Use of fertility treatments is growing with as many as 10% of births occurring after use of assisted reproductive
technologies (ART) or non-IVF fertility treatments (NIFT). These treatments often require substantial societal
and individual investments of resources and time, yet there is little information on the long-term health or
development outcomes for children born after treatment. Indeed, most of the existing literature centers around
events occurring during birth or infancy, with limited and mixed findings about events later in life. This limited
literature also reflects major methodological challenges, including (i) insufficient follow-up time to assess the
development of medical conditions past adolescence and into adulthood; (ii) numerous potential confounders,
including the original reason for needing treatment (infertility), and variation in access to and use of treatments,
e.g., differential access by income or education; and (iii) use of analytic approaches that either do not address
relevant sources of bias, or rely on ill-defined assumptions to assess treatment effects. To both address the
need for more evidence and mitigate the methodological concerns, we propose to compare the risk of
outcomes associated with different fertility treatments using multiple population-based, nationwide rich
datasets combined with modern causal inference approaches. Our aims are to examine the impact of fertility
treatments (stratified by the type of infertility) on two types of outcomes: 1) clinical events; and 2) educational
events. We will study nearly 400,000 children born after fertility treatments during 1980-2020 in four
populations (in Denmark, the Netherlands, Sweden, and Massachusetts). Across these populations, we have
comprehensive, individual-level information on our outcomes and potential cofounders, e.g., types of infertility,
treatments, education, income, and clinic traits. We will perform both predictive and causal analyses in order to
inform clinicians and potential parents as they first consider use of fertility treatments, then choose between
available treatments. Our statistical methods encompass a series of approaches starting with traditional
survival and repeated measures analyses, and more recently developed techniques for causal analysis with
observational data, e.g., inverse-probability weighting. We also will explore alternative approaches using
instrumental variables and the g-formula. In short, this study provides the best opportunity to assess long-term
outcomes in children born after fertility treatments, with the longest follow-up time, most extensive set of
demographic, clinical, and treatment characteristics, and use of state-of-the-art analytic approaches.