Poor Mental Health Days

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Average number of mentally unhealthy days reported in past 30 days (age-adjusted). The 2024 Annual Data Release used data from 2021 for this measure.

Self-reported health status is a general measure of health-related quality of life (HRQoL) in a population. Measuring HRQoL helps characterize the experience of people with disabilities and people living with chronic conditions in a population. Self-reported health status is a widely used measure of HRQoL. In addition to measuring how long people live, it is important to include measures that consider how well people live. 

Reliability for the healthy days measures in the Behavioral Risk Factor Surveillance System (BRFSS) is high.1 Additionally, 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.2 Self-reported health outcomes differ by race and ethnicity, in part, because cultural differences in reporting patterns may exist due to different definitions.3 It is important to be aware of these differences when comparing across population groups.

Data and methods

Data Source

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 United States 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. Data from the BRFSS are used to measure various health behaviors and health-related quality of life (HRQoL) indicators in the Health Snapshots and downloadable datasets. HRQoL measures are age-adjusted to the 2000 U.S. standard population. 

Prior to the 2016 Annual Data Release, 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 Annual Data Release and beyond, the CDC produced 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, as well as county- and state-level contextual effects.1 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 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.2 This same method was used in constructing the 500 cities study, which includes BRFSS data for the 500 largest cities in the U.S.  

For the 2021 Annual Data Release, the CDC has updated their modeling procedure for producing small-area estimates. With the PLACES project, a multilevel statistical modeling framework using multilevel regression and poststratification (MRP) is performed for small-area estimation that links BRFSS data with high spatial resolution population demographic and socioeconomic data from the Census’ American Community Survey (ACS). The CDC has performed internal and external validation studies, which confirm strong consistency between their model-based estimates and the direct BRFSS survey estimates at both the state and county levels. For more technical information on the PLACES modeling procedure, please see their website.3 

1Zhang 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. 

2Zhang 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. 

3PLACES Project. Centers for Disease Control and Prevention. Accessed March 9, 2021. https://www.cdc.gov/places

Key Measure Methods

Poor Mental Health Days is an average

Poor Mental Health Days is the average number of mentally unhealthy days reported in past 30 days.

Poor Mental Health Days estimates are age-adjusted

Age is a non-modifiable risk factor, and as age increases, poor health outcomes are more likely. We report an age-adjusted rate in order to fairly compare counties with differing age structures.

The method for calculating Poor Mental Health Days has changed

Prior to the 2016 Annual Data Release, the Centers for Disease Control and Prevention's (CDC) BRFSS county-level estimates were constructed from seven years of responses from participants who used a landline phone. Beginning with the 2016 Annual Data Release, CDC has provided single-year modeled county-level estimates which include both landline and cell phone users. Beginning with the 2021 Annual Data Release, CDC introduced an updated modeling procedure to produce small-area estimates. All of these changes were implemented in order to provide users with the most accurate estimates of health in their community. 

Poor Mental Health Days estimates are created using statistical modeling

Surveys collect information about a limited portion of a population. Statistical modeling can be used to predict how people who share certain characteristics with those surveyed may have responded to the survey. Modeling can increase the power of survey data by generating more stable estimates for places with small numbers of residents or survey responses. The Poor Mental Health Days estimates are produced from one year of survey data and are created using complex statistical modeling. For more technical information on PLACES modeling using BRFSS data, please see their methodology.4

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 its methods.

Caution should be used when comparing these estimates across states

Estimates may not be comparable across states because of methodological changes discussed in “The method for calculating Poor Mental Health Days has changed”.

Caution should be used when comparing these estimates across years

Estimates may not be comparable across years because of methodological changes discussed in “The method for calculating Poor Mental Health Days has changed”.


The numerator is the number of days respondents reported to the question "Now thinking about your mental health, which includes stress, depression, and problems with emotions, for how many days during the past 30 days was your mental health not good?"


The denominator is the total number of adult respondents in a county.

Can This Measure Be Used to Track Progress

Modeled estimates have specific drawbacks with their usefulness in tracking progress in communities. Modeled data may not capture the effects of local conditions, such as health promotion policies. In order to better understand and validate modeled estimates, it can be helpful to supplement with additional local data.

Additionally, methodological changes limit the ability to track progress across years using this measure. For more information on methodological changes, see above.

Finding More Data

Disaggregation means breaking data down into smaller, meaningful subgroups. Disaggregated data are often broken down by characteristics of people or where they live. Disaggregated data can reveal inequalities that are otherwise hidden. These data can be disaggregated by:

  • Age
  • Gender
  • Race
  • Education
  • Income
  • Subcounty Area

The PLACES Project provides county-, city-, census tract-, and zip code-level small-area estimates for chronic disease risk factors, health outcomes, and clinical preventive service use, including Poor Mental Health Days, across the United States.

For larger counties, you can access county- or MSA-specific data from the CDC. 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, education, income, or race.


1 Andresen EM, Catlin TK, Wyrwich KW, Jackson-Thompson J. Retest reliability of surveillance questions on health related quality of life. Journal of Epidemiology & Community Health. 2003;57:339-343.

2 Jia H, Muennig P, Lubetkin EI, Gold MR. Predicting geographical variations in behavioural risk factors: An analysis of physical and mental healthy days. Journal of Epidemiology & Community Health. 2004;58:150-155.

3 Bombak AE. Self-rated health and public health: A critical perspective. Frontiers in Public Health. 2013;1:15. 

4 PLACES: Local data for better health. Centers for Disease Control and Prevention (CDC), Robert Wood Johnson Foundation, CDC Foundation. Accessed March 9, 2021. https://www.cdc.gov/places.

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