Browsing by Author "Byass, Peter"

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    Developmental origins of health and disease in Africa - influencing early life
    (Elsevier, 2018) Davies, Justine Ina; Macnab, Andrew John; Byass, Peter; Norris, Shane A.; Nyirenda, Moffat; Singhal, Atul; Sobngwi, Eugene; Daar, Abdallah S.
    It is well established that Africa is undergoing rapid transitions resulting in a triple burden of malnutrition, infectious diseases, and non-communicable diseases (NCDs).1,2 That health systems are unlikely to be able to cope with this burden is also widely noted.1,2 What is less often discussed outside academic circles is the degree to which infectious diseases and malnutrition in Africa are exacerbating the burden of NCDs, and the implications of this exacerbation for individuals and populations.
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    Enhancing the value of mortality data for health systems : adding Circumstances Of Mortality CATegories (COMCATs) to deaths investigated by verbal autopsy
    (Taylor & Francis, 2019-10-25) Hussain-Alkhateeb, Laith; D'Ambruoso, Lucia; Tollman, Stephen; Kahn, Kathleen; Van der Merwe, Maria; Twine, Rhian; Schioler, Linus; Petzold, Max; Byass, Peter
    Half of the world’s deaths and their causes pass unrecorded by routine registration systems, particularly in low- and middle-income countries. Verbal autopsy (VA) collects information on medical signs, symptoms and circumstances from witnesses of a death that is used to assign likely medical causes. To further contextualise information on mortality, understanding underlying determinants, such as logistics, barriers to service utilisation and health systems responses, is important for health planning. Adding systematic methods for categorising circumstantial determinants of death to conventional VA tools is therefore important. In this context, the World Health Organization (WHO) leads the development of international standards for VA, and added questions on the social and health systems circumstances of death in 2012. This paper introduces a pragmatic and scalable approach for assigning relevant Circumstances Of Mortality CATegories (COMCATs) within VA tools, and examines their consistency, reproducibility and plausibility for health policy making, as well as assessing additional effort and cost to the routine VA process. This innovative COMCAT model is integrated with InterVA-5 software (which processes WHO-2016 VA data), for assigning numeric likelihoods to six circumstantial categories for each death. VA data from 4,116 deaths in the Agincourt Health and Socio-Demographic Surveillance System in South Africa from 2012 to 2016 were used to demonstrate proof of principle for COMCATs. Lack of resources to access health care, poor recognition of diseases and inadequate health systems responses ranked highest among COMCATs in the demonstration dataset. COMCATs correlated plausibly with age, sex, causes of death and local knowledge of the demonstration population. The COMCAT approach appears to be plausible, feasible and enhances the functionality of routine VA to account for critical limiting circumstances at and around the time of death. It is a promising tool for evaluating progress towards the Sustainable Development Goals and the roll-out of Universal Health Coverage.
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    The implications of developmentel origins of health and disease for Africa : what can be learnt from available data?
    (African Sun Media, 2020) Byass, Peter
    There is a broad spectrum of knowledge globally that shows that specific exposures in early life – during pregnancy and early childhood – might affect what happens at various later stages of life. The scientific field behind this has become known as the developmental origins of health and disease (DOHaD). The underlying mechanisms may be complex, and understanding and analysing the epidemiology of the causes and effects are complicated. Long-term individual data, often across generations, are the best way into understanding the precise nature of such effects. Therefore, much of the research that has been done in this area has happened where detailed, individual data on health and welfare are a routine part of social structures – for example, in Scandinavian countries. Nevertheless, it is reasonable to suppose similar exposures and effects might apply within African populations – the difference being that relevant details are much less likely to be documented. Since there is no quick fix for implementing data collection that spans generations, the only indirect clues as to how DOHaD may be affecting Africans is to consider how available data sources might be used in derivative analyses. In this chapter, an example is presented of taking a relationship established elsewhere between breastfeeding and obesity, applied to publicdomain estimates on breastfeeding and childhood obesity in Africa, and analysed to estimate the magnitude of the likely consequences of non-breastfeeding on childhood obesity in Africa. This approach is much less rigorous than the ideal situation where specific individuals’ breastfeeding histories could be related to their later individual obesity, but it offers some clues as to the likely magnitude of this particular issue in Africa, and offers a proof-of-principle for this general approach.
