Noncommunicable Disease Mortality and Life Expectancy in Immigrants to Israel from the Former Soviet Union: Country of Origin Compared with Host country/Mortalite Par Maladies Non Transmissibles et Esperance De Vie Chez Les Immigres En Israel Provenant De L'ancienne Union Sovietique
Bibliographic record
Abstract
Introduction Much research has been carried out on mortality rates among migrants all over the world. However, with the focus usually on refugees and those who migrate for economic reasons, studies often overlook a group comprising millions of people: diaspora migrants. Several countries, including Fin]and, Germany, Greece, Hungary and Israel, have immigration policies that encourage migration on the basis of common ancestry. Such diaspora migrations were especially numerous following the fall of the Iron Curtain, a notable example being the arrival in Israel of about 1 million immigrants from the former Soviet Union (FSU) between 1989 and 2003. (1) Most were Jews, and about one quarter were non-Jewish family members. The proportion of highly educated professionals among them was much higher than among the Israeli population, but so was the proportion of single-parent families and elderly people. (1) Although these migrants have enjoyed generous conditions of reception, the new country's health system, standards of medical care, lifestyles and environment have differed greatly from those in their country of origin. Such changes, in addition to a decline in social standing and the difficulties of integration, are factors that should have an effect on mortality rates. Since the mid-1960s, mortality rates in the FSU and Russian Federation have been rising, and the differences between them and the rates in western European countries continue to widen. (2,3) The Russian Federation has some of the highest standardized death rates from noncommunicable diseases of all industrialized countries. These differences have been attributed to large numbers of deaths from external causes (such as deliberate of accidental injuries) among young men, and also from circulatory diseases in middle-aged adults. (2,4) There is evidence that unhealthy behaviours and lifestyle factors such as high levels of tobacco consumption (5) and alcohol abuse--especially binge drinking (6)--along with increased consumption of red meat, sugar and polyunsaturated fats, (7) have increased the number of deaths from preventable and chronic diseases. (8-10) An increasing prevalence of obesity in the FSU could also be contributing to increased mortality rates. (11) Various authors have examined the effects of certain negative characteristics of the health-care system in the FSU on the number of avoidable deaths, with poor health promotion and inadequate access to advanced health care mentioned as relevant factors. (8,12-14) Consequently, the arrival in Israel of migrants from the FSU was expected to cause a significant rise in Israel's mortality rates. (15) However, a study of mortality among Jews in Moscow from 1993 to 1995 showed that these men and women had lower mortality rates than did other ethnic groups within Russia, even when controlling for their relatively high educational level. These results led the study authors to argue that mortality among Jews in Moscow was similar to mortality among Jews in Israel. (16) Indeed, an analysis of data up to 1994 showed that age-adjusted mortality rates, both overall and for cardiovascular diseases, were lower among FSU immigrants to Israel than among the Jewish population residing in Israel, including native-born residents and earlier immigrants. (17) Mortality among immigrant groups is known to be affected by the stresses of acculturation and by socioeconomic challenges, both of which can cause a wide range of psychopathological symptoms, and also by pre-migration health conditions and behaviour. (18,19) Although immigrants to Israel receive substantial financial, occupational and educational support and housing subsidies for the first few years after they arrive, they are nevertheless affected by psychological stress from having to cope with a new social and cultural environment or a downward shift in employment status. (20) [FIGURE 1 OMITTED] [FIGURE 2 OMITTED] A study of Russian immigrants in Israel showed that a perceived lack of social support was significantly associated with psychological distress and that length of stay did not decrease stress levels. …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".