Measures and Impact of Caseload Surge During the COVID-19 Pandemic: A Systematic Review*
Bibliographic record
Abstract
OBJECTIVES: COVID-19 pandemic surges strained hospitals globally. We performed a systematic review to examine measures of pandemic caseload surge and its impact on mortality of hospitalized patients. DATA SOURCES: PubMed, Embase, and Web of Science. STUDY SELECTION: English-language studies published between December 1, 2019, and November 22, 2023, which reported the association between pandemic "surge"-related measures and mortality in hospitalized patients. DATA EXTRACTION: Three authors independently screened studies, extracted data, and assessed individual study risk of bias. We assessed measures of surge qualitatively across included studies. Given multidomain heterogeneity, we semiquantitatively aggregated surge-mortality associations. DATA SYNTHESIS: Of 17,831 citations, we included 39 studies, 17 of which specifically described surge effects in ICU settings. The majority of studies were from high-income countries ( n = 35 studies) and included patients with COVID-19 ( n = 31). There were 37 different surge metrics which were mapped into four broad themes, incorporating caseloads either directly as unadjusted counts ( n = 11), nested in occupancy ( n = 14), including additional factors (e.g., resource needs, speed of occupancy; n = 10), or using indirect proxies (e.g., altered staffing ratios, alternative care settings; n = 4). Notwithstanding metric heterogeneity, 32 of 39 studies (82%) reported detrimental adjusted odds/hazard ratio for caseload surge-mortality outcomes, reporting point estimates of up to four-fold increased risk of mortality. This signal persisted among study subgroups categorized by publication year, patient types, clinical settings, and country income status. CONCLUSIONS: Pandemic caseload surge was associated with lower survival across most studies regardless of jurisdiction, timing, and population. Markedly variable surge strain measures precluded meta-analysis and findings have uncertain generalizability to lower-middle-income countries (LMICs). These findings underscore the need for establishing a consensus surge metric that is sensitive to capturing harms in everyday fluctuations and future pandemics and is scalable to LMICs.
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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 | 0.017 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".