Tracing Treatment in the Face of an Epidemic An Analysis on the Accessibility of Opioid Treatment in the State of New Jersey
GEOGRAPHIC INFORMATION SYSTEMS TIHANA BULUT | RYAN EUSTACE | THIAGO LEE
COLUMBIA GSAPP
“No group is
immune to it — it is happening in our inner cities, rural and affluent communities” - Timothy R. Rourke Commissoner of New Hampshire Governor’s Commison on Alcohol and Drug Abuse
Photo by Chandana Fitzgerald
TABLE OF CONTENTS INTRODUCTION . . . . . . . . . . 4-5 RESEARCH QUESTION . . . . . . . 7 METHODOLGOY . . . . . . . . .
8-9
PROCESS . . . . . . . . . . . . . . . . 10-19 ANALYSIS. . . . . . . . . . . . . . . . . . . 20 CONCLUSION . . . . . . . . . . . . 21 REFERENCES . . . . . . . . . . . . . . . 22-23 APPENDEIX . . . . . . . . . . . . 24 - 25 2
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Introduction In the year 2017, more than 47,000 people in the United States passed away as result of an opioid overdose. That same year, roughly 1.7 million Americans suffered from opioid specific substance use disorders while 652,000 Americans suffered from heroin use disorder although these disorders are not mutually exclusive (National Institute on Drug Abuse, 2019).
Every day in the United States, 130 people die at the hands of opiates
One state that is particularly susceptible to the tragedies of opioid related deaths, is the state of New Jersey. Increasingly so, New Jersey continues to experience rampant instances of illicit drug use, overdose, and substance use disorders, all of which continue to plague the state. Year after year, New Jersey continues to break its prior record in the number of drug-related deaths, leaving policymakers at a loss as to what to do.
(National Institute on Drug Abuse, 2019)
the state ranked well over average in opioid related deaths comparatively to the United States as a whole. As result of New Jersey’s propensity to consistently provide up to date datasets for Geographic Information Systems and various other public health indicators, New Jersey was an ideal state for our study.
Currently, New Jersey ranks 12th in the nation in its amount of opioid related deaths as calculated per 100,000 residents. Overall,
Furthermore, New Jersey recently passed a policy measure that made it easier for lowincome patients to receive access to treatment for substance use disorders; the state’s 1.7 million medicaid patients would no longer have to wait for doctor approval within their health insurance network in order to receive medically assisted treatment for opioid addiction. This reduction in barriers to treatment is vital in the fight against the opioid epidemic; “When someone with an opioid addiction is ready for treatment, we shouldn’t be losing them to care while they wait for approval” quoted Department of Health Services Commisioner, Carole Johnson (Stainton, 2019). Medically Assisted Treatment, otherwise more simply referred to as MAT, is now considered the gold standard of care for treating opioid addiction. This is the result of the treatments apparent success rate and its ability to reduce rates of relapse above that of any other available treatment option. As the opioid epidemic reaches simliar scales to that of the HIV/AIDs epidemice, it is
pertinent that national efforts go into combatting the crisis. This is increasingly being seen through scaled up efforts to promote Medically Assisted Treatment, with New Jersey acting as a catalyst Studies have found that distance to treatment facilities and treatment retention are correlated. Typically, the farther a patient has to travel for treatment, the more likey they are to drop-out of treatment, and furthermore, relapse (Dove and Schneider 1981; Fortney, Booth, Blow, and Bunn 1995). Beardesley (2003) found that patients who travelled between 0 -1-miles had the highest likelihood of treatment completion; patients who travelled 4 or more miles experienced significantly lower rates of completion. This idea will be explored further throughout out study. This study will analyze accessibility to MAT facilities throughout New Jersey. By doing so, we hope to address any possible spatial barriers to treatment, allowing for comprehensive care.
