Abstract

To determine the variability of arthritis prevalence in 4 US population health surveys. We estimated annualized arthritis prevalence in 2011-2012, among adults age ≥20 years, using 2 definition methods, both based on self-report: 1) doctor-/health care provider-diagnosed arthritis in the Behavioral Risk Factor Surveillance Survey (BRFSS), National Health and Nutrition Examination Survey (NHANES), National Health Interview Survey (NHIS), and Medical Expenditure Panel Survey (MEPS); and 2) three arthritis definitions based on International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) criteria in MEPS (National Arthritis Data Workgroup on Arthritis and Other Rheumatic Conditions [NADW-AORC], Clinical Classifications Software [CCS], and Centers for Disease Control and Prevention [CDC]). Diagnosed arthritis prevalence percentages using the surveys were within 3 points of one another (BRFSS 26.2% [99% confidence interval (99% CI) 26.0-26.4], MEPS 26.1% [99% CI 25.0-27.2], NHIS 23.5% [99% CI 22.9-24.1], NHANES 23.0% [99% CI 19.2-26.8]), and those using ICD-9-CM were within 5 percentage points of one another (CCS 25.8% [99% CI 24.6-27.1]; CDC 28.3% [99% CI 27.0-29.6]; and NADW-AORC 30.7% [99% CI 29.4-32.1]). The variation in the estimated number (in millions) affected with diagnosed arthritis was 7.8 (BRFSS 58.5 [99% CI 58.1-59.1], MEPS 59.3 [99% CI 55.6-63.1], NHANES 51.5 [99% CI 37.2-65.5], and NHIS 52.6 [99% CI 50.9-54.4]), and using ICD-9-CM definitions it was 11.1 (CCS 58.7 [99% CI 54.5-62.9], CDC 64.3 [99% CI 59.9-68.6], and NADW 69.9 [99% CI 65.2-74.5]). Most (57-70%) reporting diagnosed arthritis also reported ICD-9-CM arthritis; respondents reporting diagnosed arthritis were older than those meeting ICD-9-CM definitions. Proxy response status affected arthritis prevalence differently across surveys. Public health practitioners and decision makers are frequently charged with choosing a single number to represent arthritis prevalence in the US population. We encourage them to consider the surveys' purpose, design, measurement methods, and statistical precision when choosing an estimate.

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