Incidence of tuberculosis and its predictors among under-five children with severe acute malnutrition in North Shoa, Amhara region, Ethiopia: a retrospective follow-up study
Bibliographic record
Abstract
Introduction Although tuberculosis (TB) is one of the significant public health challenges in severely malnourished children throughout the globe, it is a severe issue for countries such as Ethiopia, with significant resource limitations. Few studies have examined the incidence of tuberculosis and its predictors among children under five years of age with severe acute malnutrition in developing countries, and there is a paucity of data. This study aimed to estimate the incidence of tuberculosis and its predictors among under-five children with severe acute malnutrition (SAM) in North Shoa, Amhara region, Ethiopia. Methods An institution-based retrospective follow-up study was conducted between January 20, 2017, and June 20, 2019. The sample size was calculated using STATA, which yields a total of 345 charts that were selected with systematic random sampling. Data entry was performed using Epi-data version 4.2 and analyzed with STATA 14. Kaplan-Meier survival curves were computed. Cox proportional hazard models were fitted to detect the determinants of tuberculosis. The hazard ratio with a 95% confidence interval was subsequently calculated. Variables with p -values < 0.05 were considered statistically significant. Results The incidence rate of tuberculosis among children under five years of age with SAM was 4.6 per 100 person-day observations (95% CI: 3.29, 8.9). Predictors of TB were a history of contact with known TB cases [AHR: 1.4 (95% CI: 1.00, 2.8], HIV/AIDS [AHR: 3.71 (95% CI: 2.10, 8.71)], baseline pneumonia [AHR: 2.10 (1.76,12)], not supplying zinc at baseline [AHR: 3.1 (1.91, 4.70)], and failed appetite taste at the diagnosis of SAM [AHR: 2.4 (1.35, 3.82)]. Conclusions In this study, the incidence rate of TB was high. Not supplying zinc at baseline, failed appetite taste at the diagnosis of SAM, history of contact with known TB cases, and baseline pneumonia were significant predictors of TB. Prioritizing regular TB screenings, nutritional support, and zinc supplementation for under-five children with SAM should be implemented to reduce the risk of TB.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".