Spatial Analysis of Drug Poisoning Deaths and Access to
Substance-use Disorder Treatment in the United States
Yelena Ogneva-Himmelberger
Department of International Development, Community and Environment, Clark University,
950 Main St., Worcester, MA, U.S.A.
Keywords: Space-time Analysis, Drug Poisoning Deaths, Medication-assisted Treatment, Opioid, Hot Spot Analysis.
Abstract: Mortality rates from drug overdose have increased exponentially throughout the US for the past 30 years.
Age-adjusted death rates from drug poisoning for 1999-2016 were analyzed at the county level using space-
time cube and hot spot analysis, and a composite index of patient access to substance-use disorder treatment
and services per each county has been calculated. More than two-thirds of all US counties have been classified
as hot spots. Combining mortality hot spots with the accessibility index highlights 81 counties with high
disease burden and low access to treatment providers. These areas deserve special attention as state and local
government and public health organizations seek new prevention and intervention strategies to address the
opioid epidemic.
1 INTRODUCTION
Substance-use disorder epidemic continues to rage
through the United States, and the mortality rates
from drug overdose have been increasing
exponentially over more than 30 years (Jalal et al.,
2018). According to the Center for Disease Control,
the age-adjusted rate of drug overdose deaths in the
country was more than three times higher in 2016
than in 1999 (Hedegaard et al., 2017), with
considerable variation in mortality rates across the
United States (Rossen et al., 2013). While scientific
evidence suggests that medication-assisted treatment
(MAT) is effective in treating substance-use
disorders, only ten percent of people with this
disorder receive any type of specialty treatment (U.S.
Department of Health and Human Services, 2016).
Three medications - methadone, buprenorphine, and
naltrexone – are safe and effective in treating
substance use disorder and opioid addiction, but their
availability at treatment facilities in the U.S. is still
limited (Jones et al., 2018). A recent survey of
facilities providing addiction treatment services
revealed that “61% of counties in the U.S. did not
have any treatment programs that offered at least one
MAT drug” (amfAR, 2018).
While several studies used GIS to analyze spatial
patterns of the opioid epidemic (Jalal et al., 2018;
Jones et al., 2018; Rossen et al., 2013; Stewart et al.,
2017), only one study used GIS and clustering to
examine spatial access to treatment and services,
focusing on buprenorphine provider availability
(Jones et al., 2018). In this research, spatial and
temporal patterns of drug poisoning death rates are
compared with patterns of access to facilities with
MAT, with the goal of identifying priority areas for
improving access to MAT. Specifically, this paper
addresses the following two research questions:
Where are the hot spots of drug poisoning death rates?
How are resources for treatment and recovery
distributed within these hot spots? The unit of
analysis is a United States county, a political and
administrative division within a state.
2 DATA
To understand where the drug overdose epidemic is
the most pronounced, age-adjusted estimates of drug
poisoning deaths per 100,000 per county were
obtained from the amfAR Opioid and Health
Indicator Database (https://opioid.amfar.org/). These
estimates are based on deaths resulting from the
following underlying causes: unintentional
poisoning, intentional/suicidal poisoning; homicidal
poisoning; and poisoning from undermined intent
Ogneva-Himmelberger, Y.
Spatial Analysis of Drug Poisoning Deaths and Access to Substance-use Disorder Treatment in the United States.
DOI: 10.5220/0007828703150321
In Proceedings of the 5th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2019), pages 315-321
ISBN: 978-989-758-371-1
Copyright
c
2019 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
315
(Rossen et al., 2017). Annual estimates of death rates
covered period from 1999 to 2016, allowing for a
spatio-temporal analysis and identification of hot
spots.
To characterize availability and access to
treatment and services, the study used the following
indicators about medical facilities and providers:
Number of Facilities Providing Substance Abuse
Services;
Distance to Nearest Substance Abuse Facility
providing MAT;
Number of Facilities Providing at least one, at
least two, or all three medications used in the
treatment and Accepting Medicaid (three
indicators);
Number of Providers Licensed to Administer
Buprenorphine.
Higher number of facilities and shorter distance to the
nearest substance abuse facility providing MAT
means better availability and access to treatment and
services.
