Improving Access of Substance Abuse Facilities in CBSA 26980

Improving Access of Substance Abuse Facilities in CBSA 26980

Watson, Michael

URP: 6225, ArcGIS for Planners

Final Project

May 5, 2016

Improving Access of Substance Abuse Facilities in CBSA 26980 | 0BWatson, Michael

Page 1 of 27

Executive Summary

According to a 2009 study, only 11.2% of those who seek substance abuse treatment

actually find it (SAMHSA, 2009). That means, on average, almost 21 million people within

the United States go without the help they desperately need. Couple this lack of access with

a sudden infusion of money from programs backed by the passage of the Affordable Care

Act (ACA), and there exists currently a window of opportunity for health care professionals

and agencies to improve the access of substance abuse services for all demographic groups.

This study uses the admissions data provided by SAMHSA from its TEDS-A dataset to

ascertain what types, or clusters, of demographic groups actively seek and find treatment

and, comparing that to actual demographic make-ups of specific areas recorded in the US

Census (namely, Johnson and Washington County in Iowa), find the best general locations

for new facilities. Through the creation of density maps filtered by population

demographics, and raster analysis maps that calculate according to the desired

demographics and relative seclusion of these populations from existing facilities, this study

finds that rural areas like Washington, Iowa need facilities introduced into the area and

more urban areas, like Iowa City, Iowa, need additional facilities established in neighboring

suburban locations.


Page 2 of 27

Introduction

In 2013, the National Institute of Health (NIH) conducted a study investigating drug use

across the United States which found that people had begun to turn towards illicit drug

consumption at ever increasing rates over the last decade. According to the study, an

estimated 24.6 million Americans (age 12 or above) admitted to using an illicit drug

sometime in the last month. This number, up a full percent since 2002, represents 9.4

percent of the population and includes a sudden and recent surge in the use of marijuana,

the most commonly used illicit drug (“DrugFacts: Nationwide Trends”, 2015). This swell in

usage suggests an increased need for facilities dealing with the abuse of such substances

and the recent passage of the Affordable Care Act (ACA) includes funding for such increases

(“Read the Law”, 2015; Frakt et al, 2014; Buck 2011). However, in general, this additional

funding and need has not equated to an increase in the number of facilities available for

those who desire them (Gorman, 2014). In fact, according to statistics released by SAMSHA

for 2009, only 11.2 percent out of 23.5 million people aged 12 and over who needed

treatment, found and received it (“DrugFacts: Treatment Statistics”, 2015). This means that

almost 21 million people who want such services never receive them. In a country with

expanding health resources, such a problem of access demands a solution. The simplest

course of action is to build more facilities, placing them in areas where the need proves

most pressing. But how to ascertain what types of people need such treatments or where

such facilities should be built?

This study aims to begin the process of answering these questions and decide what facility

locations would best fill the aforementioned treatment gaps. By sifting through admissions

data provided by the Substance Abuse and Mental Health Services Administration

(SAMHSA) and comparing it to the demographic data available through the US Census,


Page 3 of 27

specific areas with a particular demographic composition can be identified as needing

substance abuse facilities.

Method

Any study dealing with providing services to specific populations needs data. This study

focused on the Treatment Episode Data Set (TEDS) collected by SAMHSA. This data set

represents over 20 years (1992-2012) of admissions data from facilities across the United

States. For the purposes of simplicity and a desire to delve deeply into one area of the

country, this study deals only with the Core Based Statistical Area (CBSA) that includes Iowa

City, Iowa. This area, CBSA 26980, is comprised of the communities which reside in Johnson

and Washington County, and represents the highest level of geographic granularity

available within the admissions data set. All collected demographic data of this CBSA comes

from the 2010 census. As such, the only attributes of the TEDS admissions data inspected

includes only demographic data collected by census.gov. Therefore, out of the 64 available

variables within the TEDS dataset, only eight (age, gender, marital status, education,

employment, vet status, living arrangement, and primary income) gained consideration for

analysis. Race and ethnicity, attributes included in the original data date, were simulated in

the clustering process. Instead of grouping patients together with their race and ethnicities

included, this study clustered each race and/or ethnicity separately. This prevented

clusters forming based on the specific races and ethnicities of the patients and allowed the

study to focus on other, less superficial, connections. This meant that a pre-clustering of

sorts occurred before applying the hierarchical method as discussed in the next subsection,

‘Grouping the Data: Clustering’. This study grouped each patient according to their racial

designation (missing values excluded), only collecting patients who identified as non-


Page 4 of 27

Hispanic. All Hispanic individuals fell into their own pre-cluster, regardless of other

associated racial classifications.

