Prev Chronic DisPreventing Chronic Disease1545-1151Centers for Disease Control and Prevention211592213044020PCDv81_09_0234Original ResearchPeer ReviewedHealth-Related Quality of Life Among Adults With Multiple Chronic Conditions in the United States, Behavioral Risk Factor Surveillance System, 2007ChenHan-YangMSCenter for Urban Population Health, University of Wisconsin School of Medicine and Public Health
1020 N 12th St, Suite 4180, Milwaukee, WI 53233414-219-5181chen25@wisc.edu
BaumgardnerDennis J.MDCenter for Urban Population Health, University of Wisconsin School of Medicine and Public Health, Milwaukee, Wisconsin. Dr Baumgardner is also affiliated with the Aurora UW Medical Group, Milwaukee, WisconsinRiceJessica P.MPHCenter for Urban Population Health, University of Wisconsin School of Medicine and Public Health, Milwaukee, Wisconsin
120111512201081A09Introduction

Little is known about health-related quality of life (HRQOL) among people with multiple chronic conditions. We examined the association between the number of chronic conditions and self-reported HRQOL outcomes among adults in the United States.

Methods

We used data from the Behavioral Risk Factor Surveillance System (BRFSS) in 2007 (n = 430,912) to compare 4 HRQOL measures for people with any of 8 chronic conditions. We also assessed the frequency of self-reported physical and mental distress and the number of days activity was limited because of chronic conditions. We estimated prevalence and adjusted odds ratios (AORs) and 95% confidence intervals (CIs) by using survey logistic regression analyses.

Results

People with 3 or more chronic conditions had the highest risk of reporting fair or poor health compared with respondents with no chronic conditions (AOR, 8.7; 95% CI, 8.0-9.4). People with cardiovascular conditions or diabetes had higher risk of reporting poor HRQOL outcomes than those with other chronic conditions. The odds ratios for frequent physical distress were consistently higher than those for frequent mental distress and frequent activity limitations for all conditions.

Conclusion

Strategies that help clinicians to manage their patients' chronic conditions may contribute to improved HRQOL among adults. Our findings may help to inform these strategies.

Introduction

As disease prevention and management improve and the population ages, the prevalence of chronic conditions is accelerating in the United States. Nearly half of adults have at least 1 chronic condition (1), which can result in extended pain and suffering and impaired quality of life.

The growing number of Americans living with chronic illness has shifted the focus of research from treatment and quantity of life to improvement of the quality of life. One of the major goals of Healthy People 2010 (2) was improving the quality and number of years of healthy life. During the past decade, the research community has increasingly focused on measuring the patient's perspective when evaluating the effect of chronic illness and the benefit of treatment. Self-assessments of health-related quality of life (HRQOL) are rapidly gaining acceptance and are widely used for tracking health status. The Centers for Disease Control and Prevention (CDC) developed a surveillance definition of HRQOL as "perceived physical and mental health over time" (3). Others characterize HRQOL as a subjective assessment of well-being and physical, mental, and social functioning. Thus, HRQOL is recognized as a health-oriented subset of the broader concept of overall quality of life, including aspects of life satisfaction and happiness (4).

The high prevalence of chronic disease in the United States does not tell the whole story. A more specific concern is that many people, especially those in the Medicare population, have multiple chronic conditions (5). Whether HRQOL varies by number of conditions has not been established, despite the research finding that multiple chronic diseases have a substantial negative effect on quality of life, not only how people feel about their lives but also the extent of their psychological distress (6). Some chronic conditions have a stronger relationship with functional impairment than others, but people with more chronic conditions experience more functional impairment and experience it sooner than people with fewer chronic conditions (7).

Our primary objective was to examine the association between the number of chronic conditions and HRQOL outcomes. Our secondary objective was to describe the prevalence of common chronic conditions among the US adult population.

Methods

We analyzed data from the 2007 Behavioral Risk Factor Surveillance System (BRFSS). BRFSS collects data from ongoing random-digit–dial telephone surveys administered to noninstitutionalized US adults aged 18 years or older on health risk behaviors, preventive health practices, and access to and use of health care services primarily related to chronic conditions. BRFSS data are directly weighted for the probability of selection of a telephone number, the number of adults in a household, and the number of telephones in a household. A final poststratification adjustment is made for nonresponse and noncoverage of households without telephones. The weights for each relevant factor are multiplied to get a final weight (8). In 2007, BRFSS was administered to 430,912 (weighted N = 230,172,178) respondents. The median response rate was 51%, and the median cooperation rate was 72%. A detailed description of the survey design and random sampling procedures is available elsewhere (8). The health sciences institutional review board at the University of Wisconsin-Madison approved this study.

