Table 1 Key features of the EMA study

Population-based

Avoids biases due to selected clinical samples

Improves generalizability to source populations

Capitalizes on existing resources (DDS, Vital Statistics, CBP)

Enables relatively large sample size

Enables prospective examination of biomarkers through use of archived biospecimens

Efficient design- no participant re-contact required

Multiple comparison groups (GP, ID)^a

Enables consideration of specificity of associations to ASD (ASD vs GP) vs broader developmental delays (ID vs GP and ASD vs ID comparisons)

Inclusion of biospecimens collected during critical developmental periods prior to diagnosis

Allows for measurement of exposure levels during mid-pregnancy or in newborn period, rather than estimation via reported or recalled information

Availability of a wide range of biomarkers (see Table 3)

Case confirmation via clinician review of records

Enhances confidence in validity of diagnostic categories under study

Reduces potential for outcome misclassification

Rich dataset of multiple exposures/biomarkers

Allows for examination of combined effects

Ability to examine mechanisms through pathway analyses considering intermediate biomarkers (e.g., thyroid hormones, immune markers as intermediates between EDCs and outcomes)

Interdisciplinary team

Supports cross-cutting science

Early stage investigator engagement and mentorship supported^b