Once set, these pre-clusters had each of their selected features endured a standardization

procedure where any ordinal categories (age, education, living arrangement) were changed

into equal increments between 0 and 1, and all non-ordinal categories were transformed

into binary features with 1 equal to the desired category (all other categories in feature

equaled 0). The following python code illustrates these transformations:

Figure 1: Python code for dictionary created to standardize attributes

Each category within the created python dictionary ‘attributeRanges’ used the list of

numbers at the end of the tuple associated with it to create the intervals. For instance, ‘AGE’

has a list of numbers 2 through 12, meaning that its range of values between 0 and 1 will

have 11 breaks (0, 0.1, 0.2, . . ., 1.0). A categorical feature, like ‘MARSTAT’ (denoted with a

‘cat’ designation in the first position of its associated tuple) has a specific value (the second

positon in the associated tuple, in this case a ‘2’) used to transform it into a binary variable.

This designated value becomes a ‘1’ during the transformation, and all other values within a

feature’s list turns to a ‘0’. This way, all features have uniform weights and allow any

analysis to consider each variable equally.

Grouping the Data: Clustering

In order to discover what types of demographic groupings existed within the TEDS

admissions data, this study employed hierarchical clustering using the Ward metric for


Page 5 of 27

measuring dissimilarity, limiting the standard error within each cluster. This method

created a much larger number of ‘pure’ cluster labels in comparison to other methods (EM

& K-means in particular), meaning that the average categorical value for each attribute

within a cluster maintained a variance such that two standard deviations in either direction

from the mean did not cause any overlap with other categories (Figure 2). Such clusters

gave a greater level of confidence as to the accuracy of the groupings created and allowed

direct connections to similar demographic categories tracked by the 2010 census.

Figure 2: Example of clustering results of the pre-clustered Hispanic patients. Bold text denotes categorical labels whose means did not overlap with other categories even when taking into consideration a possible spread of two

standard deviations in either direction.

From the clustering, three main types of groupings became apparent: Those dominated by

unmarried males (whether single, divorced, widowed, or separated), and the over-

representation of two specific racial groups, Native Americans and African Americans.

Incorporating the Census Data

Once clustering determined the types of demographic groupings that existed in the TEDS

data set, the next step relied on merging those clusters with the available census files. In

order to match the clusters found with the census data available, this study focused on

tables concerning household types, racial compositions, and ethnic identities from the

American Factfinder website maintained by census.gov. These, used in conjunction with the

2010 Census Blocks stored in the Tiger/Line shapefiles for Johnson and Washington County,

Hispanic HCLUSTClusters 0 1 2 3 4 5 6 7 8 9 10 11 12 13AGE 25-29 35-39 30-34 30-34 21-24 30-34 25-29 25-29 30-34 21-24 25-29 40-44 30-34 30-34GENDER Female Female Male Male Male Female Female Male Male Male Female Male Male MaleMARSTAT No Yes Yes No No Yes No No Yes No No No No NoEDUC 12 12 8-11 12 12 12 12 12 12 12 12 12 12 12EMPLOY No Yes Yes No No No Yes Yes No Yes No Yes No YesVET No No No No No No No No No No No Yes No NoLIVARAG Not Homeless Not Homeless Not Homeless Homeless Not Homeless Not Homeless Not Homeless Not Homeless Not Homeless Not Homeless Homeless Not Homeless Not Homeless HomelessPRIMINC Other Job Job Other Other Other Ret/Pen/Dis Job Other Other Other Ret/Pen/Dis Ret/Pen/Dis Ret/Pen/DPercent 10.7 1.7 12.9 4.3 19.2 2.7 6.3 20.8 6.4 3 3 3.4 3.8 1.9


Page 6 of 27

allowed a way of visualizing the general locations of those specific demographic groups

identified by the clustering analysis.

