The economic burden of type 2 diabetes on the public healthcare system in Kenya: a cost of illness study
Bibliographic record
Abstract
BACKGROUND: The burden of chronic non-communicable diseases (NCDs) is a growing public health concern. The availability of cost-of-illness data, particularly public healthcare costs for NCDs, is limited in Sub-Saharan Africa (SSA), yet such data evidence is needed for policy action. OBJECTIVE: The objective of this study was to estimate the economic burden of type 2 diabetes (T2D) on Kenya's public healthcare system in 2021 and project costs for 2045. METHODS: This was a cost-of-illness study using the prevalence-based bottom-up costing approach to estimate the economic burden of T2D in the year 2021. We further conducted projections on the estimated costs for the year 2045. The costs were estimated corresponding to the care, treatment, and management of diabetes and some diabetes complications based on the primary data collected from six healthcare facilities in Nairobi and secondary costing data from previous costing studies in low and middle-income countries (LMICs). The data capture and costing analysis were done in Microsoft Excel 16, and sensitivity analysis was conducted on all the parameters to estimate the cost changes. RESULTS: The total cost of managing T2D for the healthcare system in Kenya was estimated to be US$ 635 million (KES 74,521 million) in 2021. This was an increase of US$ 2 million (KES 197 million) considering the screening costs of undiagnosed T2D in the country. The major cost driver representing 59% of the overall costs was attributed to T2D complications, with nephropathy having the highest estimated costs of care and management (US$ 332 million (KES 36, 457 million). The total cost for T2D was projected to rise to US$ 1.6 billion (KES 177 billion) in 2045. CONCLUSION: This study shows that T2D imposes a huge burden on Kenya's healthcare system. There is a need for government and societal action to develop and implement policies that prevent T2D, and appropriately plan care for those diagnosed with T2D.
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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 | 0.004 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".