{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T11:55:48Z","timestamp":1785326148710,"version":"3.55.0"},"reference-count":33,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T00:00:00Z","timestamp":1755216000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Psychol."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Cardiovascular disease with multimorbidity imposes substantial long-term financial burdens. Financial toxicity, the financial burden associated with healthcare, is increasingly recognized as a psychosocial stressor affecting health outcomes among chronically ill populations. However, its psychological heterogeneity in non-cancer patients is understudied. We aimed to identify financial toxicity profiles, their determinants, and associations with anxiety\/depression in cardiovascular non-cancer multimorbidity.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This study used a cross-sectional design. Utilizing latent profile analysis to identify distinct financial toxicity subgroups. Univariate and multivariate logistic regression analyses were employed to explore factors associated with subgroup membership. Inverse probability of treatment weighting based on covariate balancing propensity scores was applied to assess the associations between financial toxicity profiles and psychological distress. Doubly robust regression models were utilized to validate the robustness of the observed associations.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Financial toxicity was categorized into three latent profiles: mild (63.8%), moderate (11.0%), and none (25.2%). Demographic, economic, social support, and coping variables significantly differed across profiles. In weighted regression models using inverse probability of treatment weighting based on covariate balancing propensity scores, both mild and moderate toxicity were associated with elevated anxiety (<jats:italic>\u03b2<\/jats:italic>\u202f=\u202f1.88 and 8.68, respectively) and depression scores (<jats:italic>\u03b2<\/jats:italic>\u202f=\u202f4.21 and 11.92; all <jats:italic>p<\/jats:italic>\u202f&amp;lt;\u202f0.01) compared to the no-toxicity group. These associations remained robust in doubly robust models adjusting for covariates (GAD-7: <jats:italic>\u03b2<\/jats:italic>\u202f=\u202f1.72 and 8.31; PHQ-9: <jats:italic>\u03b2<\/jats:italic>\u202f=\u202f4.08 and 11.48).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Financial toxicity is prevalent and heterogeneous in cardiovascular multimorbidity. Its distinct profiles predict elevated psychological distress, supporting targeted interventions to alleviate financial burden and enhance mental health resilience.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fpsyg.2025.1633445","type":"journal-article","created":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T05:27:38Z","timestamp":1755235658000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Financial toxicity and psychological distress in cardiovascular non-cancer multimorbidity: a latent profile analysis and causal weighting"],"prefix":"10.3389","volume":"16","author":[{"given":"Ruihan","family":"Wu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiali","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huafen","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shanshan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaojie","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tian","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingjing","family":"Tao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongxiang","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,8,15]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"817.e1","DOI":"10.1016\/j.ajog.2021.12.012","article-title":"Risk factors for financial toxicity in patients with gynecologic cancer","volume":"226","author":"Aviki","year":"2022","journal-title":"Am. 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