  Regression discontinuity design (RDD) is a quasi-experimental approach to study the causal effects of an intervention/treatment on later health outcomes. It exploits a continuously measured assignment variable with a clearly defined cut-off above or below which the population is at least partially assigned to the intervention/treatment. We describe the RDD and outline the applications of RDD in the context of perinatal epidemiology and birth cohort research.   There is an increasing number of studies using RDD in perinatal and pediatric epidemiology. Most of these studies were conducted in the context of education, social and welfare policies, healthcare organization, insurance, and preventive programs. Additional thematic fields include clinically relevant research questions, shock events, social and environmental factors, and changes in guidelines. Maternal and perinatal characteristics, such as age, birth weight and gestational age are frequently used assignment variables to study the effects of the type and intensity of neonatal care, health insurance, and supplemental newborn benefits. Different socioeconomic measures have been used to study the effects of social, welfare and cash transfer programs, while age or date of birth served as assignment variables to study the effects of vaccination programs, pregnancy-specific guidelines, maternity and paternity leave policies and introduction of newborn-based welfare programs.   RDD has advantages, including relatively weak and testable assumptions, strong internal validity, intuitive interpretation, and transparent and simple graphical representation. However, its use in birth cohort research is hampered by the rarity of settings outside of policy and program evaluations, low statistical power, limited external validity (geographic- and time-specific settings) and potential contamination by other exposures/interventions. 
Regression discontinuity design in perinatal epidemiology and birth   cohort research