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Mortality in Transition: Study Protocol of the PrivMort Project, a multilevel convenience cohort study

Overview of attention for article published in BMC Public Health, July 2016
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Title
Mortality in Transition: Study Protocol of the PrivMort Project, a multilevel convenience cohort study
Published in
BMC Public Health, July 2016
DOI 10.1186/s12889-016-3249-9
Pubmed ID
Authors

Darja Irdam, Lawrence King, Alexi Gugushvili, Aytalina Azarova, Mihaly Fazekas, Gabor Scheiring, Denes Stefler, Katarzyna Doniec, Pia Horvat, Irina Kolesnikova, Vladimir Popov, Ivan Szelenyi, Michael Marmot, Michael Murphy, Martin McKee, Martin Bobak

Abstract

Previous research using routine data identified rapid mass privatisation as an important driver of mortality crisis following the collapse of Communism in Central and Eastern Europe. However, existing studies on the mortality crisis relying on individual level or routine data cannot assess both distal (societal) and proximal (individual) causes of mortality simultaneously. The aim of the PrivMort Project is to overcome these limitations and to investigate the role of societal factors (particularly rapid mass privatisation) and individual-level factors (e.g. alcohol consumption) in the mortality changes in post-communist countries. The PrivMort conducts large-sample surveys in Russia, Belarus and Hungary. The approach is unique in comparing towns that have undergone rapid privatisation of their key industrial enterprises with those that experienced more gradual forms of privatisation, employing a multi-level retrospective cohort design that combines data on the industrial characteristics of the towns, socio-economic descriptions of the communities, settlement-level data, individual socio-economic characteristics, and individuals' health behaviour. It then incorporates data on mortality of different types of relatives of survey respondents, employing a retrospective demographic approach, which enables linkage of historical patterns of mortality to exposures, based on experiences of family members. By May 2016, 63,073 respondents provided information on themselves and 205,607 relatives, of whom 102,971 had died. The settlement-level dataset contains information on 539 settlements and 12,082 enterprises in these settlements in Russia, 96 settlements and 271 enterprises in Belarus, and 52 settlement and 148 enterprises in Hungary. In addition to reinforcing existing evidence linking smoking, hazardous drinking and unemployment to mortality, the PrivMort dataset will investigate the variation in transition experiences for individual respondents and their families across settlements characterized by differing contextual factors, including industrial characteristics, simultaneously providing information about how excess mortality is distributed across settlements with various privatization strategies.

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The data shown below were collected from the profiles of 3 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 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 3%
Unknown 39 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 18%
Student > Ph. D. Student 5 13%
Other 3 8%
Student > Master 3 8%
Student > Bachelor 2 5%
Other 5 13%
Unknown 15 38%
Readers by discipline Count As %
Social Sciences 8 20%
Medicine and Dentistry 5 13%
Psychology 3 8%
Biochemistry, Genetics and Molecular Biology 2 5%
Engineering 2 5%
Other 2 5%
Unknown 18 45%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 August 2016.
All research outputs
#14,553,567
of 23,306,612 outputs
Outputs from BMC Public Health
#10,568
of 15,196 outputs
Outputs of similar age
#217,151
of 367,356 outputs
Outputs of similar age from BMC Public Health
#259
of 366 outputs
Altmetric has tracked 23,306,612 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 15,196 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 27th percentile – i.e., 27% 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 367,356 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 366 others from the same source and published within six weeks on either side of this one. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.