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Using non-exceedance probabilities of policy-relevant malaria prevalence thresholds to identify areas of low transmission in Somalia

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  • Emanuele Giorgi
  • Ali Abdirahman Osman
  • Abdikarin Hussein Hassan
  • Abdi Abdilahi Ali
  • Faisa Ibrahim
  • Jamal G. H. Amran
  • Abdisalan M. Noor
  • Robert W. Snow
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Article number88
<mark>Journal publication date</mark>20/02/2018
<mark>Journal</mark>Malaria Journal
Volume17
Number of pages10
Publication StatusPublished
<mark>Original language</mark>English

Abstract

Background Countries planning malaria elimination must adapt from sustaining universal control to targeted intervention and surveillance. Decisions to make this transition require interpretable information, including malaria parasite survey data. As transmission declines, observed parasite prevalence becomes highly heterogeneous with most communities reporting estimates close to zero. Absolute estimates of prevalence become hard to interpret as a measure of transmission intensity and suitable statistical methods are required to handle uncertainty of area-wide predictions that are programmatically relevant. Methods A spatio-temporal geostatistical binomial model for Plasmodium falciparum prevalence (PfPR) was developed using data from cross-sectional surveys conducted in Somalia in 2005, 2007–2011 and 2014. The fitted model was then used to generate maps of non-exceedance probabilities, i.e. the predictive probability that the region-wide population-weighted average PfPR for children between 2 and 10 years (PfPR2–10) lies below 1 and 5%. A comparison was carried out with the decision-making outcomes from those of standard approaches that ignore uncertainty in prevalence estimates. Results By 2010, most regions in Somalia were at least 70% likely to be below 5% PfPR2–10 and, by 2014, 17 regions were below 5% PfPR2–10 with a probability greater than 90%. Larger uncertainty is observed using a threshold of 1%. By 2011, only two regions were more than 90% likely of being < 1% PfPR2–10 and, by 2014, only three regions showed such low level of uncertainty. The use of non-exceedance probabilities indicated that there was weak evidence to classify 10 out of the 18 regions as < 1% in 2014, when a greater than 90% non-exceedance probability was required. Conclusion Unlike standard approaches, non-exceedance probabilities of spatially modelled PfPR2–10 allow to quantify uncertainty of prevalence estimates in relation to policy relevant intervention thresholds, providing programmatically relevant metrics to make decisions on transitioning from sustained malaria control to strategies that encompass methods of malaria elimination.