, and clinical characteristics of the newborn using the chi-square test, t test, or Fisher’s test depending of type of variable (p < 0.05). Crude odds ratios (cOR), matched by children’s date of birth, that assessed the association of each variable with ASD diagnosis were estimated using a univariate conditional logistic regression model. A multivariable model adjusted for potential confounders was used to obtain adjusted OR (aOR). This type of analysis was chosen given that in logistic regression, the odds ratio (OR) represents the constant effect of X (each studied variable), on the likelihood that one outcome (ASD) will occur. The level of statistical significance was set at α = 0.05. All the analyses were carried out with the R software, v.3.2.3 (The R Foundation for Statistical Computing,


Cases diagnosed with ASD presented more obstetric complications than controls. Statistically significant gross associations were identified between developing ASD and the following variables: previous pregnancies, parity, mode of delivery, and male children (see Table 1). Previous pregnancy and parity showed a negative association while gender, and mode of delivery presented positive associations. The association with male gender is in line with the global prevalence of ASD by gender where it is estimated that males outnumber females 4:1 (Elsabbagh et al. 2012; Zablotsky et al. 2015). We consider male sex to be an already established predictor.

The multivariable conditional logistic regression model was adjusted for mother’s age, child’s sex, previous pregnancies, and gestation weeks. Parity was excluded from the final model to avoid collinearity with previous pregnancies. The results are provided as OR in Table 1. An odds ratio is a measure of the association between an exposure and an outcome. It represents the odds that an outcome will occur given a specific exposure, in comparison to the odds of the outcome occurring without that specific exposure. Compared to unassisted VD, assisted VD was not related with ASD (aOR = 1.50, 95% CI 0.61–3.68), and CS was closely related with developing ASD (aOR = 3.36, 95% CI 1.44–7.85). The results suggest that the probability of ASD after a birth by CS is over three times that observed after unassisted VD. The mother’s age at the time of delivery and gestational age did not seem to be associated with an increased or decreased probability of ASD. However, having had a previous pregnancy seems to be associated with a decrease of about 50% in the probability of ASD (aOR = 0.45, 95% CI 0.21–0.96). Finally, as stated above, the established predictor of male sex was strongly associated with an increase in the probability of ASD (aOR = 9.35, 95% CI 3.46–25.23). The results suggest that the probability of ASD in a male newborn was nine times that of a female newborn.


A hospital-based case-control study was conducted in a cohort of births to evaluate the possibility of using perinatal factors, namely CS, as predictors of ASD. The initial hypothesis postulated that the existence of an association of any type, causal or not, between CS and ASD, would justify its use as a predictor of ASD. The aOR obtained for CS stressed the statistically significant relation between children born by CS and ASD. This relation remained even after adjusting for the other risk factors and confounders.

Ecological, cross-sectional, or case-control studies commonly employed in similar contexts have often included several limitations, such as selection bias, memory bias, or information bias. However, one of the strengths of this research work was that a comprehensible list of risk factors was classified per the exact time they could have an effect, and the information employed originated from the hospital database. This guaranteed that the information worked with and used to carry out the standard procedure to select controls was exact (information on ASD diagnosis, CS, and potential confounders came from MH, which avoids memory bias) and that the same action protocols were applied to both cases and controls, which ensured that the ASD diagnostic criteria remained homogeneous over time.

One of the strong points of this work is that the study population belongs to the same hospital, which implies homogeneity in healthcare protocols for delivery and treatment of children suspected of having ASD (temporary consistency in the diagnostic criteria). Likewise, as the hospital is public, and offers universal healthcare free of charge, the vast majority of births that took place within the study area happened at the hospital. This not only minimizes the potential confusion of the socio-economic level between CS and ASD but also increases our study’s internal validity. The reasons for the increase in prevalence are not fully understood, but the expansion of the diagnostic criteria could play a role in this increase. This study took place at a time and place with homogeneity of ASD diagnostic criteria (DSM-IV-R) and therefore the confusion bias is minimized.

The obtained results coincide with those reported in the literature: several works have significantly related CS with an increased risk of ASD (Curran et al. 2015a, b; Dodds et al. 2011; Schieve et al. 2014), although some studies have also shown null association (Curran et al. 2016). Despite some authors having found a marked association between inducing or prolonging birth and ASD, in our study inducing birth was not associated with ASD (Gregory et al. 2013). However, this discrepancy in the results could be due to the process followed when dealing with births in our center: when a birth does not respond to induction, CS is performed to avoid further obstetric complications.

The aOR obtained for CS was 3.36 (95% CI 1.44–7.85), which was a significantly higher value than that described by a meta-analysis of 13 studies: 1.23 (95% CI 1.07–1.40) (Curran et al. 2015b). Our study design, which only included one hospital, could account for this discrepancy. The aforementioned meta-analysis identified a different pattern for elective CS and emergency CS: the estimated risk for elective CS tended to be lower and non-significant, but a significant risk was detected for emergency CS. As our study identified no elective CS because the hospital’s healthcare protocol does not contemplate this option, carrying out a differentiated analysis between elective CS and emergency CS was not feasible.

In a previous sibling study, the association between CS and ASD disappeared when using sibling controls which suggest that the association is due to familial confounding (Curran et al. 2015a). However, since our objective is not to determine the root cause of the association between CS and ASD, the adequacy of CS as an ASD predictor remains unaffected.

