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S1

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geocoded students and school
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Is the market served by your clinic, hospital or professional practice defined by who shows up at the door? Understanding healthcare market dynamics is one way these entities can improve their bottom line by using Health Data Analytics. Professionals skilled with Health Data Analytics (HDA) can help their organization, or clients, better achieve their vision and improve performance. See the HDA main section for a summary of related topics and sections.

Examining County Health Demographic-Economic Patterns
This section is focused on use of tools and methods to analyze county health demographic-economic patterns. Participants in the Certificate in Data Analytics may optionally use the tools and resources described here. The Data Analytics Methods Guide provides extended detail on use of these tools, data and development of applications to meet participant interests.

As the population ages, migrates and otherwise changes, health status and healthcare needs change by location, type and in other ways. Data on health status, characteristics and trends continue to become more available, particularly at the county geographic level ... but these data are often difficult to locate, integrate and use in a combined manner.

VA Hospitals/Facilities in Context of Urban/Rural Areas
The following graphic illustrates how Veterans Administration hospitals and facilities (red markers) can be viewed in context of urban/rural patterns. Urban areas are shown with orange fill pattern. The Appalachia 405 county area is shown with black bold boundary. Use (
GIS) resources to examine additional patterns such as the distribution of veterans by census tract based on the American Community Survey (ACS) data.

-- view created using CV XE GIS and associated county health GIS Project
-- click graphic for larger view showing details.

Supplemental views of Appalachia Region:
Appalachia counties shown with bold black boundary.
  • Cities by 2014 Population Size
  • Cities by Population Percent Change 2010-2014

County Health Analytics
This section provides an overview of accessing, integrating and analyzing demographic, economic and health data with a focus on county and sub-county geography. Geographic information system (GIS) tools are used to visually and geospatially analyze health-related patterns and characteristics. Applications reviewed here are developed using the CV XE GIS software and associated U.S. national scale health GIS Project.

The County Health Patterns GIS project includes data from:
  • ProximityOne CountyTrends and Situation & Outlook
    ... view individual county population & components of change annual estimates and trends
    ... click county link in this interactive table
  • Robert Wood Johnson Foundation County Health Rankings
  • Appalachian Regional Commission economic status
  • Other sources. See additional information


Additional ProximityOne ready-to-use shapefiles could be added containing all data from the American Community Survey demographic-economic profiles. The same scope of subject matter, annually updated, is available at the ZIP code, census tract, county and other geography. See related interactive tables (four related web sections) for subject matter details.

The CV XE GIS software is used with the County Health Patterns GIS project to develop views/applications shown below. These views/applications illustrate how the health analytics resources can be used. Select from wide ranging alternative measures.

Patterns of Population Change -- %Change 2010-2014 -- U.S. by County
The following view shows patterns of %population change 2010-2014 using the CountyTrends layer/dataset.

  -- view created using CV XE GIS and associated county health GIS Project
  -- click graphic for larger view showing details.

Site Analysis & Patterns of Population Change -- %Change 2010-2014 -- Houston Metro Area
The following view shows patterns of %population change 2010-2014 using the CountyTrends layer/dataset.
This view also illustrates use of the Site Analysis tool to aggregate and display population by year 2010 through 2014.

  -- click graphic for larger view showing details.

Patterns of Population Change -- %Change 2010-2014 -- Missouri Area Counties
The following view shows patterns of %population change 2010-2014 using the CountyTrends layer/dataset.
This view also illustrates use of the Metros layer to show outlines of Missouri metropolitan statistical areas (bold red/brown boundary). Counties are labeled with the 2014 population estimate.

  -- click graphic for larger view showing details.

Patterns of Percent Smokers -- U.S. by County
The following view shows patterns of percent smokers by county using the County Health Rankings RMD layer/dataset.

  -- click graphic for larger view showing details.

Patterns of Food Insecurity -- U.S. by County
The following view shows patterns of food insecurity by county using the County Health Rankings AMD layer/dataset.

  -- view created using CV XE GIS and associated county health GIS Project
  -- click graphic for larger view showing details.

Patterns of Food Insecurity -- Appalachia Region
The following view shows a zoom-in of the above view.

  -- click graphic for larger view showing details.

Patterns of Economic Distress -- Appalachia Region
The following view shows patterns of economic distress based on an index developed by the Appalachian Regional Commission.

  -- click graphic for larger view showing details.

