To demonstrate a generalizable approach for developing maternal-child health data resources using state administrative records and community-based program data. We used a probabilistic and deterministic linking strategy to join vital records, hospital discharge records, and home visiting data for a population-based cohort of at-risk, first time mothers enrolled in a regional home visiting program in Southwestern Ohio and Northern Kentucky from 2007 to 2010. Because data sources shared no universal identifier, common identifying elements were selected and evaluated for discriminating power. Vital records then served as a hub to which other records were linked. Variables were recoded into clinically significant categories and a cross-set of composite analytic variables was constructed. Finally, individual-level data were linked to corresponding area-level measures by census tract using the American Communities Survey. The final data set represented 2,330 maternal-infant pairs with both home visiting and vital records data. Of these, 56 pairs (2.4 %) did not link to either maternal or infant hospital discharge records. In a 10 % validation subset (n = 233), 100 % of the reviewed matches between home visiting data and vital records were true matches. Combining multiple data sources provided more comprehensive details of perinatal health service utilization and demographic, clinical, psychosocial, and behavioral characteristics than available from a single data source. Our approach offers a template for leveraging disparate sources of data to support a platform of research that evaluates the timeliness and reach of home visiting as well as its association with key maternal-child health outcomes.