Physical Inactivity

Within the report area, 30,760 or 25.8% of adults aged 20 and older self-report no leisure time for activity, based on 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?". This indicator is relevant because current behaviors are determinants of future health and this indicator may illustrate a cause of significant health issues, such as obesity and poor cardiovascular health.
Report Area Total Population Age 20+ Population with no Leisure Time Physical Activity Percent Population with no Leisure Time Physical Activity
Franklin County, PA 112,263 30,760 25.8%
Pennsylvania 9,647,635 2,317,825 22.9%
United States 231,341,061 53,415,737 22.6%
Note: This indicator is compared with the state average. Green - Better than state average, Red - Worse than state average.
Data Source: Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion. 2012. Source geography: County

Adults with No Leisure-Time Physical Activity by Gender
Report Area Total Males with No Leisure-Time Physical Activity Percent Males with No Leisure-Time Physical Activity Total Females with No Leisure-Time Physical Activity Percent Females with No Leisure-Time Physical Activity
Franklin County, PA 13,590 24% 17,170 27.4%
Pennsylvania 1,018,357 21.27% 1,299,516 24.36%
United States 24,071,561 21.2% 29,344,293 23.94%
Percent Adults Physically Inactive by Year, 2004 through 2012
Report Area 2004 2005 2006 2007 2008 2009 2010 2011 2012
Franklin County, PA 22.5% 23.1% 22.4% 23.7% 24.8% 24.9% 26.8% 25.5% 25.8%
Pennsylvania 23.71% 23.79% 23.57% 23.75% 24.66% 25.26% 24.83% 23.21% 22.93%
United States 22.96% 22.82% 22.93% 23.2% 23.51% 23.67% 23.41% 22.47% 22.64%
Website Updated August 2016

Physical Inactivity

Data Background

The Centers for Disease Control and Prevention’s National Center for Chronic Disease Prevention and Health Promotion monitors the health of the Nation and produces publically available data to promote general health. The division maintains the Diabetes Data and Trends data system, which includes the National Diabetes Fact Sheet and the National Diabetes Surveillance System. These programs provide 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.

Citation: Centers for Disease Control and Prevention, Diabetes Data & Trends: Frequently Asked Questions (FAQ). (2012).

Methodology

Data for the total adult population and the estimated population with inadequate physical activity are acquired from the County Level Estimates of Diagnosed Diabetes, a service of the Centers for Disease Control and Prevention’s National Diabetes Surveillance Program. Diabetes and other risk factor prevalence is estimated using the following formula:

Percent Prevalence = [Risk Factor Population] / [Total Population] * 100.

All data are estimates modelled by the CDC using the methods described below:

The National Diabetes Surveillance system produces data estimating the prevalence of diagnosed diabetes and population obesity by county 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?"

Three years of data were used to improve the precision of the year-specific county-level estimates of diagnosed diabetes and selected risk factors. For example, 2003, 2004, and 2005 were used for the 2004 estimate and 2004, 2005, and 2006 were used for the 2005 estimate. Estimates were restricted to adults 20 years of age or older to be consistent with population estimates from the U.S. Census Bureau. The U.S. Census Bureau provides year-specific county population estimates by demographic characteristics—age, sex, race, and Hispanic origin.

The county-level estimates were based on indirect model-dependent estimates. The model-dependent approach employs a statistical model that “borrows strength” in making an estimate for one county from BRFSS data collected in other counties. Bayesian multilevel modeling techniques were used to obtain these estimates. Separate models were developed for each of the four census regions: West, Midwest, Northeast and South. Multilevel Poisson regression models with random effects of demographic variables (age 20–44, 45–64, 65+; race; sex) at the county-level were developed. State was included as a county-level covariate.
Citation: Centers for Disease Control and Prevention, Diabetes Data & Trends: Methods and References for County-Level Estimates and Ranks. (2012).
Rates are age adjusted by the CDC for the following three age groups: 20-44, 45-64, 65+. Additional information, including the complete methodology and data definitions, can be found at the CDC’s Diabetes Data and Statistics website.

Notes

Race and Ethnicity
Statistics by race and ethnicity are not provided for this indicator from the data source. Detailed race/ethnicity data may be available at a broader geographic level, or from a local source.

Courtesy: Community Commons, <www.communitycommons.org>, August 2016