Expenditures Of Metabolic Diseases – An Estimation on National Health Care Expenditures of Diabetes and Obesity, Hungary 2013
Bibliographic record
Abstract
BACKGROUND: Obesity could be considered as the main consequence of unhealthy nutrition, responsible for many pathological alterations in human. Obese patients usually need more health care services. The aim of the study was to estimate the financial expenditures of health care provisions in Hungary, related to obesity and diabetes, as its main pathological consequence. METHODS: Data of the Hungarian National Health Insurance Fund (NHIF) were collected for 2013, regarding finances of secondary care, hospital services, reimbursement for medications and healing aids of diabetic patients together with selected morbidities linked to obesity, based on the codes of the International Classification of Diseases (ICD) and calculated their population prevalence on the population-attributable fraction (PAF). RESULTS: Financial data regarding diabetes care resulted in a 40,311 Million HUF (129 Million EUR) national fund expenses, beside a 7,173 Million HUF (23 Million EUR) contribution from patients. Estimated total health care expenditures related to obesity were 58,986 Million HUF (188 Million EUR) and the financial contribution of patients was calculated as 25,316 Million HUF (81 Million EUR). These data represent a 5.2% and 9.3% of the whole national health services, 16% and 30% of the whole drug-reimbursement budgets, respectively. CONCLUSIONS: Although expenditures for some obesity related pathologies analyzed in this paper represent 0.28% of the national GDP, considering other morbidities and other patient's expenses, the real ratio could be between 0.5-1%. The increasing number of overweight and obese persons requires more focus in public health, higher awareness in the society and more governmental support.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".