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 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.

We are exploring a number of sociodemographic covariates from various sources. The main challenges is that in general for these to be useful we need them to be in a geographically linked format. We currently use population density, distance to main roads, travel times, other accessibility/ruralness measures, livestock ownership, participation in other health program like vaccines, and water/sanitation. At the moment we are not planning to use MDA coverage and TT surgery as usually this data isn’t geolocated and at a small scale enough for it to add value to the estimations.

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.

Current models do not weight the data based on when the data were collected, so data collected many years ago will be given equal importance to data collected within the last year. In an ideal scenario, it would be possible to explicitly acknowledge time in the model, however this would increase the complexity of the models to an extent that it might increase the uncertainty of the output, potentially negating the benefits of adding in time as an additional variable.

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.

We do not combine data that come from areas in different stages of intervention - so for example, we would never combine baseline data (pre-MDA) with any other survey type (e.g. impact or surveillance survey). We do however on occasion combine data from impact and surveillance surveys, as long as they are in the same stage of intervention (post-MDA), and only when the data available are sparse. Ideally, all data would come from one survey type, and we would use the most recent data available.

Technically, any survey data may be used in MBG models, regardless of when they were collected. However, for MBG analyses being done with TD support, the data must be generated using either GTMP or Tropical Data methods. Currently the most recent data available for an area are 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 to capture variation over time 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 using real-world data is difficult as it requires knowing with the exact prevalence of an area, which is unlikely to be known due to practical survey constraints (time and cost). It is therefore not possible to use ground-truthing in this way as existing survey data provide an estimate based on random sampling, and will therefore contain a level of error which is acceptable for trachoma elimination efforts, but is not acceptable as a benchmark for validating a model. 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 is 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%.