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Prenatal alcohol exposure is associated with early motor, but not language development in a South African cohort

Published online by Cambridge University Press:  06 January 2020

Gaironeesa Hendricks Open the ORCID record for Gaironeesa Hendricks [Opens in a new window] ,
Susan Malcolm-Smith ,
Dan J. Stein ,
Heather J. Zar ,
Catherine J. Wedderburn ,
Raymond T. Nhapi ,
Tawanda Chivese ,
Colleen M. Adnams  and
Kirsten A. Donald
Gaironeesa Hendricks*
Affiliation:
Department of Psychiatry and Mental Health, and SAMRC Unit on Risk and Resilience, University of Cape Town, Cape Town, South Africa Department of Paediatrics and Child Health, Red Cross War Memorial Children’s Hospital, University of Cape Town, Cape Town, South Africa
Susan Malcolm-Smith
Affiliation:
Department of Psychology, Applied Cognitive Science and Experimental Neuropsychology Team, University of Cape Town, Cape Town, South Africa
Dan J. Stein
Affiliation:
Department of Psychiatry and Mental Health, and SAMRC Unit on Risk and Resilience, University of Cape Town, Cape Town, South Africa Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
Heather J. Zar
Affiliation:
Department of Paediatrics and Child Health, Red Cross War Memorial Children’s Hospital, University of Cape Town, Cape Town, South Africa SAMRC Unit on Child and Adolescent Health, University of Cape Town, Cape Town, South Africa
Catherine J. Wedderburn
Affiliation:
Department of Paediatrics and Child Health, Red Cross War Memorial Children’s Hospital, University of Cape Town, Cape Town, South Africa Department of Clinical Research, London School of Hygiene & Tropical Medicine, London, UK
Raymond T. Nhapi
Affiliation:
Department of Paediatrics and Child Health, Red Cross War Memorial Children’s Hospital, University of Cape Town, Cape Town, South Africa
Tawanda Chivese
Affiliation:
Department of Population Health, College of Medicine, Qatar University, Doha, Qatar
Colleen M. Adnams
Affiliation:
Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
Kirsten A. Donald
Affiliation:
Department of Paediatrics and Child Health, Red Cross War Memorial Children’s Hospital, University of Cape Town, Cape Town, South Africa Neuroscience Institute, University of Cape Town, Cape Town, South Africa
*
Author for correspondence: Gaironeesa Hendricks, Email: gaironeesahendricks@gmail.com
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Abstract

Objective:

To investigate the association of prenatal alcohol exposure (PAE) and early neurodevelopment in the first 2 years of life, adjusting for maternal socio-demographic and psychosocial factors, in the Drakenstein Child Health Study (DCHS), a South African birth cohort study.

Methods:

The DCHS comprises a population-based birth cohort of 1143 children, of which a subsample completed the Bayley Scales of Infant Development-III (BSID-III) at 6 (n = 260) and 24 months of age (n = 734). A subset of alcohol-exposed and -unexposed children was included in this analysis at age 6 (n = 52 exposed; n = 104 unexposed) and 24 months (n = 92 exposed; n = 184 unexposed). Multiple hierarchical regression was used to explore the associations of PAE with motor and language development.

Results:

PAE was significantly associated with decreased gross motor [odds ratio (OR) = 0.16, 95% confidence interval (CI) = 0.06–0.44, p = 0.001] or fine motor (OR = 0.16, 95% CI = 0.06–0.46, p = 0.001) functioning after adjusting for maternal socio-demographic and psychosocial factors at 6 months of age only. No significant effects were found in either receptive or expressive communication and cognitive outcomes at either time points.

Conclusion:

PAE has potentially important consequences for motor development in the first 2 years of life, a period during which the most rapid growth and maturation occur. These findings highlight the importance of identifying high-risk families in order to provide preventive interventions, particularly in antenatal clinics and early intervention services.

Keywords

prenatal alcohol exposuremotor developmentlanguage developmentneurodevelopment
Type
Original Article
Information
Acta Neuropsychiatrica , Volume 32 , Issue 3 , June 2020 , pp. 145 - 152
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2020. Published by Cambridge University Press on behalf of Scandinavian College of Neuropsychopharmacology

Significant outcomes

  • PAE had a significant impact on motor functioning after adjusting for variety of socio-demographic and psychosocial factors at 6 months of age, but not on cognitive or language development.

  • The findings of this study highlight the importance of identifying psychosocial risk factors, particularly in antenatal clinics and early intervention services in a South African context.

Limitations

  • The BSID-III tool assesses general ability of a given task but may have low sensitivity for detecting minor developmental impairments especially during infancy.

  • Language impairments are very subtle in the early years and may be more difficult to identify impairments than in other domains.

  • A small sample size may have reduced the power of the study, and findings may not be generalisable to other populations.

