The Problem with Pertussis: Finding Undetected Pertussis Cases in Electronic Medical Record Primary Care (EMRPC) to Improve Data Accuracy and Burden Estimates
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Pertussis is a reportable disease in many countries and surveillance is essential, but ascertainment bias has limited data accuracy. However, the true extent of bias, and its impact on burden estimates, is unknown. Within this dissertation are three novel studies which aim to evaluate and enhance the accuracy of health data used for pertussis research in Ontario to improve burden estimates which can inform disease surveillance, health interventions, and public health policy.I used a stratified strategy to sample a reference standard from a primary care electronic medical record cohort to minimize partial verification bias while optimizing sensitivity precision. Eight hundred records were abstracted, with 208 (26.0%) definite and 261 (32.6%) possible prevalent pertussis cases. Classifications demonstrated a variety of case severities. During optimization, the predicted width of 95% confidence intervals for sensitivity ranged from 12.4% to 32.8%. I used a cohort-selected cross-sectional design to evaluate pertussis detection algorithms and reasons for lack of detection in a primary care electronic medical record database. The algorithm including all data measures achieved the highest sensitivity at 20.6%. Sensitivity increased to 100% after reclassifying symptom-only cases as non-cases, but the PPV remained low. Age at first episode was significantly associated with detection in half of the tested scenarios, and false negatives often had some history of immunization. I used Chao capture-recapture models to obtain abundance and sensitivity estimates using a dataset comprised of pertussis case report, laboratory, and health administrative data. I compared results between prevalence, incidence, and adjusted false positive case definitions. Findings demonstrated that all sources consistently fail to detect pertussis cases. Estimate validity improved after adjusting for false positives, demonstrating how capture-recapture methods can be adapted to further their utility to epidemiological research when data is biased. This research strives to improve understanding of gaps in pertussis case ascertainment and burden in Ontario while suggesting mitigation strategies. Each separate aim contributes to this effort. High quality data is essential for conducting vaccine research and effectiveness studies, in addition to evaluating immunization programs. As a result, findings from this study can help direct future research and policy towards the design of optimal public health interventions for pertussis.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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 it