High Urban-Rural Inequities of Abdominal Obesity in Malawi: Insights from the 2009 and 2017 Malawi Noncommunicable Disease Risk Factors Surveys
Bibliographic record
Abstract
Geographical disparities in abdominal obesity (AO) exist in low-income countries due to major demographic and structural changes in urban and rural areas. We aimed to investigate differences in the urban–rural prevalence of AO in the Malawi population between 2009 and 2017. We conducted a secondary analysis of data from the Malawi 2009 and 2017 STEPS surveys. AO (primary outcome) and very high waist circumference (secondary outcome) were defined using WHO criteria. Prevalence estimates of AO and very high waist circumference (WC) were standardized by age and sex using the age and sex structure of the adult population in Malawi provided by the 2018 census. A modified Poisson regression analysis adjusted for sociodemographic covariates was performed to compare the outcomes between the two groups (urban versus rural). In total, 4708 adults in 2009 and 3054 adults in 2017 aged 25–64 were included in the study. In 2009, the age–sex standardized prevalence of AO was higher in urban than rural areas (40.9% vs 22.0%; adjusted prevalence ratio [aPR], 1.51; 95% confidence interval [CI], 1.36–1.67; p < 0.001). There was no significant trend for closing this gap in 2017 (urban 37.0% and rural 21.4%; aPR, 1.48; 95% CI, 1.23–1.77; p < 0.001). This urban–rural gap remained and was slightly wider when considering the ‘very high WC’ threshold in 2009 (17.0% vs. 7.1%; aPR, 1.98; 95%CI, 1.58–2.47; p < 0.001); and in 2017 (21.4% vs. 8.3%; aPR, 2.03; 95%CI, 1.56–2.62; p < 0.001). Significant urban–rural differences exist in the prevalence of AO and very high WC in Malawi, and the gap has not improved over the last eight years. More effective weight management strategies should be promoted to reduce health care disparities in Malawi, particularly in urban areas.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".