Budget analysis entities often cannot capture the full downstream impacts of investments in prevention services, programs, and interventions. This study describes and applies an approach to synthesizing existing literature to more fully account for these effects. This study reviewed meta-analyses in PubMed published between Jan 1, 2010 and Dec 31, 2019. The initial search included meta-analyses on the association between health risk factors, including maternal behavioral health, intimate partner violence, child maltreatment, depression, and obesity, with a later health condition. Through a snowball sampling-type approach, the endpoints of the meta-analyses identified became search terms for a subsequent search, until each health risk was connected to one of the ten costliest health conditions. These results were synthesized to create a path model connecting the health risks to the high-cost health conditions in a cascade. Thirty-seven meta-analyses were included. They connected early-life health risk factors with six high-cost health conditions: hypertension, diabetes, asthma and chronic obstructive pulmonary disorder, mental disorders, heart conditions, and trauma-related disorders. If confounders could be controlled for and causality inferred, the cascading associations could be used to more fully account for downstream impacts of preventive interventions. This would support budget analysis entities to better include potential savings from investments in chronic disease prevention and promote greater implementation at scale.