Pediatric post-discharge mortality in resource-poor countries: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Under-five mortality remains concentrated in resource-poor countries. Post-discharge mortality is becoming increasingly recognized as a significant contributor to overall child mortality. With a substantial recent expansion of research and novel data synthesis methods, this study aims to update the current evidence base by providing a more nuanced understanding of the burden and associated risk factors of pediatric post-discharge mortality after acute illness. Methods: Eligible studies published between January 1, 2017 and January 31, 2023, were retrieved using MEDLINE, Embase, and CINAHL databases. Studies published before 2017 were identified in a previous review and added to the total pool of studies. Only studies from countries with low or low-middle Socio-Demographic Index with a post-discharge observation period greater than seven days were included. Risk of bias was assessed using a modified version of the Joanna Briggs Institute critical appraisal tool for prevalence studies. Studies were grouped by patient population, and 6-month post-discharge mortality rates were quantified by random-effects meta-analysis. Secondary outcomes included post-discharge mortality relative to in-hospital mortality, pooled risk factor estimates, and pooled post-discharge Kaplan–Meier survival curves. PROSPERO study registration: #CRD42022350975. Findings: Of 1963 articles screened, 42 eligible articles were identified and combined with 22 articles identified in the previous review, resulting in 64 total articles. These articles represented 46 unique patient cohorts and included a total of 105,560 children. For children admitted with a general acute illness, the pooled risk of mortality six months post-discharge was 4.4% (95% CI: 3.5%–5.4%, I2 = 94.2%, n = 11 studies, 34,457 children), and the pooled in-hospital mortality rate was 5.9% (95% CI: 4.2%–7.7%, I2 = 98.7%, n = 12 studies, 63,307 children). Among disease subgroups, severe malnutrition (12.2%, 95% CI: 6.2%–19.7%, I2 = 98.2%, n = 10 studies, 7760 children) and severe anemia (6.4%, 95% CI: 4.2%–9.1%, I2 = 93.3%, n = 9 studies, 7806 children) demonstrated the highest 6-month post-discharge mortality estimates. Diarrhea demonstrated the shortest median time to death (3.3 weeks) and anemia the longest (8.9 weeks). Most significant risk factors for post-discharge mortality included unplanned discharges, severe malnutrition, and HIV seropositivity. Interpretation: Pediatric post-discharge mortality rates remain high in resource-poor settings, especially among children admitted with malnutrition or anemia. Global health strategies must prioritize this health issue by dedicating resources to research and policy innovation. Data Processing Methods: Data were extracted using a standard data extraction form developed by the review authors. Kaplan–Meier survival curves, where provided, were extracted using a plot digitizer. The data extraction file, “PDMSR2024_DataExtraction_Dataset_SD” was generated as described above and analyzed as is. Co-ordinates were extracted from the survival curves in their original, published form, using a plot digitizer (https://automeris.io/WebPlotDigitizer/). The co-ordinates for each survival curve were then cleaned up to: 1. Re-scale the time points to weeks 2. Curves which reported % mortality were converted to % survival (1 – mortality) 3. First co-ordinate was set to (0, 1), i.e., survival is 100% at time-point 0 4. Include the numbers at risk (if reported), primary reference, and subgroup information Using these cleaned co-ordinates, individual-level patient data were extracted (see Guyot et al, 2012, doi.org/10.1186/1471-2288-12-9) and the survival curves re-constructed to obtain the survival and number at risk at specified time-points (0-52 weeks). Where possible, disease and age subgroups were combined to create all admissions curves by combining the individual-level patient data from multiple curves in the same study. Additional data from the survival curves were extracted to produce the “PDMSR2024_AdditionalDataSurvivalCurves6M_Dataset_SD” and “PDMSR2024_AdditionalDataSurvivalCurves12M_Dataset_SD” files by extracting the survival rate at 6 and 12 months. Previously unpublished hazards ratios were extracted from the dataset used in the Wiens et al (2015) study on post-discharge mortality (doi:10.1136/bmjopen-2015-009449) to produce the “PDMSR2024_Wiens2015HazardsRatios_Dataset_SD.xlsx” file. These original data are published on Dataverse at: doi.org/10.5683/SP2/VBPLRM Analyses were in R version 4.3.0 (R Foundation for Statistical Computing, Vienna, Austria), and RStudio version 2023.6.1 (RStudio, Boston, MA). Additional Files: Survival curves in their original, published form, as well as survival curve coordinates files can be made available by request.
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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 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.034 |
| Bibliometrics | 0.008 | 0.010 |
| 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) | 0.005 | 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".