Exploring the double burden of malnutrition at the household level in the Philippines: Analysis of National Nutrition Survey data
Bibliographic record
Abstract
BACKGROUND: In the Philippines, the rising prevalence of obesity and related chronic diseases alongside persistent undernutrition presents a complex public health challenge. Understanding the patterns and dynamics of this 'double burden of malnutrition' (DBM) is crucial for developing effective intervention strategies. However, evidence of the occurrence of undernutrition and overnutrition within the same household is currently lacking. METHODS: Using cross-sectional data from the 2013 Philippines National Nutrition Survey this study examined the prevalence of different typologies of household-level DBM from an analytical sample of 5,837 households and 25,417 individuals. Multivariable logistic regression was performed to identify factors associated with overall occurrence of intrahousehold DBM. RESULTS: The overall prevalence of double burden households was 56% based on a comprehensive definition. The most common typology of intrahousehold DBM characterized in this study (% of all households) comprised households with at least one adult with overnutrition and at least one separate adult with undernutrition. Household size, wealth quintile, food insecurity, and household dietary diversity were all associated with household-level DBM. Double burden households were also influenced by head of household characteristics, including sex, level of education, employment status, and age. CONCLUSIONS: The findings from this study reveal that the coexistence of overnutrition and undernutrition at the household level is a major public health concern in the Philippines. Further comprehensive assessments of household-level manifestations of the DBM are needed to improve our understanding of the trends and drivers of this phenomenon in order to develop better targeted interventions.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".