In our analysis, the outcomes of interest were 4 measures of HRQOL from the CDC Healthy Days Core Module (CDC HRQOL-4): general health, mental distress, physical distress, and activity limitations. The CDC HRQOL measures have acceptable content, construct and criterion validity, and test-retest reliability (3,9-12).

In the CDC HRQOL-4, the first question asks respondents to rate their general health on a scale from excellent to poor. We dichotomized these responses as  either "fair/poor" or "good/very good/excellent." The other 3 questions ask about respondents' assessment of their health in the previous 30 days: "How many days was your physical health, which includes physical illness or injury, not good?" (physical distress), "How many days was your mental health, which includes stress, depression, and problems with emotions, not good?" (mental distress), and "How many days did poor physical or mental health keep you from doing your usual activities, such as self-care, work, or recreation?" (activity limitations). We dichotomized these 3 HRQOL variables in terms of their frequency in the previous 30 days (≥14 being frequent or <14 being infrequent). We used the 14-day minimum period because clinicians and clinical researchers often use this period as a marker for clinical depression and anxiety disorders, and longer duration of symptoms is associated with a higher level of activity limitation (13). In addition, most studies based on the BRFSS HRQOL indicators used the same dichotomized criteria as we did (13-16). Thus, our results can be compared with those of previous studies. Moreover, our outcomes of interest were not normally distributed; by dichotomizing the outcomes, we were able to conduct logistic regression analyses without violating the linearity assumption.

We examined respondents by 8 chronic conditions: asthma, arthritis, 3 cardiovascular diseases (heart attack, angina, stroke), diabetes, and hypertension, based on diagnosis of the condition by a health professional, and obesity, defined as a body mass index of at least 30 kg/m2, based on self-reported height and weight.

We estimated the prevalence of each chronic condition among adults in the United States. We estimated adjusted odds ratios (AORs) and 95% confidence intervals (CIs) by comparing having each chronic condition with having no condition. To account for potential confounding effects, we controlled for respondents' age, sex, race/ethnicity, education level, income level, employment status, marital status, and health insurance coverage status. In addition, we adjusted for 3 health behavior risk factors: current smoking (defined as ever having smoked at least 100 cigarettes and now smoking every day or some days), current heavy drinking (defined as having more than 2 alcoholic drinks per day for men and having more than 1 alcoholic drink per day for women during the previous 30 days), and no physical activity (defined as not participating in any physical activity during the previous 30 days). To account for complex survey design and produce unbiased estimates of standard errors, we used multivariate survey logistic regression models to estimate AORs and 95% CIs. We conducted all analyses using SAS version 9.2 (SAS Institute, Inc, Cary, North Carolina).

Results

Approximately 19% of respondents were smokers, 5% were heavy drinkers, and 9% did not participate in any physical activity  (Table 1). Among all survey respondents, 57% reported at least 1 chronic condition. The most prevalent chronic conditions were arthritis (27%), obesity (26%), and hypertension (28%). Among people with cardiovascular diseases, more than 90% of them had 2 or more chronic conditions (Table 2).

The most common conditions for which fair or poor health were reported were cardiovascular diseases (53% for each one) or diabetes (48%) (Table 3). People with 1 or no chronic condition had a higher prevalence of frequent mental distress than frequent physical distress. In contrast, people with 2 or more conditions had a higher prevalence of frequent physical distress than mental distress. Respondents with cardiovascular diseases or diabetes were approximately 7 to 8 times as likely to report fair or poor health as respondents with no chronic condition (Table 4). People with 3 or more chronic conditions were more likely to report poor HRQOL outcomes than those with 1 or 2 conditions. In the population of adults with at least 1 chronic illness, the odds ratios of frequent physical distress varied more widely than those for frequent mental distress and frequent activity limitations across conditions.

Discussion

Our findings that respondents with multiple chronic conditions reported worse HRQOL than those with 1 or no chronic condition and that frequent physical distress was more common than frequent mental distress were consistent with previous studies in disease-specific populations, such as those of adults with asthma (15), obesity (16), stroke (17), diabetes (18), and arthritis (19).

