Incidence of Traumatic Brain Injury among Ontarian adults with and without Intellectual and Developmental Disabilities
Bibliographic record
Abstract
Background: In Ontario, there are approximately 66,000 adults living with a diagnosis of intellectual and developmental disability (IDD). These individuals have poorer health and health outcomes compared to the general population. For instance, it is known that persons with IDD experience falls and injuries more frequently than the general population. However, little is known about traumatic brain injury (TBI) in this population. Traumatic brain injuries (TBIs) are a leading cause of death and disability in Canada, and cost the healthcare system nearly $300 million annually in direct costs from Ontario emergency departments alone. TBI captured by Ontario emergency departments also account for more than $650 million in lost productivity. Falls are a known risk factor for TBI, indicating a potential increased TBI burden among persons with IDD compared to the general population and resulting in even higher healthcare costs. Despite this, there is no research examining the risk of TBI among persons with IDD. The objective of this study was to compare TBI among Ontarian adults with and without IDD over time and by demographic information. Methods: Using administrative data, annual incidence of TBI for fiscal years 2002/03 - 2016/17 were compared across three cohorts: 1) all adults with IDD (ALL IDD), 2) persons with IDD diagnosed prior to experiencing any TBI (IDD prior to TBI), and 3) random 10% sample of Ontarians without IDD (No-IDD). Records of persons with IDD and/or TBI were identified using International Classification of Diseases (ICD) codes in three administrative health databases: the Canadian Institute for Health Information Discharge Abstract Database (CIHI-DAD), Same Day Surgery (SDS), and the National Ambulatory Care Reporting System (NACRS). Annual incidence was calculated using the first new instance of TBI in a unique individual in a given fiscal year. Incidence of TBI in the three study groups was adjusted for age and sex.Results & Discussion: Data analysis will begin in December 2018 and results will be ready in time for the conference. Based on existing literature indicating falls as a leading cause of TBI in combination with literature indicating a greater risk of falls and injury-related loss of consciousness among persons with IDD, it is anticipated that the yearly incidence of TBI will be greater among persons with IDD than the general population. This will be the first known study to quantify TBI in persons with IDD using population level data. Our findings could be used to guide TBI prevention strategies specific to the needs of people with IDD.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".