Perinatal Factors Associated with Autism Spectrum Disorder in Jamaican Children

Abstract

Mode of delivery, preterm birth, and low birth weight (LBW) are hypothesized to be associated with Autism Spectrum Disorder (ASD) in the offspring. Using data from 343 ASD cases (2–8 years) and their age- and sex-matched typically developing controls in Jamaica we investigated these hypotheses. Our statistical analyses revealed that the parish of residence could modify the association between cesarean delivery and ASD, with a difference found in this relationship in Kingston parish [matched odds ratio (MOR) (95% confidence interval (CI)): 2.30 (1.17–4.53)] and other parishes [MOR (95% CI): 0.87 (0.48–1.59)]. Although the associations of LBW and preterm birth with ASD were not significant, we observed a significant interaction between LBW and the household socioeconomic status. These findings require replication.

Introduction

Autism Spectrum Disorder (ASD) is a pervasive neurodevelopmental disorder mainly characterized by impaired social and communication skills, and repetitive, stereotyped behavior (Field 2014; Hallmayer et al. 2011; American Psychiatric Association 2013). Symptoms of ASD can be recognized from early infancy through childhood depending on the severity of the condition and have lifelong implications. Thus, some individuals affected by ASD cannot live independently and require long-term care and support (Matson and Kozlowski 2011). According to the most recent report by the Autism and Developmental Disabilities Monitoring Network (ADDM), the estimated prevalence of ASD in the United States is 1 in 59 children aged 8 years (Baio et al. 2018). Although the median of the global prevalence of ASD has been estimated to be 0.62% (Elsabbagh et al. 2012), the prevalence of ASD is unknown in many low- and middle-income countries (LMICs) such as Jamaica. The etiology of ASD is not fully understood. Multiple genetic factors, environmental factors, and their interactions are believed to be involved in ASD (Schieve et al. 2018; Kim and Leventhal 2015; Hertz-Picciotto et al. 2018). Many studies have emphasized the role of environmental exposures during the crucial stages of neurodevelopment, which begin in the first few weeks of gestation and continue into early childhood, in the manifestation of ASD (Guinchat et al. 2012; Wang et al. 2017; Modabbernia et al. 2017; Arndt et al. 2005). Therefore, exposures during the perinatal period, which comprises the time immediately before and after birth, have the potential to be associated with neurodevelopmental disorders such as ASD.

Several studies investigated the possible association of suboptimal perinatal factors and adverse birth outcomes such as cesarean delivery (Curran et al. 2015; Burstyn et al. 2010; Hultman et al. 2002), low birth weight (LBW) (Schieve et al. 2015; Burstyn et al. 2010; Lampi et al. 2012), and preterm birth (Schieve et al. 2015; Atladottir et al. 2016; Lampi et al. 2012) with ASD. The presence of these perinatal complications have been linked to increased risk of fetal and neonatal hypoxia (Gardener et al. 2011) and over-activation of dopamine in the brain (Previc 2007), which are both mechanisms suspected to have an adverse impact on brain development and manifestation of autism and autistic behaviors.

There is evidence in the literature suggesting that children with ASD are more likely to be born by cesarean section than typically developing (TD) children (Hultman et al. 2002; Zhang et al. 2010). Another study reported that only emergency cesarean delivery has a significant association with ASD after adjusting for confounders, suggesting that the association may be related to the indications for cesarean section rather than the surgical procedure (Curran et al. 2015). On the other hand, some epidemiological studies have reported no association between cesarean delivery and ASD (Burstyn et al. 2010; Glasson et al. 2004). Although conflicting findings have been reported in individual studies, two meta-analyses suggested that children who were delivered by cesarean section, regardless of whether the procedure was elective or emergency, are 20–30% more likely to be diagnosed with ASD (Wang et al. 2017; Gardener et al. 2011).

As an indicator of impaired fetal growth and development, LBW has been associated with various cognitive and mental health outcomes in children including speech and language problems (Aram et al. 1991), learning disabilities (Johnson and Breslau 2000), and hyperactivity (Pharoah et al. 1994; McCormick et al. 1990). However, findings reported in the literature are inconsistent regarding the association between LBW and ASD in children; some studies suggested that children who were born with LBW were more likely to be diagnosed with ASD, with odds ratios (ORs) ranging from 1.33 to 1.57 (Burstyn et al. 2010; Lampi et al. 2012; Schieve et al. 2015), while other studies did not support any independent association between LBW and ASD (Hultman et al. 2002; Williams et al. 2008). A direct association between LBW and ASD has also been reported in two meta-analyses (Gardener et al. 2011; Wang et al. 2017).

There are varying levels of evidence for the association between ASD and preterm birth (gestational age (GA) < 37 weeks) with ORs ranging from 1.1 to 2.5 (Lampi et al. 2012; Atladottir et al. 2016; Schieve et al. 2015), though some other epidemiological studies reported no association (Burstyn et al. 2010). This inconsistency might be due to different definitions used for preterm birth ranging from less than 28 to 36 weeks of GA at delivery. Results from a recent meta-analysis suggested that children who were born with GA ≤ 36 weeks are 30% more likely to be diagnosed with ASD (Wang et al. 2017). Another meta-analysis reported similar findings, however, they emphasized the variability of the studies and the need for further investigation (Gardener et al. 2011). There is also evidence that children with ASD and abnormal GA, defined as either pre-term (<37 gestational weeks) or post-term (≥ 42 gestational weeks) delivery, had more severe ASD symptoms when compared with children with ASD and normal GA (Movsas and Paneth 2012).

The majority of the previous studies were conducted in developed countries and the number of epidemiological studies that focused on the association between perinatal factors and ASD in LMICs are very limited (Hadjkacem et al. 2016; Say et al. 2016). This is the case for Jamiaca, which is an upper middle income county with a vulnerable and slow growing economy (The World Bank 2017b, 2017a). A very limited number of facilities provide specialty care for autism in Jamaica and there is lack of required infrastructure and documentation to conduct national epidemiological studies. To our knowledge, the Child and Family Clinic (CFC) at the University Hospital of the West Indies (UWI) is the only center that developed a comprehensive database such as the Jamaica Autism Database to keep the records of identified ASD cases in Jamaica. In collaboration with UWI, previous studies have reported that increased joint maternal and paternal age (Rahbar et al. 2012), and prenatal maternal exposures to fever or infection, physical trauma, and oil-based paints (Christian et al. 2018) are associated with ASD in Jamaica. However, to our knowledge, no study has been conducted to investigate the relationship between perinatal factors and ASD in Jamaica. The objective of this research is to evaluate the possible association between select perinatal factors, including cesarean delivery and suboptimal characteristics at birth (i.e. preterm birth and LBW), and ASD in Jamaican children. Additionally, we also explore potential effect modifications by other covariates such as sociodemographic characteristics when investigating the association between perinatal factors and ASD in Jamaican children.

