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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".