Total Population: 8,908,520 Number of MAT Facilities: 74 Selected Demographics as % of Total Population (2017) % Non-White % in Poverty % with a Bachelors Degree % Unemployed
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35
30
25
20
15
10
5
0
5
Research Question How does the presence of life altering impacts of opioid use differntiate across New Jersey counties? Furthermore, how does the availability of Medically Assisted Treatment (MAT) facilities relate to this prevalence of life altering impacts of opioid use?
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Photo by David Bonazzi
Medically Assisted Treatment (MAT)
Life Threatening Impacts of Opioid Use
Accessibility to Treatment
MAT facilities use three specific pharmacological therapies to treat opioid addiction: methadone, buprenorphine and naltrexone. In addition to providing medication, these facilities monitor patients throughout their treatment. A growing body of literature has found that treatments provided in this type of environment are more effective at treating opioid addiction an reducing relapse rates.
This study uses the phrase, “life threatening impacts of opioid use� to operationalize the detrimental impacts of the opioid epidemic on populations. Two variables have been aggregated to quantify rates of life threatening impacts of opioid use: drug related deaths as caused by opioids as well as drug related hospital visits caused by opioid use.
Research into the effectiveness of opioid treatment and proximity to treatment facilities has found that patients who had to travel 1 mile or less had the highest likelihood of completing treatment, between 1 and 4 miles had a moderate chance of completing treatment and after 4 miles chances of completion were dramatically decreased.
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Methodology
Our scope is limited to the state of New Jersey and the year 2017. Additionally, we are concerned only with opioid use rather than greater drug use.
3 The index used to score counties with the highest and lowest rates of life altering
impacts of opioid use is unweighted-- we weighed both deaths and hospitalizations with equal importance.
4 The distance of our accessibility buffers are supported by a wide body of literature
showing that as distance away from facilities increases, effectiveness of treatment subsequently decreases. The specific range of 1 and 4-mile buffers, however, are substantiated by the findings of only one study
5 Lastly, although supported by our literature review, we only used Medically Assisted
Treatment facilities in considering available treatment options. This neglects a wider breadth of treatment options available.
6 It is possible that we experienced double counting in our use of hospitalization and overdose data.
Drivable Service Spatial Distribution
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The second direction of accessibility, as defined by our methodology, focuses on the spatial manifestation of MAT facilities and the areas they serve. We will measure this by creating service areas around each facility that capture populations within a 0 to 1-mile driving distance and a 1 to 4-mile driving distance. We will continue by analysing demographic indicators within each service area and aggregate this through a proportional split. By comparing demographic indicators within our service areas as well as within the counties as a whole, we will demonstrate which target populations have high access and low access to the facilities. Following the acquisition and dataset cleaning process, phase one of our project focuses on establishing which counties in NJ were most and least affected by life altering impacts of opioid use. To establish which counties were most affected, we examine two criteria across NJ’s counties: hospitalizations due to opiod use and deaths due to opiod use, normalized per 100,000 people. Once we determined two counties with the highest and lowest impacts according to these criteria, we reclassified these rankings on a scale of 1-5-- aggregating them to create an index score. Based on our final index score which ranged from 2-9, we selected our three counties: Bergen (least impacted), Camden (median impact), and Gloucester (highest impact). We then proceeded with our analysis.
f
Accessibility as bi-directional
f ro m
Drivable Service 2
to a
study; for example, arrests, permanent health damage, losing guardianship of dependents, lost wages, etc. All are valid in contributing to life altering impacts. These were not considered however.
ity l i ac
ty cili fa
1 There are other life altering impacts caused by opioid use outside those utilized in our
This study defines accessibility to MAT facilities as having two directions, or rather, as being bi-directional. The first direction of our methodology will look at the availability of MAT facilities in each county, comparing this to other indicators. This will be informed by calculating the ratio between the number of facilities to each indicator within each county.
