{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T05:55:26Z","timestamp":1781762126532,"version":"3.54.5"},"reference-count":50,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T00:00:00Z","timestamp":1781740800000},"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. Glob. Women\u2019s Health"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Understanding the core diseases and comorbidity patterns of maternal complications during pregnancy and delivery is crucial for developing preventive strategies and improving pregnancy outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This cross-sectional study investigated maternal complications during pregnancy and delivery in women who gave birth at the Shanghai First Maternity and Infant Hospital between January 2023 and November 2025. A comorbidity network of maternal complications was constructed, and core comorbidities within the network were identified on the basis of topological network indicators. The Apriori association rule algorithm was applied to identify common binary comorbidity patterns in maternal health.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The average age of the 15,773 female participants was 31.12 (3.53) years. The most common pregnancy-related complication was anemia during pregnancy (27.95%), whereas the most common delivery-related complication was postpartum hemorrhage (21.24%). Network analysis revealed that gestational diabetes mellitus was positioned at the center of the comorbidity network and exhibited the highest strength centrality (strength\u2009=\u20091.61). When separate comorbidity networks were constructed for the advanced maternal age and nonadvanced maternal age groups, no significant differences were found between the networks (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&amp;gt;\u20090.05). The Apriori algorithm generated 40 association rules, with the strongest association based on the lift values between \u201cgestational diabetes mellitus and gestational hypertension\u201d (lift\u2009=\u20093.868).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The core complication in both the advanced maternal age and nonadvanced maternal age groups during pregnancy and delivery was gestational diabetes mellitus, with the core comorbidity pair being \u201cgestational diabetes mellitus and gestational hypertension\u201d. These findings suggest that prioritizing joint screening and integrated management of gestational diabetes mellitus and gestational hypertension may help reduce downstream complications and improve maternal outcomes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fgwh.2026.1824757","type":"journal-article","created":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T05:34:19Z","timestamp":1781760859000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Analysis of comorbidity patterns of pregnancy and delivery complications in the female population"],"prefix":"10.3389","volume":"7","author":[{"given":"Xiaoli","family":"Zhang","sequence":"first","affiliation":[{"name":"Nursing Department, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji 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