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Background: As the impact of stroke continues, the number of people who require rehabilitation close to their homes will also increase. Primary health care will continue to be a critical platform for the identification and referral of stroke survivors who need long-term rehabilitation Purpose: We aimed at identifying stroke survivors assisted by community health workers (CHW) in Brazil as they relate to rehabilitation facilities locations. Methods: We used de-identified data from a real-world database generated by a free data collection app used by CHW from May 2015 to January 2021 in Brazil to identify stroke survivors, and to assess demographics and clinical characteristics. We used data from a public database, Cadastro Nacional de Estabelecimentos de Saúde (CNES), for identifying rehabilitation facilities. Locations were obtained by a Geocoding API (Google Maps Platform), distances were measured in kilometers (km) and travel time in minutes (min). Continuous variables were measured by mean (SD) and, categorical data in frequencies and percentages. Results: Among 2,397,764 individuals assisted by CHW that used the free app, 21,785 were stroke survivors, representing a 0.9% prevalence. Among this subgroup, the majority were from the Northeast region (50.3%), and 77.7% in urban areas. Most individuals (52.8%) were women, the mean age was 66.5 years (SD 14.7) and 4313 reported physical disability. A total of 348 re-habilitation facilities were identified, mostly located in the Southeast region (40.8%). The mean distance from stroke survivor to facility was 79.13 km and, mean travel time 81.16 min. The southern region has the largest mean distance (175.58 km) and travel time (144.48 min). Among patients that reported physical disability, the mean distance from stroke survivor to facility was 83.26 km and, mean travel time 85.29 min. Conclusions: We observed that stroke survivors’ distributions as they relate to rehabilitation facilities varies widely in Brazil. As a means of evaluating improvement of the clinical pathway and resources allocation for long term care, the use of large real-world databases and adequate analysis may assist in real needs assessments and policy changes.