OBJECTIVES: Osteoporosis is a common health problem among elderly population. Patients with osteoporosis are exposed to low-energy fractures. The prevalence of the disease varies significantly throughout the country due to the strong correlation with demographic factors. National guidelines recommend secondary prevention for all osteoporosis patients suffering from low-energy fractures. The aim of treatment is to prevent further fractures. In order to effectively allocate the resources needed for osteoporosis treatment, it is essential to know the number of patients and treatment practices in local level. METHODS: Local registry data was used to estimate the total population with low-energy fractures, as well as to recognize the differences in treatment of osteoporosis in each hospital district (n=21) and municipality (n=320). Data on hip fractures from year 2009 was extrapolated to year 2012 using demographic data. The overall prevalence of low-energy fractures was estimated trough a distribution of different fracture types available from literature. A map-based application was built (Tableau 8) to visualize the differences in the prevalence and treatment of osteoporosis across Finland. The model enables comparison between municipalities or hospital districts. RESULTS: The estimated number of osteoporotic fractures was 1733, in a hospital district with 250 000 inhabitants (Northern-Savo). The estimated prevalence rates in different municipalities within the hospital district varied between 0.52–1.7/100 inhabitants. Simultaneously, basic reimbursement for medical treatment (ATC:M05B) was paid for 1.2/100 inhabitants - the range among elderly population (>65y) was 3.21– 6.56/100 inhabitants. CONCLUSIONS: There are significant local differences in the prevalence of osteoporosis as well as in its treatment. The population demographics do not explain the observed differences in the consumption of osteoporosis medication between different municipalities. The developed map-based application gives multiple possibilities to analyze these issues through data visualization. With this approach the interventions may be targeted to areas needing it the most.