a
Assumptions and Limitations
In phase 2 we looked at five demographic characteristics of the population in each of the three counties: its total population, the non-white population, population of adults holding a Bachelor’s degree, population below the poverty line, and the unemployed population. A ratio between the number of facilities and each of these parameters was calculated to enable an analysis of the availability of facilities for populations showing vulnerability. In phase 3, we established service areas of 1-mile and 4-miles based on driving, around each of our selected counties’ available MAT facilities. Those within 1-mile driving distance to a facility were considered to have “excellent” accessibility to treatment, those within 1-4 miles were deemed to have “fair” accessibility to treatment and those more than 4 miles from a facility were deemed to have “poor” accessibility to treatment. Within each service area, using the proportional split method, we analyzed demographic indicators that were used in the previous phase to enable an analysis of the population that has adequate access to treatment facilities. 9
Phase One County Selection How do life threatening impacts of opioid use differentiate across New Jersey Counties? Opioid-Related Deaths (2017)
Life Threatening Impacts of Life Threatening Opioid Use Index Impact of Opioid Low Impact Use Index (2017) Counties
Opioid-Related Hospitilizations (2017) Opioid-Related Hospitlizations (2017)
Opioid-Related Deaths (2017)
Score = 3
Score = 2
Number of Deaths
+
Number of Hospitilizations
9.3 - 10.0
53.6 - 183.4
10.0 - 15.1
183.4 - 449
15.1 - 25. 1
543.9 449 - 543.9-
15.1 - 25. 1
25.134.8 - 34.8-
High Impact Counties Index Score
449 - 543.9
25.1 - 34.8 47.6
N
931.8
N
543.9 - 931.8
931.8 - 1400
2-3
Score = 9
2-3
6-7
Score = 9
8-9
6-7 50 Miles
50 Miles
10
50 Miles
Score = 9
8-9
N
. To select our case study counties, we first had to construct an index of variables that when summed, would give us a clear picture of which counties in New Jersey were experiencing the highest rates of life threatening impacts of opioid use as well as those experiencing the lowest rates. The two criteria we chose to explore were hospitalizations due to opiod use (normalized per 100,000 people), and deaths due to opiod use (also normalized per 100,000 people). Each criterion was reclassified on a scale of 1 (least impacted) to 5 (most impacted). Following this, both criterion were summed into an index that would tell us how each county in New Jersey was impacted in regards to life threatening impacts of opioid use. While the lowest scored (Bergen) and median scored (Camden) counties were clear based on their ranks, we were presented with three counties that scored equally: Cape May, Cumberland and Gloucester. Gloucester was chosen because it had a lower rate of facilities per population.
Cumberland
4-5
Gloucester
4-5
931.8 - 1400
34.8 - 47.6
Morris
*reference maps not to scale
183.4 - 449
9.3 - 10.0
Bergen
Score = 4
53.6 - 183.4
10.0 - 15.1
N
=
Somerset
Cape May
*reference maps not to scale
50 Miles
Score
+
Opioid-Related Deaths 1
2
3
4
5
Opioid-Related Hospitilizations 1
2
3
4
5
=
2-3 4-5 6-7 8-9
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Phase One County Selection How do life threatening impacts of opioid use differentiate across New Jersey Counties? Low Impact Counties
High Impact Counties
Median Impact Counties
Bergen County
Camden County
Gloucester County
N
N N
5 Miles
5 Miles
Index Score MAT Facility
2-3 4-5 6-7 8-9
Camden County
5 Miles
MAT Facilities
Gloucester County
N
N
*reference maps not to scale
Phase Two
Where are Medically Assisted Treatment Facilities Located? Mapping
Bergen County
12
5 Miles
N
5 Miles
5 Miles 13
Phase Two Where are Medically Assisted Treatment Facilities Located?