Introduction

Prenatal alcohol exposure (PAE) has been recognised as a major global public health concern. A recent study estimated 9.8% of mothers consumed alcohol during pregnancy and 4.3% were heavy drinkers (defined as an average of two or more drinks per day; Popova et al., Reference Popova, Lange, Probst, Gmel and Rehm2017). Estimated global prevalence rates of foetal alcohol spectrum disorders (FASDs) have been reported at 7.7 (4.9–11.7) per 1000 children (Olivier et al., Reference Olivier, Viljoen and Curfs2016). In low- and middle-income countries (LMICs), such as South Africa, the estimated prevalence of FASDs is as high as 111.1 per 1000 children in some communities (Olivier et al., Reference Olivier, Viljoen and Curfs2016). The majority of previous studies exploring the impact of PAE on child development in the context of psychobiological and psychosocial factors have been performed in high-income countries, even though higher rates of PAE, poverty, post-traumatic stress disorder (PTSD) and depression exist in LMICs (May et al., Reference May, Gossage, Marais, Hendricks, Snell, Tabachnick, Stellavato, Buckley, Brooke and Viljoen2008; Keen et al., Reference Keen, Uriu-Adams, Skalny, Grabeklis, Grabeklis, Green, Yevtushok, Wertelecki and Chambers2010; Flak et al., Reference Flak, Su, Bertrand, Denny, Kesmodel and Cogswell2014). The research taking into account contextual factors such as those cited above underscores the importance of examining the adverse effects of PAE in young children (May et al., Reference May, Gossage, Marais, Hendricks, Snell, Tabachnick, Stellavato, Buckley, Brooke and Viljoen2008; Flak et al., Reference Flak, Su, Bertrand, Denny, Kesmodel and Cogswell2014), within the broader context of psychosocial and environmental risk factors that may additionally influence not only early neurodevelopmental outcomes but also lifelong health trajectories.

The adverse effects of PAE manifest a continuum of disorders, namely, FASDs. Foetal alcohol syndrome (FAS) is a pattern of often irreversible physical and mental birth deficiencies (Nayak & Murthy, Reference Nayak and Murthy2008; Safe et al., Reference Safe, Joosten and Giglia2018), while alcohol-related neurodevelopmental disorder (ARND) and alcohol-related birth defects are described as conditions in which the exposed child demonstrates some but not all features of FAS (Sokol et al., Reference Sokol, Delaney-Black and Nordstrom2003). Previous studies have shown that FAS and ARND are associated with a range of impairments in motor functioning, reading comprehension or executive functioning in the early school years (Adnams et al., Reference Adnams, Kodituwakku, Hay, Molteno, Viljoen and May2001; Cone-Wesson, Reference Cone-Wesson2005; Comasco et al., Reference Comasco, Rangmar, Eriksson and Oreland2018). Safe et al. (Reference Safe, Joosten and Giglia2018), for example, have reported that children with FAS displayed both motor function and language impairments at 12 years of age, while Coggins et al. (Reference Coggins, Timler and Olswang2007) found that school-aged children with FASDs often exhibit clinically meaningful deficits in language and social communication between 6 and 12 years of age. Previous work by Viholainen et al. (Reference Viholainen, Ahonen, Lyytinen, Cantell, Tolvanen and Lyytinen2006) reported that impaired language development has also been found to be precursor of problems with motor functioning in the school years.

While there is a rapidly growing literature detailing the effects of PAE on neurodevelopmental outcomes in school-going children, comparable data across motor and language functioning are limited in very young children. A previous cross-sectional study assessed specific developmental domains and found PAE deficits at 12 months of age: motor coordination and gross motor functioning (Hutchinson et al., Reference Hutchinson, Youssef, McCormack, Wilson, Allsop, Najman, Elliott, Burns, Jacobs, Honan and Rossen2019). Other cross-sectional analyses found that FAS was associated with abnormal walking and balance (Kaplan-Estrin et al., Reference Kaplan-Estrin, Jacobson and Jacobson1999; O’Leary, Reference O’Leary2004; Kalberg et al., Reference Kalberg, Provost, Tollison, Tabachnick, Robinson, Eugene Hoyme, Trujillo, Buckley, Aragon and May2006; Henderson et al., Reference Henderson, Gray and Brocklehurst2007; Mattson et al., Reference Mattson, Crocker and Nguyen2011) and deficits in receptive and expressive communication through 2 years (Kodituwakku, Reference Kodituwakku2007; O’leary et al., Reference O’Leary, Zubrick, Taylor, Dixon and Bower2009; Kodituwakku et al., Reference Kodituwakku, Segall and Beatty2011). However, very few studies included data at different time points in the first 2 years of life (see Hendricks et al., Reference Hendricks, Malcolm-Smith, Adnams, Stein and Donald2018). Of the few studies exploring developmental impairments over time, heavy alcohol exposure was significantly associated with delayed motor functioning in toddlers between 12 and 17 months but not at 24 months of age (Fried & Watkinson, Reference Fried and Watkinson1988; Jacobson & Jacobson, Reference Jacobson and Jacobson2002; Davies et al., Reference Davies, Dunn, Chersich, Urban, Chetty, Olivier and Viljoen2011). However, the heterogeneity in designs and methodologies of previous studies limits the ability to interpret results across different age cohorts. For example, the impact of maternal alcohol consumption on child outcomes using a clinical diagnosis of FAS without focus on children who do not meet the FASD criteria was reported in only one study (Davies et al., Reference Davies, Dunn, Chersich, Urban, Chetty, Olivier and Viljoen2011).

Much of the longitudinal research describing the developmental outcomes in early childhood has been conducted in well-resourced settings (Fried & Watkinson, Reference Fried and Watkinson1990; Fried et al., Reference Fried, O’connell and Watkinson1992; Kaplan-Estrin et al., Reference Kaplan-Estrin, Jacobson and Jacobson1999), less is known about the effects of PAE on early neurodevelopmental outcomes at different time points in LMICs and much of the work published to date has lacked control groups and/or has adjusted for very few confounders (maternal age, gestation, birth weight and parity; Fried & Watkinson, 1988, Reference Fried and Watkinson1990; Fried et al., Reference Fried, O’connell and Watkinson1992; Jacobson & Jacobson, Reference Jacobson and Jacobson2002). Few studies have adjusted for additional psychosocial factors, such as maternal PTSD, which frequently co-occurs with PAE and which may have detrimental effects on young children’s neurodevelopmental outcomes.

