Statistical methods for health studies in high-income countries do not always translate to problems in low- and middle- income countries due to the different characteristics of datasets in the latter. Administrative data, such as census-based data on risk factors, tends to be poor. Survey data can be high quality but with sparse geographical coverage. Hierarchical models which pool information across different different datasets, with appropriate model assumptions, can be used to overcome such problems. Talks will describe novel methodology related to hierarchical models motivated by different applications in Global Health.

Monica Alexander (University of Toronto)

Estimating the timing of stillbirths in countries worldwide using a Bayesian penalized splines regression

Reducing the global burden of stillbirths is an important part of the UN’s Sustainable Development Goals agenda in improving child and maternal health. Of particular interest is understanding patterns in the timing of stillbirths — that is, whether they occur in the intra- or antepartum period — because stillbirths that occur after the onset of labor are largely preventable. However, data that exist on the timing of stillbirths is highly variable across the world, with low- and middle-income countries generally having very few observations available. In this paper we develop a Bayesian penalized splines regression framework to estimate the proportion of stillbirths that are intrapartum for all countries worldwide. The model accounts for known relationships with neonatal mortality, pools information across geographic regions, accounts for different errors based on data source type, and allows for data-driven trends. Results suggest that the intrapartum proportion is generally decreasing over time, but progress is slower in some regions, particularly Sub-Saharan Africa. 

Emanuele Giorgi (Lancaster University)

Combining geostatistical and mechanistic models for efficient post-elimination surveillance strategies for neglected tropical diseases

Sujit Sahu (University of Southampton)

Spatio-temporal detection for dengue outbreaks in the Central Region of Malaysia using climatic drivers at mesoscale and synoptic scale

Organiser: Dr Patrick Brown 

Chair: Dr Patrick Brown 

Speaker: Sayantee Jana 

Speaker: Monica Alexander 

Speaker: Lucinda Hadley