Evidence from an Applied Health Research Question (AHRQ): Health care utilization of publicly funded rehab services for patients post COVID-19 diagnosis.
Bibliographic record
Abstract
ObjectivesThe Rehabilitative Care Alliance issued an Applied Health Research Question request to collect information regarding healthcare and rehabilitation use among COVID-19 positive individuals. The objective of this project is to determine the association between length of stay (LOS) in acute care and the number of rehabilitation services used post COVID-19 diagnosis. ApproachHospital and rehabilitation service use was identified among individuals diagnosed with COVID-19, using administrative health data. Admission into acute care within 30 days post COVID-19 diagnosis was recorded. Use of inpatient, physiatry and home-care rehabilitative services were collected until March 31st 2021. Outpatient rehabilitation reporting is not mandatory and was not included. Marginalization was evaluated using the Ontario Marginalization Index factor scores. The association between LOS in acute care and number of rehabilitation categories used was assessed using a negative binomial model, stratified by with or without a stay in the ICU and controlling for age, sex, comorbidities and long-term care residence. ResultsOf 181,139 individuals diagnosed with COVID-19 prior to December 31st 2020, 5% were hospitalized. Of those hospitalized 2.3% then entered rehabilitation compared to 0.06% who were not hospitalized post COVID-19 infection. Rehabilitation users had higher residential instability (mean=0.45 vs -0.01 in the overall cohort), dependency (mean=-0.02 vs -0.27) and material deprivation (mean=0.37 vs 0.19) but similar ethnic diversity (mean=0.87 vs 0.90) compared to the full cohort. LOS in acute care was associated with a 3.3% increased risk of using additional rehabilitation services for individuals without a stay in the ICU (RR 1.033, 95% CI: 1.011 to 1.055; p=0.0036), and a 3.7% increased risk for individuals with a stay in the ICU (RR 1.037, 95% CI: 1.025 to 1.048; p<.0001). ConclusionsPost COVID-19 diagnosis, a larger proportion of rehabilitation service users were hospitalized compared to all COVID-19+ individuals. Additionally, LOS in acute care was associated with the use of more rehabilitation care categories following a COVID-19 diagnosis, and the association was stronger for more severe cases requiring an ICU stay.
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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 | 0.206 | 0.489 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.008 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".