Strengths and Limitations of Health and Disability Support Administrative Databases for Population‐Based Health Research in Intellectual and Developmental Disabilities
Bibliographic record
Abstract
Abstract Individuals with intellectual and developmental disabilities ( IDD ) experience high rates of social and health disadvantage. Planning effective services that meet the needs of this vulnerable population requires good population‐based data that are collected on a routine, ongoing basis. However, in most jurisdictions, none of the commonly available data (e.g., health or disability benefits administrative data) completely captures the IDD population. To more accurately identify persons with IDD in a population, one solution is to link data across multiple sources. To do this, the authors report on an effort to create a linked database to identify a cohort of adults, aged 18–64, with IDD in Ontario and use these data to examine how the linkage can help study health and healthcare access. The linked dataset was created using four health and one disability income support databases. Standardized differences were used to compare sociodemographic and clinical characteristics of the IDD cohorts identified through the health, disability income support, and linked datasets. Indirect estimation was used to evaluate which IDD subgroups might be over‐ or underestimated if only a single source of data was available. The linked database identified a cohort of 66,484 adults with IDD (0.78% prevalence). The health and disability income support data each uniquely identified approximately a third of the cohort. Health data were more likely to identify younger adults (18–24 years), those with psychiatric illnesses, and hospitalized individuals. The disability income support data were more likely to identify adults aged 35–54 and those living in lower income neighborhoods. By linking multiple databases, the authors were able to identify a much larger cohort of individuals with IDD than if they had used a single data source. It also enabled the creation of a more accurate sociodemographic and clinical profile of this population as each source captured different segments of it.
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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 | 0.150 | 0.385 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.012 | 0.031 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".