viernes, 29 de junio de 2018

Surveillance for Certain Health Behaviors and Conditions Among States and Selected Local Areas — Behavioral Risk Factor Surveillance System, United States, 2015 | MMWR

Surveillance for Certain Health Behaviors and Conditions Among States and Selected Local Areas — Behavioral Risk Factor Surveillance System, United States, 2015 | MMWR

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MMWR Surveillance Summaries
Vol. 67, No. SS-9
June 29, 2018



Surveillance for Certain Health Behaviors and Conditions Among States and Selected Local Areas — Behavioral Risk Factor Surveillance System, United States, 2015

Surveillance Summaries / June 29, 2018 / 67(9);1–90
Cassandra M. Pickens, PhD1,2; Carol Pierannunzi, PhD1; William Garvin, MS1; Machell Town, PhD1 (View author affiliations)
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Abstract

Problem: Chronic conditions and disorders (e.g., diabetes, cardiovascular diseases, arthritis, and depression) are leading causes of morbidity and mortality in the United States. Healthy behaviors (e.g., physical activity, avoiding cigarette use, and refraining from binge drinking) and preventive practices (e.g., visiting a doctor for a routine check-up, tracking blood pressure, and monitoring blood cholesterol) might help prevent or successfully manage these chronic conditions. Monitoring chronic diseases, health-risk behaviors, and access to and use of health care are fundamental to the development of effective public health programs and policies at the state and local levels.
Reporting Period: January–December 2015.
Description of the System: The Behavioral Risk Factor Surveillance System (BRFSS) is an ongoing, state-based, random-digit–dialed landline- and cellular-telephone survey of noninstitutionalized adults aged ≥18 years residing in the United States. BRFSS collects data on health-risk behaviors, chronic diseases and conditions, access to and use of health care, and use of preventive health services related to the leading causes of death and disability. This report presents results for all 50 states, the District of Columbia, the Commonwealth of Puerto Rico (Puerto Rico), and Guam and for 130 metropolitan and micropolitan statistical areas (MMSAs) (N = 441,456 respondents) for 2015.
Results: The age-adjusted prevalence estimates of health-risk behaviors, self-reported chronic health conditions, access to and use of health care, and use of preventive health services varied substantially by state, territory, and MMSA in 2015. Results are summarized for selected BRFSS measures. Each set of proportions refers to the median (range) of age-adjusted prevalence estimates for health-risk behaviors, self-reported chronic diseases or conditions, or use of preventive health care services by geographic jurisdiction, as reported by survey respondents. Adults with good or better health: 84.6% (65.9%–88.8%) for states and territories and 85.2% (66.9%–91.3%) for MMSAs. Adults with ≥14 days of poor physical health in the past 30 days: 10.9% (8.2%–17.2%) for states and territories and 10.9% (6.6%–19.1%) for MMSAs. Adults with ≥14 days of poor mental health in the past 30 days: 11.3% (7.3%–15.8%) for states and territories and 11.4% (5.6%–20.5%) for MMSAs. Adults aged 18–64 years with health care coverage: 86.8% (72.0%–93.8%) for states and territories and 86.8% (63.2%–95.7%) for MMSAs. Adults who received a routine physical checkup during the preceding 12 months: 69.0% (58.1%–79.8%) for states and territories and 69.4% (57.1%–81.1%) for MMSAs. Adults who ever had their blood cholesterol checked: 79.1% (73.3%–86.7%) for states and territories and 79.5% (65.1%–87.3%) for MMSAs. Current cigarette smoking among adults: 17.7% (9.0%–27.2%) for states and territories and 17.3% (4.5%–29.5%) for MMSAs. Binge drinking among adults during the preceding 30 days: 17.2% (11.2%–26.0%) for states and territories and 17.4% (5.5%–24.5%) for MMSAs. Adults who reported no leisure-time physical activity during the preceding month: 25.5% (17.6%–47.1%) for states and territories and 24.5% (16.1%–47.3%) for MMSAs. Adults who reported consuming fruit less than once per day during the preceding month: 40.5% (33.3%–55.5%) for states and territories and 40.3% (30.1%–57.3%) for MMSAs. Adults who reported consuming vegetables less than once per day during the preceding month: 22.4% (16.6%–31.3%) for states and territories and 22.3% (13.6%–32.0%) for MMSAs. Adults who have obesity: 29.5% (19.9%–36.0%) for states and territories and 28.5% (17.8%–41.6%) for MMSAs. Adults aged ≥45 years with diagnosed diabetes: 15.9% (11.2%–26.8%) for states and territories and 15.7% (10.5%–27.6%) for MMSAs. Adults aged ≥18 years with a form of arthritis: 22.7% (17.2%–33.6%) for states and territories and 23.2% (12.3%–33.9%) for MMSAs. Adults having had a depressive disorder: 19.0% (9.6%–27.0%) for states and territories and 19.2% (9.9%–27.2%) for MMSAs. Adults with high blood pressure: 29.1% (24.2%–39.9%) for states and territories and 29.0% (19.7%–41.0%) for MMSAs. Adults with high blood cholesterol: 31.8% (27.1%–37.3%) for states and territories and 31.4% (23.2%–42.0%) for MMSAs. Adults aged ≥45 years who have had coronary heart disease: 10.3% (7.2%–16.8%) for states and territories and 10.1% (4.7%–17.8%) for MMSAs. Adults aged ≥45 years who have had a stroke: 4.9% (2.5%–7.5%) for states and territories and 4.7% (2.1%–8.4%) for MMSAs.
Interpretation: The prevalence of health care access and use, health-risk behaviors, and chronic health conditions varied by state, territory, and MMSA. The data in this report underline the importance of continuing to monitor chronic diseases, health-risk behaviors, and access to and use of health care in order to assist in the planning and evaluation of public health programs and policies at the state, territory, and MMSA level.
Public Health Action: State and local health departments and agencies and others interested in health and health care can continue to use BRFSS data to identify groups with or at high risk for chronic conditions, unhealthy behaviors, and limited health care access and use. BRFSS data also can be used to help design, implement, monitor, and evaluate health-related programs and policies.
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Introduction

Chronic conditions (e.g., cardiovascular diseases, diabetes, and arthritis) are leading causes of morbidity, mortality, and health care spending in the United States (1,2). Adopting healthy behaviors (e.g., eating a healthy diet, exercising, avoiding tobacco, and refraining from alcohol) and using preventive services (e.g., visiting a doctor, monitoring and treating blood pressure, and monitoring cholesterol) might prevent chronic disease and help effectively manage chronic conditions (2). At the population level, monitoring health behaviors, chronic conditions, and health care use can inform action to address these leading causes of death and disability.
The Behavioral Risk Factor Surveillance System (BRFSS) is an ongoing, state-based survey conducted via landline and cellular telephone. Since 1984, BRFSS has been conducted by U.S. states and territories with technical assistance from CDC. BRFSS is the largest continuously running health-based telephone survey in the world (3). BRFSS is a principal source of data on health-risk behaviors, chronic diseases, and health care access and use at the state and local levels. States, counties, cities, and others use BRFSS data to set objectives, track progress, and evaluate the effectiveness of health-related initiatives. Beginning in 2002, BRFSS has calculated prevalence estimates for selected counties, metropolitan divisions, and metropolitan or micropolitan statistical areas (MMSAs). This report contains age-adjusted prevalence estimates for various chronic conditions, health-risk behaviors, and use of preventive health services by state, territory, and selected MMSA for 2015.
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Methods

BRFSS is an ongoing, cross-sectional, random-digit–dialed telephone survey that completes approximately 400,000 interviews with adults residing in the United States or its territories each year. BRFSS is conducted by states and territories receiving technical assistance from CDC. BRFSS uses a multistage sampling design to select a representative sample of the noninstitutionalized adult population aged ≥18 years residing within each state and territory. The validity and reliability of the BRFSS survey have been reviewed in detail elsewhere (4).
In 2015, all 50 U.S. states, the District of Columbia, Puerto Rico, and Guam collected both landline and cellular telephone surveys (5). In the landline survey, one adult was randomly chosen from each selected household. In the cellular phone survey, each adult respondent was considered a one-person household (6).
Participants reported their county of residence in the demographics section of the core questionnaire. Persons were assigned to MMSAs on the basis of American National Standards Institute county codes (7). MMSAs are defined by the U.S. Office of Management and Budget (7). This report contains age-adjusted prevalence estimates for general health status, health-risk behaviors, self-reported chronic health conditions, and access to and use of health care for all 50 states, the District of Columbia, Puerto Rico, and Guam and for 130 MMSAs containing ≥500 total respondents.

