Category Archives: Health Professional Shortage Areas

Employment by Occupation by Census Tract; 5-Year Trends

.. data and tools to examine patterns of employment by occupation by census tract and 5-year change .. the U.S. civilian employed population increased from 142.9 million in 2012 to 155.1 million in 2017, an increase of 12.1 million (8.5%) based on the American Community Survey (ACS) 1-year estimates. See this table to see how the employed population were distributed by occupation in 2012, 2017 and the 5-year change. How did your neighborhoods or market/service areas of interest change over the past 5 years? How will occupational employment patterns by tract/neighborhood change between now and 2023?

Patterns of Percent Employed in Health Occupations by Census Tract
The following graphic shows patterns of the employed population in health occupations as a percent of total civilian employed population ages 16 and over in the Minneapolis-St. Paul metro. This view uses the occupational category MBSA40 Healthcare practitioners and technical listed in scroll section below. Tracts with blue or green pattern exceed the national average as shown in national table. Click graphic for larger view, more detail (shows schools layer) and legend color/data intervals. This map illustrates the geographic level of detail available using census tract demographics and the relative ease to gain insights using geospatial data analytics tools. View related graphic showing tract with the largest employment in the “Healthcare practitioners and technical” occupational group among all tracts.

– View developed using CV XE GIS and related GIS project.

Drill-down to Census Tract Level
Examining patterns of employment by occupation, for the same scope of subject matter, at the sub-county level can provide more insights. What is the size of the employment for a selected occupation in a neighborhood or market/service area of interest? How has the size of an occupational group by census tract changed over the past five years? How do these patterns rank/compare by tract in a particular state, metro or county? Data on employment by occupational category from the Federal statistical system on a U.S. national scale for counties, cities and census tracts are only available from the American Community Survey (ACS).

Use tools, resources and methods described here to access, integrate and analyze employment by occupation for the U.S. by census tract. Use the interactive table to view, query, rank, compare census tract occupational characteristics, patterns and trends. Data are based on the American Community Survey (ACS) 2017 5-year estimates.

Related sections with census tract interactive tables:
– General Demographics .. Social .. Economic .. Housing 

Current Estimates & Projections
ACS tract/small area estimates lag by four years or more between the current year and reference year. ACS does not produce current year annual estimates but estimates based on a 5-year period. The 2017 ACS estimates are centric to 2015. Use the ProximityOne annual tract estimates and projections 2010 through 2023 for current year (e.g., characteristics as of 2018) estimates and anticipated change 5 years ahead.

Using the Interactive Table
An example of using the interactive table to view, query, rank, compare census tract occupational characteristics, patterns and trends is shown by the graphic presented below. The table shows 6 columns of employment data for all tracts in Harris County, TX. The table is ranked on the ACS 2017 health occupations employment (MBSA40) column. Tract 48-201-312600 had largest ACS 2017 health employment of 1,078 among all tracts in the county. Compare to 2012 patterns. Use settings below table to develop a similar view your geography and occupations of interest.

Occupational Categories
The interactive table includes occupational categories listed below.
Total population
Total Civilian employed population 16 years and over
MBSA00 . Management, business, science, and arts
MBSA10 . . Management, business, and financial
MBSA11 . . . Management
MNSA12 . . . Business and financial operations
MBSA20 . . Computer, engineering, and science
MBSA21 . . . Computer and mathematical
MBSA22 . . . Architecture and engineering
MBSA23 . . . Life, physical, and social science
MBSA30 .. Education, legal, community service, arts, and media
MBSA31 … Community and social service
MBSA32 … Legal
MBSA34 … Education, training, and library
MBSA35 … Arts, design, entertainment, sports, and media
MBSA40 .. Healthcare practitioners and technical
MBSA41 … Health diagnosing & treating practitioners & other tech
MBSA42 … Health technologists and technicians
SVC00 . Service
SVC10 . . Healthcare support
SVC20 . . Protective service
SVC21 . . . Fire fighting/prevention & other protective services
SVC22 . . . Law enforcement workers including supervisors
SVC30 . . Food preparation and serving related
SVC40 . . Building and grounds cleaning and maintenance
SVC50 . . Personal care and service
SOF00 . Sales and office
SOF10 . . Sales and related
SOF20 . . Office and administrative support
NRC00 . Natural resources, construction, and maintenance
NRC10 . . Farming, fishing, and forestry
NRC20 . . Construction and extraction
NRC30 . . Installation, maintenance, and repair
PTM00 . Production, transportation, and material moving
PTM10 . . Transportation
PTM20 . . Material moving

Data Analytics Web Sessions
See these applications live/demoed. Run the applications on your own computer.
Join me in a Data Analytics Lab session to discuss more details about accessing and using wide-ranging demographic-economic data and data analytics. Learn more about using these data for areas and applications of interest.

