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Aetiological Pathways of Congenital Anomalies in High-Burden Settings: A Multi-site Epidemiological Study - NU/LSHTM project

Supervisory team

LSHTM

Nagasaki University

Project

Background 

Congenital anomalies represent a major, yet poorly understood, global health challenge, contributing significantly to perinatal mortality and lifelong disability[1]. The prevention of these conditions is hindered by a limited understanding of their complex aetiologies[2]. Identifying high-risk pregnancies and developing targeted preventative strategies requires a clearer understanding of the complex interplay between maternal factors (e.g., nutrition and infections), environmental exposures, and fetal outcomes[3]. This project will address this critical knowledge gap by using an epidemiological approach to analyse data from large-scale, multi-site surveillance networks, aiming to quantify associations and identify high-risk profiles.

Aims and Objectives 

The overarching aim of this research is to elucidate the complex aetiological pathways leading to major congenital anomalies in diverse, low- and middle-income settings.

The specific objectives are:

  1. To identify and quantify the associations between key maternal risk factors, specific pathogens and intermediate biological markers, and the occurrence of major congenital anomalies.
  2. To conduct a comparative analysis across different geographical regions to explore context-specific versus universal risk factors and pathways.
  3. To develop an integrated epidemiological model that clarifies the interplay between maternal exposures, intermediate factors, and foetal outcomes.

Methods 

This project will be a large-scale epidemiological study, employing a case-control design, using existing data from pregnancy and birth defect surveillance and registries for example the sub-Saharan African Congenital Anomaly Network (sSCAN)[4], South-East Asia Region Newborn and Birth Defects Surveillance Initiative and Child Health and Mortality Prevention Surveillance (CHAMPS) network[5]. Cases will be liveborn and stillborn babies with major congenital anomalies, while appropriately selected controls will be births without such conditions.

A comprehensive range of variables - including maternal health history, environmental exposures, and detailed clinical, microbiological and pathological data from the perinatal period where available - will be analysed. Quantitative analytical methods, including advanced multivariable modelling and pathway analysis, will be applied to assess the strength of associations. By investigating how maternal and environmental factors relate to specific biological markers and subsequently to the risk of congenital anomalies, this research will provide crucial epidemiological evidence to inform future public health interventions.

References

  1. Perin J, Mai CT, De Costa A, et al. Systematic estimates of the global, regional and national under-5 mortality burden attributable to birth defects in 2000-2019: a summary of findings from the 2020 WHO estimates. BMJ Open. 2023;13(1):e067033. Published 2023 Jan 30. doi:10.1136/bmjopen-2022-067033
  2. Moorthie S, Blencowe H, W Darlison M, et al. An overview of concepts and approaches used in estimating the burden of congenital disorders globally. J Community Genet. 2018 Oct;9(4):347-362. doi: 10.1007/s12687-017-0335-3
  3. Blencowe H, Kancherla V, Moorthie S et al. Estimates of global and regional prevalence of neural tube defects for 2015: a systematic analysis. Ann N Y Acad Sci. 2018 Feb;1414(1):31-46. doi: 10.1111/nyas.13548
  4. Leke AZ, Malherbe H, Kalk E, et al. The burden, prevention and care of infants and children with congenital anomalies in sub-Saharan Africa: A scoping review. PLOS Glob Public Health. 2023 Jun 28;3(6):e0001850. doi: 10.1371/journal.pgph.0001850.
  5. Mahtab S, Madhi SA, Baillie VL, et al. Causes of death identified in neonates enrolled through Child Health and Mortality Prevention Surveillance (CHAMPS), December 2016 -December 2021. PLOS Glob Public Health. 2023;3(3):e0001612. doi:10.1371/journal.pgph.0001612.

The role of LSHTM and NU in this collaborative project

This project leverages the distinct, complementary strengths of both institutions to address the complex aetiology of congenital anomalies.

LSHTM provides world-leading expertise in global health epidemiology, statistics, and maternal/child health. The supervisor (Assoc. Prof. Blencowe) is a leading expert in the epidemiology of stillbirths, congenital anomalies and neonatal health, ensuring robust methodological leadership in advanced statistical modelling and the analysis of large-scale, multi-site surveillance data.

NU offers specialist expertise in paediatric infectious diseases and clinical tropical medicine. The supervisor (Prof. Moriuchi) is an expert in vertical transmission, which is critical for interpreting the complex interplay of clinical, pathological, and microbiological data (e.g., infectious agents).

The collaboration integrates LSHTM's advanced quantitative epidemiology with NU's deep clinical and infectious disease expertise. This supervisory team is ideally positioned to analyse existing surveillance data and elucidate the complex pathways leading to congenital anomalies in high-burden settings.

Particular prior educational requirements for a student undertaking this project

  • A master’s degree (or equivalent professional experience) in public health, reproductive health, epidemiology, or a related field relevant to the project.
  • Demonstrable experience in handling and analysing large-scale quantitative health datasets.
  • Previous experience related to the understanding of congenital anomalies.

Skills we expect a student to develop/acquire whilst pursuing this project

  • Expertise in conducting literature reviews and critical appraisal of evidence related to congenital anomalies and maternal/child health.
  • Advanced skills in managing and analysing complex, multi-site epidemiological data.
  • Proficiency in advanced quantitative analysis using statistical software (primarily Stata or R).
  • Competency in developing and applying sophisticated epidemiological methods, including causal pathway modelling and mediation analysis.
  • High-level skills in scientific writing for peer-reviewed publications, data visualisation, and effective communication of research findings.