Supplemental Views SubCounty Geography -- Primary Care Service Areas (PCSA)
... focus on Appalachia -- bold boundary; PCSAs are comprised of one or more contiguous Census 2010 census tracts
... patterns of Crude Rate: Total number of Deaths per 1,000 Medicare beneficiaries residing in the PCSA
... this PCSA is located in Mingo County, WV and Pike County, KY.

  -- click graphic for larger view showing details.

Supplemental Views SubCounty Geography -- Primary Care Service Areas -- Zoom-in
... focus on Appalachia -- bold boundary; PCSAs are comprised of one or more contiguous Census 2010 census tracts
... patterns of Crude Rate: Total number of Deaths per 1,000 Medicare beneficiaries residing in the PCSA
... zoom-in to pointer shown in above view
... pointer in following view in located in one PCSA shown in profile
... this is a multi-tract, multi-state PCSA

  -- click graphic for larger view showing details.

Supplemental Views SubCounty Geography -- Primary Care Service Areas -- Zoom-in -- Tract
... focus on Appalachia -- bold boundary; PCSAs are comprised of one or more contiguous Census 2010 census tracts
... patterns of Crude Rate: Total number of Deaths per 1,000 Medicare beneficiaries residing in the PCSA
... same view as above with census tract layer turned on.
... pointer in following view in located in one census tract shown in profile
... note census tract outline

  -- click graphic for larger view showing details.

About the County Health Patterns GIS Project
Use the County Health Patterns GIS Project to develop maps, such as those shown above, and perform geospatial analysis operations. Steps to develop custom thematic pattern maps are summarized below (requires Windows computer with Internet connection)
1. Install the ProximityOne CV XE GIS
... run the CV XE GIS installer
... take all defaults during installation
2. Download the County Health Patterns GIS project fileset
... requires DataAnalytics User ID
... unzip County Health Patterns GIS project files to local folder c:\healthpatterns
3. Open the c:\healthpatterns\chp1.gis project
... after completing the above steps, click File>Open>Dialog
... open the file named c:\healthpatterns\chp1.gis
4. Done. The start-up view is shown above.

About the Data & GIS Project
The following scroll sections provide field names and subject matter descriptions for each of the shapefiles/datasets used in the GIS project.