Questionnaire

The BRFSS questionnaire consists of three sections: a core component, optional modules, and state-added questions. All questions in the core component and optional modules undergo technical review, cognitive testing, and field testing (6).
States must ask all core component questions without modification (6). In 2015, questions in the BRFSS core component addressed participants’ self-reported health status, number of physically and mentally healthy days in the past 30 days, health care access and use, high blood pressure awareness, high cholesterol awareness, chronic health conditions, demographics, tobacco use, alcohol consumption, fruit and vegetable consumption, physical activity, arthritis burden, seatbelt use, immunization, and testing for human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) (6).
In addition to the core questionnaire component, states could include up to 25 optional questionnaire modules. In 2015, a total of 24 optional modules were used by at least one state or territory as follows: adult asthma history (two states), adult human papillomavirus (nine states), anxiety and depression (five states), arthritis management (14 states), breast and cervical cancer screening (seven states), cardiovascular health (five states), caregiver (24 states), childhood asthma prevalence (32 states), clinical breast examination for breast cancer screening (three states), cognitive decline (35 states), colorectal cancer screening (12 states), diabetes (39 states), emotional support and life satisfaction (three states), industry and occupation (25 states), prediabetes (20 states), prostate cancer screening (three states), prostate cancer screening decision making (one state), sexual orientation and gender identity (22 states), shingles (nine states), social context (16 states), sodium or salt-related behavior (10 states), tetanus-diphtheria vaccination in adults (11 states), visual impairment and access to eye care (one state), and random child selection (36 states). The random child selection module collects demographic information on one randomly selected child in the household (data include age, sex, race/ethnicity, and relation of the child to the respondent). This module is also used to randomly select a child for the Asthma Call-back Survey, although various states that use the random child selection module do not participate in the Asthma Call-back Survey (8).
To address state-specific needs, states also can add their own questions to the BRFSS questionnaire. State-added questions were not evaluated by CDC and are not released in public-use data sets (6).
In 2015, certain states used a split questionnaire design. Up to three different questionnaire versions were permitted. Although core questions were required to be used in all versions, state-added questions and optional modules did not have this requirement. Using a split questionnaire design allowed states to include a larger variety of optional modules or state-added questions (6). CDC provided a Spanish translation of the BRFSS core questionnaire and optional modules in 2015. States could translate the BRFSS questionnaire into other languages (6).

Data Collection and Processing

Since 2007, BRFSS surveys have been collected monthly in all 50 states, the District of Columbia, Puerto Rico, and Guam. In 2015, all BRFSS interviews were conducted according to standard protocols, which ensure interview quality and confidentiality (9). Data collected by the states are submitted to CDC for processing, checking, and weighting.

Sampling

A BRFSS sample record was one telephone number in the list of all telephone numbers that were randomly selected for dialing (6). In 2015, Puerto Rico and Guam used simple random sampling to collect their landline samples, and all 50 states and the District of Columbia used disproportionate stratified sampling (DSS) for the landline portion of the sample. In the DSS approach, telephone numbers were separated into two strata (high-density and medium-density) on the basis of the number of listed telephone numbers in their hundred block. Both strata were expected to contain mostly household telephone numbers, but high-density strata were sampled at a higher rate than medium-density strata (6). The DSS design resulted in a probability sample of all households with telephones (6).
Cellular telephone sampling frames were provided by the Telecordia database of telephone exchanges. Cell phone numbers were randomly selected from these sampling frames (6). The target population for the cellular telephone sample was adults aged ≥18 years with a functioning cell phone who resided in a private residence or college housing. The states’ cell phone samples often reached adults who had moved to a different state. These records were transferred to the appropriate state (i.e., the participant’s state of residence) at the end of the year. In 2015, a total of 46 states or territories (all except the District of Columbia, Florida, Guam, North Dakota, Oregon, West Virginia, and Wyoming) sampled disproportionately by geographic stratum to ensure adequate sample sizes in sub-state geographic regions (6).

Data Weighting

Design Weights
BRFSS created design weights that account for unequal selection probabilities, noncoverage, and nonresponse (6). Design weights of dual cell phone and landline users were adjusted to account for the complete overlap of cell phone and landline sampling frames. Design weights were truncated by quartile within geographic region or state (6). BRFSS used weight trimming to reduce the value of extremely high weights and to increase the value of extremely low weights, with the objective of reducing errors in prevalence estimates (6).
Raking
Beginning in 2011, BRFSS data have been weighted using a process known as iterative proportional fitting (“raking”), rather than previous poststratification methods. In the past, BRFSS poststratification weights were based only on three sociodemographic characteristics: age, sex, and race/ethnicity. In contrast, the new raking process permits the inclusion of additional sociodemographic characteristics (e.g., marital status and home ownership) as well as cellular telephone survey data (10). Raking allows the sociodemographic makeup of BRFSS to more closely match the known sociodemographic makeup of states and MMSAs (10).
The 2015 BRFSS data were raked using the following demographic characteristics: sex by age group, race/ ethnicity, education, marital status, home renter/owner, sex by race/ethnicity, age group by race/ethnicity, and phone ownership (6). If states collected BRFSS data by geographic region, then BRFSS data were raked by four additional margins: region, region by age group, region by sex, and region by race/ethnicity (6). BRFSS data were raked to each of these margins in an iterative process until a convergence of a set value was reached.
Persons were assigned to MMSAs on the basis of American National Standards Institute county codes (7). For MMSAs with ≥500 respondents, BRFSS data were raked by five additional margins at the MMSA level: age group, sex, race/ethnicity, sex by age group, and sex by race/ethnicity (7). More detailed information on MMSA weighting is located on the BRFSS SMART webpage (11).

Statistical Analyses

Age adjustment is a standard analytical technique used to compare estimates between populations with different age distributions (e.g., between states) and over time. In this report, prevalence estimates were directly age adjusted so that the reader can compare estimates across states and MMSAs with different age distributions. Age adjusted prevalence estimates were standardized to the 2000 projected U.S. population, which is consistent with recommendations from the CDC National Center for Health Statistics (12).
For prevalence estimates among adults aged ≥18 years, three age adjustment categories were used: 18–44 years (standardized proportion: 0.5305), 45–64 years (standardized proportion: 0.2992), and ≥65 years (standardized proportion: 0.1703). For prevalence estimates among adults aged 18–64 years, two age adjustment categories were used: 18–44 years (standardized proportion: 0.6394) and 45–64 years (standardized proportion: 0.3606). For prevalence estimates among adults aged ≥45 years, four age adjustment categories were used: 45–54 years (standardized proportion: 0.3869), 55–64 years (standardized proportion: 0.2504), 65–74 years (standardized proportion: 0.1895), and ≥75 years (standardized proportion: 0.1732). Age-adjusted prevalence estimates are taken from direct responses and are not the results of modeling. Age was imputed for the limited number of persons who were missing data on age. To account for BRFSS’s complex sampling design, all prevalence estimates in this report were calculated using weights and strata in SAS version 9.3 (SAS Institute Inc., Cary, North Carolina) or SAS-callable SUDAAN Version 11 (RTI International, Research Triangle Park, North Carolina). Crude (unadjusted) estimates for each state and MMSA are available on the BRFSS website (13). Most prior BRFSS reports (i.e., those reporting on 2012 survey data and earlier) included crude prevalence estimates rather than age-adjusted prevalence estimates. The age-adjusted prevalence estimates in this BRFSS report should not be directly compared with crude prevalence estimates in most prior BRFSS reports.
This report presents unweighted sample sizes; age-adjusted, weighted prevalence estimates with standard errors; and 95% confidence intervals for the prevalence of chronic health conditions, health-risk behaviors, and use of preventive health care services by state, territory, and MMSA using 2015 BRFSS data. Only MMSAs with ≥500 respondents are included in this report. County-level estimates are not presented in this report. Modeled small area estimates at the county level will be released at a future date.
If the unweighted sample size of any jurisdiction or subpopulation was <50 or if the relative standard error was >30%, the findings were suppressed to avoid unstable estimates. Relative standard error was calculated by dividing the standard error of the estimated prevalence by the estimated prevalence and multiplying by 100 (for percent). Responses coded as “refused” or “do not know” were excluded from the given analysis.
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About This Report

This report presents age-adjusted prevalence estimates and a discussion of the following topics: 1) health status indicators (general health status, poor physical health, poor mental health, and health care coverage for adults aged 18–64 years), 2) preventive practices (recent routine physical checkup, ever having blood cholesterol checked), 3) health-risk behaviors (current cigarette smoking, binge drinking, no leisure-time physical activity, consuming fruit less than once per day, and consuming vegetables less than once per day), and 4) chronic conditions (among adults aged ≥18 years: obesity, arthritis, depressive disorder, high blood pressure, and high blood cholesterol; among adults aged ≥45 years: diabetes, coronary heart disease, and stroke). Respondents self-reported their height and weight. Body mass index (BMI) was calculated by dividing weight (in kilograms) by height (in meters) squared. Obesity was defined as BMI ≥30.0 kg/m2. The prevalence of all other chronic conditions was based on self-report of the specific condition: i.e., participants were asked if they had ever been told by a health professional that they had the specific condition. Selected chronic conditions (e.g., coronary heart disease) were evaluated among adults aged ≥45 years because the prevalence of these conditions is so low among adults aged 18–44 years. For instance, the prevalences of coronary heart disease and stroke were less than 1% among adults aged 18–44 years in the 2015 National Health Interview Survey (14).
The 2015 BRFSS questionnaire and all related support documents can be accessed from the BRFSS webpage (15). Crude (unadjusted) prevalence estimates for selected health indicators are presented on the BRFSS website (13).
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Results

