The Impact of COVID-19 on Sepsis-Related Mortality in the United States
Bibliographic record
Abstract
Background: Coronavirus disease 2019 (COVID-19)-related organ dysfunction is increasingly considered as sepsis of viral origin. In recent clinical and autopsy studies, sepsis has been present in the majority of decedents with COVID-19. Given the high mortality toll of COVID-19, sepsis epidemiology would be expected to be substantially transformed. However, the impact of COVID-19 on sepsis-related mortality at the national level has not been quantified. We aimed to estimate the contribution of COVID-19 to sepsis-related mortality in the USA during the first year of the pandemic. Methods: We used the Centers for Disease Control Wide-Ranging Online Data for Epidemiological Research (CDC WONDER) Multiple Cause of Death dataset to identify decedents with sepsis during 2015 - 2019, and those with a diagnosis of sepsis, COVID-19, or both in 2020. Negative binomial regression was used on the 2015 - 2019 data to forecast the number of sepsis-related deaths in 2020. We then compared the observed vs. predicted number of sepsis-related deaths in 2020. In addition, we examined the frequency of a diagnosis of COVID-19 among decedents with sepsis and the proportion of a diagnosis of sepsis among decedents with COVID-19. The latter analysis was repeated within each of the Department of Health and Human Services (HHS) regions. Results: In 2020, there were 242,630 sepsis-related deaths, 384,536 COVID-19-related deaths, and 35,807 deaths with both in the USA. The predicted number of sepsis-related deaths for 2020 was 206,549 (95% confidence interval (CI): 201,550 - 211,671). COVID-19 was reported in 14.7% of decedents with sepsis, while a diagnosis of sepsis was reported in 9.3% of all COVID-19-related deaths, ranging from 6.7% to 12.8% across HHS regions. Conclusions: A diagnosis of COVID-19 was reported in less than one in six of decedents with sepsis in 2020, with corresponding less than one in 10 diagnoses of sepsis among decedents with COVID-19. These findings suggest that death certificate-based data may have substantially underestimated the toll of sepsis-related deaths in the USA during the first year of the pandemic.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".