Aim of the study

This study aimed to investigate the association of PAE and early neurodevelopment through 2 years of age, adjusting for socio-demographic and psychosocial factors in the Drakenstein Child Health Study (DCHS), a South African birth cohort study.

Materials and methods

Design and setting

This study formed part of the DCHS, a multidisciplinary birth cohort study investigating the early determinants of child health (Stein et al., Reference Stein, Koen, Donald, Adnams, Koopowitz, Lund, Marais, Myers, Roos and Sorsdahl2015; Zar et al., Reference Zar, Barnett, Stadler, Gardner-Lubbe, Myer and Nicol2016; Donald et al., Reference Donald, Hoogenhout, du Plooy, Wedderburn, Nhapi, Barnett, Hoffman, Malcolm-Smith, Zar and Stein2018). The DCHS enrolled pregnant women (20–28 weeks’ gestation) from two primary health care clinics, Mbekweni (a predominantly black African community) and TC Newman (a mixed-ancestry community) in the Western Cape, South Africa. Both communities are characterised by low socio-economic status (SES) and a high prevalence of multiple psychosocial risk factors (Zar et al., Reference Zar, Barnett, Stadler, Gardner-Lubbe, Myer and Nicol2016). Pregnant women were eligible to participate if they were 18 years or older, had access to one of the two primary health care clinics for antenatal care and had stated no intention to move out of the district within the following year. Mother–child dyads were followed longitudinally until children were at least 6 years of age.

Participants

This study utilised a subgroup from the DCHS. The PAE group comprised mothers with a minimum score of 11 on the alcohol questions of the Alcohol, Smoking and Substance Involvement Screening Test (ASSIST; Humeniuk et al., Reference Humeniuk, Ali, Babor, Farrell, Formigoni, Jittiwutikarn, De Lacerda, Ling, Marsden, Monteiro and Nhiwatiwa2008). A follow-up cohort completed a measure on alcohol questions at 3–6 weeks and 24 months of age. Mothers were asked postpartum to provide a positive history of alcohol use in any of the three trimesters of pregnancy at levels consistent with the World Health Organization’s moderate-to-severe alcohol use. The unexposed group included children whose mothers had a score less than 11 on the ASSIST antenatally. After birth, infants identified were included for the study unless the mothers had a positive urine screen for any other drug abuse (opiates, marijuana, cocaine, methamphetamine and barbiturates). Infants born prematurely or with any other congenital malformations as well as sets of twins and triplets were excluded from the study.

In total, there were 1143 live births in the DCHS (see Donald et al., Reference Donald, Hoogenhout, du Plooy, Wedderburn, Nhapi, Barnett, Hoffman, Malcolm-Smith, Zar and Stein2018). A subsample of the larger cohort completed the Bayley Scales of Infant Development-III (BSID-III) at 6 months (n = 260), whereas the full cohort was invited to participate at 24 months (n = 734) making a larger sample available. At 6 and 24 months of age, a subset of infants and toddlers were selected whose mothers reported moderate-to-severe levels of alcohol consumption and for whom BSID-III data were available. Of the 260 infants, 52 were exposed to alcohol at 6 months, and of the 734 toddlers, 92 were exposed to alcohol at 24 months. The unexposed group comprised 104 at 6 months and 184 at 24 months. Unexposed control children were randomly matched for maternal education and clinic site in a 1:2 ratio (Fig. 1).

Fig. 1. Study sample selection.

Measures

Participants were asked to complete self-reported and clinician-administered measures at antenatal and postnatal study visits in their preferred language, English, Afrikaans or isiXhosa. At the point of assessments (6 and 24 months of age), every effort was made to ensure a safe, anonymous, confidential and supportive environment. Translation of the measures from English to Afrikaans and isiXhosa included a standard forward and back-translation process (see Stein et al., Reference Stein, Koen, Donald, Adnams, Koopowitz, Lund, Marais, Myers, Roos and Sorsdahl2015). Prior to the administration of the measures, adult mothers or legal guardians of the children received enough information about the study and were asked to complete an informed consent form in their preferred language.

Maternal socio-demographic, psychosocial and infant measures for this study have previously been described (Stein et al., Reference Stein, Koen, Donald, Adnams, Koopowitz, Lund, Marais, Myers, Roos and Sorsdahl2015; Donald et al., Reference Donald, Hoogenhout, du Plooy, Wedderburn, Nhapi, Barnett, Hoffman, Malcolm-Smith, Zar and Stein2018) and included:

Socio-demographic measures

Measures included data on SES (maternal income, education, employment status and asset sum), marital status and HIV status (Myer et al., Reference Myer, Stein, Grimsrud, Seedat and Williams2008). Higher scores on this validated composite score indicated higher SES.

Psychosocial measures

Measures included data on composite scores of maternal smoking (cigarette and cannabis use) and psychological variables (PTSD and depression) administered antenatally. Maternal smoking was assessed using the ASSIST (Humeniuk et al., Reference Humeniuk, Ali, Babor, Farrell, Formigoni, Jittiwutikarn, De Lacerda, Ling, Marsden, Monteiro and Nhiwatiwa2008), maternal PTSD was assessed using the Modified Post-traumatic Stress Disorder Symptom Scale (Foa et al., Reference Foa, Riggs, Dancu and Rothbaum1993) and maternal depression was assessed using the Beck Depression Inventory (Beck et al., Reference Beck, Steer and Brown1996).