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    An integrated approach to processing WHO-2016 verbal autopsy data : the InterVA-5 model
    (BMC (part of Springer Nature), 2019-05-30) Byass, Peter; Hussain-Alkhateeb, Laith; D’Ambruoso, Lucia; Clark, Samuel; Davies, Justine; Fottrell, Edward; Bird, Jon; Kabudula, Chodziwadziwa; Tollman, Stephen; Kahn, Kathleen; Schioler, Linus; Petzold, Max
    Background: Verbal autopsy is an increasingly important methodology for assigning causes to otherwise uncertified deaths, which amount to around 50% of global mortality and cause much uncertainty for health planning. The World Health Organization sets international standards for the structure of verbal autopsy interviews and for cause categories that can reasonably be derived from verbal autopsy data. In addition, computer models are needed to efficiently process large quantities of verbal autopsy interviews to assign causes of death in a standardised manner. Here, we present the InterVA-5 model, developed to align with the WHO-2016 verbal autopsy standard. This is a harmonising model that can process input data from WHO-2016, as well as earlier WHO-2012 and Tariff-2 formats, to generate standardised cause-specific mortality profiles for diverse contexts. The software development involved building on the earlier InterVA-4 model, and the expanded knowledge base required for InterVA-5 was informed by analyses from a training dataset drawn from the Population Health Metrics Research Collaboration verbal autopsy reference dataset, as well as expert input. Results: The new model was evaluated against a test dataset of 6130 cases from the Population Health Metrics Research Collaboration and 4009 cases from the Afghanistan National Mortality Survey dataset. Both of these sources contained around three quarters of the input items from the WHO-2016, WHO-2012 and Tariff-2 formats. Cause-specific mortality fractions across all applicable WHO cause categories were compared between causes assigned in participating tertiary hospitals and InterVA-5 in the test dataset, with concordance correlation coefficients of 0.92 for children and 0.86 for adults. The InterVA-5 model’s capacity to handle different input formats was evaluated in the Afghanistan dataset, with concordance correlation coefficients of 0.97 and 0.96 between the WHO-2016 and the WHO-2012 format for children and adults respectively, and 0.92 and 0.87 between the WHO-2016 and the Tariff-2 format respectively. Conclusions: Despite the inherent difficulties of determining “truth” in assigning cause of death, these findings suggest that the InterVA-5 model performs well and succeeds in harmonising across a range of input formats. As more primary data collected under WHO-2016 become available, it is likely that InterVA-5 will undergo minor reversioning in the light of practical experience. The model is an important resource for measuring and evaluating cause-specific mortality globally.
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    Understanding and acting on the developmental origins of health and disease in Africa would improve health across generations
    (Taylor & Francis Open, 2017) Norris, Shane A.; Daar, Abdallah; Balasubramanian, Dorairajan; Byass, Peter; Kimani-Murage, Elizabeth; Macnab, Andrew; Pauw, Christoff; Singhal, Atul; Yajnik, Chittaranjan; Akazili, James; Levitt, Naomi; Maatoug, Jihene; Mkhwanazi, Nolwazi; Moore, Sophie E.; Nyirenda, Moffat; Pulliam, Juliet R. C.; Rochat, Tamsen; Said-Mohamed, Rihlat; Seedat, Soraya; Sobngwi, Eugene; Tomlinson, Mark; Toska, Elona; Van Schalkwyk, Cari
    Data from many high- and low- or middle-income countries have linked exposures during key developmental periods (in particular pregnancy and infancy) to later health and disease. Africa faces substantial challenges with persisting infectious disease and now burgeoning non-communicable disease.This paper opens the debate to the value of strengthening the developmental origins of health and disease (DOHaD) research focus in Africa to tackle critical public health challenges across the life-course. We argue that the application of DOHaD science in Africa to advance life-course prevention programmes can aid the achievement of the Sustainable Development Goals, and assist in improving health across generations. To increase DOHaD research and its application in Africa, we need to mobilise multisectoral partners, utilise existing data and expertise on the continent, and foster a new generation of young African scientists engrossed in DOHaD.