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Autism spectrum disorder in under 19s: recognition, referral and diagnosis
[A] Evidence review for factors and
neurodevelopmental disorders that increase the likelihood of a diagnosis of autism spectrum disorder
NICE guideline, No. 128
London: National Institute for Health and Care Excellence (NICE); 2017 Dec.
Factors and neurodevelopmental disorders that increase the likelihood of a diagnosis of autism spectrum disorder
Review questions
Do the following risk factors increase the likelihood of a diagnosis of autism spectrum disorders (ASD) and assist in the decision to refer for a formal ASD diagnostic assessment?
- Small for gestational age
- Prenatal use of selective serotonin reuptake inhibitors (SSRIs)
- Fertility treatments
Do neurodevelopmental disorders (such as attention deficit hyperactivity disorder [ADHD] and learning [intellectual] disability) increase the likelihood of a diagnosis of ASD and assist in the decision to refer for a formal ASD diagnostic assessment?
Introduction
The NICE guideline (CG128) on diagnosing autism spectrum disorders (ASD) was reviewed by the surveillance programme in September 2016. The surveillance process identified new evidence indicating that the current recommendations on factors associated with an increased prevalence of ASD should be updated. The following key factors were identified for consideration - being small for gestational age, prenatal use of selective serotonin reuptake inhibitors (SSRIs), use of fertility treatments and the presence of neurodevelopmental disorders. This update reviews the evidence for these factors and considers whether they may alter the likelihood that a person has ASD and whether clinicians should take account of these factors when considering referral for an ASD diagnostic assessment.
Terminology in the NICE guideline on diagnosing ASD (CG128) was also amended throughout to reflect the updated DSM-5 diagnostic criteria. This involved replacing references to the old DSM-IV criteria with references to the new DSM-5 criteria.
PICO table
| Population | Children and young people from birth up to their 19th birthday without a diagnosis of ASD |
|---|---|
| Predictive factors |
|
| Outcomes | Clinical diagnosis of ASD |
| Measures | Adjusted and unadjusted:
|
Methods and process
This evidence review was developed using the methods and process described in Developing NICE guidelines: the manual (2014). Methods specific to this review question are described in the review protocol in Appendix A and Appendix B.
Declarations of interest were recorded according to NICE’s 2014 conflicts of interest policy.
Clinical evidence
Included studies
A systematic search was carried out to identify observational studies and systematic reviews of observational studies, which found 11,223 references (see Appendix C for literature search strategy). Evidence included in the original guideline, evidence identified from the surveillance review and studies referenced in identified systematic reviews were also reviewed, which included a total of 60 references. An additional reference (Rai 2017) which was published after the date of the systematic search was identified by a member of the guideline committee which was considered to be relevant for the update. In total, 11,284 references were identified to be screened at title and abstract level. Using priority screening software, from the first 8,000 references screened, 7,786 were excluded based on their titles and abstracts and 214 references were ordered to be screened based on their full texts. Of these, 23 references were included based on their relevance to the review protocol (Appendix A). The clinical evidence study selection is available in Appendix C.
No relevant papers were identified at title and abstract level in the last 3,000 screened (records 5,000-8,000), and therefore it was agreed to be appropriate to stop screening at this point (based on the priority screening functionality in the EPPI-reviewer systematic reviewing software, see Appendix B for more details). Therefore, the final 3,284 references were not screened on their titles and abstracts, but were automatically excluded.
Studies met the protocol criteria for clinical diagnosis of ASD if they reported that the diagnosis was made by a health professional. In the case of registry-based studies, a clinical diagnosis of ASD was assumed if International Statistical Classification of Diseases and Related Health Problems (ICD) or Diagnostic and Statistical Manual of Mental Disorders (DSM) codes were looked at from the databases. A clinical diagnosis of ASD was not assumed and studies were excluded if the reference reported that diagnoses were made with a questionnaire by researchers, parents or teachers.
No standard definition was found for small for gestation age, and therefore all references were included if they provided the definition of small for gestational age used in their analysis.
Excluded studies
For the full list of excluded studies, with reasons for exclusion, see Appendix H.
Summary of clinical studies included in the evidence review
| Author (year) | Title | Study characteristics |
|---|---|---|
|
Country: UK | Morbidity and medication in a large population of individuals with Down syndrome compared to the general population | Study type
|
|
Country: Denmark | Fertility treatment and risk of childhood and adolescent mental disorders: register based cohort study. |
Study type
Outcome(s)
|
|
Country: Canada | Antidepressant Use During Pregnancy and the Risk of Autism Spectrum Disorder in Children | Study type
|
|
Country: Canada | Association Between Serotonergic Antidepressant Use During Pregnancy and Autism Spectrum Disorder in Children | Study type
|
|
Country: US | Advanced parental age and the risk of autism spectrum disorder. |
Study type
Outcome(s)
|
|
Country: Denmark | Psychiatric disorders in Danish children aged 5-7 years: A general population study of prevalence and risk factors from the Copenhagen Child Cohort (CCC 2000) | Study type
|
|
Country: Sweden | The familial co-aggregation of ASD and ADHD: a register-based cohort study |
Study type
Predictive factor(s)
Outcome(s)
|
|
Country: Denmark | Risk of autism spectrum disorders in children born after assisted conception: a population-based follow-up study |
Study type
Outcome(s)
|
|
Country: Denmark | Use of selective serotonin reuptake inhibitors during pregnancy and risk of autism |
Study type
Outcome(s)
|
|
Country: US | Extremely low gestational age and very low birthweight for gestational age are risk factors for autism spectrum disorder in a large cohort study of 10-year-old children born at 23-27 weeks’ gestation. |
Study type
Outcome(s)
|
|
Country: US | Association of assisted reproductive technology (ART) treatment and parental infertility diagnosis with autism in ART-conceived children. |
Study type
Outcome(s)
|
|
Country: US | Prevalence and neonatal factors associated with autism spectrum disorders in preterm infants. |
Study type
Outcome(s)
|
|
Country: Finland | Gestational Exposure to Selective Serotonin Reuptake Inhibitors and Offspring Psychiatric Disorders: A National Register-Based Study |
Study type
Outcome(s)
|
|
Country: Sweden | Mediators of the association between parental severe mental illness and offspring neurodevelopmental problems |
Study type
Outcome(s)
|
|
Country: US | Timing of the Diagnosis of Attention-Deficit/Hyperactivity Disorder and Autism Spectrum Disorder. | Study type
|
|
Country: US | Autism risk in small- and large-for-gestational-age infants | Study type
|
|
Country: Denmark | Neurological sequelae in twins born after assisted conception: controlled national cohort study. | Study type
|
|
Country: Stockholm | Antidepressants during pregnancy and autism in offspring: population based cohort study. | Study type
|
|
Country: UK | Prevalence of Parent-Reported ASD and ADHD in the UK: Findings from the Millennium Cohort Study | Study type
|
|
Country: Sweden | Autism and mental retardation among offspring born after in vitro fertilization. |
Study type
Outcome(s)
|
|
Country: Denmark | Antidepressant exposure in pregnancy and risk of autism spectrum disorders |
Study type
Outcome(s)
|
|
Country: Sweden | Associations of Maternal Antidepressant Use During the First Trimester of Pregnancy With Preterm Birth, Small for Gestational Age, Autism Spectrum Disorder, and Attention-Deficit/Hyperactivity Disorder in Offspring | Study type
|
|
Country: Sweden | Autism risk following antidepressant medication during pregnancy | Study type
|
See appendix D for full evidence tables.
Quality assessment of clinical studies included in the evidence review
See appendix F for full GRADE tables and appendix G for Forest plots.
Economic evidence
No health economics work was planned for this guideline update, as it was agreed that any recommendations made were highly unlikely to result in a substantial resource impact.
Evidence statements
Small for gestational age
Birth weight for gestational age >2 SDs below the mean
- Very low-quality evidence from 1 retrospective cohort containing 185,164 children (8 years old) could not detect a difference in numbers of clinical diagnoses of ASD between children with a birth weight for gestational age >2 SDs below the mean compared to children with a birth weight for gestational age within 1 SD of the mean.
- Moderate-quality evidence from 1 retrospective cohort containing 866,272 children (younger than 18 years old) found that more children with a birth weight for a given gestational age >2 SDs below the average had a clinical diagnosis of ASD compared to children not being born small for gestational age.
Birth weight for gestational age Z-score <-2
- Moderate-quality evidence from 1 prospective cohort containing 737 children (10 years old) found that more children with a birth weight for gestational age Z-score <-2 had a clinical diagnosis of ASD (reference category was not reported).
Birth weight for gestational age <5th percentile
- Moderate-quality of evidence from 1 retrospective cohort containing 185,506 children (age was not reported) could not detect a difference in numbers of clinical diagnoses of ASD in children with birth weight for gestational age <5th percentile (reference category was not reported).
- Moderate-quality of evidence from 1 retrospective cohort containing 4,692,129 children (4 years and older) could not detect a difference in numbers of clinical diagnoses of ASD in children with birth weight for gestational age <5th percentile compared to children with birth weight percentile >10 to <90.
Birth weight for gestational age 5 to 10th percentile
- Moderate-quality of evidence from 1 retrospective cohort containing 4,711,838 children (4 years and older) could not detect a difference in numbers of clinical diagnoses of ASD in children with birth weight for gestational age 5 to 10th percentile compared to children with birth weight percentile >10 to <90.
Prenatal use of selective serotonin reuptake inhibitors (SSRIs)
SSRIs used during pregnancy
- Low-quality evidence from 1 retrospective cohort containing 35,618 children (4 to 10 years old) could not detect a difference in numbers of clinical diagnoses of ASD between children born to mothers using SSRIs during pregnancy compared to children born to mothers without a prescription of serotonergic antidepressants during pregnancy.
- High-quality evidence from 1 retrospective cohort containing 16,997 children (10 years or younger) found that, in a sub-analysis of a cohort restricted to women with a documented diagnosis of a mood or anxiety disorder within 2 years previous to conception, more children born to mothers using SSRIs or selective norepinephrine reuptake inhibitors during pregnancy had a diagnosis of ASD compared to children born to mothers without a prescription of serotonergic antidepressants during pregnancy.
- Low-quality evidence from 1 retrospective cohort containing 626,875 children (10 years or younger) could not detect a difference in numbers of clinical diagnoses of ASD between children born to mothers using SSRIs during pregnancy compared to children born to mothers not using SSRIs during pregnancy.
- Low-quality evidence from 1 retrospective cohort containing 27,842 children (10 years or younger) could not detect a difference in numbers of clinical diagnoses of ASD between children born to mothers using SSRIs during pregnancy compared to children born to mothers not using SSRIs during pregnancy in both subgroup analyses: mothers with previous psychiatric diagnoses (n=24,360) or mothers with previous diagnosis of depression (n=3,482).
- Moderate-quality evidence from 1 retrospective cohort containing 15,035 children (4 to 7 years old) found that more children born to mothers using SSRIs during pregnancy had a diagnosis of ASD compared to children born to mothers with a psychiatric disorder not using antidepressants during pregnancy.
- Moderate-quality evidence from 1 retrospective cohort containing 15,035 children (4 to 7 years old) found that more children born to mothers using SSRIs during pregnancy had a diagnosis of ASD without learning (intellectual) disability compared to children born to mothers with a psychiatric disorder not using antidepressants during pregnancy.
- High-quality evidence from 3 retrospective cohorts containing 877,235 children (younger than 14 years old) found that more children born to mothers using SSRIs during pregnancy had a diagnosis of ASD compared to children born to mothers not using antidepressants during pregnancy.