Composite scores were created for maternal smoking and psychological variables. The indicators for SES included maternal income, education, employment status and asset sum; smoking included cigarette and cannabis use and psychological variables included PTSD and depression. Composite variables were used to combine data into a single score as they are considered more robust than a unidimensional measure (Field, Reference Field2013).

ASSIST

As above, the ASSIST assessed alcohol or substance use. This measure includes seven items with scores from 0 to 10 for alcohol and 0 to 3 for illicit drugs indicating low risk, 11 to 26 for alcohol and 4 to 26 for illicit drugs indicating moderate risk, and above 26 as high risk of severe problems, with the likelihood of alcohol dependence (Group, Reference Group2002). The higher the score, the greater the alcohol-related risk. The ASSIST has good reliability and validity in several countries including Australia, Brazil, Ireland, India, Israel, the Palestinian Territories, Puerto Rico, the United Kingdom and Zimbabwe (Group, Reference Group2002) and in South Africa (Humeniuk et al., Reference Humeniuk, Ali, Babor, Farrell, Formigoni, Jittiwutikarn, De Lacerda, Ling, Marsden, Monteiro and Nhiwatiwa2008).

Bayley Scales of Infant Development-III

The BSID-III was conducted at the 6- and 24-month visits, to assess child development in infants and toddlers between 0 and 42 months (Bayley, Reference Bayley2006). This is an international, well-validated test that was used to measure language and motor development. The BSID-III has been standardised with a stratified sample of 1000 children ranging from 0 to 42 months that was representative of the US population with respect to gender, race/ethnicity, geographic region and parent education level having high reliability and validity (Bayley, Reference Bayley2006). The Bayley-III has been shown to be a reliable tool for use among the South African population (Rademeyer & Jacklin, Reference Rademeyer and Jacklin2013).

The motor scale evaluated early fine and gross motor development (Bayley, Reference Bayley2006). The gross motor subset included 72 items that assessed movement of the limbs, static positioning (e.g. sitting and standing) and dynamic movement, including locomotion, coordination, balance and motor planning. The fine motor subtest included 66 items that assessed prehension, perceptual-motor integration, motor planning, speed, visual tracking, reaching, object grasping, object manipulation, functional hand skills and responses to tactile information. The motor assessments were administered using directly observed items for the infant and toddler (Bayley, Reference Bayley2006).

The language scale assessed receptive and expressive communication and was directly administered to the infant or toddler (Bayley, Reference Bayley2006). The receptive communication subtest includes 49 items that assessed pre-verbal behaviour, vocabulary development (identifying objects and pictures), understanding morphological development (pronouns and prepositions), morphological markers (e.g. plural, tense markings and the possessive), social referencing and verbal comprehension (Bayley, Reference Bayley2006). The expressive communication subtest included 48 items that assessed pre-verbal communication (babbling and gesturing), vocabulary development (naming objects, pictures or naming attributes) and morpho-syntactic development. Composite scores were based on the composite equivalents of the scaled scores. Scaled scores were based on scores with a mean of 10 and a standard deviation of 3 and range from 1 to 19. At 6 months, scaled scores were corrected for prematurity. The assessors were trained by a paediatric neurologist who ensured quality control and scoring precision. A trained paediatric occupational therapist or physiotherapist administered the BSID-III scales in the home language of the infants and toddlers. The assessors had background experience in paediatric clinical and research environments and were blinded to the exposure status of the children.

The DCHS was approved by the Faculty of Health Sciences Human Research Ethics Committees of the University of Cape Town and Stellenbosch University in South Africa, and by the Western Cape Department of Health Provincial Research Committee. All study participants provided written informed consent.

Statistical analysis

The data were analysed using descriptive statistics which included frequencies and percentages for categorical data, while means (SD) were presented for normally distributed data. Medians [interquartile range (IQR)] were presented for data that were not normally distributed and for all BSID-III scores. For comparisons between alcohol-exposed and -unexposed children, chi-squared tests were used for categorical variables, while t-tests or, in the case of data that were not normally distributed, Mann–Whitney U-test was used. Variables that were associated with PAE at an alpha level of 0.05 or less were included in the final model to determine whether the outcome measures that were significantly associated with PAE remained significant after adjusting for potential confounders (see Appendices A and B). Multiple hierarchical regression was used to explore the associations of PAE with motor and language development. The model was adjusted for the maternal socio-demographic and psychosocial confounding variables, which are known to be associated with child neurodevelopment (motor, language and cognitive outcomes). Potential confounding variables included composites of SES (Fried & Watkinson, Reference Fried and Watkinson1988), smoking (cigarette and cannabis) and psychological variables (Fried & Watkinson, Reference Fried and Watkinson1990), and child’s body mass index (BMI) z-score and child’s nutritional status according to their gender and age. Significance was set at 0.05, and 95% confidence intervals (CIs) were reported for all estimates, where applicable.

