{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T05:20:39Z","timestamp":1764566439504,"version":"3.46.0"},"reference-count":42,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100009392","name":"Prince Sattam Bin Abdulaziz University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100009392","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Pharmacol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Cell and gene therapies (CGTs) challenge traditional Health Technology Assessment (HTA), creating a fragmented global access landscape. This study identifies the determinants of CGT reimbursement outcomes by quantifying the influence of key variables and identifying the configurations leading to a positive recommendation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A dual-methodology approach was employed. We constructed a comprehensive dataset of all HTA decisions for CGTs across seven major jurisdictions between January 2017 and July 2025. Hierarchical Linear Modeling (HLM) was used to identify independent predictors of HTA outcomes, and Fuzzy-Set Qualitative Comparative Analysis (fsQCA) was used to identify sufficient pathways to success. Novel composite indicators were developed to measure system-level adaptability and the influence of patient advocacy groups (PAGs).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The HLM analysis, accounting for data clustering (Intraclass Correlation Coefficients (ICCs): 42% country-level, 24% agency-level variance), confirmed that strong clinical efficacy (Coef. = 0.40), high unmet need, and disease rarity were significant positive predictors. High therapy cost was a powerful negative predictor (Coef. = \u22120.29 per $1M USD). Crucially, high System Adaptability (Coef. = 0.35) and strong PAG Influence (Coef. = 0.28) emerged as major positive determinants. The fsQCA revealed three distinct pathways to a positive recommendation with high consistency: a \u201cTransformative Value\u201d path (consistency: 0.93), a \u201cStrategic Mitigation\u201d path (consistency: 0.90), and an \u201cEconomic Dominance\u201d path (consistency: 0.94). The overall QCA solution explained a majority of positive outcomes (solution coverage: 0.68).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>HTA success for CGTs is not determined by isolated attributes but by the strategic alignment of therapy-level evidence, agency-level processes, and country-level context. The influence of organized patient advocacy and the structural flexibility of HTA systems are critical, previously under-quantified components of this alignment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fphar.2025.1695961","type":"journal-article","created":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T05:16:40Z","timestamp":1764566200000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A hierarchical and configurational analysis of Health Technology Assessment outcomes for cell and gene therapies"],"prefix":"10.3389","volume":"16","author":[{"given":"Ziyad S.","family":"Almalki","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Renad M.","family":"Alshammari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nadia A.","family":"Dahduli","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mouaddh Abdulmalik","family":"Nagi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Syeda","family":"Juweria","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Moayad M.","family":"Alhamdani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yazan M.","family":"Alzahrani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saja H.","family":"Almazrou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nehad Jaser","family":"Ahmed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdullah K.","family":"Alahmari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Areej A.","family":"Alshlowi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,12,1]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1297","DOI":"10.1007\/s40273-020-00956-w","article-title":"Recalibrating health technology assessment methods for cell and gene therapies","volume":"38","author":"Angelis","year":"2020","journal-title":"Pharmacoeconomics"},{"key":"B2","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1177\/1558689820937882","article-title":"A systematic methodological review of hierarchical linear modeling in mixed methods research","volume":"15","author":"Bash","year":"2020","journal-title":"J. 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