Methods

Jamiaca and the distribution of healthcare providers

Jamaica is the fourth-largest island country in the Northwest region of the Caribbean Sea. According to the World Bank, Jamaica is an upper middle income county which has experienced an avarage of only 1% annual increase in the gross domestic product (GDP) per capita over the last 30 years (The World Bank 2017b, 2017a). Jamaica’s health system is mainly government-based. There is no national health insurance coverage, however, the public health system is free of cost. Within the public sector, health services are delivered through a network of primary, secondary and tertiary healthcare facilities consisting of 22 hospitals, and 317 health centers to a population of nearly 2.9 million people (Government of Jamaica 2014). Jamaica is divided into 14 parishes that are the principal units of civic administration, and Kingston is the capital and the largest city of Jamaica (Jamaica Information Service 2019). Most of the financial institutions, government agencies, and other businesses in either the public or private sector are headquartered in Kingston. Relevant to the healthcare system, all parishes in Jamaica have at least one public hospital. However, all type S hospitals that are national referral specialty hospitals (e.g., one maternity hospital, and one pediatric hospital), as well as two type A hospitals that provide comprehensive multidiciplinary secondary and tertiary health care services are centralized in Kingston. There is only one type A hospital outside of Kingston that provides most of the specialized services in St. James in west Jamaica, and only 5 parishes outside of Kingston have type B hospitals that provide comprehensive essential obstetrics, pediatrics, general medicine and surgery secondary care. All other hospitals are classified as type C that are only able to provide primary care and very basic newborn and obstetric care (Samms-Vaughan et al. 2001; Foster-Williams et al. 2010). The health centers are strategically distributed across the country and provide only primary care (Jamaica Ministry of Health 2019). [Online Resource 1: a map of Jamaica with boundaries of the parishes, as well as the distribution and type of the public health care providers in the country; Online Resource 2: Types of health Centers and health services that each type provides].

The access to well-child care services is high in Jamaica and the immunization rate for young children is about 95% (United Nations Children’s Fund (UNICEF) 2013). Most of the children attend public clinics or visit private family doctors and general pediatricians for routine well-child care, where developmental concerns are usually first raised. However, no specialist pediatric services are available in the public well-child clinics. Since 2010, the Child Health and Development Passport (CHDP) has been implemented in Jamaica to track information about the growth and development of a child from birth to 17 years (Jamaica Information Service 2010). There are tools incorporated in CHDP for initial screening of children for indications of ASD at the internationally recommended ages of 6 weeks, 9 months, 18 months and 3 years. However, there is no report available about the use and success of this screening program. Young children suspected to have ASD or other developmental disabilities who are identified by health care workers at different levels of the public or private sector, educators, social workers, parents and other agencies serving young children will be referred to a public hospital that provides general pediatric services across the country, or directly to the public specialist clinic for children with developmental disabilities in Kingston. The ASD diagnosis needs to be confirmed by experienced specialists. Currently 11 of Jamaica’s 14 parishes have at least one hospital with general pediatric services (Jamaica Ministry of Health 2019). Facilities that could provide proper assessment and care for ASD children are very limited and are mostly located in Kingston. The CFC at the Department of Child and Adolescent Health in UWI is the only established center in the public sector for the objective diagnosis of children with ASD (Samms-Vaughan and Franklyn-Banton 2008). There are only two developmental pediatricians in Jamaica who evaluate children who are referred to the CFC with all types of developmental disabilities and use gold standard diagnostic tools including the second edition of the Autism Diagnostic Observation Schedule (ADOS-2) (Lord et al. 1999), and the Autism Diagnostic Interview-Revised (ADI-R) (Rutter et al. 2003) for ASD diagnosis. The Early Stimulation Program (ESP) is another publicly funded service that provides assessment and early interventions for young children (0–6 years) with developmental disabilities including ASD (Jamaica Ministry of Labour and Social Security 2018). The ESP operates from four centers in the country including two in Kingston, one in Portland, and one in St. James parishes. Although the ESP facilities, general pediatricians and a few psychologists across the country could make a clinical diagnosis of ASD, they are not trained to use objective diagnostic tools including ADOS or ADI-R (Samms-Vaughan et al. 2017). Therefore, they refer children to CFC at UWI to confirm the diagnosis. Furthermore, most of the theraputic services (e.g. behavioral therapy and speech therapy) and early interventions are only available in private or public facilities located in Kingston, except for the early interventions offered by ESP facilities in other parishes.

Study design and population

This study is based on data from two age- and sex-matched case-control studies, “Epidemiological Research on Autism in Jamaica (ERAJ)” and ERAJ-Phase2 (ERAJ-2). Since 2009, these studies have been conducted in Jamaica in collaboration between faculty at the University of Texas Health Science Center at Houston (UTHealth) in the United States of America (US), and UWI, Mona campus in Jamaica. Details regarding the study population and the eligibility criteria for enrollment have been reported previously (Rahbar et al. 2013; Rahbar et al. 2012). Briefly, the sample in the present study includes 343 1:1 matched pairs of children (one ASD case and one age-(±6 months) and sex-matched TD control) 2–8 years old, who were born in Jamaica and enrolled in the ERAJ and ERAJ-2 studies between December 2009 and September 2017. Eligible ASD cases were recruited from the UWI Jamaica Autism Database, which has been identifying about 25–30 new cases per year since 1995. This database includes children who were referred from all over the country to the specialists at UWI and who were diagnosed with ASD using the Diagnostic Statistical Manual of Mental Disorders (DSM-IV-TR) (American Psychiatric Association 2000) criteria and the Childhood Autism Rating Scale (CARS) (Schopler et al. 1980). To confirm the ASD diagnosis at the time of enrollment, ADOS-2 (Lord et al. 1999), and ADI-R (Rutter et al. 2003) were administered to children and their parents/guardians, respectively. Cases were included in the study only if both the ADOS-2 and ADI-R confirmed the ASD diagnosis. The TD controls were recruited from local schools and well-child clinics in Jamaica. The lifetime version of the Social Communication Questionnaire (SCQ) (Rutter et al. 2003) was administered to parents/guardians to rule out the possibility of ASD or any other developmental disabilities in TD controls at the time of enrollment. Children were enrolled as TD controls only if they had an SCQ score of 6 or below. This cut-off point of 6 is one standard deviation above the mean SCQ score for TD children (Mulligan et al. 2009). We obtained the parents’/guardians’ informed consent and child’s assent (if applicable) for all ASD cases and TD controls at the time of enrollment. The ERAJ study protocol has been previously approved by the Institutional Review Boards (IRBs) of both UTHealth and UWI.

