The National Health and Nutrition Examination Surveys (NHANES) is a program of studies designed to assess the health and nutritional status of adults and children in the United States. The NHANES combines personal interviews and physical examinations, which focus on different population groups or health topics. These surveys have been conducted by the National Center for Health Statistics (NCHS) on a periodic basis from 1971 to 1994. In 1999 the NHANES became a continuous program with a changing focus on a variety of health and nutrition measurements which were designed to meet current and emerging concerns. The surveys examine a nationally representative sample of approximately 5,000 persons each year. These persons are located in counties across the United States, 15 of which are visited each year. The 1999-2000 NHANES contains data for 9,965 individuals (and MEC examined sample size of 9,282) of all ages. Many questions that were asked in NHANES II, 1976-1980, Hispanic HANES 1982-1984, and NHANES III, 1988-1994, were combined with new questions in the NHANES 1999-2000. The 1999-2000 NHANES collected data on the prevalence of selected chronic conditions and diseases in the population and estimates for previously undiagnosed conditions, as well as those known to and reported by respondents. Risk factors, those aspects of a person's lifestyle, constitution, heredity, or environment that may increase the chances of developing a certain disease or condition, were examined. Data on smoking, alcohol consumption, sexual practices, drug use, physical fitness and activity, weight, and dietary intake were collected. Information on certain aspects of reproductive health, such as use of oral contraceptives and breastfeeding practices, were also collected. The interview includes demographic, socioeconomic, dietary, and health-related questions. The examination component consists of medical, dental, and physiological measurements, as well as laboratory tests. Demographic data file variables are grouped into three broad categories: (1) Status Variables: Provide core information on the survey participant. Examples of the core variables include interview status, examination status, and sequence number. (Sequence number is a unique ID assigned to each sample person and is required to match the information on this demographic file to the rest of the NHANES 1999-2000 data). (2) Recoded Demographic Variables: The variables include age (age in months for persons through age 19 years, 11 months; age in years for 1-84 year olds, and a top-coded age group of 85+ years), gender, a race/ethnicity variable, an education variable (high school, and more than high school education), country of birth (United States, Mexico, or other foreign born), and pregnancy status variable. Some of the groupings were made due to limited sample sizes for the two-year dataset. (3) Interview and Examination Sample Weight Variables: Sample weights are available for analyzing NHANES 1999-2000 data. For a complete listing of survey contents for all years of the NHANES see the document -- Survey Content -- NHANES 1999-2010.

analyze the national health and nutrition examination survey (nhanes) with r nhanes is this fascinating survey where doctors and dentists accompany survey interviewers in a little mobile medical center that drives around the country. while the survey folks are interviewing people, the medical professionals administer laboratory tests and conduct a real doctor's examination. the b lood work and medical exam allow researchers like you and me to answer tough questions like, "how many people have diabetes but don't know they have diabetes?" conducting the lab tests and the physical isn't cheap, so a new nhanes data set becomes available once every two years and only includes about twelve thousand respondents. since the number of respondents is so small, analysts often pool multiple years of data together. the replication scripts below give a few different examples of how multiple years of data can be pooled with r. the survey gets conducted by the centers for disease control and prevention (cdc), and generalizes to the united states non-institutional, non-active duty military population. most of the data tables produced by the cdc include only a small number of variables, so importation with the foreign package's read.xport function is pretty straightforward. but that makes merging the appropriate data sets trickier, since it might not be clear what to pull for which variables. for every analysis, start with the table with 'demo' in the name -- this file includes basic demographics, weighting, and complex sample survey design variables. since it's quick to download the files directly from the cdc's ftp site, there's no massive ftp download automation script. this new github repository co ntains five scripts: 2009-2010 interview only - download and analyze.R download, import, save the demographics and health insurance files onto your local computer load both files, limit them to the variables needed for the analysis, merge them together perform a few example variable recodes create the complex sample survey object, using the interview weights run a series of pretty generic analyses on the health insurance ques tions 2009-2010 interview plus laboratory - download and analyze.R download, import, save the demographics and cholesterol files onto your local computer load both files, limit them to the variables needed for the analysis, merge them together perform a few example variable recodes create the complex sample survey object, using the mobile examination component (mec) weights perform a direct-method age-adjustment and matc h figure 1 of this cdc cholesterol brief replicate 2005-2008 pooled cdc oral examination figure.R download, import, save, pool, recode, create a survey object, run some basic analyses replicate figure 3 from this cdc oral health databrief - the whole barplot replicate cdc publications.R download, import, save, pool, merge, and recode the demographics file plus cholesterol laboratory, blood pressure questionnaire, and blood pressure laboratory files match the cdc's example sas and sudaan syntax file's output for descriptive means match the cdc's example sas and sudaan synta x file's output for descriptive proportions match the cdc's example sas and sudaan syntax file's output for descriptive percentiles replicate human exposure to chemicals report.R (user-contributed) download, import, save, pool, merge, and recode the demographics file plus urinary bisphenol a (bpa) laboratory files log-transform some of the columns to calculate the geometric means and quantiles match the 2007-2008 statistics shown on pdf page 21 of the cdc's fourth edition of the report click here to view these five scripts for more detail about the national health and nutrition examination survey (nhanes), visit: the cdc's nhanes homepage the national cancer institute's page of nhanes web tutorials notes: nhanes includes interview-only weights and interview + mobile examination component (mec) weights. if you o nly use questions from the basic interview in your analysis, use the interview-only weights (the sample size is a bit larger). i haven't really figured out a use for the interview-only weights -- nhanes draws most of its power from the combination of the interview and the mobile examination component variables. if you're only using variables from the interview, see if you can use a data set with a larger sample size like the current population (cps), national health interview survey (nhis), or medical expenditure panel survey (meps) instead. confidential to sas, spss, stata, sudaan users: why are you still riding around on a donkey after we've invented the internal combustion engine? time to transition to r. :D

