{
  "abstract": "Background 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.Methods 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.Results 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’s ρ>0.97), though some middle-ranked strata shifted by up to six positions.Conclusions Applying a multilevel survival (shared frailty) model within MAIHDA enables examination of when disparities emerge, not just whether they exist. This approach retains MAIHDA’s interpretability while leveraging time-to-event data, offering advantages in settings with incomplete follow-up or irregular observation windows.",
  "authors": [
    {
      "affiliations": [
        "Seoul National University Institute of Health and Environment, Seoul, Korea (the Republic of)"
      ],
      "name": "Jin-Hwan Kim"
    },
    {
      "affiliations": [
        "Seoul National University Institute of Health and Environment, Seoul, Korea (the Republic of)",
        "Department of Public Health Science, Seoul National University Graduate School of Public Health, Seoul, Korea (the Republic of)"
      ],
      "name": "Woojoo Lee"
    }
  ],
  "title": "Extending multilevel analysis of individual heterogeneity and discriminatory accuracy to time-to-event outcomes: an application of survival MAIHDA to Korean health data",
  "uid": "5d8579fa-06e0-537d-8bc8-7d1c376750e4"
}