In 2015, approximately 441,000 adults completed BRFSS interviews via landline or cellular telephone. A total of 254,645 respondents completed landline telephone interviews, and the numbers of participants ranged from 1,259 in Guam to 11,356 in Kansas (median: 4,048). For the cellular telephone survey, a total of 186,811 respondents completed interviews, and the numbers of participants ranged from 410 in Guam to 11,880 in Kansas (median: 2,924).
Response rates for BRFSS were calculated using standards set by the American Association of Public Opinion Research Response Rate Formula 4 (RR4), which is the number of respondents who completed the survey as a proportion of all eligible and likely-eligible persons (16). The RR4 response rate for the landline survey ranged from 31.6% in Alabama to 63.7% in Utah (median: 48.2%), whereas the RR4 response rate for the cellular telephone survey ranged from 33.6% in California to 69.6% in Alaska (median: 47.2%). The RR4 response rate for the combined sample, which was weighted by the respective size of the two samples, ranged from 33.9% in California to 61.1% in Utah (median: 47.2%). More detailed information on response rates, cooperation rates, interview completion rates, and eligibility factors is included in the 2015 BRFSS Summary Data Quality Report (17).

Health Status Indicators

General Health Status

In the 2015 BRFSS, adults aged ≥18 years were asked to rate their general health as poor, fair, good, very good, or excellent. Among 53 states and U.S. territories in 2015, the age-adjusted prevalence estimates of adults who reported good or better health ranged from 65.9% in Puerto Rico to 88.8% in New Hampshire (median: 84.6%) (Table 1). Among 130 metropolitan and micropolitan statistical areas (MMSAs) with ≥500 respondents in the 2015 BRFSS, the age-adjusted prevalence estimates of adults reporting good, very good, or excellent health ranged from 66.9% in San Juan-Carolina-Caguas, Puerto Rico, to 91.3% in Burlington-South Burlington, Vermont (median: 85.2%) (Table 2).

Poor Physical Health

Respondents aged ≥18 years were asked for how many of the past 30 days their physical health was not good. Poor physical health was defined as physical illness or injury. Among 53 states and U.S. territories in 2015, the age-adjusted prevalence estimates of adults reporting ≥14 days of poor physical health in the past 30 days ranged from 8.2% in North Dakota to 17.2% in West Virginia (median: 10.9%) (Table 3). Among selected MMSAs, the age-adjusted prevalence estimates ranged from 6.6% in San Jose-Sunnyvale-Santa Clara, California, to 19.1% in Kingsport-Bristol-Bristol, Tennessee-Virginia (median: 10.9%) (Table 4).

Poor Mental Health

Poor mental health was defined as stress, depression, or problems with emotions. Respondents were asked for how many of the past 30 days their mental health was not good. In 2015, the age-adjusted prevalence estimates of adults aged ≥18 years who reported ≥14 days of poor mental health during the past 30 days ranged from 7.3% in South Dakota to 15.8% in West Virginia (median: 11.3%) (Table 5). Among selected MMSAs, the age-adjusted prevalence estimates ranged from 5.6% in Rochester, Minnesota, to 20.5% in Hagerstown-Martinsburg, Maryland-West Virginia (median: 11.4%) (Table 6).

Health Care Coverage

Health care coverage was defined as having health insurance, prepaid plans (e.g., health maintenance organizations), or government plans (e.g., Medicare or Medicaid). In 2015, the age-adjusted prevalence estimates of adults aged 18–64 who had health care coverage ranged from 72.0% in Texas to 93.8% in Massachusetts (median: 86.8%) (Table 7). Among 130 selected MMSAs, the age-adjusted prevalence estimates of 18–64 year-olds with health care coverage ranged from 63.2% in El Paso, Texas, to 95.7% in Montgomery County-Bucks County-Chester County, Pennsylvania (median: 86.8%) (Table 8).

Preventive Practices

Recent Routine Physical Checkup

In 2015, the age-adjusted prevalence estimates of adults aged ≥18 years who visited a doctor for a routine checkup during the preceding 12 months ranged from 58.1% in Alaska to 79.8% in Rhode Island (median: 69.0%) (Table 9). Among selected MMSAs, the 2015 age-adjusted prevalence estimates ranged from 57.1% in Spartanburg, South Carolina, to 81.1% in Providence-Warwick, Rhode Island-Massachusetts (median: 69.4%) (Table 10).

Ever Had Blood Cholesterol Checked

In 2015, the age-adjusted prevalence estimates of adults aged ≥18 years who reported ever having their blood cholesterol checked ranged from 73.3% in New Mexico to 86.7% in the District of Columbia (median: 79.1%) (Table 11). Among 130 selected MMSAs, the age-adjusted prevalence estimates ranged from 65.1% in Logan, Utah-Idaho to 87.3% in Raleigh, North Carolina (median: 79.5%) (Table 12).

Health-Risk Behaviors

Current Cigarette Smoking

Adults were considered current smokers if they reported having smoked at least 100 cigarettes in their lifetime and currently smoked every day or on certain days. In 2015, the estimated age-adjusted prevalence of current smoking among adults aged ≥18 years ranged from 9.0% in Utah to 27.2% in West Virginia (median: 17.7%) (Table 13). Among 130 selected MMSAs, the age-adjusted prevalence estimates of current smokers ranged from 4.5% in Logan, Utah-Idaho, to 29.5% in Akron, Ohio (median: 17.3%) (Table 14).

Binge Drinking

Males were considered binge drinkers if they had five or more drinks on one or more occasions during the past 30 days. Females were considered binge drinkers if they had four or more drinks on one or more occasions during the past 30 days. The age-adjusted prevalence estimates of binge drinking among adults aged ≥18 years ranged from 11.2% in Tennessee to 26.0% in the District of Columbia in 2015 (median: 17.2%) (Table 15). Among selected MMSAs, the age-adjusted prevalence estimates of adults who reported binge drinking ranged from 5.5% in Provo-Orem, Utah, to 24.5% in Duluth, Minnesota-Wisconsin (median: 17.4%) (Table 16).

No Leisure-Time Physical Activity

Respondents were asked if, during the past month, they participated in any physical activities or exercises (e.g., running, calisthenics, golfing, gardening, or walking for exercise) outside of their regular job. In 2015, the age-adjusted prevalence estimates of adults aged ≥18 years who reported no leisure time physical activity during the preceding month ranged from 17.6% in Colorado to 47.1% in Puerto Rico (median: 25.5%) (Table 17). Among selected MMSAs, the age-adjusted prevalence estimates ranged from 16.1% in San Jose-Sunnyvale-Santa Clara, California, to 47.3% in San Juan-Carolina-Caguas, Puerto Rico (median: 24.5%) (Table 18).

Consuming Fruit Less than Once per Day

Adults aged ≥18 years were asked how frequently they consumed fruit (fresh, frozen, or canned) or 100% pure fruit juice during the preceding month. The age-adjusted prevalence estimates of adults who consumed fruit or fruit juice less than once per day during the preceding month ranged from 33.3% in New Hampshire to 55.5% in Puerto Rico (median: 40.5%) (Table 19). Among 130 selected MMSAs, the age-adjusted prevalence estimates ranged from 30.1% in Silver Spring-Frederick-Rockville, Maryland, to 57.3% in Tuscaloosa, Alabama (median: 40.3%) (Table 20).

Consuming Vegetables Less than Once per Day

Respondents aged ≥18 years were asked how frequently they consumed dark green vegetables, orange-colored vegetables, beans, or other vegetables during the preceding month. In 2015, the age-adjusted prevalence estimates of adults who reported consuming vegetables less than once per day during the preceding month ranged from 16.6% in Oregon to 31.3% in Mississippi (median: 22.4%) (Table 21). Among selected MMSAs, the age-adjusted prevalence estimates ranged from 13.6% in San Jose-Sunnyvale-Santa Clara, California to 32.0% in Jackson, Mississippi (median: 22.3%) (Table 22).

