Data_Sheet_1_Regression discontinuity design for the study of health effects of exposures acting early in life.pdf

Regression discontinuity design (RDD) is a quasi-experimental approach to study the causal effect of an exposure on later outcomes by exploiting the discontinuity in the exposure probability at an assignment variable cut-off. With the intent of facilitating the use of RDD in the Developmental Origins of Health and Disease (DOHaD) research, we describe the main aspects of the study design and review the studies, assignment variables and exposures that have been investigated to identify short- and long-term health effects of early life exposures. We also provide a brief overview of some of the methodological considerations for the RDD identification using an example of a DOHaD study. An increasing number of studies investigating the effects of early life environmental stressors on health outcomes use RDD, mostly in the context of education, social and welfare policies, healthcare organization and insurance, and clinical management. Age and calendar time are the mostly used assignment variables to study the effects of various early life policies and programs, shock events and guidelines. Maternal and newborn characteristics, such as age, birth weight and gestational age are frequently used assignment variables to study the effects of the type of neonatal care, health insurance, and newborn benefits, while socioeconomic measures have been used to study the effects of social and welfare programs. RDD has advantages, including intuitive interpretation, and transparent and simple graphical representation. It provides valid causal estimates if the assumptions, relatively weak compared to other non-experimental study designs, are met. Its use to study health effects of exposures acting early in life has been limited to studies based on registries and administrative databases, while birth cohort data has not been exploited so far using this design. Local causal effect around the cut-off, difficulty in reaching high statistical power compared to other study designs, and the rarity of settings outside of policy and program evaluations hamper the widespread use of RDD in the DOHaD research. Still, the assignment variables’ cut-offs for exposures applied in previous studies can be used, if appropriate, in other settings and with additional outcomes to address different research questions.

断点回归设计（Regression Discontinuity Design，RDD）是一种准实验研究方法，通过利用分配变量临界值处暴露概率的断点，探究暴露因素对后续结局的因果效应。为推动断点回归设计在健康与疾病的发育起源（Developmental Origins of Health and Disease，DOHaD）研究中的应用，本文阐述了该研究设计的核心要点，并综述了已开展的、旨在识别早期生命暴露对健康短期与长期影响的相关研究、分配变量及暴露因素。此外，本文以一项DOHaD研究为例，简要概述了断点回归设计识别因果效应时需关注的若干方法论要点。当前，越来越多探究早期生命环境应激因素对健康结局影响的研究采用了断点回归设计，这类研究多聚焦于教育、社会与福利政策、医疗组织与保险以及临床管理等场景。年龄与日历时间是探究各类早期生命政策与项目、突发公共事件及指南影响时最常用的分配变量。产妇与新生儿的特征（如年龄、出生体重、胎龄）常被用作分配变量，以探究新生儿护理类型、医疗保险及新生儿福利的影响；而社会经济指标则被用于研究社会与福利项目的效应。断点回归设计具备诸多优势，例如解读直观、图形展示清晰简洁。相较于其他非实验研究设计，该方法的假设条件相对宽松，若假设得以满足，则可得到有效的因果效应估计值。目前，断点回归设计在探究早期生命暴露对健康的影响时，仅局限于基于登记库与行政数据库的研究，而出生队列数据尚未有采用该设计的相关应用。但该方法仍存在一定局限：仅能估计临界值附近的局部因果效应、相较于其他研究设计较难获得较高的统计效力，且除政策与项目评估外的适用场景较为稀缺，这些因素均阻碍了断点回归设计在DOHaD研究中的广泛应用。不过，若条件适宜，过往研究中使用的暴露因素分配变量临界值可被复用至其他场景，并结合额外的结局指标以解答不同的研究问题。