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Neurobehavioral consequences of chronic intrauterine opioid exposure in infants and preschool children: a systematic review and meta-analysis
© Baldacchino et al.; licensee BioMed Central Ltd. 2014
Received: 26 September 2013
Accepted: 26 March 2014
Published: 8 April 2014
The Erratum to this article has been published in BMC Psychiatry 2015 15:134
It is assumed within the accumulated literature that children born of pregnant opioid dependent mothers have impaired neurobehavioral function as a consequence of chronic intrauterine opioid use.
Quantitative and systematic review of the literature on the consequences of chronic maternal opioid use during pregnancy on neurobehavioral function of children was conducted using the Meta-analysis of Observational Studies in Epidemiology (MOOSE) and the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. We searched Cinahl, EMBASE, PsychINFO and MEDLINE between the periods of January 1995 to January 2012.
There were only 5 studies out of the 200 identified that quantitatively reported on neurobehavioral function of children after maternal opioid use during pregnancy. All 5 were case control studies with the number of exposed subjects within the studies ranging from 33–143 and 45–85 for the controls. This meta-analysis showed no significant impairments, at a non-conservative significance level of p < 0.05, for cognitive, psychomotor or observed behavioural outcomes for chronic intra-uterine exposed infants and pre-school children compared to non-exposed infants and children. However, all domains suggested a trend to poor outcomes in infants/children of opioid using mothers. The magnitude of all possible effects was small according to Cohen’s benchmark criteria.
Chronic intra-uterine opioid exposed infants and pre-school children experienced no significant impairment in neurobehavioral outcomes when compared to non-exposed peers, although in all domains there was a trend to poorer outcomes. The findings of this review are limited by the small number of studies analysed, the heterogenous populations and small numbers within the individual studies. Longitudinal studies are needed to determine if any neuropsychological impairments appear after the age of 5 years and to help investigate further the role of environmental risk factors on the effect of ‘core’ phenotypes.
Substance abuse has been a global problem for many decades and in recent years there has been a significant increase in the numbers of people using opioids . Opioid use was seen as a predominately male problem but today there are many women using opioids which could lead to an increase in problem pregnancies . During pregnancy drugs will cross the placenta and can have an effect on the foetus. This effect is often hard to quantify as there are other aspects that could be considered as having a larger effect on child outcomes, for example, the quality of care or the environment . Many studies examining the impact of opioid use during pregnancy on child outcomes have concentrated on treatment populations (methadone and/or buprenorphine) for recruitment as this group is easier to reach than heroin users . Research has attempted to address birth problems, neonatal abstinence syndrome, mortality and co-morbidities as well as neuro-developmental issues in children sometimes with conflicting results . There are many reports into neonatal abstinence syndrome and birth parameters but fewer reports on neuro-developmental issues surrounding prenatal exposure to opioids [6–10].
In the U.K. it is estimated that around 280,000 people use opioids and that around 30% are women [11, 12]. In 2009/2010 925 pregnancies in Scotland reported drug misuse, a rate of 16.1 per 1,000 pregnancies, with opioids reported in 506 (55%) of these pregnancies . Over half the pregnant mothers who report drug use are opioid dependent with consequential increase in risk to both mother and expected child.
Replacement prescribing with methadone and recently buprenorphine forms the main plank of medical treatment for opioid dependency in the United Kingdom, reflecting a comprehensive and evolving evidence base which consistently demonstrates the effectiveness of methadone in delivering positive outcomes in a complex and demanding population [13–15]. Properly prescribed and adequately supported, methadone prescribing achieves harm reduction outcomes in opioid dependent patients [16, 17]. It is also associated with reduced mortality and improved quality of life . The duration and dosage of methadone was also closely observed to be relevant factors in treatment outcomes with longer duration and higher dosages showing positive outcomes [19–21].
In contrast, some neuropsychological studies of chronic methadone users have identified deficits in executive function measures. These have included impairments in cognitive flexibility [22, 23], in strategic planning [24, 25] and decision making . Other studies found no clear deficits when comparing the performance of healthy controls, with that of opioid abstinent or methadone users [27, 28]. The accumulated literature tends to assume that neuropsychological function is commonly impaired as a consequence of chronic methadone use justifying an abstinence agenda with premature termination of methadone treatment . Furthermore a recent meta-analysis on the neuropsychology of chronic opioid use suggested impairment in verbal working memory, cognitive impulsivity (risk taking) and cognitive flexibility (verbal fluency) with a medium effect size .
An early study has suggested that methadone-exposed children have better birth outcomes compared to heroin-exposed children, suggesting that opioid treatment during pregnancy is beneficial for the neonate . Despite evidence of the beneficial effects of methadone in the care of pregnant opioid-dependent women, approximately half of all infants prenatally exposed to methadone require medical treatment for neonatal abstinence syndrome . Accordingly, there are risks associated with prenatal exposure to methadone or buprenorphine . A recent Cochrane systematic review identified four trials comparing methadone in pregnancy with buprenorphine in three studies and oral slow-release morphine in the other . Patients using methadone had lower dropout rates than for the other treatment options but there were no differences in neo-natal abstinence syndrome in the trials. Infant birth weight was higher for buprenorphine users in two trials but no different in the other two trials. Women on slow-release morphine were less likely to also use heroin in the third trimester than methadone users. The authors highlighted the lack of available evidence to inform treatment decisions for pregnant women with opioid dependence.
The literature pertaining to the long-term developmental effects of prenatal methadone and buprenorphine exposure is relatively sparse and contradictory . While some studies report no long-term effects [35–37] others report reduced performance on tests of cognitive development [38–41].
