Insufficient Sleep*
The University of Wisconsin Population Health Institute Model of Health
Use the model to explore the measures of how long and how well we live and what shapes these trends. Select a measure in the diagram or browse the list of all measures.
Measures marked with an asterisk (*) are not included in summary calculations for Population Health and Well-being or Community Conditions.
About
Percentage of adults who report fewer than 7 hours of sleep on average (age-adjusted). The 2025 Annual Data Release used data from 2022 for this measure.
Sleep is an important part of a healthy lifestyle, and a lack of sleep can have serious negative effects on one’s own health as well as the health of others. Ongoing sleep deficiency has been linked to chronic health conditions, including cardiovascular disease and stroke, as well as psychiatric disorders such as depression and anxiety, risky behavior, and even suicide.1,2 Sleepiness can lead to motor vehicle crashes and put the lives of others in jeopardy.3 Sleep duration has also been found to be inversely related to diabetes mellitus.1
The University of Wisconsin Population Health Institute Model of Health
Use the model to explore the measures of how long and how well we live and what shapes these trends. Select a measure in the diagram or browse the list of all measures.
Measures marked with an asterisk (*) are not included in summary calculations for Population Health and Well-being or Community Conditions.
Data and methods
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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.
Website to download data
For more detailed methodological information
Key Measure Methods
Insufficient Sleep is a percentage
Insufficient Sleep is the percentage of adults who report that they sleep less than 7 hours per night on average.
Insufficient Sleep estimates are age-adjusted
Age is a non-modifiable risk factor, and certain health behaviors may be associated with different age groups in the population. We report an age-adjusted rate in order to fairly compare counties with differing age structures.
The method for calculating Insufficient Sleep has changed
The Centers for Disease Control and Prevention (CDC) provides single-year modeled county-level estimates that 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. This measure has been age-adjusted since the 2021 Annual Data Release. All of these changes were implemented in order to provide users with the most accurate estimates of health in their community.
Insufficient Sleep 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 Insufficient Sleep 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
BRFSS survey data are collected independently by each state, which could result in data collection differences. Additionally, estimates may not be comparable across states because of methodological changes discussed in “The method for calculating Insufficient Sleep 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 Insufficient Sleep has changed”.
Numerator
The numerator is the number of adults who responded to the following question by stating they sleep less than 7 hours per night: “On average, how many hours of sleep do you get in a 24-hour period?"
Denominator
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 Insufficient Sleep, 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. For 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.
References
- Seixas AA, Gyamfi L, Newsome V, et al. Moderating effects of sleep duration on diabetes risk among cancer survivors: Analysis of the National Health Interview Survey in the USA. Cancer Management and Research. 2018;10(1):4575-4580.
- Harris LM, Huang X, Linthicum KP, Bryen CP, Ribeiro JD. Sleep disturbances as risk factors to suicidal thoughts and behaviours: A meta-analysis of longitudinal studies. Scientific Reports. 2020;10(13888).
- Tefft BC. Acute sleep deprivation and culpable motor vehicle crash involvement. Sleep. 2018;41(10).
- 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.