The National Health and Nutrition Examination Survey (NHANES) is a program of studies designed to assess the health and nutritional status of adults and children in the United States. Participants are selected via a random sampling method. The survey is unique in that it combines interviews and physical examinations. The NHANES interview includes demographic, socioeconomic, dietary, and health-related questions. The examination component consists of medical, dental, and physiological measurements, as well as laboratory tests administered by highly trained medical personnel.

The National Health and Nutrition Examination Survey (NHANES) is designed to assess the health and nutritional status of adults and children in the United States. The survey is unique in that it combines interviews with standardized physical examinations and laboratory tests.
NHANES was conducted on a periodic basis from 1971 to 1994. In 1999 NHANES became continuous. Every year, approximately 5,000 people of all ages are interviewed in their homes and complete the health examination conducted in a mobile examination center.
The NHANES interview includes demographic, socioeconomic, dietary, and health-related questions. The examination component consists of medical, dental, and physiological measurements, as well as the collection of biospecimens, such as blood and urine for laboratory testing.

This set of restricted data contains indirect identifying and/or sensitive information collected in continuous NHANES since 1999. Please refer to the links below for additional data available from NHANES:

• NHANES Public Use Data at: https://wwwn.cdc.gov/nchs/nhanes/Default.aspx
• NHANES Genetic Restricted Data at: https://data.cdc.gov/dataset/National-Health-and-Nutrition-Examination-Survey-N/hdx4-e4uu
• NHANES Restricted Data: Prior to 1999 at: https://data.cdc.gov/dataset/National-Health-and-Nutrition-Examination-Survey-N/ic32-yq9m
• NHANES National Youth Fitness Survey (NNYFS) Restricted Data at: https://data.cdc.gov/dataset/3-NHANES-National-Youth-Fitness-Survey-NNYFS-Restr/5u84-m4rs

At a glance - NHANES is the National Health and Nutrition Examination Survey. - NHANES is a national survey that measures the health and nutrition of adults and children in the United States. The National Health and Nutrition Examination Survey (NHANES) collects data about the health of adults and children in the United States. We also collect data about what participants eat, drink, and take as supplements to determine how many nutrients are in their diet. These dietary interviews and blood tests help us measure the nutritional status of U.S. adults and children. CDC's National Center for Health Statistics (NCHS) conducts NHANES. NHANES is the only national health survey that includes health exams, laboratory tests, and dietary interviews for participants of all ages. NHANES data can help improve the health of Americans. Survey data have driven changes in how doctors treat patients and how public policy supports good health. The NHANES program began in the early 1960s. It started as a series of surveys that focused on different population groups and health topics. Since 1999, NCHS has conducted NHANES without interruption—what we call continuous NHANES. Now, NHANES can focus on different health and nutrition measurements to meet changing information needs. Each year, about 5,000 adults and children in communities across the United States participate in NHANES. We use a random, scientific process to select the people we invite to participate. This process ensures that this group of people can accurately represent the health and nutritional status of everyone in our diverse nation. To collect data about a wide range of health and nutrition topics, NHANES includes— - Interviews about health, diet, and personal, social, and economic characteristics - Visits to our mobile exam center for dental exams and health and body measurements - Laboratory tests by highly trained medical professionals (Text above quoted from https://www.cdc.gov/nchs/nhanes/about/ on 2025-03-23)