- Low-quality evidence from 1 retrospective cohort containing 5,799 children (younger than 14 years old) could not detect a difference in numbers of clinical diagnoses of ASD between children born to mothers using SSRIs during pregnancy compared to children born to mothers not using SSRIs during pregnancy in an analysis restricted to mothers with a hospital-diagnosed affective disorder.
- Low-quality evidence from 1 retrospective cohort containing 25,380 children (younger than 14 years old) could not detect a difference in numbers of clinical diagnoses of ASD between children born to mothers using SSRIs during pregnancy compared to children born to mothers with psychiatric disorder but no antidepressant use.
SSRIs used during first trimester
- Low-quality evidence from 1 retrospective cohort containing 626,875 children (10 years or younger) could not detect a difference in numbers of clinical diagnoses of ASD between children born to mothers using SSRIs during first trimester of pregnancy compared to children born to mothers not using SSRIs during pregnancy.
- High-quality evidence from 2 retrospective cohorts containing 1,580,210 children (younger than 14 years old) found that more children born to mothers using SSRIs during first trimester of pregnancy had a diagnosis of ASD compared to children born to mothers not using antidepressants during pregnancy.
- Low-quality evidence from 1 retrospective cohort containing 654,288 children (10 years or younger) could not detect a difference in numbers of clinical diagnoses of ASD between children born to mothers using SSRIs during first trimester of pregnancy compared to children born to mothers not using SSRIs during pregnancy in an analysis restricted to mothers with a hospital-diagnosed affective disorder.
SSRIs used during second and/or third trimester
- Low-quality evidence from 3 retrospective cohorts containing 852,957 children (younger than 14 years old) could not detect a difference in numbers of clinical diagnoses of ASD between children born to mothers using SSRIs during second and/or third trimester of pregnancy compared to children born to mothers not using antidepressants during pregnancy.
Fertility treatment
Assisted conception including in vitro fertilisation (IVF) and ovulation induction (OI)
- Moderate-quality evidence from 1 retrospective cohort containing 588,967 children (age was not reported) could not detect a difference in numbers of clinical diagnoses of ASD between children born after assisted conception including IVF and OI compared to children born after natural conception.
IVF with or without intracytoplasmic sperm injection (ICSI)
- Low-quality evidence from 2 retrospective cohorts containing 3,111,944 children (age was not reported) could not detect a difference in numbers of clinical diagnoses of ASD between children born after IVF with or without ICSI compared to children born after natural conception.
IVF and ICSI
- Moderate-quality evidence from 1 retrospective cohort containing 570,819 children (age was not reported) could not detect a difference in numbers of clinical diagnoses of ASD between children born after IVF and ICSI compared to children born after natural conception.
- Very low-quality evidence from 1 retrospective cohort containing 13,632 twin children (2 to 7 years old) could not detect a difference in numbers of clinical diagnoses of ASD between children born after IVF and ICSI compared to children born after natural conception.
Different procedures of fertility treatments
- Low-quality evidence from 1 retrospective cohort containing 2,541,125 children (age was not reported) could not detect a difference in numbers of clinical diagnoses of ASD between children born after natural conception compared to children born after the following procedures:
- IVF without ICSI, fresh embryo transfer
- IVF without ICSI, frozen embryo transfer
- ICSI using ejaculated sperm with fresh embryos
- ICSI with ejaculated sperm and frozen embryos
- Spontaneously conception with hormone treatment as the only fertility treatment
ICSI with surgical extracted sperm and fresh embryos
- Moderate-quality evidence from 1 retrospective cohort containing 2,510,794 children (age was not reported) found that more children born after ICSI with surgical extracted sperm and fresh embryos had a diagnosis of ASD compared to children born after natural conception.
OI or intrauterine insemination (IUI)
- Moderate-quality evidence from 1 retrospective cohort containing 573,976 children (age was not reported) could not detect a difference in numbers of clinical diagnoses of ASD between children born after OI/IUI compared to children born after natural conception.
- Moderate-quality evidence from 1 retrospective cohort containing 573,976 children (age was not reported) could not detect a difference in numbers of clinical diagnoses of ASD between children born after OI compared to children born after natural conception.
ICSI
- Moderate-quality evidence from 1 retrospective cohort containing 35,481 children (5 years and younger) could not detect a difference in numbers of clinical diagnoses of ASD between children born after ICSI compared to children born after IVF without ICSI.
Neurodevelopmental disorders
Attention deficit hyperactivity disorder (ADHD)
- Moderate-quality evidence from 3 cross-sectional studies containing 1,914,808 children (9 years and younger) found that more children with ADHD had a diagnosis of ASD compared to children without ADHD.
Down’s syndrome
- Very low-quality evidence from 1 case-control study containing 25,606 children and adults found that more people with Down’s syndrome had a clinical diagnosis of ASD compared to people without Down’s syndrome.
Learning (intellectual) disability
- Moderate-quality evidence from 1 prospective cohort containing 737 children (10 years old) found that more children with learning (intellectual) disability had a clinical diagnosis of ASD compared to children without learning (intellectual) disability.
ADHD before ASD
- Low-quality evidence from 1 cross-sectional study containing 1,059 children (2 to 17 years old) found that more children had a clinical diagnosis of ASD delayed until after 6 years of age if they were diagnosed with ADHD before ASD compared to children who were only diagnosed with ASD.
ADHD same/after ASD
- Very low-quality evidence from cross-sectional study containing 1,138 children (2 to 17 years old) could not detect a difference in numbers of clinical diagnoses of ASD delayed until after 6 years of age between children diagnosed with ADHD and ASD at the same time compared to children who were diagnosed with only ASD.
The committee’s discussion of the evidence
Interpreting the evidence
The outcomes that matter most
The committee agreed that for all the factors included in the review, the critical outcome was whether the presence of those factors increased the likelihood of a diagnosis of ASD. For small for gestational age, maternal use of SSRIs during pregnancy and use of fertility treatments the committee agreed cohort studies would be the most appropriate study design, as the focus is on the impact of these maternal and neonatal factors on long-term rates of ASD diagnosis. For neurodevelopmental disorders, they agreed that cross-sectional studies would also be an appropriate study design, as the focus here is less on the time course of which diagnoses comes first, and simply on how often the two diagnosis co-exist.
The quality of the evidence
There was considerable variety in the quality of the evidence available, with ratings ranging from very low to high. The main reasons for downgrading were imprecision in effect estimates and risk of bias, in particular resulting from not appropriately adjusting for relevant confounding variables. The committee agreed that unadjusted odds ratios were an acceptable outcome measure for ADHD as a factor that increased the likelihood of a clinical diagnosis of ASD, because this update was not about causation and it was expected that studies would provide evidence of diagnosis of ADHD and ASD at the same point in time. Therefore, studies on ADHD were not downgraded if they reported unadjusted odds ratios. The committee agreed the effect sizes from the different studies on the association between ADHD and ASD were not clinically meaningfully different from each other (and all demonstrated a substantial increase in ASD rates in people with ADHD) and therefore the evidence was not downgraded for inconsistency as a result of this unexplained heterogeneity.
The committee also highlighted that indirectness was not a problem in the evidence from the meta-analysis of ADHD. One of the studies (Elberling 2016) reported on hyperkinetic disorders according to ICD-10 codes which includes a list of disorders apart from ADHD. However, the committee agreed that the most common hyperkinetic disorder is ADHD and they were confident that this was the case in Elberling 2016. Another study in the meta-analysis (Ghirardi 2017) reported that diagnosis of ASD was recorded with ICD-9 or ICD-10 codes which included Rett’s syndrome. However, the committee agreed that Rett’s syndrome is a rare disorder and they did not expect the numbers of such cases to make a meaningful difference to the results.
Benefits and harms
The committee agreed that ADHD was the most important factor identified that increased the likelihood of a clinical diagnosis of ASD, with the included studies showing a clear association between ADHD and ASD. The committee also highlighted that in clinical practice, ADHD is often already considered when diagnosing ASD (and vice versa). Therefore, the committee agreed to include ADHD to the list of factors that could assist in the decision to refer for a formal ASD diagnostic assessment and in the decision to carry out an ASD diagnostic assessment. The committee suggested that ADHD be added to the current list of factors after learning (intellectual) disability as both were considered neurodevelopmental disorders.
The committee noted that this recommendation change also interacted with the decision made to refer to the new DSM-5 criteria for ASD diagnosis. Comorbid diagnosis of ADHD and ASD was not allowed in the previous DSM-IV version, but is allowed in the DSM-5. The committee discussed a list of benefits from adding ADHD as a factor associated with an increased prevalence of ASD, such as the consideration of a joint assessment of both ADHD and ASD in children who show signs and symptoms that may be attributable to either diagnosis. Another benefit could be the early diagnosis of ASD in children with signs and symptoms of ADHD. Otherwise, having only an ADHD diagnosis without assessing for ASD might lead to diagnostic overshadowing of ADHD over ASD. Having an early diagnosis of ASD allows families to access educational and financial support, to adjust their life and coping mechanisms; to seek support groups for children and their family.
The committee agreed that there was insufficient evidence on the rest of the factors reviewed in this update for them to be added to the list of factors associated with an increased prevalence of autism. The committee agreed that the evidence for small for gestational age was mixed with some studies finding that more children identified as small for gestational age were diagnosed with ASD compared to other studies which were unable to detect a difference. Results from Joseph (2017) showed a large effect for the association between small for gestational age and ASD diagnosis but this was a small study and the population was limited to very preterm children (children born between 23 and 27 weeks gestation), which was considered to be an unusual cohort of children. The committee also agreed that gestational age less than 35 weeks was a considerably more important association than being small for gestational age, and this was already a factor associated with an increased prevalence of ASD in the recommendation.
The committee highlighted that whilst there appeared to be an association between SSRI use and an the increased number of clinical diagnoses of ASD when mothers taking SSRIs were compared to all mothers not taking SSRIs, this weakened substantially when studies compared mothers with psychiatric or mood disorders using SSRI treatment to mothers with such disorders not using SSRI treatment, which was agreed to be the most relevant comparison. Therefore, the committee agreed there was no robust evidence that SSRI use increased rates of ASD, above the effects known from ‘parental schizophrenia-like psychosis or affective disorder’, a factor already included in the recommendation.
The evidence for fertility treatments only showed an effect on clinical diagnosis of ASD for the least common fertility treatment procedure (ICSI with surgical extracted sperm and fresh embryos). The evidence for this procedure was from a smaller sample size compared to the rest of procedures (n=628), with only 8 cases of ASD. The committee also agreed that this procedure is considered to be the least common fertility treatment, and agreed there was insufficient evidence to add it to the list of factors.
Cost effectiveness and resource use
The committee agreed that the addition of ADHD as a relevant factor to consider was unlikely to lead to a substantial resource impact, as it reflects current clinical practice which is already changing to reflect the known interaction between ASD and ADHD.
Other factors the committee took into account
The committee agreed there was no evidence identified to suggest that Down’s syndrome and learning (intellectual) disability should be removed from the list of factors, and therefore these were retained in the list of factors that increased the likelihood of a clinical diagnosis of ASD.
The rest of the factors included in the existing recommendations were not reviewed as part of this update and therefore no further changes were made to the list.
Sex, language and cultural background were discussed by the committee as part of the equality impact assessment. The committee agreed that ASD and ADHD are both often underdiagnosed in females. The addition of ADHD to the list was not expected to either improve or worsen the potential for under-diagnosis of ASD in females. The committee also agreed that language and familiarity with the health system might have an effect on the time of ASD diagnosis and that there might be an ASD diagnosis stigma from some cultural backgrounds but the addition of ADHD to the recommendation was not expected to modify these effects.
The committee discussed that registry-based studies from the UK were not available for most of the factors except for Down’s syndrome. However, the committee agreed that they did not expect to find differences between studies from the UK and other European or American countries. Therefore, additional UK registry-based studies would not add more evidence to that currently available and a research recommendation was not considered necessary.
