Mapping Caveats

Survey data maps

Survey data maps can only be as reliable as the data they display.  Whilst strict inclusion and exclusion criteria are applied, users should bear a few things in mind:

  • Area-level averages: for much of the world, in particular Asia and the Middle East, survey data is only reported at a district or provincial level. Whilst work is ongoing to locate the co-ordinates of these surveys, in such instances area-level averages are currently shown. However, included surveys may have been conducted in single schools or communities and as such may not be representative of the whole district/province population.
  • Changes over time: country data may reflect surveys conducted over a wide period of time.  These maps cannot reflect temporal changes in prevalence, which may be due to changes in socio-economic conditions, improvements in hygiene and sanitation, or the implemenation of school-based and mass treatment campaigns. 


Predictive risk maps

The predictive risk maps are not without their limitations. When using the maps, the following limitations should be borne in mind:

  • Risk in urban areas: Urban slums, with their inadequate sanitation and overcrowding, can be ideal settings for STH transmission, leading to a higher prevalence of infection in urban than in rural areas. However, this is not always the case, and prevalence can also be lower in urban areas because of improved health care and socio-economic status. It is therefore difficult to reliably predict prevalence in urban areas, and the risk maps may under-estimate or over-estimate prevalence in such areas.
  • Areas without survey data: The distribution of surveys across Africa is uneven. In areas without suitable data, the ability of the models to predict infection risk may be reduced. In such cases, the model is limited to using environmental information alone, and is pulled towards the mean estimate of prevalence, which in some regions may over-estimate the true prevalence.
  • Areas with few data points: In areas with only a few data points, the model gives emphasis to these data. For example, if there is a single data point with a high prevalence in an area with no other data, the models will sometimes predict a high prevalence for the surrounding area, again over-estimating prevalence.
  • Areas with extreme environments: In extreme environments the relationships between infection prevalence and environmental characteristics may well be different to less extreme areas.  In such cases, the model may over- or under-predict the true prevalence, particularly when survey data is lacking.


Intervention Coverage maps

When using the Intervention Coverage maps, it should be borne in mind that these are generated using standard reporting data.

Intervention coverage figures are thus based upon the reported distribution of treatment within the intervention unit and the recorded unit population.  Whilst standardised methods do exist for accurately recording this information, distribution of medication is not the same as direct observation of ingestion. Therefore, in practice, the true intervention coverage (proportion of the total population actually taking the medication) may be lower than that reported.