Estimating the Economic Burden of Diabetes Mellitus in Kenya: a Cost of Illness Study
Bibliographic record
Abstract
Diabetes mellitus is one of the non-communicable diseases that depletes the wealth of any individual directly and indirectly due to the cost associated with treating the illness and its complications. The study aims to estimate the economic burden of Diabetes mellitus in Kenya from a societal perspective using a cost-of-illness approach. The study’s results and findings for the economic burden of diabetes mellitus in Kenya relied on the cost of illness approach. The approach identifies and measures all the costs of Diabetes mellitus, including direct and indirect costs. The 552,400 adult cases reported in 2019 resulted in a total economic cost of USD 372,184,585, equivalent to USD 674 per diabetes mellitus patient. The total direct costs accounted for the highest proportion of the overall costs at 61% (USD 227,980,126), whereas indirect costs accounted for 39% of the total economic costs (USD 144,204,459). Costs of medicines accounted for the highest costs over the total economic costs at about 29%, followed by the income lost while seeking care at 19.7%. Other costs that accounted for more than 10% of the total costs include productivity losses (19%), diagnostic tests (13%), and travel (12%). The rest of the cost categories accounted for less than 5%. Efforts should be made to reduce the costs of these medicines to enhance care. The high indirect costs reported, majorly in income lost by patients while seeking medical care, are 19%. Access to affordable health services such as diabetes mellitus education, regular blood glucose screening initiatives, and increasing local manufacturing of medicines can reduce the economic burden of diabetes mellitus and increase the health outcomes of the population and their contributions to society.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".