About

Julie L. Daniels, PhD, is a professor in the Department of Epidemiology and the Department of Maternal and Child Health at UNC Gillings School of Global Public Health. Her research focuses on prenatal environmental and nutritional exposures that may impact children’s growth, neurodevelopment, and overall health. She has developed a platform for studying early life exposure to brominated and organophosphate flame retardants, persistent organic pollutants, and long-chain fatty acids as they relate to children's health, particularly within the Pregnancy, Infection & Nutrition Kids Study. Dr. Daniels has directed the North Carolina sites of the Study to Explore Early Development and the Autism and Developmental Disabilities Monitoring Network, with a strong emphasis on understanding the epidemiology of autism spectrum disorder. Her work specifically investigates how gene expression and environmental exposures interact to influence neurodevelopment, aiming to improve understanding and communication of the environment's role in children's health.

Research topics

• Medicine
• Psychology
• Psychiatry
• Demography
• Pediatrics
• Gerontology
• Internal medicine
• Biology
• Developmental psychology
• Endocrinology
• Bioinformatics
• Genetics
• Environmental health

Selected publications

• Factors Associated With the Use of Complementary and Alternative Medicine by Autistic Preschoolers

  Journal of Autism and Developmental Disorders · 2025-11-06

  article
  PublisherDOI

• Pregnancy Length Measurement Error: A Comparison of Last Menstrual Period and Ultrasonography with Ovulation-based Estimation

  Epidemiology · 2025-09-19

  articleOpen access

  BACKGROUND: Last menstrual period (LMP) and ultrasound are commonly used to estimate pregnancy length. Ovulation, which precedes fertilization by ≤24 hours, should give a more accurate estimate. METHODS: The Effects of Aspirin in Gestation and Reproduction (EAGeR) trial preconceptionally enrolled participants from four US medical centers from 2006 to 2012. Participants in our analyses delivered a singleton live birth, had prospectively recorded LMP, ovulation detected by a fertility monitor, and early first-trimester crown-rump length measurements. We estimated pregnancy length, preterm birth (<37 weeks) prevalence, and sex-specific size for gestational age by LMP, ultrasound, and ovulation. We report the sensitivity and specificity of LMP and ultrasound for detecting preterm birth compared with our gold standard, ovulation. RESULTS: In our analytic sample (n = 392), pregnancies were longest, preterm birth was least common (prevalence = 0.07, 95% confidence interval [CI]: 0.04, 0.10), and small for gestational age was most common when measured by LMP. Pregnancies were shortest, preterm birth was most common (prevalence = 0.10, 95% CI: 0.07, 0.13), and small for gestational age was least common when measured by ultrasound. The prevalence of preterm birth was 0.08 (95% CI: 0.06, 0.12) by ovulation. Using ovulation as the gold standard measure, LMP was less sensitive in detecting preterm birth (0.76, 95% CI: 0.61, 0.90) than ultrasound (0.94, 95% CI: 0.86, 1.00). The specificity of LMP was 1.00 (95% CI: 0.99, 1.00), and the specificity of ultrasound was 0.97 (95% CI: 0.96, 0.99). CONCLUSION: While this study's pregnancy length information is the best-case scenario, we observed misclassification of outcomes that may inform future bias analyses.

  PublisherOA PDFDOI

• Correction to: Brief Report: Maternal Opioid Prescription from Preconception Through Pregnancy and the Odds of Autism Spectrum Disorder and Autism Features in Children

  UNC Libraries · 2025-08-22

  articleOpen access
  PublisherDOI

• Changes in Autism Traits from Early Childhood To Adolescence in the Study To Explore Early Development

  Journal of Autism and Developmental Disorders · 2025-11-08

  articleSenior author
  PublisherDOI

• Fertility Treatment, Female‐Factor Infertility, and Autism Spectrum Disorder: Study to Explore Early Development

