Identifying hospitalization episodes of care among people with and without HIV in British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND: Hospitalizations are a resource-intensive form of healthcare use, particularly for persons with chronic conditions such as HIV. In standardized Canadian hospitalization databases, it can be unclear whether a hospitalization record is an independent hospitalization, a planned interhospital transfer, or an unplanned readmission. Misclassifying hospitalization records can bias metrics (e.g., counting transfers as readmissions can inflate readmission counts) and hence yield incorrect results. We compared definitions for combining sequential, related hospitalization records to create hospitalization episodes of care (HEoC) within a cohort of persons with and without HIV (PWH; PWoH) in British Columbia (BC), Canada. METHODS: Acute care hospitalization records (April 1992 to March 2020) were sourced from the Discharge Abstract Database within the Comparative Outcomes And Service Utilization Trends (COAST) study, a BC data linkage that includes samples of PWH and PWoH. Guided by published approaches and data quality considerations, we compared eight HEoC definitions applied to PWH and PWoH. Definitions varied by the date gap between records (0 day [same-day] or ≤ 1 day), and transfer indication (none required, populated transfer fields, one-way matching of hospital transfer identifiers, or two-way matching of hospital transfer identifiers). Comparisons were primarily informed by the percentage of multi-record HEoCs (HEoCs involving multiple hospitalization records, including interhospital transfers), and feasibility given data quality. RESULTS: The sample included 56,455 hospitalization records from 10,826 PWH, and 973,430 hospitalization records from 299,053 PWoH. Across the eight HEoC definitions, the percentage of multi-record HEoCs varied from 2.8 to 6.0% among PWH and 3.6 to 5.5% among PWoH. Definitions yielding the highest percentage of multi-record HEoCs combined records without requiring a transfer indication; definitions yielding the lowest percentage of multi-record HEoCs required two-way agreement of hospital identifiers. Patterns were generally comparable among PWH and PWoH, and similar in sensitivity analyses. CONCLUSIONS: Various approaches can be used to define HEoCs. We recommended a balanced HEoC definition - requiring at least one populated hospital identifier field (without requiring matching of hospital identifiers) and ≤ 1 day gap between each hospitalization record for general use purposes in HIV research. Future work may examine these definitions in other settings and populations.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".