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Abstract Background Diarrhoea is one of the leading causes of morbidity and mortality in South African children, accounting for approximately 20% of under-five deaths. Though progress has been made in scaling up multiple interventions to reduce diarrhoea in the last decade, challenges still remain. In this paper, we model the cost and impact of scaling up 13 interventions to prevent and treat childhood diarrhoea in South Africa. Methods Modelling was done using the Lives Saved Tool (LiST). Using 2014 as the baseline, intervention coverage was increased from 2015 until 2030. Three scale up scenarios were compared: by 2030, 1) coverage of all interventions increased by ten percentage points; 2) intervention coverage increased by 20 percentage points; 3) and intervention coverage increased to 99%. Results The model estimates 13 million diarrhoea cases at baseline. Scaling up intervention coverage averted between 3 million and 5.3 million diarrhoea cases. In 2030, diarrhoeal deaths are expected to reduce from an estimated 5,500 in 2014 to 2,800 in scenario one, 1,400 in scenario two and 100 in scenario three. The additional cost of implementing all 13 interventions will range from US$510 million (US$9 per capita) to US$960 million (US$18 per capita), of which the health system costs range between US$40 million (less than US$1 per capita) and US$170 million (US$3 per capita). Conclusion Scaling up 13 essential interventions could have a substantial impact on reducing diarrhoeal deaths in South African children, which would contribute toward reducing child mortality in the post-MDG era. Preventive measures are key and the government should focus on improving water, sanitation and hygiene. The investments required to achieve these results seem feasible considering current health expenditure.

AbstractMultimorbidity is an increasing global public health challenge, however, most existing research focuses on high‐income countries, with limited evidence from low‐ and middle‐income countries. This paper aims to estimate the prevalence of multimorbidity in South Africa and analyse the associations between multimorbidity and social determinants of health in the adult population. Multimorbidity will be defined as the coexistence of two or more chronic diseases in an individual throughout this paper. Data from the South African National Income Dynamics Survey of 2017 was used with a total sample of 20,833. A binary logistic regression was performed to analyse the associations between multimorbidity and several social determinants of health indicators based on the Commission on Social Determinants of Health Framework. Multimorbidity was prevalent in 5.4% of the South African adult population surveyed, with 71.35% of those with multimorbidity being female. Hypertension was the most common NCD and the highest contributor to multimorbidity. Multimorbidity was found to have statistically significant associations with age, obesity, being female, being of Colored or Asian/Indian ethnicity, being in employment, and having no schooling. This study highlights the importance of analysing the associations between multimorbidity and the social determinants of health. Further research on multimorbidity is required in low‐ and middle‐income countries to understand the specific challenges not identifiable in the existing research predominately based in high‐income nations.

INTRODUCTION: Despite the documented benefits of using health technology assessments (HTA) to inform resource allocation in health care systems, HTA remains underused, especially in low- and middle-income countries. A survey of global health practitioners was conducted to reveal the top reasons ("excuses") that they had heard from colleagues, policymakers or other stakeholders for not using HTA in their settings. METHODS: There were 193 respondents to the survey. Most responses were from individuals in research organisations (37%), ministries of health (27%) and other government agencies (14%). Participants came from Southeast Asia (40%), the Western Pacific (30%), Africa (15%), Europe (7%), the Americas (7%) and the Eastern Mediterranean region (2%). RESULTS: The top five reasons encountered by respondents related to lack of data, lack of technical skills for HTA, the technocratic nature of the work, the lack of explicit decision rules and the perception that HTA puts a "price on life". CONCLUSIONS: This study aimed to understand and address the top reasons for not using HTA. They fall into three categories: (1) misconceptions about HTA; (2) feasibility issues; and (3) values, attitudes and politics. Previous literature has shown that these reasons can be addressed when identified, and even imperfect HTA analyses can provide useful information to a decision-maker. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12962-021-00308-1.