Frequently Asked Questions

General Questions

When printing county snapshots with the "Areas to Explore" highlighting turned on, you may find that the colors used to highlight these areas don't print. This is a browser specific issue for Internet Explorer and Firefox. These browsers will not print background colors or images by default, as these frequently take much more ink and can serve to obscure the foreground text on a black-and-white printout. Here are the steps you can take to resolve this:

  • Open either Internet Explorer or Firefox
  • Press "CTRL + P" or click the "Print" button to display the Print dialog
  • Check the "Print Background Colors" and "Print Background Images" boxes in the dialog
  • Click "Print" to print the Web page

We are happy to provide copies of both of these graphics for your use: County Health Rankings model and our Take Action graphic. If you use these images in a publication, please use the following citation:

University of Wisconsin Population Health Institute. County Health Rankings & Roadmaps 2017. www.countyhealthrankings.org.

  1. Raising awareness in the general community about the multiple factors that influence health via media interviews and follow-up conversations.
  2. Initiating community health assessment and planning efforts where none previously existed.
  3. Celebrating successes and promoting existing community health improvement efforts.
  4. Informing policy makers about the many factors that affect a community's health and about community health improvement planning.
  5. Revitalizing or refining existing community health improvement strategies.
  6. Citing the County Health Rankings as justification in securing grant funding to conduct community health improvement efforts and/or to address the determinants of health.

Visit the Community Stories section to learn more.

There is not one formula for where communities should put their efforts. Because the County Health Rankings are based on broad measures and include multiple years of data, it is important for communities to look at further information prior to making a decision about next steps. Learn more about how to assess your community's needs and resources via the Action Center.

America’s Health Rankings ranks the health of states -- we have tried to align our measures as closely as possible with these Rankings.

The purpose of the County Health Rankings is to compare counties within states. We discourage comparisons between states for several reasons:

  1. The County Health Rankings are not intended to be a national report that finds the 10 or 15 least healthy counties in the nation and focuses only on that. The Rankings are intended to provide a tool for communities in each state as a call to identify opportunities for improvement
  2. We focus on state-specific county rankings, and we do not provide any county rankings across state boundaries.

If you would like to compare specific counties from different states, please visit our Compare Counties tool by selecting a state and then the “Compare Counties” tab. Users should refer to our guidelines for comparing measures across states to find the differences that occur for some measures (such as high school graduation rates and violent crime).

The County Health Rankings are designed as a call to action – the use of ranks can often serve as a more effective tool for drawing attention to community health issues than lengthy listings of indicators. We encourage any community that has not already done so to use the Rankings as a stimulus to engage community members in a more detailed community health assessment, using whatever additional data sources they have available. The Rankings can be used as a pointer to suggest areas where more in-depth analysis might be helpful.

We believe that there are two separate sets of messages to convey. One set (Health Outcomes) addresses how healthy a county currently is and the other (Health Factors) addresses how healthy a county might be in the future based on the many factors that influence health. However, when a single ranking of the “healthiest” counties is desired, we use the Health Outcomes rank.

To serve as a call to action for communities to:

  1. Understand the health problems in their community
  2. Get more people involved in improving the health of communities
  3. Recognize that factors outside medical care influence health

Ranking the health of counties using not only traditional health outcomes, but also the broad range of health factors, can mobilize action on the part of governmental public health and in many other sectors that can influence and are affected by health.

Methodology

A county’s rank tells a community how healthy it is today compared to other counties in its state but a rank alone cannot fully capture progress. Because ranks are dependent on how other counties are doing, they are not as helpful as a standalone measure of progress. A county’s rank could actually get worse even though its health is getting better. For example, the premature death rate for Bexar County in Texas (home to San Antonio) improved by 6 percent from our initial 2010 Rankings to 2015 while its rank for length of life dropped by 8 places (from 58 to 66). People are living longer lives in Bexar but its rate of improvement has been outpaced by other counties in Texas.

To examine progress beyond ranks, we might suggest exploring:

Specific measure estimates over time
Look to the underlying CHR measures to examine change over time. For instance, take a look at the trend graph for your county’s premature death estimate and how your county trends compare to state and national trends. Of note, it is also important to consider the error margins associated with measure estimates. Year-to-year, even if a county’s true rate does not change, there will be some fluctuation in the estimate due to random variation.

Diverse data sources and local information
Look for information from existing local data sources. These sources may contain measures that can better capture the health needs and opportunities that are important to measuring progress in your community.

