Prevalence and associated factors of abdominal obesity among the adult population in Woldia town, Northeast Ethiopia, 2020: Community-based cross-sectional study
Bibliographic record
Abstract
BACKGROUND: The prevalence of abdominal obesity has been dramatically increasing both in developed and developing countries, including Ethiopia. It is an independent risk factor for cardiovascular diseases, type-2 diabetes mellitus, high blood pressure, and cancer. However, there is inadequate data regarding the prevalence and associated factors of abdominal obesity among adult population in Ethiopia. OBJECTIVE: This study aimed to assess the prevalence and associated factors of abdominal obesity among the adult population in Woldia town, Northeast Ethiopia in 2020. METHOD: A community-based cross-sectional study was conducted in Woldia town from March 20 to April 20, 2020. Data on socio-demographic, dietary history, and anthropometric characteristics were collected from 802 adults using the World Health Organization stepwise technique. Multi-stage sampling was employed to select the study subjects. Data were cleaned, coded, and entered by EPI-info Version 7 and were exported to SPSS Version 20 for further analysis. To indicate the strength of association, odds ratios (OR) and 95% confidence intervals (95% CI) were used. RESULT: From a total of 823 respondents 802 were involved with a response rate of 97.4%. The overall prevalence of central obesity based on waist to hip ratio was 16.5% with 95% CI (14.2-19.2). Female sex [AOR = 13.3, 95% CI: 7.01-25.39), high wealth rank (AOR = 2.95, 95% CI: 1.21-7.17), single marital status (AOR = 0.16, 95%CI: 0.04-0.58), age from 35 to 55 years (AOR = 4.3, 95% CI: 2.22-7.99), age greater than 55 years (AOR = 3.8, 95%CI: 1.36-10.78), secondary educational level (AOR = 1.83, 95% CI: 1.05-3.18), eat more protein (AOR = 4.22, 95% CI: 1.26-14.22), and consumption of snacks (AOR = 2.78, 95% CI: 1.68-4.61) were significantly associated with abdominal obesity. CONCLUSION: The prevalence of abdominal obesity among adults in Woldia town is high, and has become an emerging nutrition-related problem. Being female, older age, being in a high wealth rank, consuming more meat, having secondary education level, and consuming snacks were the risk factors of abdominal obesity. Nutrition interventions should target adults mainly focusing on the alarmingly increase in nutrition problems, such as abdominal obesity, in Ethiopia with specific attention to females.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".