Key Laboratory of Environmental Medicine and Engineering of Ministry of Education, Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, No.87 Dingjiaqiao Rd, Gulou District, Nanjing, Jiangsu, China.
Department of Obstetrics and Gynecology, Zhong Da Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China.
Reprod Health. 2022 Jun 25;19(1):150. doi: 10.1186/s12978-022-01450-6.
As problems associated with infertility and population aging increase, there is a growing interest in the factors that cause a decline in human fertility. Time-to-pregnancy (TTP) is a good indicator with which to reflect human fecundability. Here, we present a comprehensive overview of this topic.
Relevant qualitative and quantitative studies were identified by searching the Web of science and PubMed electronic databases. We included all literature, written in English, from inception to the 10th April 2021 providing the focus was on TTP. We conducted a narrative synthesis using thematic analysis.
Traditional TTP-related study protocols include prospective and retrospective cohorts that provide a wealth of data to reveal potential influences on TTP. Thus far, a variety of factors have been shown to be associated with TTP in couples preparing for pregnancy, including basic demographic characteristics, menstrual status, chronic disease status, environmental endocrine disruptor exposure, and lifestyles. However, there are inevitable epidemiological bias in the existing studies, including recall bias, selection bias and measurement bias. Some methodological advances have brought new opportunities to TTP research, which make it possible to develop precision interventions for population fertility. Future TTP studies should take advantage of artificial intelligence, machine learning, and high-throughput sequencing technologies, and apply medical big data to fully consider and avoid possible bias in the design.
There are many opportunities and future challenges for TTP related studies which would provide a scientific basis for the "precise health management" of the population preparing for pregnancy.
随着与不孕和人口老龄化相关问题的增加，人们对导致人类生育能力下降的因素越来越感兴趣。妊娠时间（TTP）是反映人类生育力的一个很好的指标。在这里，我们对这个主题进行了全面的概述。
通过搜索 Web of Science 和 PubMed 电子数据库，确定了相关的定性和定量研究。我们纳入了所有以英语撰写的文献，从最初到 2021 年 4 月 10 日，重点关注 TTP。我们使用主题分析进行了叙述性综合。
传统的 TTP 相关研究方案包括前瞻性和回顾性队列研究，这些研究提供了丰富的数据，揭示了对 TTP 的潜在影响。迄今为止，已经有多种因素被证明与准备怀孕的夫妇的 TTP 有关，包括基本人口特征、月经状况、慢性疾病状况、环境内分泌干扰物暴露和生活方式。然而，现有研究中存在不可避免的流行病学偏倚，包括回忆偏倚、选择偏倚和测量偏倚。一些方法学的进步为 TTP 研究带来了新的机会，这使得为人口生育能力开发精确的干预措施成为可能。未来的 TTP 研究应利用人工智能、机器学习和高通量测序技术，并将医疗大数据应用于充分考虑和避免设计中可能存在的偏倚。
TTP 相关研究有很多机会和未来的挑战，这将为准备怀孕的人群的“精准健康管理”提供科学依据。
