The Mortality Crisis in Transitional Economies (WIDER Studies in Development Economics)
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With appropriate modelling, this information may be used to produce standard indicators such as age-specific mortality rates. One example is educational differences in mortality in Russia. Since , educational level is no longer collected on death certificates, so routine information is no longer available, but estimates can be made by collecting information form respondents on educational level and survival of their relatives. In highly literate and numerate societies, information on age and date of death can be collected so that more refined estimates may be made.
Life and death during the Great Depression
The PrivMort project used a multilevel approach by sampling mono-industrial towns with radical privatization and then matching them with towns with slow privatization experiences. This approach provides an opportunity to estimate the effect of privatization on health, while controlling for individual-level confounding factors.
Mono-industrial towns were defined as where a single industrial enterprise provided employment for at least 7. After that, a matching control group of four multi-industrial towns with fast privatisation and one multi-industrial town with slow privatization were selected. We chose this strategy mainly to address the far greater diversity of privatisation strategies applied in Hungary.
There are 11 mono-towns in Belarus in total, so all of them were included into the dataset. After that, 9 multi-slow towns in Belarus were matched to the 15 mono-fast towns selected in Russia. The full list of settlements can be found in the Additional file 2. Towns were matched using standard propensity score matching based on the pre-transition demographic and socio-economic conditions in the settlements. We used eight potential predictors of mortality levels, all measured for the pre-transition year, , with the exception of wage in USD, which was available from 1 crude death rates per population in ; 2 pre-reform population size, 3 dependency ratio in ; 4 average wage in US dollars in ; 5 number of physicians per 10, population in ; 6 floor area per person in ; 7 death rates from alcohol poisoning per , population in ; and 8 emission of pollutants into the atmosphere from stationary sources, thousand tons in In Russia, for example, this means that we had two groups of mono-industrial towns that were close to identical on these 8 variables, but one group experienced fast privatization and one group slow privatization.
The same procedure was employed to match multi-industrial towns to mono-industrial towns. The data on the population of the settlements came from the historical state dataset on Russian towns since The enterprise-level data were collected from a variety of state sources. In Russia many enterprises changed their registry code after privatization, which required a rigorous selection process. First, enterprises with less than employees were eliminated from the dataset in Russia. Then, the datasets were matched based on the OKPO National Classification of Enterprises and Organizations by the State Statistics Service registration code for enterprises, while for those firms that changed their code after being privatized, the matching was based on the address, enterprise name and the approximate size of the enterprise within a settlement in some cases.
The Hungarian settlement-level data were obtained from the Hungarian Central Statistical Office and a variety of other government sources. The largest companies in the selected settlements based on registered capital in were identified through the Company Information Service of the Ministry of Justice. Some data were acquired from the Hungarian Privatization Agency. After selecting the three biggest companies in each settlement data on the number of employees, on ownership structure and profitability were collected from the archives of the local courts of registry and from various private digital company information archives.
Most of the companies that existed in changed their names or their legal form. To ensure continuity the successors of the original parent companies were identified and data on them were obtained. Overall, altogether we collected data on Hungarian companies. For analytical reasons, original parent companies and successors were later treated as one company.
When there was more than one successor company, the biggest company by registered capital or the one closest to the original company by type of activity was selected. A random walk procedure was used in the PrivMort for sampling the respondents. First, the settlements were divided into street-centered clusters, which were then distributed among the interviewers using the method of random numbers. Each route could generate up to 25 interviews. Starting from the first house on the street in each cluster, a step of four was applied in private houses and in apartment blocks.
Only one respondent was selected from each household, even in cases when more than one family shared the same house. Interviewers had to make four attempts at interviewing the person who matched the screening criteria if he or she was temporarily unavailable. All respondents were born before to ensure that they and their relatives were of working age in and hence could potentially be affected by the transition. The selection was conditional on the fact that their family members lived in the same settlement for a prolonged period of time during and after the transitions.
While the criterion for women was to have at least one family member parents, siblings or a spouse living in the same settlement in this time period, the survey excluded male respondents who only had their spouses residing in the same settlement. Information on survival is collected for a maximum of two siblings who survived to age 20, the age at which our analysis of adult mortality starts.
The Mortality Crisis in Transitional Economies : Giovanni Andrea Cornia :
The proportion of informants with larger number of siblings is small; for reasons of interview efficiency, additional information collected would be counter-productive, and more siblings would lead to even greater over-representation of those from large sibships. The third group of relatives consists of the first partners married or long-term cohabiters of female respondents. We ask about survival of the first partner, since if current partner were included, information on men who died early would be differentially excluded and therefore bias results. Since our main interest was in male mortality, we did not ask for information on female partners from male respondents.
Data were collected in face-to-face interviews using structured questionnaires, covering characteristics of the respondents and their relatives, including the residency history including some questions on international and domestic migration ; education levels; marital status; religious affiliation; lifestyle habits such as smoking and alcohol consumption; vital status of relatives, a substantial separate block of questions on issues such as the labour market position and employment history; and some questions on economic conditions.
The section containing questions on alcohol consumption is particularly detailed and contains measures and estimations of the frequency and the amount of drinking, of the character of drinking or the type alcohol consumed, and on the consumption of health-hazardous spirits. The full questionnaire is available upon request. The questionnaire was developed by a multidisciplinary team of researchers, followed by cognitive testing of questions on respondents sampled from mono-industrial towns using the snowball sampling. The cognitive tests were carried out in a controlled environment to identify problematic wording and sensitive questions.