While this literature, together with the neurobehavioral effects of intrauterine opioid use on children, has been reviewed by Konijnenberg and Melinder (2011) , Whitham (2012)  and Hutchings (1987) , traditional narrative reviews typically assume statistically significant group differences to be evidence for cognitive and/or psychomotor impairment, without giving due consideration to the magnitude of such differences by reporting effect sizes.
This paper will determine the strength and consistency of neurobehavioral impairment in cognitive and psychomotor function in opioid exposed infants and pre-school children when compared to healthy non-opioid exposed controls by performing a systematic literature review and consequently quantitatively synthesising the existing literature using meta-analytic methodology [44, 45].
Inclusion and exclusion criteria
The systematic review of the literature was conducted accordingly to the Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines  and the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) guidelines .
For the purpose of this review, the meanings of the terms ‘opioid’ and ‘opiate’ were considered as largely synonymous, with opioid being used, as it has a broader definition. An Infant was defined as a child up to 2 years old, pre-school child as one between 3 and 5 years of age and a school child as one between 6 and 12 years of age. Neurobehavioral function was defined as ‘growth of perceptual, emotional, intellectual, and behavioural capabilities and functioning during childhood (prior to puberty) which includes development of language, symbolic thought, logic, memory, emotional awareness, empathy, a moral sense, and a sense of identity, including sex-role identity’ .
Only studies that recruited opioid users were included in the meta-analysis. Furthermore all trial methodologies, not only RCTs, were considered. Studies had to use a validated diagnostic system and explicitly define whether their participants were opioid/methadone dependent [49, 50].
We excluded studies that recruited mothers who were polydrug users during term pregnancy even though they might have also been taking opioids. Studies that only investigated the immediate effects of opioid use on neonates including neonatal abstinence syndrome and the neurological consequences of opioid exposure were also excluded. Sufficient study statistics not convertible to effect size (d) e.g. means, standard deviation, F, t, X 18 were also excluded, as well as studies with less than 15 in the total sample size.
Articles were identified using an electronic and hand strategy based search. A computer based search was performed using the following database: Cinahl, EMBASE, PsychINFO and MEDLINE between the periods of January 1995 to January 2012 (17 years). No language constraints were applied. Subject headings originally included ‘child, opioid, prenatal exposure and substance misuse’. (Refer to Additional file 1: Table S1)
This was followed with the term ‘neurobehavioral’ which was subsequently replaced with a succession of terms describing names of a list of cognitive and psychomotor tests and using wild cards.
Two of the authors (AB and KA) independently reviewed all the identified abstracts from the electronic search, selected studies and published reviews. A snowballing technique was employed so that the reference list of the identified articles was screened to find other suitable studies. The literature search was further enhanced by hand searching 22 journals for the last 5 years (2008–2012). They include Drug and Alcohol Dependence, Addictive Behaviours, Addiction, European Addiction Research, Journal of Substance Abuse Treatment, Child Neuropsychology, Neurotoxicology, Neurotoxicology and Teratology, Toxicology Letters, Psychological Medicine, European Journal of Paediatrics, Paediatrics, Developmental and Behavioral Paediatrics, Archives of Diseases in Childhood, Paediatric Research, NeuroImage, Early Human Development, Women and Birth, Obstetrics, British Journal of Gyneacology and Obstetrics, British Medical Journal, Neuroscience and Biobehavioral Reviews.
Data analysis and study detail
Standard meta-analytic techniques were employed to this review . Magnitude is indexed with the effect size d that is meant to reflect the degree to which the dependent variable is present in the sample group or the degree to which the null hypothesis is false . In mathematical terms d is the difference between two group means standardised via pooled standard deviation units. Effect sizes (i.e. Cohen’s d statistics) were calculated for each neurobehavioral test and then adjusted for sampling bias . A value of 0.80 is regarded as a large effect size, 0.5 as a medium effect and 0.2 small [54, 55]. Formulae were appropriately adjusted so that all derived statistics informally represented the same direction; that is the same polarity of performance when comparing groups. Negative scores always represented worse performance on the part of the opiate exposed group.
Child’s ability to learn and solve problems
WPPSI-R, DAS, SBIS, MSCA,MSEL, GDS, BSID
Child’s ability to both understand and use language
PPVT-III, NEPSY, RDLS, GDS
Non verbal processing
Child’s ability to organize the visual-spatial field, adapt to new or novel situations, and/or accurately read nonverbal signals and cues.
K-ABC, Non verbal subtests of DAS, WPPSI, MSCA
The child’s ability to connect thoughts with muscle movements
Vineland Motor Domain, MSCA, NEPSY, GDS, BSID
Child’s ability to analyze situations, plan and take action, focus and maintain attention, and adjust actions as needed to get the job done
WPPSI-R Animal Pegs, NEPSY, GDS
Child’s ability to hold and manipulate information over brief periods of time, in the course of ongoing cognitive activities
DAS, MSCA, BSID, NEPSY
Child’s ability to interact with others, including helping themselves and self-control.
VSS, CBC, CBRS, GDS, IBR, RPD-Q, VSMS
To meet the assumption of independence, when multiple test variables in a study contributed to any one neuropsychological domain, the effect size for each measure was assessed separately and then the mean effect size of these measures were combined to assess the overall outcome in the respective area of functioning. Multiple measurements can increase the likelihood of Type 1 errors and so a p value over 0.01 will be interpreted with caution even though analysis will use a significant level of p < 0.05.
