As expected, a large amount of variation in prevalence across studies was found by graphical representation of estimates and by indices of heterogeneity. Despite this wide variation, pooled estimates are useful to indicate the public health burden of the disorder. The study variation is reflected in the very large intervals on the summaries of overall prevalence. The estimates of around 7.1 per 10
000 for typical autism, and 20.0 per 10
000 for all ASD are slightly lower than those estimated previously at 8.7–10.0 per 10
000 and 27.5 per 10
The covariate most strongly associated with prevalence estimates for typical autism and all ASD was the diagnostic criteria used. This association has been recognised previously.2,3,4
The time variation in prevalence is so closely linked to changes in diagnostic criteria, the two could not be examined separately. Furthermore, it was not possible to account entirely for the effect of the diagnostic criteria on the prevalence estimates as the ICD‐10 and DSM‐IV diagnostic schema leave some scope for variation in their interpretation and application.
The age of the children screened was strongly associated with the prevalence estimates. Manifestations of ASD may be more obvious in younger children. Alternatively, some screening methods may be more sensitive for younger children. Methods of screening were found to be significantly associated with the prevalence estimates in the univariate analyses of typical autism, but not after adjusting for the age of the children screened.
The multivariate model that explained most among‐study variance in studies of typical autism included the region studied, with studies from Japan having significantly higher estimates than North American studies. This could be due to other study factors. For example, a higher proportion of the Japanese studies were from urban areas (4/7 (57%) studies) compared to those in North America (1/6 (17%) studies). All the Japanese studies used prospective diagnostic assessments, and all but one drew on whole population rather than clinical samples. Due to the imposed limit of three covariates in the model, it was not possible to adjust for further potential effect modifiers. Countries differ in their diagnostic practice both in their theoretical background and their training procedures for healthcare workers. This may, in part, account for between‐region variation in prevalence.
In an alternative model for typical autism, when adjusting for age and diagnostic criteria, studies including prospective diagnostic assessments gave rise to higher prevalence estimates than those using retrospective records. This may be linked to the use of different diagnostic methodology at different times. Alternatively, an assessor taking part in prospective research studies might observe children more closely for symptoms of ASD.
When adjusting for diagnostic criteria, urban location was also observed to be associated with higher prevalence estimates for both typical autism and all ASD. If the screen method relied on records, these may have been more complete in urban locations. If the screen method used referrals from clinicians, it is possible that a higher proportion of children were known to services in urban locations. There may have been different diagnostic practices in urban locations where staff were more likely to be employed at specialist healthcare centres than in rural locations. It is easier to access the population in urban locations, and response rates may have been higher, but data on response were too limited to investigate this.
Limitations and recommendations for future research
Publication bias was not investigated in this review, as funnel plots were not considered appropriate due to the large degree of variation across studies. It is unlikely that the set of papers published is biased with respect to prevalence reported. However, it is possible that some studies were not identified in the searches if they were not published in mainstream journals. There may have been some time lag bias, with smaller studies, or studies with unremarkable results, coming through to publication slower than larger studies.
Of the papers identified for detailed examination, five potentially eligible studies were excluded as they did not have a detailed English summary or were not peer reviewed. There is no reason to suspect that the lack of availability of data from these studies is a direct consequence of the prevalences they might have observed.
The choice of coding of the covariates may have affected the model, such as using the midpoint of the age range or grouping diverse diagnostic criteria. Furthermore, it was only possible to assess the impact of reported covariates, or easily quantifiable covariates. Qualitative influences on prevalence such as awareness of autism in each population could not be included. As more studies are published, it may be possible to include new covariates or more precise coding of existing covariates in such a model. It would be valuable to have even more thorough recording of study characteristics in future studies to facilitate meta‐analyses of studies.
It is unlikely that it would ever be possible to measure and record all potentially important covariates. An alternative approach to investigating trends in prevalence, through ongoing monitoring of defined school aged populations using standard methodology, has been recommended.1
This would enable researchers to investigate changes in prevalence over time, and geographical variations while controlling for study methodology.
This review has contributed to explaining some of the influences on variation among prevalence estimates. Over half of the variation among study estimates can be explained by the age of the children screened, the diagnostic criteria used, and the country studied. Other important factors were whether the study was in a rural or urban location and whether cases were assessed prospectively or retrospectively. The impact of these identified factors on prevalence estimates should now be further investigated as they may be acting as proxies for other influences on prevalence. For example, the effect of geographical location on prevalence may be due to the services available, or variation in awareness of the disorder. By taking this quantitative approach, this review has shown that using meta‐analytic techniques can be a valuable additional tool in deepening our understanding of the influences of study and population characteristics on variation in prevalence estimates in autism spectrum disorders.