This data represents the age-adjusted prevalence of high total cholesterol, hypertension, and obesity among US adults aged 20 and over between 1999-2000 to 2017-2018. Notes: All estimates are age adjusted by the direct method to the U.S. Census 2000 population using age groups 20–39, 40–59, and 60 and over. Definitions Hypertension: Systolic blood pressure greater than or equal to 130 mmHg or diastolic blood pressure greater than or equal to 80 mmHg, or currently taking medication to lower high blood pressure High total cholesterol: Serum total cholesterol greater than or equal to 240 mg/dL. Obesity: Body mass index (BMI, weight in kilograms divided by height in meters squared) greater than or equal to 30. Data Source and Methods Data from the National Health and Nutrition Examination Surveys (NHANES) for the years 1999–2000, 2001–2002, 2003–2004, 2005–2006, 2007–2008, 2009–2010, 2011–2012, 2013–2014, 2015–2016, and 2017–2018 were used for these analyses. NHANES is a cross-sectional survey designed to monitor the health and nutritional status of the civilian noninstitutionalized U.S. population. The survey consists of interviews conducted in participants’ homes and standardized physical examinations, including a blood draw, conducted in mobile examination centers.

The National Health and Nutrition Examination Survey (NHANES) is a nationally representative study that collects demographic, socioeconomic, dietary, and health-related information from 10,000 Americans annually. In Wave G (2011-2012) and Wave H (2013-2014), participants wore an ActiGraph GT3X+ on the non-dominant wrist for seven consecutive days. Publicly available data include minute-level wear predictions (wake wear, sleep wear, unknown, nonwear), data quality flags, and monitor-independent movement summary units (MIMS). Additionally, raw triaxial accelerometer data recorded at 80 Hz are provided, enabling detailed analyses beyond MIMS. We applied five step counting algorithms and the ActiGraph Activity Count (AC) algorithm to the raw data to create a dataset of minute-level step counts. The dataset also includes minute level AC, MIMS, wear predictions, and wear flags for all participants who wore accelerometers in NHANES 2011–2014. These data facilitate detailed investigations of accelerometry-measured physical activity patterns in a nationally representative U.S. population. Potential analyses include examination of within-day activity patterns and their association with health outcomes.

Background: Nocturia, a prevalent chronic condition, impacts individuals' quality of life but remains underexplored. This study aimed to assess the association between serum albumin levels and nocturia.Methods: Based on the analysis of the National Health and Nutrition Examination Survey (NHANES) database (2005-2012), our study included a total of 6345 adults (≥20 years old). Nocturia was defined as ≥2 nocturnal voiding episodes. Logistic regression and smooth curve fitting analyzed the linear and nonlinear correlations between serum albumin and nocturia, with subgroup analysis.Results: Among 6345 participants, 1821 (28.7%) experienced nocturia. Logistic regression analysis revealed a linear negative correlation between serum albumin and nocturia risk (OR = 0.9549, 95% CI = 0.9280 ~ 0.9827, P = 0.002). Even after quartile division of serum albumin concentration, this correlation persisted within each group, and a smooth curve fitting validated the nonlinear negative correlation between the two. Subgroup analysis further demonstrated significant impacts of body mass index (BMI), alcohol consumption, and age on this association.Conclusion: This cross-sectional study indicated that higher serum albumin levels were associated with a reduced risk of nocturia in U.S. adults aged 20 and older, highlighting the importance of serum albumin in the prevention and treatment of nocturia and providing clinical guidance.