Appendices
Appendix A. Review protocols
Review protocol for factors and neurodevelopmental disorders with an increased likelihood of a diagnosis of ASD
| Field | Content |
|---|---|
| Review questions | Do the following risk factors increase the likelihood of a diagnosis of ASD and assist in the decision to refer for a formal ASD diagnostic assessment?
|
| Type of review questions | Association |
| Objective of the review | To update the list of factors for ASD referred to in the current NICE diagnosis of ASD guideline. |
| Eligibility criteria – population | Children and young people from birth up to their 19th birthday without a diagnosis of ASD at the time of factors evaluation. |
| Eligibility criteria – factors |
|
| Measures |
|
| Outcomes | Clinical diagnosis of ASD |
| Eligibility criteria – study design |
|
| Other inclusion/exclusion criteria | Other inclusion criteria:
|
| Proposed sensitivity/sub-group analysis, or meta-regression | Subgroups
|
| Selection process – duplicate screening/selection/analysis |
10% of the abstracts were reviewed by two reviewers, with any disagreements resolved by discussion or, if necessary, a third independent reviewer. If meaningful disagreements were found between the different reviewers, a further 10% of the abstracts were reviewed by two reviewers, with this process continued until agreement is achieved between the two reviewers. From this point, the remaining abstracts will be screened by a single reviewer. This review made use of the priority screening functionality with the EPPI-reviewer systematic reviewing software. See Appendix B for more details. |
| Data management (software) | See Appendix B |
| Information sources – databases and dates |
See Appendix C Sources searched Clinical searches:
Supplementary search techniques
|
| Identify if an update |
Update of 2011 ASD in under 19s: recognition, referral and diagnosis guideline question: In children with suspected autism (based on signs and symptoms) what information assists in the decision to refer for a formal autism diagnostic assessment?
|
| Author contacts | Guideline update |
| Highlight if amendment to previous protocol | For details please see section 4.5 of Developing NICE guidelines: the manual |
| Search strategy – for one database | For details please see appendix C |
| Data collection process – forms/duplicate | A standardised evidence table format will be used, and published as appendix E (clinical evidence tables). |
| Data items – define all variables to be collected | For details please see evidence tables in appendix E (clinical evidence tables). |
| Methods for assessing bias at outcome/study level | See Appendix B |
| Criteria for quantitative synthesis | See Appendix B |
| Methods for quantitative analysis – combining studies and exploring (in)consistency | See Appendix B |
| Meta-bias assessment – publication bias, selective reporting bias | See Appendix B |
| Confidence in cumulative evidence | See Appendix B |
| Rationale/context – what is known | For details please see the introduction to the evidence review in the main file. |
| Describe contributions of authors and guarantor |
A multidisciplinary committee developed the evidence review. The committee was convened by the NICE Guideline Updates Team and chaired by Tessa Lewis in line with section 3 of Developing NICE guidelines: the manual. Staff from the NICE Guideline Updates Team undertook systematic literature searches, appraised the evidence, conducted meta-analysis where appropriate, and drafted the evidence review in collaboration with the committee. For details please see Developing NICE guidelines: the manual. |
| Sources of funding/support | The NICE Guideline Updates Team is an internal team within NICE. |
| Name of sponsor | The NICE Guideline Updates Team is an internal team within NICE. |
| Roles of sponsor | The NICE Guideline Updates Team is an internal team within NICE. |
| PROSPERO registration number | N/A |
Appendix B. Methods
Priority screening
The reviews undertaken for this guideline all made use of the priority screening functionality with the EPPI-reviewer systematic reviewing software. This uses a machine learning algorithm (specifically, an SGD classifier) to take information on features (1, 2 and 3 word blocks) in the titles and abstracts of papers marked as being ‘includes’ or ‘excludes’ during the title and abstract screening process, and re-orders the remaining records from most likely to least likely to be an include, based on that algorithm. This re-ordering of the remaining records occurs every time 25 additional records have been screened.
Research is currently ongoing as to what are the appropriate thresholds where reviewing of abstracts can be stopped, assuming a defined threshold for the proportion of relevant papers which it is acceptable to miss on primary screening. As a conservative approach until that research has been completed, the following rules were adopted during the production of this guideline:
- In every review, at least 50% of the identified abstracts (or 1,000 records, if that is a greater number) were always screened.
- After this point, the number of included studies was recorded after every 1,000 records were screened. If, assuming studies were to be found in the remainder of the dataset at the same rate as in that 1,000 records (for example, if 5 includes were found, every subsequent 1,000 records would contain 5 includes), it was estimated that at least 95% of the includable studies in the database had been identified, then the screening was stopped.
As an additional check to ensure this approach did not miss relevant studies, the included studies lists of included systematic reviews were searched to identify any papers not identified through the primary search. If a meaningful number of studies were found that had been eliminated by the priority screening feature, the full original database was then screened.
Incorporating published systematic reviews
For all review questions where a literature search was undertaken looking for a particular study design, systematic reviews containing studies of that design were also included. All included studies from those systematic reviews were screened to identify any additional relevant primary studies not found as part of the initial search.
Quality assessment
Individual systematic reviews were quality assessed using the ROBIS tool, with each classified into one of the following three groups:
- High quality – It is unlikely that additional relevant and important data would be identified from primary studies compared to that reported in the review, and unlikely that any relevant and important studies have been missed by the review.
- Moderate quality – It is possible that additional relevant and important data would be identified from primary studies compared to that reported in the review, but unlikely that any relevant and important studies have been missed by the review.
- Low quality – It is possible that relevant and important studies have been missed by the review.
Each individual systematic review was also classified into one of three groups for its applicability as a source of data, based on how closely the review matches the specified review protocol in the guideline. Studies were rated as follows:
- Fully applicable – The identified review fully covers the review protocol in the guideline.
- Partially applicable – The identified review fully covers a discrete subsection of the review protocol in the guideline (for example, some of the factors in the protocol only).
- Not applicable – The identified review, despite including studies relevant to the review question, does not fully cover any discrete subsection of the review protocol in the guideline.
Using systematic reviews as a source of data
If systematic reviews were identified as being sufficiently applicable and high quality, and were identified sufficiently early in the review process (for example, from surveillance review or early in the database search), they were used as the main source of data, rather than extracting information from primary studies. The extent to which this was done depended on the quality and applicability of the review, as defined in Table 1. When systematic reviews were used as a source of primary data, any unpublished or additional data included in the review which is not in the primary studies was also included. Data from these systematic reviews was then quality assessed and presented in GRADE tables as described below, in the same way as if data had been extracted from primary studies. In questions where data was extracted from both systematic reviews and primary studies, these were cross-referenced to ensure none of the data had been double counted through this process.
Table 1Criteria for using systematic reviews as a source of data
| Quality | Applicability | Use of systematic review |
|---|---|---|
| High | Fully applicable | Data from the published systematic review were used instead of undertaking a new literature search or data analysis. Searches were only done to cover the period of time since the search date of the review. |
| High | Partially applicable | Data from the published systematic review were used instead of undertaking a new literature search and data analysis for the relevant subsection of the protocol. For this section, searches were only done to cover the period of time since the search date of the review. For other sections not covered by the systematic review, searches were undertaken as normal. |
| Moderate | Fully applicable | Details of included studies were used instead of undertaking a new literature search. Full-text papers of included studies were still retrieved for the purposes of data analysis and evaluation of risk of bias. Searches were only done to cover the period of time since the search date of the review. |
| Moderate | Partially applicable | Details of included studies were used instead of undertaking a new literature search for the relevant subsection of the protocol. For this section, searches were only done to cover the period of time since the search date of the review. For other sections not covered by the systematic review, searches were undertaken as normal. |
Association studies
In this guideline, association studies are defined those reporting data showing an association of a predictor (either a single variable or a group of variables) and an outcome variable, where the data are not reported in terms of outcome classification (i.e. diagnostic/prognostic accuracy). Data were reported as hazard ratios (if measured over time), or odds ratios or risk ratios (if measured at a specific time-point). Data reported in terms of model fit or predictive accuracy were not assessed using this method. Odds ratios were calculated when studies did not report any of the measures of interest (hazard ratios, risk ratios or odds ratios) but reported extractable data for the calculation of odds ratios.
Quality assessment
Individual cohort and case-control studies were quality assessed using the CASP cohort study and case-control checklists, respectively. Each individual study was classified into one of the following three groups:
- Low risk of bias – The true effect size for the study is likely to be close to the estimated effect size.
- Moderate risk of bias – There is a possibility the true effect size for the study is substantially different to the estimated effect size.
- High risk of bias – It is likely the true effect size for the study is substantially different to the estimated effect size.
Individual cross sectional studies were quality assessed using the Joanna Briggs Institute critical appraisal checklist for analytical cross sectional studies (2016), which contains 8 questions covering: inclusion criteria, description of the sample, measures of exposure, measures of outcomes, confounding factors, and statistical analysis. Each individual study was classified into one of the following groups:
- Low risk of bias – Evidence of non-serious bias in zero or one domain.
- Moderate risk of bias – Evidence of non-serious bias in two domains only, or serious bias in one domain only.
- High risk of bias – Evidence of bias in at least three domains, or of serious bias in at least two domains.
Each individual study was also classified into one of three groups for directness, based on if there were concerns about the population, predictors and/or outcomes in the study and how directly these variables could address the specified review question. Studies were rated as follows:
- Direct – No important deviations from the protocol in population, predictors and/or outcomes.
- Partially indirect – Important deviations from the protocol in one of the population, predictors and/or outcomes.
- Indirect – Important deviations from the protocol in at least two of the population, predictors and/or outcomes.
Methods for combining predictive modelling evidence
Where appropriate and from univariate analyses, hazard ratios were pooled using the inverse-variance method, and odds ratios or risk ratios were pooled using the Mantel-Haenszel method. Adjusted odds ratios from multivariate models were only pooled if the same set of predictor variables were used across multiple studies and if the same thresholds to measure predictors were used across studies.
Fixed- and random-effects models (der Simonian and Laird) were fitted for all syntheses, with the presented analysis dependent on the degree of heterogeneity in the assembled evidence. Fixed-effects models were the preferred choice to report, but in situations where the assumption of a shared mean for fixed-effects model were clearly not met, even after appropriate pre-specified subgroup analyses were conducted, random-effects results are presented. Fixed-effects models were deemed to be inappropriate if one or both of the following conditions was met:
- Significant between study heterogeneity in methodology, population, predictors or outcomes was identified by the reviewer in advance of data analysis. This decision would need to be made and recorded before any data analysis is undertaken.
- The presence of significant statistical heterogeneity, defined as I2≥50%.
Meta-analyses were performed in Cochrane Review Manager v5.3.
Minimal clinically important differences
The Guideline Committee were asked to prospectively specify any outcomes where they felt a consensus MID could be defined from their experience.
The Guideline Committee agreed to use a MID of 10% as a starting point for discussion of association between predictors and outcomes. The same parameter was used as a starting point to assess imprecision.
Modified GRADE for predictive evidence
GRADE has not been developed for use with predictive studies; therefore a modified approach was applied using the GRADE framework. Data from cohort studies was initially rated as high quality, data from case-control studies as low quality, with the quality of the evidence for each outcome then downgraded or not from this initial point. Cross-sectional studies were only included for evidence on neurodevelopmental disorders, and were initially rated as high quality because it was expected that studies reporting on ASD and neurodevelopmental disorders were likely to diagnose both conditions at the same time, and this study design was felt to be appropriate to address the review question, as it focuses only on association rather than causation.
