National estimates of arthritis prevalence rely on a single survey question about doctor-diagnosed arthritis without using survey information on joint symptoms, even though some subjects with only the latter have been shown to have arthritis. The sensitivity of the current surveillance definition is only 53% and 69% in subjects ages 45-64 years and ages ≥65 years, respectively, resulting in misclassification of nearly one-half and one-third of subjects in those age groups. This study was undertaken to estimate arthritis prevalence based on an expansive surveillance definition that is adjusted for the measurement errors in the current definition.
Using the 2015 National Health Interview Survey, we developed a Bayesian multinomial latent class model for arthritis surveillance based on doctor-diagnosed arthritis, joint symptoms, and whether symptom duration exceeded 3 months.
Of 33,672 participants, 19.3% of men and 16.7% of women ages 18-64 years and 15.7% of men and 13.5% of women ages ≥65 years affirmed joint symptoms without doctor-diagnosed arthritis. The measurement error-adjusted prevalence of arthritis was 29.9% (95% Bayesian probability interval [95% PI] 23.4-42.3) in men ages 18-64 years, 31.2% (95% PI 25.8-44.1) in women ages 18-64 years, 55.8% (95% PI 49.9-70.4) in men ages ≥65 years, and 68.7% (95% PI 62.1-79.9) in women ages ≥65 years. Arthritis affected 91.2 million adults (of 247.7 million; 36.8%) in the US in 2015, which included 61.1 million persons between 18 and 64 years of age (of 199.9 million; 30.6%). Our arthritis prevalence estimate was 68% higher than the previously reported national estimate.
Arthritis prevalence in the US population has been substantially underestimated, especially among adults younger than 65 years of age.