{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T06:45:38Z","timestamp":1782456338342,"version":"3.54.5"},"reference-count":27,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T00:00:00Z","timestamp":1782432000000},"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>Traditional risk factors like smoking and air pollution remain insufficient to explain the persistent geographical variation in respiratory failure mortality across the United States, with significantly higher mortality observed in specific regions such as the Mississippi River Basin.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted a state-level ecological study using Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research (CDC WONDER) mortality data and 68 socio-environmental risk factors from Global Burden of Disease (GBD). Predictive modeling leveraged 12 machine learning algorithms (from linear to tree-based ensembles) with Recursive Feature Elimination (RFE) and SHapley Additive exPlanations (SHAP) interpretation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The optimal model Gradient Boosting Machine (GBM) achieved high accuracy (Root Mean Square Error (RMSE) = 0.7447, R-squared (R\n                      <jats:sup>2<\/jats:sup>\n                      ) = 0.872), identifying four dominant state-level predictors: short gestation, residential radon, intimate partner violence, and occupational nickel exposure. Key interactions, such as nickel-radon ecological co-patterns, were uncovered.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>These findings suggest an \u201cearly-life and cumulative exposure\u201d framework as a hypothesis-generating model. This framework identifies early-life vulnerability, environmental factors, and social stress as key state-level predictors that warrant further investigation. Our findings suggest a potential shift in research focus, moving from a reactive focus on adult-life risk factors toward proactive interventions targeting early-life vulnerabilities and cumulative environmental and social exposures.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1839753","type":"journal-article","created":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T05:45:38Z","timestamp":1782452738000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Identifying early-life, environmental, and social stressors as key predictors of U.S. respiratory failure mortality: a machine learning study"],"prefix":"10.3389","volume":"14","author":[{"given":"Changhua","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Emergency Medicine, Fuzong Clinical Medical College of Fujian Medical University, Fuzhou","place":["Fujian, China"]},{"name":"Department of Emergency, 900th 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Medical College of Fujian Medical University, Fuzhou","place":["Fujian, China"]},{"name":"Department of Emergency, 900th Hospital of PLA Joint Logistic Support Force, Fuzhou","place":["Fujian, China"]},{"name":"Department of Emergency Medicine, Fuzong Teaching Hospital of Fujian University of Traditional Chinese Medicine (900th Hospital), Fuzhou","place":["Fujian, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,6,26]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"2100","DOI":"10.1016\/s0140-6736(24)00367-2","article-title":"Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 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