New data available in HealthLandscape!

We have added the most recent data from the CDC Diabetes Surveillance System to HealthLandscape, both at www.healthlandscape.org and, in Quick Map form, at beta.healthlandscape.org. Variables include the Percent of Adults who are Physically Inactive, the Percent of Adults who are Obese, and the Percent of Adults who have Diabetes.

Figure 1. Percent of Adults Who Are Physically Inactive, 2008



Figure 2. Percent of Adults Who Are Physically Inactive, 2008 (HL3)



Diabetes Data and Trends, which includes the National Diabetes Fact Sheet and the National Diabetes Surveillance System, provides resources documenting the public health burden of diabetes and its complications in the United States. The surveillance system also includes county-level estimates of diagnosed diabetes and selected risk factors for all U.S. counties to help target and optimize the resources for diabetes control and prevention.

The prevalence of diagnosed diabetes and selected risk factors by county was estimated using data from CDC's Behavioral Risk Factor Surveillance System (BRFSS) and data from the U.S. Census Bureau's Population Estimates Program. The BRFSS is an ongoing, monthly, state-based telephone survey of the adult population. The survey provides state-specific information on behavioral risk factors and preventive health practices. Respondents were considered to have diabetes if they responded "yes" to the question, "Has a doctor ever told you that you have diabetes?" Women who indicated that they only had diabetes during pregnancy were not considered to have diabetes. Respondents were considered obese if their body mass index was 30 or greater. Body mass index (weight [kg]/height [m]2) was derived from self-report of height and weight. Respondents were considered to be physically inactive if they answered "no" to the question, "During the past month, other than your regular job, did you participate in any physical activities or exercises such as running, calisthenics, golf, gardening, or walking for exercise?"

See the CDC Diabetes Surveillance System for more information.

New data available in HealthLandscape!

Yesterday, the U.S. Census Bureau released their 2009 Small Area Income and Poverty Estimates. According to their analysis, the poverty rate for children ages 5 to 17 in families rose in 295 counties and declined in 19 counties between 2007 and 2009. Most counties saw no statistically significant change between these years.

The U.S. Census Bureau, with support from other Federal agencies, created the Small Area Income and Poverty Estimates (SAIPE) program to provide more current estimates of selected income and poverty statistics than those from the most recent decennial census.

The U.S. Census Bureau's Small Area Income and Poverty Estimates (SAIPE) program provides annual estimates of income and poverty statistics for all states, counties, and school districts. The main objective of the program is to provide estimates of income and poverty for the administration of federal programs and the allocation of federal funds to local jurisdictions. In addition to these federal programs, there are hundreds of state and local programs that depend on income and poverty estimates for distributing funds and managing programs.

The SAIPE program produces the following county estimates:
• total number of people in poverty
• number of related children ages 5 to 17 in families in poverty
• number of children under age 18 in poverty
• median household income


The estimates are not direct counts from enumerations or administrative records, nor direct estimates from sample surveys. Instead, income and poverty estimates are modeled by combining survey data with population estimates and administrative records. Beginning with the SAIPE program's estimates for 2005, data from the American Community Survey (ACS) are used in the estimation procedure; all prior years used data from the Annual Social and Economic Supplements of the Current Population Survey. Further details are given in a 2007 SAIPE report, Use of ACS Data to Produce SAIPE Model-Based Estimates of Poverty for Counties [PDF 3.4M]. For more information, see Small Area Income & Poverty Estimates.

Figure 1. Percent of Population in Poverty by County, 2009


Figure 2. Percent of Population Under 18 Years of Age in Poverty by County, 2009

Good news for all of the data-minded readers out there! While we're all anxiously awaiting the release of the Census 2010 data, we can begin to enjoy the first wave of releases from the American Community Survey.

The American Community Survey replaced the traditional long-form questionnaire that was sent to a smaller subset of households through Census 2000. The ACS sample includes about 3 million housing and group quarter units in the US, including representation from every county. The survey asks about professions, earnings, health insurance, modes of transportation, and housing costs. Census 2010 gives us the actual count of the population on April 1, 2010, but it's the ACS that describes how that population lives - the portrait of America. The 2009 ACS 1-Year Estimates are now available in the American Fact Finder for geographic areas with populations of 65,000 or more.

The highlights?

Median Household Income - Real median household income in the United States fell between 2008 and 2009 -- decreasing by 2.9 percent from $51,726 to $50,221.

Poverty - Thirty-one states saw increases in both the number and percentage of people in poverty between 2008 and 2009.

Health Insurance - In 2009, the uninsured rate for children under 19 in the United States was 9.0 percent, and the uninsured rate in the states ranged from 18.4 percent in Nevada to 1.5 percent in Massachusetts.

Industry and Occupation - Work hours fell in 46 of the 50 most populous U.S. metro areas between 2008 and 2009.

Home Values - After adjusting for inflation, the median property value decreased in the United States by 5.8 percent between 2008 and 2009.

