Congenital anomaly registries have two main surveillance aims: firstly to define baseline epidemiology of important congenital anomalies to facilitate programme, policy and resource planning, and secondly to identify clusters of cases and any other epidemiological changes that could give early warning of environmental or infectious hazards. However, setting up a sustainable registry and surveillance system is resource-intensive requiring national infrastructure for recording all cases and diagnostic facilities to identify those malformations that that are not externally visible. Consequently, not all countries have yet established robust surveillance systems. For these countries, methods are needed to generate estimates of prevalence of these disorders which can act as a starting point for assessing disease burden and service implications. Here, we describe how registry data from high-income settings can be used for generating reference rates that can be used as provisional estimates for countries with little or no observational data on non-syndromic congenital malformations.

In the absence of intervention, early-onset congenital disorders lead to pregnancy loss, early death, or disability. Currently, lack of epidemiological data from many settings limits the understanding of the burden of these conditions, thus impeding health planning, policy-making, and commensurate resource allocation. The Modell Global Database of Congenital Disorders (MGDb) seeks to meet this need by combining general biological principles with observational and demographic data, to generate estimates of the burden of congenital disorders. A range of interventions along the life course can modify adverse outcomes associated with congenital disorders. Hence, access to and quality of services available for the prevention and care of congenital disorders affects both their birth prevalence and the outcomes for affected individuals. Information on this is therefore important to enable burden estimates for settings with limited observational data, but is lacking from many settings. This paper, the third in this special issue on methods used in the MGDb for estimating the global burden of congenital disorders, describes key interventions that impact on outcomes of congenital disorders and methods used to estimate their coverage where empirical data are not available.

Chromosomal disorders, of which Down syndrome is the most common, can cause multi-domain disability. In addition, compared to the general population, there is a higher frequency of death before the age of five. In many settings, large gaps in data availability have hampered policy-making, programme priorities and resource allocation for these important conditions. We have developed methods, which overcome this lack of data and allow estimation of the burden of affected pregnancies and their outcomes in different settings worldwide. For example, the methods include a simple equation relating the percentage of mothers 35 and over to Down syndrome birth prevalence. The results obtained provide a starting point for consideration of services that can be implemented for the care and prevention of these disorders.

As child mortality rates overall are decreasing, non-communicable conditions, such as genetic disorders, constitute an increasing proportion of child mortality, morbidity and disability. To date, policy and public health programmes have focused on common genetic disorders. Rare single gene disorders are an important source of morbidity and premature mortality for affected families. When considered collectively, they account for an important public health burden, which is frequently under-recognised. To document the collective frequency and health burden of rare single gene disorders, it is necessary to aggregate them into large manageable groupings and take account of their family implications, effective interventions and service needs. Here, we present an approach to estimate the burden of these conditions up to 5 years of age in settings without empirical data. This approaches uses population-level demographic data, combined with assumptions based on empirical data from settings with data available, to provide population-level estimates which programmes and policy-makers when planning services can use.

Congenital disorders are an important cause of pregnancy loss, premature death and life-long disability. A range of interventions can greatly reduce their burden, but the absence of local epidemiological data on their prevalence and the impact of interventions impede policy and service development in many countries. In an attempt to overcome these deficiencies, we have developed a tool-The Modell Global Database of Congenital Disorders (MGDb) that combines general biological principles and available observational data with demographic data, to generate estimates of the birth prevalence and effects of interventions on mortality and disability due to congenital disorders. MGDb aims to support policy development by generating country, regional and global epidemiological estimates. Here we provide an overview of the concepts and methodological approach used to develop MGDb.