Importance
Diagnoses of attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) have increased substantially in recent decades, but it is unclear whether associations of prediagnostic characteristics with these diagnoses have changed over time.
Objective
To examine temporal trends in associations between well-defined risk factors and subsequent diagnoses of ADHD and ASD.
Design, Setting, and Participants
This population-based matched case-control study was conducted using nationwide Danish registry data for individuals born between January 1, 1994, and December 31, 2022, followed up to age 18 years. Individuals diagnosed with ADHD or ASD before age 18 years between 2012 and 2022 were included as cases. Each case was matched to 10 controls without these diagnoses by sex, birth year, municipality of residence, and country of birth. Data were analyzed from April 2025 through June 2026.
Exposure
A list of 19 prediagnostic characteristics, including parental and family factors, birth and perinatal factors, and health care use.
Main Outcomes and Measures
Associations between prediagnostic characteristics and ADHD or ASD diagnoses by year of diagnosis, estimated as odds ratios (ORs) with 95% CIs using conditional logistic regression.
Results
Of 2 194 951 children and adolescents in the registry, 100 323 individuals (4.6%) were diagnosed with ADHD or ASD before age 18 years, among whom 71 317 children and adolescents were diagnosed between 2012 and 2022 and included as cases (26 452 female [37.1%]; median [IQR] age at diagnosis, 11.4 [8.1 to 14.9] years), matched with 713 170 controls. Individuals diagnosed with ADHD or ASD differed from controls across all characteristics, but differences diminished over time, with ORs attenuating toward the null, particularly for socioeconomic and perinatal factors. For example, the OR for low birth weight decreased from 1.54 (95% CI, 1.41 to 1.68) in 2012 to 2013 to 1.17 (95% CI, 1.10 to 1.24) in 2020 to 2022. Attenuation was greater for ADHD than ASD (eg, household income: mean yearly change in OR, 6.5% [95% CI, 5.7% to 7.3%] for ADHD; mean yearly change in OR, 0.7% [95% C, -0.1% to 1.5%] for ASD) and more pronounced among individuals diagnosed at ages 10 to 17 years than at younger ages. Patterns were similar across sexes and in analyses restricted to cases with higher diagnostic certainty.
Conclusions and relevance
This study found that from 2012 to 2022, children and adolescents diagnosed with ADHD or ASD became increasingly similar to peers without these diagnoses. These findings suggest that increasing diagnostic rates may reflect changes in diagnostic practices or health care capacity rather than changes in underlying risk alone and should not be interpreted as evidence that ADHD or ASD have become less impairing at the individual level.
Magnus Elias Tarp, M. L. Lousdal, C. U. Rask et al.· JAMA psychiatry· 1 citation
Background: Polygenic scores (PGSs) are increasingly used to investigate the genetic architecture of complex traits. In genetics, family study designs are often used to adjust for confounders such as population structure and shared environment. However, family studies may also be particularly vulnerable to to non-random ascertainment, for example when individual case status affects the probability of inclusion, leading to differential representation of sibling pairs. In sibling samples, PGS associations can be decomposed into within-family and between-family components, where the within-family estimate captures associations between sibling differences in PGS and differences in outcome, thereby providing an estimate that is less affected by shared familial confounding. In this study, we examined the impact of non-random sampling on estimated genetic effects in family-based studies using both simulations and real-world data. Further, we leveraged the iPSYCH study design to estimate within-family PGS effects for six common mental health outcomes and whether accounting for these can improve prediction accuracy. Methods: We conducted simulations and applied the same framework to real-world data to evaluate the impact of selection bias on within- and between-family PGS estimates. Selection bias was modelled through differential sampling of sibling pairs based on case status, and inverse probability weighting (IPW) was applied to adjust for known heterogeneous inclusion probabilities. Analyses were replicated in the iPSYCH cohort using registry-based sampling weights and PGSs for six major psychiatric disorders. Predictive performance of models was assessed using five-fold cross-validation. Results: In simulation studies, biased sampling led to deviations in estimated PGS effects, with greater distortion observed for between-family components. IPW adjustment reduced the discrepancy between estimates obtained from biased and true underlying data. In the iPSYCH cohort, between-family estimates from unweighted models were larger than within-family estimates across traits. IPW weighted attenuated several of these estimates. Prediction analyses comparing models using total PGS versus decomposed within- and between-family components showed minimal differences in area under the curve and scaled R2 in the iPSYCH data, while modest gains were observed in selected simulation scenarios. Conclusions: Non-random ascertainment distorts effect estimates in family-based models, with particular sensitivity when estimating between-family effects. Incorporating IPWs derived from known or estimable inclusion probabilities can reduce this bias. Our findings highlight the importance of accounting for selection bias in family studies when estimating genetic effects
T. Gholipourshahraki, Ciarrah Barry, O. Plana-Ripoll et al.· medRxiv· 0 citations
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