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Health Data Analytics
  -- Metropolitan Area Health Insurance Coverage
Decision-Making Information
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$median household income
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113th Congressional Districts

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US Asian Indian population 2010

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geocoded students and school
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geocoded students and school
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high school attendance zones
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%high school graduates by
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Appalachia counties (green) &
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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.

Analyzing Metro Health Insurance Coverage
This section is focused on tools, methods and data to analyze health insurance coverage. 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 of 2009, among the largest 10 MSAs, Boston, Washington, Philadelphia and New York all had less than 13% of the population without health insurance. Of the largest 10 MSAs, the Miami MSA had the largest percent population without health insurance (25.4%, 1.4 million people). Nationally, an estimated 45.6 million had no health insurance (15.1%). These data are based on the 2009
American Community Survey. Use the interactive ranking table below to view rankings of interest.

  • MSAs by percent population with no health insurance coverage:
      - range from 3% (Worcester, MA) to 36% (McAllen, TX)
  • MSAs by percent population under 18 years with no health insurance coverage:
      - range from 0.4% (Ames, IA) to 24.8% (Odessa, TX)

These data are presented as an integrated component of the demographic profiles for metros and other types of geography. See example for a congressional district.

Metro/NonMetro and Urban/Rural Patterns
Compared to the variation in percent population with no health insurance from MSA to MSA, there is relatively little variation in MSA versus non-MSA population as shown in the following table. The rural population may be impacted by employment situations less likely to provide health insurance.

    Civilian noninstitutionalized population (PopCNI) with no health insurance
  Geography PopCNI with No Health Insurance
  United States45.715.1
    Within MSAs38.115.0
    Outside MSAs7.615.7

What Accounts for the Wide Variation Among Metros?
Metro median household income and percent population in poverty are included in the ranking table. While ability to pay and relative affordability are important factors explaining why many metros have high percent population without health insurance, other factors are at play. Among these factors may be lifestyle and and household budget allocation priorities. When ranked on percent with no health insurance (%w/no HI), many of the top 50 metros have a percent population in poverty below the 2009 14.3% national level.

Examining Patterns Among Metros
The interactive ranking table provides selected data on the population by metro and health insurance coverage. Portions of the ACS 2009 DP3 dataset have been integrated into the Metro GIS Toolset. Download and use the Metro GIS Toolset; optionally combine your own data; examine your markets. Data presented in the ranking table are part of a broader set of demographic-economic profiles. Use the pick-from list tables to view profiles for individual states, congressional districts, metro areas.

Health Insurance Coverage by Metro Interactive Ranking Table ... rank by column
  Click column header to sort; click again to sort other direction. See related Ranking Tables Main Page

Usage Notes
  • Click on a column header to sort on that column; click column header again to sort in other direction.
  • Click ShowAll button to show all areas and restore full set of data view.
  • Click State to view metros in a selected state (click ShowAll between selections).
  • Find by Name: key in partial area name in text box to right of Find-in-Name button
      then click button to locate all matches (case sensitive).
  • See related ranking tables.

Column Headers
All items are for calendar years 2009
  • CBSA - Core-Based Statistical Area Code
  • State - dominant MSA state
  • $MHI - median household income
  • PopCNI - Civilian Noninstitutionalized Population
      - w/HI - With health insurance coverage
      - %w/HI - With health insurance coverage - percent
      - w/PubHI - With public health insurance coverage
      - %w/PubHI- With public health insurance coverage - percent
      - w/PrvHI - With private health insurance coverage
      - %w/PrvHI - With private health insurance coverage - percent
      - NoHI - No health insurance coverage
      - %NoHI - No health insurance coverage - percent
  • <18 - Civilian Noninstitutionalized Population Under 18 years
  • <18NoHI - "<18" -- with no health insurance coverage
  • %<18NoHI - "<18" -- with no health insurance coverage - percent
  • %PopinPov - percent of all people with incomes in during last 12 months below poverty level
  • %<18inPov - percent of all people under 18 years with incomes in during last 12 months below poverty level

Limitations of the Data
The universe for most health insurance coverage estimates is the civilian noninstitutionalized population, which excludes active-duty military personnel and the population living in correctional facilities and nursing homes. Some noninstitutionalized group quarters (GQ) populations have health insurance coverage distributions that are different from the household population (e.g., the prevalence of private health insurance among residents of college dormitories is higher than the household population). The proportion of the universe that is in the noninstitutionalized GQ populations could therefore have a noticeable impact on estimates of the health insurance coverage. Institutionalized GQ populations may also have health insurance coverage distributions that are different from the civilian noninstitutionalized population.

Data Comparability
Health insurance coverage was added to the 2008 ACS and so no equivalent measure is available from previous ACS surveys or Census 2000. Because coverage in the ACS references an individual’s current status, caution should be taken when making comparisons to other surveys which may define coverage as “at any time in the last year” or “throughout the past year.”

ProximityOne User Group
Join the ProximityOne User Group to keep up-to-date with new developments relating to metros and component geography decision-making information resources. Receive updates and access to tools and resources available only to members. Use this form to join the User Group.

Support Using these Resources
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 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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