{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T06:27:22Z","timestamp":1784010442414,"version":"3.55.0"},"reference-count":47,"publisher":"BMJ","issue":"6","funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RS-2023-00271195"],"award-info":[{"award-number":["RS-2023-00271195"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["J Epidemiol Community Health"],"accepted":{"date-parts":[[2025,12,15]]},"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) is a leading quantitative approach for intersectionality-informed health research, but most applications analyse binary or cross-sectional outcomes, ignoring event timing. We applied a multilevel survival (shared frailty) model within the MAIHDA framework to examine intersectional disparities in time-to-diagnosis of hypertension.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Using 2019 Korean Community Health Survey data (n=228 632), we defined intersectional strata by sex, education, income and residential area. Three survival specifications were implemented: accelerated failure time (AFT), parametric proportional hazards (PHs) and semi-parametric Cox PH models, each with stratum-level random intercepts (shared frailty terms). Between-stratum variance was summarised with the variance partition coefficient (VPC) where estimable and proportional change in variance quantified fixed-effect contributions. Stratum-specific random effects were compared across model types to assess ranking stability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Between-stratum variance was small overall (AFT VPC: 1.8%), but several strata deviated markedly from the grand mean. Strata with low education and low income were diagnosed earlier than average, while high-education, low-income strata were diagnosed later. Geographic context modified these effects. Time-to-diagnosis patterns often diverged from prevalence patterns. Across models, random effect estimates and ranks were highly correlated (Spearman\u2019s\n                      <jats:italic>\u03c1<\/jats:italic>\n                      &gt;0.97), though some middle-ranked strata shifted by up to six positions.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Applying a multilevel survival (shared frailty) model within MAIHDA enables examination of when disparities emerge, not just whether they exist. This approach retains MAIHDA\u2019s interpretability while leveraging time-to-event data, offering advantages in settings with incomplete follow-up or irregular observation windows.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/jech-2025-224939","type":"journal-article","created":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T18:20:32Z","timestamp":1767205232000},"page":"444-451","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":5,"title":["Extending multilevel analysis of individual heterogeneity and discriminatory accuracy to time-to-event outcomes: an application of survival MAIHDA to Korean health data"],"prefix":"10.1136","volume":"80","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8229-9613","authenticated-orcid":false,"given":"Jin-Hwan","family":"Kim","sequence":"first","affiliation":[{"name":"Seoul National University Institute of Health and Environment, Seoul, Korea (the Republic of)"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Woojoo","family":"Lee","sequence":"additional","affiliation":[{"name":"Seoul National University Institute of Health and Environment, Seoul, Korea (the Republic of)"},{"name":"Department of Public Health Science, Seoul National University Graduate School of Public Health, Seoul, Korea (the Republic of)"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"239","published-online":{"date-parts":[[2025,12,31]]},"reference":[{"key":"2026050806500658000_80.6.444.1","doi-asserted-by":"publisher","DOI":"10.2307\/1229039"},{"key":"2026050806500658000_80.6.444.2","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-soc-073014-112142"},{"key":"2026050806500658000_80.6.444.3","doi-asserted-by":"publisher","DOI":"10.1016\/j.socscimed.2014.03.022"},{"key":"2026050806500658000_80.6.444.4","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1057\/sth.2015.16","article-title":"What can health inequalities researchers learn from an intersectionality perspective? 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