Tests for the presence and degree of heterogeneity were conducted using the Q statistic  and I2 index  respectively. However, quantification of heterogeneity is only one component of a wider investigation of variability across studies, the most important being diversity in clinical and methodological domains, and the observed degree of inconsistency across studies with regards to the direction of effects . As different scales were sometimes used by different studies, standardised mean difference (SMD) effect-size estimates were calculated. In case of significant heterogeneity, random effect models were applied [63, 64].
Research with statistically significant results are potentially more likely to be submitted and published than studies with non-significant results. The presence of such publication bias was assessed informally by visual inspection of funnel plots and formally by its statistical analogue, Fail Safe N, according to Orwin .
A Fail-Safe N is the number of non significant, unpublished, or missing studies that would need to be added to the meta-analysis in order to change the overall result from significance to non-significance. More than two studies are needed to enable a Fail-Safe N to be calculated.
Eligible research studies comprising a common dependent variable as well as statistics that can be transformed into effect sizes were systematically surveyed. Individual study results (typically means and standard deviations from each group) and relevant moderator variables considered as relevant by previous reviews (e.g. dosage of maternal methadone during pregnancy, gestational age, presence of Neonatal Abstinence Syndrome (NAS), quality of the study, and population studied) were used as moderators if needed during this review. They were abstracted, quantified,coded and assembled into a database and analysed using Comprehensive Meta-Analysis Version 2 . The significance level was p = 0.01 and in Q statistics p = 0.10.
Assessment of study quality
For all review questions, data were extracted by one reviewer and checked by another. Discrepancies were resolved by referral to the original studies and, if necessary, arbitration done by a third reviewer. Duplicate publications were actively screened for and, when found, the latest and most complete report was used. The Effective Public Health Practice Project (EPHPP) quality assessment checklist (amended) was used in this study . For pragmatic reasons no papers were excluded on quality grounds as all papers were weak to moderate (Refer to Additional file 2: Table S2).
Ethical approval and informed patient consent was not required as this study was a literature review and had no direct patient contact or influence on patient care.
Studies selected and population studied
Combined searches yielded 1452 references. In total 65 articles were retrieved for further assessment from which 65 studies identified intrauterine exposure to opioids and reported health and developmental outcomes for the opioid exposed children. From these studies, 8 articles were found to investigate the cognitive, psychomotor and behavioral outcomes in opioid exposed infants, pre-school and school children when compared to healthy non-opioid exposed controls. Only 5 studies could be further utilised in this meta-analysis since 1 study measured motor rather than psychomotor skills  and 2 other studies had small sample sizes of < 15 [69, 70].
All studies were case controlled observational studies conducted with a population living in urbanised and low socioeconomic communities exposed to either heroin or methadone.
The global quality assessment reported four studies as of moderate quality and one as weak. The assessment for analysis performed was moderate for all studies but all other domains were reported as either weak or moderate (see Additional file 2: Table S2).
General characteristics of selected studies comparing opioid exposed infants and children with non opioid exposed controls (n = 5)
Age in months when tested
Type of opioid exposure
Measures used (Neurobehavioral)
Hunt et al. 
VSMS, MSCA, BSID
Moe et al. 
3.9OE*1 v 4.1C
Hans et al. 
Bunikowski et al. 
4OE*3 v 6C
Hunt et al. 
Moe et al. 
3.9OE*1 v 4.1C
Ornoy et al. 
BSID, MCSA, Achenbach CBC
Specific characteristics of selected studies comparing opioid exposed infants and children with non opioid exposed controls (n = 5)
Age in months when tested
Gestational age in weeks
Mean birth weight in g
Mean head circumference (cms)
Mother’s age (median) in years
Mother’s methadone dose in mg
Hunt et al. 
33.6 in OE
3300 in C
34.5 in C
Moe et al. 
3037 in OE
34 in OE
3754 in C
35.8 in C
Hans et al. (2011)
2922 in OE
27.1 in OE
3236 in C
25.8 in C
Bunkowski et al. (1998)
2783 in OE
27 in OE
3240 in C
30.2 in C
Hunt et al. 
33.6 in OE
3300 in C
34.5 in C
Moe et al. 
3037 in OE
34 in OE
3754 in C
35.8 in C
Ornoy et al. 
2487 in OE
27.8 in OE
3346 in C
39.8 in C
There were six effect size measures possible (3 for the infant cohort and 3 for the pre-school cohort groups) from the selected studies. There were no effect sizes identified as greater than 2× inter-quartile range (25 and 75 percentile) from the nearest quartile (outliers) .
Opioid exposed infants compared with non-opioid exposed infants
Opioid exposed pre-school children compared with non-opioid exposed pre-school children
Effect sizes and associated statistics for neurobehavioral domains in opioid exposed infants and pre-school children compared to others who have no history of opioid (or any other illicit and/or alcohol use) exposure during pregnancy
Lower Limit 5
Upper Limit 6
p for Q 8
pfor Z 10
Fail safe N 12
The result of this systematic review is in accordance with Whitham  who conducted an open label non randomised flexible dosing longitudinal study with results showing that children prenatally exposed to illicit heroin and/or methadone did not differ to non-exposed infants and other children in cognitive, psychomotor and caregiver rated temperament outcomes. As a recent review observed, the conflicting results of traditional systematic reviews on this subject could be that most children in these studies were exposed to other drugs in addition to opioids such as methadone . Another explanation given to the conflicting results may be that various studies were conducted using different neurobehavioral tests at different ages of development. Prenatal opioid exposure may affect children’s cognitive and psychomotor performance differently at different ages resulting in neurobehavioral outcomes that might improve or worsen over time . This analysis could only be conducted during the two early stages of a child’s development (infancy and pre-school children). It was not possible to conduct a similar analysis on children aged between 6–12 years due to the presence of only one study meeting the criteria for analysis.