Chronic Conditions

Obesity

Obesity was defined as BMI ≥30.0 kg/m2 (BMI was calculated by dividing weight [in kilograms] by height [in meters] squared). Both height and weight were self-reported. In 2015, the age-adjusted prevalence estimates of obesity among adults aged ≥18 years ranged from 19.9% in Colorado to 36.0% in Louisiana (median: 29.5%) (Table 23). Among selected MMSAs, the age-adjusted prevalence estimates of obesity among adults aged ≥18 years ranged from 17.8% in Oakland-Hayward-Berkeley, California, to 41.6% in Corpus Christi, Texas (median: 28.5%) (Table 24).

Diabetes

Adults aged ≥45 years were considered to have diabetes if they had ever been told by a health professional that they had diabetes (excluding diabetes during pregnancy, prediabetes, or borderline diabetes in adults). Among adults aged ≥45 years, age-adjusted prevalence estimates of diabetes in 2015 ranged from 11.2% in Colorado to 26.8% in Puerto Rico (median: 15.9%) (Table 25). Among selected MMSAs, the age-adjusted prevalence estimates ranged from 10.5% in Rochester, Minnesota, to 27.6% in Corpus Christi, Texas (median: 15.7%) (Table 26).

Arthritis

Respondents were identified as having a form of arthritis if they had ever been told by a health professional that they had arthritis, rheumatoid arthritis, gout, lupus, or fibromyalgia. The 2015 age-adjusted prevalence estimates of arthritis among adults aged ≥18 years ranged from 17.2% in Hawaii to 33.6% in West Virginia (median: 22.7%) (Table 27). Among selected MMSAs in 2015, the age-adjusted prevalence estimates of arthritis among adults aged ≥18 years ranged from 12.3% in San Jose-Sunnyvale-Santa Clara, California, to 33.9% in Charleston, West Virginia (median: 23.2%) (Table 28).

Depressive Disorder

Adults aged ≥18 years of age were identified as having a depressive disorder if they were ever told by a health professional that they had a depressive disorder (including depression, major depression, dysthymia, or minor depression). In 2015, the age-adjusted prevalence estimates of depressive disorder ranged from 9.6% in Guam to 27.0% in Oregon (median: 19.0%) (Table 29). Among selected MMSAs, the age-adjusted prevalence estimates of depressive disorder ranged from 9.9% in Los Angeles-Long Beach-Anaheim, California, to 27.2% in Spartanburg, South Carolina (median: 19.2%) (Table 30).

High Blood Pressure

Respondents were considered to have high blood pressure if they had ever been told by a health professional that they had high blood pressure (excluding high blood pressure during pregnancy). The 2015 age-adjusted prevalence estimates of high blood pressure among adults aged ≥18 years ranged from 24.2% in Minnesota to 39.9% in Mississippi (median: 29.1%) (Table 31). Among selected MMSAs, the age-adjusted prevalence estimates ranged from 19.7% in Rochester, Minnesota, to 41.0% in Gulfport-Biloxi-Pascagoula, Mississippi (median: 29.0%) (Table 32).

High Blood Cholesterol

Adults were classified as having high cholesterol if, after having their blood cholesterol checked, they had ever been told by a health professional that their cholesterol was high. (Adults who had never had their blood cholesterol checked were excluded from analysis.) In 2015, the age-adjusted prevalence estimates of high blood cholesterol among adults aged ≥18 years ranged from 27.1% in Montana to 37.3% in Puerto Rico (median: 31.8%) (Table 33). Among selected MMSAs, the age-adjusted prevalence estimates of high blood cholesterol ranged from 23.2% in Aberdeen, South Dakota, to 42.0% in Kahului-Wailuku-Lahaina, Hawaii (median: 31.4%) (Table 34).

Coronary Heart Disease

Respondents were classified as having coronary heart disease if they had ever been told by a health professional that they had a heart attack (i.e., myocardial infarction) or angina. In 2015, the age-adjusted prevalence estimates of coronary heart disease among adults aged ≥45 years ranged from 7.2% in Hawaii to 16.8% in West Virginia (median: 10.3%) (Table 35). Among selected MMSAs, the age-adjusted prevalence estimates of adults aged ≥45 years who reported coronary heart disease ranged from 4.7% in San Jose-Sunnyvale-Santa Clara, California, to 17.8% in Wichita Falls, Texas (median: 10.1%) (Table 36).

Stroke

Adults were classified as having had a stroke if they had ever been told by a health professional that they had a stroke. In 2015, the age-adjusted prevalence estimates of stroke among adults aged ≥45 years ranged from 2.5% in Puerto Rico to 7.5% in Mississippi (median: 4.9%) (Table 37). Among selected MMSAs, the age-adjusted prevalence estimates of stroke among adults aged ≥45 years ranged from 2.1% in College Station-Bryan, Texas, to 8.4% in Huntington-Ashland, West Virginia-Kentucky-Ohio (median: 4.7%) (Table 38).
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Discussion

The findings in this report reveal considerable geographic variation in the age-adjusted estimated prevalence of health care access and use, health-risk behaviors, and chronic health conditions among U.S. adults at the state, territory, and MMSA level. Variations in age-adjusted prevalence estimates might be because of differences in sociodemographic characteristics, cultural contexts, behavioral risk factors for health conditions, health care access and affordability, state and municipal laws, or combinations of these factors. BRFSS is one of the main sources of health information at the state and local level. Prevalence estimates from BRFSS are used at the state and local level to monitor changes in population health status over time, to determine the needs of public health programming, and to evaluate the effectiveness of public health initiatives.

Health Status Indicators

Self-reported general health status is a strong risk factor for mortality independent of other medical and sociodemographic characteristics (18). Likewise, physical and mental healthy days measures are independent predictors of physician visits, hospitalization, and mortality (19). These self-reported health measures have been found to be reliable and valid (20,21). In the 2015 BRFSS, the estimated prevalence of self-reported fair or poor general health status ranged from 11.2% to 34.1% among states and territories and from 8.7% to 33.1% in selected MMSAs (i.e., this prevalence represents adults who did not report good or better general health status in Tables 1–2). The estimated prevalence of adults reporting poor physical or mental health for ≥14 of the last 30 days also varied by geographic region. To reduce the prevalence of poor physical and mental health status, it is essential to investigate and address the underlying causes of these conditions.

Health Care Coverage

According to BRFSS data, the median age-adjusted prevalence of health care coverage among adults aged 18–64 years increased from 78.4% in 2011 to 86.8% in 2015. The Affordable Care Act (ACA), passed in 2010, includes multiple provisions to increase access to health care coverage for the U.S. population. As of July 1, 2016, a total of 32 states had elected to expand Medicaid eligibility under ACA, extending eligibility to a new group of adults aged <65 years with incomes up to 138% of the federal poverty level (22). In addition, ACA offers tax credits to numerous families who purchase insurance coverage through the Health Insurance Marketplace to subsidize the cost of premiums (23). Furthermore, ACA prevents health insurers from denying coverage or charging more because of a pre-existing condition (24). ACA also requires insurers to allow children to remain on their parents’ health insurance plans until age 26 years (25). As of March 2016, the number of uninsured persons in the United States (of all ages) had decreased by 21.3 million since the enactment of ACA (26). Despite this progress, approximately 13.2% (age-adjusted prevalence estimate) of adults aged 18–64 lacked health insurance coverage in 2015. Not having health insurance is associated with higher morbidity and mortality and poorer quality of life (27). Additional work to enhance insurance affordability and coverage might generate essential gains in health and other outcomes.

Recent Routine Physical Checkup

Although routine checkups are no longer generally recommended, they might provide opportunities to deliver certain types of high-value care (e.g., early detection of high blood pressure, high cholesterol, cancer, and other adverse health conditions), establish relationships between patients and providers, and gauge access to or use of health care (28,29). In 2015, a large proportion of U.S. adults (up to 41.9% in Alaska) had not visited a doctor for a routine checkup during the preceding year. Lack of health insurance and transportation are barriers to health care for many adults (27,30). In addition, adults might not be aware of free or low-cost health care options in their community. The Health Resources and Services Administration provides a list of these health care centers by geographic region (31).

Current Cigarette Smoking

Tobacco use is the leading preventable cause of death in the United States (32). Annually, approximately 480,000 U.S. deaths are attributable to smoking or exposure to tobacco smoke; approximately 9% of these are caused by second-hand smoke (33). Smoking causes coronary heart disease, stroke, diabetes, chronic obstructive pulmonary disease, and cancers of the lung, colon, stomach, and other areas of the body (34). Approximately 80% of lung cancer deaths among U.S. adults aged ≥35 years are attributable to smoking (33). During 2013–2015, the median age-adjusted prevalence estimate of current smoking decreased from 19.3% to 17.7%. However, the 2015 prevalence is still substantially higher than the Healthy People 2020 goal of ≤12.0% (35). Culturally appropriate tobacco prevention and control programs are needed, particularly among subgroups at high risk for tobacco use (e.g., adults of low socioeconomic status) (36). Tobacco prevention campaigns that discourage smoking initiation among adolescents might reduce the number of future adult smokers (37). More data are needed on the prevalence of electronic cigarette and marijuana use, which appear to be increasing (38,39). In 2016, BRFSS added questions about electronic cigarettes to the core questionnaire. Marijuana use was added as an optional BRFSS module in 2016.