Results

The maternal and child socio-demographic and psychosocial characteristics are presented in Table 1. At 6 months, the median maternal age at enrolment was 24 years (IQR = 21–30). In the alcohol group, 15.4% of the mothers were HIV infected, 46.2% were classified as having PTSD and depression and in the unexposed group, 14.4% of the mothers were HIV infected and 55.8% had PTSD and depression. At 24 months, the median maternal age at enrolment was 26 years (IQR = 22–31). In the alcohol-exposed group, 16.3% of the mothers were HIV infected and 44.6% were classified as having PTSD and depression and in the unexposed group, 16.8 % of the mothers were HIV infected and 43.1% had PTSD and depression. There were no differences across the groups in socio-demographic or psychosocial variables, except for smoking, where mothers who consumed alcohol were more likely to smoke at both 6 (65.4% vs. 37.5%, respectively, p = 0.001) and 24 months of age (69.6% vs. 37.5%, respectively, p = 0.001). There were no significant differences between the exposed and unexposed groups regarding infants’ birth weight and BMI.

Table 1. Maternal and infant baseline socio-demographic and psychosocial characteristics

* p < 0.05.

** p < 0.01.

Table 2 compares the median scores of the alcohol-exposed and -unexposed groups for motor and language development at both 6 and 24 months of age. At 6 months, alcohol-exposed infants had significantly lower median scores for gross and fine motor functioning compared with the unexposed infants [gross: median scores = 9.0 (IQR = 7.2–11.0) vs. 11.0 (IQR = 9.0–12.0), respectively, p = 0.006; fine: median scores = 11.5 (IQR = 10.0–13.0) vs. 13.0 (IQR = 12.0–15.0), respectively, p = 0.001)]. At 24 months, there were no significant differences, although there remained a trend towards impairment for fine motor functioning in exposed children [median scores = 8.0 (IQR = 7.0–11.0) vs. 9.0 (IQR = 8.0–11.0), respectively, p = 0.068]. There were no significant differences in language and cognitive functioning at 6 or at 24 months.

Table 2. Motor, language and cognitive development in the exposed and unexposed groups at 6 and 24 months of age

* p < 0.05.

** p < 0.01.

Table 3 demonstrates the regression analysis for gross and fine motor functioning. PAE was significantly associated with gross motor functioning [odds ratio (OR) = 0.16, 95% CI = 0.06–0.44, p = 0.001] and fine motor functioning (OR = 0.16, 95% CI = 0.06–0.46, p = 0.001) after controlling for BMI, SES, and smoking and psychological variables at 6 months of age. BMI was significantly associated with both gross (OR = 0.83, 95% CI = 0.57–1.21, p = 0.001) and fine motor functioning (OR = 0.67, 95% CI = 0.46–0.97, p = 0.004), while SES was significantly associated with gross motor functioning (OR = 2.28, 95% = CI 1.24–4.19, p = 0.001) at 6 months.

Table 3. Coefficients for predictors in final model of gross motor functioning at 6 months of age (after adjusting for SES, smoking, PTSD and depression)

* p < 0.05.

** p < 0.01.

The final model explained a significant amount of variance in gross motor functioning (F(4) = 3.98, p = 0.002, adj R 2 = 0.10), and the R 2 showed that the amount of variance increased from 5% (BMI, SES, smoking, PTSD and depression) to 14% after adding PAE into the model (Appendix A). Similarly, the final model explained a significant amount of variation in fine motor functioning (F(4) = 3.66, p = 0.004, adj R 2 = 0.13), and the R 2 showed that the explained variance accounted for an extra 9% (0.04–0.13) in fine motor functioning (Appendix A).

Discussion

This study comprehensively assessed motor, language and cognitive functioning in a population-based cohort over the first 2 years of life. The findings of our study indicated that PAE is associated with both gross and fine motor functioning at 6 months of age, even after adjusting for maternal socio-demographic and psychosocial factors. While PAE was not associated with receptive and expressive communication nor cognitive performance at either time point in this group, there remained a trend towards significance for poorer fine motor functioning at 24 months of age.

Our findings demonstrate that PAE is associated with deficits in motor functioning across the first 2 years of life. This is consistent with previously reported cohort studies in preschool age (Fried & Watkinson, Reference Fried and Watkinson1988; Jacobson & Jacobson, Reference Jacobson and Jacobson2002; Davies et al., Reference Davies, Dunn, Chersich, Urban, Chetty, Olivier and Viljoen2011). In particular, Fried and Watkinson (Reference Fried and Watkinson1988) found a significant association between PAE and motor functioning in early infancy (12 months), even after adjusting for maternal age, gestation, birth weight and parity but found the effect to wane at later ages. These same investigators continued to report a lack of association between PAE and motor outcomes in a follow-up of these children into school age but reported associations between PAE and language comprehension at 36 months (Fried & Watkinson, Reference Fried and Watkinson1990). Important differences between our study and the cohort in these studies include middle-to-high-income samples, no control groups and the authors adjusted for primarily physical confounders (maternal age, gestation, birth weight and parity), but not psychosocial factors. Our study adds to the growing body of scientific evidence implicating PAE in motor functioning impairment at 6 months of age even after adjusting for important psychosocial factors such as PTSD and depression when compared to a matched control group.

In our cohort, PAE was not found to be associated with receptive or expressive communication or cognitive functioning at the age of 24 months. Previous studies have reported impairments in language and cognitive functioning in toddlers between the ages of 12 and 24 months (Fried & Watkinson, Reference Fried and Watkinson1988; Davies et al., Reference Davies, Dunn, Chersich, Urban, Chetty, Olivier and Viljoen2011); however, reports indicated that as children grew into the school years, PAE was not significantly associated with language or cognitive outcomes (Coggins et al., Reference Coggins, Timler and Olswang2007) using standard measures. Lack of associated impact of PAE on early language outcomes in this study may, in part, be a result of language impairments being subtle in infancy, and it is therefore being more difficult to identify these outcomes than in other domains. It may be useful for future studies to consider the extent to which specific language outcomes affect the pragmatic or conversational patterns of children affected by PAE (not just general categories of receptive or expressive communication).