As of March 2017, private for-profit organizations
operated 60 percent of facilities with opioid treatment
programs certified by the Substance Abuse and
Mental Health Services Administration
(Substance Abuse and Mental Health Services
Administration, 2017), meaning that some parts of the
country lack affordable options for any treatment
(U.S. Department of Health and Human Services,
2016). To include the affordability aspect, this
research analyzes facilities that provide MAT and
also accept Medicaid, a government health care
program. It is important to differentiate between the
facilities that provide only one, or two, or all three
medications because providing multiple options for
MAT increases chances of a successful treatment. A
separate variable on buprenorphine providers is also
included, because buprenorphine has several
advantages over the other two medications, including
the option of receiving weekly or monthly
prescriptions in the general office setting (Jones et al.,
2018). Physicians, physician assistants, and nurse
practitioners who have received specific training and
obtained a waiver to prescribe buprenorphine for
treatment of opioid use disorder are separate from
providers working at the substance abuse facilities, so
it is important to include them in the analysis.
All indicators mentioned above were downloaded
in a tabular format from the amfAR Opioid and
Health Indicator Database. To map these indicators,
GIS layer of county boundaries was downloaded from
the U.S. Census Bureau (https://www.census.gov/
geo/maps-data/data/cbf/cbf_counties.html), and
indicator tables were joined to GIS layer using county
FIPS codes. There are 3142 counties in the United
States. Table 1 provides details about data used in this
study.
3 METHODS
To identify spatio-temporal hot spots of drug
poisoning mortality, first a space-time cube was
created in ArcGIS Pro (ESRI, 2019). Space-time cube
approaches were previously applied to analyze
spatio-temporal patterns of traffic accidents (Cheng et
al., 2019; Rahman et al.,, 2018), crimes (Bunting et
al., 2018), and anthrax epidemics in livestock
(Abdrakhmanov et al., 2017), but haven’t yet been
applied to drug overdose mortality.
Table 1: Indicators used in the study.
Description Year
Age-adjusted Drug Poisoning Deaths per 100,000 (Modeled) 1999-2016
Number of facilities that provide substance abuse services (per 100,000) 2017
Average geographic distance in miles to travel to a substance use disorder treatment facility providing at least one
form of MAT
2017
Number of substance abuse treatment facilities offering all three MAT services (Buprenorphine, Methadone,
Naltrexone) and accepting Medicaid (per 100,000)
2017
Facilities providing at least two of the three forms of MAT and accepting Medicaid (per 100,000) 2017
Facilities providing at least one form of MAT and accepting Medicaid (per 100,000) 2017
Number of healthcare providers licensed to administer buprenorphine (per 100,000) 2018
HGIS 2019 - Special Session on GIS in Healthcare
316
Space-time cube is a collection of spatial units (in
this case, counties) layered vertically according to
time. The bottom layer of the cube corresponds to
1999, the earliest year in the dataset, and the top layer
of the cube corresponds to 2016, the latest year. Thus,
a particular county at a given year is referred to as a
bin within the space-time cube. Following this,
Emerging Hot spot analysis tool in ArcGIS Pro was
applied to identify hot spots and cold spots of
mortality. A hot/cold spot has mortality that is
significantly higher/lower at a given time than the
mean mortality for the entire space-time cube. This
tool uses the Getis-Ord Gi* statistic and Mann-
Kendall test (Getis and Ord, 1992) and categorizes
hot/cold spots into several categories based on their
temporal trends: new, consecutive, intensifying,
persistent, diminishing, sporadic, oscillating, and
historic (ESRI, 2019). To be considered a hot/cold
spot, each bin is evaluated in the context of its spatial
and temporal neighbors. Selection of the
neighborhood parameter can influence the results, so
the tool was applied multiple times with different
neighborhood parameters to explore variation in the
outcome.
To identify different levels of availability and
access to treatment and services, a composite index of
six indicators was created using the following
procedure. First, indicators measuring the number of
facilities or providers per county, were recalculated
per 100,000 persons. It was necessary to normalize by
population, because some counties are much more
populated than others. Second, all six indicators were
standardized to a range of 0-100 to be comparable,
using the following formula:
X
inew
= (X
ioriginal
– X
imin
) /(X
imax
– X
imin
) *100
Where X
inew
is the standardized value of an indicator
i for each county, ranging from 0 to 100;
X
ioriginal
is the original value of an indicator i for a
county;
X
imin
is the minimum value of an indicator i for the
entire country;
X
imax
is the maximum value of an indicator i for the
entire country
.
