  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.