Table 2Rationale for downgrading quality of evidence for predictive modelling questions
| GRADE criteria | Reasons for downgrading quality |
|---|---|
| Risk of bias |
Not serious: If less than 33.3% of the weight in a meta-analysis came from studies at moderate or high risk of bias, the overall outcome was not downgraded. Serious: If greater than 33.3% of the weight in a meta-analysis came from studies at moderate or high risk of bias, the outcome was downgraded one level. Very serious: If greater than 33.3% of the weight in a meta-analysis came from studies at high risk of bias, the outcome was downgraded two levels. Outcomes meeting the criteria for downgrading above were not downgraded if there was evidence the effect size was not meaningfully different between studies at high and low risk of bias. In addition, unadjusted odds ratio outcomes from univariate analyses were downgraded one level, in addition to any downgrading for risk of bias in individual studies. Adjusted odds ratios from multivariate analyses were not similarly downgraded. |
| Indirectness |
Not serious: If less than 33.3% of the weight in a meta-analysis came from partially indirect or indirect studies, the overall outcome was not downgraded. Serious: If greater than 33.3% of the weight in a meta-analysis came from partially indirect or indirect studies, the outcome was downgraded one level. Very serious: If greater than 33.3% of the weight in a meta-analysis came from indirect studies, the outcome was downgraded two levels. Outcomes meeting the criteria for downgrading above were not downgraded if there was evidence the effect size was not meaningfully different between direct and indirect studies. |
| Inconsistency |
Concerns about inconsistency of effects across studies, occurring when there is unexplained variability in the association strength demonstrated across studies (heterogeneity). This was assessed using the I2 statistic. N/A: Inconsistency was marked as not applicable if data on the outcome was only available from one study. Not serious: If the I2 was less than 33.3%, the outcome was not downgraded. Serious: If the I2 was between 33.3% and 66.7%, the outcome was downgraded one level. Very serious: If the I2 was greater than 66.7%, the outcome was downgraded two levels. Outcomes meeting the criteria for downgrading above were not downgraded if there was evidence the effect size was not meaningfully different between studies with the smallest and largest effect sizes. |
| Imprecision |
If an MID other than the line of no effect was defined for the outcome, the outcome was downgraded once if the 95% confidence interval for the effect size crossed one line of the MID, and twice if it crosses both lines of the MID. If the line of no effect was defined as an MID for the outcome, it was downgraded once if the 95% confidence interval for the effect size crossed the line of no effect (i.e. the outcome was not statistically significant), and twice if the sample size of the study was sufficiently small that it is not plausible any realistic effect size could have been detected. Outcomes meeting the criteria for downgrading above were not downgraded if the confidence interval was sufficiently narrow that the upper and lower bounds would correspond to clinically equivalent scenarios. |
The quality of evidence for each outcome was upgraded if either of the following conditions were met:
- Data showing an effect size sufficiently large that it cannot be explained by confounding alone.
- Data where all plausible residual confounding is likely to increase our confidence in the effect estimate.
Publication bias
Publication bias was assessed in two ways. First, if evidence of conducted but unpublished studies was identified during the review (e.g. conference abstracts or protocols without accompanying published data), available information on these unpublished studies was reported as part of the review. Secondly, where 10 or more studies were included as part of a single meta-analysis, a funnel plot was produced to graphically assess the potential for publication bias.
Appendix C. Literature search strategies
Search summary
The search strategies were based on the population strategy used in CG128, (appendix F, page 34). The final cut-off date for searches in the original guideline was 11 October 2010 (page 41). A date limit was added to the new strategies to reflect this.
The clinical searches were conducted in July 2017.
Sources searched for this guideline are shown below.
| Databases | Date searched | Version/files |
|---|---|---|
| Cochrane Central Register of Controlled Trials (CENTRAL) | 06/07/17 | Issue 6 of 12, June 2017 |
| Cochrane Database of Systematic Reviews (CDSR) | 06/07/17 | Issue 7 of 12, July 2017 |
| Database of Abstracts of Reviews of Effect (DARE) | 06/07/17 | Issue 2 of 4, April 2015 |
| Embase (Ovid) | 06/07/17 | 1980 to 2017 Week 27 |
| Health Technology Assessment Database (HTA) | 06/07/17 | Issue 4 of 4, October 2016 |
| MEDLINE (Ovid) | 06/07/17 | 1946 to June Week 5 2017 |
| MEDLINE In-Process (Ovid) | 06/07/17 | June 29, 2017 |
| PsycINFO (Ovid) | 06/07/17 | 2002 to June Week 4 2017 |
| PubMed | 06/07/17 | n/a |
Clinical search strategy (Medline)
The MEDLINE search strategy is presented below. It was translated for use in all other databases.
| Database: Medline | |
|---|---|
| 1 | Autistic Disorder/ |
| 2 | Autism Spectrum Disorder/ |
| 3 | asperger syndrome/ |
| 4 | (autistic or autism or kanner* or asperger*).tw. |
| 5 | child development disorders, pervasive/ |
| 6 | ((pervasive* or child* or young* or youth*) adj2 (development* or neurodevelopmental*) adj2 disorder*).tw. |
| 7 | (ASD or PDD or PDD-NOS).tw. |
| 8 | or/1-7 |
| 9 | (2010* or 2011* or 2012* or 2013* or 2014* or 2015* or 2016* or 2017*).ed. (6224514) |
| 10 | 8 and 9 |
| 11 | Observational Studies as Topic/ |
| 12 | Observational Study/ |
| 13 | Epidemiologic Studies/ |
| 14 | exp Case-Control Studies/ |
| 15 | exp Cohort Studies/ |
| 16 | Cross-Sectional Studies/ |
| 17 | Interrupted Time Series Analysis/ |
| 18 | case control$.tw. |
| 19 | (cohort adj (study or studies)).tw. |
| 20 | cohort analy$.tw. |
| 21 | (follow up adj (study or studies)).tw. |
| 22 | (observational adj (study or studies)).tw. |
| 23 | longitudinal.tw. |
| 24 | prospective.tw. |
| 25 | retrospective.tw. |
| 26 | cross sectional.tw. |
| 27 | or/11-26 |
| 28 | exp Risk/ |
| 29 | (Risk* adj1 (factor* or assess*)).tw. |
| 30 | Logistic* model*.tw. |
| 31 | Protective* factor*.tw. |
| 32 | (association* or regression*).tw. |
| 33 | or/28-32 |
| 34 | 27 or 33 |
| 35 | 10 and 34 |
| 36 | Comment/ or Letter/ or Editorial/ or Historical article/ or (conference abstract or conference paper or “conference review” or letter or editorial or case report).pt. |
| 37 | 35 not 36 |
| 38 | Animals/ not Humans/ |
| 39 | 37 not 38 |
| 40 | limit 39 to english language |
Appendix D. Clinical evidence study selection
Appendix E. Clinical evidence tables
Download PDF (487K)
Appendix F. GRADE tables
Outcome: ASD diagnosis >6 years of age
| No. of studies | Study design | Sample size | Effect size (95% CI) | Absolute risk: unexposed | Absolute risk: exposed (95% CI) | Risk of bias | Inconsistency | Indirectness | Imprecision | Quality |
|---|---|---|---|---|---|---|---|---|---|---|
| ADHD before ASD (reference category: ASD only) | ||||||||||
| 1 (Miodovnik 2015) | Cross-sectional | 1,059 | aOR 16.7 (7.03, 39.7) | N/E | N/E | Very serious1 | N/A | Not serious | Not serious | LOW |
| ADHD same/after ASD (reference category: ASD only) | ||||||||||
| 1 (Miodovnik 2015) | Cross-sectional | 1,138 | aOR 0.57 (0.28, 1.15) | N/E | N/E | Very serious1 | N/A | Not serious | Very serious2 | VERY LOW |
- 1
Study rated as being at high risk of bias.
- 2
95% confidence interval crosses both ends of a defined MID interval.
aOR: adjusted odds ratio; N/E: not extractable (data was not reported in an extractable format).
Appendix G. Forest plots
Outcome: Clinical diagnosis of ASD; Predictor: ADHD
Appendix H. Excluded studies
| Author (year) | Title | Reason for exclusion |
|---|---|---|
| Abel (2013) | Deviance in fetal growth and risk of autism spectrum disorder. |
|
| Alkandari (2015) | Fetal ultrasound measurements and associations with postnatal outcomes in infancy and childhood: a systematic review of an emerging literature |
|
| Arvidsson (1997) | Autism in 3-6-Year-Old Children in a Suburb of Goteborg, Sweden |
|
| Atladottir (2016) | Gestational Age and Autism Spectrum Disorder: Trends in Risk Over Time |
|
| Bagal (2016) | To study the age of recognition of symptoms and their correlates in children diagnosed with autism spectrum disorders: A retrospective study |
|
| Bakare (2012) | Prevalence of autism spectrum disorder among Nigerian children with intellectual disability: A stopgap assessment |
|
| Bay (2013) | Assisted reproduction and child neurodevelopmental outcomes: A systematic review |
|
| Bay (2014) | Fertility treatment: long-term growth and mental development of the children |
|
| Ben (2011) | Advanced parental ages and low birth weight in autism spectrum disorders--rates and effect on functioning |
|
| Boulet (2011) | Birth Weight and Health and Developmental Outcomes in US Children, 1997-2005 |
|
| Bowers (2015) | Phenotypic differences in individuals with autism spectrum disorder born preterm and at term gestation |
|
| Brock (2010) | Distinguishing features of autism in boys with fragile X syndrome |
|
| Brown (2017) | The association between antenatal exposure to selective serotonin reuptake inhibitors and autism: A systematic review and meta-analysis |
|
| Bryson (2008) | Prevalence of autism among adolescents with intellectual disabilities. |
|
| Canals (2016) | ADHD Prevalence in Spanish Preschoolers: Comorbidity, Socio-Demographic Factors, and Functional Consequences |
|
| Caravella (2017) | Adaptive skill trajectories in infants with fragile X syndrome contrasted to typical controls and infants at high risk for autism |
|
| Cassimos (2016) | Perinatal and parental risk factors in an epidemiological study of children with autism spectrum disorder |
|
| Castro (2016) | Absence of evidence for increase in risk for autism or attention-deficit hyperactivity disorder following antidepressant exposure during pregnancy: a replication study |
|
| Catford (2017) | Long-term follow-up of intra-cytoplasmic sperm injection-conceived offspring compared with in vitro fertilization-conceived offspring: a systematic review of health outcomes beyond the neonatal period |
|
| Class (2014) | Fetal growth and psychiatric and socioeconomic problems: population-based sibling comparison |
|
| Clements (2015) | Prenatal antidepressant exposure is associated with risk for attention-deficit hyperactivity disorder but not autism spectrum disorder in a large health system |
|
| Close (2012) | Co-occurring conditions and change in diagnosis in autism spectrum disorders |
|
| Cochran (2015) | Contrasting age related changes in autism spectrum disorder phenomenology in Cornelia de Lange, Fragile X, and Cri du Chat syndromes: Results from a 2.5 year follow-up |
|
| Conti (2013) | Are children born after assisted reproductive technology at increased risk of autism spectrum disorders? A systematic review |
|
| Cooper (2014) | Autistic traits in children with ADHD index clinical and cognitive problems |
|
| Cornish (2013) | Do behavioural inattention and hyperactivity exacerbate cognitive difficulties associated with autistic symptoms? Longitudinal profiles in fragile X syndrome |