Data collection

A socioeconomic status (SES) questionnaire was administered to the parents/guardians of all ASD cases and TD controls in both the ERAJ and ERAJ-2 studies at the time of enrollment. This SES questionnaire utilizes questions that have been previously used in large Jamaican birth cohort studies (Samms-Vaughan 2001; McCaw-Binns et al. 2010) to obtain information about various sociodemographic characteristics including parental education levels, ownership of a car by the family as a measure of SES in Jamaica, and the parish of residence. The SES questionnaire also included parent-reported data on perinatal characteristics of the enrolled children such as the mode of delivery (cesarean section vs. vaginal delivery), birth weight (weight measured after delivery), and GA (weeks of pregnancy at the time of delivery), which we considered as independent variables for this analysis. For the mode of delivery, we compared cesarean delivery with vaginal delivery as the reference category. We defined LBW (< 2500 grams) according to the World Health Organization (WHO) definition (Cutland et al. 2017) and compared it with normal birth weight (≥ 2500 g) as the reference category. We also used the WHO definition for preterm birth as birth before 37 weeks GA (Quinn et al. 2016; World Health Organization 2018) and compared it with full-term birth (≥ 37 weeks GA) as the reference category in our analysis.

Statistical analysis

Using data from 343 age- and sex-matched pairs, we performed descriptive statistics to compare the distributions of demographic and socioeconomic characteristics between ASD cases and TD controls. We used conditional logistic regression (CLR) models to assess the univariable association of various sociodemographic characteristics, as well as cesarean delivery, LBW, and preterm birth with ASD. For assessing the association of each of the three perinatal exposures of interest (cesarean delivery, LBW, and preterm birth) with ASD, we identified a specific set of potential confounding variables. Specifically, variables were considered as potential confounders when they were associated with both ASD and each of the exposures (Online Resource 3) with P-values less than 0.20 in the univariable CLR models. Furthermore, maternal age as suggested in the literature (Zhang et al. 2010; Schieve et al. 2015; Curran et al. 2015) was considered as a potential confounder a priori. In order to minimize any potential effect of multicollinearity due to high association between the age of the mother and the father, as well as the maternal and paternal education levels, we created variables that indicate the age, and the education levels of both parents. For example, we created a binary variable indicating whether both parents were younger than 35 years or at least one of the parents was aged 35 years or older. Similarly, for parental education, we created a binary variable indicating whether both parents had education up to high school or at least one of the parents obtained education beyond high school. Parental education level or age were included in the multivariable models when both maternal and paternal age or education level were identified as potential confounders. Additionally, the association between identified potential confounders was evaluated using univariable CLRs (Online Resource 4). Covariates that were highly associated with each other (P-value ≤ 0.01) such as child’s parish of residence, SES, and parental education were not included simultaneously in the multivariable CLRs to avoid potential effects of multicollinearity. Similarly, since marital status was strongly associated with all other identified confounders including maternal and paternal age, parental education, SES, and child’s parish, we did not adjust for marital status in our multivariable models (Online Resource 4). The age of parents and parental education level at the child’s birth, child’s parish, and SES were included in the final set of potential confounders for the associations of cesarean delivery, and premature birth with ASD. The potential confounders for the association of LBW and ASD were age of mother at the child’s birth, and SES. Separate multivariable models were constructed to adjust for potential confounders and each of the collinear variables. For additive models, we calculated unadjusted and adjusted matched odds ratios (MORs), and their corresponding 95% confidence intervals (CIs). Furthermore, we explored all possible two-way interactions within each multivariable model (Online Resource 5). For models with an interaction term, we used the CONTRAST statement in SAS statistical software to calculate the MORs and 95% CIs for different levels of SES factors (e.g., child’s parish, household SES, or parental education) as potential effect modifiers for the associations between each of the three main exposures and ASD. Final multivariable CLR models were constructed to account for potential confounders and effect modification. All statistical tests were performed at 0.05 level of significance and were conducted using SAS 9.4 statistical software (SAS Institute 2013).

Results

We compared the sociodemographic characteristics of the children and their parents between ASD cases and TD controls (Table 1). The mean age at enrollment of the ASD cases and TD controls was approximately 61 months (61.00 ± 19.57, and 61.47 ± 19.28, respectively). As expected, a majority of the ASD cases and TD controls were male (82.2%). More than 94% of the ASD cases, TD controls, and their parents were Afro-Caribbean. Compared to TD controls, a significantly higher proportion of ASD cases had at least one parent who was 35 years or older (47% vs. 32.9%, P-value <0.001) and educated beyond the high school level (61.1% vs. 46.2%, P–value = 0.001), and parents who were married (29.7% vs. 15.6%, P-value <0.001) at the time of the child’s birth. A higher proportion of ASD cases were from families with higher SES (56% vs. 40.8%, P-value <0.001) compared to the TD controls. Families of 28% of ASD cases and 61.9% of TD controls were living in Kingston parish (P-value <0.001). We did not find any significant difference between ASD cases and TD controls in terms of the person who acts as mother.

Table 1-.

Association of child, parental and sociodemographic characteristics with ASD (343 case-control pairs).

