Friday, May 27, 2016

It's that Time Again - Gearing up for Esri User Conference

Every summer, tens of thousands of geo-enthusiasts descend upon San Diego, CA to learn from and connect with industry peers, get the latest information on new technologies, try out new applications, see ground-breaking innovations from other industries, and develop new skills.

This year's conference is quickly approaching, and we're getting ready to spend a week talking about the work we're doing and hearing from others about how they're helping to improve health across the country and around the world. There are nine health-related paper sessions on the agenda this year, and HealthLandscape will be featured in two of them. If you'll be at the Esri User Conference, plan to attend our talks during the Place Based Health and Progressing Access to Care sessions. Mark Carrozza will be talking about our Community Vital Signs API, a novel way to place patient outcome data in the context of patient communities. I will be discussing the initial results of research on health care quality measures - specifically, whether competition and proximity to other health care organizations affects performance and health outcomes. We're both looking forward to presenting our work and getting feedback from our peers.

In addition to the paper sessions and technical demonstrations, the conference features a User Application Fair. This year, the applications are moving out of the EXPO and will be showcased in the Map Gallery hall. The HealthLandscape Health Workforce Mapper will be among the entries - come check out the distribution of physicians and non-physicians in your community, and cast a vote for our app while you're there.

Not able to attend the conference? We'll be tweeting about the cool things we learn while we're there, so follow us @healthlandscape to keep up! Both HealthLandscape presentations will be available after the conference, and we'll feature a full conference recap on the blog.

Hope to see you there!

Jené Grandmont
Senior Manager, Application Development & Data Services

Thursday, May 19, 2016

How Hashbrowns Keep Us Safe (Really!)

Those who know me well, know that I’m not overly fond of air travel and spend an unfortunate amount of time traveling by car from Cincinnati to far-flung locations like Washington DC, Atlanta, Tampa,  Chicago, Kansas City and all places in between.  In all my travels, there is really only one consistency:  The Waffle House. With over 2,100 location in the United States, it’s hard to miss this American classic. 

As many of you may know, when visiting a Waffle House, particular attention MUST be paid to how a person orders their Hashbrowns.  There are few joys in a person’s life as great as when they finally decide “smother, covered, and chunked” or “smothered, peppered, and diced” or whatever combination satisfies them the most.  It's a rite of passage, much like buying your first car, or deciding whether to attend college. Knowing your Hashbrown preference is what separates the newbies from the Waffle House veterans. Just ask renowned chef Anthony Bourdain . . .

How is this keeping us safe and healthy?  While the jury is still out on the ‘healthy’ part, it’s been known for several years that FEMA monitors the “Waffle House Index” as a proxy for the availability of basic infrastructure and workforce resources after a wide-spread disaster strikes an area.  The logic being, if the event (hurricane, flood, tornado, wildfire, etc) is so widespread as to disrupt supply chains, electricity, workforce transportation, and other basic business necessities, it would be apparent by the closing of popular, geographically 24-hour retail locations . . . like the venerable Waffle House.

Since the creation of the original “Waffle House Index” after hurricane Katrina, FEMA has expanded the corporate monitoring to include retailers Walmart, Lowes, and Home Depot.  But it was this little American diner that first caused FEMA Director Fugate to say  you get there and the Waffle House is closed? That's really bad”.  Very, very true. Bad for so many reasons.

So, what does all this have to do with GIS and health?  It follows from my own ongoing fascination with innovative community metrics that provide insight into health and healthy lifestyle questions, that are difficult to quantify other ways.  The “Waffle House Index” is a great example.  So is research using wastewater analysis to determine substance abuse rates for metro areas.  And crowdsourced air quality monitoring with wearable devices.

As I've said in previous blog posts and in presentations around the country (or at least the eastern half, within driving distance), Health and GIS are intrinsically linked, because "Everything Happens Somewhere". The cool part of my job is trying to figure out how to measure and described that "happening".

Mark Carrozza

Tuesday, May 10, 2016

Making Sense of Hot-Spots & Cold Spots

Today HealthLandscape is releasing Geospatial Brief #2 “Where are “Hot Spots” of Medicare Spending and Preventable Hospitalizations and “Cold Spots” of Preventive Care.  Brief #2 focuses on using advanced geospatial methods to identify priority areas for preventive care. This blog briefly describes different ways of defining hot spots and cold spots. We argue that it doesn’t necessarily matter how these terms are defined, as there are multiple methods for using these techniques to identify priority regions.

