Evidence map›Paper›PMID 37261238›Full record

ArticleFrontiers in public health2023

Small-area deprivation index does not improve the capability of multisource comorbidity score in mortality prediction.

Federico Rea, Mauro Ferrante, Salvatore Scondotto, Giovanni Corrao

Abstract read
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Article in Frontiers in public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Federico ReaNational Centre for Healthcare Research and Pharmacoepidemiology, University of Milano-Bicocca, Milan, Italy.
Mauro FerranteDepartment of Culture and Society, University of Palermo, Palermo, Italy.
Salvatore ScondottoEpidemiologic Observatory, Sicily Regional Health Service, Palermo, Italy.
Giovanni CorraoNational Centre for Healthcare Research and Pharmacoepidemiology, University of Milano-Bicocca, Milan, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The stratification of the general population according to health needs allows to provide better-tailored services. A simple score called Multisource Comorbidity Score (MCS) has been developed and validated for predicting several outcomes. The aim of this study was to evaluate whether the ability of MCS in predicting 1-year mortality improves by incorporating socioeconomic data (as measured by a deprivation index). Methods: Beneficiaries of the Italian National Health Service who in the index year (2018) were aged 50-85 years and were resident in the Sicily region for at least 2 years were identified. For each individual, the MCS was calculated according to his/her clinical profile, and the deprivation index of the census unit level of the individual's residence was collected. Frailty models were fitted to assess the relationship between the indexes (MCS and deprivation index) and 1-year mortality. Akaike information criterion and Bayesian information criterion statistics were used to compare the goodness of fit of the model that included only MCS and the model that also contained the deprivation index. The models were further compared by means of the area under the receiver operating characteristic curve (AUC). Results: The final cohort included 1,062,221 individuals, with a mortality rate of 15.6 deaths per 1,000 person-years. Both MCS and deprivation index were positively associated with mortality.The goodness of fit statistics of the two models were very similar. For MCS only and MCS plus deprivation index models, Akaike information criterion were 17,013 and 17,038, respectively, whereas Bayesian information criterion were 16,997 and 17,000, respectively. The AUC values were 0.78 for both models. Conclusion: The present study shows that socioeconomic features as measured by the deprivation index did not improve the capability of MCS in predicting 1-year risk of death. Future studies are needed to investigate other sources of data to enhance the risk stratification of populations.

Indexed as

State MedicineBayes TheoremComorbidityFemaleHumansItalyMaleROC Curvecomorbidityhealthcaremortalitypopulation-based studyprognostic scoresocioeconomic position

Identifiers

PMID37261238
PMCPMC10228715

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