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Multiple causes of death analysis of chronic diseases: the example of diabetes

Overview of attention for article published in Population Health Metrics, August 2015
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Title
Multiple causes of death analysis of chronic diseases: the example of diabetes
Published in
Population Health Metrics, August 2015
DOI 10.1186/s12963-015-0056-y
Pubmed ID
Authors

Ugo Fedeli, Giacomo Zoppini, Carlo Alberto Goldoni, Francesco Avossa, Giuseppe Mastrangelo, Mario Saugo

Abstract

Identifying a single disease as the underlying cause of death (UCOD) is an oversimplification of the clinical-pathological process leading to death. The multiple causes of death (MCOD) approach examines any mention of a disease in death certificates. Taking diabetes as an example, the study investigates: patterns of death certification, differences in mortality figures based on the UCOD and on MCOD, factors associated to the mention of diabetes in death certificates, and potential of MCOD in the analysis of the association between chronic diseases. The whole mortality archive of the Veneto Region-Italy was extracted from 2008 to 2010. Mortality rates and proportional mortality were computed for diabetes as the UCOD and as MCOD. The position of the death certificate where diabetes was mentioned was analyzed. Conditional logistic regression was applied with chronic liver diseases (CLD) as the outcome and diabetes as the exposure variable. A subset of 19,605 death certificates of known diabetic patients (identified from the archive of exemptions from medical charges) was analyzed, with mention of diabetes as the outcome and characteristics of subjects as well as other diseases reported in the certificate as predictors. In the whole mortality archive, diabetes was mentioned in 12.3 % of death certificates, and selected as the UCOD in 2.9 %. The death rate for diabetes as the UCOD was 26.8 × 10(5) against 112.6 × 10(5) for MCOD; the UCOD/MCOD ratio was higher in males. The major inconsistencies of certification were entering multiple diseases per line and reporting diabetes as a consequence of circulatory diseases. At logistic regression the mention of diabetes was associated with the mention of CLD (mainly non-alcohol non-viral CLD). In the subset of known diabetic subjects, diabetes was reported in 52.1 %, and selected as the UCOD in 13.4 %. The probability of reporting diabetes was higher with coexisting circulatory diseases and renal failure and with long duration of diabetes, whereas it was lower in the presence of a neoplasm. The use of MCOD makes the analysis of mortality data more complex, but conveys more information than usual UCOD analyses.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 61 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Nigeria 1 2%
Unknown 60 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 18%
Student > Ph. D. Student 8 13%
Student > Master 6 10%
Student > Bachelor 6 10%
Professor 4 7%
Other 14 23%
Unknown 12 20%
Readers by discipline Count As %
Medicine and Dentistry 21 34%
Nursing and Health Professions 4 7%
Social Sciences 4 7%
Psychology 3 5%
Economics, Econometrics and Finance 2 3%
Other 10 16%
Unknown 17 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 March 2022.
All research outputs
#7,359,053
of 23,365,820 outputs
Outputs from Population Health Metrics
#208
of 393 outputs
Outputs of similar age
#86,369
of 268,737 outputs
Outputs of similar age from Population Health Metrics
#6
of 11 outputs
Altmetric has tracked 23,365,820 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 393 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.0. This one is in the 46th percentile – i.e., 46% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 268,737 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.
We're also able to compare this research output to 11 others from the same source and published within six weeks on either side of this one. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.