What We Eat in America (WWEIA) is the dietary intake interview component of the National Health and Nutrition Examination Survey (NHANES). WWEIA is conducted as a partnership between the U.S. Department of Agriculture (USDA) and the U.S. Department of Health and Human Services (DHHS). Two days of 24-hour dietary recall data are collected through an initial in-person interview, and a second interview conducted over the telephone within three to 10 days. Participants are given three-dimensional models (measuring cups and spoons, a ruler, and two household spoons) and/or USDA's Food Model Booklet (containing drawings of various sizes of glasses, mugs, bowls, mounds, circles, and other measures) to estimate food amounts. WWEIA data are collected using USDA's dietary data collection instrument, the Automated Multiple-Pass Method (AMPM). The AMPM is a fully computerized method for collecting 24-hour dietary recalls either in-person or by telephone. For each 2-year data release cycle, the following dietary intake data files are available: Individual Foods File - Contains one record per food for each survey participant. Foods are identified by USDA food codes. Each record contains information about when and where the food was consumed, whether the food was eaten in combination with other foods, amount eaten, and amounts of nutrients provided by the food. Total Nutrient Intakes File - Contains one record per day for each survey participant. Each record contains daily totals of food energy and nutrient intakes, daily intake of water, intake day of week, total number foods reported, and whether intake was usual, much more than usual or much less than usual. The Day 1 file also includes salt use in cooking and at the table; whether on a diet to lose weight or for other health-related reason and type of diet; and frequency of fish and shellfish consumption (examinees one year or older, Day 1 file only). DHHS is responsible for the sample design and data collection, and USDA is responsible for the survey’s dietary data collection methodology, maintenance of the databases used to code and process the data, and data review and processing. USDA also funds the collection and processing of Day 2 dietary intake data, which are used to develop variance estimates and calculate usual nutrient intakes. Resources in this dataset:Resource Title: What We Eat In America (WWEIA) main web page. File Name: Web Page, url: https://www.ars.usda.gov/northeast-area/beltsville-md-bhnrc/beltsville-human-nutrition-research-center/food-surveys-research-group/docs/wweianhanes-overview/ Contains data tables, research articles, documentation data sets and more information about the WWEIA program. (Link updated 05/13/2020)

Copper, zinc, and selenium are essential trace elements for human and have important effects on sex hormones. There are few studies on the relationships between the three trace elements and sex hormones. Therefore, our study aimed to investigate the relationships between serum copper, zinc, selenium and testosterone, estradiol, SHBG using data from the National Health and Nutrition Examination Survey (NHANES) 2013-2016 in participants 6-19 years. 1097 participants were enrolled and stratified into male/female children and adolescents. Weighted linear regression models combined regression diagnosis were used to estimate the relationships between trace elements and sex hormones according to the different stratifications. Our results showed that copper was inversely associated with testosterone and estradiol but positively correlated with SHBG. Zinc had positive relationships with testosterone in male adolescents and female children but an inverse relationship with testosterone in female adolescents. Furthermore, a negative association was observed between zinc and SHBG. With the rise of selenium level, testosterone and estradiol were increased but SHBG was decreased. In general, this study used more standardized statistical methods to investigate the relationships between copper, zinc, selenium and testosterone, estradiol, SHBG. Further study should pay attention to some details in statistical methods.

Abstract (en): The third National Health and Nutrition Examination Survey (NHANES III, ICPSR 2231), conducted in 1988-1994, was designed to obtain nationally representative information on the health and nutritional status of the population of the United States through interviews and direct physical examinations. This release, Series II, No. 3A, contains data obtained from a second exam of selected survey participants who had had a primary exam. This release does not replace any previous NHANES III data releases. The second exam sample consists of seven separate data files. The Combination Foods file contains information on food weight, nutrient data, and descriptions about combination foods. The Total Nutrient Intake file records respondent intake of foods and beverages in a 24-hour time period. The Examination file consists of a comprehensive physical/dental examination. The Individual Foods file lists the food records and component food records for single and multi-component combination foods. The Laboratory file contains data collected through whole blood, serum, plasma, and urine specimens collected from respondents. The Second Laboratory file contains blood and urine assessments by specimen type and age group. The Variable Ingredient file reports data pertaining to the variable ingredients for many recipe foods in the Individual Foods file. The civilian noninstitutionalized population two months of age and over in the 50 states and the District of Columbia. Select survey participants from the primary NHANES III who completed an exam. The primary survey used a complex, stratified, multistage, probability design. 2006-01-18 File CB4010.ALL.PDF was removed from any previous datasets and flagged as a study-level file, so that it will accompany all downloads. Per agreement with NCHS, ICPSR distributes the data files and text of the technical documentation for this collection as prepared by NCHS.The second exam data files can be linked to the primary exam data and the household interview data using the unique identifier (SEQN). This is necessary to obtain the demographic data for the sample. Further, NCHS recommends that the survey design variables (e.g., sample weights) not be linked with these second exam data files, since the survey design variables were created for the full sample. There are no sample weights or other design variables specifically created for the second exam sample.The codebook is provided by ICPSR as a Portable Document Format (PDF) file.