The studies included in the meta-analysis had differences in the measurement of exposure to opiates. Most studies involved the use of illicit heroin wherein the dosage is highly variable and based on a large number of factors, while two of the studies involved methadone prescribed in a controlled environment. None of the studies commented on the use of illicit drugs and alcohol which may also have a bearing on the outcomes of interest.
The results of our analysis must be cautiously interpreted bearing its limitations in mind. The inclusion criteria used for this meta-analysis was very stringent so as to exclude neurobehavioral effects as a result of intra-uterine maternal polydrug use and the potential confusion of neurobehavioral outcomes associated with the neonatal abstinent syndrome and other opioid withdrawal presentations during the neonatal period (Jones et al., 2010). The main limitation of using a meta-analytic technique is the small number of primary studies available for analysis and also their small sample size. This limits the generalisability of the result  and the small sample size of individual studies means they may miss an increased risk of the occurrence of relatively rare outcomes like ADHD, autism or psychosis. The quality assessment of the individual studies would also raise some concerns about the generalisability of the findings as all were reported as of moderate or weak quality.
Meta-analysis tends to present results as composite scores for broad neurobehavioral functions using different neuropsychological tests. This is a convenient way to summarise findings but it combines data from tests potentially exploring different neuropsychological processes (e.g. memory tests assessing immediate or delayed recall, learning or recognition) and possibly generating results of questionable theoretical relevance. This study attempted to minimise this by utilising neuropsychological domains agreed by consensus and used in systematic reviews on chronic substance use effects [24, 76].
Even though the meta-analysis grouped together studies that used the same rating scales on a cohort who clearly were exposed to opioids, it was not possible from the information presented in the studies to exclude other confounding effects such as dosage of maternal opioid use during pregnancy , timing during pregnancy when the fetus was exposed to opioids with potential resulting behavioral teratogenicity , the differential effects of exposure to different opioids  and/or gender specific neurobehavioral influences , other illicit drugs including cocaine and alcohol use during pregnancy . Each included study reported that there were no polydrug using mothers within their cohorts but it is not possible to be certain that this was achieved.
This meta-analysis helps in supporting certain clinical observations in this population. The observed, if any, neurobehavioral outcomes in infants and pre-school children prenatally exposed to opioids are very often attributed to substance exposure. However it is important to examine the contribution of other influences on a child’s development. Ongoing maternal depressive illness is correlated with poorer cognitive and motor development and increase in teacher and parent rated behavior problems in pre-school children [78, 79]. Poverty and low socio-economic status is inversely related to children’s developmental performance [80, 81]. A study examining the relationship between birth weight and cognitive functioning among children in South Australia indicated that cognition at 2 years of age was significantly related to birth weight . However the magnitude of the association attenuated over time became non-significant in childhood. Factors that became significantly associated with neurobehavioral outcomes included low socio-economic status, low maternal IQ, poor quality of the home environment and child’s lead exposure. Overall it is increasingly becoming evident that the risk factors that can predict poor neurobehavioral outcomes is not the drug fuelled lifestyle or actual substance exposure during pregnancy but the presence of multiple, inter-related and weighted variables cumulatively influencing neurobehavioral outcomes . The risk factors were: maternal mental health, maternal attitudes toward parenting and maternal child–parent interaction, maternal education, parental occupation, minority status, stressful life events and family size with not one risk factor contributing exclusively to one cognitive or other neurobehavioral outcomes.
In many countries, including the UK, pharmacological maintenance with methadone is the first line of treatment for pregnant opioid dependent women [17, 83]. Whilst treatment with methadone during pregnancy results in fewer complications for both mother and infant when compared with the use of illicit opioids such as heroin, its use in pregnancy is associated with high rates of neonatal abstinence syndrome [84, 85]with its treatment involving using another opioid, morphine . Prenatal exposure to opioids also significantly increases the risk of low birth weight and small head circumference as shown in the cohort of children in the studies selected for this review. However this analysis did not observe any increased risk in neurobehavioral problems in opioid exposed infants and pre-school children compared to non-exposed peers suggesting that there is no neurobehavioral sequelae to the chronic prenatal and, if treated for NAS with opioids, also postnatal, exposure to opioids with any effects being short term and/or reversible.
Chronic intra-uterine opioid exposed infants and pre-school children experience no significant impairment in neurobehavioral outcomes when compared to non-exposed peers, although in all domains there was a trend to poorer outcomes. Interpretation of this meta-analysis needs to appreciate the heterogeneous population studied, the limited number of studies analysed due to the stringent inclusion criteria and the small numbers within the individual studies. Additional studies are needed to improve the power of a future meta-analysis to produce significant results. And longitudinal studies are needed to determine if any neuropsychological impairments appear after the age of 5 years and to help investigate further the role of environmental risk factors on the effect of ‘core’ phenotypes.
Dr Alex Baldacchino*: MD, FRCPsych, MPhil, PhD. Clinical Senior Lecturer (University of Dundee) and Consultant Psychiatrist (NHS Fife), Division of Neuroscience, Medical Research Institute, University of Dundee, Ninewells Hospital and Medical School, Dundee, DD1 9SY, UK
Ms Kathleen Arbuckle: BSc, MPH. ESRC/MRC PhD student (University of Dundee), Division of Health Population, Medical Research Institute, University of Dundee, Ninewells Hospital and Medical School, Dundee, DD1 9SY, UK
Dr Dennis J Petrie: BEcon, BSc, PhD. Senior Research Fellow, Centre for Health Policy, , Melbourne School of Population and Global Health, University of Melbourne, Victoria, Australia
Dr Colin McCowan: BSc, MSc, PhD. Reader in Health Informatics, Robertson Centre for Biostatistics, Institute of Health and Wellbeing, College of Medical, Veterinary and Life Sciences, University of Glasgow, Boyd Orr Building, Level 11, Glasgow, G12 8QQ, UK
We wish to acknowledge the financial support of the Economic and Social Research Council (ESRC) and Medical Research Council (MRC) PhD Studentship ES/H045813/1.