About the Author
— Warren Glimpse is former senior Census Bureau statistician responsible for innovative data access and use operations. He is also the former associate director of the U.S. Office of Federal Statistical Policy and Standards for data access and use. He has more than 20 years of experience in the private sector developing data resources and tools for integration and analysis of geographic, demographic, economic and business data. Contact Warren. Join Warren on LinkedIn.

Health Insurance Coverage by Census Tract

.. the overall percent civilian non-institutionalized population with health insurance coverage changed from 85.2% in 2012 to 88.3% in 2014. Health insurance coverage is one measure among many others that are important in Healthcare Data Analytics. This section uses healthcare data analytics tools to view/analyze healthcare coverage by census tract and other geographies. See more about using health insurance coverage data in context with other health-related data in this related section.

Percent Civilian Non-institutionalized Population
    with Health Insurance by Census Tract


.. view developed with ProximityOne CV XE GIS and related GIS project.

Health Insurance Coverage Data & Interactive Table Access
Health insurance coverage data are one of several types of health-related data available in the 2014 ACS 5-year estimates. At the national level, the overall population with health insurance coverage changed from 85.2% in 2012 to 88.3% in 2014. The upper two intervals shown in the health insurance coverage by census tract map above are for the percent population with health insurance coverage at or above the national 85.2% level in 2012 (census tract data are only available from the 5-year estimates, the ACS 2014 5 year estimates are centric to 2012).

While health insurance coverage data are available in a range of demographic combinations, 25 health insurance coverage items (see table below) are available from the economic characteristics dataset for selected types of geography in these interactive tables:

ACS 2014 1-Year Estimates – data centric to mid-2014
U.S., State, CBSA/Metro
114th Congressional Districts

ACS 2014 5-Year Estimates – data are centric to mid-2012
Census Tracts
ZIP Code Areas
School Districts
State Legislative Districts

Join me in a Data Analytics Lab session to discuss more details about analyzing health and healthcare characteristics and patterns and use of data analytics to develop further detail related to your interests.

About the Author
— Warren Glimpse is former senior Census Bureau statistician responsible for innovative data access and use operations. He is also the former associate director of the U.S. Office of Federal Statistical Policy and Standards for data access and use. He has more than 20 years of experience in the private sector developing data resources and tools for integration and analysis of geographic, demographic, economic and business data. Contact Warren. Join Warren on LinkedIn.

Healthcare Data Analytics: Market Analysis

What factors best determine where a clinic, hospital or professional practice is located? For those that exist, how to best determine the scope and needs of the market served? Understanding healthcare market dynamics is one way these entities can improve their business and operation by using Health Data Analytics. Professionals skilled with Health Data Analytics can help their organization, or clients, better achieve their vision and improve performance.

This section is focused on analyzing healthcare markets and infrastructure using Geographic Information System (GIS) tools and related data resources. Participants in the Certificate in Data Analytics may optionally use the tools and resources described here. See overview of steps to install and use the GIS project and datasets illustrated in more detail in the related Web section.

Using GIS to Analyze Healthcare Market Characteristics
Illustrating GIS start-up view discussed in this section.

– View developed using CV XE GIS; click graphic for larger view.

Analyzing healthcare markets involves examining characteristics of healthcare facilities in context of competitive position and market potential. Geographic Information System (GIS) tools can be used to knit together geographic, demographic, economic and business data to perform these analyses. This document makes use of the Atlanta area to illustrate applications. In an actual study, the geographic focus could be a city, county, metro, state or some combination, anywhere in the U.S., or the U.S. overall.

Understanding Needs and Visions
A first step involves an assessment of your situation — your needs, visions and data that you have to work with. The results of this assessment and data that you provide help develop/frame a market study in context of GIS project(s).

Market Infrastructure Analytical Framework
The following graphic shows the start-up view of the Atlanta area Healthcare Data Analytics (HCDA) GIS project. This GIS project involves use of many layers and types of data as shown in the legend at left of map window. Selection of the type of geography, scope of geography and scope of subject matter are key elements in setting up the market infrastructure analytical framework. This is a proxy/example for the GIS project that would be developed to meet your needs/application focus.

The above view shows a thematic pattern of median household income by census tract (averaging 4,000 population). Pattern analysis helps you visualize demographic-economic characteristics by census tract — in this example you can easily see patterns of economic prosperity. This example uses median household income; we can draw upon hundreds of subject matter items and depict other types of patterns.

Examining the Healthcare Infrastructure
The graphic below shows selected types of healthcare facilities.
See legend to the left of map:
• Hospitals – blue triangle markers
• Assisted Living Facilities (ALF) – green circle markers
• Nursing Homes – red square markers

Site Analysis — Examining Characteristics of Healthcare Facilities
The yellow circle marker shows the hypothetical location of a prospective new facility. A 5-mile radius site study area — from the yellow marker — is used to select existing nursing homes; characteristics of the competition. Nursing homes show as cross-hatched; circular area is study zone.

