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Abstract: Populations of interest are often hidden from data for a variety of reasons, though their magnitude remains important in determining resource allocation and appropriate policy. Increasing data collection and linkage across diverse fields suggests accessible methods of estimating population size with synthesized data are needed. In public health and epidemiology, these linkages often admit a tree structure, with the target population represented by the root, and paths from root-to-leaf representing pathways of care after a health event. We propose an extension to the well-known multiplier method which is applicable to tree-structured data, where multiple subpopulations and corresponding proportions combine to generate a population size estimate via the minimum variance estimator. The methodology is compared a Bayesian hierarchical model, for both simulated and real world data, the latter provided by BC's opioid overdose cohort. Finally, two R packages developed to facilitate the use of these methods on similar applications and lower the technical barrier of implementation will be discussed.