俄罗斯1984-94年死亡率的巨大变化:艺术品,酒精,还是什么?
D A Leon1, L Chenet, V M Shkolnikov
1European Centre on Health of Societies in Transition, London School of Hygiene and Tropical Medicine, UK.
Lancet (London, England)
|August 9, 1997
概括
俄罗斯的死亡率在1984年至1994年间大幅波动. 增加的酒精消费是对这些预期寿命急剧变化的最合理的解释.
科学领域:
- 公共卫生 公共卫生
- 人口统计学 人口统计学
- 流行病学 流行病学
背景情况:
- 在1984年至1994年期间,俄罗斯的死亡率出现了显著的波动,男性预期寿命显著下降.
- 人们对死亡率数据中潜在的人工制造物表示担忧,尽管酒精消费被认为是其中的一个因素.
研究的目的:
- 分析俄罗斯的特定年龄和特定死因的死亡模式.
- 通过重建的死亡序列,检查观察到的死亡趋势的各种解释的合理性.
主要方法:
- 对俄罗斯 (1984-1994) 年龄特定死亡率数据的分析.
- 特定原因死亡率分析,重点关注各种疾病类别和外部原因的趋势.
- 重建死亡数据系列以确保数据的准确性.
主要成果:
- 死亡率下降 (1984-1987) 随后增加 (1987-1994) 被观察到大多数原因,不包括瘤.
- 在40-50岁的年龄组中,死亡率的比例变化最大,影响男性和女性均等.
- 与酒精有关的死亡,事故,暴力,感染,循环和呼吸系统疾病显示出显著的波动;瘤保持稳定.
结论:
- 癌症 (新生体) 死亡率的稳定性表明,观察到的预期寿命变化并非主要是人工制造.
- 显著的酒精消费变化可能解释了1984年至1994年期间俄罗斯死亡率波动的主要特征.
- 这些发现挑战了俄罗斯的公共卫生战略以及对酒精在全球死亡率模式中的作用的理解.
更多相关视频
相关概念视频
01:52Regression Toward the Mean
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
01:16Hypothesis Test for Test of Independence
The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
H0: The two variables (factors)...
H0: The two variables (factors)...
01:08Determination of Expected Frequency
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
01:22Life Tables
A life table is a statistical tool that summarizes the mortality and survival patterns of a population, providing detailed insights into the likelihood of survival or death across different age intervals within a cohort. By organizing data on survival probabilities and mortality rates, life tables offer a clear snapshot of population dynamics over time. They are extensively used in demography, public health, actuarial science, and ecology to analyze life expectancy, design health interventions,...
01:20Actuarial Approach
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
01:20Comparing the Survival Analysis of Two or More Groups
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...