Frequent itemset mining (FIM), a technique used for finding patterns in consumer purchasing behavior, can be applied to data from large-scale biomonitoring studies to identify combinations of chemicals that frequently co-occur in people. As a proof of concept, we applied FIM to biomonitoring data from the National Health and Nutrition Examination Survey. In this way, we identified 90 chemical combinations consisting of relatively few chemicals that occur in at least 30% of the US population, as well as 3 super-combinations consisting of relatively many chemicals that occur in a small but non-negligible proportion of the US population. Thus, we have demonstrated a technique for narrowing a large number of possible chemical combinations down to a much smaller collection of prevalent chemical combinations.

This dataset is associated with the following publication: Kapraun, D.F., J.F. Wambaugh, R. Tornero-Velez, and R.W. Setzer. (ENVIRONMENTAL HEALTH PERSPECTIVES) Identifying Prevalent Chemical Mixtures in the US Population. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 125(8): 1-16, (2017).

BackgroundOsteoporosis (OP) and osteopenia are common bone disorders in old age, and lots of patients suffering from OP or osteopenia need to take antiplatelet agents to treat basic diseases. However, clinical data on the link between osteopenia or OP and antiplatelet agents are limited.MethodsData in this study were collected and screened from the NHANES from 2013 to 2014 and 2017 to 2018. The variables were extracted from interviews and compared between OP or osteopenia participants and normal. The relationship between OP or osteopenia and taking antiplatelet drugs was analyzed by weighted multivariate logistic regressionResultsAfter excluding individuals who were not eligible and had invalid data, we finally identified 894 participants for inclusion in the study. We found a negative association between OP or osteopenia and taking antiplatelet agents (OR = 0.53; 95% CI, 0.33–0.84; p < 0.05). These results did not change on multiple imputations (OR = 0.32, 95% CI, 0.19–0.56; p

NCHS has linked data from various surveys with death certificate records from the National Death Index (NDI). Linkage of the NCHS survey participant data with the NDI mortality data provides the opportunity to conduct a vast array of outcome studies designed to investigate the association of a wide variety of health factors with mortality. The Linked Mortality Files (LMF) have been updated with mortality follow-up data through December 31, 2019. Public-use Linked Mortality Files (LMF) are available for 1986-2018 NHIS, 1999-2018 NHANES, and NHANES III. The files include a limited set of mortality variables for adult participants only. The public-use versions of the NCHS Linked Mortality Files were subjected to data perturbation techniques to reduce the risk of participant re-identification. For select records, synthetic data were substituted for follow-up time or underlying cause of death. Information regarding vital status was not perturbed.

BackgroundFood insecurity, the uncertain ability to access adequate food, can limit adherence to dietary measures needed to prevent and manage cardiometabolic conditions. However, little is known about temporal trends in food insecurity among those with diet-sensitive cardiometabolic conditions.MethodsWe used data from the Continuous National Health and Nutrition Examination Survey (NHANES) 2005–2012, analyzed in 2015–2016, to calculate trends in age-standardized rates of food insecurity for those with and without the following diet-sensitive cardiometabolic conditions: diabetes mellitus, hypertension, coronary heart disease, congestive heart failure, and obesity.Results21,196 NHANES participants were included from 4 waves (4,408 in 2005–2006, 5,607 in 2007–2008, 5,934 in 2009–2010, and 5,247 in 2011–2012). 56.2% had at least one cardiometabolic condition, 24.4% had 2 or more, and 8.5% had 3 or more. The overall age-standardized rate of food insecurity doubled during the study period, from 9.06% in 2005–2006 to 10.82% in 2007–2008 to 15.22% in 2009–2010 to 18.33% in 2011–2012 (p for trend < .001). The average annual percentage change in food insecurity for those with a cardiometabolic condition during the study period was 13.0% (95% CI 7.5% to 18.6%), compared with 5.8% (95% CI 1.8% to 10.0%) for those without a cardiometabolic condition, (parallelism test p = .13). Comparing those with and without the condition, age-standardized rates of food insecurity were greater in participants with diabetes (19.5% vs. 11.5%, p < .0001), hypertension (14.1% vs. 11.1%, p = .0003), coronary heart disease (20.5% vs. 11.9%, p < .001), congestive heart failure (18.4% vs. 12.1%, p = .004), and obesity (14.3% vs. 11.1%, p < .001).ConclusionsFood insecurity doubled to historic highs from 2005–2012, particularly affecting those with diet-sensitive cardiometabolic conditions. Since adherence to specific dietary recommendations is a foundation of the prevention and treatment of cardiometabolic disease, these results have important implications for clinical management and public health.