Binge Drinking

Binge drinking costs the U.S. economy approximately $191 billion annually (40). Binge drinking increases the risk for alcohol poisoning, injury (e.g., falls and motor vehicle accidents), high blood pressure, and liver damage (41). Binge drinking also can contribute to sexually transmitted diseases and unplanned pregnancy (41). Younger adults are more likely to binge drink than older adults, but binge drinking remains a problem throughout life (42). Among states and territories in the 2015 BRFSS, 11.2%–26.0% of adults engaged in binge drinking within 30 days of participating in the survey. Health care systems could screen for and counsel about risky or hazardous drinking (43). At the population level, certain policies have been proven effective against binge drinking. For instance, increasing alcohol taxes and limiting days or hours of sale are effective interventions against alcohol misuse (44).

No Leisure-Time Physical Activity

Physical activity might help reduce the risk for weight gain, high blood pressure, coronary heart disease, stroke, type 2 diabetes, depression, and various types of cancers (45). Federal guidelines recommend that adults engage in ≥2.5 hours of moderate intensity aerobic activity (e.g., brisk walking) or ≥1.25 hours of vigorous intensity aerobic activity (e.g., running) or an equivalent combination of moderate and vigorous intensity physical activity per week. In addition, adults should perform muscle-strengthening activities at least 2 days each week (46). In 2015, the proportion of adults who reported no leisure-time physical activity during the preceding month was substantial (median: 25.5%). Community-level policies that increase access to sidewalks, bicycle lanes, outdoor recreation spaces, and safe neighborhoods might help facilitate physical activity among U.S. adults (47).

No Daily Fruit or Vegetable Consumption

Regular fruit and vegetable consumption is associated with reduced risk for obesity, high blood pressure, high cholesterol, cardiovascular diseases, type 2 diabetes, and various cancers (48,49). Fruits and vegetables are rich in fiber, vitamins, and minerals, which have myriad health benefits (48,49). For example, certain fruits and vegetables are major sources of vitamin C, which plays an important role in tissue repair (48,49). Federal guidelines for fruit and vegetable consumption vary by age, sex, and level of physical activity (50). Sedentary adults (i.e., those who obtain less than 30 minutes of moderate exercise daily, not counting regular daily activities) should consume between 1.5 and 2 cup-equivalents of fruit per day and between 2 and 3 cup-equivalents of vegetables per day (50). Moderately active and active adults, who have increased caloric needs, should consume larger quantities (50). Results from the 2015 BRFSS indicate that a large proportion of U.S. adults fail to meet these guidelines. Among states and territories, 33.3%–55.5% of adults reported consuming fruit less than once daily during the preceding month, and 16.6%–31.3% of adults reported consuming vegetables less than once daily during the preceding month. The United States Department of Agriculture’s MyPlate campaign encourages adults to fill half of their plates with fruits and vegetables and provides consumers with tips on how to increase consumption of healthy foods (51). In addition, the 2015–2020 Federal Dietary Guidelines describe strategies that persons, schools, workplaces, food retailers, and communities can implement to increase healthy eating (50).

Obesity

Obesity increases the risks for coronary heart disease, stroke, cancer, and type 2 diabetes, all of which are leading causes of death (1). Obesity is also associated with increased risk for metabolic syndrome, high blood pressure, and osteoarthritis (52). Annual obesity-related health care costs in the United States were estimated at $147 billion as of 2008 (53). Modifiable risk factors for obesity include sedentary lifestyle, excess caloric intake, and lack of sleep (54). Community-level interventions for obesity prevention include improving access to healthy foods and beverages and enhancing community infrastructure to support walking or bicycling (55,56).

Diabetes

Diabetes is the seventh leading cause of death in the United States (1). Approximately 29 million persons in the United States have diabetes (57). An additional 86 million have prediabetes; of these, 90% are unaware that they have this condition (58). Complications of diabetes include cardiovascular diseases, blindness, kidney failure, and amputation of extremities (58). Diagnosed diabetes costs the United States approximately $245 billion in medical costs and lost productivity per year (59). The age-adjusted prevalence estimate of diabetes among adults aged ≥45 years ranged from 11.2% to 26.8% among states and territories in the 2015 BFRSS. Interventions that promote physical activity and reduce obesity might be helpful in preventing diabetes. For persons at high risk, lifestyle interventions (e.g., the CDC’s National Diabetes Prevention Program) are proven to help adults with prediabetes prevent type 2 diabetes by exercising, eating healthier, and losing weight (60).

Arthritis

There are approximately 100 types of arthritis, which is characterized by inflammation of the joints or connective tissue (e.g., cartilage) surrounding joints (61). Osteoarthritis is the most common type of arthritis (61). Common arthritis symptoms include joint pain, stiffness, and swelling (61). Approximately 40% of adults with arthritis encounter activity limitations (62), and approximately 30% experience work limitations (63). The age-adjusted prevalence estimates of arthritis among adults aged ≥18 years in 2015 ranged from 17.2% to 33.6% among states and territories. Women, older adults, and persons who have obesity are at increased risk for receiving an arthritis diagnosis (62,64). Physical activity and maintaining a healthy body weight might help arthritis patients successfully manage their condition (64,65).

Depressive Disorder

An estimated 16.1 million U.S. adults (6.7%) had one or more major depressive episodes in 2015 (66). Women and younger adults were at increased risk (66). The estimated lifetime prevalence of depressive disorder ranged from 9.6% to 27.0% among states and territories in the 2015 BRFSS. Depression is associated with increased risk for anxiety disorders, sleep disturbance, substance abuse, smoking, and obesity (67). In the workplace, depression is associated with unemployment and lost productivity (67). Depression is also a risk factor for cardiovascular disease-related mortality and suicide (67). Approximately half of adults with depression do not seek treatment (68). Reducing stigma related to mental illness and increasing access to mental health care could help adults with depression better manage this condition.

High Blood Pressure

High blood pressure increases the risk for coronary heart disease, chronic kidney disease, and stroke (69). The estimated prevalence of high blood pressure was high in the 2015 BRFSS (median: 29.1% among states and territories). Approximately 17% of adults with high blood pressure remain undiagnosed (70). Enhancing systematic approaches to screening and treatment of hypertension in health care and community settings could improve high blood pressure detection and control (71–73). Public health initiatives should emphasize the importance of modifiable risk factors for high blood pressure, which include obesity, physical inactivity, diabetes, excess sodium consumption, excess alcohol use, and smoking (74). Addressing these lifestyle factors might also help adults with high blood pressure control their blood pressure (75).

Cholesterol Screening and High Blood Cholesterol

High blood cholesterol is associated with increased risk for coronary heart disease and stroke (76). Risk factors for high blood cholesterol include obesity, diabetes, lack of exercise, smoking, and a diet high in trans fat and saturated fat (77). The age-adjusted prevalence estimates of high cholesterol in adults aged ≥18 years ranged from 27.1% to 37.3% among states and territories in the 2015 BRFSS. Because high blood cholesterol is an asymptomatic condition, regular risk assessment, testing, and appropriate treatment is essential (78). Low-risk adults aged 40–75 years (i.e., adults who do not have heart disease, do not have diabetes, do not consume cholesterol medication, and who have low-density lipoprotein (LDL) cholesterol between 70–189 mg/dL) should have their blood cholesterol checked every 4 to 6 years (79). The potential benefits of cholesterol screening among low-risk younger adults is unknown (80). In 2015, 20.9% of the U.S. adult population reported that they had never had their blood cholesterol checked, and approximately 24% reported not having their blood cholesterol checked during the preceding 5 years (median age-adjusted prevalence at the state level). Furthermore, approximately half of U.S. adults with high cholesterol are not treated for this disorder (81). Health care system approaches to enhancing risk assessment, screening, and treatment for high cholesterol could help.

Coronary Heart Disease

Heart disease is the leading cause of death in the United States. In 2015, approximately 633,000 persons died because of heart disease in the United States; approximately 365,000 of these deaths were attributed to coronary heart disease (1). The median age-adjusted prevalence of coronary heart disease among adults aged ≥45 years was approximately 10% among states and territories in the 2015 BRFSS, and there were notable disparities by geographic region. Adults in the southern United States had a higher prevalence of coronary heart disease compared with adults in other parts of the United States. The highest prevalence was in West Virginia, where 16.8% of adults aged ≥45 years reported a history of coronary heart disease. Adults residing in the southern United States are also more likely to die from heart disease, compared with adults in other parts of the country (82). During 2012–2013, heart disease cost the U.S. economy approximately $199.6 billion annually. In addition, health costs of coronary heart disease specifically are predicted to double during 2013–2030 (83). Medical risk factors for coronary heart disease include diabetes, high blood pressure, high cholesterol, and obesity (84,85). Engaging in regular physical activity, eating a healthy diet (e.g., high in fruits and vegetables, low in red meats, and low in added sugars) and avoiding smoking and excessive alcohol consumption might reduce the risk for coronary heart disease (85,86).