County Population & Components of Change -- scroll section; use scroll bar at right of table
The following table lists items available in the ProximityOne County Trends dataset. Create thematic pattern map views of any of these items alone or in combination with other items. These fields are contained in project shapefile cb_2013_us_county_500k_countytrends.shp. Availability of 2015 estimates and projections to 2020 vary by version installed.
FieldName Description
STAB State Abbreviation
SUMLEV Summary Level (050)
REGION Region code
DIVISION Division code
STATE State FIPS code
COUNTY County FIPS code
STCTY State+County FIPS code
STNAME State name
NAME County Nsme
STATUS Status
CEN2010POP Population, Census 2010
ESTBAS2010 Population, Estimates base 2010
POP2010 Population, 7/1/2010
POP2011 Population, 7/1/2011
POP2012 Population, 7/1/2012
POP2013 Population, 7/1/2013
POP2014 Population, 7/1/2014
POP2015 Population, 7/1/2015
POP2016 Population, 7/1/2016
POP2017 Population, 7/1/2017
POP2018 Population, 7/1/2018
POP2019 Population, 7/1/2019
POP2020 Population, 7/1/2020
NCHG2010 Net Population Change, 2010
NCHG2011 Net Population Change, 2011
NCHG2012 Net Population Change, 2012
NCHG2013 Net Population Change, 2013
NCHG2014 Net Population Change, 2014
NCHG2015 Net Population Change, 2015
NCHG2016 Net Population Change, 2016
NCHG2017 Net Population Change, 2017
NCHG2018 Net Population Change, 2018
NCHG2019 Net Population Change, 2019
NCHG2020 Net Population Change, 2020
B2010 Births, 2010
B2011 Births, 2011
B2012 Births, 2012
B2013 Births, 2013
B2014 Births, 2014
B2015 Births, 2015
B2016 Births, 2016
B2017 Births, 2017
B2018 Births, 2018
B2019 Births, 2019
B2020 Births, 2020
D2010 Deaths, 2010
D2011 Deaths, 2011
D2012 Deaths, 2012
D2013 Deaths, 2013
D2014 Deaths, 2014
D2015 Deaths, 2015
D2016 Deaths, 2016
D2017 Deaths, 2017
D2018 Deaths, 2018
D2019 Deaths, 2019
D2020 Deaths, 2020
NI2010 Natural Increase, 2010
NI2011 Natural Increase, 2011
NI2012 Natural Increase, 2012
NI2013 Natural Increase, 2013
NI2014 Natural Increase, 2014
NI2015 Natural Increase, 2015
NI2016 Natural Increase, 2016
NI2017 Natural Increase, 2017
NI2018 Natural Increase, 2018
NI2019 Natural Increase, 2019
NI2020 Natural Increase, 2020
IMIG2010 International Migration, 2010
IMIG2011 International Migration, 2011
IMIG2012 International Migration, 2012
IMIG2013 International Migration, 2013
IMIG2014 International Migration, 2014
IMIG2015 International Migration, 2015
IMIG2016 International Migration, 2016
IMIG2017 International Migration, 2017
IMIG2018 International Migration, 2018
IMIG2019 International Migration, 2019
IMIG2020 International Migration, 2020
DMIG2010 Domestic Migration, 2010
DMIG2011 Domestic Migration, 2011
DMIG2012 Domestic Migration, 2012
DMIG2013 Domestic Migration, 2013
DMIG2014 Domestic Migration, 2014
DMIG2015 Domestic Migration, 2015
DMIG2016 Domestic Migration, 2016
DMIG2017 Domestic Migration, 2017
DMIG2018 Domestic Migration, 2018
DMIG2019 Domestic Migration, 2019
DMIG2020 Domestic Migration, 2020
NMIG2010 Net Migration, 2010
NMIG2011 Net Migration, 2011
NMIG2012 Net Migration, 2012
NMIG2013 Net Migration, 2013
NMIG2014 Net Migration, 2014
NMIG2015 Net Migration, 2015
NMIG2016 Net Migration, 2016
NMIG2017 Net Migration, 2017
NMIG2018 Net Migration, 2018
NMIG2019 Net Migration, 2019
NMIG2020 Net Migration, 2020
RES2010 Residual, 2010
RES2011 Residual, 2011
RES2012 Residual, 2012
RES2013 Residual, 2013
RES2014 Residual, 2014
RES2015 Residual, 2015
RES2016 Residual, 2016
RES2017 Residual, 2017
RES2018 Residual, 2018
RES2019 Residual, 2019
RES2020 Residual, 2020
GQBASE2010 Group Quarters, 2010 Base
GQE2010 Group Quarters, 2010
GQE2011 Group Quarters, 2011
GQE2012 Group Quarters, 2012
GQE2013 Group Quarters, 2013
GQE2014 Group Quarters, 2014
GQE2015 Group Quarters, 2015
GQE2016 Group Quarters, 2016
GQE2017 Group Quarters, 2017
GQE2018 Group Quarters, 2018
GQE2019 Group Quarters, 2019
GQE2020 Group Quarters, 2020
RB2011 Birth Rate, 2011
RB2012 Birth Rate, 2012
RB2013 Birth Rate, 2013
RB2014 Birth Rate, 2014
RD2011 Death Rate, 2011
RD2012 Death Rate, 2012
RD2013 Death Rate, 2013
RD2014 Death Rate, 2014
RNI2011 Natural Increase Rate, 2011
RNI2012 Natural Increase Rate, 2012
RNI2013 Natural Increase Rate, 2013
RNI2014 Natural Increase Rate, 2014
RIMIG2011 International Migration Rate, 2011
RIMIG2012 International Migration Rate, 2012
RIMIG2013 International Migration Rate, 2013
RIMIG2014 International Migration Rate, 2014
RDMIG2011 Domestic Migration Rate, 2011
RDMIG2012 Domestic Migration Rate, 2012
RDMIG2013 Domestic Migration Rate, 2013
RDMIG2014 Domestic Migration Rate, 2014}
RNMIG2011 Net Migration Rate, 2011
RNMIG2012 Net Migration Rate, 2012
RNMIG2013 Net Migration Rate, 2013
RNMIG2014 Net Migration Rate, 2014

