Epidemiology and economic burden of Huntington’s Disease: a Canadian provincial public health system perspective
Bibliographic record
Abstract
<b><i>Aims:</i></b> To evaluate the epidemiology, healthcare resource utilization, and direct healthcare costs associated with Huntington’s Disease in a Canadian setting with a universal healthcare system. <b><i>Materials and Methods</i></b><i>:</i> Using Albertan administrative health data, a retrospective cohort was identified applying an algorithm requiring two HD diagnostic codes within two years, using the first record as the index date (i.e., proxy for diagnosis date), from April 1, 2010 to March 31, 2019 for patients ≥21 years old. Incidence/prevalence measures were evaluated from April 1, 2010 to March 31, 2019, while healthcare resource utilization and healthcare costs per person-year (inflated to 2020 Canadian dollars) were evaluated from index to the end of follow-up (death, moved out of province, or March 31, 2020). <b><i>Results</i></b><i>:</i> Mean [standard deviation] age at index (n = 395) was 53.9 [13.8] years and 53.7% were female. From 2010-2019, annual HD incidence varied between 0.47-1.21/100,000 person-years and HD prevalence increased from 7.25-9.33/100,000 persons. The mean number of visits per person-year for general and specialist practitioners was 19.2 [18.8] and 12.2 [25.5], respectively. The mean total all-cause direct healthcare costs were $23,211 [$38,599] per person-year, with hospitalizations accounting for 57.8% of all-cause costs. Costs were higher among individuals with a long-term care stay, a proxy for disease severity. <b><i>Limitations and Conclusions</i></b><i>:</i> This study utilizes administrative health data to describe the epidemiology of HD and utilization of publicly funded care by individuals with HD. While administrative data presents limitations since it is not collected for research purposes, it provides a population-level examination of the burden of HD. There was a substantial economic burden associated with HD in a Canadian setting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".