Map Visualization

The last stage of the analysis centered on map creation. Using the demographic labels of the

clusters described above, all generated maps incorporated visualizations of these

population characteristics as well as raster calculations involving marital status, racial

identity, and proximity (3-mile radius) to existing facilities geocoded according to listed

address (“National Directory of Drug and Alcohol Abuse Treatment Programs”, 2013).

Based on those maps, location possibilities for new or additional facilities emerged.


Page 7 of 27

The Maps

Each of the following used ArcGIS by ESRI for creation. They all incorporate the GCS North

American 1983 coordinate system and each follow these initial design steps:

1. Downloaded the 2010 Census Block tables for Johnson and Washington counties for

Race, Hispanic, Household Type, and the TIGER/LINE 2010 Census Block shapefile for

each county.

2. Both the tables and shapefiles for Johnson and Washington county were joined together

to create one table each for Race, Hispanic, and Household Type for the CBSA 26980,

and one shapefile incorporating both counties.

3. Any census block within the new, joined, CBSA shapefile which had a land area equal to

zero was deleted. This eliminated all blocks comprised of only water.

4. Data from SAMHSA containing the addresses of each known substance abuse facility

across the United States was used to geo-locate each location within the state of Iowa. I

used the Texas A&M geocoding services and exported the results as its own shapefile.

From there, each map endured different sets of changes depending on the information

desired to extract. The following display of maps includes both a brief description of those

additional steps (listed in further detail in the Appendix), as well as a quick summary of

observations obtained from them.


Page 8 of 27

Map 1: The Facility Locations

Using the joined county shapefiles to create the light blue highlight for the location of

Johnson and Washington County, this map also used source data from the State of Iowa in

the form of a shapefile representing a polygon conforming to the state borders. This map


Page 9 of 27

used only the outline of that shapefile and pinpointed the geographic location of each

known facility.

Besides the visualization of facility locations, the above map also gives a glimpse as to one of

the issues this analysis highlights: the difference in access between rural and urban areas.

Aside from some counties in the west, most of the facilities exist in, or relatively near,

population centers like Des Moines, Cedar Rapids, Iowa City, Waterloo, and the Quad Cities

(Davenport). This rather stark contrast in proximal accessibility provides an area of

emphasis for later raster analysis. It also uncovers another problem: only four substance

abuse facilities exist with the CBSA 26980 (light blue section on the map), and they are all

located in the Iowa City area.


Page 10 of 27

Map 2: CBSA 26980


Page 11 of 27

I created this map with the un-joined county shapefiles, coding each in a different color to

highlight the location of each relative to the other. Also, I did not include any census blocks

which had land areas equal to zero square miles. Iowa City in Johnson County (north) and

Washington in Washington County (south) act as the county seats, or political centers, of

each county.

Map 3: Unmarried Males in CBSA

From the joined county shapefiles, this map connects the Household Type data to the 2010

Census block data and projects the information found for the variable containing the values

for number of households headed by an unmarried male in each census block. This view


Page 12 of 27

offers insight into the locations of areas with larger populations of unmarried males,

displaying the 0.5 standard deviation from the mean as a way of lending the map more

contrast between census blocks (particularly in rural areas). Not surprisingly, such areas

occur most often in Johnson County, in the northern half of the CBSA, particularly close to

the Iowa City area and the University of Iowa.

Map 4: A close-up of unmarried males in Washington, Iowa

This map zooms in on the previous map, this time centered on Washington, Iowa in the

center of Washington County. This map also tracks the standard deviations of population

numbers of unmarried males and suggests that even in more remote rural areas, this

apparent at-risk population has found a home, and in a location with no nearby services.


Page 13 of 27

Map 5: Close-up of unmarried males in Iowa City, IA

Another zoom, this map also includes the facility location shapefile, with each facility

identified with a purple dot. From this view, many of the largest deviations from the mean

of unmarried males include nearby facility locations, but certain census blocks towards the

north and west (particularly in the Coralville, Tiffin, and North Liberty areas) suggest future

expansion of services to these neighborhoods.


