Adrenal Insufficiency: Investigating Prevalence and Healthcare Utilization Using Administrative Data
Bibliographic record
Abstract
Context: Adrenal insufficiency (AI) is an uncommon, life-threatening disorder requiring lifelong treatment with steroid therapy and special attention to prevent adrenal crisis. Little is known about the prevalence of AI in Canada or healthcare utilization rates by these patients. Objective: We aimed to assess the prevalence and healthcare burden of AI in Alberta, Canada. Methods: This study used a population-based, retrospective administrative health data approach to identify patients with a diagnosis of AI over a 5-year period and evaluated emergency and outpatient healthcare utilization rates, steroid dispense records, and visit reasons. Results: The period prevalence of AI was 839 per million adults. Patients made an average of 2.3 and 17.8 visits per year in the emergency department and outpatient settings, respectively. This was 3 to 4 times as frequent as the average Albertan, and only 5% were coded as visits for AI. The majority of patients were dispensed glucocorticoid medications only. Conclusion: The prevalence of AI in Alberta is higher than published data in other locations. The frequency of visits suggests a significant healthcare burden and emphasizes the need for a strong understanding of this condition across all clinical settings. Our most concerning finding is that 94.3% of visits were not labeled with AI, even though many of the top presenting complaints were consistent with adrenal crisis. Several data limitations were discovered that suggest improvements in the standardization of data submission and coding can expand the yield of future studies using this method.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".