Variables	Categories	ASD Cases N (%) a	TD Controls N (%) a	P-value b
Child Characteristics
Age of child at enrollment (months)	< 46 months	84 (24.49)	81 (23.62)	0.331
	46–71.9 months	164 (47.81)	172 (50.15)
	≥ 72 months	95 (27.70)	90 (26.24)
Sex of child	Male	282 (82.22)	282 (82.22)	1.000
	Female	61 (17.78)	61 (17.78)
Race of child c	Afro-Caribbean	324 (94.5)	331 (97.1)	0.100
	Other d	19 (5.5)	10 (2.9)
Parental and Socioeconomic Characteristics
Age of mother e (at child’s birth)	< 35 years	275 (80.4)	293 (87.2)	0.012
	≥ 35 years	67 (19.6)	43 (12.8)
Age of father f (at child’s birth)	< 35 years	188 (55.8)	231 (70.6)	<0.001
	≥ 35 years	149 (44.2)	96 (29.4)
Age of parents g (at child’s birth)	Both < 35 years	178 (53.0)	216 (67.1)	<0.001
	At least one ≥ 35 years	158 (47.0)	106 (32.9)
Race of mother c	Afro-Caribbean	326 (95.0)	333 (97.7)	0.079
	Other d	17 (5.0)	8 (2.4)
Race of father h	Afro-Caribbean	325 (95.0)	328 (97.0)	0.167
	Other d	10 (3.0)	17 (5.0)
Mother’s education level i (at child’s birth)	High school or below	178 (51.9)	214 (63.3)	0.004
	Above high school	165 (48.1)	124 (36.7)
Father’s education level j (at child’s birth)	High school or below	198 (59.5)	256 (79.0)	<0.001
	Above high school	135 (40.5)	68 (21.0)
Parental education level k (at child’s birth)	Both high school or below	131 (38.9)	177 (53.8)	0.001
	At least one above high school	206 (61.1)	152 (46.2)
Parents’ marital status l	Married	102 (29.7)	53 (15.6)	<0.001
	Other m	241 (70.3)	287 (84.4)
Other person acts as mother c	Yes	16 (4.7)	14 (4.1)	0.706
	No	327 (95.3)	327 (95.9)
Child’s parish c	Kingston	96 (28.0)	211 (61.9)	<0.001
	Other n	247 (72.0)	130 (38.1)
Household SES c	Own a car	192 (56.0)	139 (40.8)	<0.001
	Do not own a car	151 (44.0)	202 (59.2)

ASD: Autism Spectrum Disorder; TD: Typically developing; SES: Socioeconomic status evaluated by owning or not owning a car.

^aData presented as numbers (%), otherwise as indicated;

^bP-values based on Wald statistic from univariable conditional logistic regression;

^cData missing for 2 controls;

^dIncludes White, Asian, Asian-Indian and mixed;

^eData missing for 1 case and 7 controls;

^fData missing for 6 cases and 16 controls;

^gData missing for 7 cases and 21 controls;

^hData missing for 1 case and 5 controls;

ⁱData missing for 5 controls;

^jData missing for 10 cases and 19 controls;

^kData missing for 6 cases and 14 controls;

^lData missing for 3 controls;

^mIncludes divorced, separated, widowed, no relationship, visiting relationship, and common law union;

ⁿIncludes Portland, Trelawny, Westmoreland, Clarendon, St. Andrew, St. Mary, St. James, St. Elizabeth, St. Catherine, St. Thomas, St. Ann, and Manchester.

As shown in Table 2, our univariable CLR analyses revealed that cesarean delivery was significantly associated with ASD [MOR (95% CI): 1.79 (1.23–2.60), P-value = 0.002]. Whereas, no significant association was observed between LBW and ASD [MOR (95% CI): 1.00 (0.62–1.61), P-value = 0.99], or between preterm birth and ASD [MOR (95% CI): 1.54 (0.90–2.64), P–value = 0.116].

Table 2-.

Association of select perinatal characteristics with ASD status using conditional logistic regression (343 case-control pairs).

Variable	Categories	ASD Cases N (%)	TD Controls N (%)	Unadjusted		Adjusted
						Model 1		Model 2		Model 3
				MOR (95% CI)	P-value a	MOR (95% CI)	P-value b	MOR (95% CI)	P-value b	MOR (95% CI)	P-value b
Cesarean delivery c	Yes	101 (29.6)	65 (19.1)	1.79 (1.23–2.60)	0.002	1.18 (0.78–1.78)	0.437 e	1.41 (0.90–2.19)	0.131 f	1.33 (0.89–2.00)	0.165 g
	No d	240 (70.4)	275 (80.9)
Birth weight h	Low (< 2500g)	41 (12.3)	42 (12.8)	1.00 (0.62–1.61)	0.999	1.11 (0.66–1.88)	0.691 i	-	-	-	-
	Normal (≥ 2500 g) d	293 (87.7)	286 (87.2)
Premature birth j	Yes (< 37weeks)	40 (11.7)	27 (7.9)	1.54 (0.90–2.64)	0.116	1.15 (0.63–2.11)	0.645 e	1.01 (0.53–1.92)	0.986 f	1.21 (0.66–2.20)	0.539 g
	No (≥ 37weeks) d	303 (88.3)	314 (92.1)

ASD: Autism Spectrum Disorder; TD: Typically developing; MOR: Matched Odds Ratio; 95% CI: 95% Confidence Interval.

^aP-values based on Wald statistic from univariable conditional logistic regression

^bP-values based on Wald statistic from multivariable conditional logistic regression

^cData missing for 2 cases and 3 controls

^dReference category

^eAdjusted for age of parents and parental education level at the child’s birth

^fAdjusted for age of parents at the child’s birth, and child’s parish

^gAdjusted for age of parents at the child’s birth, and socioeconomic status

^hData missing for 9 cases and 15 controls

ⁱAdjusted for age of mother at child birth, and socioeconomic status

^jData missing for 2 controls

After adjusting for sociodemographic related confounders, the association between cesarean delivery and ASD became non-significant, and all adjusted models were fairly consistent (Table 2). Specifically, no significant associations between ASD and cesarean delivery were observed after adjusting for age of parents and parental education level at the child’s birth in Model 1 [adjusted MOR (95% CI): 1.18 (0.78–1.78), P-value = 0.437], after adjusting for age of parents at the child’s birth and child’s parish in Model 2 [adjusted MOR (95% CI): 1.41 (0.90–2.19), P-value = 0.131], and after adjusting for age of parents at the child’s birth and SES in Model 3 [adjusted MOR (95% CI): 1.33 (0.89–2.00), P-value = 0.165]. The association between LBW and ASD remained non-significant after adjusting for age of mother at the child’s birth, and SES [adjusted MOR (95% CI): 1.11 (0.66–1.88), P-value = 0.691]. Similar non-significant findings were observed for the association between premature birth and ASD after adjusting for parental age and education level at the child’s birth in Model 1 [adjusted MOR (95% CI): 1.15 (0.63–2.11), P-value = 0.645], after adjusting for age of parents at the child’s birth and child’s parish in Model 2 [adjusted MOR (95% CI): 1.01 (0.53–1.92), P-value = 0.986], and after adjusting for age of parents at the child’s birth and SES in Model 3 [adjusted MOR (95% CI): 1.21 (0.66–2.20), P-value = 0.539].