Mapping MAT Facilities
Bergen County
Facilities per Opioid-Related Deaths
Facilities per Total Population
Per 100,000 People
Gloucester
0.07 0.04
Camden
N
Gloucester
73.7 57.4
Camden
5 Miles
Bergen
0.06
Bergen -4
0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 (x10 )
Camden County
Facilities per Persons in Poverty
0
100
200
300
400
500
-4 600 (x10 )
Facilities per Area (Acres)
Per 100,000 People
Gloucester
2.2
Gloucester
598.8
0.9
N
5 Miles
1.2
Camden Bergen
Gloucester County
Bergen
6.1 0
1
1.4
Camden
2
3
4
5
6
7
8
(x10-4)
3.9 0
1
2
3
4
(x10-5 )
Index Score 2-3 4-5 6-7
N
8-9 N
5 Miles
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5 Miles
To compare county wide access to MAT facilities, and more specifically, to understand if the number of facilities in a given county was adequate provided that county’s specific sociodemographic context, ratios of facilities to various county-specific metrics were examined. These ratios include: facilities to persons in poverty (per 100k people), facilities to total county acreage, facilities to number of overdoses (per 100k people), and facilities to total county population. As is discussed in our conclusions, Bergen over-performs in most categories in regards to its apparent accessibility of facilities to the county population. 15
Phase Three Demographic Analysis Who are these facilities accessible to?
Unemployment Rate (%) Bergen 1-Mile Service Area 4.7 Bergen 4- Mile Service Area 5.0 Bergen County 3.4
N
Camden 1-Mi. SA 13.3 Camden 4-Mile Service Area 9.7 Camden County 5.2 Gloucester 1-Mile Service Area 6.6 Gloucester 4-Mile Service Area 8.3
N
5 Miles
N
5 Miles
5 Miles
Unemployment Rate (%) 2.9 - 4 4 - 8
8 - 12
3
00
Gloucester County 5 All of NJ 4.6 12 6 9
15
% Unemployment Rate
12 - 16 16 - 27
% Poverty Rate
Poverty Rate (%) Bergen 1-Mile Service Area 10.1 Bergen 4-Mile Service Area 7.6 Bergen County 9.8
Camden 1-Mi SA 34.
N
N
5 Miles
5 Miles
Poverty Rate (%)
16
0.5 - 6 6 - 12
N
12 - 18 18 - 24 24 - 60
5 Miles
00
05
Camden 4-Mile SA 23.7 Camden County 16.3 Gloucester 1-Mile Service Area 7.8 Gloucester 4-Mile Service Area 9.9 Gloucester County 9 All of NJ 10.7 10 15 20 25 30 35
MAT Facility 1-Mile Service Area 4-Mile Service Area
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Phase Three Demographic Analysis Who are these facilities accessible to?
Received Bachelors Degree (%) Bergen 1-Mile SA 23.7 Bergen 4-Mile SA 27.7 Bergen County 29.8 Camden 1-Mile Service Area 9.2 Camden 4-Mile Service Area 15.4 Camden County 19.7 Gloucester 1-Mile Service Area 21.2 N
N
5 Miles
5 Miles
Gloucester 4-Mile Service Area 20.8 Gloucester County 20.6 All of NJ 23.4
N
5 Miles
00
Received Bachelors Degree Rate (%)
05
10
15
20
25
30
% Bachelors Degrees % Non-White
7.6 - 10 10 - 20 20 - 30 30 - 40 40 - 50
Non-White (%) Bergen 1-Mile Service Area 58.5 Bergen 4-Mile Service Area 45.4 Bergen County 29 Camden 1-Mile SA 82 Camden 4-Mile Service Area 56.7 Camden County 37
N
N
N
5 Miles
Non-White (%)
18 3.8 - 20 20 - 40 40 - 60 60 - 80 80 - 100
Gloucester 1-Mile Service Area 24.3 Gloucester 4-Mile Service Area 23.9 Gloucester County 18 All of NJ 32
5 Miles
5 Miles
00
20
40
60
80
100
MAT Facility 1-Mile Service Area 4-Mile Service Area
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Analysis
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Upon the completion of our analysis, it was evident that Bergen county’s comparatively high number of facilities is able to provide 75% of its population within what we have deemed as an accessible treatment range. In regards to the three case studies we have provided, Bergen county maintains the largest coverage of its population within an adequate treatment range.