  Paediatric and Perinatal Epidemiology · 2025-11-27

  articleCorresponding

  BACKGROUND: Prior research on fertility treatments and autism spectrum disorder (ASD) suggests minimal association but confounding by indication limits inference. To make clinically relevant conclusions, studies should include populations who receive treatment specifically for female-factor infertility. OBJECTIVES: We investigated the association between ovulation-inducing medications and assisted reproductive technology (ART) and ASD. We conducted analyses in a subsample reporting female-factor infertility to reduce confounding by indication. METHODS: We used data from the Study to Explore Early Development (SEED), a 2007-2020 U.S. population-based case-control study. Children 2.5-5 years old with and without ASD were classified using in-person assessments. We identified fertility treatment via interview and included ovulation-inducing medications, ART, and a combination of both. The subsample included those who were told it would be difficult to conceive and/or who attempted to conceive for > 12 months. We estimated odds ratios and 95% confidence intervals for the whole sample and the subsample using logistic regression models adjusted for age, education, parity, pre-pregnancy body mass index, pregnancy history, smoking status, pre-existing hypertension, and other hormonal fertility treatments. RESULTS: There were 5210 participants in the whole sample and 1091 in the subsample. There was no association between ovulation-inducing medications and ASD in the full sample (adjusted odds ratio [aOR] 1.04, 95% confidence interval [CI] 0.77, 1.39) and the subsample (aOR 0.87, 95% CI 0.61, 1.2). There was an increased likelihood of ASD for ART and a combination of treatments in the whole sample (ART: aOR 1.33, 95% CI 0.70, 2.52; combination: aOR 1.39, 95% CI 0.95, 2.03) compared to the subsample (ART: aOR 1.16, 95% CI 0.57, 2.36; combination: aOR 1.08, 95% CI 0.69, 1.68). CONCLUSIONS: In our data, fertility treatment was not associated with ASD. Additional research should restrict analyses to populations with similar indications to untangle whether observed associations are due to treatment or factors related to uptake.

  PublisherDOI

• Changes in Autism Traits from Early Childhood to Adolescence in the Study to Explore Early Development

  medRxiv · 2025-04-19 · 1 citations

  preprintOpen accessSenior author

  Purpose: The objectives of this study were to investigate associations between co-occurring developmental, psychiatric, behavioral, and medical symptoms and conditions and autism spectrum disorder (ASD) traits, as well as predictors of changes in autistic traits from early childhood to adolescence. Methods: Participants from the Study to Explore Early Development (SEED) were identified as having autism spectrum disorder (ASD) (n=707), another developmental disorder (DD) (n=995), or as a population comparison group (POP) (n=898). Caregivers completed the Social Responsiveness Scale-2nd edition (SRS-2) to measure autistic traits and were asked about co-occurring symptoms and conditions when their child was 2-5 years old and 12-16 years old. Children completed the Mullen Scales of Early Learning (MSEL) when they were 2-5 years old. Results: Regression models revealed that in early childhood and adolescence, multiple co-occurring symptoms and conditions were significantly associated with higher SRS-2 scores (e.g., motor, sensory, and sleep problems for children with ASD and DD). Within the ASD and DD groups, but not the POP group, lower MSEL scores at childhood were associated with greater increases in SRS-2 scores between early childhood and adolescence. Conclusions: Findings suggest that motor, sensory, and sleep problems may be important intervention targets for ASD and DD youth with elevated SRS-2 scores and that interventions that target cognitive functioning in childhood may be important to modify trajectories of autistic traits from childhood to adolescence.