Third, these standardized indicators were aggregated
(using averaging) into a composite index of access to
treatment and services (from here on referred to as
“access index” for brevity). Averaging approach was
used as the aggregation method because it weighs all
indicators equally, is intuitive, and allows
maintaining the same scale (0-100).
To answer the second research question (“How
are resources for treatment and recovery distributed
within hot spots?”), the access index and the hot spots
were overlaid and examined using various “select by
attribute” queries in GIS. The level of access to
treatment and services for counties that fall inside hot
spots was further evaluated state by state.
4 RESULTS
Space-time cube consisted of 3142 locations
(counties) and 18 time slices resulting in 56,556
space-time bins. Emerging Hot Spot Analysis tool
was used several times, with different configurations
of neighborhood (contiguity with edges and corners;
contiguity with edges only; eight nearest neighbors)
to evaluate sensitivity. Resulting spatio-temporal
patterns of hot/cold spots were very similar, with
minor variations. Results reported below are based on
the contiguity with edges and corners because this
conceptualization of neighborhood was used in a
previous study of the drug overdose epidemic in the
United States (Jalal et al, 2018).
Figure 1 and Table 2 show distribution of hot and
cold spots over the 18-year period. More than two-
thirds of the counties (68%) experienced hot spot
trends; 30% of counties did not show any pattern, and
only 2% of counties experienced cold spot trends. Of
the counties with hot spots, 50% were an “oscillating”
hot spot, 37% - a “consecutive” hot spot, 11% - a
“new” hot spot, and 1.5% - an “intensifying” hot spot.
While significant hot spots are present in every state,
there is considerable variation in the extent of the hot
spots within each state (Map 1). For example, some
states have only a few counties in hot spots (Dakotas,
Nebraska, Kansas, Iowa, New York), while other
states are entirely covered with hot spots
(Washington, California, Nevada, Utah, New
Mexico, Michigan, Oklahoma, Florida, Tennessee,
West Virginia, Vermont, New Hampshire, Maine,
Massachusetts). There was a small number of
counties with cold spots, clustered in North Dakota,
South Dakota and Nebraska. Even though these areas
show rates lower than the national mean, they are in
diminishing and historic cold spot categories,
meaning that the rates are increasing and areas are
becoming less cold over time
.
Spatial Analysis of Drug Poisoning Deaths and Access to Substance-use Disorder Treatment in the United States
317
Figure 1: Hot/cold spots of Age-adjusted Drug Poisoning Deaths per 100,000.
Table 2: Number of counties in Hot/cold spots of Age-adjusted Drug Poisoning Deaths per 100,000 (1999-2016).
Type Hot spot Cold spot Description (ESRI, 2019)
New 237 0
A location that is a statistically significant hot spot for the final time step and has never been
a statistically significant hot spot before.
Consecutive 794 0
A location with a single uninterrupted run of statistically significant hot spot bins in the final
time-step intervals. The location has never been a statistically significant hot spot prior to the
final hot spot run and less than ninety percent of all bins are statistically significant hot spots.
Intensifying 32 0
A location that has been a statistically significant hot spot for ninety percent of the time-step
intervals, including the final time step. In addition, the intensity of clustering of high counts
in each time step is increasing overall and that increase is statistically significant.
Oscillating 1080 0
A statistically significant hot spot for the final time-step interval that has a history of also
being a statistically significant cold spot during a prior time step. Less than ninety percent of
the time-step intervals have been statistically significant hot spots.
Diminishing 0 43
A location that has been a statistically significant cold spot for ninety percent of the time-step
intervals, including the final time step. In addition, the intensity of clustering of low counts in
each time step is decreasing overall and that decrease is statistically significant.
Historical 0 28
The most recent time is not cold, but at least ninety percent of the time-step intervals have
b
een statistically significant cold spots.
Figure 2: Composite index of access to substance-use disorder treatment and services.
HGIS 2019 - Special Session on GIS in Healthcare
318
The access index ranges from 2.8 to 57.8,
representing the worst/the best availability and access
to treatment and services respectively. The mean
value of the access index for the entire country is 17.4,
and the median – 16.9. To facilitate the analysis,
composite index values were mapped using standard
deviation classification – three below the national
mean and three above the national mean, each class
corresponding to one standard deviation (Figure 2).
