Spatial 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. We 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. None 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.
Helminth infections from as soil-transmitted helminths (STHs) and schistosomes are among the most prevalent neglected tropical diseases (NTDs) affecting human populations living in countries where clean water, sanitation, and hygiene (WASH) are limited. STHs and schistosomes, affect more than 1. 7 billion and 252 million [1,2] people worldwide respectively. The majority of these infections are concentrated in sub-Saharan [3,4] and North Africa, Asia, and central and Andean regions of Latin America [1]. STH and schistosome infections influence directly the nutrition status, educational development, individual productivity, physical and mental development in human populations [5]. The World Health Organization (WHO), the World Bank and other agencies defined control and elimination targets in the poorest populations [6]. Although the global burden of NTDs declined by 27% from 1990 to 2010 in upper-middle income countries [6], low and lower middle income countries still need attention. Besides, according to the Global Burden of Disease Study 2010 [1], STHs due to intestinal nematode infections, and schistosomiasis, caused the largest number of cases reported in 2010. In order to improve population health and accomplish WHO targets, the 2012 London declaration for Neglected Tropical Diseases and the 2013 World Health Assembly resolution highlighted the importance of mass drug administration (MDA) with benzimidazoles [7,8] to communities at risk. To identify communities at risk, indirect indicators of morbidity such as prevalence of infection and intensity of infection can be measured via surveying at-risk populations [9]. Communities at risk can then be categorized into disease prevalence classes (e. g. low, moderate, high) based on WHO guidelines [10]. In the absence of empirical data on infection at unsampled communities, one way to identify communities at risk is to study the role of the environment (physical and biological) to characterize potential habitats of parasites and intermediate hosts, as well as to understand the ecology and epidemiology of infections. Statistical modelling of the spatial distribution of helminth infections provides empirical relationships between infections and risk factors, which can then be used to predict the level of infection prevalence at unsampled locations [9,11–13]. In the statistical model, prevalence or another morbidity indicator, is treated as the response variable. Although statistical modelling of helminth infections is useful to effectively and efficiently manage surveillance, control and prevention of the infection [14], the mapped outputs should be interpreted with care because these can be weakened by several sources of uncertain information [15]. Sources of uncertainty that need to be accounted for in the modelling process include differences in variable selection criteria, statistical methods used, selected spatial and temporal scales of analysis [16], sampling design, sensitivity and specificity of diagnostic techniques as well as the quality of the spatial data used. Uncertainty has been the subject of extensive discussion in Geographic Information Science (GIScience) [17–32] and related subjects [33–43]. Uncertainty may relate to (1) a state of mind and our perception of the world or (2) statements about the world or observations on natural phenomena [17,18,22,32] and is relevant in terms of specifications and representations, measurement and the transformations, processing and modelling performed on raw data to turn them into usable information [17,22]. In order to address uncertainty, a more formal approach is required [17,18]. Here we conceptualize uncertainty as imperfection, which is further categorized as inaccuracy or imprecision. Imprecision may arise because the phenomenon is vague (i. e. , the phenomenon is not clearly defined), ambiguous (i. e. , different definitions can be applied to the phenomenon) [23,32] or due to the granularity of the observation [17]. In the spatial setting granularity relates to the resolution or spatial support (area or volume) of the observation and affects our ability to discern objects [17,44]. Imprecision may also arise due to natural variability, measurement error and model variability and may be described statistically, for example by the variance or standard deviation [32,45,46]. In this context, model variability may arise due to uncertain data, stochastic processes within the model or variability between competing models. The reader may be familiar with the narrow statistical definition of precision as the inverse of the variance [47], whereas the imprecision that is applied here encompasses a wider set of concepts [17,18]. Put another way, in this conceptualization, variance is not the only measure of precision. Accuracy is a measure of closeness between the observed phenomenon and reference observations, considered representative of the reality [17,45,48]. Accuracy assessment is often referred to as validation [20,49]. Common measures of accuracy include the root mean square error (RMSE) for continuous data [45,48], the overall accuracy (OA) for categorical data [27,28,50] and the area under the receiver operator characteristic curve (AUC) for binary data [45]. Bias relates to accuracy and refers to systematic differences between the observations and reference data. Accounting for uncertainty in disease mapping is important for the assessment of the applicability and validity of the predicted morbidity indicators [15]. Furthermore, it will allow a complete risk assessment and the identification of potential sources of bias [51]. Ignoring uncertainty can lead to incorrect predictions, thus wrong estimates of disease burden, which can result in misleading public health advocacy and decisions regarding disease control. Consideration of information about uncertainty is critical for control programs, health care workers, populations at risk, and other involved users who attempt to reduce prevalence and incidence of helminth infections across the affected areas [51,52]. For example, control programs need accurate information to decide about drug distribution strategies and the frequency of treatment of the target populations. Decision makers can use information about uncertainty to target more resources (e. g. , data acquisition) or to focus investigative efforts on low or highly uncertain risk areas [53,54]. This paper is a systematic review that aims at the identification of the gaps in knowledge of the different components of uncertainty associated with mapping and modelling helminth infections. It also aims at providing a basis for a complete uncertainty communication, by evaluating the impact of uncertainty on the predicted morbidity indicators. This paper starts by investigating how uncertainty is informative for decision makers, public health scientists and the affected community. It then identifies main sources of uncertainty in helminth infection mapping studies, and how uncertainties have been defined and quantified. Regarding the sources of uncertainty, their definition and quantification, the focus will be put on sources relating to Earth Observation. The significance of this paper is that it contributes to inform control programs and health workers about the importance of uncertainty in mapping and modeling helminth infections, by putting special attention on relevant sources of uncertainty, and analyzing their real influence on the predicted morbidity indicator values used to guide mass drug administration strategies and their cost effectiveness.
