Comparison of Mental Health Estimates by Sociodemographic Characteristics in the Research and Development Survey 3 and the 2019 National Health Interview Survey.

Objectives-This report compares national and subgroup estimates of any (mild, moderate, or severe) level of major depressive disorder (depression) and generalized anxiety disorder (GAD) symptoms among the U.S. adult population from two data sources, the 2019 National Health Interview Survey (NHIS) and the third round of the Research and Development Survey (RANDS 3). Methods-Data from the 2019 NHIS (n = 31,997) and RANDS 3 (n = 2,646) were used. The eight-item Patient Health Questionnaire (PHQ-8), scores ranging from 0 to 24, and the seven-item GAD scale (GAD-7), scores ranging from 0 to 21, were used to measure the severity of depression and GAD symptoms, respectively. Binary indicators of exhibiting symptoms were based on scores of 5 to 24 for depression and 5 to 21 for GAD. The estimates were compared by the following sociodemographic characteristics: age, sex, race and Hispanic origin, education, and region. Results-Nearly all of the national and subgroup estimates of adults with depression and GAD symptoms were significantly higher based on RANDS 3 compared with the 2019 NHIS. The only exception was the depression symptoms estimate among adults aged 65 and over, where the estimates were comparable across the two data sources. Both data sources found that depression symptoms were associated with sex, age, race and Hispanic origin, and education, and GAD symptoms were associated with age, race and Hispanic origin, and education. However, NHIS identified a few associations that RANDS did not, including associations between depression symptoms and region and GAD symptoms and sex. Conclusions-Mental health estimates from RANDS, a web-based survey, may be overestimated when compared with a traditional in-person household survey. These results may inform potential strategies to improve the comparability of mental health estimates from RANDS and other surveys like NHIS, such as calibration weights or other model-based methods.