Special Analysis: How Healthy Is Your Congressional District?
How Healthy is Your Congressional District? is a one-year (not longitudinal) snapshot of seven preventive health measures for each Congressional District and for each state and the District of Columbia. It uses data from the Centers for Disease Control and Prevention’s (CDC) 2015 Behavioral Risk Factor Surveillance System (BRFSS).
TFAH is sharing this resource with decision-makers and constituents who want to know more about the health of their Congressional Districts and state. The data and maps provide an opportunity for policymakers and advocates to work together to create a culture of health. The data were obtained from the CDC and prepared and analyzed by Elena Ong of Ong & Associates.
"To our knowledge, this is the first-time key preventive health measures are available for each Congressional district," said John Auerbach, president and CEO, TFAH. "We hope providing information in this format will be helpful to elected officials and their constituents."
These seven BRFSS questions are not intended as the definitive definition of “healthy." Rather they represent a quick dashboard of indications of self-reported health, access to clinical care and healthy behaviors. Additional data is needed to truly assess the overall health of a community. The seven indicators include the proportion of:
- Adults Reporting Fair or Poor Health
- Adults Reporting They Are Frequently Mentally Distressed
- Nonelderly Adults Who Are Uninsured
- Adults Who Couldn’t See a Physician Due to Cost
- Adults Who Had a Check-Up Last Year
- Adults Who Had a Cholesterol Screening
- Adults Who Are Currently Smoking
There are 4 excel spread sheets with information about the 7 indicators by either Congressional District or by state. They are:
- Congressional Districts: Listed from the healthiest to the least healthy*
- Congressional Districts: Listed in alphabetical order by state; and below each state, the Congressional Districts are listed from healthiest to least healthy*
- Congressional Districts: Listed in alphabetical order by state*
- States: Listed in alphabetical order with rank shown*
- States: The top ten healthiest and the bottom ten least healthy*
There are seven maps that are color-coded to indicate the percent response to each of the seven indicators by Congressional District.
*Important Note With Regard To Ranking Methodology
When listing the relative health of the Congressional Districts and the states, there are times when there is a statistical tie. This term tie is used in connection with rank order statistics. Tied observations are observations having the same value, which prohibits the assignment of unique rank numbers. As a way out, tied observations are assigned to the average of their hypothetical ranks.
The ranks of the following 11 observations should be calculated: 1.0, 8.2, 2.2, -2.0, -1.0, 2.2, 5.3, 2.2, 8.1, 7.0 und 5.3.
Sorting the values in ascending order results in the following (hypothetical) rank order:
As the observations 2.2 and 5.3 occur several times their ranks are replaced by the mean of these hypothetical ranks:
2.2 5 (tie)
5.3 7.5 (tie)
The data were produced with guest contributor Elena Ong, PHN, MS, Ong & Associates. The data were drawn from CDC’s 2015 Behavioral Risk Factor Surveillance Survey (BRFSS), which is run by the CDC and conducted by state health departments. It is the world largest such survey. TFAH selected seven data points because of the significance of their potential impact on the health and wellbeing of the public and their relevance to current policy considerations.
The data were sorted based upon the 2015 and 2017 Congressional districts. The method of generating small area estimation (SAE) of the measures reported for the Congressional Districts is a multilevel statistical modeling framework. Specifically, CDC uses an innovative peer-reviewed multilevel regression and poststratification (MRP) approach that links geocoded health surveys and high spatial resolution population demographic and socioeconomic data. The approach also accounts for the associations between individual health outcomes, individual characteristics, and spatial contexts and factors at multiple levels (e.g., state, county); predicts individual disease risk and health behaviors in a multilevel modeling framework, and estimates the geographic distributions of population disease burden and health behaviors.
The MRP approach is flexible and allows the ability to provide modeled estimates of the prevalence of each indicator at the various levels of geography, including Congressional Districts. CDC’s internal and external validation studies confirm the strong consistency between MRP model-based SAEs and direct BRFSS survey estimates at both state and county levels.
Further information on the small area estimation methodology can be obtained from:
Zhang X, Holt JB, Lu H, Wheaton AG, Ford ES, Greenlund K, Croft JB, 2014. Multilevel regression and postratification for small area estimation of population health outcomes: a case study of chronic obstructive pulmonary disease prevalence using BRFSS. American Journal of Epidemiology 179:1025-1033.
Zhang X et. al. Validation of Multilevel Regression and Poststratification Methodology for Small Area Estimation of Health Indicators from the Behavioral Risk Factor Surveillance System. American Journal of Epidemiology. 2015;182(2):127-137.
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