Category Archives: Health

America’s Cities: Demographic-Economic Characteristics Annual Update

.. tools and data to interactively examine demographic-economic characteristics of America’s 29,321 cities/places .. understanding demographic-economic characteristics of cities and places is essential for business development, market analysis, planning, economic development, program management and general awareness of patterns and trends. This section provides access to data and tools to examine characteristics of all cities/places in the U.S. This annual update includes data for 29,321 cities/places based on ACS 2015 data.

Accessing the Data; Using Interactive Tables
Each of the four links below opens a new page providing access to U.S. by city/place interactive tables — by type of subject matter. Use tools and usage notes below table to select operations to perform queries, sort and select columns.
General Demographics
Social Characteristics
Economic Characteristics
Housing Characteristics

How the the Tables/Data Can be Used
The following table shows data derived from the Economic Characteristics table. The top 10 cities/places having the highest median household income ($MHI) are shown. The table also shows population, median family income ($MFI) and per capita income ($PCI). The $250,000 value is a cap; the actual value is $250,000 or higher. Use the interactive tables to create similar views for states of interest. Use the button below the table to select/view cities within a selected metro. Compare attributes of cities of interest to a peer group based on population size.

Visual Analysis of City/Place Population Patterns
Use GIS resources to visually examine city/place demographic-economic patterns. The following view shows patterns of population percent change by city in the Charlotte, NC-SC metro area.

… view developed using the CV XE GIS software.
… click map for larger view and details.

Related Data
Cities/Places Main Section
Citie Population Estimates & Trends, 2010-15

More About Using These Data
Using ACS 1-year and 5-year data

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.

Examining Health Characteristics by Census Tract

.. new data, new ways to examine health characteristics at the city and census tract/subcounty level.  For example, among the 500 largest U.S. cities in 2014, the incidence of high blood pressure ranged from 22.5% (Longmont, CO) to 47.8% (Gary, IN). Use the interactive table to view, rank, compare this and other new wide-ranging health statistics for the 500 largest U.S. cities and associated census tracts. See the related Web section for more detail.

At the census tract/neighborhood level, 937 tracts have more than 10% of the population ages 18 years and over with coronary heart disease. What are characteristics of health-related factors in your city, neighborhood and census tracts of interest? Use tools reviewed in this section to access/analyze a wide range of health-related characteristics (see items list below) — not available at the city or census tract level before.

Patterns of High Blood Pressure: Honolulu, HI by Census Tract
This graphic illustrates visual analysis and analytical potential for tracts in cities covered.

– Click graphic for larger view with high blood pressure %population label
– View developed with CV XE GIS software and related GIS project/fileset.

Accsss/analyze these data for approximately 28,000 tracts (of a total approximate 74,000) on topics including chronic disease risk factors, health outcomes and clinical preventive service use for the largest 500 cities in the U.S. These small area data enable stakeholders in cities, local health departments, neighborhoods and study areas to better understand the characteristics and geographic distribution of health-related measures and how they might impact health-related programs and other demographic-economic issues.

Scope of 500 Cities
The following graphic shows the 500 cities (green areas) included in project. Data for these cities and intersecting tracts are available. Click graphic for larger view providing county visibility and city name labels. Expand browser to full window for best quality view.

– View developed with CV XE GIS software and related GIS project/fileset.

The 500 Cities data have been developed as a part of the CDC 500 Cities project, a collaboration between the Centers for Disease Control (CDC), the Robert Wood Johnson Foundation and the CDC Foundation. These data are being integrated into the Situation & Outlook (S&O) database and included in the S&O metro reports. Examine health-related characteristics of metro cities and drill-down areas in combination with other demographic-economic measures.

Analytical Potential
These data provide only the health characteristics attributes. They are a small, but important, subset of a larger set of key health metrics. These data are estimates subject to errors of estimation and provide a snapshot view of one point in time.

The value of these data can be leveraged by linking them with other demographic-economic data from the American Community Survey (ACS 2015) tract and city data. Integrate and analyze these data with related data and alternative geography. See related health data analytics section.

Patterns of Heart Disease; Charlotte, NC-SC Area by Tract
This graphic illustrates coronary heart disease patterns by census tract for cities included in the database. Gray areas are census tracts not included in the 500 cities database. Click graphic for larger view.
– View developed with CV XE GIS software and related GIS project/fileset.

Using the Interactive Table
Use the interactive table to view, rank, compare, query these health measures by city. The following graphic illustrates how the table can be used to examine patterns of Texas cities. Table operations are used to selected Texas cities then rank the cities based on the “Access” column — “Current lack of health insurance among adults aged 18-64 Years”.

Try it yourself. Use the table to examine a set of cities in a state of interest.

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.

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.

Examining Life Expectancy by County

Life expectancy in the United States in 2007 ranged from 65.9 to 81.1 years for men and 73.5 to 86.0 years for women. When compared against a time series of life expectancy in the 10 nations with the lowest mortality, U.S. counties range from being 15 calendar years ahead to over 50 calendar years behind for men and 16 calendar years ahead to over 50 calendar years behind for women. Between 2000 and 2007, 80% (men) and 91% (women) of U.S. counties fell in standing against this international life expectancy standard. These are among the findings reported in Population Health Metrics.

This section reviews interactive tools and resources that you can use to examine county life expectancy and related data.

Male Life Expectancy by County, 2007

County Life Expectancy Interactive Table
Use the Web interactive table to view, compare, analyze the U.S. by county level data from the above study in combination with related demographic-economic measures.

The following graphic illustrates how the table can be used to examine life expectancy patterns by county. A query is used to select only Charlotte NC-SC metro counties, Each row shows data for a county. The 2000 and 2007 life expectancy data are shown at right in the graphic,

Visual Analysis using GIS Resources
Use GIS tools to visually examine life expectancy patterns. Create thematic pattern maps such as the male life expectancy by county pattern map shown above or corresponding female pattern map shown below. Members of the ProximityOne User Group may download and use the county life expectancy GIS project to develop maps like these. Zoom into regions of interest. Add other layers. Label the maps as desired. Use other subject matter to develop related pattern views. Modify the legend defining life expectancy intervals.

Female Life Expectancy by County, 2007

Healthcare Analysis and Situation & Outlook
The ProximityOne Situation & Outlook (S&O) Program is a combined database and interpretive resource that you can use to better understand the existing healthcare situation, find out how area demographic-economic conditions are changing, and assess how changing conditions might impact various components of the healthcare infrastructure.

Support Using these Resources
Learn more about healthcare geographic-demographic-economic data, patterns and related analytical tools. Join us in a Decision-Making Information Web 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.