{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T08:28:35Z","timestamp":1765960115491,"version":"3.48.0"},"reference-count":48,"publisher":"BMJ","issue":"10","license":[{"start":{"date-parts":[[2020,10,20]],"date-time":"2020-10-20T00:00:00Z","timestamp":1603152000000},"content-version":"unspecified","delay-in-days":19,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31771468"],"award-info":[{"award-number":["31771468"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["BMJ Open"],"accepted":{"date-parts":[[2020,9,8]]},"published-print":{"date-parts":[[2020,10]]},"abstract":"<jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To investigate time trends of preterm birth and estimate the contributions of risk factors to the changes in preterm birth rates over a decade (2009\u20132018) of transitional period in Shenzhen, China.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design<\/jats:title>\n                    <jats:p>Retrospective cohort study between 2009 and 2018.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Setting<\/jats:title>\n                    <jats:p>All births in Baoan during January 2009 and December 2018 registered in the Shenzhen Birth Registry Database.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Participants<\/jats:title>\n                    <jats:p>478\u2009044 live births were included with sociodemographic and medical records for both women and infants.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Outcome measures<\/jats:title>\n                    <jats:p>The incidence rate of preterm birth stratified by different maternal and infant characteristics. Multiple logistic regression was used to identify significant risk factors associated with preterm birth. The population attributable risk fraction of each factor was calculated to estimate its contribution to variations of preterm birth rate over the 10 years.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 27\u2009829 preterm births from 478\u2009044 (5.8%) live births were recorded and the preterm birth rate increased from 5.5% in 2009 to 6.2% in 2018. Medically induced preterm birth rate increased from 2.0% in 2009 to 3.4% in 2018 while spontaneous preterm labour rate decreased from 3.3% to 2.7% over the decade years. Risk factors including multiple pregnancy (0.28% increase) drove the rise of preterm birth rate, whereas changes in maternal educational attainment (0.22% reduction) and prenatal care utilisation (0.45% reduction) had contributed to the decline in preterm birth rate.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>An uptrend of preterm birth rate was observed in an area under rapid sociodemographic transitions during 2009\u20132018 and the changes were associated with these sociodemographic transitions. Continued investments in girls\u2019 education and prenatal care have the potential of reducing preterm birth rate.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/bmjopen-2020-037266","type":"journal-article","created":{"date-parts":[[2020,10,20]],"date-time":"2020-10-20T17:10:29Z","timestamp":1603213829000},"page":"e037266","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":12,"title":["Ten-year time trends in preterm birth during a sociodemographic transition period: a retrospective cohort study in Shenzhen, China"],"prefix":"10.1136","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0910-3164","authenticated-orcid":false,"given":"Rui","family":"Ma","sequence":"first","affiliation":[{"name":"Center for Big Data Research in Health, Shenzhen Baoan Women's and Children's Hospital, Jinan University, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yali","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Prevention & Health Care, Shenzhen Baoan Women's and Children's Hospital, Jinan University, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Wang","sequence":"additional","affiliation":[{"name":"Center for Big Data Research in Health, Shenzhen Baoan Women's and Children's Hospital, Jinan University, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanxia","family":"Zhou","sequence":"additional","affiliation":[{"name":"Center for Big Data Research in Health, Shenzhen Baoan Women's and Children's Hospital, Jinan University, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haiyang","family":"Sun","sequence":"additional","affiliation":[{"name":"Center for Big Data Research in Health, Shenzhen Baoan Women's and Children's Hospital, Jinan University, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xi","family":"Ren","sequence":"additional","affiliation":[{"name":"Center for Big Data Research in Health, Shenzhen Baoan Women's and Children's Hospital, Jinan University, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Quan","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Prevention & Health Care, Shenzhen Baoan Women's and Children's Hospital, Jinan University, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lian","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Neonatology, Shenzhen Baoan Women's and Children's Hospital, Jinan University, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1109-5709","authenticated-orcid":false,"given":"Lingyun","family":"Zou","sequence":"additional","affiliation":[{"name":"Center for Big Data Research in Health, Shenzhen Baoan Women's and Children's Hospital, Jinan University, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"239","published-online":{"date-parts":[[2020,10,20]]},"reference":[{"key":"2025121510251218000_10.10.e037266.1","doi-asserted-by":"publisher","DOI":"10.1126\/science.1251816"},{"key":"2025121510251218000_10.10.e037266.2","doi-asserted-by":"crossref","first-page":"1423","DOI":"10.1016\/j.jogc.2019.02.005","article-title":"Phenotypic classification of preterm birth among nulliparous women: a population-based cohort study","volume":"41","author":"Maghsoudlou","year":"2019","journal-title":"J Obstet Gynaecol Can"},{"key":"2025121510251218000_10.10.e037266.3","doi-asserted-by":"publisher","DOI":"10.1186\/1742-4755-10-S1-S6"},{"key":"2025121510251218000_10.10.e037266.4","unstructured":"United Nations Inter-agency Group for Child Mortality Estimation (UNIGME) . 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