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      Comparison of adverse perinatal outcomes between Asians and Caucasians: a population-based retrospective cohort study in Ontario

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          Abstract

          Background

          Racial disparities in adverse perinatal outcomes have been studied in other countries, but little has been done for the Canadian population. In this study, we sought to examine the disparities in adverse perinatal outcomes between Asians and Caucasians in Ontario, Canada.

          Methods

          We conducted a population-based retrospective cohort study that included all Asian and Caucasian women who attended a prenatal screening and resulted in a singleton birth in an Ontario hospital (April 1st, 2015-March 31st, 2017). Generalized estimating equation models were used to estimate the independent adjusted relative risks and adjusted risk difference of adverse perinatal outcomes for Asians compared with Caucasians.

          Results

          Among 237,293 eligible women, 31% were Asian and 69% were Caucasian. Asians were at an increased risk of gestational diabetes mellitus, placental previa, early preterm birth (< 32 weeks), preterm birth, emergency cesarean section, 3rd and 4th degree perineal tears, low birth weight (< 2500 g, < 1500 g), small-for-gestational-age (<10 th percentile, <3 rd percentile), neonatal intensive care unit admission, and hyperbilirubinemia requiring treatment, but had lower risks of preeclampsia, macrosomia (birth weight > 4000 g), large-for-gestational-age neonates, 5-min Apgar score < 7, and arterial cord pH ≤7.1, as compared with Caucasians. No difference in risk of elective cesarean section was observed between Asians and Caucasians.

          Conclusion

          There are significant differences in several adverse perinatal outcomes between Asians and Caucasians. These differences should be taken into consideration for clinical practices due to the large Asian population in Canada.

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          Most cited references44

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          Multiple imputation of discrete and continuous data by fully conditional specification.

          The goal of multiple imputation is to provide valid inferences for statistical estimates from incomplete data. To achieve that goal, imputed values should preserve the structure in the data, as well as the uncertainty about this structure, and include any knowledge about the process that generated the missing data. Two approaches for imputing multivariate data exist: joint modeling (JM) and fully conditional specification (FCS). JM is based on parametric statistical theory, and leads to imputation procedures whose statistical properties are known. JM is theoretically sound, but the joint model may lack flexibility needed to represent typical data features, potentially leading to bias. FCS is a semi-parametric and flexible alternative that specifies the multivariate model by a series of conditional models, one for each incomplete variable. FCS provides tremendous flexibility and is easy to apply, but its statistical properties are difficult to establish. Simulation work shows that FCS behaves very well in the cases studied. The present paper reviews and compares the approaches. JM and FCS were applied to pubertal development data of 3801 Dutch girls that had missing data on menarche (two categories), breast development (five categories) and pubic hair development (six stages). Imputations for these data were created under two models: a multivariate normal model with rounding and a conditionally specified discrete model. The JM approach introduced biases in the reference curves, whereas FCS did not. The paper concludes that FCS is a useful and easily applied flexible alternative to JM when no convenient and realistic joint distribution can be specified.
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            A new and improved population-based Canadian reference for birth weight for gestational age.

            Existing fetal growth references all suffer from 1 or more major methodologic problems, including errors in reported gestational age, biologically implausible birth weight for gestational age, insufficient sample sizes at low gestational age, single-hospital or other non-population-based samples, and inadequate statistical modeling techniques. We used the newly developed Canadian national linked file of singleton births and infant deaths for births between 1994 and 1996, for which gestational age is largely based on early ultrasound estimates. Assuming a normal distribution for birth weight at each gestational age, we used the expectation-maximization algorithm to exclude infants with gestational ages that were more consistent with 40-week births than with the observed gestational age. Distributions of birth weight at the corrected gestational ages were then statistically smoothed. The resulting male and female curves provide smooth and biologically plausible means, standard deviations, and percentile cutoffs for defining small- and large-for-gestational-age births. Large-for-gestational age cutoffs (90th percentile) at low gestational ages are considerably lower than those of existing references, whereas small-for-gestational-age cutoffs (10th percentile) postterm are higher. For example, compared with the current World Health Organization reference from California (Williams et al, 1982) and a recently proposed US national reference (Alexander et al, 1996), the 90th percentiles for singleton males at 30 weeks are 1837 versus 2159 and 2710 g. The corresponding 10th percentiles at 42 weeks are 3233 versus 3086 and 2998 g. This new sex-specific, population-based reference should improve clinical assessment of growth in individual newborns, population-based surveillance of geographic and temporal trends in birth weight for gestational age, and evaluation of clinical or public health interventions to enhance fetal growth. fetal growth, birth weight, gestational age, preterm birth, postterm birth.
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              Overadjustment bias and unnecessary adjustment in epidemiologic studies.

              Overadjustment is defined inconsistently. This term is meant to describe control (eg, by regression adjustment, stratification, or restriction) for a variable that either increases net bias or decreases precision without affecting bias. We define overadjustment bias as control for an intermediate variable (or a descending proxy for an intermediate variable) on a causal path from exposure to outcome. We define unnecessary adjustment as control for a variable that does not affect bias of the causal relation between exposure and outcome but may affect its precision. We use causal diagrams and an empirical example (the effect of maternal smoking on neonatal mortality) to illustrate and clarify the definition of overadjustment bias, and to distinguish overadjustment bias from unnecessary adjustment. Using simulations, we quantify the amount of bias associated with overadjustment. Moreover, we show that this bias is based on a different causal structure from confounding or selection biases. Overadjustment bias is not a finite sample bias, while inefficiencies due to control for unnecessary variables are a function of sample size.
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                Author and article information

                Contributors
                yguo@bornontario.ca
                Journal
                BMC Pregnancy Childbirth
                BMC Pregnancy Childbirth
                BMC Pregnancy and Childbirth
                BioMed Central (London )
                1471-2393
                5 January 2021
                5 January 2021
                2021
                : 21
                : 9
                Affiliations
                [1 ]GRID grid.28046.38, ISNI 0000 0001 2182 2255, School of Epidemiology and Public Health, University of Ottawa, ; Ottawa, Ontario Canada
                [2 ]GRID grid.412687.e, ISNI 0000 0000 9606 5108, OMNI Research Group, Clinical Epidemiology Program, , Ottawa Hospital Research Institute, ; Ottawa, Ontario Canada
                [3 ]GRID grid.414148.c, ISNI 0000 0000 9402 6172, Better Outcomes Registry & Network Ontario, Children’s Hospital of Eastern Ontario, ; Ottawa, Ontario Canada
                [4 ]GRID grid.412687.e, ISNI 0000 0000 9606 5108, Ottawa Hospital Research Institute, ; Ottawa, Ontario Canada
                [5 ]GRID grid.414148.c, ISNI 0000 0000 9402 6172, Children’s Hospital of Eastern Ontario Research Institute, ; Ottawa, Ontario Canada
                [6 ]GRID grid.28046.38, ISNI 0000 0001 2182 2255, Department of Obstetrics and Gynecology, , University of Ottawa Faculty of Medicine, ; Ottawa, Ontario Canada
                Article
                3467
                10.1186/s12884-020-03467-w
                7786932
                33402112
                bbb66441-b7de-45bc-8ecf-85660c678285
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 20 September 2020
                : 1 December 2020
                Funding
                Funded by: Canadian Institutes of Health Research (CIHR)
                Award ID: FDN-148438
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2021

                Obstetrics & Gynecology
                Obstetrics & Gynecology

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