{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:16:53Z","timestamp":1787026613630,"version":"build-2736575974"},"reference-count":46,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"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. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Multiple sclerosis (MS) shows marked female predominance, particularly among women of childbearing age (WCBA, 15\u201349 years), yet their specific disease burden remains under characterized. Using Global Burden of Disease (GBD) 1990\u20132021 data, we conducted the first comprehensive analysis of MS trends in WCBA globally, with projections to 2040, to guide targeted interventions for this vulnerable population.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analyzed age-standardized incidence (ASIR), prevalence (ASPR), mortality (ASMR), and disability-adjusted life years (ASDR) rates across 204 countries using GBD 2021 data. Our analytical approach incorporated Pearson correlation to examine Socio-demographic Index (SDI) relationships, decomposition analysis to identify burden drivers, and frontier analysis to assess disease control efficiency gaps. We employed Bayesian Age-Period-Cohort modeling alongside ARIMA for 20-year projections, with joinpoint regression analyzing temporal trends through annual percentage changes. Age-period-cohort effects were further disentangled through APC modeling, with stratification by age group, SDI level, and geographic region.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Between 1990-2021, WCBA experienced a 48% rise in MS incidence and 66% increase in prevalence, with mortality growing by 17% despite a 7% DALYs reduction. Age-standardized rates improved (ASMR:-0.35; ASDR:-0.3), though burden varied regionally: Australasia showed greatest incidence growth versus East Asia\u2019s decline, while Central Latin America led prevalence increases. High-SDI countries (&amp;gt;0.4) carried disproportionate burden, peaking at ages 45-49 (incidence:30\u201334 years). Projections indicate rising crude rates but falling ASDR. Low-SDI nations (e.g., Cambodia) demonstrated optimal control versus high-SDI underperformers (e.g., Sweden). Birth cohorts revealed generally declining risk (RR1.146\u21920.805) except 2002-2006(RR1.413), with elevated DALYs(20-25y) and prevalence(30-35y) warranting targeted action.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>This study reveals key disparities in MS burden among WCBA, with high-SDI countries showing paradoxical high burden yet improvement potential. The 2002\u20132006 cohort rebound and persistent burdens in 30-49-year-olds highlight critical intervention windows. While low-SDI countries demonstrate unexpected control efficiency, rising crude rates globally underscore the growing women\u2019s health challenge of MS, particularly given projected prevalence increases. These findings advocate for tailored, age-specific management strategies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2026.1677178","type":"journal-article","created":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T04:19:02Z","timestamp":1777609142000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["The epidemiological trends of multiple sclerosis among women of child-bearing age: a global analysis from 1990 to 2021 and forecasts to 2040"],"prefix":"10.3389","volume":"17","author":[{"given":"Zhuo-lei","family":"Cai","sequence":"first","affiliation":[{"name":"Department of Neurology, The Second Affiliated Hospital of Anhui Medical University","place":["Hefei, Anhui, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Dong","sequence":"additional","affiliation":[{"name":"Department of Neurology, The First Affiliated Hospital of Zhengzhou 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