Page 14 of 27

Map 6: Minority Populations in the CBSA

Beginning with the same source materials as the other maps, this one uses the Race table

available from Census.gov to calculate the percentage of minority peoples within each

census block. However, it omits anyone identifying as Asian as that particular ethnic group

makes up only about a quarter of a percent of all of the admissions data for this CBSA. As

such, many of the largest clusters of high percentage minority (not Asian) blocks exist

around the Iowa City and Washington, Iowa areas. Using the border shapefile adapted from

the state of Iowa, the extent of the map’s coverage is revealed in the insert in the lower

right-hand corner.


Page 15 of 27

Map 7: Minority Populations in Washington, Iowa

This map also uses the shapefile from the state of Iowa for the border inset, and zooms in on

the Washington, Iowa area, located in the inset by a black dot within the purple CBSA

region, and displays the population percentages of minorities within each census block.

This, combined with the map revealing a relatively large population of unmarried males

within the town limits, suggests that a need exists for a substance abuse facility in this area

to address the high population levels of at-risk groups identified by the earlier clustering

phase of the study.


Page 16 of 27

Map 8: Minority Populations in Iowa City, Iowa

As with the previous two maps focused on the minority population within CBSA 26980, this

map also incorporates a zoom; on the Iowa City area. This reveals a large number of high

percentage minority blocks to the southeast and northwest, towards North Liberty (upper

left corner). As with Washington, Iowa, the high concentrations of minorities combined

with the relatively large numbers of unmarried males suggests that this regions requires a

large number of substance abuse facilities. Even with four already available, more could be

built towards the northwest, near North Liberty and Coralville.


Page 17 of 27

Map 9: Raster Analysis in CBSA

Created by taking the Race table from Census.gov and the geocoded locations of the existing

facilities, this map assigns service levels according to the size of population densities of

African-American and Native American minorities, and lack of a nearby facility (each

concentric circle indicates a 3-mile radius) for each census block. According to these

results, the western half of Washington County (southwest corner) includes the most

underserved areas, with certain census blocks in the Iowa City area also needing further

attention.


Page 18 of 27

Map 10: Raster Analysis in Washington, Iowa

Once again zooming in on Washington, Iowa, this map reveals the location of the area in

Washington County where the Level of Service values start to decline towards the lowest half of the

scale without any recovery by certain census blocks. At this distance from the existing facilities in

Iowa City, any occurrences of at-risk populations pushes values towards the minimum

(underserved). As such, this, and locations to the west, indicate a need for substance facilities of

any kind in such isolated communities.


Page 19 of 27

Map 11: Raster Analysis in Iowa City, Iowa

For Iowa City, most of the area falls under the ‘Best Served’ category, with some isolated

clusters of census blocks indicating a large at-risk population. With the four current located

towards the south side of the map (green circles), it suggests that, despite the large amount

of red, a basic amount of coverage exists. However as the red moves to the north, and no

other facilities exist to provide such coverage, it appears as if future locations should focus

on the Coralville and North Liberty areas. Despite their proximity to local services, these

areas still register high in need.


Page 20 of 27

Conclusions

When taking into consideration the proximity of communities in relation to current

facilities and the existence of at-risk minority groups, the above maps tend to highlight one

important point: census blocks with relatively high numbers of African American and/or

Native American populations AND located more than 9 miles away from current facilities

rated as most in need of intervention. Places like Washington, IA in the center of

Washington County, with its relatively large Hispanic and Native American population and

lack of nearby facilities, require greater access to substance abuse services. These places

contain high concentrations of at-risk groups and have the added barrier of distance

preventing ease of access making them prime candidates for an infusion of substance and

mental health resources.

One area, though, provides an exception to the above rule. Iowa City, including its

surrounding suburbs of Coralville and North Liberty, also contains high ratings of need

despite its relative proximity to four current facilities. This suggests that, though services

exist in the area, there may not be enough to assure that the available facilities match the

surrounding population in scope of need or physical availability. For instance, do the sheer

numbers of at-risk populations far outweigh the current capabilities of the local facilities?

