Post-acute health care burden after SARS-CoV-2 infection: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: The post-acute burden of health care use after SARS-CoV-2 infection is unknown. We sought to quantify the post-acute burden of health care use after SARS-CoV-2 infection among community-dwelling adults in Ontario by comparing those with positive and negative polymerase chain reaction (PCR) test results for SARS-CoV-2 infection. METHODS: We conducted a retrospective cohort study involving community-dwelling adults in Ontario who had a PCR test between Jan. 1, 2020, and Mar. 31, 2021. Follow-up began 56 days after PCR testing. We matched people 1:1 on a comprehensive propensity score. We compared per-person-year rates for health care encounters at the mean and 99th percentiles, and compared counts using negative binomial models, stratified by sex. RESULTS: Among 531 702 matched people, mean age was 44 (standard deviation [SD] 17) years and 51% were female. Females who tested positive for SARS-CoV-2 had a mean of 1.98 (95% CI 1.63 to 2.29) more health care encounters overall per-person-year than those who had a negative test result, with 0.31 (95% CI 0.05 to 0.56) more home care encounters to 0.81 (95% CI 0.69 to 0.93) more long-term care days. At the 99th percentile per-person-year, females who tested positive had 6.48 more days of hospital admission and 28.37 more home care encounters. Males who tested positive for SARS-CoV-2 had 0.66 (95% CI 0.34 to 0.99) more overall health care encounters per-person-year than those who tested negative, with 0.14 (95% CI 0.06 to 0.21) more outpatient encounters and 0.48 (95% CI 0.36 to 0.60) long-term care days, and 0.43 (95% CI -0.67 to -0.21) fewer home care encounters. At the 99th percentile, they had 8.69 more days in hospital per-person-year, with fewer home care (-27.31) and outpatient (-0.87) encounters. INTERPRETATION: We found significantly higher rates of health care use after a positive SARS-CoV-2 PCR test in an analysis that matched test-positive with test-negative people. Stakeholders can use these findings to prepare for health care demand associated with post-COVID-19 condition (long COVID).
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".