Technical FAQs

MBG uses regression models to predict the prevalence of trachoma (TF or TT). A regression model describes the relationship between a target (in this case, TF or TT prevalence), and one or more independent variables. These variables could include things such as age, gender, proximity to healthcare infrastructure, population density, temperature, and many other factors relevant to trachoma epidemiology. MBG also allows for the inclusion of spatial effects, which takes into account the observation that trachoma prevalence is often spatially correlated (i.e., trachoma prevalence is more similar when the geographical locations are closer). When estimating trachoma prevalence, MBG can also use existing survey data from nearby EUs to improve the estimate precision for the region of interest. The MBG output includes a point prevalence for the region of interest, and a Probability of being Below the elimination Threshold (PBT).

Current research conducted by RTI International and Lancaster University is focused on investigating the most useful covariates that consistently demonstrate a significant relationship with trachoma prevalence. From a starting point of over 60 covariates (environmental, social, demographic), researchers are aiming to reduce this to a standard pool of ~20, which will be included in all starting models when using MBG. The models themselves will then be used to determine which of these 20 covariates best explain the variation in the data for each region of interest, and these covariates will then be taken forward into the final models used for estimating trachoma prevalence.

The covariate data are sourced from a number of places, and these may change and be updated over time. Current sources include WorldPop, Malaria Atlas Project, DHS, CHIRPS, etc.

The extent to which we're able to borrow information from other EUs depends on the data and the strength of spatial correlation, and is decided on a case-by-case basis. For example, in some cases correlations between trachoma prevalence spans a long geographic range, indicating that the same factors that influence trachoma prevalence in one EU are the same factors that influence the trachoma prevalence in an EU many miles away. In this case, it would be possible to use data from EUs from a wide geographic range. However, if the correlation between trachoma prevalence was shown to be much more localised, it would be more beneficial to use data from contiguous EUs rather than ones further away.

Technically, as long as data were generated using either GTMP or Tropical Data methods, they can be used regardless of when they were collected. Currently the most recent data available for an area are preferentially selected for use in MBG, as these are likely to be the most informative of the current situation. The use of older data can be made as long as the area covered is the same as the newer data, as this allows variation over time to be captured through the MBG model. In cases where the older data cover a different area from the newer data, the decision on the use of the old data depends on several factors such as differences in the intervention history, environment and socio-demographic traits of the populations from the two areas. In cases where such differences are deemed to be small enough, the older data can be used in the analysis. If not, the older data should not be combined with the newer data.

 Ground-truthing is difficult using field data because surveys take a sample of the total EU population to provide a prevalence estimate. To know the true prevalence, we would need to examine every individual in the EU. An alternative to ground-truthing that is currently being investigated is using a simulation model to generate datasets with known true prevalence, and see if the model estimates the prevalence precisely.

It could be possible to run a model with small amounts of “positive” data, however with fewer data there is a higher chance of more uncertainty in the model, which would be demonstrated by a PBT close to 50%.