As shown in Table 3, further exploration of possible interactions in multivariable models indicated that the child’s parish of residence may modify the association between cesarean delivery and ASD after adjusting for the age of the parents at the child’s birth (overall interaction P-value = 0.049). This finding remained consistent after further adjustment for SES in the final adjusted model (overall interaction P-value = 0.039). Specifically, for children from the Kingston parish (urban), we observed that the odds of cesarean delivery is significantly higher among ASD cases than the TD controls after adjusting for age of the parents at the child’s birth and SES [adjusted MOR (95% CI): 2.30 (1.17–4.53), P-value = 0.016], whereas the association between cesarean delivery and ASD was not significant for children from other parishes that include a higher percentage of rural residences [adjusted MOR (95% CI): 0.87 (0.48–1.59), P-value = 0.660]. Additionally, we explored possible interaction between SES and parish of residence in relation to ASD in the final adjusted model to check if the difference observed among parishes is related to the SES, however, this interaction was not statistically significant (Online Resource 5, overall interaction P-value = 0.383), therefore, was not included in the final model. Furthermore, the association between cesarean delivery and ASD did not differ by SES (overall interaction P-value = 0.310), or by parental education level at the child’s birth (overall interaction P-value = 0.524).

Table 3-.

Matched odds ratios for the association between cesarean delivery as main perinatal exposure and ASD stratified by potential effect modifiers, based on interactive multivariable conditional logistic regression model.

Potential effect modifiers	Categories	Cesarean delivery	ASD Cases a	TD Controls b	Adjusted c			Final adjusted model d
					MOR (95% CI)	P-value		MOR (95% CI)	P-value
Child’s Parish	Kingston	Yes	30 (31.3)	36 (17.1)	2.34 (1.19–4.60)	0.013	0.049 e	2.30 (1.17–4.53)	0.016	0.039 e
		No f	66 (68.8)	174 (82.9)
	Other	Yes	71 (29.0)	29 (22.3)	0.93 (0.52–1.69)	0.824		0.87 (0.48–1.59)	0.660
		No f	174 (71.0)	101 (77.7)
Household SES	Own a car	Yes	64 (33.3)	33 (23.9)	1.10 (0.65–1.90)	0.707	0.310 e
		No f	128 (66.7)	105 (79.1)
	Do not own a car	Yes	37 (24.8)	32 (15.8)	1.65 (0.92–2.95)	0.090
		No f	112 (75.2)	170 (84.2)
Parental education level (at child’s birth)	Both high school or below	Yes	30 (23.1)	27 (15.2)	1.37 (0.74–2.53)	0.321	0.524 e
		No f	100 (76.9)	150 (84.8)
	At least one above high school	Yes	69 (33.7)	37 (24.3)	1.07 (0.63–1.79)	0.812
		No f	136 (66.3)	115 (75.7)

ASD: Autism Spectrum Disorder; TD: Typically developing; SES: Socioeconomic status evaluated by owning or not owning a car; MOR: Matched Odds Ratio; 95% CI: 95% Confidence Interval.

^aStratum specific information missing for 2 cases when child parish and SES are the potential effect modifiers, and for 8 cases when parental education level at child’s birth is the potential effect modifiers

^bStratum specific information missing for 3 TD controls when child parish and SES are the potential effect modifiers, and for 14 TD controls when parental education level at child’s birth is the potential effect modifier

^cAdjusted for age of parents at the child’s birth

^dAdjusted for age of parents at the child’s birth and household socioeconomic status

^eOverall interaction P-value

^fReference category

We also observed a significant interaction between household SES and LBW in relation to ASD status (overall interaction P-value = 0.047). Specifically, as shown in Table 4, after adjusting for the age of the mother at the child’s birth, the direction of the association between LBW and ASD appears to differ by household SES level, with an inverse association for high SES families [adjusted MOR (95% CI): 0.64 (0.30–1.36), P-value = 0.248], and a direct association among low SES families [adjusted MOR (95% CI): 1.79 (0.89–3.64), P-value = 0.100].

Table 4-.

Matched odds ratios for the association between birth weight as main perinatal exposure and ASD stratified by household SES, based on final interactive multivariable conditional logistic regression model.

Potential effect modifier	Categories of SES	Birth weight	ASD Cases a (n = 334)	TD Controls b (n = 328)	Adjusted c
					MOR (95% CI)	P-value d
Household SES	Own a car	Normal (≥ 2500 gr) f	173 (91.1)	118 (87.4)
		Low (< 2500gr)	17 (9.0)	17 (12.6)	0.64 (0.301.36)	0.248
	Do not own a car	Normal (≥ 2500 gr) f	120 (83.3)	168 (87.1)
		Low (< 2500gr)	24 (16.7)	25 (13.0)	1.79 (0.893.64)	0.100

ASD: Autism Spectrum Disorder; TD: Typically developing; SES: Socioeconomic status evaluated by owning or not owning a car; MOR: Matched Odds Ratio; 95% CI: 95% Confidence Interval; SES: socioeconomic status.

^aInformation was missing for 9 cases

^bInformation was missing for 15 controls

^cAdjusted for age of mother at the child’s birth

^dOverall interaction P-value = 0.047

^fReference category

As shown in Table 5, we did not find any significant interactions between preterm birth and child’s parish, SES, or parental education level at the child’s birth in relation to ASD (overall interaction P-value = 0.921, 0.345, and 0.860, respectively). Therefore, our findings from both additive and interactive multivariable models do not support a significant association between preterm birth and ASD.

Table 5-.

Matched odds ratios for the association between premature birth as main perinatal exposure and ASD stratified by potential effect modifiers, based on interactive multivariable conditional logistic regression model.