- Manchikanti L, Fellows B, Ailinani H, Pampati V: Therapeutic use, abuse, and nonmedical use of opioids: a ten-year perspective. Pain Physician. 2010, 13 (5): 401-435.PubMedGoogle Scholar
- Drug misuse statistics in Scotland 2011. [http://www.isdscotland.org],
- Sameroff A, Seifer R, Zax M, Barocas R: Early indicators of developmental risk: Rochester Longitudinal Study. Schizophr Bull. 1987, 13 (3): 383-394. 10.1093/schbul/13.3.383.View ArticlePubMedGoogle Scholar
- Hanson B, Beschner G, Walters JM, Bovell E: Life with Heroin: Voices from the Inner City. 1985, Lexington: D.C. HeathGoogle Scholar
- Konijnenberg C, Melinder A: Prenatal exposure to methadone and buprenorphine: a review of the potential effects on cognitive development. Child Neuropsychol. 2011, 17: 495-519. 10.1080/09297049.2011.553591.View ArticlePubMedGoogle Scholar
- Hunt RW, Tzioumi D, Collins E, Jeffery HE: Adverse neurodevelopmental outcome of infants exposed to opiate in-utero. Early Hum Dev. 2008, 84 (1): 29-35. 10.1016/j.earlhumdev.2007.01.013.View ArticlePubMedGoogle Scholar
- Hamilton R, McGlone L, MacKinnon JR, Russell HC, Bradnam MS, Mactier H: Ophthalmic, clinical and visual electrophysiological findings in children born to mothers prescribed substitute methadone in pregnancy. Br J Ophthalmol. 2010, 94 (6): 696-700. 10.1136/bjo.2009.169284.View ArticlePubMedGoogle Scholar
- McGlone L, Hamilton R, McCulloch DL, MacKinnon JR, Bradnam M, Mactier H: Visual outcome in infants born to drug-misusing mothers prescribed methadone in pregnancy. Br J Ophthalmol. 2014, 98 (2): 238-245.View ArticlePubMedGoogle Scholar
- Drug use during pregnancy. [http://www.drugabuse.gov],
- Spiteri Cornish K, Hrabovsky M, Scott NW, Myerscough E, Reddy AR: The short- and long-term effects on the visual system of children following exposure to maternal substance misuse in pregnancy. Am J Ophthalmol. 2013, 156 (1): 190-194. 10.1016/j.ajo.2013.02.004.View ArticlePubMedGoogle Scholar
- NICE: Drug Misuse: Psychosocial Interventions. 2007, London: National Institute for Health and Clinical ExcellenceGoogle Scholar
- Keegan J, Parva M, Finnegan M, Garson A, Belden M: Addiction in pregnancy. J Addict Dis. 2010, 29: 175-191. 10.1080/10550881003684723.View ArticlePubMedGoogle Scholar
- Simeons S, Matheson C, Inkster K, Ludbrook A, Bond C: The Effectiveness of Treatment for Opiate Dependent Drug Users: An International Systematic Review of the Evidence. 2002, Edinburgh: Scottish Executive Drug Misuse ProgrammeGoogle Scholar
- Kimber J, Copeland L, Hickman M, Macleod J, McKenzie J, de Angelis D, Robertson JR: Survival and cessation in injecting drug users: prospective observational study of outcomes and effect of opiate substitution treatment. BMJ. 2010, 341: c3172-10.1136/bmj.c3172.View ArticlePubMedPubMed CentralGoogle Scholar
- Cornish R, Macleod J, Strang J, Vickerman P, Hickman M: Risk of death during and after opiate substitution treatment in primary care: prospective observational study in UK general practice research database. BMJ. 2010, 341: c5475-10.1136/bmj.c5475.View ArticlePubMedPubMed CentralGoogle Scholar
- Scottish Government: Review of Methadone in Drug Treatment: Prescribing Information and Practice. 2007, Edinburgh: Scottish GovernmentGoogle Scholar
- Department of Health (England) and the devolved administrations: Drug Misuse and Dependence: UK Guidelines on Clinical Management. 2007, London: Department of Health (England), the Scottish Government, Welsh Assembly Government and Northern Ireland ExecutiveGoogle Scholar
- Connock M, Juarez-Garcia A, Jowett S, Frew E, Liu Z, Taylor RJ, Fry-Smith A, Day E, Lintzeris N, Roberts T, Burls A, Taylor RS: Methadone and buprenorphine for the management of opioid dependence: a systematic review and economic evaluation. Health Technol Assess. 2007, 11: 1-171.PubMedGoogle Scholar
- Farrell M, Ward J, Mattick R, Hall W, Stimson GV, des Jarlais D: Forthnightly review: methadone maintenance in opiate dependence: a review. BMJ. 1994, 309 (14): 1724-1731.Google Scholar
- van Beusekom I, Iguchi M: A review of recent advances in knowledge about methadone maintenance treatment. 2001, Santa Monica: RAND, ISBN 0-8330-3091-4Google Scholar
- Faggiano F, Vigna-Taglianti F, Versino E, Lemma P: Methadone maintenance at different dosages for opioid dependence. Cochrane Database Syst Rev. 2003, CD002208-3Google Scholar
- Pirastu M, Fais R, Messina M, Bini V, Spiga S, Falconieri D, Dianne M: Impaired decision-making in opiate-dependent subjects: effect of pharmacological therapies. Drug Alcohol Depend. 2006, 83: 163-168. 10.1016/j.drugalcdep.2005.11.008.View ArticlePubMedGoogle Scholar
