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Identifying potential differences in cause-of-death coding practices across Russian regions

Overview of attention for article published in Population Health Metrics, March 2016
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  • Above-average Attention Score compared to outputs of the same age and source (57th percentile)

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1 policy source
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Title
Identifying potential differences in cause-of-death coding practices across Russian regions
Published in
Population Health Metrics, March 2016
DOI 10.1186/s12963-016-0078-0
Pubmed ID
Authors

Inna Danilova, Vladimir M. Shkolnikov, Dmitri A. Jdanov, France Meslé, Jacques Vallin

Abstract

Reliable and comparable data on causes of death are crucial for public health analysis, but the usefulness of these data can be markedly diminished when the approach to coding is not standardized across territories and/or over time. Because the Russian system of producing information on causes of death is highly decentralized, there may be discrepancies in the coding practices employed across the country. In this study, we evaluate the uniformity of cause-of-death coding practices across Russian regions using an indirect method. Based on 2002-2012 mortality data, we estimate the prevalence of the major causes of death (70 causes) in the mortality structures of 52 Russian regions. For each region-cause combination we measured the degree to which the share of a certain cause in the mortality structure of a certain region deviates from the respective inter-regional average share. We use heat map visualization and a regression model to determine whether there is regularity in the causes and the regions that is more likely to deviate from the average level across all regions. In addition to analyzing the comparability of cause-specific mortality structures in a spatial dimension, we examine the regional cause-of-death time series to identify the causes with temporal trends that vary greatly across regions. A high level of consistency was found both across regions and over time for transport accidents, most of the neoplasms, congenital malformations, and perinatal conditions. However, a high degree of inconsistency was found for mental and behavioral disorders, diseases of the nervous system, endocrine disorders, ill-defined causes of death, and certain cardiovascular diseases. This finding suggests that the coding practices for these causes of death are not uniform across regions. The level of consistency improves when causes of death can be grouped into broader diagnostic categories. This systematic analysis allows us to present a broader picture of the quality of cause-of-death coding at the regional level. For some causes of death, there is a high degree of variance across regions in the likelihood that these causes will be chosen as the underlying causes. In addition, for some causes of death the mortality statistics reflect the coding practices, rather than the real epidemiological situation.

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The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 48 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 19%
Researcher 6 13%
Student > Doctoral Student 3 6%
Professor > Associate Professor 3 6%
Lecturer 2 4%
Other 8 17%
Unknown 17 35%
Readers by discipline Count As %
Medicine and Dentistry 12 25%
Social Sciences 6 13%
Agricultural and Biological Sciences 2 4%
Philosophy 1 2%
Nursing and Health Professions 1 2%
Other 6 13%
Unknown 20 42%
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 13 August 2018.
All research outputs
#6,461,165
of 22,931,367 outputs
Outputs from Population Health Metrics
#187
of 392 outputs
Outputs of similar age
#92,183
of 300,310 outputs
Outputs of similar age from Population Health Metrics
#7
of 14 outputs
Altmetric has tracked 22,931,367 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 392 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.7. This one has gotten more attention than average, scoring higher than 51% of its peers.
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 300,310 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 68% of its contemporaries.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.