Opioid overdoses
Furthermore, Medically Assisted Treatment facility placement tends to fall within areas that are consistent with Bergen’s average poverty rate, below average attainment of a bachelor’s degree, and higher rates of nonwhite populations alongside unemployed populations as well. Given the counties high rate of coverage according to our analysis, it is unsurprising that Bergen county has the lowest impact index comparatively to the rest of New Jersey. It is sufficient to say that this analysis indicates that the high number of Medically Assisted Treatment facilities in Bergen county is either the reasoning behind the low rates of life altering impacts of opioid use, or a misallocation of facilities compared to counties with higher rates of
2017 in 52 areas
increased 30 percent from July 2016 to September in 45 states (National Institute on Drug Abuse, 2019)
life altering impacts of opioid use. Alternatively, it appears that Camden county has the highest placement of Medically Assisted Treatment facilitie areas with higher than average unemployment, higher rates of non-white residents, lower than average education attainement and higher than average poverty rates. That being said, Camden’s Medically
Treatment facilities are only able to provide 34% of its population with what we have deemed as an accessible treatment range. If Camden were to continue to further place Medically Assisted Treatment facilities not relying solely on its current criteria, it would likely have a much greater impact than we are able to analyze at the moment. The counties ability to range as median on our impact index seems appropriate given that much of the county’s population still remains outside of reasonable access (according to our criteria). Lastly, the county of Gloucester scored the highest on our impact index, indicating a high level of life altering impacts of opioid use. Given that Goucester’s facilities provide what we have deemed as adequate access to 47% of its residents, this does not come as a surprise. However, Gloucester county covers more residents as a percent of its total population than Camden county. It’s higher index score is likely a function of its less optimized placement criteria for high risk populations as well as some limitations of our index criteria. Where Camden’s
placement is to maximal impact, Gloucester’s placement is sparse thereby providing only moderate impact. While its facilities serve areas that experince higher unemployment and have more nonwhite residents on average, they are also in areas with generally higher educational attainment than average as well as average levels of poverty. Where Camden needs to increase its coverage, Gloucester needs to both increase its coverage and better select is locations to maximize impact from these Medically Assisted Treatment facilities.
Conclusions This spatial analysis of New Jersey’s counties was able to offer some interesting results in our effort to answer our original research question: “How does the presence of life altering impacts of opioid use differentiate across New Jersey counties?”. GIS can be a powerful tool in analyzing treatment accessibility, and even more so when faced with an epidemic of this scale.
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References DATASETS
New Jersey Department of Health (2019). Confirmed Drug-Related Deaths in New Jersey [interactive dataset]. Retrieved from: https://www.nj.gov/health/populationhealth/opioid/opioid_ deaths.shtml New Jersey Department of Health (2019). Drug-Related Hospital Visits 2008-2017 [interactive dataset]. Retrieved from: https://www.state.nj.us/health/populationhealth/opioid/opioid_ hospital.shtml New Jersey Office of Information Technology (NJOIT) - Office of Geographic Information Systems (OGIS) (2016). Counties of New Jersey, New Jersey State Plane NAD83 [shapefile]. Retrieved from: http://njogis-newjersey.opendata.arcgis.com New Jersey Office of Information Technology (NJDOIT), Office of Geographic Information Systems (OGIS) (2013). Census Tracts - Tiger. Retrieved from: http://www.census.gov/geo/mapsdata/data/tiger.html
Stainton, L. (2019, April 2). Access to treatment for opioid addiction eased for those on Medicaid in N.J. WHYY - PBS. Retrieved from https://whyy.org/articles/access-to-treatment-for-opioidaddiction-eased-for-those-on-medicaid-in-nj/