Stroke

Stroke was the fourth most frequent cause of death among adults aged ≥45 years in 2015 (1). Stroke is also a leading cause of disability (87). The direct and indirect costs of stroke cost the U.S. economy approximately $34 billion each year (83). In 2015, approximately 5% of U.S. adults aged ≥45 years reported having a history of stroke (median age-adjusted prevalence at the state level). Stroke prevalence is higher among older adults, blacks, American Indians/Alaska natives, and those without a high school diploma (87). Risk factors for stroke include high blood pressure, high cholesterol, heart disease, diabetes, obesity, smoking, and physical inactivity (88,89).
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Limitations

The findings in this report are subject to at least five limitations. First, these findings might not be generalizable to the U.S. population because the BRFSS survey design excludes persons who reside in military installations, correctional institutions, long-term care facilities, and nursing homes. Adults without telephone access are also excluded from the BRFSS. An estimated 2.4% of the U.S. population and approximately 5.7% of Puerto Rico’s population did not have telephone access in 2015 (6).
Second, prevalence estimates from the BRFSS are based on self-reporting, which is likely to be less accurate than physical measurements (4). For example, survey respondents underreport their weight (90), recent alcohol intake (91), and tobacco use (92), and they overreport physical activity (93). These tendencies might be related to concerns about social desirability (4,94). Alternatively, respondents might have trouble recalling their past health behaviors or receipt of health care services, or they might not be aware of their underlying health conditions (e.g., high blood pressure) (4).
Third, the prevalence of chronic diseases might be underestimated in the BRFSS. BRFSS prevalence estimates are estimates of diagnosed disease. Multiple chronic diseases remain undiagnosed for long periods of time, so the actual prevalence of these conditions might be higher than what is captured in BRFSS.
Fourth, although BRFSS surveys are conducted in several languages other than English (i.e., Spanish, Mandarin, and Portuguese), the survey does not apply to persons who exclusively speak languages not represented in the BRFSS. Finally, because of small sample sizes or unstable estimates, the prevalence of certain conditions (e.g., stroke) could not be estimated for particular MMSAs.
The BRFSS data set has various strengths. BRFSS data have been shown to be valid and reliable for certain indicators (4). With certain exceptions, many prevalence estimates from the BRFSS are comparable to those of the National Health and Nutrition Examination Survey (NHANES) and National Health Interview Survey, which are conducted using face-to-face interviews (95–97). However, obesity prevalence estimates in BRFSS (which are based on self-reported height and weight) are approximately 4%–10% lower than obesity prevalence estimates in NHANES (which are based on measured height and weight) (95,98). In addition, the estimated prevalence of U.S. adults who are physically active was 15%–18% higher in the 2005 BRFSS than in the 2005 National Health Interview Survey or 2005–2006 NHANES (99).
Questions on the BRFSS are cognitively tested to optimize the validity of survey response (6). Likewise, BRFSS interviewers are thoroughly trained, and their performance regularly evaluated, to ensure interview quality (6).
BRFSS is the largest continuously conducted, health-based telephone survey in the world (3) with approximately 440,000 interviews conducted in 2015. All 50 states and the District of Columbia, Puerto Rico, and Guam collected data via both landline and cell phones in 2015 (6). The telephone-based approach of BRFSS is cost-effective (3). BRFSS data are used in numerous capacities at the state and local level, including surveillance, needs assessments, and program evaluations (3).
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Conclusion

This report highlights the estimated prevalence of selected chronic diseases, health-risk behaviors, and health care access and use among adults residing in the United States in 2015. The chronic conditions in this report are leading causes of U.S. morbidity and mortality. However, many of these conditions can be effectively managed or prevented through lifestyle modifications (e.g., avoiding tobacco use) and use of preventive health care services (e.g., blood pressure screening). Since 1984, BRFSS has been a unique source of data on chronic diseases and their risk factors. States and municipalities use BRFSS data to monitor health conditions and behaviors over time, design public health initiatives, conduct public health needs assessments, and evaluate the impact of public health programs and policies.
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Acknowledgments

States and territories BRFSS coordinators; Population Health Surveillance Branch, Division of Population Health, National Center for Chronic Disease Prevention and Health Promotion, CDC; A contributor to previous versions of the report is David Flegel, Population Health Surveillance Branch, Division of Population Health, National Center for Chronic Disease Prevention and Health Promotion, CDC and Northrop Grumman Corporation, Atlanta, Georgia.
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Conflict of Interest

No conflicts of interest were reported.
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Corresponding author: Cassandra M. Pickens, National Center for Chronic Disease Prevention and Health Promotion, CDC. Telephone: 404-498-1702; E-mail: kdv2@cdc.gov.
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1Division of Population Health, National Center for Chronic Disease Prevention and Health Promotion, CDC; 2TEKsystems and Northrop Grumman Corporation, Atlanta, Georgia
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References

