Pediatric Post-Discharge Mortality in Resource Poor Countries: A Systematic Review
Bibliographic record
Abstract
OBJECTIVES: Mortality following hospital discharge is an important and under-recognized contributor to overall child mortality in developing countries. The primary objective of this systematic review was to identify all studies reporting post-discharge mortality in children, estimate likelihood of death, and determine the most important risk factors for death. SEARCH STRATEGY: MEDLINE and EMBASE were systematically searched using MeSH terms and keywords from the inception date to October, 2012. Key word searches using Google Scholar™ and hand searching of references of retrieved articles was also performed. Studies from developing countries reporting mortality following hospital discharge among a pediatric population were considered for inclusion. RESULTS: Thirteen studies that reported mortality rates following discharge were identified. Studies varied significantly according to design, underlying characteristics of study population and duration of follow-up. Mortality rates following discharge varied significantly between studies (1%-18%). When reported, post-discharge mortality rates often exceeded in-hospital mortality rates. The most important baseline variables associated with post-discharge mortality were young age, malnutrition, multiple previous hospitalizations, HIV infection and pneumonia. Most post-discharge deaths occurred early during the post-discharge period. Follow-up care was examined in only one study examining malaria prophylaxis in children discharged following an admission secondary to malaria, which showed no significant benefit on post-discharge mortality. CONCLUSIONS: The months following hospital discharge carry significant risk for morbidity and mortality. While several characteristics are strongly associated with post-discharge mortality, no validated tools are available to aid health workers or policy makers in the systematic identification of children at high risk of post-discharge mortality. Future research must focus on both the creation of tools to aid in defining groups of children most likely to benefit from post-discharge interventions, and formal assessment of the effectiveness of such interventions in reducing morbidity and mortality in the first few months following hospital discharge.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".