Social Determinants of Health Associated With the Development of Sepsis in Adults: A Scoping Review
Bibliographic record
Abstract
Evaluating risk for sepsis is complicated due to limited understanding of how social determinants of health (SDoH) influence the occurence of the disease. This scoping review aims to identify gaps and summarize the existing literature on SDoH and the development of sepsis in adults. DATA SOURCES: A literature search using key terms related to sepsis and SDoH was conducted using Medline and PubMed. STUDY SELECTION: Studies were screened by title and abstract and then full text in duplicate. Articles were eligible for inclusion if they: 1) evaluated at least one SDoH on the development of sepsis, 2) participants were 18 years or older, and 3) the studies were written in English between January 1970 and January 2022. Systematic reviews, meta-analyses, editorials, letters, commentaries, and studies with nonhuman participants were excluded. DATA EXTRACTION: Data were extracted in duplicate using a standardized data extraction form. Studies were grouped into five categories according to the SDoH they evaluated (race, socioeconomic status [SES], old age and frailty, health behaviors, and social support). The study characteristics, key outcomes related to incidence of sepsis, mortality, and summary statements were included in tables. DATA SYNTHESIS: The search identified 637 abstracts, 20 of which were included after full-text screening. Studies evaluating SES, old age, frailty, and gender demonstrated an association between sepsis incidence and the SDoH. Studies that examined race demonstrated conflicting conclusions as to whether Black or White patients were at increased risk of sepsis. Overall, a major limitation of this analysis was the methodological heterogeneity between studies. CONCLUSIONS: There is evidence to suggest that SDoH impacts sepsis incidence, particularly SES, gender, old age, and frailty. Future prospective cohort studies that use standardized methods to collect SDoH data, particularly race-based data, are needed to inform public health efforts to reduce the incidence of sepsis and help clinicians identify the populations most at risk.
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 | 0.014 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".