  PublisherOA PDFDOI

• Factor Structure of RRBs in Verbal and Non-Verbal Preschoolers With ASD or Related Characteristics

  Journal of Autism and Developmental Disorders · 2025-11-28

  articleSenior author
  PublisherDOI

• Mothers’ Ideas About Causes of Autism Spectrum Disorder (ASD): Differences Over Time and by Household Experience with ASD

  Journal of Autism and Developmental Disorders · 2025-05-22

  articleOpen accessSenior author
  PublisherOA PDFDOI

• Accounting for nonmonotone missing data using inverse probability weighting

  UNC Libraries · 2025-01-30

  articleOpen access

  Inverse probability weighting can be used to correct for missing data. New estimators for the weights in the nonmonotone setting were introduced in 2018. These estimators are the unconstrained maximum likelihood estimator (UMLE) and the constrained Bayesian estimator (CBE), an alternative if UMLE fails to converge. In this work we describe and illustrate these estimators, and examine performance in simulation and in an applied example estimating the effect of anemia on spontaneous preterm birth in the Zambia Preterm Birth Prevention Study. We compare performance with multiple imputation (MI) and focus on the setting of an observational study where inverse probability of treatment weights are used to address confounding. In simulation, weighting was less statistically efficient at the smallest sample size and lowest exposure prevalence examined (n&thinsp;=&thinsp;1500, 15% respectively) but in other scenarios statistical performance of weighting and MI was similar. Weighting had improved computational efficiency taking, on average, 0.4 and 0.05 times the time for MI in R and SAS, respectively. UMLE was easy to implement in commonly used software and convergence failure occurred just twice in &gt;200&thinsp;000 simulated cohorts making implementation of CBE unnecessary. In conclusion, weighting is an alternative to MI for nonmonotone missingness, though MI performed as well as or better in terms of bias and statistical efficiency. Weighting's superior computational efficiency may be preferred with large sample sizes or when using resampling algorithms. As validity of weighting and MI rely on correct specification of different models, both approaches could be implemented to check agreement of results.

  PublisherDOI

• Leveraging External Validation Data: The Challenges of Transporting Measurement Error Parameters

  UNC Libraries · 2025-03-02

  articleOpen accessSenior author

  Approaches to address measurement error frequently rely on validation data to estimate measurement error parameters (e.g., sensitivity and specificity). Acquisition of validation data can be costly, thus secondary use of existing data for validation is attractive. To use these external validation data, however, we may need to address systematic differences between these data and the main study sample. Here, we derive estimators of the risk and the risk difference that leverage external validation data to account for outcome misclassification. If misclassification is differential with respect to covariates that themselves are differentially distributed in the validation and study samples, the misclassification parameters are not immediately transportable. We introduce two ways to account for such covariates: (1) standardize by these covariates or (2) iteratively model the outcome. If conditioning on a covariate for transporting the misclassification parameters induces bias of the causal effect (e.g., M-bias), the former but not the latter approach is biased. We provide proof of identification, describe estimation using parametric models, and assess performance in simulations. We also illustrate implementation to estimate the risk of preterm birth and the effect of maternal HIV infection on preterm birth. Measurement error should not be ignored and it can be addressed using external validation data via transportability methods.

  PublisherDOI

Recent grants

• Training Program in Reproductive, Perinatal and Pediatric Epidemiology

  NIH · $3.5M · 2008–2028

• NIH Grant R21ES023904

  NIH · $436k · 2017

• NIH Grant U01DD000749

  NIH · $5.0M · 2017

• NIH Grant UR3DD000089

  NIH · $1.3M · 2011

• Component A: NC Study to Explore Early Development Phase 3 (SEED 3)

  NIH · $6.4M · 2016–2021

Frequent coauthors

• Laura A. Schieve

  National Center for Chronic Disease Prevention and Health Promotion

  106 shared

• Lisa D. Wiggins

  Centers for Disease Control and Prevention

  75 shared

• Amy H. Herring

  Duke University

  63 shared

• Carolyn DiGuiseppi

  Colorado School of Public Health

  59 shared

• Lisa Croen

  Kaiser Permanente

  59 shared

• Maureen S. Durkin

  University of Wisconsin–Madison

  52 shared

• Kate Hoffman

  Duke University

  50 shared

• Gayle C. Windham

  California Department of Public Health

  41 shared

Education

• PhD, Epidemiology

  University of North Carolina at Chapel Hill

  1999