We found that people without any chronic condition reported a higher prevalence of frequent mental distress than frequent physical distress. However, as the number of chronic conditions increased, frequent physical distress outpaced frequent mental distress. Although our results were consistent with previous findings that the burden of chronic illness is primarily carried in terms of physical health (20), the observation that mental distress is less frequent than physical distress does not imply that mental distress is an unimportant consideration in managing chronic conditions. People with chronic illness may have lived with their conditions for years and feel that they are able to manage their illness and therefore report less mental distress. For example, diabetes patients often rate their well-being positively despite the presence of diabetes-related complications or poor glycemic control (21). These findings suggest that in addition to medical care, the mental health quality of life of the chronically ill population may benefit from social support and be mitigated by socioeconomic status, personality characteristics, and styles of coping with illness.

We found that cardiovascular diseases and diabetes are frequently associated with other surveyed diseases; they may also be associated with many other unmeasured comorbidities. Because our statistical analysis did not adjust for the number of comorbidities, our finding that physical distress is higher in participants with cardiovascular diseases and diabetes may be due to unmeasured comorbidities. In addition, cardiovascular diseases are the primary causes of illness and death among people with diabetes (22) and have a negative effect on quality of life (23).

Our finding of more frequent activity limitations among respondents with at least 1 chronic condition may be a consequence of impaired physical health among the chronically ill population. Physical pain, fatigue, or other limitations may prohibit chronically ill people from engaging in exercise or physical activities. Engaging in such health promotion behaviors, however, and being able to make choices that reflect personal needs and goals are positive characteristics related to quality of life among older adults (24). Thus, applying motivational interviewing techniques (25) to help patients identify their problems and adopt a health-promoting lifestyle early in a disease course, combined with customized medication or treatment that empowers patients to manage their conditions, may improve their quality of life.

Various disease-specific quality-of-life scales have been developed and validated (26-29). Although disease-specific measures provide additional valuable information, they could be more time-consuming than a simple general health questionnaire for respondents to complete. In a general health survey such as BRFSS, a short, valid, generic scale that is applicable across conditions and groups is practical and preferable (30). The CDC HRQOL-4 measures used in BRFSS reflect general HRQOL and compare well against other HRQOL measures, such as the Medical Outcomes Study 36-Item Short-Form Health Survey and the Quality of Well-Being Scale (12,31-33).

Managing chronic illness, especially for people with multiple conditions, presents substantial challenges to professionals in all arenas of health care. Health professionals seek not only to develop better strategies to manage chronic disorders and prevent complications but also to maintain or enhance the functional abilities of people who are chronically ill. Clinician awareness of patients' needs early in their care may reduce the effect of chronic comorbidities on HRQOL. By targeting outcomes that patients seem to value most, clinicians could provide customized treatment plans that patients are more motivated to follow. Thus, a better understanding of HRQOL related to chronic conditions may lead to more effective preventive education and improved care of patients with chronic illness.

Our study had several limitations. First, BRFSS does not survey people who are hospitalized or institutionalized. People with severe conditions might not have been able to answer the telephone or be interviewed. For example, stroke survivors interviewed through BRFSS may have less severe disabilities than the total population of stroke survivors. BRFSS also excludes people with no telephones or people who use only cellular telephones. People who use only cellular telephones tend to be younger and may have fewer chronic conditions (34), whereas people with no telephones are usually from a lower socioeconomic group, which is associated with poor HRQOL (35). Thus, BRFSS may either underestimate or overestimate the prevalence of people with impaired physical or mental health. Second, the analyses were based on self-reported data, which may be influenced by reporting bias. However, results from previous validation studies showed substantial agreement between self-reported disease status and disease status as documented in medical records (36). Third, since BRFSS did not include questions about the severity of impairment resulting from conditions or comorbidities, we were unable to assess the association between severity of impairment and HRQOL. It is possible that people who report better physical health or fewer activity limitations had a less severe impairment from their conditions than those who reported worse HRQOL. However, in our analyses, we were able to categorize respondents by the number of conditions they had and to assess the association with self-reported HRQOL. Finally, the cross-sectional study design allowed us to demonstrate only an association. Future studies using a longitudinal design are necessary to assess the temporal sequence of the onset of the chronic conditions and the change in HRQOL.

