Prevalence and factors associated with pre-diabetes and diabetes mellitus in Kenya: results from a national survey
Bibliographic record
Abstract
BACKGROUND: Diabetes Mellitus is one of the four major non-communicable diseases causing about 4 million deaths in 2017. By 2040, low income countries are projected to experience 92% increase in mortality due to diabetes. Undiagnosed diabetes poses a public health concern with costly public health implications especially in Africa. It is therefore crucial to examine the burden and risk factors for diabetes at national level to inform policy and national programs. METHODS: Data from the 2015 Kenya national STEPs survey of adults aged 18-69 years were used. Pre-diabetes was defined as impaired fasting blood glucose level (6.1 mmol/l to < 7 mmol/l) while diabetes was defined as impaired fasting blood glucose level ≥ 7 mmol/l. Descriptive statistics were used to determine the prevalence of pre-diabetes and diabetes and logistic regression was used to identify associated factors. RESULTS: Complete data for 4069 respondents (51% females), with 46% aged 18-29 and 61% in rural areas were analyzed. The age-standardized prevalence for pre-diabetes and diabetes were 3.1% (95% CI: 2.2, 4.0) and 2.4% (1.8, 3.0) respectively. Only 43.7% were aware of their glycemic condition, one in five of those who had diabetes had received treatment, and only 7% of those diagnosed with diabetes had their blood glucose under control. Primary education ((both incomplete (0.21, 95%CI 0.10-0.47) and complete (0.40, 95%CI 0.23-0.71)) were associated with lower odds of pre-diabetes. Older age (60-69 years, AOR; 5.6, 95%CI 2.1-15.1) and raised blood pressure (2.8, 95% CI 1.5-5.0) were associated diabetes while overweight/obesity among women was associated with diabetes. CONCLUSION: The overall diabetes prevalence in Kenya is consistent with what has been reported in other sub-Saharan African countries. Of concern is the higher prevalence of pre-diabetes and undiagnosed diabetes that can progress to complications in the absence of interventions and the low diabetes awareness and control. This is the first nationally representative study to identify important groups at risk of pre-diabetes and diabetes that can be targeted for screening, health promotion and treatment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".