Analysing observational data on early life survival |
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The RSS General Applications and Medical Sections have organised an afternoon of talks on the challenges around analysing observational data on early life survival (including capturing information about terminations, still births, and infant deaths). Confirmed speakers are: Free to attend for Fellows (pre-registration required), with a small charge for non-Fellows (please click on the 'register now button' for further details). Dr. Brad Manktelow (senior Lecturer, The Infant Mortality and Morbidity Studies, University of Leicester): Since 2013 MBRRACE-UK has been commissioned to undertake the national surveillance of stillbirth and neonatal death in the UK. As part of the MBRRACE-UK annual report, stabilised & adjusted mortality rates are estimated using mixed-effects logistic regression models and are reported for organisations based on residence and on place of care. In this talk the data collection methods, the statistical models and the reported outcomes of MBRRACE-UK will be described and discussed.
England has some of the highest child mortality rates in Western Europe, while Sweden has some of the lowest. In both countries the majority of deaths occur in the first year of life. These differences have been commonly attributed to differences in quality of healthcare for children. However, any differences are also likely to be at least partly explained by the prevalence of risk factors at birth, such as low birthweight, preterm birth or presence of congenital anomalies. The use of individual-level data with detailed information about birth characteristics allows us to determine which factors before and after birth contribute most to the gap in mortality between England and Sweden. Such comparisons can be used to guide policy decisions on preventing infant deaths through improving maternal health before and during pregnancy or focusing on the care of babies after birth. In this talk I will present methods for cross-country comparisons of child mortality using administrative-linked datasets. I will discuss the challenges for developing comparable birth cohorts and show how results can be used to inform preventive strategies. These methods are also highly relevant to other cross-country comparisons. Abstract: Large sample sizes are required to achieve adequate statistical power to study the most severe pregnancy outcomes such as stillbirth, using a cohort design. Complications including fetal growth restriction or excessive growth, pre-eclampsia, gestational diabetes, preterm birth and emergency caesarean section contribute to the risk of stillbirth and neonatal morbidity, and they can be used as alternative outcomes in risk prediction when the sample size is limited. The availability of multiple types of exposure data (ultrasonic measurements, blood biomarkers and ‘omics’ data) measured repeatedly throughout gestation is expected to improve the prediction of pregnancy outcomes. Identification of high-risk pregnancies will inform the design of trials and interventions. I will give examples from the Pregnancy Outcome Prediction study.
Patau and Edwards syndrome are severely disabling syndrome with only ten percent of live born babies surviving more than 1 year. Both syndromes can be screened for in pregnancy and if the mother is at a high risk an invasive diagnostic test can be performed to determine whether the fetus is affected or not. On receiving a diagnosis most women decide to terminate the pregnancy, but some do not. Recent studies have been interpreted to suggest that a prenatal diagnosis may be detrimental to the survival of the baby. This poses a difficult dilemma for women who would not consider a termination – should they risk the diagnostic test or not? To register, please follow this link. |
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Free to RSS Fellows, £25 for non-Fellows Organiser Name Pia Hardelid (for any queries, not to register) Email Address This email address is being protected from spambots. You need JavaScript enabled to view it. Organising Group(s) RSS General Applications Section and Medical Section
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