Evidence from previous studies is limited because of their observational nature and given the risk of an unmeasured bias or residual confounding. Therefore, a possible causal relation seemed unlikely. Notwithstanding, indications of an emergency CS (Curran et al. 2015b), inducing birth (Gregory et al. 2013), or even prolonging delivery times (Maramara et al. 2014), coincide with underlying fetal distress. However, in their final statistical model, Polo-Kantola et al. (2014) referred to scheduled CS as still being significant. These observations suggest that factors distinct from hypoxia or distress related to emergency CS could play a role in the association between ASD and CS. The interpretation of this finding is still a challenge but indicates the possible role of CS in ASD, which would not necessarily be linked to perinatal complications. This independent association would further strengthen the use of CS as a predictor of ASD to be considered when performing pediatric follow-up examinations.


This study has limitations, with the main one being its small sample size, although our results reflected a strong significant effect. Given our study design, the interpretation of the results did not allow any causality to be inferred. Another possible limitation was selection bias, although this bias type could be considered minimal because, since as mentioned earlier, (a) cases and controls were selected from the same population who received the same healthcare, (b) only one medical team made diagnoses by following homogeneous criteria throughout the study period, and (c) cases and controls were matched using potential confounders as the matching criteria. A certain type of information bias could have been incurred, although the information on exposure to various risk factors was obtained from medical histories which were filled in before diagnosing ASD. If this bias occurred, it would be a non-differential type. Another limitation of this study was the use of data from birth certificates as the only source of information for obstetric and neonatal risk factors. Traditionally, birth certificate data have underestimated the rate of complications during pregnancy (e.g., high blood pressure induced by pregnancy, hemorrhaging), although this circumstance is also common to both cases and controls.

The literature describes some risk factors associated with diagnosing ASD which could confuse its relation with CS. They were not considered because either no information about them was available or their contribution was not considered sufficiently significant. This could lead to a certain type of residual confounding bias, which is unfortunately very difficult to deal with. These risk factors include the mother’s ethnic group or race and her nationality (Becerra et al. 2014; Lehti et al. 2013), mother’s weight increase while pregnant (Bilder et al. 2013), parents’ level of education (Duan et al. 2014), direct or indirect exposure to smoking (Duan et al. 2014; Zhang et al. 2010), mother’s depressive mental status (Zhang et al. 2010), and genetic susceptibility indicated by some close family relative having been diagnosed with ASD (Dodds et al. 2011). While these possible confounding factors may pose a problem when trying to establish a causal relationship between CS and ASD, in our study their non-inclusion was deemed to not have significantly altered our results on the use of CS as an ASD predictor. Although the obtained CI was wide, the estimated association was quite strong. This phenomenon, along with the way participants were selected for the present study, confers the feasibility that these associations existed, even when certain possible confounding variables were not considered when adjusting statistical models.

Our findings support the notion that CS relates to the development of ASD in children, and are consistent with other previous studies. An early diagnosis of ASD depends on the recognition of a series of clinical manifestations and it is key that clinicians have predictors that may serve as a signal for the need to more carefully evaluate the neurological development of the child. Establishing CS as a predictor for ASD would allow for vulnerable children to be more closely monitored and increase the rates of early detection and intervention which would then improve the children’s outcomes and quality of life.



The authors would like to thank Vicente Huerta-Biosca for his help in the data collection phase of this study.

Author Contributions

APM collaborated with the design and execution of the study and the editing of the final manuscript. ALG collaborated with the design of the study, the data analyses, and the editing of the final manuscript. IPC collaborated with the data analyses, the writing and editing of the manuscript. PCB collaborated with the execution of the study and the editing of the final manuscript. MTM collaborated with the execution of the study and the editing of the final manuscript. SMB collaborated with the data analyses, writing, and editing of the final manuscript. MMSV collaborated with the design and execution of the study, the data analyses, and the writing and editing of the final manuscript.


This work has been financed through research project SMI 19/2014 of the Regional Valencian Ministry of Health (Spain).

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The Ethics Committee of the La Fe Hospital provided IRB approval for this study.

Informed Consent

Informed consent was obtained from all individual participants included in the study.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Alfredo Perales-Marín
    • 1
    • 2
  • Agustín Llópis-González
    • 3
    • 4
  • Isabel Peraita-Costa
    • 3
  • Pablo Cervera-Boada
    • 5
  • Montserrat Téllez de Meneses
    • 6
  • Salvador Marí-Bauset
    • 3
  • María Morales-Suárez-Varela
    • 3
    • 4
  1. 1.Department of ObstetricsLa Fe University Polytechnic HospitalValenciaSpain
  2. 2.Department of Pediatrics, Obstetrics and GynecologyUniversity of ValenciaValenciaSpain
  3. 3.Public Health and Environmental Care Unit, Department of Preventive MedicineUniversity of ValenciaBurjassotSpain
  4. 4.CIBER of Epidemiology and Public Health (CIBERESP)MadridSpain
  5. 5.Department of PsychiatryDr. Peset University HospitalValenciaSpain
  6. 6.Department of NeuropediatricsLa Fe University Polytechnic HospitalValenciaSpain

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