{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T01:55:32Z","timestamp":1777600532161,"version":"3.51.4"},"reference-count":46,"publisher":"BMJ","issue":"2","license":[{"start":{"date-parts":[[2025,8,16]],"date-time":"2025-08-16T00:00:00Z","timestamp":1755302400000},"content-version":"unspecified","delay-in-days":46,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Amsterdam Public Health Research Institute","award":["NA"],"award-info":[{"award-number":["NA"]}]}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["bmjph"],"accepted":{"date-parts":[[2025,6,5]]},"published-print":{"date-parts":[[2025,7]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The first 1000 days of life are a crucial foundational period during which many different factors can impact development. It is unknown to what extent different factors cluster and how this affects later-life outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this population-based cohort study, we used registry data of all children born in the Netherlands in 2006. We used latent class analysis to investigate clustering of circumstances in the first 1000 days of life, including socioeconomic indicators (household income, parental education), prenatal and perinatal biomedical factors (maternal age, late-start antenatal care, preterm birth\/born small for gestational age\/poor start in life), and adverse childhood experiences in the first 1000 days (parental death, separation, mental health problems and detention) and associated clusters with school performance (ie, highest secondary school level advice at age 12).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In the study population of 181\u2009575 children, we identified five clusters. We labelled cluster 1 (39%) and cluster 2 (27%) as \u2018resource-richest\u2019, clusters 3 (15%) and 4 (15%) as \u2018intermediate\u2019, and cluster 5 (5%) as \u2018resource-poorest\u2019, with the latter having the highest probabilities of low socioeconomic resources, adverse prenatal and perinatal biomedical factors and adverse childhood experiences in the first 1000 days. Compared with those in the resource-richest cluster (cluster 2), children in the resource-poorest cluster (cluster 5) had poorer school performance (OR 0.13, 95%\u2009CI 0.11 to 0.14), also after adjustment for parental education and household income (OR 0.20, 95%\u2009CI 0.18 to 0.24).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Clustering of risk factors across different domains during the first 1000 days of life was associated with poorer school performance at age 12, suggesting that children growing up in resource-limited environments during this critical developmental window may face challenges in reaching their full developmental and educational potential. If we find similar associations with health-related outcomes, this would further underscore the importance of policies that strengthen resources across multiple domains early in life to support long-term human potential.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/bmjph-2024-002176","type":"journal-article","created":{"date-parts":[[2025,8,17]],"date-time":"2025-08-17T22:20:36Z","timestamp":1755469236000},"page":"e002176","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":1,"title":["Clustering of circumstances during the first 1000 days after conception and their association with school performance: a population-based cohort study from the Netherlands"],"prefix":"10.1136","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4058-0487","authenticated-orcid":false,"given":"Malon","family":"Van den Hof","sequence":"first","affiliation":[{"name":"Department of Epidemiology and Data Science, Amsterdam UMC, 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