Burden of Cardiovascular Morbidity and Mortality Following Humanitarian Emergencies: A Systematic Literature Review
Bibliographic record
Abstract
BACKGROUND: The global burden of cardiovascular mortality is increasing, as is the number of large-scale humanitarian emergencies. The interaction between these phenomena is not well understood. This review aims to clarify the relationship between humanitarian emergencies and cardiovascular morbidity and mortality. METHODS: With assistance from a research librarian, electronic databases (PubMed, Scopus, CINAHL, and Global Health) were searched in January 2014. Findings were supplemented by reviewing citations of included trials. Observational studies reporting the effect of natural disasters and conflict events on cardiovascular morbidity and mortality in adults since 1997 were included. Studies without a comparison group were not included. Double-data extraction was utilized to abstract information on acute coronary syndrome (ACS), acute decompensated heart failure (ADHF), and sudden cardiac death (SCD). Review Manager 5.0 (Version 5.2, The Nordic Cochrane Centre; Copenhagen Denmark,) was used to create figures for qualitative synthesis. RESULTS: The search retrieved 1,697 unique records; 24 studies were included (17 studies of natural disasters and seven studies of conflict). These studies involved 14,583 cardiac events. All studies utilized retrospective designs: four were population-based, 15 were single-center, and five were multicenter studies. Twenty-three studies utilized historical controls in the primary analysis, and one utilized primarily geographical controls. DISCUSSION: Conflicts are associated with an increase in long-term morbidity from ACS; the short-term effects of conflict vary by study. Natural disasters exhibit heterogeneous effects, including increased occurrence of ACS, ADHF, and SCD. CONCLUSIONS: In certain settings, humanitarian emergencies are associated with increased cardiac morbidity and mortality that may persist for years following the event. Humanitarian aid organizations should consider morbidity from noncommunicable disease when planning relief and recuperation projects.
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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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".