{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T05:04:39Z","timestamp":1789362279309,"version":"build-2803163510"},"reference-count":16,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T00:00:00Z","timestamp":1789344000000},"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. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Cognitive health-related vulnerability is closely associated with multidimensional public health factors, including chronic disease burden, functional limitation, healthcare access, socioeconomic vulnerability, health behaviors, and preventive service utilization. However, latent population heterogeneity embedded in routine public health survey variables remains insufficiently explored.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aims to develop an unsupervised semantic stratification framework based on BRFSS 2024 data to identify adult population subgroups associated with cognitive health-related vulnerability from a public health perspective.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Routine public health variables were selected, cleaned, imputed, and standardized, while cognitive reference variables were excluded from model training. An autoencoder was used to learn low-dimensional latent representations, followed by MiniBatchKMeans clustering to identify semantic subgroups. Each subgroup was further analyzed through public health profiling, held-out cognitive reference validation, non-redundant feature signature analysis, and intervention priority assessment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Four interpretable semantic subgroups were identified: the low-burden subgroup, general health vulnerability subgroup, health behavior risk subgroup, and preventive service gap subgroup. These subgroups showed distinct patterns in public health burden, cognitive reference variables, key feature signatures, and intervention priorities.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Routine public health survey data can support the identification of latent adult subgroups associated with cognitive health-related vulnerability and provide exploratory evidence for population monitoring, priority screening, resource allocation, and stratified intervention design. This framework is not a diagnostic, prognostic, or causal inference model, and further validation using weighted, longitudinal, cross-year, and external data is needed.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1878119","type":"journal-article","created":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T04:34:26Z","timestamp":1789360466000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Unsupervised discovery of public health subgroups associated with cognitive health vulnerability in adults: an analysis of BRFSS 2024"],"prefix":"10.3389","volume":"14","author":[{"given":"Ziyue","family":"Wang","sequence":"first","affiliation":[{"name":"School of Nursing, Midwifery & Evidence Science, University of Galway","place":["Galway, Ireland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joshi","family":"Dookhy","sequence":"additional","affiliation":[{"name":"School of Nursing, Midwifery & Evidence Science, University of Galway","place":["Galway, Ireland"]},{"name":"School of Nursing & Midwifery, University College Dublin","place":["Dublin, Ireland"]},{"name":"Galway University Hospitals","place":["Galway, Ireland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hossein","family":"Khaleghy","sequence":"additional","affiliation":[{"name":"School of Computer Science, University of Galway","place":["Galway, Ireland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Soumyajyoti","family":"Maji","sequence":"additional","affiliation":[{"name":"Discipline of Electrical & Electronic Engineering, University of Galway","place":["Galway, Ireland"]},{"name":"Institute for Clinical Trials, University of Galway","place":["Galway, Ireland"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuejia","family":"Yang","sequence":"additional","affiliation":[{"name":"Xiangya School of Nursing, Central South 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