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      Understanding the cumulative risk of maternal prenatal biopsychosocial factors on birth weight: a DynaHEALTH study on two birth cohorts

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          Abstract

          Background

          There are various maternal prenatal biopsychosocial (BPS) predictors of birth weight, making it difficult to quantify their cumulative relationship.

          Methods

          We studied two birth cohorts: Northern Finland Birth Cohort 1986 (NFBC1986) born in 1985–1986 and the Generation R Study (from the Netherlands) born in 2002–2006. In NFBC1986, we selected variables depicting BPS exposure in association with birth weight and performed factor analysis to derive latent constructs representing the relationship between these variables. In Generation R, the same factors were generated weighted by loadings of NFBC1986. Factor scores from each factor were then allocated into tertiles and added together to calculate a cumulative BPS score. In all cases, we used regression analyses to explore the relationship with birth weight corrected for sex and gestational age and additionally adjusted for other factors.

          Results

          Factor analysis supported a four-factor structure, labelled closely to represent their characteristics as ‘Factor1-BMI’ (body mass index), ‘Factor2-DBP’ (diastolic blood pressure), ‘Factor3-Socioeconomic-Obstetric-Profile’ and ‘Factor4-Parental-Lifestyle . In both cohorts, ‘Factor1-BMI’ was positively associated with birth weight, whereas other factors showed negative association. ‘Factor3-Socioeconomic-Obstetric-Profile’ and ‘Factor4-Parental-Lifestyle’ had the greatest effect size, explaining 30% of the variation in birth weight. Associations of the factors with birth weight were largely driven by ‘Factor1-BMI’. Graded decrease in birth weight was observed with increasing cumulative BPS score, jointly evaluating four factors in both cohorts.

          Conclusion

          Our study is a proof of concept for maternal prenatal BPS hypothesis, highlighting the components snowball effect on birth weight in two different European birth cohorts.

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          Most cited references37

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          Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives

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            The need for a new medical model: a challenge for biomedicine.

            The dominant model of disease today is biomedical, and it leaves no room within tis framework for the social, psychological, and behavioral dimensions of illness. A biopsychosocial model is proposed that provides a blueprint for research, a framework for teaching, and a design for action in the real world of health care.
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              The cost of dichotomising continuous variables.

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                Author and article information

                Journal
                J Epidemiol Community Health
                J Epidemiol Community Health
                jech
                jech
                Journal of Epidemiology and Community Health
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                0143-005X
                1470-2738
                November 2020
                1 November 2020
                : 74
                : 11
                : 933-941
                Affiliations
                [1 ]departmentCenter for Life Course Health Research, University of Oulu , Oulu, Finland
                [2 ]departmentSchool of Natural and Built Environment, Queen’s University Belfast , Belfast, UK
                [3 ]departmentDepartment of Epidemiology, Erasmus Medical Center , Rotterdam, Netherlands
                [4 ]departmentThe Generation R Study Group, Erasmus Medical Centre , Rotterdam, The Netherlands
                [5 ]departmentDepartment of Pediatrics, Erasmus Medical Centre , Rotterdam, The Netherlands
                [6 ]departmentDepartment of Child and Adolescent Psychiatry, Erasmus MC – Sophia Children’s Hospital , Rotterdam, The Netherlands
                [7 ]departmentDepartment of Psychology, Education and Child Studies, Erasmus School of Social and Behavioral Sciences, Erasmus University Rotterdam , Rotterdam, The Netherlands
                [8 ]departmentDepartment of Medical Statistics, London School of Hygiene and Tropical Medicine, Faculty of Epidemiology and Population Health , London, UK
                [9 ]departmentDepartment of Epidemiology and Bio-statistics, School of Public Health, Imperial College London , London, UK
                [10 ]departmentMedical Research Center (MRC) Oulu, University of Oulu , Oulu, Finland
                [11 ]departmentDepartment of Gastroenterology and Metabolism, Poznan University of Medical Sciences , Poznan, Poland
                [12 ]departmentDepartment of Clinical and Experimental Medicine, School of Biosciences and Medicine, University of Surrey , Guildford, UK
                [13 ]departmentDepartment of Metabolism, Digestion and Reproduction, Genomic Medicine, Faculty of Medicine, Imperial College London , London, UK
                [14 ]departmentUnit of Primary Health Care, Oulu University Hospital , Oulu, Finland
                [15 ]departmentMRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London , London, UK
                [16 ]departmentDepartment of Life Sciences, College of Health and Life Sciences, Brunel University London , London, UK
                Author notes
                Correspondence to Sylvain Sebert, Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland; sylvain.sebert@ 123456oulu.fi and Marjo-Riitta Järvelin, Department of Epidemiology and Biostatistics, Faculty of Medicine, Imperial College London, London, UK; m.jarvelin@ 123456imperial.ac.uk
                Author information
                http://orcid.org/0000-0001-6681-6983
                Article
                jech-2019-213154
                10.1136/jech-2019-213154
                7577640
                32581064
                e3c51224-c3fa-4386-93f4-a271b7189f0c
                © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

                This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 31 August 2019
                : 21 May 2020
                : 30 May 2020
                Funding
                Funded by: EU Horizon 2020;
                Award ID: 633595
                Award ID: 733206
                Funded by: FundRef http://dx.doi.org/10.13039/501100002341, Academy of Finland;
                Award ID: 285547
                Categories
                Original Research

                Public health
                ageing,diabetes,disability,child health,maternal health,birth defects,cardiovascular disease,birth weight,epidemiology of chronic non communicable diseases,life course epidemiology,cohort studies,biostatistics

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