Potential effect modifiers	Categories	Premature birth	ASD Cases a	TD Controls b	Adjusted c
					MOR (95% CI)	P-value
Child’s Parish	Kingston	Yes (<37weeks)	8 (8.3)	11 (5.2)	1.05 (0.343.22)	0.928	0.921 d
		No (≥37weeks) e	88 (91.7)	200 (94.8)
	Other	Yes (<37weeks)	32 (13.0)	16 (12.3)	0.98 (0.462.12)	0.970
		No (≥37weeks) e	215 (87.0)	114 (87.7)	.
Household SES	Own a car	Yes (<37weeks)	21 (10.9)	14 (10.1)	0.94 (0.432.06)	0.883	0.345 d
		No (≥37weeks) e	171 (89.1)	125 (89.9)
	Do not own a car	Yes (<37weeks)	19 (12.6)	13 (6.4)	1.63 (0.683.92)	0.270
		No (≥37weeks) e	132 (87.4)	189 (93.6)
Parental education level (at child’s birth)	Both high school or below	Yes (<37weeks)	15 (11.4)	11 (6.2)	1.23 (0.483.14)	0.665	0.860 d
		No (≥37weeks) e	116 (88.6)	166 (93.8)
	At least one above high school	Yes (<37weeks)	25 (12.1)	15 (9.9)	1.10 (0.502.44)	0.813
		No (≥37weeks) e	181 (87.9)	137 (90.1)

ASD: Autism Spectrum Disorder; TD: Typically developing; SES: Socioeconomic status evaluated by owning or not owning a car: MOR: Matched Odds Ratio; 95% CI: 95% Confidence Interval.

^aStratum specific information missing for 6 cases when parental education level at child’s birth is the potential effect modifier

^bStratum specific information missing for 2 TD controls when child parish and SES are the potential effect modifiers, and for 14 TD controls when parental education level at child’s birth is the potential effect modifier

^cAdjusted for age of parents at the child’s birth

^dOverall interaction P-value

^eReference category

Discussion

In this study we have investigated the association of three perinatal outcomes with ASD in Jamaican children. In the following, we discuss our main findings for each of these three investigations separately.

Cesarean delivery

Based on univariable analysis in this study we have reported that ASD cases had about 79% higher odds of being born by cesarean section compared to TD controls, which is consistent with findings reported by several other studies. For example, several age- and sex-matched case-control studies from Australia (Glasson et al. 2004), China (Zhang et al. 2010), and Sweden (Hultman et al. 2002) reported that ASD cases were about 1.5–2.0 times more likely to be delivered by cesarean section than the control children in univariable analyses [OR (95% CI): 2.05 (1.49–2.82); 1.79 (0.99–3.25); and 1.8 (1.4–2.4), respectively]. Our findings about the association between cesarean delivery and ASD are also consistent with a retrospective cohort study from Canada (Dodds et al. 2011) [Relative risk (RR) (95% CI): 1.23(1.1–1.4)], and a prospective cohort study in Sweden (Curran et al. 2015) [RR (95% CI): 1.39 (1.33–1.45) for elective cesarean, and 1.40 (1.34–1.46) for emergency cesarean], indicating that children born by cesarean section are 20–40% more likely to be diagnosed with ASD. However, findings from the multivariable analysis from the previous studies are inconsistent. Some case-control studies, including the ones from Australia (Glasson et al. 2004) and Sweden (Hultman et al. 2002), reported that the association between cesarean delivery and ASD remained significant after adjusting for maternal age, SES, and other delivery complications [adjusted OR (95% CI): 1.83 (1.32–2.54), and 1.6 (1.1–2.3), respectively]. The case-control study from China (Zhang et al. 2010), which had a relatively smaller sample size reported a marginally significant association between cesarean delivery and ASD after adjusting for the age of the parents at the birth of the child and the child’s gender [adjusted OR (95% CI): 1.83 (0.98–3.44)]. In contrast, a large Canadian retrospective cohort study (Burstyn et al. 2010) reported no significant association between cesarean delivery and the diagnosis of ASD in children [adjusted RR (95% CI): 1.04 (0.88–1.22)].

To our knowledge, none of the previously published studies have reported the interactions between cesarean delivery and the area of residence in relation to ASD. Findings from our study suggest that in the Kingston parish (with urban residences), the odds of cesarean delivery of children with ASD was 2.30 times higher compared to TD children, whereas in other parishes (with a higher percentage of rural residences), there was no association between cesarean delivery and ASD. The modifying effect of parish of residence on the cesarean delivery-ASD association could possibly be attributable to factors that are related to either cesarean delivery or ASD and may vary between urban and non-urban areas of residence, such as the SES of the family, and the availability of prenatal care facilities. While there are specific medical indications for cesarean delivery, there is evidence suggesting associations between many non-medical factors and cesarean delivery even after adjusting for medical indications (O’Leary et al. 2007). For example, several studies have reported that cesarean delivery is more likely among mothers with lower levels of education (Guihard and Blondel 2001; Cesaroni et al. 2008; Lee et al. 2005; Tollånes et al. 2007), or those with lower SES (Kottwitz 2014; Simoes et al. 2005; Linton et al. 2004; Joseph et al. 2006). Our findings do not support a modifying effect of SES or parental education level at the child’s birth on the cesarean delivery-ASD association. In addition, since we have controlled for the potential confounding effect of SES in our final multivariable model, SES is less likely to directly explain the modifying effect of the parish of residence on the cesarean delivery-ASD association. However, SES may affect other factors such as access to health services that may be different across parishes. For example, findings from a recent study in France (Milcent and Zbiri 2018) suggested that cesarean delivery is significantly more likely for pregnant women who do not participate in prenatal education and visits compared to those who do. In addition, they have reported that the attendance at prenatal visits varies according to SES, and women with lower SES are less likely to participate in prenatal visits. Prenatal visits that provide routine health assessments, education, counseling, and necessary treatments throughout pregnancy are crucial to prevent many health conditions that could result in cesarean delivery (Alexander and Kotelchuck 2001). As a part of the standards for maternal and neonatal care, WHO recommends at least four prenatal assessments for all pregnant women under the supervision of a skilled attendant, starting as early as possible in the first trimester, and spaced at regular intervals (World Health Organization 2007). However, this optimal prenatal care may remain unmet, especially in rural areas. For example, a study from India has reported that three-fifths of the women in rural residences did not receive any prenatal check-up during their last pregnancy (Pallikadavath et al. 2004). In Jamaica, there is evidence that 87% of the mothers complete at least 4 antenatal visits (United Nations Children’s Fund (UNICEF) 2013), however, there is no data available on whether receiving sufficient prenatal care varies across parishes. In our study, we observed that the proportion of ASD cases born by cesarean section is relatively similar, and larger than TD controls in both Kingston and other parishes [31.3% (about 80% larger than that of the TD controls), and 29% (about 30% larger than that of TD controls), respectively]. However, the difference was only significant in Kingston because a smaller proportion of TD controls were born by cesarean section in Kingston as compared to other parishes (17.1% vs. 22.3%, respectively). As mentioned earlier, Kingston is the capital of Jamaica and most of the health care services including Jamaica’s largest maternity hospital are centralized in this region (Jamaica Ministry of Health 2019; Government of Jamaica 2014). Consequently, facilities that provide prenatal care would be easily accessible to mothers who live in Kingston parish. On the other hand, women who live in urban areas such as Kingston are more likely to have higher SES and education levels (Statistical Institute of Jamaica 2011b, 2011a), which could potentially increase their tendency to seek adequate prenatal care compared to women in parishes with a higher percentage of rural residences. Therefore, receiving suboptimal prenatal care could be a possible explanation for the high proportions of children being born by cesarean delivery among both ASD cases and TD controls in parishes with a higher percentage of rural residences. In our study, we did not have data on the quality and quantity of prenatal care in different parishes to explore if SES interacts with access to prenatal care in relation to ASD and this requires further investigation.

