Predictors of Severe Acute Malnutrition among Children Aged 6 to 59 Months Attended out Patient Therapeutic Program Center in Kavre District of Nepal - A Case Control Study
Bibliographic record
Abstract
Background: Severe acute malnutrition is an excessive loss of weight due to the acute shortage of food or illness. It is one of the major public health problems in developing countries including Nepal. According to multiple indicator cluster survey (MICS) 2014, 2.6% severely malnourished in Nepal and 4.4% are severely malnourished in Kavre district. However, there are limited studies about predictors of severe acute malnutrition in Nepal. Thus, this study was aimed to identify the predictors of severe acute malnutrition in Kavre district of Nepal.Methods: Health facility based matched case control study was conducted among 210 (70 cases and 140 controls) children aged 6-59 months from November 2015 to April 2016. Data was collected through face to face interview with mother of eligible children using structured questionnaires. Multivariate analysis was applied to estimate adjusted odds ratio along with 95% confidence interval.Results: Children with severe acute malnutrition were 11.32 times more likely than control to have recurrent diarrhea in past six months (95% CI=4.64-28.21). Similarly, severe acute malnutrition was associated with female sex (AOR=2.44, 95% CI=1.88-6.78), fathers occupation daily labor (AOR=4.69, 95% CI=1.17-13.76) and agriculture (AOR=6.850, 95%CI=3.81-12.93), improper exclusive breast feeding (AOR=6.646, 95%CI=2.11-20.90), not feeding colostrum (AOR=3.89, 95% CI=2.88-11.21), severe food insecurity access (AOR=3.55, 95% CI=1.85-9.77) and monthly income less than average level (AOR=8.214, 95% CI=1.43-22.16).Conclusion: Severe acute malnutrition was independently associated with sex of child, occupation of father, monthly household income, not feeding colostrum, improper exclusive breast feeding, severe household food insecurity access and recurrent diarrhea.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".