Environmental Profiles and COVID-19 Mortality Risk: A Latent Class Analysis of Intensive Care Patients in the Early Pandemic
Bibliographic record
Abstract
Abstract Background Coronavirus disease 2019 (COVID-19) continues to strain intensive care units (ICUs), particularly during seasonal surges and in conjunction with other respiratory infections. While clinical and demographic mortality predictors are well-established, the impact of environmental conditions remains less understood, especially at the individual level. We therefore analysed subgroups of ICU patients based on environmental conditions and evaluate differences in mortality risk. Methods In this retrospective, multi-centre cohort study, we analysed data from 1,166 adults admitted with COVID-19 to three Birmingham (UK) ICUs between March 1, 2020, and February 28, 2021. Using latent class analysis (LCA), we grouped patients by ambient temperature, relative humidity, and wind speed preceding ICU admission. Associations between class membership and ICU mortality were assessed using multivariable logistic regression, adjusting for age, sex, ethnicity, deprivation, BMI, frailty, and ICNARC Physiology Score. Relative risk (RR) of mortality was compared across classes. Results Four latent classes were identified. Two showed significantly increased mortality risk: one with high temperature and low humidity (RR=1.62, 95% CI: 1.14-2.29) and another with low temperature and moderate-to-high wind speed (RR=1.47, 95% CI: 1.15-1.89), compared to the reference class. Environmental and patient characteristics, particularly relative humidity, female sex, BMI, ICNARC score, and frailty, demonstrated class-specific associations with mortality. Conclusions Environmental exposures prior to ICU admission contribute to mortality risk in critically ill COVID-19 patients. Incorporating these conditions into patient risk stratification could enhance clinical decision-making, particularly during public health emergencies.
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".