Abstract MP23: Does Predictors Of Overweight/obesity Among Women Vary Based On The Residential Setting? A Multilevel Analysis Of Repeated Cross-sectional Data In Nigeria
Bibliographic record
Abstract
Background: Overweight/obesity is increasingly becoming a major public health problem in Nigeria despite the persistence of underweight burden. A major contributor of this is the urban sprawl experienced in the country with approximately half of the Nigerian population living in urban areas. Evidence suggests that similar socio-demographic factors could have different influences on an individual’s nutrition status by virtue of their residential setting. Yet, little is known about this phenomenon in the Nigerian context. Objectives: This study aimed to explore the following objectives: 1) to determine whether the anticipated increase in overweight/obesity prevalence is subsequently paralleled by substantial decrease in undernutrition in the last decade; and 2) to examine both the individual and household predictors’ overweight/obesity among women of reproductive age in rural and urban Nigeria. Methods: We used data acquired from the 2008, 2013 and 2018 Nigeria Demographic and Health Surveys (DHS). Our outcome was defined using standard Body Mass Index (BMI) categories calculated from height and weight measurements of the study participants. The national and stratified (urban/rural) prevalence estimates of underweight (BMI <18.5 Kg/m 2 ), overweight (BMI 25 - 29.9 Kg/m 2 ), and obese (BMI ≥ 30 Kg/m 2 ) were computed for each survey. Afterwards, the underweight subjects were excluded from the study. Two-level (individual and household) logistic regression models were applied to examine the influence of individual and household characteristics on women’s overweight/obese status. Results: Findings show a steady increase in both the prevalence of overweight and obesity from 16.1% and 6.1% in 2008 to 18.2% and 10.0% in 2018, respectively, while underweight prevalence was averagely 12% across each survey. Regardless of the residential setting, age, marital status, education, occupation, household wealth and survey year were consistently associated with increased risk of overweight/obesity, whereas breastfeeding showed a protective association with overweight/obesity. Having multiple children and living in female-headed homes were unique risk factors for overweight/obesity among urban women while ethnicity, media exposure and state of residence were unique risk factors for overweight/obesity among rural women. Conclusion: Public health interventions aimed at lowering the risk of overweight/obesity should embrace the differences in risk factors that exists between rural and urban dwellers.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".