The impact of multimorbidity on severe COVID-19 outcomes in community and congregate settings
Bibliographic record
Abstract
Purpose: This study examined the impact of multimorbidity on severe COVID-19 outcomes in community and long-term care (LTC) settings, alone and in interaction with age and sex. Methods: We conducted a retrospective cohort study of all Ontarians who tested positive for COVID-19 between January-2020 and May-2021 with follow-up until June 2021. We used cox regression to evaluate the adjusted impact of multimorbidity, individual characteristics, and interactions on time to hospitalization and death (any cause). Results: 24.5% of the cohort had 2 or more pre-existing conditions. Multimorbidity was associated with 28% to 170% shorter time to hospitalization and death, respectively. However, predictors of hospitalization and death differed for people living in community and LTC. In community, increasing multimorbidity and age predicted shortened time to hospitalization and death. In LTC, we found none of the predictors examined were associated with time to hospitalization, except for increasing age that predicted reduced time to death up to 40.6 times. Sex was a predictor across all settings and outcomes: among male the risk of hospitalization or death was higher shortly after infection (e.g. HR for males at 14 days = 30.3) while among female risk was higher for both outcome in the longer term (e.g. HR for males at 150 days = 0.16). Age and sex modified the impact of multimorbidity in the community. Conclusion: Community-focused public health measures should be targeted and consider sociodemographic and clinical characteristics such as multimorbidity. In LTC settings, further research is needed to identify factors that may contribute to improved outcomes.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".