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On the plausibility of socioeconomic mortality estimates derived from linked data: a demographic approach

Overview of attention for article published in Population Health Metrics, July 2017
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  • Above-average Attention Score compared to outputs of the same age (53rd percentile)

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7 X users

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Title
On the plausibility of socioeconomic mortality estimates derived from linked data: a demographic approach
Published in
Population Health Metrics, July 2017
DOI 10.1186/s12963-017-0143-3
Pubmed ID
Authors

Mathias Lerch, Adrian Spoerri, Domantas Jasilionis, Francisco Viciana Fernandèz

Abstract

Reliable estimates of mortality according to socioeconomic status play a crucial role in informing the policy debate about social inequality, social cohesion, and exclusion as well as about the reform of pension systems. Linked mortality data have become a gold standard for monitoring socioeconomic differentials in survival. Several approaches have been proposed to assess the quality of the linkage, in order to avoid the misclassification of deaths according to socioeconomic status. However, the plausibility of mortality estimates has never been scrutinized from a demographic perspective, and the potential problems with the quality of the data on the at-risk populations have been overlooked. Using indirect demographic estimation (i.e., the synthetic extinct generation method), we analyze the plausibility of old-age mortality estimates according to educational attainment in four European data contexts with different quality issues: deterministic and probabilistic linkage of deaths, as well as differences in the methodology of the collection of educational data. We evaluate whether the at-risk population according to educational attainment is misclassified and/or misestimated, correct these biases, and estimate the education-specific linkage rates of deaths. The results confirm a good linkage of death records within different educational strata, even when probabilistic matching is used. The main biases in mortality estimates concern the classification and estimation of the person-years of exposure according to educational attainment. Changes in the census questions about educational attainment led to inconsistent information over time, which misclassified the at-risk population. Sample censuses also misestimated the at-risk populations according to educational attainment. The synthetic extinct generation method can be recommended for quality assessments of linked data because it is capable not only of quantifying linkage precision, but also of tracking problems in the population data. Rather than focusing only on the quality of the linkage, more attention should be directed towards the quality of the self-reported socioeconomic status at censuses, as well as towards the accurate estimation of the at-risk populations.

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X Demographics

The data shown below were collected from the profiles of 7 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 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 24 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 17%
Student > Ph. D. Student 4 17%
Other 2 8%
Student > Bachelor 2 8%
Student > Master 2 8%
Other 4 17%
Unknown 6 25%
Readers by discipline Count As %
Social Sciences 7 29%
Medicine and Dentistry 4 17%
Engineering 2 8%
Economics, Econometrics and Finance 1 4%
Nursing and Health Professions 1 4%
Other 1 4%
Unknown 8 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 31 July 2017.
All research outputs
#12,852,900
of 22,988,380 outputs
Outputs from Population Health Metrics
#252
of 391 outputs
Outputs of similar age
#144,823
of 312,506 outputs
Outputs of similar age from Population Health Metrics
#8
of 11 outputs
Altmetric has tracked 22,988,380 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 391 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.8. This one is in the 35th percentile – i.e., 35% 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 312,506 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 53% 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 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.