P206 The specific contribution of each data source in a population-based administrative data cohort from manitoba, canada
Bibliographic record
Abstract
<h3>Background</h3> In the development of administrative data case-definitions for HIV, it is important to understand the contribution of each data source to prevalence estimates, especially as it pertains to generalizability of methods. <h3>Methods</h3> HIV case-definitions were constructed from four population-based databases available in Manitoba: physician claims, hospital discharge, pharmaceutical dispensations, and provincial laboratory tests. Performance was assessed using sensitivity, specificity, positive/negative predictive value (PPV & NPV), and Youden’s index (YI). Cases identified by HIV case-definitions, and those reported to public health surveillance were compared using annualized incidence. The distribution of those flagged as HIV-positive was compared by database. <h3>Results</h3> <h3>The best performing case-definition (YI</h3> 0.71) was two or more HIV diagnoses in two years in physician claims, or in hospital discharge abstracts; or 14 or more HAART dispensations in two years; or one positive HIV laboratory. Sensitivity, specificity, PPV and NPV was 82.3% (95%CI: 79.1%-85.5%), 86.8% (95%CI: 84.9%-88.7%), 74.1% (95%CI: 70.6%-77.6%), 91.4% (95%CI: 89.8%-93.1%), respectively. Annualized incidence (2009–2015) calculated from this case-definition was 7.4/100,000 persons (95%CI: 6.8–8.1)]; annualized incidence calculated from surveillance data was 7.7/100,000 persons (95%CI: 7.1–8.3). Approximately 76% of cases would have been flagged through a positive laboratory; 43% through pharmaceutical claims; 34% through physician claims; and 11% through hospital abstracts. 95% of cases would have been flagged through the combination of laboratory and pharmaceutical databases. Only 4% of cases were flagged in all four data sources. <h3>Conclusion</h3> Although the combination of four databases produced the most complete prevalence snapshot, laboratory data was the most important contributor. The combination of laboratory and pharmaceutical databases would have identified the predominant majority of cases in our sample. Findings can be used to inform the construction of administrative data cohorts where the availability of population-based data sources may be more limited. <h3>Disclosure</h3> No significant relationships.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 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".