An investigation into the impact of congenital anomalies on infant morbidity and mortality
Keywords: hospitalisation of infants born with congenital anomalies: a structured literature review
reasons for and outcomes of hospitalisation comparing infants with and without congenital anomalies
an investigation into the nature and clinical features of congenital anomalies leading to infant mortality
an investigation into the patterns
neonatal and infant mortality associated with encephalocele: a systematic review
neonatal and infant mortality associated with spina bifida: a systematic review and meta-analysis

Background
Congenital anomalies consist of a heterogeneous group of conditions involving structural, metabolic, endocrine, and genetic defects that occur during intrauterine development; they contribute significantly to infant morbidity and mortality. However, little is known about how particular anomalies and their associated conditions lead to excess infant morbidity and mortality. Population-based study evidence is limited and inadequate to inform clinical and public health practices. The aim of this research is to investigate the impact of congenital anomalies on infant mortality and infant hospitalisation as a proxy for morbidity.
Methods
Two systematic reviews were conducted specifically to examine the contemporary evidence concerning infant mortality associated with spina bifida and encephalocele respectively, and a structured literature review to examine the contemporary evidence relating to infant hospitalisation associated with congenital anomalies.
A population-based cohort study was conducted using linked de-identified data from the Welsh national Congenital Anomaly Register and Information Service (CARIS) and Office for National Statistics (ONS) between 1998 and 2017. Descriptive statistics were used to analyse the rates and patterns of infant mortality by anomaly subgroup. Logistic regression was used to estimate the odds ratio for risk factors contributing to excess infant case fatality due to congenital anomalies; confounders were adjusted for in multivariable logistic regression analysis.
The second population-based cohort study used linked de-identified data from the Patient Episode Database for Wales (PEDW), CARIS and ONS between 1999 and 2016. Survival analysis was conducted to estimate the admission rate, and descriptive statistics to examine patterns of infant hospitalisation by anomaly subgroup. Multinomial logistic regression was carried out to investigate the relative risk ratios of hospital admission in infancy for infants with congenital anomalies.
Results
Anomaly-related deaths represented almost one-third of all infant deaths. Multiple anomalies and structural anomalies involving vital organ systems were associated with the highest infant mortality rate and case fatality. Congenital anomalies and associated morbidities were the most common causes of death for infants with anomalies who died. Preterm birth was associated with a four-fold increased odds of infant case fatality in all anomaly subgroups. The effects of maternal ethnicity, maternal smoking, parity, infant sex, anomaly severity and surgery varied according to anomaly subgroups.
Infants with congenital anomalies were at a greater odds of more frequent and longer admissions compared to infants without an anomaly. Congenital anomalies and the associated diseases, and respiratory conditions were the most common primary causes of hospital admissions among infants with anomalies. Preterm birth, younger mother, maternal smoking and male sex (except for genitourinary anomalies) were independently associated with increased risks of anomaly-related infant hospitalisation.
Conclusions
Congenital anomalies and their associated complications are significant causes of infant hospitalisation and mortality, with various risk factors playing a significant role. Generalisability of the analytical findings beyond the UK may be limited due to variations in anomaly definitions, health care systems, admission thresholds, neonatal practices and registration of extremely premature births over time and between countries. Further studies should aim at using big data with large national population-based studies to minimise this heterogeneity.