|
| Corsello (2007) | Between a ROC and a hard place: decision making and making decisions about using the SCQ. |
|
| Croen (2002) | Descriptive Epidemiology of Autism in a California Population: Who Is at Risk? |
|
| Croen (2011) | Antidepressant use during pregnancy and childhood autism spectrum disorders |
|
| Darcy-Mahoney (2016) | Probability of an Autism Diagnosis by Gestational Age |
|
| Darcy-Mahoney (2016) | Maternal and Neonatal Birth Factors Affecting the Age of ASD Diagnosis |
|
| David (2014) | Prevalence and characteristics of children with mild intellectual disability in a French county |
|
| Davidovitch (2015) | Late diagnosis of autism spectrum disorder after initial negative assessment by a multidisciplinary team |
|
| de Bildt (2005) | Prevalence of pervasive developmental disorders in children and adolescents with mental retardation. |
|
| de Bruin (2007) | High rates of psychiatric co-morbidity in PDD-NOS. |
|
| Dietz (2006) | Screening for autistic spectrum disorder in children aged 14-15 months. II: population screening with the Early Screening of Autistic Traits Questionnaire (ESAT). Design and general findings. |
|
| DiGuiseppi (2010) | Screening for autism spectrum disorders in children with Down syndrome: population prevalence and screening test characteristics |
|
| D’Onofrio (2013) | Preterm birth and mortality and morbidity: a population-based quasi-experimental study. |
|
| Duan (2014) | Perinatal and background risk factors for childhood autism in central China |
|
| Dudova (2014) | Comparison of three screening tests for autism in preterm children with birth weights less than 1,500 grams |
|
| Dudova (2014) | Screening for autism in preterm children with extremely low and very low birth weight |
|
| Ehlers (1999) | A screening questionnaire for Asperger syndrome and other high-functioning autism spectrum disorders in school age children. |
|
| El Marroun (2014) | Prenatal exposure to selective serotonin reuptake inhibitors and social responsiveness symptoms of autism: population-based study of young children. |
|
| El-Baz (2011) | Risk factors for autism: An Egyptian study |
|
| Emerson (2003) | Prevalence of psychiatric disorders in children and adolescents with and without intellectual disability |
|
| Emerson (2007) | Mental health of children and adolescents with intellectual disabilities in Britain. |
|
| Fevang (2016) | Mental health in children born extremely preterm without severe neurodevelopmental disabilities |
|
| Fountain (2015) | Association between assisted reproductive technology conception and autism in California, 1997-2007 |
|
| Frenette (2013) | Factors affecting the age at diagnosis of autism spectrum disorders in Nova Scotia, Canada |
|
| Frolli (2015) | Developmental changes in cognitive and behavioural functioning of adolescents with fragile-X syndrome |
|
| Gadow (2005) | Clinical significance of tics and attention-deficit hyperactivity disorder (ADHD) in children with pervasive developmental disorder. |
|
| Gadow (2016) | Clinical Correlates of Co-occurring Psychiatric and Autism Spectrum Disorder (ASD) Symptom-Induced Impairment in Children with ASD |
|
| Gardener (2011) | Perinatal and neonatal risk factors for autism: a comprehensive meta-analysis |
|
| Geier (2015) | A Prospective Longitudinal Assessment of Medical Records for Diagnostic Substitution among Subjects Diagnosed with a Pervasive Developmental Disorder in the United States |
|
| Geier (2017) | Neonatal factors among subjects diagnosed with a pervasive developmental disorder in the US |
|
| Gellec (2011) | Neurologic outcomes at school age in very preterm infants born with severe or mild growth restriction |
|
| Gentile (2015) | Prenatal antidepressant exposure and the risk of autism spectrum disorders in children. Are we looking at the fall of Gods? |
|
| Gidaya (2014) | In Utero Exposure to Selective Serotonin Reuptake Inhibitors and Risk for Autism Spectrum Disorder |
|
| Giltaij (2015) | Psychiatric diagnostic screening of social maladaptive behaviour in children with mild intellectual disability: differentiating disordered attachment and pervasive developmental disorder behaviour. |
|
| Goldin (2016) | Premature birth as a risk factor for autism spectrum disorder |
|
| Goldstein (2004) | The comorbidity of Pervasive Developmental Disorder and Attention Deficit Hyperactivity Disorder: results of a retrospective chart review. |
|
| Grandgeorge (2013) | Autism spectrum disorders: head circumference and body length at birth are both relative |
|
| Gray (2015) | Screening for autism spectrum disorder in very preterm infants during early childhood |
|
| Green (2015) | Autism spectrum disorder symptoms in children with ADHD: A community-based study. |
|
| Green (2016) | Association between autism symptoms and functioning in children with ADHD |
|
| Grefer (2016) | The emergence and stability of attention deficit hyperactivity disorder in boys with fragile X syndrome |
|
| Grether (2013) | Is Infertility Associated with Childhood Autism? |
|
| Guinchat (2012) | Pre-, peri- and neonatal risk factors for autism |
|
| Guy (2015) | Infants born late/moderately preterm are at increased risk for a positive autism screen at 2 years of age. |
|
| Hack (2009) | Behavioral outcomes of extremely low birth weight children at age 8 years. |
|
| Haglund (2011) | Risk factors for autism and Asperger syndrome |
|
| Haglund (2011) | Risk factors for autism and Asperger syndrome. Perinatal factors and migration |
|
| Harrington (2013) | Association of autism with maternal SSRi use during pregnancy |
|
| Harrington (2014) | Prenatal SSRI use and offspring with autism spectrum disorder or developmental delay |
|
| Hart (2013) | The longer-term health outcomes for children born as a result of IVF treatment. Part II--Mental health and development outcomes |
|
| Hassiotis (2012) | Mental health needs in adolescents with intellectual disabilities: cross-sectional survey of a service sample |
|
| Healy (2016) | Links between serotonin reuptake inhibition during pregnancy and neurodevelopmental delay/spectrum disorders: A systematic review of epidemiological and physiological evidence |
|
| Hoffmire (2014) | High prevalence of sleep disorders and associated comorbidities in a community sample of children with Down syndrome |
|
| Honda (2009) | Extraction and Refinement Strategy for detection of autism in 18-month-olds: a guarantee of higher sensitivity and specificity in the process of mass screening. |
|
| Hultman (2002) | Perinatal risk factors for infantile autism. |
|
| Hwang (2013) | Higher prevalence of autism in Taiwanese children born prematurely: a nationwide population-based study. |
|
| Imran (2012) | Children’s mental health: Pattern of referral, distribution of disorders and service use in child psychiatry outpatient setting |
|
| Indredavik (2010) | Perinatal risk and psychiatric outcome in adolescents born preterm with very low birth weight or term small for gestational age |
|
| Jacob (2016) | Co-morbidity in Attention-Deficit Hyperactivity Disorder: A Clinical Study from India |
|
| Jaspers (2013) | Early childhood assessments of community pediatric professionals predict autism spectrum and attention deficit hyperactivity problems |
|
| Jauhari (2012) | Comorbidities associated with intellectual disability among pediatric outpatients seen at a teaching hospital in Northern India |
|
| Jensen (2015) | Comorbid mental disorders in children and adolescents with attention-deficit/hyperactivity disorder in a large nationwide study |
|
| Johnson (2010) | Psychiatric disorders in extremely preterm children: longitudinal finding at age 11 years in the EPICure study. |
|
| Johnson (2010) | Autism spectrum disorders in extremely preterm children. |
|
| Johnson (2011) | Screening for autism in preterm children: diagnostic utility of the Social Communication Questionnaire |
|
| Johnson (2016) | Preschool outcomes following prenatal serotonin reuptake inhibitor exposure: differences in language and behavior, but not cognitive function |
|
| Joseph (2017) | Prevalence and associated features of autism spectrum disorder in extremely low gestational age newborns at age 10 years |
|
| Kamowski-Shakibai (2015) | Parent-reported use of assisted reproduction technology, infertility, and incidence of autism spectrum disorders |
|
| Kamp-Becker (2009) | Dimensional structure of the autism phenotype: relations between early development and current presentation. |
|
| Kantzer (2013) | Autism in community pre-schoolers: developmental profiles. |
|
| Kaplan (2016) | Prenatal selective serotonin reuptake inhibitor use and the risk of autism spectrum disorder in children: A systematic review and meta-analysis |
|
| Karmel (2010) | Early medical and behavioral characteristics of NICU infants later classified with ASD |
|
| Kato (2016) | Extremely preterm infants small for gestational age are at risk for motor impairment at 3 years corrected age |
|
| Kaufmann (2017) | Autism spectrum disorder in fragile X syndrome: Cooccurring conditions and current treatment |
|
| Khandake (2014) | A population-based longitudinal study of childhood neurodevelopmental disorders, IQ and subsequent risk of psychotic experiences in adolescence |
|
| Kim (2000) | The Prevalence of Anxiety and Mood Problems among Children with Autism and Asperger Syndrome |
|
| Kim (2016) | Predictive Validity of the Modified Checklist for Autism in Toddlers (M-CHAT) Born Very Preterm. |
|
| Kobayashi (2016) | Autism spectrum disorder and prenatal exposure to selective serotonin reuptake inhibitors: A systematic review and meta-analysis |
|
| Kochhar (2011) | Autistic spectrum disorder traits in children with attention deficit hyperactivity disorder |
|
| Kommu (2017) | Profile of two hundred children with Autism Spectrum Disorder from a tertiary child and adolescent psychiatry centre |
|
| Kotte (2013) | Autistic traits in children with and without ADHD |
|
| Kuban (2009) | Positive Screening on the Modified Checklist for Autism in Toddlers (M-CHAT) in Extremely Low Gestational Age Newborns |
|
| Kuban (2016) | Girls and Boys Born before 28 Weeks Gestation: Risks of Cognitive, Behavioral, and Neurologic Outcomes at Age 10 Years |
|
| Kumar (2017) | Prevalence of autism spectrum disorders and its association with Epileptiform activity among children with intellectual disability in a tertiary centre |
|
| Lampi (2012) | Risk of autism spectrum disorders in low birth weight and small for gestational age infants. |
|
| Langridge (2013) | Maternal conditions and perinatal characteristics associated with autism spectrum disorder and intellectual disability |
|
| Larsson (2005) | Risk factors for autism: perinatal factors, parental psychiatric history, and socioeconomic status. |
|
| Leavey (2013) | Gestational age at birth and risk of autism spectrum disorders in Alberta, Canada |
|
| Lehti (2013) | Autism spectrum disorders in IVF children: a national case-control study in Finland |
|
| Levy (2010) | Autism spectrum disorder and co-occurring developmental, psychiatric, and medical conditions among children in multiple populations of the United States. |
|
| Leyfer (2006) | Comorbid psychiatric disorders in children with autism: interview development and rates of disorders. |
|
| Linsell (2016) | Prognostic Factors for Behavioral Problems and Psychiatric Disorders in Children Born Very Preterm or Very Low Birth Weight: A Systematic Review |