- Darke S, Sims J, McDonald S, Wickes W: Cognitive impairment among methadone maintenance patients. Addiction. 2000, 95 (5): 687-695. 10.1046/j.1360-0443.2000.9556874.x.View ArticlePubMedGoogle Scholar
- Ersche KD, Clark L, London M, Robbins TW, Sahakian BJ: Profile of executive and memory function associated with amphetamine and opiate dependence. Neuropsychopharmacology. 2006, 31 (5): 1036-1047. 10.1038/sj.npp.1300889.View ArticlePubMedPubMed CentralGoogle Scholar
- Ornstein TJ, Iddon JL, Baldacchino AM, Sahakian BJ, London M, Everitt BJ, Robbins TW: Profiles of cognitive dysfunction in chronic amphetamine and heroin abusers. Neuropsychopharmacology. 2000, 23 (2): 113-126. 10.1016/S0893-133X(00)00097-X.View ArticlePubMedGoogle Scholar
- Prosser J, Cohen LJ, Steinfeld M, Eisenberg D, London ED, Galynker II: Neuropsychological functioning in opiate-dependent subjects receiving and following methadone maintenance treatment. Drug Alcohol Depend. 2006, 84: 240-247. 10.1016/j.drugalcdep.2006.02.006.View ArticlePubMedPubMed CentralGoogle Scholar
- Lombardo WK, Lombardo B, Goldstein A: Cognitive functioning under moderate and low dosage methadone maintenance. Int J Addict. 1976, 11 (3): 389-401.View ArticlePubMedGoogle Scholar
- Rotheram-Fuller E, Shoptaw S, Berman SM, London ED: Impaired performance in a test of decision-making by opiate-dependent tobacco smokers. Drug Alcohol Depend. 2004, 73: 79-86. 10.1016/j.drugalcdep.2003.10.003.View ArticlePubMedGoogle Scholar
- King R, Best D: Cognitive functioning and cognitive style among drug users in maintenance substitution treatment. Drug Educ Prev Polic. 2011, 18 (2): 132-139. 10.3109/09687631003705553.View ArticleGoogle Scholar
- Baldacchino A, Balfour D, Passetti F, Humphris G, Matthews K: The neuropsychological consequences of chronic opioid use: a quantitative review and meta-analysis. Neur BioBeh Rev. 2012, 36: 2056-2068. 10.1016/j.neubiorev.2012.06.006.View ArticleGoogle Scholar
- Kandall SR, Albin S, Lowinson J, Berle B, Eidelman AI, Gartner LM: Differential effects of maternal heroin and methadone use on birthweight. Pediatrics. 1976, 58 (5): 681-685.PubMedGoogle Scholar
- Jones HE, Kaltenbach K, Heil SH, Stine SM, Coyle MG, Arria AM, O’Grady KE, Selby P, Martin PR, Fischer G: Neonatal abstinence syndrome after methadone or buprenorphine exposure. Obstet Gynecol Surv. 2011, 66: 191-193. 10.1097/OGX.0b013e318225c419.View ArticleGoogle Scholar
- Cleary BJ, Eogan M, O’Connell MP, Fahey T, Gallagher PJ, Clarke T, White MJ, McDermott C, O’Sullivan A, Carmody D, Gleeson J, Murphy DJ: Methadone and perinatal outcomes: a prospective cohort study. Addiction. 2012, 107 (8): 1482-1492. 10.1111/j.1360-0443.2012.03844.x.View ArticlePubMedGoogle Scholar
- Minozzi S, Amato L, Bellisario C, Ferri M, Davoli M: Maintenance agonist treatments for opiate-dependent pregnant women. Cochrane Database Syst Rev. 2013, 12: CD006318-PubMedGoogle Scholar
- de Cubas MM, Field T: Children of methadone-dependent women: developmental outcomes. Am J Orthopsych. 1993, 63 (2): 266-276.View ArticleGoogle Scholar
- Hans SL: Developmental consequences of prenatal exposure to methadone. Ann N Y Acad Sci. 1989, 562: 195-207. 10.1111/j.1749-6632.1989.tb21018.x.View ArticlePubMedGoogle Scholar
- Rosen TS, Johnson HL: Long-term effects of prenatal methadone maintenance. Current Research on the Consequences of Maternal Drug Abuse NIDA Research Monograph. Edited by: Pinkert TM. 1985, Rockville, Maryland: National Institute on Drug Abuse, 73-83. 59Google Scholar
- Bauman PS, Levine SA: The development of children of drug addicts. Int J Addict. 1986, 21 (8): 849-863.View ArticlePubMedGoogle Scholar
- Konijnenberg C, Melinder A: Neurodevelopmental investigation of the mirror neurone system in children of women receiving opioid maintenance therapy during pregnancy. Addiction. 2013, 108 (1): 154-160. 10.1111/j.1360-0443.2012.04006.x.View ArticlePubMedGoogle Scholar
- Salo S, Kivistö K, Korja R, Biringen Z, Tupola S, Kahila H, Kivitie-Kallio S: Emotional availability, parental self-efficacy beliefs, and Child Devin caregiver-child relationships with buprenorphine-exposed 3-year-olds. Parent Sci Pract. 2009, 9: 244-259. 10.1080/15295190902844563.View ArticleGoogle Scholar
- Soepatmi S: Developmental outcomes of children of mothers dependent on heroin or heroin/methadone during pregnancy. Acta Paediatr. 1994, 83: 36-39.View ArticleGoogle Scholar