Mixed methods approaches
You will not be able to measure progress fully with a simple quantitative approach. Consider ways to collect information through interviews, focus groups, or surveys, particularly for near-term progress measures.

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To make the best use of your Rankings data and in appropriate ways, it is important to understand how the measures you are interested in are created and where the information comes from. Measures created from modeled estimates, such as BRFSS measures of adult smoking, can have specific drawbacks with regard to their usefulness in tracking progress in communities. Modeled data like these are not particularly good at generating estimates that incorporate the effects of local conditions such as health promotion policies or unique population characteristics. Counties trying to measure the effects of programs and policies should take caution when using modeled estimates and look to other data sources, particularly local community information, that can help to understand the effects of interventions or changes over time. To explore how you can use individual measures to track progress in your community, visit the “Learn More About this Measure” link on each measure page in your county snapshot.

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Ranks can improve or worsen for one or more reason:

Your county experienced health gains or losses
Change in a rank can be due to actual change in the underlying measures that comprise the summary rank.

Other counties experienced health gains or losses
A rank may also improve or worsen not due to change in your own county’s measures, but rather because of changes in counties ranked just above or below your county within the state.

Random variation in measure estimates
A fluctuation in county rank may be caused by random variation in the measures that comprise the rank.

Changes in ranked measures or their methods
Ranks can be influenced by the introduction of new measures or a change in the methods for current measures.

In order to better understand why your county’s rank may have changed from the previous year, the best place to start is by examining the individual measures and z-scores that comprise the rank. When examining each of these underlying components, it is helpful to explore the following:

Your county’s estimates over time;
Are measure values increasing, decreasing, or staying the same? For instance, take a look at the trend graph for your county’s premature death estimate. Is premature death in your county increasing, decreasing, or staying the same? How does your county’s trend compare to the state or national trend? Looking more closely at changes over time in your county’s underlying estimates can help you better understand your county’s progress and potential influence on the change in your rank.

The error margins associated with your county’s estimates.
Another thing to consider is the error associated with the estimates comprising the rank. Year-to-year, even if your county’s true rate does not change, there will be some fluctuation in the estimate due to random variation. For instance, it is possible to see substantial change in your county’s premature death rate from the previous year, but if the rate is within the margin of error for the previous year’s premature death estimate, the change may be partially or almost completely due to random fluctuation in estimates from year-to-year.

You can contact us through the ‘get help’ button on the website with any specific questions regarding why your county’s rank changed.

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The county-level estimates based on BRFSS data are calculated for the County Health Rankings by staff at the Centers for Disease Control and Prevention.

The Behavioral Risk Factor Surveillance System (BRFSS) is a state-based random digit dial (RDD) telephone survey that is conducted annually in all states, the District of Columbia, and U.S. territories. Data obtained from the BRFSS are representative of each state’s total non-institutionalized population over 18 years of age and has included more than 400,000 annual respondents with landline telephones or cellphones since 2011. Data are weighted using iterative proportional fitting (also called "raking") methods to reflect population distributions. For the County Health Rankings, data from the BRFSS are used to measure various health behaviors and health-related quality of life (HRQoL) indicators. HRQoL measures are age-adjusted to the 2000 U.S. standard population.

Prior to the 2016 County Health Rankings, up to seven survey years of landline only BRFSS data were aggregated to produce county estimates. However, even with multiple years of data, these did not provide reliable estimates for all counties, particularly those with smaller respondent samples. Since the 2016 County Health Rankings, the CDC produces county estimates using single-year BRFSS data and a multilevel modeling approach based on respondent answers and their age, sex and race/ethnicity, combined with county-level poverty and county and state level contextual effects1. To produce estimates for those counties where there was no or limited data, the modeling approach borrowed information from the entire BRFSS sample as well as Census population estimates. CDC used a parametric bootstrapping method to produce standard errors and confidence intervals for those point estimates. This estimation methodology was validated for all U.S. counties, including those with no or small (<50 respondents) samples2.

One limitation of the BRFSS is that all measures are based on self-reported information, which cannot be validated with medical records. Another limitation is that these model-based estimates were created by borrowing information from the entire BRFSS, which may or may not accurately reflect those counties’ local intervention experiences. Additionally, the confidence intervals constructed from these methods appear much smaller than confidence intervals reported for direct survey methods in previous years.

