Wednesday, May 10, 2017

We were SDOH before SDOH was cool

Here at HealthLandscape, we have been developing online geospatial analytic tools for more than 10 years and that entire time, we have focused on the Social Determinants of Health (SDOH).  Our inter-disciplinary team of sociologists, informaticians, geographers and training specialists regularly work side-by-side with providers, researchers and social service organizations to synthesize data and build custom tools to meet their analytic needs.  Most of our mapping tools incorporate social determinants of health and other community, contextual information ensuring that workforce, utilization or other client-provided data can be viewed with potentially explanatory details and resources.

Our first ever (and continually updated) tool, Community HealthView, contains a comprehensive library of data one can use to predict health outcomes based on poverty status, crime, education levels and much, much more.  Data are available via map and data table and can be exported by the user.  The user can change the visualization of the data as well.  Community HealthView can be found at and as an add-on tool in most of our mapping tools.

More recently we have built tools that allow users to “cold spot” areas based on SDOH.  Proposed by Jack Westfall, cold spotting is identifying areas that have poor values for multiple or overlapping SDOH.  Our tools that allow this type of analysis are the UDS Mapper’s Population Indicators Tool, the Social Determinants of Health Mapper, Population Health Mapper and our newest SDOH tool, the 500 Cities Project Mapper.  You’ve read about these in our recent blogs.  These tools can be found at:

Our experience developing geospatial tools and our continued collaboration with primary care researchers have opened new avenues for research and development as well.  As health system transformation is occurring, more and more people are embracing the idea of SDOH and looking for ways to incorporate these data into patient records.  The determinants data that we have been collecting over the past 10 years form the basis of a community profile for patients that will help health care teams, researchers and administrators understand the factors that may influence success and failure of treatment plans; see the factors that may be contributing to poor outcomes and high costs; and address these non-clinical factors either at the patient, practice or community level (with partners who focus on these issues) will improve health outcomes and improve population health.

For more about our mapping tools we invite you to attend one of our free webinars where you can learn how to use them. We cover the data included and we welcome your questions in these interactive forums.

  • For the UDS Mapper

Jennifer Rankin
Senior Manager for Research and Product Services

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