Environmental factors associated with autism spectrum disorder: a scoping review for the years 2003–2013
Bibliographic record
Abstract
INTRODUCTION: The number of children diagnosed with autism spectrum disorder (ASD) has been rapidly rising in the past decade. The etiology of this disorder, however, is largely unknown, although the environmental relative to the genetic contribution is substantial. We conducted a scoping review to comprehensively assess the current state of knowledge of the environmental factors present from preconception to early life associated with ASD, and to identify research gaps. METHODS: We searched electronic databases MEDLINE, PsycINFO and ERIC for articles on potential risk factors or protective factors from the physical and social environments associated with ASD and its subclassifications published between 1 January, 2003, and 12 July, 2013. We categorized articles into broad themes: chemical, physiological, nutritional and social factors, based on environmental exposure. RESULTS: We identified over 50 000 publications, but after ineligible studies were screened out, 315 articles remained. Most of these studies examined physiological factors, followed closely by chemical factors, and to a much lesser extent, nutritional and social factors, associated with ASD. Despite a vast literature and many heterogeneous studies, several risk factors emerged consistently: chemical factors such as traffic-related air pollutants; physiological factors including advanced parental age, preterm birth, low birth weight, hyperbilirubinemia and clustering of pregnancy complications; and maternal immigrant status. Despite extensive research on vaccines, findings overwhelmingly demonstrate no support for an association with ASD. CONCLUSION: The lack of consistency, temporality and specificity of associations between environmental factors and ASD remains the largest barrier to establishing causal relationships. More robust research is required to resolve inconsistencies in the literature. Future research should explore underlying mechanisms of associations between the risk factors that we identified and ASD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.024 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".