Risk of neuropsychiatric and related conditions associated with SARS-CoV-2 infection: a difference-in-differences analysis
Bibliographic record
Abstract
The COVID-19 pandemic has been associated with increased neuropsychiatric conditions in children and youths, with evidence suggesting that SARS-CoV-2 infection may contribute additional risks beyond pandemic stressors. This study aims to assess the full spectrum of neuropsychiatric conditions in COVID-19 positive children (ages 5–12) and youths (ages 12–20) compared to a matched COVID-19 negative cohort, accounting for factors influencing infection risk. Using EHR data from 25 institutions in the RECOVER program, we conduct a retrospective analysis of 326,074 COVID-19 positive and 887,314 negative participants matched for risk factors and stratified by age. Neuropsychiatric outcomes are examined 28 to 179 days post-infection or negative test between March 2020 and December 2022. SARS-CoV-2 positivity is confirmed via PCR, serology, or antigen tests, while negativity requires negative test results and no related diagnoses. Risk differences reveal higher frequencies of neuropsychiatric conditions in the COVID-19 positive cohort. Children face increased risks for anxiety, OCD, ADHD, autism, and other conditions, while youths exhibit elevated risks for anxiety, suicidality, depression, and related symptoms. These findings highlight SARS-CoV-2 infection as a potential contributor to neuropsychiatric risks, emphasizing the importance of research into tailored treatments and preventive strategies for affected individuals. The COVID-19 pandemic has raised concerns about increased neuropsychiatric conditions in children and youths, with potential links to SARS-CoV-2 infection. Here, the authors analyze EHR data from 25 institutions, showing that COVID-19 positive children and youths have a modestly increased risk of developing neuropsychiatric conditions, such as anxiety, depression, and ADHD, compared to those who tested negative.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".