|
| Losh (2012) | Lower birth weight indicates higher risk of autistic traits in discordant twin pairs |
|
| Lyall (2012) | Fertility therapies, infertility and autism spectrum disorders in the Nurses’ Health Study II |
|
| Lyall (2013) | Infertility and its treatments in association with autism spectrum disorders: a review and results from the CHARGE study |
|
| Mackay (2013) | Obstetric factors and different causes of special educational need: retrospective cohort study of 407,503 schoolchildren |
|
| Magnúsdóttir (2016) | The impact of attention deficit/hyperactivity disorder on adaptive functioning in children diagnosed late with autism spectrum disorder—A comparative analysis |
|
| Maimburg (2006) | Perinatal risk factors and infantile autism |
|
| Malm (2012) | Prenatal exposure to selective serotonin reuptake inhibitors and infant outcome |
|
| Mamidala (2013) | Prenatal, perinatal and neonatal risk factors of Autism Spectrum Disorder: a comprehensive epidemiological assessment from India |
|
| Mamidala (2013) | Maternal hormonal interventions as a risk factor for Autism Spectrum Disorder: An epidemiological assessment from India |
|
| Man (2015) | Exposure to selective serotonin reuptake inhibitors during pregnancy and risk of autism spectrum disorder in children: a systematic review and meta-analysis of observational studies. |
|
| Mann (2010) | Pre-eclampsia, birth weight, and autism spectrum disorders |
|
| Mannion (2013) | An investigation of comorbid psychological disorders, sleep problems, gastrointestinal symptoms and epilepsy in children and adolescents with Autism Spectrum Disorder |
|
| Maramara (2014) | Pre- and perinatal risk factors for autism spectrum disorder in a New Jersey cohort |
|
| Mathewson (2017) | Mental health of extremely low birth weight survivors: A systematic review and meta-analysis |
|
| Mattila (2010) | Comorbid psychiatric disorders associated with Asperger syndrome/high-functioning autism: a community- and clinic-based study. |
|
| Meijerink (2016) | Behavioral, cognitive, and motor performance and physical development of five-year-old children who were born after intracytoplasmic sperm injection with the use of testicular sperm |
|
| Mezzacappa (2017) | Risk for autism spectrum disorders according to period of prenatal antidepressant exposure: A systematic review and meta-analysis |
|
| Mohammed (2016) | Incidence of autism in high risk neonatal follow up |
|
| Moore (2012) | Screening for autism in extremely preterm infants: problems in interpretation. |
|
| Moster (2008) | Long-term medical and social consequences of preterm birth. |
|
| Movsas (2012) | The Effect of Gestational Age on Symptom Severity in Children with Autism Spectrum Disorder |
|
| Mpaka (2016) | Prevalence and comorbidities of autism among children referred to the outpatient clinics for neurodevelopmental disorders |
|
| Nærland (2017) | Age and gender-related differences in emotional and behavioural problems and autistic features in children and adolescents with Down syndrome: a survey-based study of 674 individuals |
|
| Nilsen (2013) | Analysis of self-selection bias in a population-based cohort study of autism spectrum disorders |
|
| Nomura (2014) | A clinical study of attention-deficit/hyperactivity disorder in preschool children--prevalence and differential diagnoses |
|
| Oberman (2015) | Autism spectrum disorder in Phelan-McDermid syndrome: initial characterization and genotype-phenotype correlations |
|
| Oeseburg (2010) | Pervasive developmental disorder behavior in adolescents with intellectual disability and co-occurring somatic chronic diseases |
|
| Oeseburg (2010) | Prevalence of chronic diseases in adolescents with intellectual disability |
|
| Oeseburg (2011) | Prevalence of chronic health conditions in children with intellectual disability: a systematic literature review |
|
| Ortiz (2017) | Early warning signs of autism spectrum disorder in people with Down syndrome |
|
| Oshodi (2016) | Autism spectrum disorder in a community-based sample with neurodevelopmental problems in Lagos, Nigeria |
|
| Padilla (2016) | Intrinsic Functional Connectivity in Preterm Infants with Fetal Growth Restriction Evaluated at 12 Months Corrected Age |
|
| Pinto-Martin (2011) | Prevalence of autism spectrum disorder in adolescents born weighing <2000 grams. |
|
| Polyak (2015) | Comorbidity of intellectual disability confounds ascertainment of autism: implications for genetic diagnosis |
|
| Pondé (2010) | Frequency of symptoms of attention deficit and hyperactivity disorder in autistic children |
|
| Pringsheim (2013) | Social behavior and comorbidity in children with tics |
|
| Pritchard (2016) | Autism in Toddlers Born Very Preterm |
|
| Rais (2014) | Association Between Antidepressants Use During Pregnancy and Autistic Spectrum Disorders: A Meta-analysis |
|
| Rellini (2004) | Childhood Autism Rating Scale (CARS) and Autism Behavior Checklist (ABC) correspondence and conflicts with DSM-IV criteria in diagnosis of autism. |
|
| Rimal (2016) | Prevalence of attention deficit hyperactivity disorder among school children and associated co-morbidities - a hospital based descriptive study |
|
| Roberts (2012) | Heart activity and autistic behavior in infants and toddlers with fragile X syndrome |
|
| Roberts (2012) | Visual Attention and Autistic Behavior in Infants with Fragile X Syndrome |
|
| Roberts (2016) | Infant Development in Fragile X Syndrome: Cross-Syndrome Comparisons |
|
| Ryland (2012) | Autism spectrum symptoms in children with neurological disorders |
|
| Saemundsen (2013) | Prevalence of autism spectrum disorders in an Icelandic birth cohort |
|
| Saltik (2012) | Neurological disorders combined with autism in children |
|
| Sanmaneechai (2013) | Treatment outcomes of West syndrome in infants with Down syndrome |
|
| Scheirs (2009) | Differentiating among children with PDD-NOS, ADHD, and those with a combined diagnosis on the basis of WISC-III profiles. |
|
| Schieve (2014) | Population attributable fractions for three perinatal risk factors for autism spectrum disorders, 2002 and 2008 autism and developmental disabilities monitoring network |
|
| Schieve (2015) | Comparison of Perinatal Risk Factors Associated with Autism Spectrum Disorder (ASD), Intellectual Disability (ID), and Co-occurring ASD and ID |
|
| Schieve (2016) | Population impact of preterm birth and low birth weight on developmental disabilities in US children |
|
| Schrieken (2013) | Head circumference and height abnormalities in autism revisited: the role of pre- and perinatal risk factors |
|
| Shimada (2012) | Parental age and assisted reproductive technology in autism spectrum disorders, attention deficit hyperactivity disorder, and Tourette syndrome in a Japanese population |
|
| Simonoff (2008) | Psychiatric Disorders in Children With Autism Spectrum Disorders: Prevalence, Comorbidity, and Associated Factors in a Population-Derived Sample |
|
| Singh (2013) | Mental Health Outcomes in US Children and Adolescents Born Prematurely or with Low Birthweight |
|
| Skotko (2013) | Contributions of a specialty clinic for children and adolescents with Down syndrome |
|
| Srebnicki (2013) | Adolescent outcome of child ADHD in primary care setting: stability of diagnosis |
|
| Stahlberg (2010) | Mental health problems in youths committed to juvenile institutions: Prevalences and treatment needs |
|
| Stephens (2012) | Screening for autism spectrum disorders in extremely preterm infants. |
|
| Tonnsen (2016) | Prevalence of autism spectrum disorders among children with intellectual disability |
|
| Treyvaud (2013) | Psychiatric outcomes at age seven for very preterm children: rates and predictors. |
|
| Unenge (2012) | Is autism spectrum disorder common in schizophrenia? |
|
| Ververi (2012) | Clinical and laboratory data in a sample of Greek children with autism spectrum disorders |
|
| Wang (2017) | Prenatal, perinatal, and postnatal factors associated with autism: A meta-analysis |
|
| Webb (2003) | Prevalence of autistic spectrum disorder in children attending mainstream schools in a Welsh education authority. |
|
| Weisbrot (2005) | The presentation of anxiety in children with pervasive developmental disorders. |
|
| Williams (2008) | Perinatal and maternal risk factors for autism spectrum disorders in New South Wales, Australia. |
|
| Wong (2014) | Evaluation of early childhood social-communication difficulties in children born preterm using the Quantitative Checklist for Autism in Toddlers. |
|
| Worley (2011) | Prevalence of autism spectrum disorders in toddlers receiving early intervention services |
|
| Wu (2016) | Risk of Autism Associated With Hyperbilirubinemia and Phototherapy |
|
| Zachor (2011) | Assisted reproductive technology and risk for autism spectrum disorder |
|
| Zachor (2013) | Do risk factors for autism spectrum disorders affect gender representation? |
|
| Zhang (2010) | Prenatal and perinatal risk factors for autism in China |
|
| Zuckerman (2015) | Parental concerns, provider response, and timeliness of autism spectrum disorder diagnosis. |
|
Appendix I. References
Included studies
- Alexander Myriam, Petri Hans, Ding Yingjie, Wandel Christoph, Khwaja Omar, and Foskett Nadia (2016) Morbidity and medication in a large population of individuals with Down syndrome compared to the general population. Developmental medicine and child neurology 58, 246–54 [PubMed: 26282180]
- Bay B, Mortensen EL, Hvidtjorn D, and Kesmodel US (2013) Fertility treatment and risk of childhood and adolescent mental disorders: register based cohort study. BMJ (Clinical research ed.) 347, f3978 [PMC free article: PMC3702157] [PubMed: 23833075]
- Boukhris Takoua, Sheehy Odile, Mottron Laurent, and Berard Anick (2016) Antidepressant Use During Pregnancy and the Risk of Autism Spectrum Disorder in Children. JAMA pediatrics 170, 117–24 [PubMed: 26660917]
- Brown Hilary K, Ray Joel G, Wilton Andrew S, Lunsky Yona, Gomes Tara, and Vigod Simone N (2017) Association Between Serotonergic Antidepressant Use During Pregnancy and Autism Spectrum Disorder in Children. JAMA 317, 1544–1552 [PubMed: 28418480]
- Durkin MS, Maenner MJ, Newschaffer CJ, Lee LC, Cunniff CM, Daniels JL, Kirby RS, Leavitt L, Miller L, Zahorodny W, and Schieve LA (2008) Advanced parental age and the risk of autism spectrum disorder. American journal of epidemiology 168(11), 1268–76 [PMC free article: PMC2638544] [PubMed: 18945690]
- Elberling Hanne, Linneberg Allan, Rask Charlotte Ulrikka, Houman Tine, Goodman Robert, Mette Skovgaard, and Anne (2016) Psychiatric disorders in Danish children aged 5-7 years: A general population study of prevalence and risk factors from the Copenhagen Child Cohort (CCC 2000). Nordic journal of psychiatry 70, 146–55 [PubMed: 26509656]
- Ghirardi L, Brikell I, Kuja-Halkola R, Freitag C M, Franke B, Asherson P, Lichtenstein P, and Larsson H (2017) The familial co-aggregation of ASD and ADHD: a register-based cohort study. Molecular Psychiatry, 17, 1038 [PMC free article: PMC5794881] [PubMed: 28242872]
- Hvidtjørn D, Grove J, Schendel D, Schieve L A, Sværke C, Ernst E, and Thorsen P (2011) Risk of autism spectrum disorders in children born after assisted conception: a population-based follow-up study. Journal of Epidemiology & Community Health 65, 497–502 [PubMed: 20584728]