An online search was performed using two search engines, the Web of Knowledge (Core collection and MEDLINE) and PubMed. Only articles published in English were considered. The date range was 1 January 1980 to 24 October 2016. The search strategy aimed at the identification of primary research studies that have looked into establishing the geographical limits of STH and schistosomiasis present only in humans; therefore the search strategy combined variations of three terms: spatial, helminth infection, and uncertainty terms. The full list of terms used in the systematic review is shown in Table 1. Six searches were performed by combining the three terms in each search engine, using the keywords described in Table 2. After removing duplicates, the abstracts of 139 papers were read. Papers written in languages other than English (11 papers) were automatically excluded. Review papers (14 papers) were also excluded. Further criteria were then applied to select the final papers to read, but also to make the reading process more efficient. The inclusion criteria considered were (i) the presence of the three spatial, uncertainty and helminth infection search terms in the abstracts and (ii) also articles related to only STH and schistosomiasis helminth infections. The papers were classified into schistosomiasis and soil transmitted helminth studies. The selection of the papers, data acquisition and analysis was undertaken by the first author. The PRISMA flow diagram is given in Fig 1. Data collection from each paper focused on addressing three main research questions. (1) How is uncertainty informative for decision making in the public health context? (2) What are the different uncertainty sources reported in the reviewed studies? (3) How were uncertainty and its sources defined and quantified in the studies? Papers addressing these questions were enumerated. Fig 2 illustrates the relevant three uncertainty stages that drive the final mapping and modelling of STH and schistosomiasis infections. The first stage (pink box) describes the origin of uncertainty coming from data sources, including uncertainties in the response variable and covariates. The second stage (orange box) shows how uncertainty from the pink box propagates through the predictive model (green box). The green box incorporates uncertainties derived from the selection of the predictive model, considering that there could be different ways to model the same helminth infection. It also includes uncertainties in model structure, which refers to all possible limitations and assumptions in the selected model, such as: the lack of understanding about the interaction between the environment, helminth infections and human populations, as well as the assumptions of stationarity and spatial isotropy [9]. Finally, the green box includes uncertainties in the methods used to estimate the model parameters. The third stage (yellow box), shows how uncertainty in the predicted morbidity indicator is addressed, firstly in policy and decision making settings and secondly in a scientific setting. This stage aims to understand how information on uncertainty is used practically and how is it defined and quantified. The blue box represents different elements of data quality that relate to the sources of information (pink box), and the predicted morbidity indicators (yellow box), which due to its wide field of study and importance was separated into a different box.
The total number of papers found in each search is shown in Table 5. Table 6 shows the resulting number of read and discarded papers presented per infection. In total 73 papers were selected, from which 14 were review papers. While the identified review papers were not included in this review we examined their reference lists; this yielded another 14 valuable references that had not been identified by our original search. Finally 73 primary research papers were included in our systematic review. Our results demonstrate that the annual number of publications on mapping and modelling STH and schistosome infections was constant until the year 2007 and steadily increased since then; since 2008 a total of 49 (67% of the total) papers were published (Fig 3).