Do waiting lists exist? If so, how long? Do these facilities provide services in sync with the

substance abuse issues common in the area? How might other new facilities established in

outlying rural areas free up the already established ones in regards to how they can serve

their community?


Page 21 of 27

Future Study

All of these questions point to one over-arching lesson when dealing with data: more study

is required. Besides the fact that this data only deals with those admitted to substance

abuse facilities and does not include anyone needing but unable to find such services, this

data also in no way represents the quality or types of treatment offered. With the ever

shifting complexity of the drug use problem within the United States, facilities often have to

adjust their tactics as well as their services. This means staying on top of current trends of

drug use and issues that stem from such use can create a system of services tailored only to

specific groups and not necessarily to those still in need. The data used for this study

cannot account for such structural and societal fluctuations.

This means any further study should expand and incorporate information and insights from

the facilities themselves. This data could lend researchers a greater sense of both the

problem and area involved as well as suggesting further analytical methods. Armed with

such additional knowledge, researchers can be more effective in the ongoing endemic war

against substance abuse. In order for studies like this to truly provide actionable

intelligence regarding this problem, analysts will have to go much deeper than simple data

exploration to tell the stories the need to be told in order to guide future policy.


Page 22 of 27

Data Sources

Coordinate Systems: GCS North American 1983, NAD 1983

Datum: D North American 1983

Other Data:

“2010 Census Blocks, Johnson County”. Tiger/Line Shapefiles. United States Census. Web. 22 March

2016. https://www.census.gov/cgi-bin/geo/shapefiles/index.php

“2010 Census Blocks, Washington County”. Tiger/Line Shapefiles. United States Census. Web. 22

March 2016. https://www.census.gov/cgi-bin/geo/shapefiles/index.php

“Boundary of the State of Iowa”. Iowa Department of Natural Resources. State of Iowa. Web. 22

March 2016. http://programs.iowadnr.gov/nrgislibx

“Household Type for the Population in Households, Johnson County, Iowa”. American Factfinder.

United States Census. Web. 22 March 2016.

http://factfinder.census.gov/faces/nav/jsf/pages/index.xhtml

“Household Type for the Population in Households, Washington County, Iowa”. American Factfinder.

United States Census. Web. 22 March 2016.


“Race, Johnson County, Iowa”. American Factfinder. United States Census. Web. 22 March 2016.


https://www.census.gov/cgi-bin/geo/shapefiles/index.php

https://www.census.gov/cgi-bin/geo/shapefiles/index.php

http://programs.iowadnr.gov/nrgislibx





Page 23 of 27

“Race, Washington County, Iowa”. American Factfinder. United States Census. Web. 22 March 2016.


“Treatment Episode Data Set - Admissions (TEDS-A) Series.” ICPSR. University of Michigan. n.d.

Web. 14 December 2015.

References

Buck, J.A., “The Looming Expansion and Transformation of Public Substance Abuse Treatment

Under the Affordable Care Act.” Health Affairs. 30(8) (2011): 1402-1410. PDF.

“DrugFacts: Nationwide Trends.” National Institute of Health; National Institute on Drug Abuse.

drugabuse.gov, June 2015. Web. 9 December 2015.

“DrugFacts: Treatment Statistics.” National Institute of Health; National Institute on Drug Abuse.

drugabuse.gov, March 2011. Web. 14 December 2015.

Frakt, Austin B., Trafton, Jodie, Pizer, Steven. “Maintenance of Access as Demand for Substance

Abuse Use Disorder Grows.” Journal of Substance Abuse Treatment. 55 (2014): 58-63. PDF.

“Geography: Geographic Terms and Concepts.” United States Census Bureau. census.gov, n.d., Web.

18 December 2015.

Gorman, Anna. “Barriers Remain Despite Health Law’s Push to Expand Access to Substance Abuse

Treatment.” Kaiser Health News. Kaiser Family Foundation, 10 Apr. 2014. Web. 14

December 2015.

“Homepage for PyPDF2 Project”. Phaseit. github.io n.d. Web. 18 December 2015.

