扩大丹麦双胞胎注册到第三个世纪:双胞胎出生和生存的世俗趋势
Kaare Christensen1, Lisbeth Aagaard Larsen2, Dorthe Almind Pedersen2
1The Danish Twin Registry, Department of Public Health, University of Southern Denmark, Campusvej 55, 5230, Odense M, Denmark. kchristensen@health.sdu.dk.
Behavior genetics
|February 18, 2026
概括
丹麦双胞胎登记处追踪了三个世纪的双胞胎出生率和生存率. 双胞胎生殖率大幅波动,受到医疗辅助生殖 (MAR) 的影响,而双胞胎生存率大幅提高,接近单生子水平.
科学领域:
- 流行病学 流行病学
- 生殖健康 生殖健康
- 人类遗传学 人类遗传学
背景情况:
- 1954年建立的丹麦双胞胎登记册已被更新为2001-2021年双胞胎出生队列.
- 这种扩展允许对跨越三个世纪的双胞胎出生频率和生存趋势进行全面分析.
研究的目的:
- 从19世纪中叶到现在,分析双胞胎出生频率和生存率的变化.
- 调查诸如孕产妇年龄和医疗辅助生殖 (MAR) 等因素对双胞胎生育率的影响.
- 为了在不同的历史时期比较双胞胎和单胞胎的生存率.
主要方法:
- 在2001-2021年期间使用全国性注册表在丹麦出生的22680对双胞胎的识别和联系.
- 通过电子问卷收集15005对双胞胎的双胞胎性数据,并通过遗传标记器进行验证.
- 从丹麦双胞胎登记 (1870-2000) 和当代队列 (2001-2021) 的历史数据分析.
主要成果:
- 双胞胎生育率保持稳定,直到20世纪中叶,从1950年至1970年减少,然后从20世纪80年代到2000年代翻了一番,这是由于母亲的年龄增加和MAR.
- 当代的结伴率已经下降,在MAR程序的变化之后,恢复到19世纪的水平.
- 在6岁之前的双胞胎死亡率从19世纪的43%显著下降到21世纪的2.0%,尽管它仍然高于单身死亡率.
结论:
- 医疗辅助生殖 (MAR) 和不断增长的母亲年龄在过去一个世纪里对结生率产生了深刻的影响.
- 三个世纪以来,双胞胎存活率的显著改善已经被观察到,缩小了与单身存活率的差距.
- 虽然双胞胎死亡率在幼儿时期仍然高于单胞胎死亡率,但在最近的队列中,差异随着年龄的增长而减少.
更多相关视频
相关概念视频
Applications of Life Tables
378
Life tables are versatile across various fields, providing a quantitative basis for analyzing mortality and survival rates. Whether used by demographers, actuaries, epidemiologists, or sociologists, life tables offer valuable insights into the dynamics of life and death, facilitating informed decisions in public health, insurance, conservation, and beyond. Their broad applicability highlights the interconnectedness of demographic data with practical outcomes in everyday life and strategic...
378
Actuarial Approach
325
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,...
325
Longitudinal Research
13.5K
Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
13.5K
Survival Curves
761
Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
761
Life Tables
559
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,...
559
Introduction To Survival Analysis
862
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
The primary goal of survival analysis is to estimate survival time—the time...
862