Despite these potential limitations, our findings suggest that HRQOL varies substantially by the category and number of chronic conditions. The prevalence and AORs of frequent physical distress vary more widely across the chronic conditions and appear to be higher than those of frequent mental distress; HRQOL consistently decreases as the number of conditions increases. Strategies by individual clinicians and teams providing customized medication or treatment to improve the HRQOL of their patients should focus on preventing sequelae and comorbidities of the patient's chronic disease and targeting the areas that the patient values most, such as the ability to perform daily activities, a desired recreational activity, or playing with grandchildren. Motivating patients to take charge of their disease management and adopt healthy lifestyles that improve physical health may improve their HRQOL. On a broad scale, health care organizations could focus care management resources on enhancing communication with patients and guiding them in making choices to improve their health and HRQOL.

The Center for Urban Population Health is a collaborative partnership of the University of Wisconsin School of Medicine and Public Health, the University of Wisconsin-Milwaukee, and Aurora Health Care, Inc, Milwaukee, Wisconsin. This study received no external funding.

The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.

Suggested citation for this article: Chen H-Y, Baumgardner DJ, Rice JP. Health-related quality of life among adults with multiple chronic conditions in the United States, Behavioral Risk Factor Surveillance System, 2007. Prev Chronic Dis 2011;8(1) http://www.cdc.gov/pcd/issues/2011/jan/09_0234.htm. Accessed [date].