A separate cohort study was conducted at a Japanese urogynecology and urology clinic from June 2018 to May 2024 to supplement NHANES findings. The study included 1,869 women, 60 years or older, who presented with urinary symptoms. The dataset comprises anonymized clinical data, featuring participants with stress urinary incontinence (SUI) and age-matched controls. Frailty was evaluated using both NHANES-based classification and Fried's Frailty Phenotype. To ensure group comparability, propensity score matching (PSM) was employed, adjusting for age and BMI. Urinary incontinence severity and frailty status were assessed using standardized NHANES criteria. The study also examined additional clinical factors, such as smoking history, arrhythmia, cerebral infarction, chronic lung disease, and grip strength, to explore their relationships with frailty and urinary dysfunction. This dataset offers researchers a valuable resource for investigating the connections between aging, urinary symptoms, and frailty in an East Asian population.

Mean Neutrophil, Lymphocyte, and NLR values according to demographic and clinical characteristics for children age 2–18 years (n = 5286) - NHANES data set. (DOCX)

Title: "Association between various physical activity domains and overall cancer risk, National Health and Nutrition Examination Survey (NHANES) 2007-2018" Purpose: There are very few studies concurrently evaluating the association between multiple physical activity (PA) domains and cancer prevalence. Therefore, this study aims to fill this gap by investigating the link between multiple PA subdomains [occupational PA (OPA), transportation-related PA (TPA), leisure-time PA (LTPA), and total PA] and the likelihood of cancer. Method: The data from National Health and Nutrition Examination Survey (NHANES) 2007-2008, 2009-2010, 2011-2012, 2013-2014, 2015-2016, and 2017-2018 were used in this study. Cancers are the primary outcome variable of interest in this study. PA was self- or proxy-reported using the Global Physical Activity Questionnaire (GPAQ). Multivariable logistic regression models were used, adjusted for covariates. Results: The trend analysis revealed that the prevalence of cancer statistically decreased with the increase in total PA amount. The participants achieving twice the minimum recommended PA guidelines (≥300 minutes) for total PA were 32% [0.68 (0.54, 0.86)] less likely to have cancer. However, significant associations between three PA subdomains (OPA, TPA, and LTPA) and cancers were not found in this study. Conclusion: There is no significant association between any of these three single PA subdomains and cancer prevalence other than total PA. Therefore, this study recommends clinical practice should prioritize promoting comprehensive PA that integrates OPA, TPA, and LTPA to achieve at least 150 minutes per week (i.e. per seven days) initially and progressing towards 300 minutes for optimal cancer prevention.

AimThe aim of this study was to assess the relationship of circadian syndrome and stroke.MethodsWe performed a cross-sectional analysis of 11,855 participants from the National Health and Nutrition Examination Survey (NHANES) database between 2005 and 2018, and collected the baseline characteristics. Multivariate logistic regression models were developed to explore the association between circadian syndrome and stroke. Simultaneously, subgroup analyses based on the difference of gender, race, and components associated with circadian syndrome also were performed. The odds ratio (OR) and 95% CI were calculated in this study.ResultsAll the participants were divided into the non-stroke group and the stroke group. There were approximately 3.48% patients exclusively with stroke and 19.03% patients exclusively with circadian syndrome in our study. The results suggested that the risk of stroke in patients with circadian syndrome was higher than that in patients without circadian syndrome (OR = 1.322, 95 CI%: 1.020–1.713). Similar associations were found in women with circadian syndrome (OR = 1.515, 95 CI%: 1.086–2.114), non-Hispanic whites with circadian syndrome (OR = 1.544, 95 CI%: 1.124–2.122), participants with circadian syndrome who had elevated waist circumference (OR = 1.395, 95 CI%: 1.070–1.819) or short sleep (OR = 1.763, 95 CI%: 1.033–3.009).ConclusionCircadian syndrome was associated with the risk of stroke. Particularly, we should pay more close attention to the risk of stroke in those populations who were female, non-Hispanic whites, had the symptoms of elevated waist circumference or short sleep.

NCHS has linked 1999-2018 National Health Interview Survey (NHIS) and 1999-2018 National Health and Nutrition Examination Survey (NHANES) to administrative data through 2019 for the Department of Housing and Urban Development’s (HUD) largest housing assistance programs: the Housing Choice Voucher program, public housing, and privately owned, subsidized multifamily housing. Linkage of NCHS survey participants with HUD administrative records provides the opportunity to examine relationships between housing and health.