Clinical burden of diabetes in Italy in 2018: a look at a systemic disease from the ARNO Diabetes Observatory
Bibliographic record
Abstract
INTRODUCTION: Diabetes is a highly prevalent disease worldwide and represents a challenge for patients and healthcare systems. This population-based study evaluated diabetes burden in Italy in 2018 by assessing all aspects of outpatient and hospital care. RESEARCH DESIGN AND METHODS: We investigated data of 11 300 750 residents in local health districts contributing to ARNO Diabetes Observatory (~20% of Italian inhabitants). All administrative healthcare claims were analyzed to gather information on access to medical resources. Subjects with diabetes, identified by antihyperglycemic drug prescriptions, disease-specific copayment exemption and hospital discharge codes, were compared with age, sex and residency-matched non-diabetic individuals. RESULTS: We identified 697 208 subjects with ascertained diabetes, yielding a prevalence of 6.2% (6.5% in men vs 5.9% in women, p<0.001). Age was 69±15 (mean±SD). As compared with non-diabetic subjects, patients with diabetes received more prescriptions of any drugs (+30%, p<0.001), laboratory tests, radiologic exams and outpatient specialist consultations (+20%, p<0.001) and were hospitalized more frequently (+86%, p<0.001), with a longer stay (+1.4 days, p<0.001). Although cardiovascular diseases accounted for many hospital discharge diagnoses, virtually all diseases contributed to the higher rate of hospital admissions in diabetic subjects (235 vs 99 per 1000 person-years, p<0.001). Healthcare costs were >2-fold higher in subjects with diabetes, mainly driven by hospitalizations and outpatient care related to chronic complications rather than to glucose-lowering drugs, diabetes-specific devices, or metabolic monitoring. CONCLUSIONS: The burden of diabetes in Italy is particularly heavy and, as a systemic disease, it includes all aspects of clinical medicine, with consequent high expenses in all areas of healthcare.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".