Validity of diagnoses of SARS-CoV-2 infection in Canadian administrative health data: a multiprovince, population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Accurate coding of diagnoses of SARS-CoV-2 infection in administrative data benefits population-based studies about the epidemiology, treatment and outcomes of COVID-19. We describe the validity of diagnoses of SARS-CoV-2 infection recorded in hospital discharge abstracts, emergency department records and outpatient physician service claims from 3 Canadian provinces. METHODS: In this cohort study, population-based inpatient, emergency department and outpatient records were linked to SARS-CoV-2 polymerase chain reaction (PCR; reference standard) test results from British Columbia, Manitoba and Ontario for Apr. 1, 2020, to Mar. 31, 2021. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of diagnoses of SARS-CoV-2 infection were estimated for each quarter in the study period, overall and by province, age group and sex. RESULTS: Our study encompassed more than 13 million SARS-CoV-2 PCR test results. Specificity and NPV of diagnoses of SARS-CoV-2 infection were consistently high (i.e., most estimates were > 95%). Overall sensitivity estimates were 86.2%, 60.4% and 20.3% in the first quarter for inpatient, emergency department and outpatient cohorts, and 66.2%, 47.5% and 25.0% in the last quarter, respectively. For inpatients, overall PPV estimates ranged from 50.0% to 66.4%. For emergency department patients, overall PPV estimates were 76.9% and 68.3% in the first and last quarters, respectively. For outpatients, PPV estimates were 6.8% and 29.1% in the first and last quarters, respectively. INTERPRETATION: We found variations in the validity of diagnoses for SARS-CoV-2 infection recorded in different health care settings, geographic areas and over time. Our multiprovince validation study provides evidence about the potential use of inpatient and emergency department records as an alternative to population-based laboratory data for identification of patients with SARS-CoV-2 infection, but does not support the use of outpatient claims for this purpose.
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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 | 0.002 |
| 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".