Project summary Very low birth weight (VLBW) is associated with a host of long-term health and neurodevelopmental difficulties. The prevalence of VLBW and the severity of neurodevelopmental and physical difficulties are substantially higher for low-income infants. To optimize the developmental trajectories of VLBW infants, the American Academy of Pediatrics recommends follow-up developmental services. One of the most common sources of follow-up therapy and developmental services for VLBW infants is Part C early intervention (EI). Part C of the Individuals with Disabilities Education Act authorizes states, with the incentive of financial support, to provide a state-wide system of developmental services for children with developmental delays and disabilities. EI spending for VLBW infants represents the largest spending for any EI service sub-group. Although the clinical effectiveness of EI has been demonstrated in controlled randomized clinical trials, the intensity of therapy interventions in these trials far outweighs the typical intervention dosage for the majority of Part C EI programs. To date, the he epidemiologic evidence suggesting positive effects of Part C EI as it is actually implemented is weak, and the political will to maintain or enhance the program requires strong empirical evidence of its effectiveness. In part, the weak EI evidence is due to methodological challenges associated with conducting EI outcomes research. EI program databases are limited to children who receive EI services, but children who receive EI services systematically differ from those who do not. Moreover, datasets that include all EI-eligible children do not include relevant clinical information and do not track health outcomes over time. In the proposed study, we address methodological challenges by using a regression discontinuity design that will allow us to establish the causal effects of Part C EI on both growth trajectories and neurodevelopment. Our empirical approach exploits the fact that VLBW strongly predicts receipt of EI services. Moreover, we proposed to use electronic health records from a large metropolitan safety net health system that serves high-risk, low- income children. Our proposed data contain rich information on demographic, clinical characteristics, and outcomes. Thus, our study will yield important new knowledge about the effectiveness of a clinically and policy- relevant sub-group of children.