County Health Rankings "Ranked Measurement Data" (RMD) -- scroll section; use scroll bar at right of table
The following table lists items available in the County Health Rankings RMD dataset. Create thematic pattern map views of any of these items alone or in combination with other items. These fields are contained in project shapefile cb_2013_us_county_500k_ch_rmd.shp. List of items shown below is also summarized starting on page 1 of this document.
FieldName Description
FIPSFIPS
StateState
CountyCounty
RMD004# Deaths
RMD005Years of Potential Life Lost Rate
RMD00695% CI - Low
RMD00795% CI - High
RMD008Z-Score
RMD009Sample Size
RMD010% Fair/Poor
RMD01195% CI - Low
RMD01295% CI - High
RMD013Z-Score
RMD014Sample Size
RMD015Physically Unhealthy Days
RMD01695% CI - Low
RMD01795% CI - High
RMD018Z-Score
RMD019Sample Size
RMD020Mentally Unhealthy Days
RMD02195% CI - Low
RMD02295% CI - High
RMD023Z-Score
RMD024Unreliable
RMD025# Low Birthweight Births
RMD026# Live births
RMD027% LBW
RMD02895% CI - Low
RMD02995% CI - High
RMD030Z-Score
RMD031Sample Size
RMD032% Smokers
RMD03395% CI - Low
RMD03495% CI - High
RMD035Z-Score
RMD036% Obese
RMD03795% CI - Low
RMD03895% CI - High
RMD039Z-Score
RMD040Food Environment Index
RMD041Z-Score
RMD042% Physically Inactive
RMD04395% CI - Low
RMD04495% CI - High
RMD045Z-Score
RMD046# With Access
RMD047% With Access
RMD048Z-Score
RMD049Sample Size
RMD050% Excessive Drinking
RMD05195% CI - Low
RMD05295% CI - High
RMD053Z-Score
RMD054# Alcohol-Impaired Driving Deaths
RMD055# Driving Deaths
RMD056% Alcohol-Impaired
RMD057Z-Score
RMD058# Chlamydia Cases
RMD059Chlamydia Rate
RMD060Z-Score
RMD061Teen Births
RMD062Teen Population
RMD063Teen Birth Rate
RMD06495% CI - Low
RMD06595% CI - High
RMD066Z-Score
RMD067# Uninsured
RMD068% Uninsured
RMD06995% CI - Low
RMD07095% CI - High
RMD071Z-Score
RMD072# Primary Care Physicians
RMD073PCP Rate
RMD074PCP Ratio
RMD075Z-Score
RMD076# Dentists
RMD077Dentist Rate
RMD078Dentist Ratio
RMD079Z-Score
RMD080# Mental Health Providers
RMD081MHP Rate
RMD082MHP Ratio
RMD083Z-Score
RMD084# Medicare Enrollees
RMD085Preventable Hosp. Rate
RMD08695% CI - Low
RMD08795% CI - High
RMD088Z-Score
RMD089# Diabetics
RMD090% Receiving HbA1c
RMD09195% CI - Low
RMD09295% CI - High
RMD093Z-Score
RMD094# Medicare Enrollees
RMD095% Mammography
RMD09695% CI - Low
RMD09795% CI - High
RMD098Z-Score
RMD099Cohort Size
RMD100Graduation Rate
RMD101Z-Score
RMD102# Some College
RMD103Population
RMD104% Some College
RMD10595% CI - Low
RMD10695% CI - High
RMD107Z-Score
RMD108# Unemployed
RMD109Labor Force
RMD110% Unemployed
RMD111Z-Score
RMD112# Children in Poverty
RMD113% Children in Poverty
RMD11495% CI - Low
RMD11595% CI - High
RMD116Z-Score
RMD11780th Percentile Income
RMD11820th Percentile Income
RMD119Income Ratio
RMD120Z-Score
RMD121# Single-Parent Households
RMD122# Households
RMD123% Single-Parent Households
RMD12495% CI - Low
RMD12595% CI - High
RMD126Z-Score
RMD127# Associations
RMD128Association Rate
RMD129Z-Score
RMD130# Violent Crimes
RMD131Violent Crime Rate
RMD132Z-Score
RMD133# Injury Deaths
RMD134Injury Death Rate
RMD13595% CI - Low
RMD13695% CI - High
RMD137Z-Score
RMD138Average Daily PM2.5
RMD139Z-Score
RMD140Pop. In Viol
RMD141% Pop in Viol
RMD142Z-Score
RMD143# Households with Severe Problems
RMD144% Severe Housing Problems
RMD14595% CI - Low
RMD14695% CI - High
RMD147Z-Score
RMD148# Drive Alone
RMD149# Workers
RMD150% Drive Alone
RMD15195% CI - Low
RMD15295% CI - High
RMD153Z-Score
RMD154# Workers who Drive Alone
RMD155% Long Commute - Drives Alone
RMD15695% CI - Low
RMD15795% CI - High
RMD158Z-Score

