Evidence map›Paper›PMID 42216077›Full record

ArticlePopulation health metrics2026

Assessing the risk of worsening conditions for diabetes mellitus patients in Italy: a non-parametric and semi-parametric approach to time-to-event data.

Cristiana Abbafati, Luciano Nieddu

Abstract read
In one paragraph

Article in Population health metrics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Cristiana AbbafatiDepartment of Legal and Economic Studies, Sapienza University of Rome, P.le A. Moro, 5, 00185, Rome, Italy. cristiana.abbafati@uniroma1.it.
Luciano NiedduDepartment of Humanistic and Social Studies, UNINT University, Via C. Colombo, 200, 00147, Rome, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDiabetes is associated with the development of sequelae that further reduce patients' quality of life. A valuable instrument for assessing the severity of sequelae is the disability weight. The evaluation of the risks associated with the potential deterioration of sequelae has direct implications for healthcare.

objectivesThe objective of this study is to estimate the risk of progression from no complications to any complications in both type 2 and type 1 diabetes and to examine the progression of risk over time.

methodsSurvival analysis methods are applied. The Kaplan-Meier and the Cox proportional hazards models are employed to ascertain the probability of developing complications in the future, given that the subject is currently free of such complications. Patients were obtained from the longitudinal dataset of the Italian Association of Diabetologists, collected between 2005 and 2017.

resultsThis is the first study in Italy to estimate the risk of transition to complications in people with diabetes. The results indicate that older males with type 2 diabetes living in the central and northern regions of the country are associated with a higher risk profile. Since the patients excluded from the baseline have the same risk characteristics as the patients studied, the results can be generalized. Our findings provide evidence within a large clinical cohort and suggest potential applicability of the proposed approach in other settings beyond the Italian context.

conclusionQuantifying risk in a way that is easily understood by policymakers and the general public is a valuable tool, as diabetes complications are a significant burden for individuals and society as a whole.

Indexed as

Diabetes ComplicationsDiabetes Mellitus, Type 1Diabetes Mellitus, Type 2Disease ProgressionAdultAgedFemaleHumansItalyKaplan-Meier EstimateLongitudinal StudiesMaleMiddle AgedProportional Hazards ModelsQuality of LifeRisk AssessmentDiabetesIndividual and societal costsSequelaeTime-to-event dataYLDs related risk

Identifiers

PMID42216077
PMCPMC13330368

What Socratic holds

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.