Application and Validation of Case-Finding Algorithms for Identifying Individuals with Human Immunodeficiency Virus from Administrative Data in British Columbia, Canada
Bibliographic record
Abstract
OBJECTIVE: To define a population-level cohort of individuals infected with the human immunodeficiency virus (HIV) in the province of British Columbia from available registries and administrative datasets using a validated case-finding algorithm. METHODS: Individuals were identified for possible cohort inclusion from the BC Centre for Excellence in HIV/AIDS (CfE) drug treatment program (antiretroviral therapy) and laboratory testing datasets (plasma viral load (pVL) and CD4 diagnostic test results), the BC Centre for Disease Control (CDC) provincial HIV surveillance database (positive HIV tests), as well as databases held by the BC Ministry of Health (MoH); the Discharge Abstract Database (hospitalizations), the Medical Services Plan (physician billing) and PharmaNet databases (additional HIV-related medications). A validated case-finding algorithm was applied to distinguish true HIV cases from those likely to have been misclassified. The sensitivity of the algorithms was assessed as the proportion of confirmed cases (those with records in the CfE, CDC and MoH databases) positively identified by each algorithm. A priori hypotheses were generated and tested to verify excluded cases. RESULTS: A total of 25,673 individuals were identified as having at least one HIV-related health record. Among 9,454 unconfirmed cases, the selected case-finding algorithm identified 849 individuals believed to be HIV-positive. The sensitivity of this algorithm among confirmed cases was 88%. Those excluded from the cohort were more likely to be female (44.4% vs. 22.5%; p<0.01), had a lower mortality rate (2.18 per 100 person years (100PY) vs. 3.14/100PY; p<0.01), and had lower median rates of health service utilization (days of medications dispensed: 9745/100PY vs. 10266/100PY; p<0.01; days of inpatient care: 29/100PY vs. 98/100PY; p<0.01; physician billings: 602/100PY vs. 2,056/100PY; p<0.01). CONCLUSIONS: The application of validated case-finding algorithms and subsequent hypothesis testing provided a strong framework for defining a population-level cohort of HIV infected people in BC using administrative databases.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".