Additional limitations deserve consideration. Firstly, the substudy comprised a small sample size which may have limited the power to detect differences between the groups. Secondly, despite assurances of confidentiality, some women may have chosen not to disclose or minimise reporting alcohol use to the research teams and the low reported alcohol consumption may therefore represent an element of response bias. Thirdly, the BSID-III tool measures general ability in completing a given task but may have low sensitivity for detecting minor developmental impairments especially during infancy. Further, although this tool has been validated for use in South Africa, this study may not be generalisable to other populations.

A large proportion of very young children in LMICs do not reach their developmental potential due to a wide variety of socio-demographic and psychosocial factors that may impact early developmental outcomes. Our study, reporting the association of PAE and early motor functioning, is one of only a few studies that have additionally addressed important potential psychosocial confounders which frequently co-occur with alcohol use in these communities. These findings highlight the importance of identifying high-risk families in order to provide preventive interventions, particularly in antenatal clinics and early intervention services.

Authors contribution

HJZ is the principal investigator (PI) of the umbrella DCHS cohort and played a central role in the design and operational aspects of the study. DJS is the PI of the psychosocial aspects of the DCHS cohort and contributed to the design and decision-making involving the psychosocial tools and measures used. KAD, the PI of the child psychosocial aspects of the DCHS, was involved in the design of the study and operational aspects of the study and played a key role in the child psychosocial measures used. GH and KAD conceived and designed this substudy. GH drafted article, analysed, interpreted findings and discussed the conclusion of the study with input from KAD, SM-S and TC. RTN and CJW contributed to the operational aspects of the study and in particular the developmental assessment data. All the authors were involved in revising the article, contributed to intellectual content and provided approval of the final version to be published.

Financial support

GH was supported by the Centre of Excellence, National Research Foundation (D20160042) and Oppenheimer Memorial Trust Fund (R: 21246/-1). The DCHS was funded by Bill and Melinda Gates Foundation (OPP 1017641). Additional support included National Institute on Alcohol Abuse and Alcoholism (R21AA023887), US Brain and Behaviour Foundation (24467), the Collaborative Initiative on Fetal Alcohol Spectrum Disorders (U24 AA014811), Medical Research Council of South Africa, Wellcome Trust (203525/Z/16/Z), National Research Foundation and Newton Fund (NAF002\1001).

Conflict of interest

None.

Ethical standards

The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2013.

Appendix A. Summary of regression analysis: regressing gross motor functioning onto BMI, SES, smoking, psychological variables risk and PAE at 6 months of age

Predictors: Model 1 – BMI; Model 2 – BMI and SES; Model 3 – BMI, SES and smoking; Model 4 – BMI, SES, smoking and psychological variables; Model 5 – BMI, SES, smoking, psychological variables and PAE.

* p < 0.05.

** p < 0.01.

Appendix B. Summary of regression analysis: regressing fine motor functioning onto SES, smoking, psychological variables risk and PAE at 6 months of age

Predictors: Model 1 – BMI; Model 2 – BMI and SES; Model 3 – BMI, SES and smoking; Model 4 – BMI, SES, smoking, SES and psychological variables; Model 5 – BMI, SES, smoking, SES, psychological variables and PAE.

* p < 0.05.

