Effects of undernutrition on survival of human immunodeficiency virus positive children on antiretroviral therapy
Bibliographic record
Abstract
The relationship between undernutrition and HIV is bidirectional, ultimately contributing to quality of life and survival of affected individuals. Ethiopia is a sub-Saharan nation influenced by both undernutrition and HIV. In Ethiopia, although individuals are often dually impacted, the effect of undernutrition on the survival of HIV positive children on anti-retroviral therapy (ART) has not been well investigated. Therefore, this study assessed the effect of undernutrition on survival rates of HIV positive children on ART in Amhara Regional State of Ethiopia. An institution-based retrospective cohort study was conducted among 390 HIV positive children on ART from the 1st of January, 2012 to the 28th of February, 2017 in Amhara Regional State Referral Hospitals. A simple random sampling technique was used to select the study participants. Data were extracted by reviewing patients' ART intake and follow-up forms. Data were entered into Epi-Data Version 3.1, and analysis was done using STATA Version 13. The Kaplan-Meier survival curve was used to estimate the cumulative survival time of the sample. Log rank tests were employed to compare the survival time between different categories of explanatory variables. Bivariable and multivariable Cox proportional hazards models were fitted to identify predictors of mortality. Among the 390 records included in the final analysis, 9.7% of the individuals died within the follow-up period. In this study, the overall mortality rate was found to be 4.4 per 100 child-years (95% CI: 3.2, 6.0) while undernourished children had a lower survival time than well-nourished children. Low hemoglobin level (AHR: 3.2, 95% CI: 1.4, 7.4), CD4 cell count or percent below the threshold (AHR: 5.2, 95% CI: 1.9, 14.1), severe stunting (AHR: 3.9, 95% CI: 1.7, 9.4), severe wasting (AHR: 3.0, 95% CI: 1.3, 6.9) and advanced disease stage (III and IV) (AHR: 2.6, 95% CI: 1.1, 6.6) were found to be predictors of mortality. There was a high rate of mortality. A significant difference was observed in the survival rate of undernourished and well-nourished children. Low hemoglobin level, CD4 count or percent below the threshold, severe wasting, severe stunting, and advanced disease stage were found to be predictors of mortality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".