“List of Metropolitan Statistical Areas.” Wikipedia. Wikimedia Foundation, Inc. 30 November 2015.

Web. 18 December 2015.



Page 24 of 27

Müllner, Daniel. “fastcluster: Fast Hierarchical, Agglomerative Clustering Routines for R and

Python”. Journal of Statistical Software. 53(9) (2013): 1-18. URL.

http://www.jstats.org/v53/i09/

“National Directory of Drug and Alcohol Abuse Treatment Programs”. Substance Abuse and Mental

Health Services Administration. United States Department of Health and Human Services.

samhsa.gov. June 2013. Web. December 2015.

“Read the Law: The Affordable Care Act, Section by Section.” U.S. Department of Health & Human

Services. hhs.gov. 28 August 2015. Web. 18 December 2015.

“SciPy.org.” SciPy Developers & Enthought. scipy.org. 14 December 2015. Web. 18 December 2015.

“Texas A&M Geoservices”. Department of Geography. Texas A&M University. 30 November 2015.

Web. 22 March 2016.

“Welcome to RStudio.” RStudio. rstudio.com. 2015. Web. 18 December 2015.

“Weka 3: Data Mining Software in Java.” The University of Waikato. cs.waikato.ac.nz. n.d. Web. 18

December 2015.

http://www.jstats.org/v53/i09/


Page 25 of 27

Appendices: Map Creation Steps

Map 1: The Facility Locations

• Extracted facility addresses for state of Iowa with python-created .pdf scraper that

used the PyPDF2 package for access.

• Extracted addresses geocoded by Texas A&M batch geocoding services, with any

failures done manually through Google Maps.

• Imported geocoded data set and exported as a shapefile in NAD 1983 coordinate

system.

• Imported Facility Locations shapefile into blank document and added state of Iowa

border shapefile from Iowa Department of Natural Resources.

• Formatted point locations and border color and size

Map 2: CBSA 26980

• Imported Tiger/Line shapefiles for the 2010 census block data for Johnson and

Washington Counties.

• Formatted color of each shapefile and zoom level of map

Map 3-5: Unmarried Males

• Imported 2010 Household Types census block data for both Johnson and


• Imported Tiger/Line shapefiles for the 2010 census block data for Johnson and



Page 26 of 27

• Merged the Johnson and Washington County shapefiles into one shapefile. Exported

it as new shapefile, CBSA 26980

• Imported CBSA 26980 shapefile and joined the Household Type census block data to

it.

• Symbolized number of households with unmarried males as head of household as ½

standard deviations from mean

• Imported Facility Locations shapefile.

• Formatted points of locations and zoom levels of each map

Map 6-8: Minority Populations

• Imported 2010 Race census block data for both Johnson and Washington Counties.

• Calculated new field that subtracted number of white and Asian peoples from the

total population in each census block, and represented the result as a percentage of

the whole.

• Imported CBSA 26980 shapefile and joined the Race census block data to it.

• Symbolized percentage of minorities (not Asian) as stretched color ramp.

• Imported Facility Locations shapefile.

• Formatted points of locations and zoom levels of each map

Map 9-11: Raster Analyses

• Imported 2010 Race and Household Type census block data for both Johnson and


• Imported CBSA 26980 shapefile.

• Made same calculations and joins as mentioned for maps 3-8.

• Converted joined CBSA 269808 shapefile into polygon-raster data set


Page 27 of 27

• Imported Facility Locations shapefile

• Converted Facility Locations dataset into a point-raster data set

• Calculated Euclidean distance buffer of 3 miles for Facility Locations point-raster

• Reclassified all raster datasets on a 1-7 scale, with low numbers of minorities or

unmarried males registering as a 7, highest as a 1, and small proximal distance to

current facilities registering as a 7, and greatest distances as a 1.

• Calculated the reclassified rasters according to their adjusted scales and symbolized

the data as a stretched color ramp.

• Formatted point locations and zoom levels for each map.

Improving Access of Substance Abuse Facilities in CBSA 26980

Documents

Transcript of Improving Access of Substance Abuse Facilities in CBSA 26980