Rental Housing Costs - Housing cost burdens ranged from a low of 23.2 percent of renting households in the Casper, Wyo., metro area to a high of 62.8 percent of renting households in the College Station-Bryan, Texas, metro area.

Education -- Science and Technology - The estimated number of people in the United States 25 and over with a bachelor's degree or higher was 56.3 million. Of this group, 20.5 million, or 36.4 percent, held at least one science and engineering degree.

Even more exciting than newly updated 1-Year Estimates; for the first time ever we will be able to get regularly-updated county- and place-level information for ALL US COUNTIES AND PLACES, including those with fewer than 20,000 people, through 5-year estimates. The first release of these 5-year estimates is scheduled for December, 2010. This is a big deal to those of us who regularly use data to describe populations and solve problems, as the best data we have right now for those smaller areas are 10 years old. You can imagine what kind of problems this can cause. Think about how much has changed in your own personal life over the last 10 years. 10 years ago, would you have been able to accurately predict where you are today? Population estimates and extrapolation can only take us so far.

In the meantime, there are plenty of other tools and articles to explore. Check out this page from USA Today, which includes an interactive mini-map that displays 2009 ACS data, by state.



You can also follow the official blog of the US Census Bureau, Random Sampling.

Of course, HealthLandscape is continuously uploading data to the newly added 2009 American Community Survey 1-Year Estimates section, so be sure to check back regularly for new additions.

Figure 1. Journey to Work: Percent of Population Using Public Transportation

Figure 2. Educational Attainment: Percent of Population with Graduate Degrees

New data available in HealthLandscape!

The US Census Bureau's Small Area Health Insurance Estimates (SAHIE) for 2007 are estimates of health insurance coverage for all counties. This dataset includes county-level estimates on the number of people and the percentages of people with and without health insurance coverage for ages 18 to 64 years. For more information, see, SAHIE.

The Small Area Health Insurance Estimates (SAHIE) program was created to develop model-based estimates of health insurance coverage for counties and states. The program builds on the work of the Small Area Income and Poverty Estimates (SAIPE) program.

SAHIE released 2007 county estimates of people with and without health insurance coverage by:

•Ages 0-18; 0-64; 18-64; 40-64; and 50-64;

•Sex;

•People of all incomes and people at or below 200 percent or 250 percent of the poverty threshold; and

•Measures of uncertainty of the estimates.

This research is partially funded by the Centers for Disease Control and Prevention, National Breast and Cervical Cancer Early Detection Program (NBCCEDP). The CDC has a congressional mandate to provide screening services for breast and cervical cancer to low-income, uninsured, and underserved women through the NBCCEDP. Most state NBCCEDP programs define low-income as 200 or 250 percent of the poverty threshold.

Figure 1. Percent of Population Uninsured by County, 2007

Figure 2. Percent of Population at or Below 200% of Poverty Uninsured by County, 2007

The Local Area Unemployment Statistics (LAUS) program is a federal-state cooperative effort in which monthly estimates of total employment and unemployment are prepared. These estimates are key indicators of local economic conditions. The Bureau of Labor Statistics (BLS) of the U.S. Department of Labor is responsible for the concepts, definitions, technical procedures, validation and publication of the estimates that state employment security agencies prepare under agreement with BLS.

A wide variety of customers use these estimates. Federal programs use the data for allocations to states and areas, as well as eligibility determinations for assistance. State and local governments use the estimates for planning and budgetary purposes and to determine the need for local employment and training services. Private industry, researchers, the media, and other individuals use the data to assess localized labor market developments and make comparisons across areas.

The concepts and definitions underlying LAUS data come from the Current Population Survey (CPS), the household survey that is the official measure of the labor force for the nation. State monthly model estimates are controlled in "real time" to sum to national monthly labor force estimates from the CPS. These models combine current and historical data from the CPS, the Current Employment Statistics (CES) program.

For more information, see the Local Area Unemployment Statistics Home Page.

Figure 1. Unemployment Rate by County, 2009

The Regional Economic Profile (Table CA30) provides general economic data that are derived from other, more detailed tables (CA05, CA05N, CA25, CA25N, and CA35). Estimates are organized by both place of residence and place of work. The place of residence profile includes estimates of total personal income, population, and per capita personal income. The place of work profile includes estimates of total earnings, total employment, and average earnings per job. For more information, see BEA Regional Economic Accounts.

Figure 1. Personal Income (Thousands of Dollars), 2008

Figure 2. Number of Non-Farm Proprietors, 2008

2008 estimates from the BEA County Income and Employment Summary HealthLandscape.

Bureau of Economic Analysis data released in April of 2010 are new estimates for 2008.

The first part of Table CA04 presents the summary statistics: Personal income, nonfarm personal income, farm income, population (estimated as of July 1 of each year by the Census Bureau), and per capita personal income, which is personal income divided by population.

