Deforestation and Household- and Individual-Level Double Burden of Malnutrition in Sub-saharan Africa
Bibliographic record
Abstract
INTRODUCTION: Although forests and forest-based ecosystems have been shown to influence health and sustainable diets, there is limited evidence on how deforestation affects the current nutrition transition and the double burden of malnutrition. We examined the relationship between deforestation and the individual- and household-level double burden of malnutrition in 15 countries in Sub-Saharan Africa. MATERIALS AND METHODS: We combined data from geolocated Demographic and Health Surveys and the Global Forest Change dataset. We defined household-level double burden of malnutrition as the co-occurrence of an overweight woman of childbearing age (WCBA) and a stunted pre-school child (PSC) within the same household. We defined individual-level double burden in two ways: 1) as the co-occurrence of overweight and anemia within an individual WCBA, and 2) as the co-occurrence of overweight and stunting within a PSC. We used logistic regression analysis to examine the association between forest cover loss and these three measures after adjusting for potential confounders. We also assessed the mechanisms linking forest cover loss and nutritional status, such as livestock ownership and access to clean water. RESULTS: In our sample, the prevalence rates of the three measures of the double burden were: overweight and anemic WCBA: 8.4%, overweight WCBA and stunted PSC: 6.9%, overweight and stunted PSC: 2.7%. After adjusting for the confounders as well as country fixed effects and the month of the survey, forest cover loss was marginally associated with a higher odds of an overweight WCBA and stunted PSC (odds ratio (95% CI): 4.80 (0.82, 28.25)). We found no association between forest cover loss and odds of an overweight and stunted PSC (odds ratio (95% CI): 2.47 (0.80, 7.60)) or the odds of an anemic and overweight WCBA (odds ratio (95% CI): 0.71 (0.15, 3.32)). DISCUSSION: Deforestation does not seem to be an important driver of the double burden of malnutrition in SSA. However, deforestation influences several intermediate factors which, in turn, may influence the double burden. The overall weak association between forest cover loss and double burden measures mask significant heterogeneity across regions within SSA. Future research should unpack the mechanisms behind these regional differences.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".