On the other hand, compared to TD controls, we observed that a slightly higher proportion of ASD cases in the better served urban environment of Kingston were born by cesarean delivery as compared to other parishes (31.3% and 29%, respectively). This could be due to possible interactions between cesarean delivery and other environmental exposures in relation to ASD that may vary by geographical area of residence, such as traffic-related air pollution. For instance, several studies have reported that prenatal and perinatal exposure to air pollutants including carbon monoxide, and particulates in urban areas are associated with ASD (Dickerson et al. 2016; Dickerson et al. 2015; Weisskopf et al. 2015). Residential proximity to heavy-traffic areas has also been associated with adverse birth outcomes such as fetal distress, preterm birth, and LBW which may be related to cesarean delivery (Brender et al. 2011; Wilhelm and Ritz 2003). However, in this study we did not have data on environmental factors such as air pollutants to investigate their effect on the cesarean delivery-ASD association. Although the actual mechanism behind the modifying effect of the parish of residence on the association between cesarean and ASD in Jamaica requires further investigation, our findings imply the complexity of the cesarean delivery-ASD association, and the importance of investigating possible interactions and effect modifications. In other words, in addition to the methodological differences, the inconsistent findings of the previous studies on the cesarean delivery-ASD relationship could possibly be attributed to the modiffying effect of other factors such as parish of residence on this relationship, which has not been accounted for. This warrants further investigation into the possible mechanisms underlying the association between cesarean delivery and ASD, as well as replication in study populations outside of Jamaica.

Low birth weight

The present study does not support a univariable association between LBW and ASD, which is consistent with a previous case-control study from China (Zhang et al. 2010) and a retrospective cohort study from Canada (Dodds et al. 2011). However, a retrospective cohort study from Canada (Burstyn et al. 2010) and a nested case-control study from Denmark (Larsson et al. 2005) reported that children with LBW were more likely to be diagnosed with ASD [RR (95%CI):1.33 (1.01–1.75), and 1.79 (1.28, 2.51), respectively]. Furthermore, a study that evaluated the effects of LBW on mental health outcomes (Singh et al. 2013) in the US using the National Survey of Children’s Health (NSCH) data also reported that children with LBW had 80% higher odds of ASD compared to children born with normal weight after adjusting for sociodemographic characteristics and SES. It is important to note that in most of these previous studies, LBW was more common among ASD cases compared to TD controls even when it was not significantly associated with ASD. In our study, however, the proportion of ASD cases and TD controls who reported LBW were very similar (about 12.3% and 12.8%, respectively) and were comparable to the estimated prevalence of LBW (about 10%) reported in the general population in Jamaica (Bernard et al. 2018).

Although not statistically significant, our multivariable analysis revealed that the direction of association between LBW and ASD differed by SES. Among families with low SES, ASD cases were 79% more likely to have LBW compared to TD controls, whereas, among families with high SES, ASD cases were 36% less likely to have LBW compared to TD controls. Other studies have shown that LBW is more prevalent among families with low SES (Parker et al. 1994). Although almost all studies that investigated the association between LBW and ASD or other mental and cognitive conditions considered SES as a potential confounder, to our knowledge, only a few explored the modifying effect of SES on the association between LBW and neurodevelopmental disabilities including ASD. For example, there is evidence in the literature that the impact of LBW on other mental conditions including intellectual disability (ID) and low cognitive test scores is different when stratified by SES. A study in Australia (Malacova et al. 2009) has reported a direct association between optimal birth weight and higher reading scores especially for children born in higher SES areas, whereas for children born in low SES areas, reading scores were low regardless of birthweight. Another study has reported an association between LBW and higher proportion of cognitive deficits in high SES families, whereas no association between birthweight and cognitive deficits was evident in families with lower SES (Drews-Botsch et al. 2011). In contrast to IDs, having a child with ASD has been reported to be more common among families with high SES compared to families with lower SES (Christensen et al. 2014; Durkin et al. 2010; Windham et al. 2011). To our knowledge, only one study in the US that used the ADDM network surveillance data (Schieve et al. 2015) to compare the effect of perinatal factors in ASD, and in ASD either with or without co-occurring ID, has further evaluated whether the association of being small for gestational age (SGA) with the combinations of ASD and ID differ by maternal race-ethnicity and education as two key SES factors. Although they did not assess the modifying effect of SES on the association of LBW with combinations of ASD and ID, their findings suggested that among non-Hispanic white children whose mothers had educational levels above high school, full term SGA was more than 2 times higher in children with ID only than in those with ASD only or both ASD and ID. Furthermore, they did not compare children with ASD and TD children in each of the maternal race-ethnicity and education strata. Our findings add to the growing body of literature suggesting a role of SES in the relationship between LBW and neurodevelopmental outcomes, though additional studies, on ASD specifically, are warranted. Most studies of interactive effects, including ours, might also be underpowered, so findings should be replicated in studies with larger sample sizes to produce more conclusive evidence.