On this map, the darker the color the further away
from the mean is the value: dark red corresponds to
the lowest index value, and the dark green – to the
highest index value. The map shows that access to
treatment and services is distributed unevenly
throughout the country. Northeastern states and
selected counties within West Virginia, Kentucky,
Ohio, Wisconsin, Colorado, New Mexico, and
Oregon have the best access. The lowest access is
observed in the Plains region, especially Montana,
and in Texas and Nevada.
To explore access to treatment and services within
the hot spots only, the access index and hot spots were
overlaid, and three classes with access index values
below the national mean were mapped separately
(Figure 3). This map reveals that 55% of the hot spot
counties have access index below the national mean.
Of particular importance are 81 counties that fall
within two lowest access categories: 72 counties that
have very low access (i.e. their access index is
between one and two standard deviations below the
national mean), and nine counties that have extremely
low access (i.e. their access index is below two
standard deviations from the national mean). These
81 counties account for 3% of total U.S. population
and are primarily located in Texas (26 counties),
Montana (13 counties), Alaska (11 counties),
Oklahoma (six counties), Nevada (six counties) and
New Mexico (five counties). A smaller number of
counties in this category are located in Washington,
Wyoming, Utah, Arkansas, South Dakota, Michigan,
Kansas, Iowa and Idaho.
5 DISCUSSION
This study of data from 3142 counties found a
significant geographic variation in the concentration
of the drug poisoning deaths, with the majority of
counties (68%) falling inside a hot spot – an area
where the death rates are statistically significantly
higher than the national average. Among all types of
hot spots, new and intensifying hot spots are of
particular concern. The new hot spots are counties
that were never a hot spot in previous years, and
became a hot spot in 2016. These new hot spots are
often located at the fringes of previously existing hot
spots and symbolize a current frontier or opioid
epidemic. The majority of the new hot spots are
located in the South – in Texas, Georgia, Arkansas,
Louisiana, but also in Illinois and Virginia.
Figure 3: Composite index in hot spot counties. Red color shows hot spots with the lowest access to treatment and services;
orange color shows hot spots with second lowest access. Light brown color shows hot spots with access within one standard
deviation below the mean. Hatched green shows hot spots with access above the national mean.
Spatial Analysis of Drug Poisoning Deaths and Access to Substance-use Disorder Treatment in the United States
319
The intensifying hot spots are counties that
experienced increasing intensity of clustering of high
mortality rates in each time step. There are two
distinct areas of intensifying hot spots – a big cluster
of contiguous 30 counties in the Appalachia region
(Kentucky, West Virginia, Virginia) and a small
cluster of two counties in north-central New Mexico.
This study aggregated six indicators into one
composite index of availability and access to
treatment and services, instead of analyzing access-
related data separately. The averaging approach used
here is an intuitive and easy to understand, especially
when relative importance of contributing variables is
unknown. One limitation of this approach is that it
potentially compensates low scores in one variable
with high scores in another variable, thus masking a
more nuanced distribution and interaction between
the variables. Future research will consider other
ways of creating an access index.
The final index map shows that the availability of
treatment and services varies widely. When it is
overlaid with the hot spot map, some alarming
patterns of high drug overdose deaths and low
availability of treatment become evident. The study
identified 81 hot spot counties that have extremely
low access to treatment and services. In these
counties, the average distance to the closest facility
with MAT is 90 miles (minimum = 52 mi, maximum
= 415 mi). Sixty-five of these counties have no
facilities providing substance abuse services. The
remaining 16 counties have 29 such facilities, and
only one of them provides MAT with one medication.
Only seven of 81 counties have Buprenorphine
providers (total of 28 providers). These areas in
Texas, Montana, Alaska, Oklahoma, Nevada and
New Mexico need immediate attention for the local
and state public health organizations.
6 CONCLUSION
This study used a novel approach to analyze opioid
overdose death rates concurrently through space and
time, by creating a space-time cube and identifying
hot spots using GIS. Resulting hot spot maps provide
a comprehensive assessment of the geographical
patterns of death rates from drug overdose. This study
also illustrates how a composite indicator can
facilitate the assessment of accessibility and
availability of treatment and services. Combining
mortality hot spots with accessibility index spotlights
areas with high disease burden and low availability of
treatment centers and providers. These areas deserve
special attention as state and local government and
public health organizations seek new prevention and
intervention strategies to address the opioid epidemic.
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