Prevalence of Diabetes and the Relationship Between Wealth and Social Demographic Characteristics Across 6 Low-and-Middle Income Countries
Bibliographic record
Abstract
Background: As the global burden of diabetes persists, research is needed to understand the role of wealth and correlates of diabetes across regions of the world. The purpose of this study is to examine the prevalence and role of wealth and diabetes across 6 low- and middle- income countries while also accounting for independent correlates of diabetes by country. Methods: Data from the Study on Global Ageing and Adult Health (SAGE), SAGE Wave 1 was used. Self-reported diabetes status was the primary dependent variable and wealth quintile, number of dwelling characteristics and possession of a set of assets, was the independent variable. Logistic regression models examined the relationship between wealth and presence of diabetes across 6 countries with the highest wealth quintile, quintile 1, serving as the reference group. Results: Sample size by country included Ghana N = 5573, South Africa N = 4227, Russia N = 4947, Mexico N = 5448, India N = 12198, and China N = 15050. Average age across country ranged from 49 to 63 years of age. Prevalence of diabetes across country included 3.4% and 9.2% for Ghana and South Africa, respectively. In Russia, 8.3%; Mexico, 18.1%; India, 4.9%; and China, 5.9% of the sample reported having diabetes. In the adjusted logistic model, wealth was associated with higher odds of diabetes in Ghana (OR 2.26; CI 1.28; 4.13), South Africa (OR 4.57; CI 2.25; 10.32), Mexico (OR 2.00; CI 1.14; 3.60), India (OR 2.45; CI 1.60; 3.86), and China (OR 2.16; CI 1.62, 2.93). Conclusions: These findings add to the growing body of evidence in our understanding between wealth and diabetes. As diabetes persists as a leading cause of death globally, future work should focus on mechanisms underlying the relationship between wealth and diabetes while also developing interventions to mitigate his burgeoning disease affecting communities across low- and middle-income countries.
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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 | 0.001 |
| 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".