Preterm birth

Results of the present study do not support a significant association between preterm birth (GA < 37 weeks at birth) and ASD, which is consistent with many studies that used the same WHO definition for preterm birth including a Canadian retrospective cohort study (Burstyn et al. 2010) [RR adjusted for numerous maternal and perinatal factors (95% CI): 0.97 (0.75–1.25)], a Finnish case-control study (Lampi et al. 2012) [(OR adjusted for maternal age and psychiatric history (95% CI): 1.12 (0.9, 1.4)], and an American nested case-control study (Bilder et al. 2009) [(unadjusted OR (95% CI): 0.924 (0.537–1.590)]. In contrast, another Canadian retrospective cohort study (Dodds et al. 2011) has reported that children born preterm are about 40% more likely to be diagnosed with ASD. Another study, that used the 2011–2012 NSCH survey data in the US to evaluate the effects of preterm birth on mental health outcomes among children aged 2–17 years (Singh et al. 2013), has reported that the odds of ASD in preterm children were 2.26 times that of the full-term children after adjusting for sociodemographic characteristics and SES.

The substantial heterogeneity in the literature on the association between preterm birth and ASD was also discussed in recent meta-analyses (Gardener et al. 2011; Wang et al. 2017) and attributed to using various definitions for preterm birth ranging from less than 28 weeks to 36 weeks of GA at delivery. Specifically, some case-control studies defined preterm birth as delivery at less than 36 weeks of GA (Hultman et al. 2002), whereas others used 35 weeks of GA as the cut-off for preterm birth (Larsson et al. 2005; Zhang et al. 2010) and reported significant associations between preterm birth and ASD with adjusted ORs ranging from 1.31 to 1.76. Furthermore, some previous studies in the US that used ADDM network surveillance data have reported that there were stronger associations of extremely preterm (<28 weeks of gestation), and very preterm (28–31 weeks of gestation) delivery with ASD when compared to full-term delivery after adjusting for SES factors (Schieve et al. 2010; Schieve et al. 2015; Durkin et al. 2008). In the present study, a limited number of ASD cases and TD controls were extremely preterm (only 1 ASD case and 2 TD controls with <28 weeks of gestation) and very preterm (only 4 ASD cases and 8 TD controls with 28–31 weeks of gestation). Therefore, we did not analyze the possible association of the various levels of preterm birth and ASD.

Study strengths and limitations

To our knowledge, this study is the first that investigates the association of perinatal characteristics with ASD in Jamaican children. Most of the existing literature regarding these associations is from developed countries. Therefore, our findings could contribute to the literature on ASD in LMICs. Furthermore, this is the first study that explored possible interactions between perinatal factors and SES characteristics in relation to ASD in Jamaica. This study also has some limitations that should be acknowledged. The current research used data from the ERAJ and ERAJ-2 case-control studies. Since perinatal exposures were not the main focus of the ERAJ studies, the SES questionnaire used for data collection obtained limited details about these exposures. For instance, the SES questionnaire did not distinguish between complications of labor, abnormal presentation at birth and other indications for cesarean delivery. No data were collected on whether the mothers in different parishes had received adequate prenatal visits as recommended by WHO before the child’s birth. Therefore, our findings could have been impacted by potential unmeasured confounding. Similarly, detailed information was not available to evaluate the association between SGA and ASD in this study. All the data collected were self-reported exposures, therefore the results may be affected by potential recall bias and exposure misclassification. In addition, the SES questionnaire collected data on the parish of residence, but it did not ask clearly whether the area of residence is urban or rural. Kingston, which is the capital of Jamaica has all urban residences. However, other parishes that are considered as mainly rural may have different proportions of rural residents. It should also be acknowledged that, although the UWI Jamaica Autism Database includes ASD children from all over Jamaica, ASD cases who live in Kingston or parishes close to Kingston might be more likely to be included in the UWI database which could introduce bias to some of our findings. However, we believe that our ASD cases are fairly representative of the children with confirmed ASD diagnosis in the country. The first reason is that the number of centers that offers assessment and early interventions for children with ASD is very limited in Jamaica, and to our knowledge, except for 1 center located in St. James parish in west Jamaica, 4 out of 5 centers are located in Kingston and parishes close to Kingston (St. Andrew and Portland)(Jamaica Ministry of Labour and Social Security 2018). In addition, only the CFC at UWI has specialists who could objectively diagnose ASD using gold standard methods. Therefore, children with varying types of developmental disabilities will be referred to this center from all over the country for confirmation of ASD diagnosis. Secondly, the proportion of our cases from parishes located in the north, south, and west of Jamaica (from outside Kingston) seems to be reasonably comparable to the distribution of the national population by parish in Jamaica (Statistical Institute of Jamaica 2011). For example, we had 24 (7%) ASD cases from St. Ann in north Jamaica (6.4% of the national population), and 24 (7%) cases from Manchester parish in south Jamaica (7% of the Jamaica national population). Similarly, we had 22 (6.4%) ASD cases from St. James parish in west Jamaica, which includes 6.8% of the national population and is the only parish far from Kingston that has a facility providing assessment and early intervention for ASD children. On the other hand, TD controls in the ERAJ studies were more likely to be selected from the Kingston parish to match the ASD cases based on age and sex, hence, they may not represent a random sample from the population of all children in Jamaica. Finally, we obtained wide confidence intervals for some of the estimates possibly due to instability from small proportions of exposed study participants, thus some of our findings should be replicated in a larger study.

Conclusions

Our study suggests a significant modifying effect of the child’s parish of residence on the association between cesarean delivery and ASD in Jamaica. Among children from Kingston parish which includes urban residents, the odds of cesarean delivery among ASD cases was 2.30 times that of the odds of cesarean delivery among TD controls, though there was no association among children from other parishes with higher percentages of rural residences. We also found that SES may modify the association between LBW and ASD. Specifically, ASD cases from families with lower SES had 79% higher odds of having LBW compared to TD controls, whereas, ASD cases from families with higher SES had 36% lower odds of having LBW compared to TD controls. However, these findings were not statistically significant. Although findings of this research will add evidence to the literature on the association of perinatal characteristics with ASD in Jamaica, they require replication in studies with less limitations than that of the present study.