** p < 0.01.

References

Adnams, CM, Kodituwakku, PW, Hay, A, Molteno, CD, Viljoen, D and May, PA (2001) Patterns of cognitive-motor development in children with fetal alcohol syndrome from a community in South Africa. Alcoholism: Clinical and Experimental Research 25, 557–562. doi: 10.1111/j.1530-0277.2001.tb02250.xCrossRefGoogle ScholarPubMed
Bayley, N (2006) Bayley Scales of Infant and Toddler Development. San Antonio, TX: Pearson.Google Scholar
Beck, AT, Steer, RA and Brown, GK (1996) Beck depression inventory-II. San Antonio 78, 490–498.Google Scholar
Coggins, TE, Timler, GR and Olswang, LB (2007) A state of double jeopardy: impact of prenatal alcohol exposure and adverse environments on the social communicative abilities of school-age children with fetal alcohol spectrum disorder. Language, Speech, and Hearing Services in Schools 38, 117–127. doi: 10.1044/0161-1461(2007/012)CrossRefGoogle ScholarPubMed
Comasco, E, Rangmar, J, Eriksson, UJ and Oreland, L (2018) Neurological and neuropsychological effects of low and moderate prenatal alcohol exposure. Acta Physiologica 222, 1–18. doi: 10.1111/apha.12892CrossRefGoogle ScholarPubMed
Cone-Wesson, B (2005) Prenatal alcohol and cocaine exposure: influences on cognition, speech, language, and hearing. Journal of Communication Disorders 38, 279–302. doi: 10.1016/j.jcomdis.2005.02.004CrossRefGoogle ScholarPubMed
Davies, L, Dunn, M, Chersich, M, Urban, M, Chetty, C, Olivier, L and Viljoen, D (2011) Developmental delay of infants and young children with and without fetal alcohol spectrum disorder in the Northern Cape Province, South Africa. African Journal of Psychiatry 14, 298–305. doi: 10.4314/ajpsy.v14i4.7CrossRefGoogle ScholarPubMed
Donald, KA, Hoogenhout, M, du Plooy, CP, Wedderburn, CJ, Nhapi, RT, Barnett, W, Hoffman, N, Malcolm-Smith, S, Zar, HJ and Stein, DJ (2018) Drakenstein Child Health Study (DCHS): investigating determinants of early child development and cognition. BMJ Paediatrics 2, 1. doi: 10.1136/bmjpo-2018-000282Google ScholarPubMed
Group, WAW (2002) The alcohol, smoking and substance involvement screening test (ASSIST): development, reliability and feasibility. Addiction 97, 1183–1194. doi: 10.1046/j.1360-0443.2002.00185.xCrossRefGoogle Scholar
Field, A (2013) Discovering Statistics using IBM SPSS Statistics. North America: Sage, pp. 1–915.Google Scholar
Flak, AL, Su, S, Bertrand, J, Denny, CH, Kesmodel, US and Cogswell, ME (2014) The association of mild, moderate, and binge prenatal alcohol exposure and child neuropsychological outcomes: a meta-analysis. Alcoholism: Clinical and Experimental Research 38, 214–226. doi: 10.1111/acer.12214CrossRefGoogle ScholarPubMed
Foa, EB, Riggs, DS, Dancu, CV and Rothbaum, BO (1993) Reliability and validity of a brief instrument for assessing post-traumatic stress disorder. Journal of Traumatic Stress 6, 459–473. doi: 10.1002/jts.2490060405CrossRefGoogle Scholar
Fried, P and Watkinson, B (1988) 12- and 24-Month neurobehavioural follow-up of children prenatally exposed to marihuana, cigarettes and alcohol. Neurotoxicology and Teratology 10, 305–313. doi: 10.1016/0892-0362(88)90032-3CrossRefGoogle ScholarPubMed
Fried, PA, O’connell, CM and Watkinson, B (1992) 60- and 72-Month follow-up of children prenatally exposed to marijuana, cigarettes, and alcohol: cognitive and language assessment. Journal of Developmental and Behavioral Pediatrics 13, 299–311. doi: 10.1097/00004703-199212000-00001Google ScholarPubMed
Fried, PA and Watkinson, B (1990) 36- and 48-Month neurobehavioral follow-up of children prenatally exposed to marijuana, cigarettes, and alcohol. Journal of Developmental and Behavioral Paediatrics 11, 49–58. doi: 10.1097/00004703-199004000-00003Google ScholarPubMed
Henderson, J, Gray, R and Brocklehurst, P (2007) Systematic review of effects of low–moderate prenatal alcohol exposure on pregnancy outcome. BJOG: An International Journal of Obstetrics & Gynaecology 114, 243–252. doi: 10.1111/j.1471-0528.2006.01163.xCrossRefGoogle ScholarPubMed
Hendricks, G, Malcolm-Smith, S, Adnams, C, Stein, DJ and Donald, KA (2018) Effects of prenatal alcohol exposure on language, speech and communication outcomes: a review longitudinal studies. Acta Neuropsychiatrica 31, 74–83. doi: 10.1017/neu.2018.28CrossRefGoogle ScholarPubMed
Humeniuk, R, Ali, R, Babor, TF, Farrell, M, Formigoni, ML, Jittiwutikarn, J, De Lacerda, RB, Ling, W, Marsden, J, Monteiro, M and Nhiwatiwa, S (2008) Validation of the alcohol, smoking and substance involvement screening test (ASSIST). Addiction 103(6), 1039–1047. doi: 10.1111/j.1360-0443.2007.02114.xCrossRefGoogle Scholar
Hutchinson, D, Youssef, GJ, McCormack, C, Wilson, J, Allsop, S, Najman, J, Elliott, E, Burns, L, Jacobs, S, Honan, I and Rossen, L (2019) Correction to: Prenatal alcohol exposure and infant gross motor development: a prospective cohort study. BMC Pediatrics 19(1), 222.CrossRefGoogle ScholarPubMed
Jacobson, JL and Jacobson, SW (2002) Effects of prenatal alcohol exposure on child development. Alcohol Research and Health 26, 282–286. doi: 10.1111/j.1530-0277.1993.tb00744.xGoogle ScholarPubMed
Kalberg, WO, Provost, B, Tollison, SJ, Tabachnick, BG, Robinson, LK, Eugene Hoyme, H, Trujillo, PM, Buckley, D, Aragon, AS and May, PA (2006) Comparison of motor delays in young children with fetal alcohol syndrome to those with prenatal alcohol exposure and with no prenatal alcohol exposure. Alcoholism: Clinical and Experimental Research 30, 2037–2045. doi: 10.1111/j.1530-0277.2006.00250.xCrossRefGoogle ScholarPubMed