IMAGES
Chan, E. (2018). Retrieved from http://www.davidebonazzi.com/news/columbia-medicineopioids-addiction Fitzgerald, C. (2018). Retrieved from https://www.healthxl.com/blog/humans-of-the-opioidcrisis
McDonald, Patrick (2016). Road Centerlines of New Jersey, New Jersey State Plane NAD 83 [shapefile]. Retrieved from: https://njgin.state.nj.us/NJ_NJGINExplorer/jviewer.jsp?pg=ROADS State of New Jersey - DHS - Division of Mental Health and Addiction Services (2019). Addiction Services Treatment Directory [dataset]. Retrieved from: https://njsams.rutgers.edu/ TreatmentDirectory/License U.S. Census Bureau (2017). 2013-2017 American Community Survey 5-year Estimates [dataset]. Retrieved from: https://factfinder.census.gov/faces/tableservices/jsf/pages/productview. xhtml?pid=ACS_17_5YR_DP05&prodType=table U.S. Census Bureau (2018) . Cartographic Boundary Shapefiles - States [Shapefile]. Retrieved from https://census.gov/geo/maps-data/data/cbf/cbf_state.html U.S. Department of Health and Human Services - Substance Abuse and Mental Health Services Administration (SAMHSA) (n.d.). Search for treatment [Interactive dataset]. Retrieved from: https://findtreatment.samhsa.gov/locator
RESEARCH
Beardsley, K., Wish, E. D., Fitzelle, D. B., Ogrady, K., & Arria, A. M. (2003). Distance traveled to outpatient drug treatment and client retention. Journal of Substance Abuse Treatment, 25(4), 279–285. doi: 10.1016/s0740-5472(03)00188-0 Dove, H. G., & Schneider, K. C. (1981). The Usefulness of Patients?? Individual Characteristics in Predicting No-Shows in Outpatient Clinics. Medical Care, 19(7), 734–740. doi: 10.1097/00005650198107000-00004\
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Fortney, J. C., Booth, B. M., Blow, F. C., Bunn, J. Y., & Cook, C. A. L. (1995). The Effects of Travel Barriers and Age on the Utilization of Alcoholism Treatment Aftercare. The American Journal of Drug and Alcohol Abuse, 21(3), 391–406. doi: 10.3109/00952999509002705
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Appendix: Methodology Primary Datasets
New Jersey Counties (Polygon)
2017 Opioid-Use Related Deaths for Counties in NJ (Table)
New Jersey Department of Health (2019). Confirmed Drug-Related Deaths in New Jersey [interactive dataset].
Make Table with Online Data
New Jersey Department of Health (2019). Drug-Related Hospital Visits 2008-2017 [interactive dataset]. New Jersey Technology Geographic (OGIS) (2016). New Jersey [shapefile].
Phase 3 - Service Areas of Facilities
Phase 1 - Selecting counties
Verify Projection
Office of Information (NJOIT) - Office of Information Systems Counties of New Jersey, State Plane NAD83
Clean Table
Make Table with Online Data
Table Join
Table Join
Classify using Jenks Method
Classify using Jenks Method
Choropleth Map of OpioidUse Related Deaths per County in NJ (5 classes)
2019 State of New Jersey MAT Facilities Addresses (Online Directory)
2017 Opioid-Use Related Hospitalization for Counties in NJ (Table)
2017 ACS Data by Census Tract for each County (Table)
2013 NJ Census Tracts (Polygon)
Collect Data Clean Tables
Verify Projection
Join Data to One Table
Select by Location, the Roads inside each County
Table Join the ACS Data into Attribute Table of Census Tracts Shapefile
Clip Export Data
Select by Attribute Census Tracts of each County
The 3 Counties with respective Roads Centerlines
Calculate Areas of each Census Tract
Create Address Locator Geocode Addresses
Sum indexes county by county (in Excel)
Review and Rematch
Facilities Geocoded into each County
Table Join to NJ Counties
Create New Feature Dataset For each County, Create New Network Dataset, based on their Roads Centerlines
Choropleth Map of Index for LifeAltering Impacts of Opioid Use (Range: 2-9 - divided into 4 classes)
Make Service Areas in each County (Break Values of 1 and 4 miles)
Select: - 3 Counties with the Highest Score - 3 with Median Scores - 3 with Lowest Scores
Add Location to Service Areas Solve Service Areas
Compare their Death and Hospitalization Rates
State of New Jersey - DHS - Division of Mental Health and Addiction Services (2019). Addiction Services Treatment Directory [dataset].
