Modelled epidemiological data for selected congenital disorders in South Africa.
Helen Louise MalherbeColleen AldousArnold L ChristiansonMatthew W DarlisonBernadette ModellPublished in: Journal of community genetics (2021)
Congenital disorders (CD) remain an unprioritized health care issue in South Africa with national surveillance underreporting by > 95%. This lack of empiric data contributes to an underestimation of the CD disease burden, resulting in a lack of services for those affected. Modelling offers estimated figures for policymakers to plan services until surveillance is improved. This study applied the Modell Global Database (MGDb) method to quantify the South African CD disease burden in 2012. The MGDb combines birth prevalence data from well-established registries with local demographic data to generate national baseline estimates (birth prevalence and outcomes) for specific early-onset, endogenous CDs. The MGBd was adapted with local South African demographic data to generate baseline (no care) and current care national and provincial estimates for a sub-set of early-onset endogenous CDs. Access to care/impact of interventions was quantified using the infant mortality rate as proxy. With available care in 2012, baseline birth prevalence (27.56 per 1000 live births, n = 32,190) decreased by 7% with 2130 less affected births, with 5400 (17%) less under-5 CD-related deaths and 3530 (11%) more survivors at 5 years, including 4720 (15%) effectively cured and 1190 (4%) less living with disability. Results indicate a higher proportion of CD-affected births than currently indicated by national surveillance. By offering evidence-based estimates, the MGDb may be considered a tool for policymakers until accurate empiric data becomes available. Further work is needed on key CD groups and costing of specific interventions.
Keyphrases
- healthcare
- early onset
- quality improvement
- electronic health record
- south africa
- big data
- risk factors
- palliative care
- public health
- late onset
- gestational age
- affordable care act
- type diabetes
- mental health
- primary care
- nk cells
- quantum dots
- multiple sclerosis
- emergency department
- machine learning
- cardiovascular disease
- deep learning
- insulin resistance
- men who have sex with men
- chronic pain
- young adults
- hiv infected
- skeletal muscle
- adverse drug