Kaplan-Estrin, M, Jacobson, SW and Jacobson, JL (1999) Neurobehavioral effects of prenatal alcohol exposure at 26 months. Neurotoxicology and teratology 21(5), 503–511.CrossRefGoogle ScholarPubMed
Keen, CL, Uriu-Adams, JY, Skalny, A, Grabeklis, A, Grabeklis, S, Green, K, Yevtushok, L, Wertelecki, WW and Chambers, CD (2010) The plausibility of maternal nutritional status being a contributing factor to the risk for fetal alcohol spectrum disorders: the potential influence of zinc status as an example. Biofactors 36(2), 125–135. doi: 10.1002/biof.89Google ScholarPubMed
Kodituwakku, P (2007) Defining the behavioral phenotype in children with fetal alcohol spectrum disorders: a review. Neuroscience & Biobehavioral Review 31, 192–201. doi: 10.1016/j.neubiorev.2006.06.020CrossRefGoogle ScholarPubMed
Kodituwakku, PW, Segall, JM and Beatty, GK (2011) Cognitive and behavioral effects of prenatal alcohol exposure. Future Neurology 6, 237–259. doi: 10.2217/fnl.11.4CrossRefGoogle Scholar
Mattson, SN, Crocker, N and Nguyen, TT (2011) Fetal alcohol spectrum disorders: neuropsychological and behavioral features. Neuropsychology Review 21, 81–101. doi: 10.1007/s11065-011-9167-9CrossRefGoogle ScholarPubMed
May, PA, Gossage, JP, Marais, AS, Hendricks, LS, Snell, CL, Tabachnick, BG, Stellavato, C, Buckley, DG, Brooke, LE and Viljoen, DL (2008) Maternal risk factors for fetal alcohol syndrome and partial fetal alcohol syndrome in South Africa: a third study. Alcoholism: Clinical and Experimental Research 32(5), 738–753. https://doi.org/10.1111/j.1530-0277.2008.00634.xCrossRefGoogle ScholarPubMed
Myer, L, Stein, DJ, Grimsrud, A, Seedat, S and Williams, DR (2008) Social determinants of psychological distress in a nationally-representative sample of South African adults. Social Science & Medicine 66, 1828–1840. doi: 10.1016/j.socscimed.2008.01.025CrossRefGoogle Scholar
Nayak, RB and Murthy, P (2008) Fetal alcohol spectrum disorder. Indian Pediatrics 977, 977–983.Google Scholar
O’Leary, C, Zubrick, SR, Taylor, CL, Dixon, G and Bower, C (2009) Prenatal alcohol exposure and language delay in 2-year-old children: the importance of dose and timing on risk. Pediatrics 123, 547–554.CrossRefGoogle ScholarPubMed
O’Leary, CM (2004) Fetal alcohol syndrome: diagnosis, epidemiology, and developmental outcomes, Journal of Paediatrics and Child Health 40, 2–7. doi: 10.1111/j.1440-1754.2004.00280CrossRefGoogle ScholarPubMed
Olivier, L, Viljoen, D and Curfs, L (2016) Fetal alcohol spectrum disorders: prevalence rates in South Africa: the new millennium. South African Medical Journal 106(1), 103–106. doi: 10.7196/SAMJ.2016.v106i6.11009CrossRefGoogle Scholar
Popova, S, Lange, S, Probst, C, Gmel, G and Rehm, J (2017) Estimation of national, regional, and global prevalence of alcohol use during pregnancy and fetal alcohol syndrome: a systematic review and meta-analysis. The Lancet Global Health 5(3), 290–299. doi: 10.1016/S2214-109X(17)30021-9CrossRefGoogle ScholarPubMed
Rademeyer, V and Jacklin, L (2013) A study to evaluate the performance of black South African urban infants on the Bayley Scales of Infant Development III. South African Journal of Child Health 7, 54–59. doi: 10.7196/sajch.547CrossRefGoogle Scholar
Safe, B, Joosten, A and Giglia, R (2018) Assessing motor skills to inform a fetal alcohol spectrum disorder diagnosis focusing on persons older than 12 years: a systematic review of the literature. Journal of Population Therapeutics and Clinical Pharmacology 25, 25–38. doi: 10.22374/1710-6222.25.1.3CrossRefGoogle ScholarPubMed
Sokol, RJ, Delaney-Black, V and Nordstrom, B (2003) Fetal alcohol spectrum disorder. JAMA 290, 2996–2999. doi: 10.1001/jama.290.22.2996CrossRefGoogle ScholarPubMed
Stein, D, Koen, N, Donald, K, Adnams, C, Koopowitz, S, Lund, C, Marais, A, Myers, B, Roos, A and Sorsdahl, K (2015) Investigating the psychosocial determinants of child health in Africa: the Drakenstein Child Health Study. Journal of Neuroscience Methods 252, 27–35. doi: 10.1016/j.jneumeth.2015.03.016CrossRefGoogle ScholarPubMed
Viholainen, H, Ahonen, T, Lyytinen, P, Cantell, M, Tolvanen, A and Lyytinen, H (2006) Early motor development and later language and reading skills in children at risk of familial dyslexia. Developmental Medicine and Child Neurology 48(5), 367–373. doi: 10.1017/S001216220600079XCrossRefGoogle ScholarPubMed
Zar, HJ, Barnett, W, Stadler, A, Gardner-Lubbe, S, Myer, L and Nicol, MP (2016) Aetiology of childhood pneumonia in a well vaccinated South African birth cohort: a nested case-control study of the Drakenstein Child Health Study. The Lancet Respiratory Medicine 4(6), 463–472. doi: 10.1016/S2213-2600(16)00096-5CrossRefGoogle Scholar
Figure 0

Fig. 1. Study sample selection.

Figure 1

Table 1. Maternal and infant baseline socio-demographic and psychosocial characteristics

Figure 2

Table 2. Motor, language and cognitive development in the exposed and unexposed groups at 6 and 24 months of age

Figure 3

Table 3. Coefficients for predictors in final model of gross motor functioning at 6 months of age (after adjusting for SES, smoking, PTSD and depression)