- Hviid Anders, Melbye Mads, and Pasternak Bjorn (2013) Use of selective serotonin reuptake inhibitors during pregnancy and risk of autism. The New England journal of medicine 369, 2406–15 [PubMed: 24350950]
- Joseph RM, Korzeniewski SJ, Allred EN, O’Shea TM, Heeren T, Frazier JA, Ware J, Hirtz D, Leviton A, and Kuban K (2017) Extremely low gestational age and very low birthweight for gestational age are risk factors for autism spectrum disorder in a large cohort study of 10-year-old children born at 23-27 weeks’ gestation.. American journal of obstetrics and gynecology 216(3), 304.e1–304.e16 [PMC free article: PMC5334372] [PubMed: 27847193]
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- Kuzniewicz MW, Wi S, Qian Y, Walsh EM, Armstrong MA, and Croen LA (2014) Prevalence and neonatal factors associated with autism spectrum disorders in preterm infants.. The Journal of pediatrics 164(1), 20–5 [PubMed: 24161222]
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- McCoy Brittany M, Rickert Martin E, Class Quetzal A, Larsson Henrik, Lichtenstein Paul, and D’Onofrio Brian M (2014) Mediators of the association between parental severe mental illness and offspring neurodevelopmental problems. Annals of epidemiology 24, 629–634.e1 [PMC free article: PMC4135008] [PubMed: 25037304]
- Miodovnik A, Harstad E, Sideridis G, and Huntington N (2015) Timing of the Diagnosis of Attention-Deficit/Hyperactivity Disorder and Autism Spectrum Disorder.. Pediatrics 136(4), e830–7 [PubMed: 26371198]
- Moore Gaea Schwaebe, Kneitel Anna Weber, Walker Cheryl K, Gilbert William M, and Xing Guibo (2012) Autism risk in small- and large-for-gestational-age infants. American journal of obstetrics and gynecology 206, 314.e1–9 [PMC free article: PMC9884028] [PubMed: 22464070]
- Pinborg A, Loft A, Schmidt L, Greisen G, Rasmussen S, and Andersen AN (2004) Neurological sequelae in twins born after assisted conception: controlled national cohort study.. BMJ (Clinical research ed.) 329(7461), 311 [PMC free article: PMC506847] [PubMed: 15256418]
- Rai D, Lee BK, Dalman C, Newschaffer C, Lewis G, and Magnusson C (2017) Antidepressants during pregnancy and autism in offspring: population based cohort study.. BMJ (Clinical research ed.) 358, j2811 [PMC free article: PMC5516223] [PubMed: 28724519]
- Russell Ginny, Rodgers Lauren, Ukoumunne Obioha, and Ford Tamsin (2014) Prevalence of Parent-Reported ASD and ADHD in the UK: Findings from the Millennium Cohort Study. Journal of Autism & Developmental Disorders 44, 31–40 [PubMed: 23719853]
- Sandin S, Nygren KG, Iliadou A, Hultman CM, and Reichenberg A (2013) Autism and mental retardation among offspring born after in vitro fertilization. JAMA 310(1), 75–84 [PubMed: 23821091]
- Sorensen Merete Juul, Gronborg Therese Koops, Christensen Jakob, Parner Erik Thorlund, Vestergaard Mogens, Schendel Diana, and Pedersen Lars Henning (2013) Antidepressant exposure in pregnancy and risk of autism spectrum disorders. Clinical epidemiology 5, 449–59 [PMC free article: PMC3832387] [PubMed: 24255601]
- Sujan Ayesha C, Rickert Martin E, Oberg A Sara, Quinn Patrick D, Hernandez-Diaz Sonia, Almqvist Catarina, Lichtenstein Paul, Larsson Henrik, and D’Onofrio Brian M (2017) Associations of Maternal Antidepressant Use During the First Trimester of Pregnancy With Preterm Birth, Small for Gestational Age, Autism Spectrum Disorder, and Attention-Deficit/Hyperactivity Disorder in Offspring. JAMA 317, 1553–1562 [PMC free article: PMC5875187] [PubMed: 28418479]
- Viktorin A, Uher R, Reichenberg A, Levine S Z, and Sandin S (2017) Autism risk following antidepressant medication during pregnancy. Psychol Med, 1–10 [PMC free article: PMC6421839] [PubMed: 28528584]
Excluded studies
- Abel KM, Dalman C, Svensson AC, Susser E, Dal H, Idring S, Webb RT, Rai D, and Magnusson C (2013) Deviance in fetal growth and risk of autism spectrum disorder.. The American journal of psychiatry 170(4), 391–8 [PubMed: 23545793]
- Alkandari Farah, Ellahi Awaiss, Aucott Lorna, Devereux Graham, and Turner Steve (2015) Fetal ultrasound measurements and associations with postnatal outcomes in infancy and childhood: a systematic review of an emerging literature. Journal of epidemiology and community health 69, 41–8 [PubMed: 25190820]
- Arvidsson T, Danielsson B, Forsberg P, Gillberg C, Johansson M, and Kjellgren G (1997) Autism in 3-6-Year-Old Children in a Suburb of Goteborg, Sweden. Autism 1(2), 163–173
- Atladottir H O, Schendel D E, Henriksen T B, Hjort L, and Parner E T (2016) Gestational Age and Autism Spectrum Disorder: Trends in Risk Over Time. Autism research: official journal of the International Society for Autism Research 9, 224–31 [PubMed: 26363410]
- Bagal R, Kadam K, and Parkar S (2016) To study the age of recognition of symptoms and their correlates in children diagnosed with autism spectrum disorders: A retrospective study. Journal of Indian Association for Child and Adolescent Mental Health 12, 291–308
- Bakare M O, Ebigbo P O, and Ubochi V N (2012) Prevalence of autism spectrum disorder among Nigerian children with intellectual disability: A stopgap assessment. Journal of Health Care for the Poor and Underserved 23, 513–518 [PubMed: 22643602]
- Bay B, Mortensen E L, and Kesmodel U S (2013) Assisted reproduction and child neurodevelopmental outcomes: A systematic review. Fertility and Sterility 100, 844–853 [PubMed: 23810272]
- Bay Bjorn (2014) Fertility treatment: long-term growth and mental development of the children. Danish medical journal 61, B4947 [PubMed: 25283630]
- Ben Itzchak, Esther, Lahat Eli, and Zachor Ditza A (2011) Advanced parental ages and low birth weight in autism spectrum disorders-rates and effect on functioning. Research in developmental disabilities 32, 1776–81 [PubMed: 21498045]
- Boulet Sheree, Schieve Laura, and Boyle Coleen (2011) Birth Weight and Health and Developmental Outcomes in US Children, 1997-2005. Maternal & Child Health Journal 15, 836–844 [PubMed: 19902344]
- Bowers Katherine, Wink Logan K, Pottenger Amy, McDougle Christopher J, and Erickson Craig (2015) Phenotypic differences in individuals with autism spectrum disorder born preterm and at term gestation. Autism: the international journal of research and practice 19, 758–63 [PubMed: 25192860]
- Brock M, and Hatton D (2010) Distinguishing features of autism in boys with fragile X syndrome. Journal of intellectual disability research: JIDR 54, 894–905 [PubMed: 20704635]
- Brown H K, Hussain-Shamsy N, Lunsky Y, Dennis C L. E, and Vigod S N (2017) The association between antenatal exposure to selective serotonin reuptake inhibitors and autism: A systematic review and meta-analysis. Journal of Clinical Psychiatry 78, e48–e58 [PubMed: 28129495]
- Bryson SE, Bradley EA, Thompson A, and Wainwright A (2008) Prevalence of autism among adolescents with intellectual disabilities. Canadian journal of psychiatry. Revue canadienne de psychiatrie 53(7), 449–59 [PubMed: 18674403]
- Canals J, Morales-Hidalgo P, Jane M C, and Domenech E (2016) ADHD Prevalence in Spanish Preschoolers: Comorbidity, Socio-Demographic Factors, and Functional Consequences. J Atten Disord, 359, 312–5 [PubMed: 27009923]
- Caravella K E, and Roberts J E (2017) Adaptive skill trajectories in infants with fragile X syndrome contrasted to typical controls and infants at high risk for autism. Research in Autism Spectrum Disorders 40, 1–12 [PMC free article: PMC5695720] [PubMed: 29170682]
- Cassimos D C, Syriopoulou-Delli C K, Tripsianis G I, and Tsikoulas I (2016) Perinatal and parental risk factors in an epidemiological study of children with autism spectrum disorder. International Journal of Developmental Disabilities 62, 108–116
- Castro V M, Kong S W, Clements C C, Brady R, Kaimal A J, Doyle A E, Robinson E B, Churchill S E, Kohane I S, and Perlis R H (2016) Absence of evidence for increase in risk for autism or attention-deficit hyperactivity disorder following antidepressant exposure during pregnancy: a replication study. Translational psychiatry 6, e708 [PMC free article: PMC5068870] [PubMed: 26731445]
- Catford S R, McLachlan R I, O’Bryan M K, and Halliday J L (2017) Long-term follow-up of intra-cytoplasmic sperm injection-conceived offspring compared with in vitro fertilization-conceived offspring: a systematic review of health outcomes beyond the neonatal period. Andrology, 5(4), 610–21 [PubMed: 28632930]
- Class Quetzal A, Rickert Martin E, Larsson Henrik, Lichtenstein Paul, and D’Onofrio Brian M (2014) Fetal growth and psychiatric and socioeconomic problems: population-based sibling comparison. The British journal of psychiatry: the journal of mental science 205, 355–61 [PMC free article: PMC4217026] [PubMed: 25257067]
- Clements C C, Castro V M, Blumenthal S R, Rosenfield H R, Murphy S N, Fava M, Erb J L, Churchill S E, Kaimal A J, Doyle A E, Robinson E B, Smoller J W, Kohane I S, and Perlis R H (2015) Prenatal antidepressant exposure is associated with risk for attention-deficit hyperactivity disorder but not autism spectrum disorder in a large health system. Molecular psychiatry 20, 727–34 [PMC free article: PMC4427538] [PubMed: 25155880]
- Close H A, Lee L C, Kaufmann C N, and Zimmerman A W (2012) Co-occurring conditions and change in diagnosis in autism spectrum disorders. Pediatrics 129, e305–e316 [PubMed: 22271695]
- Cochran Lisa, Moss Joanna, Nelson Lisa, and Oliver Chris (2015) Contrasting age related changes in autism spectrum disorder phenomenology in Cornelia de Lange, Fragile X, and Cri du Chat syndromes: Results from a 2.5 year follow-up. American journal of medical genetics. Part C, and Seminars in medical genetics 169, 188–97 [PubMed: 25989416]
- Conti E, Mazzotti S, Calderoni S, Saviozzi I, and Guzzetta A (2013) Are children born after assisted reproductive technology at increased risk of autism spectrum disorders? A systematic review. Human reproduction (Oxford, and England) 28, 3316–27 [PubMed: 24129612]
- Cooper Miriam, Martin Joanna, Langley Kate, Hamshere Marian, and Thapar Anita (2014) Autistic traits in children with ADHD index clinical and cognitive problems. European child & adolescent psychiatry 23, 23–34 [PMC free article: PMC3899449] [PubMed: 23616179]
- Cornish K, Cole V, Longhi E, Karmiloff-Smith A, and Scerif G (2013) Do behavioural inattention and hyperactivity exacerbate cognitive difficulties associated with autistic symptoms? Longitudinal profiles in fragile X syndrome. International Journal of Developmental Disabilities 59, 80–94
- Corsello C, Hus V, Pickles A, Risi S, Cook EH Jr, Leventhal BL, and Lord C (2007) Between a ROC and a hard place: decision making and making decisions about using the SCQ.. Journal of child psychology and psychiatry, and and allied disciplines 48(9), 932–40 [PubMed: 17714378]
- Croen Lisa A, Grether Judith K, and Selvin Steve (2002) Descriptive Epidemiology of Autism in a California Population: Who Is at Risk? Journal of Autism and Developmental Disorders 32(3), 217–224 [PubMed: 12108623]
- Croen Lisa A, Grether Judith K, Yoshida Cathleen K, Odouli Roxana, and Hendrick Victoria (2011) Antidepressant use during pregnancy and childhood autism spectrum disorders. Archives of general psychiatry 68, 1104–12 [PubMed: 21727247]
- Darcy-Mahoney Ashley, Minter Bonnie, Higgins Melinda, Guo Ying, Head Zauche, Lauren, and Hirst Jessica (2016) Maternal and Neonatal Birth Factors Affecting the Age of ASD Diagnosis. Newborn & Infant Nursing Reviews 16, 340–347 [PMC free article: PMC5630129] [PubMed: 28989330]