  1. CDC Wonder [Internet]. Atlanta, GA: CDC, National Center for Health Statistics; 2016. https://wonder.cdc.gov/ucd-icd10.html
  2. CDC. Chronic disease overview. Atlanta, GA: US Department of Health and Human Services, CDC; 2016. https://www.cdc.gov/chronicdisease/overview/index.htm
  3. CDC. About the Behavioral Risk Factor Surveillance System (BRFSS). Atlanta, GA: US Department of Health and Human Services, CDC; 2014. https://www.cdc.gov/brfss/about/about_brfss.htm
  4. Pierannunzi C, Hu SS, Balluz L. A systematic review of publications assessing reliability and validity of the Behavioral Risk Factor Surveillance System (BRFSS), 2004–2011. BMC Med Res Methodol 2013;13:49. CrossRef PubMed
  5. CDC. Comparability of data BRFSS 2015. Atlanta, GA: US Department of Health and Human Services, CDC; 2016. https://www.cdc.gov/brfss/annual_data/2015/pdf/compare_2015.pdf
  6. CDC. Overview: BRFSS 2015. Atlanta, GA: US Department of Health and Human Services, CDC; 2016. https://www.cdc.gov/brfss/annual_data/2015/pdf/overview_2015.pdf
  7. CDC. 2015 SMART BRFSS MMSA methodology. Atlanta, GA: US Department of Health and Human Services, CDC; 2017. https://www.cdc.gov/brfss/annual_data/2015/pdf/2015_smart_brfss_mmsa_methodology.pdf
  8. CDC. 2015 BRFSS modules used by category. Atlanta, GA: US Department of Health and Human Services, CDC; 2016. https://www.cdc.gov/brfss/questionnaires/modules/category2015.htm
  9. CDC. The BRFSS data user guide, August 15, 2013. Atlanta, GA: US Department of Health and Human Services, CDC; 2013. https://www.cdc.gov/brfss/data_documentation/PDF/UserguideJune2013.pdf
  10. Pierannunzi C, Town M, Garvin W, Shaw FE, Balluz L. Methodologic changes in the Behavioral Risk Factor Surveillance System in 2011 and potential effects on prevalence estimates. MMWR Morb Mortal Wkly Rep 2012;61:410–3. PubMed
  11. CDC. 2015 SMART: BRFSS city and county data and documentation. Atlanta, GA: US Department of Health and Human Services, CDC; 2017. https://www.cdc.gov/brfss/smart/smart_2015.html
  12. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy people 2010 statistical notes, no 20. Hyattsville, MD: US Department of Health and Human Services, CDC, National Center for Health Statistics; 2001.
  13. CDC. BRFSS prevalence & trends data. Atlanta, GA: US Department of Health and Human Services, CDC; 2017. https://www.cdc.gov/brfss/brfssprevalence
  14. CDC. Summary health statistics: National Health Interview Survey, 2015. Atlanta, GA: US Department of Health and Human Services, CDC; 2015. https://ftp.cdc.gov/pub/Health_Statistics/NCHS/NHIS/SHS/2015_SHS_Table_A-1.pdf
  15. CDC. 2015 BRFSS survey data and documentation. Atlanta, GA: US Department of Health and Human Services, CDC; 2016. https://www.cdc.gov/brfss/annual_data/annual_2015.html
  16. American Association for Public Opinion Research. Standard definitions: final dispositions of case codes and outcome rates for surveys. 9th ed. Oakbrook Terrace, IL: American Association for Public Opinion Research; 2016. http://www.aapor.org/AAPOR_Main/media/publications/Standard-Definitions20169theditionfinal.pdf
  17. CDC. BRFSS 2015 summary data quality report. Atlanta, GA: US Department of Health and Human Services, CDC; 2016. https://www.cdc.gov/brfss/annual_data/2015/pdf/2015-sdqr.pdf
  18. Idler EL, Benyamini Y. Self-rated health and mortality: a review of twenty-seven community studies. J Health Soc Behav 1997;38:21–37. CrossRef PubMed
  19. Dominick KL, Ahern FM, Gold CH, Heller DA. Relationship of health-related quality of life to health care utilization and mortality among older adults. Aging Clin Exp Res 2002;14:499–508.CrossRef PubMed
  20. Moriarty DG, Zack MM, Kobau R. The Centers for Disease Control and Prevention’s healthy days measures—population tracking of perceived physical and mental health over time. Health Qual Life Outcomes 2003;1:37. CrossRef PubMed
  21. CDC. Measuring healthy days: population assessment of health-related quality of life. Atlanta, GA: US Department of Health and Human Services, CDC; 2000.
  22. Kaiser Family Foundation. Current status of state Medicaid expansion decisions. Washington, DC: Kaiser Family Foundation; 2018. http://kff.org/health-reform/slide/current-status-of-the-medicaid-expansion-decision
  23. The Patient Protection and Affordable Care Act, Pub L No. 111–148, 124 Stat. 213, Sect. 1401 (March 2010).
  24. The Patient Protection and Affordable Care Act, Pub L No. 111–148, 124 Stat. 154, Sect. 1201 (March 2010).
  25. The Patient Protection and Affordable Care Act, Pub L No. 111–148, 124 Stat. 132, Sect. 1001 (March 2010).
  26. Cohen RA, Martinez ME, Zammitti EP. Health insurance coverage: early release of estimates from the National Health Interview Survey, January–March 2016. Atlanta, GA: CDC, National Center for Health Statistics; 2016. https://www.cdc.gov/nchs/data/nhis/earlyrelease/insur201609.pdf
  27. Institute of Medicine (US) Committee on Health Insurance Status and Its Consequences. America’s uninsured crisis: consequences for health and health care. Washington, DC: National Academies Press, US; 2009.
  28. Society of General Internal Medicine. Five things physicians and patients should question. Philadelphia, PA: The ABIM Foundation; 2017. http://www.choosingwisely.org/wp-content/uploads/2015/02/SGIM-Choosing-Wisely-List.pdf
  29. Mehrotra A, Prochazka A. Improving value in health care—against the annual physical. N Engl J Med 2015;373:1485–7. CrossRef PubMed
  30. Syed ST, Gerber BS, Sharp LK. Traveling towards disease: transportation barriers to health care access. J Community Health 2013;38:976–93. CrossRef PubMed
  31. Health Resources and Services Administration. Find a health center. Rockville, MD: US Department of Health and Human Services, Health Resources and Services Administration; 2016. https://findahealthcenter.hrsa.gov/index.html
  32. CDC. Fast facts. Atlanta, GA: US Department of Health and Human Services, CDC; 2018. https://www.cdc.gov/tobacco/data_statistics/fact_sheets/fast_facts
  33. CDC. Reports of the Surgeon General. The health consequences of smoking—50 years of progress: a report of the Surgeon General. Atlanta, GA: CDC; 2014.
  34. CDC. Health effects of cigarette smoking. Atlanta, GA: US Department of Health and Human Services, CDC; 2017. https://www.cdc.gov/tobacco/data_statistics/fact_sheets/health_effects/effects_cig_smoking
  35. US Department of Health and Human Services. 2020 topics and objectives—objectives A–Z. Washington, DC: US Department of Health and Human Services; 2018. https://www.healthypeople.gov/2020/topics-objectives
  36. Jamal A, King BA, Neff LJ, Whitmill J, Babb SD, Graffunder CM. Current cigarette smoking among adults—United States, 2005–2015. MMWR Morb Mortal Wkly Rep 2016;65:1205–11.CrossRef PubMed
  37. CDC. Youth and tobacco use. Atlanta, GA: US Department of Health and Human Services, CDC; 2016. https://www.cdc.gov/tobacco/data_statistics/fact_sheets/youth_data/tobacco_use
  38. King BA, Patel R, Nguyen KH, Dube SR. Trends in awareness and use of electronic cigarettes among U.S. adults, 2010–2013. Nicotine Tob Res 2015;17:219–27. CrossRef PubMed
  39. Azofeifa A, Mattson ME, Schauer G, McAfee T, Grant A, Lyerla R. National estimates of marijuana use and related indicators—National Survey on Drug Use and Health, United States, 2002–2014. MMWR Surveill Summ 2016;65:1–28. CrossRef PubMed
  40. Sacks JJ, Gonzales KR, Bouchery EE, Tomedi LE, Brewer RD. 2010 National and state costs of excessive alcohol consumption. Am J Prev Med 2015;49:e73–9. CrossRef PubMed
  41. CDC. Fact sheets—binge drinking. Atlanta, GA: US Department of Health and Human Services, CDC; 2015. https://www.cdc.gov/alcohol/fact-sheets/binge-drinking.htm
  42. CDC. Vital signs: binge drinking prevalence, frequency, and intensity among adults—United States, 2010. MMWR Morb Mortal Wkly Rep 2012;61:14–9. PubMed
  43. US Preventive Services Task Force. Final recommendation statement: alcohol misuse: screening and behavioral counseling interventions in primary care. Rockville, MD: US Preventive Services Task Force; 2013. https://www.uspreventiveservicestaskforce.org/Page/Document/RecommendationStatementFinal/alcohol-misuse-screening-and-behavioral-counseling-interventions-in-primary-care
  44. Community Preventive Services Task Force. CPSTF findings for excessive alcohol consumption. Atlanta, GA: Community Preventive Services Task Force; 2017. https://www.thecommunityguide.org/content/task-force-findings-excessive-alcohol-consumption
  45. Physical Activity Guidelines Advisory Committee. Physical Activity Guidelines Advisory Committee report, 2008. Washington, DC: US Department of Health and Human Services; 2008. https://health.gov/paguidelines/report/pdf/CommitteeReport.pdf
  46. CDC. How much physical activity do adults need? Atlanta, GA: US Department of Health and Human Services, CDC; 2015. https://www.cdc.gov/physicalactivity/basics/adults/index.htm
  47. US Department of Health and Human Services, Office of the Surgeon General. Step it up! The Surgeon General’s call to action to promote walking and walkable communities. Washington, DC: US Department of Health and Human Services, Office of the Surgeon General; 2015.
  48. US Department of Agriculture. Why is it important to eat fruit? Washington, DC: US Department of Agriculture; 2015. https://www.choosemyplate.gov/fruits-nutrients-health
  49. US Department of Agriculture. Why is it important to eat vegetables? Washington, DC: US Department of Agriculture; 2015. https://www.choosemyplate.gov/vegetables-nutrients-health