As a result of the cognitive interviews, the questionnaire has been modified to ensure a smooth flow of the conversation and to make the respondents as comfortable and confident as possible. There were then small pilot surveys conducted in each country before large scale surveying began.
The back-checks were mostly performed randomly by phone, while in some cases the regional supervisors carried out the back-checks by visiting individual households. During the cognitive tests, we discovered that respondent sensitivity was less of a problem than initially feared, consistent with previous experience in Russia where people often appreciated the opportunity to talk about their deceased relatives, knowing that they were contributing to research that may benefit public health in the future [ 12 ].
PrivMort is currently spring and summer conducting a new set of surveys. First, 1, representative interviews will be conducted in both the European part of Russia and in Hungary. This will significantly increase the generalizability of the findings. In addition, 8, interviews containing a set of questions on self-reported health and psychological wellbeing will be collected in Russia.
All of these interviews will be collected in 23 randomly selected towns in Sverdlovsk Oblast - one of the most typical industrial regions of the European part of Russia, where the mortality trends on average match the country averages perfectly. Finally, the PrivMort project conducts qualitative fieldwork in Russia and Hungary in order to compliment the surveys. The interviews intend to also address the issues stemming from the unaccountable domestic migration, which might have not been captured by the sampling design.
For reasons noted above, relevant individual-level data do not exist, but we have shown that we can use reports for relatives as an alternative data source [ 3 , 16 — 18 ].
Our data permit the allocation of each mortality event to a cell with a specific set of characteristics, such as, for instance, the number of deaths among well-educated men age 45 in a rapid-privatization town in The corresponding person-years of exposure to risk in the group may be calculated using standard software e. Mortality rates may be fitted using a standard Poisson GLM with logarithm of exposure as offset, with both individual and macro-level data as covariates.
The model coefficients may be used to show the relative risks associated with various covariates, and the fitted values of mortality rates may be used to construct variables such as life expectancy at age 40 for an individual with a specified set of characteristics or variables such as the probability of survival from 20 to These data may be used to fit a number of alternative survival models including Cox regression models, with or without time-dependent covariates, or parametric models such a Gompertz or Weibull distributions.
The data have a multi-level structure, with subjects i. These structures can be modeled using multi-level models. Since there is a rich settlement-level macro-level dataset, these variables can be included in such models where appropriate. The PrivMort project investigates the distal and proximal causes of mortality in post-communist transition countries.
To achieve this ambitious objective, the project established a retrospective indirect convenience cohort of relatives of respondents in population surveys. These individual level-data are complemented by settlement level data from other sources. The complex methodology has several limitations.
First, it was not possible to cover the whole post-communist region. We focus on the European part of Russia and Belarus mainly because the post-Communist mortality crisis was especially severe in these regions of the Former Soviet Union. These two former Soviet countries are relatively homogeneous, with similar religious, cultural and socio-economic characteristics but different pace and type of privatization, which makes the comparison especially favourable, as it allows us to test for overall transition strategy at the country level.
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Excluding the region of the North Caucasus from the sample is crucial as the region was torn by several conflicts in the s and s. Including Hungary, which applied a gradual privatization model with a significantly higher share of foreign ownership [ 19 ], makes it possible to investigate the same issues in a non-Soviet country. Second, the convenience cohort based on the Brass method is not directly representative of a defined population and the chance of inclusion is not uniform. However, since we obtain information from a range of different types of individuals, very few people are excluded an older only child unmarried woman whose parents are dead would be an example.
However, there is no evidence that this would lead to biases that would invalidate our findings given that we are concerned with adult mortality in a developed country, even though concerns have been raised about such issues for infant and toddler mortality in least-developed countries [ 20 ]. A similar issue relates to the modest response rates. As non-respondents tend to have lower socioeconomic status and worse health, and since their relatives are likely to be of similar background and have similar health status, the convenience cohort is likely to be less well-off and less healthy than the general population.
This, however, should not affect the associations within the study. Third, an additional potential source bias relates to family-level factors. Subjects relatives who left the selected settlement area were excluded from the study, so future investigations should look into the migrant differentials on the settlement level. However, if there was a strong family-level mortality effect on migration, this would lead to high mortality families being less likely to have any surviving relative alive in to report on their mortality two decades earlier, which would differentially exclude high mortality families.
Analysis of family-level clustering on earlier data sets suggests that this is not a major source of bias, but further work will be undertaken. We have noted some of the possible concerns about the use of such an approach, but we emphasize that under reasonable assumptions the method produces largely unbiased estimates of mortality that can be directly compared with other sources such as official statistics. Fourth, recall bias and measurement error can be a major issue in a cohort where all the data come from proxy informants i.
Such misclassification leads to underestimation of the strengths of associations between risk factors and mortality and reduce the statistical power of the study.
However, this is at least partly compensated by the large size of the study although we are aware that the scale of such bias depends on the magnitude of misclassification of the variables of interest. In addition, to further reduce misclassification, only male spouses were included in the convenience cohort, as the literature suggests that men are more likely to exclude non-residential: former, and especially first, partners in surveys.