County Health Rankings "Additional Measurement Data" (AMD) -- scroll section; use scroll bar at right of table
The following table lists items available in the County Health Rankings AMD dataset. Create thematic pattern map views of any of these items alone or in combination with other items. These fields are contained in project shapefile cb_2013_us_county_500k_ch_amd.shp. List of items shown below is also summarized starting on page 3 of this document.
FieldName Description
FIPSFIPS
StateState
CountyCounty
amd004Population
amd005# < 18
amd006% < 18
amd007# 65 and over
amd008% 65 and over
amd009# African American
amd010% African American
AMD011# American Indian/ Alaskan Native
AMD012% American Indian/ Alaskan Native
AMD013# Asian
AMD014% Asian
AMD015# Native Hawaiian/ Other Pacific Islander
AMD016% Native Hawaiian/ Other Pacific Islander
AMD017# Hispanic
AMD018% Hispanic
AMD019# Non-Hispanic white
AMD020% Non-Hispanic white
AMD021# Not Proficient in English
AMD022% Not Proficient in English
AMD02395% CI - Low
AMD02495% CI - High
AMD025# Female
AMD026% Female
AMD027# Rural
AMD028% Rural
AMD029# Diabetic
AMD030% Diabetic
AMD03195% CI - Low
AMD03295% CI - High
AMD033# HIV Cases
AMD034HIV Prevalence Rate
AMD035# Deaths
AMD036Age-Adjusted Mortality
AMD03795% CI - Low
AMD03895% CI - High
AMD039# Deaths
AMD040Infant Mortality Rate
AMD04195% CI - Low
AMD04295% CI - High
AMD043# Deaths
AMD044Child Mortality Rate
AMD04595% CI - Low
AMD04695% CI - High
AMD047# Food Insecure
AMD048% Food Insecure
AMD049# Limited Access
AMD050% Limited Access
AMD051# Motor Vehicle Deaths
AMD052MV Mortality Rate
AMD05395% CI - Low
AMD05495% CI - High
AMD055# Drug Poisoning Deaths
AMD056Drug Poisoning Mortality Rate
AMD057# Uninsured
AMD058% Uninsured
AMD05995% CI - Low
AMD06095% CI - High
AMD061# Uninsured
AMD062% Uninsured
AMD06395% CI - Low
AMD06495% CI - High
AMD065Costs
AMD066Sample Size
AMD067% Couldn't Access
AMD06895% CI - Low
AMD06995% CI - High
AMD070Other PCP Rate
AMD071Other PCP Ratio
AMD072Household Income
AMD07395% CI - Low
AMD07495% CI - High
AMD075% Free Lunch
AMD076Homicide Rate
AMD07795% CI - Low
AMD07895% CI - High

Appalachian Regional Commission Economic Status Data -- scroll section; use scroll bar at right of table
The following table lists items available in the "County Economic Status in Appalachia, Fiscal Year 2016" dataset. Create thematic pattern map views of any of these items alone or in combination with other items. These fields are contained in project shapefile cb_2013_us_county_500k_arc.shp. The shapefile includes all counties but only those counties that are classified as Appalachia are population with subject matter fields/items 6 and above listed below. List of items shown below is also available in the excel file referenced here.
FieldName Description
STATEFPState FIPS Code
COUNTYFPCounty FIPS Code
GEOIDState + County FIPS Code
NAMEArea name
ALANDLand Area, square meters
AWATERWater Area, square meters
STATUS16County Economic Status, FY 2016
AVGUR1113Three-Year Average Unemployment Rate, 2011-2013
PCMI13Per Capita Market Income, 2013
POV0913Poverty Rate, 2009-2013
AVGURP1113Three-Year Avg. Unemp.Rate, Percent of U.S., 2011-2013
PCMIP13PCMI, Percent of U.S., 2013
PCMIPI13PCMI, Percent of U.S., Inversed, 2013
POVP0913Poverty Rate, Percent of U.S., 2009-2013
COMPIDX16Composite Index Value, FY 2016
IDXVAL16Index Value Rank (of 3,110 counties in U.S., 1 is the best), FY 2016
QUART16Quartile (1 is the best), FY 2016

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Learn more about accessing and using demographic-economic data and related analytical tools. Join us in a Data Analytics Lab session. There is no fee for these one-hour Web sessions. Each informal session is focused on a specific topic. The open structure also provides for Q&A and discussion of application issues of interest to participants.

Additional Information
ProximityOne develops geodemographic-economic data and analytical tools and helps organizations knit together and use diverse data in a decision-making and analytical framework. We develop custom demographic/economic estimates and projections, develop geographic and geocoded address files, and assist with impact and geospatial analyses. Wide-ranging organizations use our tools (software, data, methodologies) to analyze their own data integrated with other data. Follow ProximityOne on Twitter at www.twitter.com/proximityone. Contact us (888-364-7656) with questions about data covered in this section or to discuss custom estimates, projections or analyses for your areas of interest.


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