Patterns and Trends in the Attributable Fractions of under-5 Years Hospitalization and Inpatient Death for Neonates, Infectious Diseases, and Severe Acute Malnutrition in Yemen: A Retrospective Data Analysis
Bibliographic record
Abstract
Objectives: To examine trends in hospitalization and inpatient deaths of neonates, and infectious diseases (IDs) between 2005-2014; and severe acute malnutrition (SAM) between 2010 and 2014 in Yemen. Method: It was a retrospective descriptive study. Data were extracted from the clinical records of the patients admitted from 2005 to 2014 for neonates and cases aged 1-59 months with IDs. For cases with SAM data were available from 2010 to 2014. Data were analyzed using SPSS version 20. Results: Between 2005 and 2014, 39282 under-5 hospitalized and 33.6% were neonates. Of 26069 aged 1-59 months, 15224(58.4%) hospitalized with IDs. Pneumonia (44.6%), diarrhea (29.9%), and meningitis (10.6%) were the main subgroups. During the study period, there were 4183 under-5 deaths. Neonatal deaths accounting for 3671 (87.8%). Deaths aged 1-59 month were 512(12.2%) and IDs contributing 440(85.9%). Compared to 2005/06, neonatal hospitalization and death declined by 9.2% and 18.1% in 2013/14, and IDs by 56.8% and 79.2%, respectively. Pneumonia reductions were 65.4% and 83.7%, diarrhea 42% and 95.5%, and meningitis 73% and 83%, respectively. Between 2010-2014, SAM cases were 1781 of 13689 total hospitalization [13% (95%CI 12.5-13.6)] and 53 SAM deaths of 224 total deaths [23.7 % (18.6-29.6)]. SAM hospitalization rate increased from 8.5% in 2010, to 18.4% in 2014 and death rate increased from 27% (17.6-39.0) to 57.5% (42.2-71.5), respectively. Conclusion: Despite significant decline in IDs and vaccine preventable diseases, this study showed 87.8% of under-5 mortality were neonates. The increasing trends in SAM hospitalization and death are alarming. Interventions to improve neonatal survival and to reduce SAM morbidity and mortality are urgently needed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".