The prevalence and profile of autism in individuals born preterm: a systematic review and meta-analysis

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Abstract

Introduction

Preterm birth (<37 weeks) adversely affects development in behavioural, cognitive and mental health domains. Heightened rates of autism are identified in preterm populations, indicating that prematurity may confer an increased likelihood of adverse neurodevelopmental outcomes. The present meta-analysis aims to synthesise existing literature and calculate pooled prevalence estimates for rates of autism characteristics in preterm populations.

Methods

Search terms were generated from inspection of relevant high-impact papers and a recent meta-analysis. Five databases were searched from database creation until December 2020 with PRISMA guidelines followed throughout.

Results

10,900 papers were retrieved, with 52 papers included in the final analyses, further classified by assessment method (screening tools N=30, diagnostic assessment N=29). Pooled prevalence estimates for autism in preterm samples was 20% when using screening tools and 6% when using diagnostic assessments. The odds of an autism diagnosis were 3.3 times higher in individuals born preterm than in the general population.

Conclusions

The pooled prevalence estimate of autism characteristics in individuals born preterm is considerably higher than in the general population. Findings highlight the clinical need to provide further monitoring and support for individuals born preterm.

Supplementary Information

The online version contains supplementary material available at 10.1186/s11689-021-09382-1.

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Most cited references 69

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A basic introduction to fixed-effect and random-effects models for meta-analysis.

Michael R. Borenstein, Larry Hedges, Julian P.T. Higgins … (2010)

There are two popular statistical models for meta-analysis, the fixed-effect model and the random-effects model. The fact that these two models employ similar sets of formulas to compute statistics, and sometimes yield similar estimates for the various parameters, may lead people to believe that the models are interchangeable. In fact, though, the models represent fundamentally different assumptions about the data. The selection of the appropriate model is important to ensure that the various statistics are estimated correctly. Additionally, and more fundamentally, the model serves to place the analysis in context. It provides a framework for the goals of the analysis as well as for the interpretation of the statistics. In this paper we explain the key assumptions of each model, and then outline the differences between the models. We conclude with a discussion of factors to consider when choosing between the two models. Copyright © 2010 John Wiley & Sons, Ltd. Copyright © 2010 John Wiley & Sons, Ltd.

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Prevalence of Autism Spectrum Disorder Among Children Aged 8 Years — Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2016

Matthew J Maenner, Kelly Shaw, Jon Baio … (2020)

Problem/Condition Autism spectrum disorder (ASD). Period Covered 2016. Description of System The Autism and Developmental Disabilities Monitoring (ADDM) Network is an active surveillance program that provides estimates of the prevalence of ASD among children aged 8 years whose parents or guardians live in 11 ADDM Network sites in the United States (Arizona, Arkansas, Colorado, Georgia, Maryland, Minnesota, Missouri, New Jersey, North Carolina, Tennessee, and Wisconsin). Surveillance is conducted in two phases. The first phase involves review and abstraction of comprehensive evaluations that were completed by medical and educational service providers in the community. In the second phase, experienced clinicians who systematically review all abstracted information determine ASD case status. The case definition is based on ASD criteria described in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. Results For 2016, across all 11 sites, ASD prevalence was 18.5 per 1,000 (one in 54) children aged 8 years, and ASD was 4.3 times as prevalent among boys as among girls. ASD prevalence varied by site, ranging from 13.1 (Colorado) to 31.4 (New Jersey). Prevalence estimates were approximately identical for non-Hispanic white (white), non-Hispanic black (black), and Asian/Pacific Islander children (18.5, 18.3, and 17.9, respectively) but lower for Hispanic children (15.4). Among children with ASD for whom data on intellectual or cognitive functioning were available, 33% were classified as having intellectual disability (intelligence quotient [IQ] ≤70); this percentage was higher among girls than boys (40% versus 32%) and among black and Hispanic than white children (47%, 36%, and 27%, respectively). Black children with ASD were less likely to have a first evaluation by age 36 months than were white children with ASD (40% versus 45%). The overall median age at earliest known ASD diagnosis (51 months) was similar by sex and racial and ethnic groups; however, black children with IQ ≤70 had a later median age at ASD diagnosis than white children with IQ ≤70 (48 months versus 42 months). Interpretation The prevalence of ASD varied considerably across sites and was higher than previous estimates since 2014. Although no overall difference in ASD prevalence between black and white children aged 8 years was observed, the disparities for black children persisted in early evaluation and diagnosis of ASD. Hispanic children also continue to be identified as having ASD less frequently than white or black children. Public Health Action These findings highlight the variability in the evaluation and detection of ASD across communities and between sociodemographic groups. Continued efforts are needed for early and equitable identification of ASD and timely enrollment in services.