Most of the studies used information on uncertainty to guide MDA campaigns and evaluate their cost effectiveness. Information on uncertainty was also used to evaluate the role of risk factors in mapping helminth infections. Nevertheless, prevention strategies, improvements in sampling design, and mapping of uncertainty have not yet been addressed [113–116]. We advise to use information on uncertainty not only to inform about MDA campaigns, but also to inform about prevention strategies such as improving sanitation and hygiene education [117] or delineating potential transmission sites [116]. Transmission control is important for its public health relevance, since potential disease transmission sites could guide direct intervention measures at the place of infection [62,116]. Likewise, mapping of uncertainty is also recommended, since it is known to be an important tool for public health decision making, especially to determine the geographical distribution of areas for which information is lacking [112]. Mapping could be used as a tool to improve the sampling strategy and modelling efforts. Maps of uncertainty could also support communication of uncertainty to the affected communities. A complete exploration and judgement of uncertainty information would enhance the assessment of the risk of getting these infections, and would allow to understand potential impacts on human health [51]. While most studies identified and discussed different sources of uncertainty, this was mainly limited to a qualitative discussion, rather than a quantitative one [118] (Table 11). For instance, 38 (52%) papers highlighted qualitatively the importance of sampling design in mapping helminth infections, but only two studies (3%) have quantified their possible effects on the accuracy of the predicted morbidity indicator. An example is given by Clements et al [119], where uncertainties in the predictions were used to identify areas requiring further data collection before programme implementation. The lack of a quantitative assessment limits the utility of the findings in both policy/decision making setting and a scientific setting [51,118,120,121]. Communication of uncertainty will never be complete without an extensive quantification of uncertainties in all possible information sources [51,120,122], where model assumptions, selection of covariates and acquisition of survey data are clearly explained, either within the publication or as supplementary information. Fig 4 shows the three uncertainty stages previously described in Fig 2, where these stages encompass specific uncertainty components, which need to be considered for a complete uncertainty communication. Each of these components is analyzed in the next sections. Acknowledging and incorporating uncertainty in mapping and modelling helminth infections is a step-by-step process, which should be considered formally when developing geographical models of helminth infection. Geographical models aim at informing, not only about MDA campaigns and their cost-effectiveness, but also prevention strategies, where it is necessary to define transmission areas and plan and guide hygiene education and infrastructure programs in water sanitation and hygiene. A quantitative and qualitative analysis of uncertainty is necessary for a complete assessment of risk, to understand potential impacts on human health, and to allow a complete uncertainty communication to public health managers. Five components of uncertainty analysis were recognized: (1) uncertainty in the response variable, (2) uncertainty in the covariates, (3) uncertainty in the relationship between them, (4) uncertainty in the predictive model, and (5) the propagated uncertainty on the results. Our conclusions are shown diagrammatically in Fig 5, which aims at providing a framework for a full uncertainty evaluation when undertaking spatial modeling of helminth infections for policy formulation. Uncertainty analysis should start by identifying possible sources of uncertainty in the studies and categorize them such that at least the most important ones can be incorporated into the predictive model. Sampling design and EO data have been acknowledged as the major sources of uncertainty and should be given primary attention in the modelling process. In particular, sampling design, diagnosis, selection of significant risk factors, and selection of an adequate spatial support of analysis. Next, uncertainties in the response variable and covariates should be quantified and incorporated into the model. Methods used to define the relationship between covariates and response variables should also be documented, as well as the selection of the predictive model and its limitations. Finally, uncertainties in the parameters and response variables should be quantified, and uncertainty mapping should be performed as a valuable element for uncertainty communication and policy formulation.
In recent years spatial modelling studies of schistosome and soil-transmitted helminth infections have become commonplace; however there is no standard framework for uncertainty evaluation and reporting. In this study we aim to identify faults in existing studies and propose a framework for evaluation and reporting. We conducted a systematic review of the literature to identify the gaps in knowledge in relation to how uncertainty is dealt with in existing studies addressing the spatial modelling of helminth infections. It was found that none of the studies considered adequately all sources of uncertainty. Uncertainty in the response variables and covariates should be incorporated into the modelling framework. More attention should be given to mapping and interpreting uncertainty, and to quantify the different sources of uncertainty present in the observed covariates (environmental variables), measured response variable (morbidity indicators), used model and uncertainty representation and interpretation of the predicted morbidity indicators.
schistosoma
invertebrates
medicine and health sciences
helminths
tropical diseases
parasitic diseases
animals
health care
mathematics
forecasting
statistics (mathematics)
neglected tropical diseases
infectious disease control
morbidity
research and analysis methods
infectious diseases
mathematical and statistical techniques
helminth infections
schistosomiasis
health statistics
biology and life sciences
physical sciences
soil-transmitted helminthiases
statistical methods
organisms
Mapping Soil Transmitted Helminths and Schistosomiasis under Uncertainty: A Systematic Review and Critical Appraisal of Evidence