Poor physical health days
Average number of physically unhealthy days reported in past 30 days (age-adjusted).
The 2021 County Health Rankings used data from 2018 for this measure.
Reason for Ranking
Measuring health-related quality of life (HRQoL) helps characterize the burden of disabilities and chronic diseases in a population. In addition to measuring how long people live, it is also important to include measures of how healthy people are while alive – and people’s reports of days when their physical health was not good are a reliable estimate of their recent health.
Reliability for the healthy days measures in the Behavioral Risk Factor Surveillance System is high. In addition, a study examining the validity of healthy days as a summary measure for county health status found that counties with more unhealthy days were likely to have higher unemployment, poverty, percentage of adults who did not complete high school, mortality rates, and prevalence of disability than counties with fewer unhealthy days. Self-reported health outcomes differ by race/ethnicity, in part, because cultural differences in reporting patterns due to different definitions of health may exist. It is important to be aware of these differences when comparing across population groups.
Key Measure Methods
Poor Physical Health Days is an Average
Poor Physical Health Days measures the average number of physically unhealthy days reported in past 30 days. This measure is based on responses to the Behavioral Risk Factor Surveillance System (BRFSS) question: “Thinking about your physical health, which includes physical illness and injury, for how many days during the past 30 days was your physical health not good?” The value reported in the County Health Rankings is the average number of days a county’s adult respondents report that their physical health was not good.
Poor Physical Health Days is Age-Adjusted
As we age, our risk of poor health increases. This means that counties with older populations are more likely to have a higher proportion of their population in poor health compared with counties with younger populations. Every county population has a different age distribution, so we report an adjusted rate to account for the age distribution in order to fairly compare the risk of fair or poor health for residents across different counties. Adjusting for age removes the effect of age as a risk factor on poor physical health days since aging is not preventable.
The Method for Calculating This Measure Has Changed
Prior to the 2016 County Health Rankings, the CDC’s BRFSS provided the County Health Rankings with county-level estimates that were constructed from seven years of responses from participants who used a landline phone. However, even with multiple years of data, these did not provide reliable estimates for all counties, particularly those with smaller respondent samples. In 2016, the CDC began producing single-year estimates at the county level using a combination of BRFSS data and a multilevel modeling approach based on respondent answers and individual characteristics such as age, sex, and race/ethnicity, along with county-level poverty and county and state-level contextual effects.
Poor Physical Health Days Estimates Are Created Using Statistical Modeling
Statistical modeling is used to obtain more informed and reliable estimates than survey data alone can provide. Our Poor Physical Health Days estimates are produced from one year of survey data and are created using complex statistical modeling. Modeling generates more stable estimates for places with small numbers of residents or survey responses. For more technical information on BRFSS modeling, please see their methodology.
There are also drawbacks to using modeled data. The smaller the population or sample size of a county, the more the estimates are derived from the model itself and the less they are based on survey responses. Models make assumptions about statistical relationships that may not hold in all cases. Finally, there is no perfect model and each model generally has limitations specific to their methods.
Caution Should be Used When Comparing These Estimates Across State Lines
The model used to create these estimates includes a state-level factor that limits comparability between neighboring counties of adjacent states.
Can This Measure Be Used to Track Progress
This measure could be used to track progress, but only after considering its substantial limitations. Methodological changes in the Behavioral Risk Factor Surveillance System, which are discussed above and were implemented in the 2016 Rankings, make comparisons with estimates prior to that release year difficult. In addition, current estimates are produced using sophisticated modeling techniques which make them difficult to use for tracking progress in small geographic areas.
Modeled estimates have specific drawbacks with regard to their usefulness in tracking progress in communities. Modeled data are not particularly good at accounting for the influence of local conditions, such as health promotion policies or unique population characteristics, into their estimates. Counties trying to measure the effects of programs and policies should use great caution when using modeled estimates. To better understand physical health from the County Health Rankings, it is best to confirm with additional local sources of data. This type of “data triangulation” is particularly important when trying to confirm estimates from modeled data.
Years of Data Used
Behavioral Risk Factor Surveillance System
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 have 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. For the 2016 County Health Rankings, the CDC produced 2014 county estimates using single-year 2014 BRFSS data and a multilevel modeling approach based on respondent answers and their age, sex, and race/ethnicity, combined with county-level poverty, as well as county- and state-level contextual effects. To produce estimates for those counties where there were no or limited data, the modeling approach borrowed information from the entire BRFSS sample as well as Census Vintage 2014 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) samples. This same method was used in constructing the 500 cities study, which includes BRFSS data for the 500 largest cities in the U.S. and can be found here: https://www.cdc.gov/500cities.
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.
- 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.
- 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.
The 500 Cities Project provides city- and census tract-level small area estimates for chronic disease risk factors, health outcomes, and clinical preventive service use, including poor physical health days, for the largest 500 cities in the United States. For larger counties, you can access county- or MSA-specific data from the CDC at http://www.cdc.gov/brfss/smart/smart_data.htm. However, using this data requires somewhat advanced analytic capabilities. In many states, you can access county level BRFSS estimates, and in some cases you can stratify those estimates by age, gender or race.
 Andresen EM, Catlin TK, Wyrwich KW, Jackson-Thompson J. Retest reliability of surveillance questions on health related quality of life. J Epidemiol Community Health. 2003;57:339-343.
 Jia H, Muennig P, Lubetkin EI, Gold MR. Predicting geographical variations in behavioural risk factors: An analysis of physical and mental healthy days. J Epidemiol Community Health. 2004;58:150-155.
 Bombak AE. Self-rated health and public health: a critical perspective. Front Public Health. 2013;1:15. Published 2013 May 20. doi:10.3389/fpubh.2013.00015.
 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.