Incompatible National Mental Health Surveillance Systems in South Korea: Dramatically Different Prevalence Estimates between Structured Interviews and Brief Online Self-Reports
Year: 2026
Type: article
Abstract: Background: South Korea operates two major national mental health surveillance systems that employ different methodologies: an interview-based diagnostic survey and a web-based self-report survey. Understanding how these systems yield different estimates is essential for accurate policy planning. This study compared mental health reporting patterns and public perceptions between these two national surveys.Methods: We analyzed data from the National Mental Health Survey 2021 (offline, n=5,511) and National Mental Health Literacy and Attitude Survey 2021 (online, n=2,016). Logistic and linear regression analyses were performed to compare the lifetime prevalence rates of mental disorders and their perceptions, controlling for demographic variables.Results: Compared with offline respondents, online respondents were significantly more likely to report depression, anxiety, and obsessive-compulsive disorder. Conversely, offline respondents were more likely to report alcohol use disorder. Offline respondents exhibited more negative attitudes toward hiring or befriending individuals with mental disorders than did online respondents.Conclusions: The two national surveillance systems yielded substantially different prevalence estimates and perception patterns. These differences likely reflect a combination of survey modes (anonymity vs interviewer presence), assessment instruments (structured diagnostic interview vs self-report), and their interactions. Policymakers should interpret data from these systems with an awareness of their methodological differences. Future research using identical instruments across modes is required to isolate mode-specific differences.
Authors: Sangha Lee, Su-Jin Yang
Language: English
Subfield: Social Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $230
Award: G4402764956
OpenAlex ID: W7143350447
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