The second part of Table CA04 presents the derivation of personal income. Personal income is measured as the sum of wages and salaries; supplements to wages and salaries; proprietors' income; dividends, interest, and rent; and personal current transfer receipts; less contributions for government social insurance. The personal income of a local area is defined as the income received by the residents of the local area, but the estimates of wages and salaries, supplements to wages and salaries, and contributions for government social insurance by employees are based mainly on source data that are reported not by the place of residence of the income recipients but by their place of work. Accordingly, an adjustment for residence-- which is the net inflow of the earnings of wage and salary workers who are interstate commuters-- is estimated so that place-of-residence measures of earnings and personal income can be derived. Net earnings by place of residence is calculated by subtracting contributions for government social insurance from earnings by place of work and then adding the adjustment for residence. The estimates of dividends, interest, and rent, and of personal current transfer receipts are prepared by place of residence only.

The third part of Table CA04 presents the summary estimates of total employment, wage and salary employment, and proprietors employment.

For more information, see BEA Regional Economic Accounts.

Figure 1. Per Capita Personal Income (Dollars), 2008

Figure 2. Non-Farm Personal Income, 2008

Economic Status Data are now available for fiscal years 2010 and 2011.

The Appalachian region is home to 24.8 million people and consists of 420 counties across 13 states, stretching from New York and Pennsylvania in the northeast to Mississippi and Alabama in the south. Forty-two percent of the Region's population is rural, compared with 20 percent of the national population. The Appalachian population is characterized by lower levels of college completion and lower labor force participation. Southern areas of Appalachia attract better educated and higher skilled people.

In recent years, the region's economy has become more diversified, rather than relying on mining, forestry, agriculture, chemical industries, and heavy industry. In 1965, one in three Appalachians lived in poverty. In 2000, the Region's poverty rate was 13.6 percent.

The Appalachian Regional Commission (ARC) categorizes each county in the region into one of five economic levels: distressed, at-risk, transitional, competitive, and attainment. The system involves the creation of a national index of county economic status through a comparison of each county's averages for three economic indicators--three-year average unemployment rate, per capita market income, and poverty rate--with national averages. In 1965, 223 Appalachian counties were considered economically distressed. In fiscal year 2011 that number is 82. For more informatoin on how the ARC defines the economic categories, visit their website.

Figure 1. Applachian Economic Status, FY2011


The ARC uses US Bureau of Labor Statistics unemployment rates as a part of its economic classification system. The three-year average unemployment rate is a measure of long-term structural unemployment that allows for the comparison of counties across state borders. The unemployment rate is calculated by dividing the three-year sum of persons unemployed by the three-year sum of the civilian labor force.

Figure 2. Applachian Three-year Unemployment Rate 2006-2008


These data, and others for Appalachia, are now available in HealthLandscape for use in your maps. You can find these data by going to Community HealthView → United States → Appalachian Counties Economic Status.

Two indicators from the American Community Survey (ACS) 2006-2008 Estimates are now available in HealthLandscape.

The data on means of transportation to work refer to the principal mode of travel or type of conveyance that the worker usually used to get from home to work during the reference week. People who used different means of transportation on different days of the week were asked to specify the one they used most often, that is, the greatest number of days. People who used more than one means of transportation to get to work each day were asked to report the one used for the longest distance during the work trip. The category "Car, Truck or Van" includes workers using a car (including company cars, but excluding taxicabs), a truck of one-ton capacity or less, or a van. The category "Public Transportation" includes workers who used a bus or trolley bus, streetcar or trolley car, subway or elevated, railroad, or ferryboat, even if each mode is not shown separately in the tabulation. The category "Other Means" includes workers who used a mode of travel that is not identified separately within the data distribution.

Figure 1. Mean Travel Time to Work

Figure 2. Percent Using Public Transportation

The ACS Estimates on class of worker categorizes people according to the type of ownership of the employing organization. For employed people, the data refer to the person's job during the previous week. For those who worked two or more jobs, the data refer to the job where the person worked the greatest number of hours. For unemployed people, the data refer to their last job. Respondents provided the data for the tabulations by writing on the questionnaires descriptions of their kind of business or industry and the kind of work or occupation they are doing.

Figure 3. Percent Private Wage and Salary Workers

For more information on these indicators, see American Community Survey and 2008 Subject Definitions.

New data available in HealthLandscape!

The FBI collects these data through the Uniform Crime Reporting (UCR) Program.

Data provides the rate of selected offenses per 100,000 inhabitants for each state.

Any comparisons of crime among different locales should take into consideration relevant factors in addition to the area's crime statistics. Variables Affecting Crime provides more details concerning the proper use of UCR statistics.

For more information, see 2008 Crime in the United States, Data Declaration.

Figure 1. Violent Crime Per 100,000 Inhabitants, 2008

Figure 2. Property Crime Per 100,000 Inhabitants, 2008