- Whitham JN, Spurrier NJ, Sawyer MG, Baghurst PA, Taplin JE, White JM, Gordon AL: The effects of prenatal exposure to buprenorphine or methadone on infant visual evoked potentials. Neurotoxicol Teratol. 2010, 32: 280-288. 10.1016/j.ntt.2009.09.001.View ArticlePubMedGoogle Scholar
- Hutchings DE: Methadone and heroin during pregnancy: a review of behavioral effects in human and animal offspring. Neurobehav Toxicol Teratol. 1982, 4: 429-434.PubMedGoogle Scholar
- Wolf FM: Meta-Analysis: Quantitative Methods for Research Synthesis, Volume 59. 1986, London: Sage PublicationsView ArticleGoogle Scholar
- Borenstein M, Hedges L, Higgins JPT, Rothstein HR: Introduction to Meta-Analysis. 2009, Chichester, Sussex: WileyView ArticleGoogle Scholar
- Stroup DF, Berlin JA, Morton SC, Olkin I, Williamson GD, Rennie D, Moher D, Becker BJ, Sipe TA, Thacker SB: Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group. JAMA. 2000, 283 (15): 2008-2012. 10.1001/jama.283.15.2008.View ArticlePubMedGoogle Scholar
- Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gotzsche PC, Ioannidis JP, Clarke M, Devereaux PJ, Kleijnen J, Moher D: The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ. 2009, 339: b2700-10.1136/bmj.b2700.View ArticlePubMedPubMed CentralGoogle Scholar
- Britannica: Child Development: process and definition. 2013, Accessed http://www.britannica.com/EBchecked/topic/111044/child-development Google Scholar
- Organization WH: The ICD-10 Classification of Mental and Behavioural Disorders: Diagnostic Criteria for Research. 1993, Geneva: WHOGoogle Scholar
- American Psychiatric Association: Diagnostic and statistical manual of mental disorders fourth edition. 1994, Accessed:http://www.dsm.psychiatryonline.org/book.aspx?bookid=556 (June 2013),Google Scholar
- Cooper H, Hodges LV: The Handbook of Research Synthesis. 1984, New York: Russel Sage FoundationGoogle Scholar
- Cohen J: Statistical Power Analysis for the Behavioural Sciences. 1988, Abington: Massachusetts Lawrence Erlbaum AssociatesGoogle Scholar
- Hunter JE, Schmidt FL: Methods of Meta-Analysis. 1990, London: Sage PublicationsGoogle Scholar
- Cohen J: A power primer. Psychol Bull. 1992, 112 (1): 155-159.View ArticlePubMedGoogle Scholar
- Hedges LV, Olkin I: Statistical Methods for Meta-Analysis. 1985, Cambridge: Academic PressGoogle Scholar
- Sternberg RJ, Grigorenko EL: Genetics of childhood disorders: I. Genetics and intelligence. J Am Acad Child Adolesc Psychiatry. 1999, 38: 486-488. 10.1097/00004583-199904000-00024.View ArticlePubMedGoogle Scholar
- Lezak MD, Howieson DB, Loring DW: Neuropsychological Assessment. 2004, Oxford: Oxford University Press, 4Google Scholar
- Anderson LW, Krathwohl DR, Airasian PW, Cruikshank KA, Mayer RE, Pintrich PR, Raths J, Wittrock MC: A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives. 2001, New York: Addison Wesley LongmanGoogle Scholar
- Demakis GJ: Meta-analysis in neuropsychology: basic approaches, findings, and applications. Clin Neuropsychol. 2006, 20 (1): 10-26. 10.1080/13854040500203282.View ArticlePubMedGoogle Scholar
- Strauss E, Sherman EMS, Spreen O: A Compendium of Neuropsychological Tests: Administration, Norms, and Commentary. 2006, Oxford: Oxford University PressGoogle Scholar
- Huedo-Medina TB, Sanchez-Meca J, Marin-Martinez F, Botella J: Assessing heterogeneity in meta-analysis: Q statistic or I2 index?. Psychol Methods. 2006, 11: 193-206.View ArticlePubMedGoogle Scholar
- Higgins JP, Thompson SG, Deeks JJ, Altman DG: Measuring inconsistency in meta-analyses. BMJ. 2003, 327 (7414): 557-560. 10.1136/bmj.327.7414.557.View ArticlePubMedPubMed CentralGoogle Scholar
- Hedges LV, Verea JL: Fixed-and random effects models in meta-analysis. Psychol Methods. 1998, 3: 486-504.View ArticleGoogle Scholar
- Quinhana SM, Minami T: Guidelines for meta-analyses of J Couns Psycholresearch. J Couns Psychol. 2006, 34: 839-877. 10.1177/0011000006286991.View ArticleGoogle Scholar
- Orwin RG: A fail safe N for effect size. J Stat Educ. 1983, 8: 157-159.View ArticleGoogle Scholar
- Borenstein M, Hedges L, Higgins J, Rothstein H: Comprehensive Meta Analysis. 2005, Englewood, New Jersey: Biostat, 2Google Scholar
- National Collaborating Centre for Methods and Tools: Quality Assessment Tool for Quantitative Studies. 2008, Hamilton, ON: McMaster University, (Updated 13 April, 2010). Retrieved from http://www.nccmt.ca/registry/view/eng/14.html,Google Scholar
- Grattan M, Hans S: Motor behaviour in children exposed prenatally to drugs. Phys Occup Ther Pediatr. 1996, 16 (1/2): 89-109.View ArticleGoogle Scholar