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Identification and evaluation of children with autism spectrum disorders.

C P Johnson, S Myers (2007)

Autism spectrum disorders are not rare; many primary care pediatricians care for several children with autism spectrum disorders. Pediatricians play an important role in early recognition of autism spectrum disorders, because they usually are the first point of contact for parents. Parents are now much more aware of the early signs of autism spectrum disorders because of frequent coverage in the media; if their child demonstrates any of the published signs, they will most likely raise their concerns to their child's pediatrician. It is important that pediatricians be able to recognize the signs and symptoms of autism spectrum disorders and have a strategy for assessing them systematically. Pediatricians also must be aware of local resources that can assist in making a definitive diagnosis of, and in managing, autism spectrum disorders. The pediatrician must be familiar with developmental, educational, and community resources as well as medical subspecialty clinics. This clinical report is 1 of 2 documents that replace the original American Academy of Pediatrics policy statement and technical report published in 2001. This report addresses background information, including definition, history, epidemiology, diagnostic criteria, early signs, neuropathologic aspects, and etiologic possibilities in autism spectrum disorders. In addition, this report provides an algorithm to help the pediatrician develop a strategy for early identification of children with autism spectrum disorders. The accompanying clinical report addresses the management of children with autism spectrum disorders and follows this report on page 1162 [available at www.pediatrics.org/cgi/content/full/120/5/1162]. Both clinical reports are complemented by the toolkit titled "Autism: Caring for Children With Autism Spectrum Disorders: A Resource Toolkit for Clinicians," which contains screening and surveillance tools, practical forms, tables, and parent handouts to assist the pediatrician in the identification, evaluation, and management of autism spectrum disorders in children.

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Author and article information

Contributors

Catherine Laverty:

ORCID: http://orcid.org/0000-0003-1101-3942

cml704@student.bham.ac.uk

Andrew Surtees: A.Surtees@bham.ac.uk

Rory O’Sullivan: R.OSullivan-17@student.lboro.ac.uk

Daniel Sutherland: DLS740@student.bham.ac.uk

Christopher Jones: C.A.Jones@bham.ac.uk

Caroline Richards: C.R.Richards@bham.ac.uk

Journal

Journal ID (nlm-ta): J Neurodev Disord

Journal ID (iso-abbrev): J Neurodev Disord

Title: Journal of Neurodevelopmental Disorders

Publisher: BioMed Central (London )

ISSN (Print): 1866-1947

ISSN (Electronic): 1866-1955

Publication date (Electronic): 21 September 2021

Publication date PMC-release: 21 September 2021

Publication date Collection: 2021

Volume: 13

Electronic Location Identifier: 41

Affiliations

[1 ]GRID grid.6572.6, ISNI 0000 0004 1936 7486, School of Psychology, , University of Birmingham, ; Birmingham, B15 2TT UK

[2 ]GRID grid.498025.2, Forward Thinking Birmingham, , Birmingham Women’s and Children’s NHS Foundation Trust, ; Birmingham, UK

[3 ]GRID grid.6571.5, ISNI 0000 0004 1936 8542, School of Psychology, , Loughborough University, ; Loughborough, LE11 3TU UK

Author information

Catherine Laverty http://orcid.org/0000-0003-1101-3942

Article

Publisher ID: 9382

DOI: 10.1186/s11689-021-09382-1

PMC ID: 8454175

PubMed ID: 34548007

SO-VID: f234c5b5-2765-4304-970f-fa98e93a19be

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 1 June 2021

Date accepted : 16 August 2021

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ScienceOpen disciplines: Neurosciences

Keywords: autism,prematurity,meta-analysis,preterm,low birth weight

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ScienceOpen disciplines: Neurosciences

Keywords: autism, prematurity, meta-analysis, preterm, low birth weight

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The prevalence and profile of autism in individuals born preterm: a systematic review and meta-analysis

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Abstract

Introduction

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Results

Conclusions

Supplementary Information

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Most cited references 69

A basic introduction to fixed-effect and random-effects models for meta-analysis.

Prevalence of Autism Spectrum Disorder Among Children Aged 8 Years — Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2016

Identification and evaluation of children with autism spectrum disorders.

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Cited by 7

Most referenced authors 980