The term “hot-spotting” has become popular in the healthcare realm thanks to the work of Dr. Jeffrey Brenner in Camden, NJ, who identified a very small group of super-utilizers, which made up a disproportionate share of hospitalizations, ER visits, and healthcare costs.  These “super-utilizers” were often concentrated in small geographic areas, identified as hot spots, such as apartment buildings or city blocks, which were poor, under-resourced areas. Thus, by focusing on these hot-spots and providing coordinated care and social services, Brenner and his team were able to improve health, reduce unnecessary hospital visits, and lower costs (Gawande, 2011).  Brenner’s hot-spotting approach has been very successful and is being replicated in regions throughout the U.S., with the Robert Wood Johnson Foundation dedicating substantial funding for hot-spotting programs (RWJ, 2012).

Dr. John Westfall took an alternate view, wondering if the problem wasn’t really hot spots, but rather cold spots – “communities in which the social determinants of health, support, and access to primary care have broken down.” (Westfall, 2013).  Westfall explains that identifying cold spots and working to improve the conditions in these areas could have a larger overall impact on improving population health.  However, Westfall argues that addressing the needs of cold-spot communities is much more complex than dealing with a small group of super-utilizers, and requires a broader, communities of solution approach (Griswold et al., 2013).

Hot Spots and Cold Spots in Geospatial Analysis
While general awareness of the terms hot spots and cold spots have increased, these terms have different meanings in the field of geospatial analysis, where hot spots are defined as clusters of high values and cold spots as clusters of low values. These clusters are compared to random geographic patterns to test if they are statistically significant. Several methods exist for exploring hot spots and cold spots, including the Local Moran’s I (Anselin et al., 2006). For example, if you map Diabetes prevalence (Medicare) for counties in the U.S. (see map below), you would find clusters of high values throughout the southeast and Appalachia, and clusters of low values in the upper Midwest and throughout the Western part of the U.S. To test for statistically significant Diabetes hot spots, you would have to use advanced geospatial methods to determine if the clusters of high values are significantly different from random geographic patterns.

Diabetes Prevalence by County (Medicare) 
Source: CMS Geographic Variation, 2013; HealthLandscape Medicare Data Portal

Making Sense of Hot Spots and Cold Spots
There are similarities in how hot spots and cold spots described above are defined.  For example, if you had census tract data for 30-day readmission rates from a local hospital, you could map these data and visually identify census tracts with high rates (i.e., hot spots using Brenner’s definition).  Next, you could use geospatial methods (such as a Local Moran’s I) to determine if the clusters of high readmission rates are significantly different from a random pattern of hospital readmissions (i.e., hot spots using geospatial definition).  Similarly, you could map census tract education data to visually identify census tracts with low levels of education (i.e., cold spots using Westfall’s definition), and then use Local Moran’s I to determine if clusters of low education census tracts are statistically different from random patterns of education levels (cold spots using geospatial definition).

While there are many different ways to define hot spots and cold spots, there are all useful for identifying priority areas for place-based interventions. The key issue for future research is how we use the results of hot-spot and cold-spot analyses to target interventions.

Michael Topmiller
GIS Strategist

Anselin, Luc, Ibnu Syabri and Youngihn Kho (2006). GeoDa: An Introduction to Spatial Data Analysis. Geographical Analysis 38 (1), 5-22.

Centers for Medicare and Medicaid (CMS), 2013. Geographic Variation Public Use File

Dartmouth Atlas of Health Care, 2013. Data Downloads.

Gawande, Atul. (2011). “The Hot Spotters: Can we lower medical costs by giving the neediest of patients better care?” The New Yorker. January 24, 2011.

Griswold, Kim S., Sarah E. Lesko, and John M. Westfall (Folsom Group). (2013). Communities of Solution: Partnerships for Population Health. Journal of the American Board of Family Medicine 26(3): 232-238.

HealthLandscape Medicare Data Portal

Robert Wood Johnson Foundation, (2012). “Expanding “Hot Spotting” to New Communities: What We’re Learning about Coordinating Health Care for High-Utilizers.”

Topmiller, Michael. (2016). “Do Regions with More Preventive Care have Lower Spending and Fewer Hospitalizations?”  HealthLandscape Geospatial Research Brief #1.
Accessed at

Westfall, John M. (2013). Cold Spotting: Linking Primary Care and Public Health to Create Communities of Solution. Journal of the American Board of Family Medicine, 26(3): 239-240.