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Clean Data Removing: - Restricted Roads - Inactive Roads
Join Data to One Table
Service Areas for each Facility
Choose 1 County from each Group
U.S. Department of Health and Human Services Substance Abuse and Mental Health Services Administration (SAMHSA) (n.d.). Search for treatment [Interactive dataset].
2016 NJ Roads Centerlines (Polygon)
Clean Table
Reclassify with new index: 1-5 (in Excel)
McDonald, Patrick (2016). Road Centerlines of New Jersey, New Jersey State Plane NAD 83 [shapefile].
U.S. Census Bureau (2018) . Cartographic Boundary Shapefiles States [Shapefile].
The 3 selected New Jersey Counties (polygon)
Choropleth Map of Opioid-Use Related Hospitalizations per County in NJ (5 classes)
Reclassify with new index: 1-5 (in Excel)
New Jersey Office of Information Technology (NJDOIT), Office of Geographic Information Systems (OGIS) (2013). Census Tracts - Tiger.
U.S. Census Bureau (2017). 2013-2017 American Community Survey 5-year Estimates [dataset].
2019 SAMHSA MAT Facilities Addresses (Online Directory)
Export:
0 to 1 mile Service Areas
County of Bergen Low Score: 2
County of Camden Median Score: 6
County of Gloucester High Score: 9
Use Total Population per County
2019 State of New Jersey MAT Facilities Addresses (Online Directory)
Calculate Population Living under Poverty Line
Use Number of Facilities per County
Merge
Merge
Dissolve
Dissolve
Calculate Area
Calculate Area
2019 SAMHSA MAT Facilities Addresses (Online Directory)
2017 Opioid-Use Related Deaths for Counties in NJ (Table)
Use Number of Facilities per County
Use Number of Deaths per County
New Jersey Counties (Polygon)
Clip Census Tracts To Service Area
Use Area per County
Sum Divide Facilities/ Total Population
Ratio of Facilities per Total Population
Divide Facilities/ Death Divide Facilities/ Popul. under Poverty Line
Ratio of Facilities per Person in Poverty
Service Area (1 to 4 mi) for each County
Service Area (0 to 1 mi) for each County
Phase 2 - Acessibility of Facilities 2017 ACS Data by Census Tract for each County (Table)
1 to 4 miles Service Areas
Ratio of Facilities per Death Related to Opioid Use
Clip Census Tracts To Service Area
Create Ratio New Area/Original Area
Calculate New Areas
Multiply Ratio by each ACS Data Imported, for each Census Tract
Create Ratio New Area/Original Area
Calculate Data below in regards to the Service Area
Divide Facilities/Area
Ratio of Facilities per Area
Calculate New Areas
Unemployment Rate in SA (0 to 1 mi)
Poverty Rate in SA (0 to 1 mi)
Bachelor’s Degree in SA (0 to 1 mi)
Non-White in SA (0 to 1 mi)
Multiply Ratio by each ACS Data Imported, for each Census Tract Calculate Data below in regards to the Service Area Unemployment Rate in SA (1 to 4 mi)
Poverty Rate in SA (1 to 4 mi)
Bachelor’s Degree in SA (1 to 4 mi)
Non-White in SA (1 to 4 mi)
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Geographic Information Systems Professor Leah Meisterlin Columbia University in the City of New York GSAPP Tihana Bulut | Ryan Eustace | Thiago Lee