Chronic diseases: the power to prevent, the call to control: at a glance 2009 Centers for Disease Control and Prevention Accessed November 1, 2009 http://www.cdc.gov/nccdphp/publications/AAG/chronic.htm#aag 2000 Washington  (DC) US Government Printing Office US Department of Health and Human Services. Healthy people 2010. 2nd edition. With understanding and improving health and objectives for improving health. 2 vols MoriartyDGKobauRZackMMZahranHS Accessed August 31, 2010 2005 2 3 Tracking healthy days — a window on the health of older adults Prev Chronic Dis A16 http://www.cdc.gov/pcd/issues/2005/jul/05_0023.htm 15963318 Measuring healthy days: population assessment of health-related quality of life Centers for Disease Control and Prevention 2000 Accessed August 31, 2010 http://www.cdc.gov/hrqol/pdfs/mhd.pdf ThorpeKEHowardDH 25 5 2006 w378 w388 The rise in spending among Medicare beneficiaries: the role of chronic disease prevalence and changes in treatment intensity Health Aff (Millwood) 16926180 WalkerAE 3 3 2007 202 218 Multiple chronic diseases and quality of life: patterns emerging from a large national sample, Australia Chronic Illn 18083677 VogeliCShieldsAELeeTAGibsonTBMarderWDWeissKB 2007 22 Suppl 3 391 395 Multiple chronic conditions: prevalence, health consequences, and implications for quality, care management, and costs J Gen Intern Med 18026807 Behavioral Risk Factor Surveillance System survey data Centers for Disease Control and Prevention 2007 Accessed August 31, 2010 http://www.cdc.gov/brfss/technical_infodata/surveydata/2007.htm HennessyCHMoriartyDGZackMMScherrPABrackbillR 109 5 1994 665 672 Measuring health-related quality of life for public health surveillance Public Health Rep 7938388 AndresenEMCatlinTKWyrwichKWJackson-ThompsonJ 57 5 2003 339 343 Retest reliability of surveillance questions on health related quality of life J Epidemiol Community Health 12700216 BombardJMPowellKEMartinLMHelmickCGWilsonWH 28 3 2005 251 258 Validity and reliability of self-reported arthritis: Georgia senior centers, 2000-2001 Am J Prev Med 15766612 Measuring healthy days: population assessment of health-related quality of life Atlanta (GA) Centers for Disease Control and Prevention Accessed August 31, 2010 http://www.cdc.gov/hrqol/pdfs/mhd.pdf JiangYHesserJE 2006 4 14 Associations between health-related quality of life and demographics and health risks. Results from Rhode Island's 2002 behavioral risk factor survey Health Qual Life Outcomes 16515690 StrineTWOkoroCAChapmanDPBalluzLSFordESAjaniUA 28 2 2005 182 187 Health-related quality of life and health risk behaviors among smokers Am J Prev Med 15710274 FordESManninoDMHomaDMGwynnCReddSCMoriartyDG 123 1 2003 119 127 Self-reported asthma and health-related quality of life: findings from the Behavioral Risk Factor Surveillance System Chest 12527612 HeoMAllisonDBFaithMSZhuSKFontaineKR 11 2 2003 209 216 Obesity and quality of life: mediating effects of pain and comorbidities Obes Res 12582216 GreenlundKJGilesWHKeenanNLCroftJBMensahGA 33 2 2002 565 571 Physician advice, patient actions, and health-related quality of life in secondary prevention of stroke through diet and exercise Stroke 11823671 ValdmanisVSmithDWPageMR 91 1 2001 129 130 Productivity and economic burden associated with diabetes Am J Public Health 11189805 DominickKLAhernFMGoldCHHellerDA 2004 2 5 Health-related quality of life among older adults with arthritis Health Qual Life Outcomes 14720300 BethellCLanskyDFiorilloJ 2001 Portland (OR) Foundation for Accountability A portrait of the chronically ill in America, 2001 SnoekFJ 2000 13 24 28 Quality of life: a closer look at measuring patients' well-being Diabetes Spectr EngelgauMMGeissLSSaaddineJBBoyleJPBenjaminSMGreggEW 140 11 2004 945 950 The evolving diabetes burden in the United States Ann Intern Med 15172919 de VisserCLBiloHJGroenierKHde VisserWJongMeyboom-de B. 11 3 2002 249 261 The influence of cardiovascular disease on quality of life in type 2 diabetics Qual Life Res 12074262 MowadL. 26 3 2004 293 306 Correlates of quality of life in older adult veterans West J Nurs Res 15068553 MillerWRRollnickS 2002 New York (NY) Guilford Press Motivational interviewing: preparing people to change WilliamsLSWeinbergerMHarrisLEClarkSOBillerJ 30 7 1999 1362 1369 Development of a stroke-specific quality of life scale Stroke 10390308 PolonskyWHAndersonBJLohrerPAWelchGJacobsonAMAponteJE 18 6 1995 754 760 Assessment of diabetes-related distress Diabetes Care 7555499 JuniperEFGuyattGHEpsteinRSFerriePJJaeschkeRHillerTK 47 2 1992 76 83 Evaluation of impairment of health related quality of life in asthma: development of a questionnaire for use in clinical trials Thorax 1549827 ThompsonDRJenkinsonCRoebuckALewinRJPBoyleRMChandolaT 11 6 2002 535 543 Development and validation of a short measure of health status for individuals with acute myocardial infarction: the myocardial infarction dimensional assessment scale (MIDAS) Qual Life Res 12206574 VerbruggeLMMerrillSSLiuX 21 5-6 1999 295 306 Measuring disability with parsimony Disabil Rehabil 10381242 NewschafferCJ 1998 Atlanta (GA) Centers for Disease Control and Prevention Validation of Behavioral Risk Factor Surveillance System (BRFSS) HRQOL measures in a statewide sample SchecterSBeattyPWillisGB SchwarzNParkDCKnäuperBSudmanS Philadelphia (PA) Psychology Press 1999 Asking survey respondents about health status: judgment and response issues Cognition, aging, and self-reports AndresenEMFoutsBSRomeisJCBrownsonCA 80 8 1999 877 884 Performance of health-related quality-of-life instruments in a spinal cord injured population Arch Phys Med Rehabil 10453762 2006 The cell phone challenge to survey research. Pew Research Center for the People and the Press Accessed September 1, 2010 http://people-press.org/report/276/ RobertSACherepanovDPaltaMDunhamNCFeenyDFrybackDG 64 3 2009 378 389 Socioeconomic status and age variations in health-related quality of life: results from the National Health Measurement Study J Gerontol B Psychol Sci Soc Sci 19307286 BrownsonRCJackson-ThompsonJWilkersonJCKianiF 5 5 1994 545 549 Reliability of information on chronic disease risk factors collected in the Missouri Behavioral Risk Factor Surveillance System Epidemiology 7986871

Sample Characteristics, Behavioral Risk Factor Surveillance System (n = 430,912), United States, 2007

CharacteristicWeighted %a
Age, y
18-4450
45-6433
≥6517
Sex
Men49
Women51
Race/ethnicity
Non-Hispanic white69
Non-Hispanic black10
Hispanic15
Other6
Education
Less than high school12
High school diploma29
More than high school60
Annual household income, $
<25,00022
25,000-49,99923
50,000-74,99915
≥75,00027
Don't know/not sure/refused13
Employment status
Employed61
Unemployed5
Homemaker/student13
Retired16
Unable to work5
Health insurance coverage
No15
Yes85
Marital status
Married61
Single, previously married18
Single, never married18
Member of an unmarried couple4
Smoking behavior
Current smokingb19
No current smoking81
Drinking behavior
Heavy drinkingc5
No heavy drinking95
Physical activity behavior
No physical activityd9
Some physical activity91

Weighted N = 230,172,178.

