An increase of the health care cost of diabetes mellitus type 2: a precise review on economic impact of diabetes
Bibliographic record
Abstract
Since the last decade chronic diseases is a major contributor for rapid rise in healthcare cost in developing countries. It is apparent from published economic studies that people with diabetes account for between 2 and 3% of the total health care budget in every country consequently, an increase in the incidence, and therefore the prevalence, of diabetes will have a considerable economic impact. It is also apparent, however, that better health economic studies are needed. Objective: The study aim was to highlight the economic burden for diabetes mellitus type2 on world economy and individual patients care cost. Results: It was estimated that around 54% of deaths in developing countries are due to chronic non- communicable diseases which is predicted to rise by 65% by 2030. Diabetes mellitus is among the most prevalent chronic diseases suffered by more than 180 million people worldwide. By 2030 it is estimated that around 400 million people in the world will be afflicted with diabetes. Annual deaths attributable to diabetes are probably as high as 3 million with more than 80% occur in developing countries. In Bangladesh, the average annual cost of care was found US $ 314 (direct cost US $ 283 and indirect cost US $ 37) ranging from US $ 23 to US $ 1334. By 2020 it is expected that the Canadian economy will lose $11 billion annually as a result of the net mortality of diabetes patients, and long-term care costs for diabetes patients are expected to jump from $1 billion to $2.7 billion annually over that time period. The average annual cost per diabetic patient of Mexican was $708 USD, the total annual cost of diabetics was 2,618,000 USD,15.46% of health spending and 0.79% of GDP. Conclusion: Furthermore, although it is relatively easy to assess the direct costs associated with the disease, better methods are needed to measure the indirect and intangible costs of diabetes to enable full assessment of the economic impact of the disease.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".