Abstract
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.
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Notes
Congenital anomalies are defined as macroscopic morphological anomalies present at birth and represented by chapter XVII Congenital malformations, deformations and chromosomal abnormalities of the International Statistical Classification of Diseases and Related Health Problems 10th Revision ICD-10)(World Health Organization 1992, 2006).
Supplementary file TA01-Bottom-Line-WHO-2017-04.xlsx at https://discovery.ucl.ac.uk/id/eprint/1532179/
Serious birth defects cause death or disability in the absence of intervention (Christianson et al. 2006).
Baseline outcomes include fetal deaths/still births; live births; neonatal, infant and under-5 deaths (CD related); deaths from other causes; survivors with disability at age 5; and mean life expectancy.
In the MGDb context, optimal care is defined as the standard of care available in high-income settings with equitable access to services, at any given point in time.
Within the School of Clinical Medicine 2013-2019 and with the KwaZulu Natal Research Innovation and Sequencing Platform (KRISP), School of Laboratory Medicine and Medical Sciences from 2019 to 2020.
A wider review suggested that major differences are uncommon but the possibility should be considered.
For details on this calculation see Blencowe et al. 2018a.
Primary prevention, e.g. folate fortification, genetic counselling etc. resulting in the prevention of affected conceptions.
Secondary preventions, e.g. PND, genetic counselling, option of TOP resulting in the avoidance of affected births.
Tertiary prevention (care) includes newborn screening, diagnosis, therapeutic and surgical interventions, rehabilitation and palliative care, mitigating the impact of affected births and improving outcomes.
University of Cape Town/Groote Schuur Hospital/Red Cross War Memorial Children’s Hospital; Stellenbosch University/Tygerburg Hospital; University of the Free State/Universitas Hospital; University of KwaZulu-Natal/Inkhosi Albert Luthuli Central Hospital (pending registration); National Health Laboratory Service/University of the Witwatersrand
Congenital abnormalities are considered equivalent to congenital anomalies (Pillay-Van Wyk et al. 2014).
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Acknowledgements
Thank you to Prof Rob Dorrington, Centre for Actuarial Research (CARe), University of Cape Town, for providing demographic data for use in this study; Prof Debbie Bradshaw, Burden of Disease Unit, South African Medical Research Council for valuable advice; and Statistics South Africa (StatsSA) for providing 2012 vital registration data sets which, although not used in the final modelling process, were key in the decision-making process at the data-gathering stage of this study.
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This research was funded by the University of KwaZulu Natal (UKZN), initially via PhD (2013–2016) and Post-Doctoral (2017–2019) bursaries awarded to HM by the College of Health Sciences, UKZN, and subsequently by UKZN APACHE Flagship Post-Doctoral Research Scholarship (2019—to date) via the KwaZulu Natal Research, Innovation and Sequencing Platform (KRISP).
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All authors contributed to the study conception and design. Methodology and source material preparation: Bernadette Modell, Helen Malherbe, Arnold Christianson and Matthew Darlison. Data collection, modelling and analysis were performed by Helen Malherbe, Bernadette Modell and Colleen Aldous. The first draft of the manuscript was written by Helen Malherbe with detailed input by Bernadette Modell with all authors providing feedback on early versions of the manuscript. All authors read and approved the final manuscript.
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Helen Malherbe was the Honorary Chair of Genetic Alliance South Africa (NPO: 001-029) until March 2020 and was appointed as a (Honorary) Director of Rare Diseases South Africa in April 2020. Colleen Aldous declares she has no conflict of interest. Arnold Christianson declares he has no conflict of interest. Matthew Darlison declares he has no conflict of interest. Bernadette Modell declares she has no conflict of interest.
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Malherbe, H.L., Aldous, C., Christianson, A.L. et al. Modelled epidemiological data for selected congenital disorders in South Africa. J Community Genet 12, 357–376 (2021). https://doi.org/10.1007/s12687-021-00513-8
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DOI: https://doi.org/10.1007/s12687-021-00513-8