MOESM1 of Economic evaluations of interventions to reduce neonatal morbidity and mortality: a review of the evidence in LMICs and its implications for South Africa

Additional file 1: Database searches

Additional file 2: of Bibliometric trends of health economic evaluation in Sub-Saharan Africa

This is the R code for generating the visual network maps. (R 1 kb

Additional file 3: of Bibliometric trends of health economic evaluation in Sub-Saharan Africa

Database of included studies and characteristics, network articles, authorship and collaborations. (XLSX 182 kb

Causes of child, new born and maternal deaths in SA used in the LiST model.

<p>Causes of child, new born and maternal deaths in SA used in the LiST model.</p

Health equity in endocrinology

Health equity is when every person can achieve their full potential for health and wellbeing. In this Viewpoint, global experts discuss the root causes and contributing factors to health inequity in endocrinology. Potential action points and research directions to help reduce health disparities are also discussed.</p

Base case results for total commodity (number of units) requirements for each contraceptive method per year.

<p>CPR = Contraceptive prevalence rate. IUD = Intrauterine device. Figures rounded to the nearest 100.</p><p>Base case results for total commodity (number of units) requirements for each contraceptive method per year.</p

Total annual costs (2012 US$) of family planning projected by the model.

<p>Total annual costs (2012 US$) of family planning projected by the model.</p

Additional file 1: of Modelling the potential impact of a sugar-sweetened beverage tax on stroke mortality, costs and health-adjusted life years in South Africa

Estimates of the parameters used in the model.docx. Tables displaying parameter estimates used as inputs for the model. (DOCX 24 kb

Adapted theoretical framework for available multilevel factors driving adult obesity in South Africa.

<p>Adapted theoretical framework for available multilevel factors driving adult obesity in South Africa.</p

Characteristics of the study sample: South-African adults (≥15 years of age) participating in the NIDS Wave 1–3 surveys, 2008–2012.

<p>ⁱ: excludes missing values and erroneous extreme values (negative or zero weight or height measurements, extreme BMI values)</p><p>Characteristics of the study sample: South-African adults (≥15 years of age) participating in the NIDS Wave 1–3 surveys, 2008–2012.</p