Display of the 9 facilities selected above.

See the related Web section to view further details.

Weekly Data Analytics Lab Sessions
Join me in a Data Analytics Lab session to discuss more details about using these data in context of data analytics with other geography and other subject matter.  Learn more about integrating these data with other geography, your data and use of data analytics that apply to your situation.

About the Author
— Warren Glimpse is former senior Census Bureau statistician responsible for innovative data access and use operations. He is also the former associate director of the U.S. Office of Federal Statistical Policy and Standards for data access and use. He has more than 20 years of experience in the private sector developing data resources and tools for integration and analysis of geographic, demographic, economic and business data. Contact Warren. Join Warren on LinkedIn

Analyzing County Health Patterns

.. 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.

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. See more detail in the related Web section.

The County Health Patterns GIS project includes data from:
• ProximityOne CountyTrends and Situation & Outlook
… view individual county population & components of change 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. Choose from a list of wide-ranging health-related subject matter items.

— 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. Choose from a list of wide-ranging health-related subject matter items.

— 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. Economic characteristics of an area can have a direct impact on health and well-being and access to healthcare resources.

— click graphic for larger view showing details.

An upcoming section will review more detail about analyzing regional health and healthcare issues with drill-down to census tract and other sub-county geography.

Join us in a Data Analytics Web Session where we review and discuss use of tools and resources like those covered in this section.

About the Author
— Warren Glimpse is former senior Census Bureau statistician responsible for innovative data access and use operations. He is also the former associate director of the U.S. Office of Federal Statistical Policy and Standards for data access and use. He has more than 20 years of experience in the private sector developing data resources and tools for integration and analysis of geographic, demographic, economic and business data.

Healthcare Demographic Economic Analytics

Healthcare providers and the healthcare delivery system are challenged to determine what resources to make available at what locations and when. Assessing supply-side characteristics of the healthcare market is difficult at best. Current data on locations and capabilities of hospitals, ambulatory health care services (physicians, dentists, other healthcare practitioners, etc.), nursing and residential care facilities, and social assistance are multi-sourced and challenging to integrate.

As the population ages, migrates and otherwise changes, healthcare needs change by location, type and in other ways. Current small area demographics of these demand measures are not readily available — when knowing about changes in demand over the next several years is even more important. Changing economic circumstances and issues with insurance coverage affect the ability to pay and the demand for various healthcare resources.

Healthcare Analytics
This section provides an overview of the healthcare analytics focused on California. Resources and applications reviewed here can be emulated for other states and regions. The purpose of this section is to illustrate how a set of diverse, multi-sourced, healthcare supply and demand attributes can be organized for visual and geospatial analysis. See more details in related Web section.

Visual Analysis of Facilities, Characteristics and Patterns
Geographic Information System (GIS) resources can help decision-makers and stakeholders more easily understand and collaborate on healthcare issues that are difficult to analyze using spreadsheets and tables. A few examples:
• Relating Medically Underserved Areas (about MUAs/MUPs) to neighborhoods by economic prosperity.
• Relating locations of healthcare facilities (hospitals, clinics) to MUAs?
• Relating MUAs relate to Health Professional Shortage Areas (about HPSAs).
• Determining neighborhoods with highest need for healthcare services.
Get answers to these types of questions using multiple layers in a GIS project.

The following view of the San Diego area and shows a mix of healthcare related demographic-economic characteristics. The cross-hatched pattern areas have been designated as Medically Underserved Areas (MUAs). Patterns of economic prosperity (median household income by block group) are shown by color patterns as highlighted in the legend at left of map view. It is easy to see where the MUAs are located. Many MUAs are located in areas with medium to higher economic prosperity. Locations of primary care facilities, shown by square markers, can be viewed contextually.


Click graphic for larger view.
View developed using CV XE GIS and California DMI GIS Project.

Using GIS resources, the map views can be displayed in different ways to accomodate the needs of geographic and/or subject matter focus. Zoom-in or out for a broader or more focused view. Label features of interest (e.g., hospital names). Click layers on or off (in legend panel at left of map panel) to view geographies or locations of specific interest. Add other data such as geocoded patient or incident data.

Additional views shown below, from the same GIS project, illustrate some of these application views. Map views are just one part of the GIS resource benefits. Query tools can be used to highlight only locations of geographies that meet certain criteria, such as hospital bed capacity.

Statewide Hospitals and Primary Care Facilities

Hospitals and Primary Care Facilities: Zoom to Southern California

San Diego Area: Medically Underserved Areas

San Diego Area: Healthcare Professional Shortage Areas

Developing Your Healthcare Analyses
GIS tools and selected demographic-economic shapefiles shown above are available at no fee to ProximityOne User Group members. Join now, there is no fee. Add your data. Examine different geographies. Extend healthcare analytical and collaborative decision-making.