Mapping Soil Transmitted Helminths and Schistosomiasis under Uncertainty: A Systematic Review and Critical Appraisal of Evidence

Abstract

BackgroundSpatial modelling of STH and schistosomiasis epidemiology is now commonplace. Spatial epidemiological studies help inform decisions regarding the number of people at risk as well as the geographic areas that need to be targeted with mass drug administration; however, limited attention has been given to propagated uncertainties, their interpretation, and consequences for the mapped values. Using currently published literature on the spatial epidemiology of helminth infections we identified: (1) the main uncertainty sources, their definition and quantification and (2) how uncertainty is informative for STH programme managers and scientists working in this domain.Methodology/Principal FindingsWe performed a systematic literature search using the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) protocol. We searched Web of Knowledge and PubMed using a combination of uncertainty, geographic and disease terms. A total of 73 papers fulfilled the inclusion criteria for the systematic review. Only 9% of the studies did not address any element of uncertainty, while 91% of studies quantified uncertainty in the predicted morbidity indicators and 23% of studies mapped it. In addition, 57% of the studies quantified uncertainty in the regression coefficients but only 7% incorporated it in the regression response variable (morbidity indicator). Fifty percent of the studies discussed uncertainty in the covariates but did not quantify it. Uncertainty was mostly defined as precision, and quantified using credible intervals by means of Bayesian approaches.Conclusion/SignificanceNone of the studies considered adequately all sources of uncertainties. We highlighted the need for uncertainty in the morbidity indicator and predictor variable to be incorporated into the modelling framework. Study design and spatial support require further attention and uncertainty associated with Earth observation data should be quantified. Finally, more attention should be given to mapping and interpreting uncertainty, since they are relevant to inform decisions regarding the number of people at risk as well as the geographic areas that need to be targeted with mass drug administration.

Scaling down of a deworming programme among school‐age children after a thirty‐year successful intervention in the Bolivian Chaco

“…Possible reasons for such discrepancy are that the STH prevalence data used were small in size and covered a short time and that the possible impact of PC intervention was not considered among the model variables, due to uncertainties as to whether it was effectively implemented in Bolivia. Although spatial modelling of STH epidemiology has become a popular way in the absence of empirical data to inform decisions regarding the geographic areas and the size of the at‐risk population to be targeted with mass drug administration, these findings stress the need for strengthening disease surveillance to reduce uncertainty in the models and better inform decision‐making .…”

“…Bolivia. Although spatial modelling of STH epidemiology has become a popular way in the absence of empirical data to inform decisions regarding the geographic areas and the size of the at-risk population to be targeted with mass drug administration, these findings stress the need for strengthening disease surveillance to reduce uncertainty in the models and better inform decision-making [14]. According to the WHO cut-off suggested for the change in frequencies of PC, the level of prevalence recorded in the Bolivian Chaco justifies a suspension of PC compounded by annual surveillance at sentinel sites in order to identify early a possible recrudescence [8].…”

Connecting Climate Information with Health Outcomes

“…Aspects of uncertainty are inadequately considered in much epidemiological research 30 but are usually considered in climate modelling. 31 A common approach taken by the climate community in the development of seasonal climate forecasts is to employ an ensemble of models to create a probability distribution of possible outcomes (see § 7.4).…”

Modelling the impact of MAUP on environmental drivers for Schistosoma japonicum prevalence

“…The robustness of SCH geographical modelling efforts is affected by uncertainties propagated from the use of EO data at various spatial and temporal scales of analysis [15]. EO data are generally constrained by their spatial and temporal scale of sampling [16].…”