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)
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
HealthLandscape
References
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
http://www.healthlandscape.org/Geospatial-Analysis.cfm
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.
