Evidence map›Paper›PMID 27473198›Full record

ArticleBMC public health2016

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

Darja Irdam, Lawrence King, Alexi Gugushvili, Aytalina Azarova, Mihaly Fazekas, Gabor Scheiring, Denes Stefler, Katarzyna Doniec, Pia Horvat, Irina Kolesnikova and 6 more

Abstract read
In one paragraph

Article in BMC public health, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Darja IrdamUniversity of Cambridge, Cambridge, UK.
Lawrence KingUniversity of Cambridge, Cambridge, UK.
Alexi GugushviliUniversity of Cambridge, Cambridge, UK.
Aytalina AzarovaUniversity of Cambridge, Cambridge, UK.
Mihaly FazekasUniversity of Cambridge, Cambridge, UK.
Gabor ScheiringUniversity of Cambridge, Cambridge, UK.
Denes SteflerDepartment of Epidemiology and Public Health, University College London, 1-19 Torrington Place, London, WC1E 6BT, UK.
Katarzyna DoniecUniversity of Cambridge, Cambridge, UK.
Pia HorvatDepartment of Epidemiology and Public Health, University College London, 1-19 Torrington Place, London, WC1E 6BT, UK.
Irina KolesnikovaInstitute of Economics of the National Academy of Sciences of Belarus, Minsk, Belarus.
Vladimir PopovNew Economic School, Moscow, Russia.
Ivan SzelenyiYale University, New Haven, USA.
Michael MarmotDepartment of Epidemiology and Public Health, University College London, 1-19 Torrington Place, London, WC1E 6BT, UK.
Michael MurphyLondon School of Economics, London, UK.
Martin McKeeLondon School of Hygiene and Tropical Medicine, London, UK.
Martin BobakDepartment of Epidemiology and Public Health, University College London, 1-19 Torrington Place, London, WC1E 6BT, UK. m.bobak@ucl.ac.uk.

Funding

British Heart Foundation RG/07/008/23674Medical Research Council G0100222Medical Research Council G0601647Medical Research Council G0701830Medical Research Council G0902037Medical Research Council G1000616Medical Research Council G19/35Medical Research Council G8802774
6 · The paper itself

Abstract

backgroundPrevious 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.

methodsThe 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. DISCUSSION: 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.

Indexed as

CommunismPrivatizationAdultAgedCohort StudiesEurope, EasternFemaleHumansInterviews as TopicMaleMiddle AgedMortalityRetrospective StudiesUnemploymentYoung AdultMortalityMulti-level analysisPost-communist transitionsPrivatization

Identifiers

PMID27473198
PMCPMC4967292

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.