Abstract P101: The Double Burden Of Malnutrition Among Women And Preschool Children In Low- And Middle-income Countries: A Scoping Review And Thematic Analysis Of The Literature
Bibliographic record
Abstract
Background: The double burden of malnutrition (DBM), the simultaneous existence of both underweight and overweight sequelae, is an emerging public health concern in low- and middle-income countries (LMICs). Women of child-bearing age (15-49 years) and preschool children (under 5 years) are among the most vulnerable groups to be affected by the DBM. However, the DBM phenomenon among these population subgroups is understudied. Objectives: We explored the following objectives: 1) To determine which nutrition indicators have been used to define the DBM among women of child-bearing age and preschool children; 2) To establish the plausible explanations for the identified DBM phenotypes women of child-bearing age and preschool children; and 3) To identify the risk factors for the DBM women of child-bearing age and preschool children. Methods: We systematically searched for literature from the following databases: EMBASE, CINAHL, MEDLINE, LILACS, Scopus and ProQuest Dissertations & Thesis Global. Studies discussing the DBM phenomenon in LMICs were included. Thematic analysis was conducted on extracted information from the literature to reveal emerging themes from included studies. The findings were reported according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. Results: Preliminary findings indicate that frequently used indicators for defining DBM across all levels of operationalization were anthropometric indices and micronutrients measurements (e.g., overweight/obesity and anemia) for women and anthropometric indices (e.g., overweight/obesity and stunting) for preschool children. The following themes emerged as plausible explanations for the DBM phenotypes: Food insecurity; diet behavior; breastfeeding; illness and metabolic programming. Age, child sex, household wealth, women’s education, occupation and urbanization were frequently occurring DBM risk factors. Of note was the use of the term ‘double burden’ as a buzz word in substantial number of studies without a proper definition or discussion of the DBM concept. Conclusion: The DBM phenomenon is loosely understood due to the varying operational definitions of the DBM construct. Emerging themes and common risk factors may provide target areas for public health interventions. Studies with robust designs are needed to succinctly understand the DBM phenomenon.
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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 | 0.042 | 0.115 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.048 | 0.043 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".