  1. Zhang X, Holt JB, Lu H, Wheaton AG, Ford ES, Greenlund K, Croft JB. Multilevel regression and poststratification for small-area estimation of population health outcomes: a case study of chronic obstructive pulmonary disease prevalence using the Behavioral Risk Factor Surveillance System. American Journal of Epidemiology 2014;179(8):1025–1033.
  2. Zhang X, Holt JB, Yun, S, Lu H, Greenlund K, Croft JB. Validation of multilevel regression and poststratification methodology for small area estimation of health outcomes. American Journal of Epidemiology 2015;182(2):127-137.
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CDC’s decision to move to this new method for the BRFSS was based on their desire to provide more reliable and current county-level estimates (due to the inclusion of cell phone only users) for all counties. (Previously, even using seven years of data, there were many counties where there were not enough BRSS respondents to provide any estimates). We knew that this might lead to differences in estimates of varying size. Our best advice for counties with unexpectedly large changes is to look to other sources of estimates of people’s quality of life and health behaviors and examine all the results together. You may want to look at the Premature Death data included in your snapshot to understand how trends in mortality align with your new estimates of quality of life. Counties might also want to consider consulting any existing locally based data sources, e.g., hospitals or health care systems that conduct their own surveys or gather information in electronic health records. One area of last resort might be to conduct your own survey – although web-based or text-based surveys come with their own limitations, they can be a relatively inexpensive way to gather data from residents.

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Our trends are estimated using linear regression for all of the years of data included in the graph. This sometimes creates unusual situations, particularly when a measure both improves and worsens over the time period. For example, in many counties, unemployment increased dramatically between 2007 and 2011. Since then, in many counties, unemployment rates have been improving. However, the overall trend for the county depends on the relative magnitude of worsening to improvement. For example, even if your county has had improving rates over the past 3 years, but had higher rates of worsening in the years prior, it might show a worsening trend.

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The Areas to Explore and Areas of Strength features highlight measures that, respectively, are potential challenges your community may want to examine more closely and measures that are assets in your community already. Accounting for the relative influence of each measure on health outcomes, the County Health Rankings team used a variety of techniques to identify the Health Factor measures for your county that seem to have the greatest potential opportunity for improvement or are the assets your community might want to build on. We identified measures where there are meaningful differences between your county's values and either your state average, the national benchmark, or the state average in the best state.

As with your county’s ranks, these Areas to Explore are just one starting point for you to consider in your journey toward improving health in your community. Using the Rankings Data provides suggestions for other sources of state and local data that you can use to examine these measures more closely.

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No, many of the measures were calculated for the County Health Rankings by other organizations. For example, measures from the National Vital Statistics System and the Behavioral Risk Factor Surveillance System were calculated by staff at the National Center for Health Statistics/Centers for Disease Control and Prevention. Similarly, the health care quality measures were calculated for us by the Dartmouth Institute.

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The most frequent reason why numbers of the Rankings web site may not match other numbers that states have will be due to different definitions of measures, different time periods, or different samples or denominators. Communities should rely on their own state's data for their own more detailed community health assessments.

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Some counties in the nation are too small to have reliable measurements for health outcome measures. Those counties are not ranked. If a county has data for enough measures to be ranked but is missing data for any individual measure, we assign the county the same value as the state mean for that measure. One way to overcome unstable and unreliable estimates due to small numbers, such as with the measures from the Behavioral Risk Factor Surveillance System (BRFSS), is to combine multiple years of data. This means that although the Rankings are useful for differentiating between places that are and are not healthy, they are not a good tool for setting objectives and tracking progress from year to year.

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In part, the County Health Rankings aim to show that where you live matters to your health and that disparities exist within every state. But disparities exist within most counties as well. We encourage communities to use the Rankings as a starting point to delve more deeply into data that may highlight disparities within counties. Communities can do this by initiating a community health assessment or using the Rankings to draw attention to thorough assessments that have already been done. We have provided information in our guide to Using the Rankings Data  for communities that need help getting started.

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Each county's ranks are calculated within a specific state so you cannot compare your county's ranks with those for a county in a different state. However, you can compare the values for the Health Outcomes measures from one state to another. We advise caution in comparing the Health Factors measures across states because our measures for Health Factors are only uniform within states not across states. If you would like to compare specific counties from different states, please visit our Compare Counties tool by selecting a state and then the “Compare Counties” tab. Users should refer to our guidelines for comparing measures across states to find the differences that occur for some measures (such as high school graduation rates and violent crime).

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