Prevalence of food insecurity in patients with diabetes in western Kenya
Bibliographic record
Abstract
AIMS: To determine the characteristics of patients with diabetes who reported food insecurity at three diabetes clinics in western Kenya. METHODS: This study includes routinely collected demographic data at the first presentation of patients with diabetes at clinics in western Kenya from 1 January 2006 to 24 September 2011. A validated questionnaire was used to assess food insecurity with descriptive and comparative statistics being used to analyse the food-secure and food-insecure populations. RESULTS: The number of patients presenting to these clinics who were food-secure and those who were food-insecure was 1179 (68.0%) and 554 (32.0%), respectively. Comparative analysis shows a statistically significant difference in weight, BMI, the presence of a caretaker, and use of insulin between the two groups. These variables were lower in the food-insecure group. The overall assessment of the clinic population revealed an abnormally high mean HbA1c concentration of 81 mmol/mol (9.6%). CONCLUSIONS: Despite the widely recognized contribution of caloric over-nutrition to the development of diabetes, this study highlights the high prevalence of food insecurity amongst patients with diabetes in rural, resource-constrained settings. Other factors, such as the lower prevalence of obesity, poor glucose control, challenges in the use of insulin because of the risk of hypoglycaemia, and varying subtypes of diabetes in this population, point to the need for additional research in understanding the aetiology, pathophysiology and optimum management of this condition, as well as understanding the effects of enhancing food security.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".