Identifying people with post-COVID condition using linked, population-based administrative health data from Manitoba, Canada: prevalence and predictors in a cohort of COVID-positive individuals
Bibliographic record
Abstract
OBJECTIVE: Many individuals exposed to SARS-CoV-2 experience long-term symptoms as part of a syndrome called post-COVID condition (PCC). Research on PCC is still emerging but is urgently needed to support diagnosis, clinical treatment guidelines and health system resource allocation. In this study, we developed a method to identify PCC cases using administrative health data and report PCC prevalence and predictive factors in Manitoba, Canada. DESIGN: Cohort study. SETTING: Manitoba, Canada. PARTICIPANTS: All Manitobans who tested positive for SARS-CoV-2 during population-wide PCR testing from March 2020 to December 2021 (n=66 365) and were subsequently deemed to have PCC based on International Classification of Disease-9/10 diagnostic codes and prescription drug codes (n=11 316). Additional PCC cases were identified using predictive modelling to assess patterns of health service use, including physician visits, emergency department visits and hospitalisation for any reason (n=4155). OUTCOMES: We measured PCC prevalence as % PCC cases among Manitobans with positive tests and identified predictive factors associated with PCC by calculating odds ratios with 95% confidence intervals, adjusted for sociodemographic and clinical characteristics (aOR). RESULTS: Among 66 365 Manitobans with positive tests, we identified 15 471 (23%) as having PCC. Being female (aOR 1.64, 95% CI 1.58 to 1.71), being age 60-79 (aOR 1.33, 95% CI 1.25 to 1.41) or age 80+ (aOR 1.62, 95% CI 1.46 to 1.80), being hospitalised within 14 days of COVID-19 infection (aOR 1.95, 95% CI 1.80 to 2.10) and having a Charlson Comorbidity Index of 1+ (aOR 1.95, 95% CI 1.78 to 2.14) were predictive of PCC. Receiving 1+ doses of the COVID-19 vaccine (one dose, aOR 0.80, 95% CI 0.74 to 0.86; two doses, aOR 0.29, 95% CI 0.22 to 0.31) decreased the odds of PCC. CONCLUSIONS: This data-driven approach expands our understanding of the prevalence and epidemiology of PCC and may be applied in other jurisdictions with population-based data. The study provides additional insights into risk and protective factors for PCC to inform health system planning and service delivery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".