- Darcy-Mahoney Ashley, Minter Bonnie, Higgins Melinda, Guo Ying, Williams Bryan, Head Zauche, Lauren M, and Birth Katie (2016) Probability of an Autism Diagnosis by Gestational Age. Newborn & Infant Nursing Reviews 16, 322–326 [PMC free article: PMC5627777] [PubMed: 28989329]
- David M, Dieterich K, Billette de Villemeur, A, Jouk P S, Counillon J, Larroque B, Bloch J, and Cans C (2014) Prevalence and characteristics of children with mild intellectual disability in a French county. Journal of Intellectual Disability Research 58, 591–602 [PubMed: 23750884]
- Davidovitch Michael, Levit-Binnun Nava, Golan Dafna, and Manning-Courtney Patricia (2015) Late diagnosis of autism spectrum disorder after initial negative assessment by a multidisciplinary team. Journal of developmental and behavioral pediatrics: JDBP 36, 227–34 [PubMed: 25651066]
- de Bildt A, Sytema S, Kraijer D, and Minderaa R (2005) Prevalence of pervasive developmental disorders in children and adolescents with mental retardation.. Journal of child psychology and psychiatry, and and allied disciplines 46(3), 275–86 [PubMed: 15755304]
- de Bruin EI, Ferdinand RF, Meester S, de Nijs PF, and Verheij F (2007) High rates of psychiatric co-morbidity in PDD-NOS.. Journal of autism and developmental disorders 37(5), 877–86 [PubMed: 17031447]
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- DiGuiseppi Carolyn, Hepburn Susan, Davis Jonathan M, Fidler Deborah J, Hartway Sara, Lee Nancy Raitano, Miller Lisa, Ruttenber Margaret, and Robinson Cordelia (2010) Screening for autism spectrum disorders in children with Down syndrome: population prevalence and screening test characteristics. Journal of developmental and behavioral pediatrics : JDBP 31, 181–91 [PMC free article: PMC4419691] [PubMed: 20375732]
- D’Onofrio BM, Class QA, Rickert ME, Larsson H, Langstrom N, and Lichtenstein P (2013) Preterm birth and mortality and morbidity: a population-based quasi-experimental study.. JAMA psychiatry 70(11), 1231–40 [PMC free article: PMC3823714] [PubMed: 24068297]
- Duan Guiqin, Yao Meiling, Ma Yating, and Zhang Wenjing (2014) Perinatal and background risk factors for childhood autism in central China. Psychiatry research 220, 410–7 [PubMed: 25085792]
- Dudova I, Kasparova M, Markova D, Zemankova J, Beranova S, Urbanek T, and Hrdlicka M (2014) Screening for autism in preterm children with extremely low and very low birth weight. Neuropsychiatric Disease and Treatment 10, 277–282 [PMC free article: PMC3931701] [PubMed: 24627633]
- Dudova Iva, Markova Daniela, Kasparova Martina, Zemankova Jana, Beranova Stepanka, Urbanek Tomas, and Hrdlicka Michal (2014) Comparison of three screening tests for autism in preterm children with birth weights less than 1,500 grams. Neuropsychiatric Disease and Treatment 10, [PMC free article: PMC4240186] [PubMed: 25484588]
- Ehlers S, Gillberg C, and Wing L (1999) A screening questionnaire for Asperger syndrome and other high-functioning autism spectrum disorders in school age children. Journal of autism and developmental disorders 29(2), 129–41 [PubMed: 10382133]
- El Marroun H, White TJ, van der Knaap NJ, Homberg JR, Fernandez G, Schoemaker NK, Jaddoe VW, Hofman A, Verhulst FC, Hudziak JJ, Stricker BH, and Tiemeier H (2014) Prenatal exposure to selective serotonin reuptake inhibitors and social responsiveness symptoms of autism: population-based study of young children.. The British journal of psychiatry : the journal of mental science 205(2), 95–102 [PubMed: 25252317]
- El-Baz F, Ismael N A, and El-Din S M. N (2011) Risk factors for autism: An Egyptian study. Egyptian Journal of Medical Human Genetics
- Emerson E (2003) Prevalence of psychiatric disorders in children and adolescents with and without intellectual disability. Journal of Intellectual Disability Research 47(1), 51–58 [PubMed: 12558695]
- Emerson E, and Hatton C (2007) Mental health of children and adolescents with intellectual disabilities in Britain.. The British journal of psychiatry: the journal of mental science 191, 493–9 [PubMed: 18055952]
- Fevang S K. E, Hysing M, Markestad T, and Sommerfelt K (2016) Mental health in children born extremely preterm without severe neurodevelopmental disabilities. Pediatrics 137, [PubMed: 26944946]
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- Frenette Priscilla, Dodds Linda, MacPherson Kathleen, Flowerdew Gordon, Hennen Brian, and Bryson Susan (2013) Factors affecting the age at diagnosis of autism spectrum disorders in Nova Scotia, Canada. Autism : the international journal of research and practice 17, 184–95 [PubMed: 21788254]
- Frolli A, Piscopo S, and Conson M (2015) Developmental changes in cognitive and behavioural functioning of adolescents with fragile-X syndrome. Journal of intellectual disability research: JIDR 59, 613–21 [PubMed: 25160119]
- Gadow KD, and DeVincent CJ (2005) Clinical significance of tics and attention-deficit hyperactivity disorder (ADHD) in children with pervasive developmental disorder.. Journal of child neurology 20(6), 481–8 [PubMed: 15996396]
- Gadow Kenneth D, Perlman Greg, Ramdhany Lianne, de Ruiter, and Janneke (2016) Clinical Correlates of Co-occurring Psychiatric and Autism Spectrum Disorder (ASD) Symptom-Induced Impairment in Children with ASD. Journal of abnormal child psychology 44, 129–39 [PubMed: 25640910]
- Gardener Hannah, Spiegelman Donna, and Buka Stephen L (2011) Perinatal and neonatal risk factors for autism: a comprehensive meta-analysis. Pediatrics 128, 344–55 [PMC free article: PMC3387855] [PubMed: 21746727]
- Geier David A, Kern Janet K, Hooker Brian S, Sykes Lisa K, and Geier Mark R (2015) A Prospective Longitudinal Assessment of Medical Records for Diagnostic Substitution among Subjects Diagnosed with a Pervasive Developmental Disorder in the United States. Frontiers in pediatrics 3, 85 [PMC free article: PMC4600915] [PubMed: 26528457]
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- Gellec Isaubelle, Lapillonne Alexandre, Renolleau Sylvain, Charlaluk Marie-Laure, Roze Jean-Christophe, Marret Stephane, Vieux Rachel, Monique Kaminski, and Ancel Pierre-Yves (2011) Neurologic outcomes at school age in very preterm infants born with severe or mild growth restriction. Pediatrics 127, e883–e891 [PubMed: 21382951]
- Gentile Salvatore (2015) Prenatal antidepressant exposure and the risk of autism spectrum disorders in children. Are we looking at the fall of Gods?. Journal of affective disorders 182, 132–7 [PubMed: 25985383]
- Gidaya Nicole, Lee Brian, Burstyn Igor, Yudell Michael, Mortensen Erik, and Newschaffer Craig (2014) In Utero Exposure to Selective Serotonin Reuptake Inhibitors and Risk for Autism Spectrum Disorder. Journal of Autism & Developmental Disorders 44, 2558–2567 [PubMed: 24803368]
- Giltaij HP, Sterkenburg PS, and Schuengel C (2015) Psychiatric diagnostic screening of social maladaptive behaviour in children with mild intellectual disability: differentiating disordered attachment and pervasive developmental disorder behaviour. Journal of intellectual disability research : JIDR 59(2), 138–49 [PubMed: 23906477]
- Goldin Rachel L, and Matson Johnny L (2016) Premature birth as a risk factor for autism spectrum disorder. Developmental neurorehabilitation 19, 203–6 [PubMed: 25992682]
- Goldstein S, and Schwebach AJ (2004) The comorbidity of Pervasive Developmental Disorder and Attention Deficit Hyperactivity Disorder: results of a retrospective chart review. Journal of autism and developmental disorders 34(3), 329–39 [PubMed: 15264500]
- Grandgeorge Marine, Lemonnier Eric, and Jallot Nelle (2013) Autism spectrum disorders: head circumference and body length at birth are both relative. Acta paediatrica (Oslo, and Norway: 1992) 102, 901–7 [PubMed: 23581647]
- Gray Peter H, Edwards Dawn M, O’Callaghan Michael J, and Gibbons Kristen (2015) Screening for autism spectrum disorder in very preterm infants during early childhood. Early human development 91, 271–6 [PubMed: 25766314]
- Green JL, Rinehart N, Anderson V, Nicholson JM, Jongeling B, and Sciberras E (2015) Autism spectrum disorder symptoms in children with ADHD: A community-based study.. Research in developmental disabilities 47, 175–84 [PubMed: 26433184]
- Green Jessica Leigh, Sciberras Emma, Anderson Vicki, Efron Daryl, and Rinehart Nicole (2016) Association between autism symptoms and functioning in children with ADHD. Archives of disease in childhood 101, 922–8 [PubMed: 27117836]
- Grefer M, Flory K, Cornish K, Hatton D, and Roberts J (2016) The emergence and stability of attention deficit hyperactivity disorder in boys with fragile X syndrome. Journal of intellectual disability research: JIDR 60, 167–78 [PMC free article: PMC4720543] [PubMed: 26610738]
- Grether Judith, Qian Yinge, Croughan Mary, Wu Yvonne, Schembri Michael, Camarano Loretta, and Croen Lisa (2013) Is Infertility Associated with Childhood Autism? Journal of Autism & Developmental Disorders 43, 663–672 [PubMed: 22777105]
- Guinchat Vincent, Thorsen Poul, Laurent Claudine, Cans Christine, Bodeau Nicolas, and Cohen David (2012) Pre-, peri- and neonatal risk factors for autism. Acta obstetricia et gynecologica Scandinavica 91, 287–300 [PubMed: 22085436]
- Guy A, Seaton SE, Boyle EM, Draper ES, Field DJ, Manktelow BN, Marlow N, Smith LK, and Johnson S (2015) Infants born late/moderately preterm are at increased risk for a positive autism screen at 2 years of age. The Journal of pediatrics 166(2), 269–75.e3 [PubMed: 25477165]
- Hack M, Taylor HG, Schluchter M, Andreias L, Drotar D, and Klein N (2009) Behavioral outcomes of extremely low birth weight children at age 8 years. Journal of developmental and behavioral pediatrics: JDBP 30(2), 122–30 [PMC free article: PMC3074440] [PubMed: 19322106]
- Haglund Nils G. S, and Kallen Karin B. M (2011) Risk factors for autism and Asperger syndrome. Perinatal factors and migration. Autism: the international journal of research and practice 15, 163–83 [PubMed: 20923887]
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Final
This evidence review was developed by the NICE Guideline Updates Team
Disclaimer: The recommendations in this guideline represent the view of NICE, arrived at after careful consideration of the evidence available. When exercising their judgement, professionals are expected to take this guideline fully into account, alongside the individual needs, preferences and values of their patients or service users. The recommendations in this guideline are not mandatory and the guideline does not override the responsibility of healthcare professionals to make decisions appropriate to the circumstances of the individual patient, in consultation with the patient and/or their carer or guardian.
Local commissioners and/or providers have a responsibility to enable the guideline to be applied when individual health professionals and their patients or service users wish to use it. They should do so in the context of local and national priorities for funding and developing services, and in light of their duties to have due regard to the need to eliminate unlawful discrimination, to advance equality of opportunity and to reduce health inequalities. Nothing in this guideline should be interpreted in a way that would be inconsistent with compliance with those duties.
NICE guidelines cover health and care in England. Decisions on how they apply in other UK countries are made by ministers in the Welsh Government, Scottish Government, and Northern Ireland Executive. All NICE guidance is subject to regular review and may be updated or withdrawn.
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