Morbidities and mortality of diagnosed attention deficit hyperactivity disorder (ADHD) over the youth lifespan: A population‐based retrospective cohort study
Bibliographic record
Abstract
OBJECTIVES: To estimate the prevalence of ADHD, and related comorbidities, mortality, and type of health service use among children and young adults, using different case definitions. METHODS: We conducted a population-based retrospective cohort study between 2000 and 2018, using the Quebec Integrated Chronic Disease Surveillance System (QICDSS) database. All residents aged less than 25 years eligible for health insurance coverage were included. We compared outcomes of three indicators (morbidity, services use and mortality) according two different algorithms of ADHD definitions, to the general population. RESULTS: The cumulative prevalence of ADHD has risen steadily over the past decade, reaching 12.6% in 2017-2018. People with ADHD have a higher prevalence of psychiatric comorbidities, make greater use of medical, mental health services, and are hospitalized more often. The comparison of prevalence between the two algorithms and the general population for the three indicators showed that the cohort having one claim was very close to that with two or more, and statistically significant higher to that of people without ADHD. CONCLUSION: This finding support that a single claim algorithm for ADHD can be used for case definition. More research is needed on the impact of potentially effective treatments in improving consequences of ADHD.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".