Current smoking defined as ever having smoked at least 100 cigarettes and now smoking every day or some days.

Heavy drinking defined as more than 2 alcoholic drinks per day for men and more than 1 alcoholic drink per day for women during the previous 30 days.

No physical activity defined as not participating in any physical activity during the previous 30 days.

Prevalence of Chronic Conditions, Behavioral Risk Factor Surveillance System (n = 430,912), United States, 2007

ConditionaOverall, %1 Condition, %2 Conditions, %≥3 Conditions, %
Any57502723
Asthma8332839
Arthritis27303139
Cardiovascular disease
Myocardial infarction461678
Angina461578
Stroke391973
Diabetes9112367
Obesity26372934
Hypertension28253441

Respondents were categorized as having a condition if they had ever been diagnosed with it by a health professional or, in the case of obesity, if their body mass index (calculated from self-reported weight and height) was ≥30 kg/m2.

Prevalence of Health-Related Quality of Life Outcomes, by Chronic Conditions, Behavioral Risk Factor Surveillance System (n = 430,912), United States, 2007

ConditionaFair or Poor Health, %Frequent Physical Distress,b %Frequent Mental Distress,b %Frequent Activity Limitations,b %
Asthma30231915
Arthritis31231514
Cardiovascular disease
Myocardial infarction53341721
Angina53351822
Stroke53382024
Diabetes48281617
Obesity25161310
Hypertension31201312
Number of conditions
07473
1149106
224161310
≥347321820

Respondents were categorized as having a condition if they had ever been diagnosed with it by a health professional or, in the case of obesity, if their body mass index (calculated from self-reported weight and height) was ≥30 kg/m2.

On ≥14 days of the preceding 30 days.

Health-Related Quality of Life Outcomes, by Chronic Conditions, Behavioral Risk Factor Surveillance System (n = 430,912), United States, 2007a

ConditionbFair or Poor Health, AOR (95% CI)Physical Distress,c AOR (95% CI)Mental Distress,c AOR (95% CI)Activity Limitations,c AOR (95% CI)
No condition1 [Reference]1 [Reference]1 [Reference]1 [Reference]
Asthma4.7 (4.3-5.1)3.9 (3.6-4.3)2.3 (2.2-2.6)3.1 (2.8-3.5)
Arthritis4.5 (4.2-4.8)4.0 (3.7-4.3)2.5 (2.3-2.6)3.3 (3.0-3.6)
Cardiovascular disease
Myocardial infarction8.3 (7.6-9.2)4.8 (4.4-5.3)2.5 (2.3-2.8)3.9 (3.5-4.4)
Angina9.2 (8.4-10.0)5.4 (4.9-6.0)2.8 (2.5-3.1)4.2 (3.8-4.7)
Stroke6.9 (6.2-7.7)4.8 (4.3-5.4)2.5 (2.2-2.9)3.7 (3.3-4.2)
Diabetes7.6 (7.0-8.3)4.2 (3.8-4.5)2.3 (2.1-2.5)3.1 (2.8-3.4)
Obesity3.5 (3.3-3.8)2.7 (2.5-2.9)1.8 (1.7-2.0)2.4 (2.2-2.6)
Hypertension4.3 (4.0-4.6)3.1 (2.8-3.3)2.0 (1.9-2.2)2.7 (2.4-2.9)
Number of conditions
12.1 (1.9-2.3)1.9 (1.7–2.0)1.5 (1.4-1.6)1.7 (1.6-1.9)
23.7 (3.4-4.0)3.0 (2.8-3.3)2.1 (1.9-2.2)2.5 (2.3-2.8)
≥38.7 (8.0-9.4)5.5 (5.1-5.9)2.9 (2.7-3.1)4.1 (3.8-4.5)

Abbreviations: AOR, adjusted odds ratio; CI, confidence interval.

Adjusted by age, sex, race/ethnicity, education, income, employment, health insurance coverage status, marital status, and 3 risk behaviors: smoking, heavy drinking, and no physical activity. All AORs are significant at P < .001.

Respondents were categorized as having a condition if they had ever been diagnosed with it by a health professional or, in the case of obesity, if their body mass index (calculated from self-reported weight and height) was ≥30 kg/m2.

On ≥14 days of the preceding 30 days.