Prevalence of malnutrition and associated factors among adult patients on Antiretroviral Therapy follow up care in Jimma Medical Center, Southwest Ethiopia
Bibliographic record
Abstract
Abstract Background Malnutrition especially under nutrition is the main problem that is seen over people living with HIV/AIDS and can occur at any age. Multiple factors contributed to malnutrition of HIV/AIDS patients and it need immediate identification and prompt action. The objective of this study was to assess the nutritional status of patients and identify factors associated with malnutrition among HIV/AIDS patients on follow-up care in Jimma medical center, Southwest Ethiopia. Methods A cross-sectional study design was conducted from March-April, 2016. Data was collected retrospectively from clinical records of HIV/AIDS patients enrolled for follow up care in ART clinic from June 2010 to January 2016. Binary and multiple variable logistic regression was done to identify independent predictor of malnutrition. Results Data of 971 patients were included in the study. The prevalence of under nutrition (BMI<18.5) was (36.8%) (95% CI: 33.8%-39.8%) and out of which severe malnutrition accounts 9.7%. Overweight and obese was 8.6%. Malnutrition was more likely among widowed patients (AOR=1.7, 95% CI, 1.034-2.798), patients in the WHO clinical AIDS staging of three (AOR=2.3, 95% CI, 1.392-3.693) and four (AOR=3.2, 95% CI, 1.667-5.943), patients with CD4 cell count of <200 cells/mm 3 (AOR=2.0, 95% CI, 1.463-2.837) and patients with a functional status of bedridden (AOR=4.677, 95% CI, 1.761-12.419) and ambulatory (AOR=2.763, 95% CI, 1.833-4.165). Conclusion Both under nutrition and overweight are prevalent among HIV/AIDS patients in Jimma Medical Center, Ethiopia. Malnutrition was significantly associated with clinical outcome of patients. Hence, nutritional assessment, care and support should be strengthened. Critical identification of malnourished patients and prompt interventions should be undertaken.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".