  50. US Department of Health and Human Services and US Department of Agriculture. Dietary guidelines for Americans 2015–2020. 8th ed. Washington, DC: US Government Printing Office; 2015. https://health.gov/dietaryguidelines/2015/guidelines
  51. US Department of Agriculture. MyPlate. Washington, DC: US Department of Agriculture; 2017. https://www.choosemyplate.gov/MyPlate
  52. National Heart, Lung, and Blood Institute. Overweight and obesity (signs, symptoms, and complications). Bethesda, MD: US Department of Health and Human Services, National Institutes of Health; 2012. https://www.nhlbi.nih.gov/health/health-topics/topics/obe/signs
  53. Finkelstein EA, Trogdon JG, Cohen JW, Dietz W. Annual medical spending attributable to obesity: payer-and service-specific estimates. Health Aff (Millwood) 2009;28:w822–31. CrossRefPubMed
  54. National Heart, Lung, and Blood Institute. Overweight and obesity (risk factors). Bethesda, MD: US Department of Health and Human Services National Institutes of Health; 2012. https://www.nhlbi.nih.gov/health/health-topics/topics/obe/risks
  55. Khan LK, Sobush K, Keener D, et al. Recommended community strategies and measurements to prevent obesity in the United States. MMWR Recomm Rep 2009;58(No. RR-7). PubMed
  56. Committee on Accelerating Progress in Obesity Prevention; Food and Nutrition Board; Institute of Medicine. Glickman D, Parker L, Sim LJ, Del Valle Cook H, Miller EA, eds. Accelerating progress in obesity prevention: solving the weight of the nation. Washington, DC: National Academies Press; 2012.
  57. CDC. About diabetes. Atlanta, GA: US Department of Health and Human Services, CDC; 2017. https://www.cdc.gov/diabetes/basics/diabetes.html
  58. CDC. Diabetes. Atlanta, GA: US Department of Health and Human Services, CDC; 2016. https://www.cdc.gov/chronicdisease/resources/publications/aag/diabetes.htm
  59. American Diabetes Association. Economic costs of diabetes in the U.S. in 2012. Diabetes Care 2013;36:1033–46. CrossRef PubMed
  60. CDC. Prediabetes. Atlanta, GA: US Department of Health and Human Services, CDC; 2016. https://www.cdc.gov/diabetes/basics/prevention.html
  61. CDC. Arthritis types. Atlanta, GA: US Department of Health and Human Services, CDC; 2017. https://www.cdc.gov/arthritis/basics/types.html
  62. CDC. Prevalence of doctor-diagnosed arthritis and arthritis-attributable activity limitation—United States, 2010–2012. MMWR Morb Mortal Wkly Rep 2013;62:869–73. PubMed
  63. CDC. State-specific 2015 BRFSS prevalence estimates. Atlanta, GA: US Department of Health and Human Services, CDC; 2017. https://www.cdc.gov/arthritis/data_statistics/state-data-current.htm
  64. CDC. Risk factors. Atlanta, GA: US Department of Health and Human Services, CDC; 2017. https://www.cdc.gov/arthritis/basics/risk-factors.htm
  65. CDC. Improving the quality of life for people with arthritis at a glance 2016. Atlanta, GA: US Department of Health and Human Services, CDC; 2016. https://www.cdc.gov/chronicdisease/resources/publications/aag/arthritis.htm
  66. National Institute of Mental Health. Major depression. Bethesda, MD: US Department of Health and Human Services, National Institutes of Health; 2017. https://www.nimh.nih.gov/health/statistics/major-depression.shtml
  67. CDC. Depression. Atlanta, GA: US Department of Health and Human Services, CDC; 2016. https://www.cdc.gov/workplacehealthpromotion/health-strategies/depression/index.html
  68. González HM, Vega WA, Williams DR, Tarraf W, West BT, Neighbors HW. Depression care in the United States: too little for too few. Arch Gen Psychiatry 2010;67:37–46. CrossRefPubMed
  69. CDC. Effects of high blood pressure. Atlanta, GA: US Department of Health and Human Services, CDC; 2014. https://www.cdc.gov/bloodpressure/effects.htm
  70. Go AS, Mozaffarian D, Roger VL, et al. ; American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics—2013 update: a report from the American Heart Association. Circulation 2013;127:e6–245. CrossRef PubMed
  71. Melnyk BM, Orsolini L, Gawlik K, et al. The Million Hearts initiative: guidelines and best practices. Nurse Pract 2016;41:46–53. CrossRef PubMed
  72. Frieden TR, King SM, Wright JS. Protocol-based treatment of hypertension: a critical step on the pathway to progress. JAMA 2014;311:21–2. CrossRef PubMed
  73. Wall HK, Hannan JA, Wright JS. Patients with undiagnosed hypertension: hiding in plain sight. JAMA 2014;312:1973–4. CrossRef PubMed
  74. CDC. High blood pressure risk factors. Atlanta, GA: US Department of Health and Human Services, CDC; 2014. https://www.cdc.gov/bloodpressure/risk_factors.htm
  75. CDC. Controlling blood pressure. Atlanta, GA: US Department of Health and Human Services, CDC; 2014. https://www.cdc.gov/bloodpressure/control.htm
  76. CDC. Cholesterol fact sheet. Atlanta, GA: US Department of Health and Human Services, CDC; 2015. https://www.cdc.gov/dhdsp/data_statistics/fact_sheets/fs_cholesterol.htm
  77. CDC. Cholesterol. Atlanta, GA: US Department of Health and Human Services, CDC; 2015. https://www.cdc.gov/cholesterol/index.htm
  78. Bibbins-Domingo K, Grossman DC, Curry SJ, et al. ; US Preventive Services Task Force. Statin use for the primary prevention of cardiovascular disease in adults: US Preventive Services Task Force recommendation statement. JAMA 2016;316:1997–2007. CrossRef PubMed
  79. Stone NJ, Robinson JG, Lichtenstein AH, et al. ; American College of Cardiology/American Heart Association Task Force on Practice Guidelines. 2013 ACC/AHA guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation 2014;129(Suppl 2):S1–45. CrossRef PubMed
  80. US Preventive Services Task Force. Lipid disorders in adults (cholesterol, dyslipidemia): screening. Atlanta, GA: US Preventive Services Task Force; 2014. https://www.uspreventiveservicestaskforce.org/Page/Document/RecommendationStatementFinal/lipid-disorders-in-adults-cholesterol-dyslipidemia-screening#
  81. CDC. Vital signs: prevalence, treatment, and control of high levels of low-density lipoprotein cholesterol—United States, 1999–2002 and 2005–2008. MMWR Morb Mortal Wkly Rep 2011;60:109–14. PubMed
  82. Casper M, Kramer MR, Quick H, Schieb LJ, Vaughan AS, Greer S. Changes in the geographic patterns of heart disease mortality in the United States: 1973 to 2010. Circulation 2016;133:1171–80. CrossRef PubMed
  83. Benjamin EJ, Blaha MJ, Chiuve SE, et al. ; American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics—2017 update: a report from the American Heart Association. Circulation 2017;135:e146–603. CrossRef PubMed
  84. CDC. Conditions that increase risk for heart disease. Atlanta, GA: US Department of Health and Human Services, CDC; 2015. https://www.cdc.gov/heartdisease/conditions.htm
  85. CDC. Heart disease behavior. Atlanta, GA: US Department of Health and Human Services, CDC; 2015. https://www.cdc.gov/heartdisease/behavior.htm
  86. American Heart Association. Coronary artery disease—coronary heart disease. Dallas, TX: American Heart Association; 2018. http://www.heart.org/HEARTORG/Conditions/More/MyHeartandStrokeNews/Coronary-Artery-Disease—Coronary-Heart-Disease_UCM_436416_Article.jsp
  87. CDC. Prevalence of stroke—United States, 2006–2010. MMWR Morb Mortal Wkly Rep 2012;61:379–82. PubMed
  88. CDC. Behaviors that increase risk for stroke. Atlanta, GA: US Department of Health and Human Services, CDC; 2017. https://www.cdc.gov/stroke/behavior.htm
  89. CDC. Conditions that increase risk for stroke. Atlanta, GA: US Department of Health and Human Services, CDC; 2017. https://www.cdc.gov/stroke/conditions.htm
  90. Wen M, Kowaleski-Jones L. Sex and ethnic differences in validity of self-reported adult height, weight and body mass index. Ethn Dis 2012;22:72–8. PubMed
  91. Feunekes GI, van ’t Veer P, van Staveren WA, Kok FJ. Alcohol intake assessment: the sober facts. Am J Epidemiol 1999;150:105–12. CrossRef PubMed
  92. Klein JD, Thomas RK, Sutter EJ. Self-reported smoking in online surveys: prevalence estimate validity and item format effects. Med Care 2007;45:691–5. CrossRef PubMed
  93. Yore MM, Ham SA, Ainsworth BE, et al. Reliability and validity of the instrument used in BRFSS to assess physical activity. Med Sci Sports Exerc 2007;39:1267–74. CrossRef PubMed
  94. Adams SA, Matthews CE, Ebbeling CB, et al. The effect of social desirability and social approval on self-reports of physical activity. Am J Epidemiol 2005;161:389–98. CrossRef PubMed
  95. Li C, Balluz LS, Ford ES, Okoro CA, Zhao G, Pierannunzi C. A comparison of prevalence estimates for selected health indicators and chronic diseases or conditions from the Behavioral Risk Factor Surveillance System, the National Health Interview Survey, and the National Health and Nutrition Examination Survey, 2007–2008. Prev Med 2012;54:381–7. CrossRef PubMed
  96. Iachan R, Pierannunzi C, Healey K, Greenlund KJ, Town M. National weighting of data from the Behavioral Risk Factor Surveillance System (BRFSS). BMC Med Res Methodol 2016;16:155.CrossRef PubMed
  97. Nelson DE, Powell-Griner E, Town M, Kovar MG. A comparison of national estimates from the National Health Interview Survey and the Behavioral Risk Factor Surveillance System. Am J Public Health 2003;93:1335–41. CrossRef PubMed
  98. Yun S, Zhu BP, Black W, Brownson RC. A comparison of national estimates of obesity prevalence from the behavioral risk factor surveillance system and the National Health and Nutrition Examination Survey. Int J Obes 2006;30:164–70. CrossRef PubMed
  99. Carlson SA, Densmore D, Fulton JE, Yore MM, Kohl HW 3rd. Differences in physical activity prevalence and trends from 3 U.S. surveillance systems: NHIS, NHANES, and BRFSS. J Phys Act Health 2009;6(Suppl 1):S18–27. CrossRef PubMed
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