- Walhovd KB, Moe V, Slinning K, Due-Tonnessen P, Bjornerud A, Dale AM, van der Kouwe A, Quinn BT, Kosofsky B, Greve D, Fischl B: Volumetric cerebral characteristics of children exposed to opiates and other substances in utero. Neuroimage. 2007, 36: 1331-1344. 10.1016/j.neuroimage.2007.03.070.View ArticlePubMedPubMed CentralGoogle Scholar
- Steinhausen HC, Blattmann B, Pfund F: Developmental outcome in children with intrauterine exposure to substances. Eur Addict Res. 2007, 13: 94-9100. 10.1159/000097939.View ArticlePubMedGoogle Scholar
- Moe V: Foster-placed and adopted children exposed in utero to opiates and other substances: prediction and outcome at four and a half years. J Dev Behav Pediatr. 2002, 23 (5): 330-339. 10.1097/00004703-200210000-00006.View ArticlePubMedGoogle Scholar
- Ornoy A: The impact of intrauterine exposure versus postnatal environment in neurodevelopmental toxicity: long-term neurobehavioral studies in children at risk for developmental disorders. Toxicol Lett. 2003, 140–141: 171-181.View ArticlePubMedGoogle Scholar
- Bunikowski R, Grimmer I, Heiser A, Metze B, Schafer A, Obladen M: Neurodevelopmental outcome after prenatal exposure to opiates. Eur J Pediatr. 1998, 157 (9): 724-730. 10.1007/s004310050923.View ArticlePubMedGoogle Scholar
- Hans SL, Jeremy RJ: Postneonatal mental and motor development of infants exposed in utero to opioid drugs. Inf Mental Health J. 2001, 22 (3): 300-315. 10.1002/imhj.1003.View ArticleGoogle Scholar
- Zakzanis KK: Statistics to tell the truth, the whole truth, and nothing but the truth Formulae, illustrative numerical examples, and heuristic interpretation of effect size analyses for neuropsychological researchers. Arch Clin Neuropsych. 2001, 16 (7): 653-667. 10.1093/arclin/16.7.653.View ArticleGoogle Scholar
- Verdejo-Garcia A, Perez-Garcia M: Profile of executive deficits in cocaine and heroin polysubstance users: common and differential effects on separate executive components. Psychopharmacology (Berl). 2007, 190 (4): 517-530. 10.1007/s00213-006-0632-8.View ArticleGoogle Scholar
- Emory EK, Israelian MK: Behavioral Profiles Related to Stress Reactivity and Ethnic Variation. Proceedings of Advancing Research on Developmental Plasticity. 1998, Bethesda, Maryland: National Institutes of HealthGoogle Scholar
- Fihrer I, McMahon CA, Taylor AJ: The impact of postnatal and concurrent maternal depression on child behaviour during early school years. J Affect Disord. 2009, 119: 116-123. 10.1016/j.jad.2009.03.001.View ArticlePubMedGoogle Scholar
- NICHD: Chronicity of maternal depressive symptoms, maternal sensitivity, and child functioning at 36 months. NICHD early child care research network. Dev Psychol. 1999, 35 (5): 1297-1310.View ArticleGoogle Scholar
- Petterson SM, Albers AB: Effects of poverty and maternal depression on early child development. Child Dev. 2001, 72 (6): 1794-1813. 10.1111/1467-8624.00379.View ArticlePubMedGoogle Scholar
- Grace SL, Evindar A, Stewart DE: The effect of postpartum depression on child cognitive development and behavior: a review and critical analysis of the literature. Arch Womens Ment Health. 2003, 6: 263-274. 10.1007/s00737-003-0024-6.View ArticlePubMedGoogle Scholar
- Tong S, Baghurst P, McMichael A: Birthweight and cognitive development during childhood. J Paediatr Child Health. 2006, 42 (3): 98-103. 10.1111/j.1440-1754.2006.00805.x.View ArticlePubMedGoogle Scholar
- Dunlop AJ, Panjari M, O’Sullivan H, Henschke P, Love V, Ritter A: Clinical Guidelines for the Use of Buprenorphine in Pregnancy. 2003, Fitzroy, Victoria: Turning Point Alcohol and Drug CentreGoogle Scholar
- Johnson RE, Jones HE, Fischer G: Use of buprenorphine in pregnancy: patient management and effects on the neonate. Drug Alcohol Depend. 2003, 70: 87-8101.View ArticleGoogle Scholar
- Bernstein IM, Mongeon JA, Badger GJ, Solomon L, Heil SH, Higgins ST: Maternal smoking and its association with birth weight. Obstet Gynecol. 2005, 106 (5 Pt 1): 986-991.View ArticlePubMedGoogle Scholar
- Jones HE, Kaltenbach K, Heil SH, Stine SM, Coyle MG, Arria AM, O’Grady KE, Selby P, Martin PR, Fischer G: Neonatal abstinence syndrome after methadone or buprenorphine exposure. N Engl J Med. 2010, 363: 2320-2331. 10.1056/NEJMoa1005359.View ArticlePubMedPubMed CentralGoogle Scholar
- The pre-publication history for this paper can be accessed here:http://0-www.biomedcentral.com.brum.beds.ac.uk/1471-244X/14/104/prepub
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