Toward an Open-Access Global Database for Mapping, Control, and Surveillance of Neglected Tropical Diseases

Abstract Background: After many years of general neglect, interest has grown and efforts came under way for the mapping, control, surveillance, and eventual elimination of neglected tropical diseases (NTDs). Disease risk estimates are a key feature to target control interventions, and serve as a benchmark for monitoring and evaluation. What is currently missing is a georeferenced global database for NTDs providing open-access to the available survey data that is constantly updated and can be utilized by researchers and disease control managers to support other relevant stakeholders. We describe the steps taken toward the development of such a database that can be employed for spatial disease risk modeling and control of NTDs

Toward an Open-Access Global Database for Mapping, Control, and Surveillance of Neglected Tropical Diseases

There is growing interest in the scientific community, health ministries, and other organizations to control and eventually eliminate neglected tropical diseases (NTDs). Control efforts require reliable maps of NTD distribution estimated from appropriate models and survey data on the number of infected people among those examined at a given location. This kind of data is often available in the literature as part of epidemiological studies. However, an open-access database compiling location-specific survey data does not yet exist. We address this problem through a systematic literature review, along with contacting ministries of health, and research institutions to obtain disease data, including details on diagnostic techniques, demographic characteristics of the surveyed individuals, and geographical coordinates. All data were entered into a database which is freely accessible via the Internet (http://www.gntd.org). In contrast to similar efforts of the Global Atlas of Helminth Infections (GAHI) project, the survey data are not only displayed in form of maps but all information can be browsed, based on different search criteria, and downloaded as Excel files for further analyses. At the beginning of 2011, the database included over 12,000 survey locations for schistosomiasis across Africa, and it is continuously updated to cover other NTDs globally

Spatially explicit Schistosoma infection risk in eastern Africa using Bayesian geostatistical modelling

Schistosomiasis remains one of the most prevalent parasitic diseases in the tropics and subtropics, but current statistics are outdated due to demographic and ecological transformations and ongoing control efforts. Reliable risk estimates are important to plan and evaluate interventions in a spatially explicit and cost-effective manner. We analysed a large ensemble of georeferenced survey data derived from an open-access neglected tropical diseases database to create smooth empirical prevalence maps for Schistosoma mansoni and Schistosoma haematobium for a total of 13 countries of eastern Africa. Bayesian geostatistical models based on climatic and other environmental data were used to account for potential spatial clustering in spatially structured exposures. Geostatistical variable selection was employed to reduce the set of covariates. Alignment factors were implemented to combine surveys on different age-groups and to acquire separate estimates for individuals aged ≤20 years and entire communities. Prevalence estimates were combined with population statistics to obtain country-specific numbers of Schistosoma infections. We estimate that 122 million individuals in eastern Africa are currently infected with either S. mansoni, or S. haematobium, or both species concurrently. Country-specific population-adjusted prevalence estimates range between 12.9% (Uganda) and 34.5% (Mozambique) for S. mansoni and between 11.9% (Djibouti) and 40.9% (Mozambique) for S. haematobium. Our models revealed that infection risk in Burundi, Eritrea, Ethiopia, Kenya, Rwanda, Somalia and Sudan might be considerably higher than previously reported, while in Mozambique and Tanzania, the risk might be lower than current estimates suggest. Our empirical, large-scale, high-resolution infection risk estimates for S. mansoni and S. haematobium in eastern Africa can guide future control interventions and provide a benchmark for subsequent monitoring and evaluation activities

Diagnostic approaches to malaria in Zambia, 2009-2014

Malaria is an important health burden in Zambia with proper diagnosis remaining as one of the biggest challenges. The need for reliable diagnostics is being addressed through the introduction of rapid diagnostic tests (RDTs). However, without sufficient laboratory amenities in many parts of the country, diagnosis often still relies on non-specific, clinical symptoms. In this study, geographical information systems were used to both visualize and analyze the spatial distribution and the risk factors related to the diagnosis of malaria. The monthly reported, district-level number of malaria cases from January 2009 to December 2014 were collected from the National Malaria Control Center (NMCC). Spatial statistics were used to reveal cluster tendencies that were subsequently linked to possible risk factors, using a non-spatial regression model. Significant, spatio-temporal clusters of malaria were spotted while the introduction of RDTs made the number of clinically diagnosed malaria cases decrease by 33% from 2009 to 2014. The limited access to road network(s) was found to be associated with higher levels of malaria, which can be traced by the expansion of health promotion interventions by the NMCC, indicating enhanced diagnostic capability. The capacity of health facilities has been strengthened with the increased availability of proper diagnostic tools and through retraining of community health workers. To further enhance spatial decision support systems, a multifaceted approach is required to ensure mobilization and availability of human, infrastructural and technological resources. Surveillance based on standardized geospatial or other analytical methods should be used by program managers to design, target, monitor and assess the spatio-temporal dynamics of malaria diagnostic resources country-wide

African map of schistosomiasis survey locations based on current progress of the GNTD database.

<p>Survey locations are represented by pink squares for <i>S. matthei</i>, blue diamonds for <i>S. margrebowiei</i>, yellow stars for <i>S. intercalatum</i>, green crosses for <i>S. bovis</i>, brown dots for <i>S. mansoni</i> and red triangles for <i>S. haematobium</i>. Surveys where subjects were screened for co-occurrence of multiple species are indicated with overlapping symbols.</p

Observed prevalence of <i>S. haematobium</i> based on current progress of the GNTD database in Africa.

<p>The data included 5807 georeferenced survey locations. Prevalence equal to 0%, low infection rates (0.1–9.9%), moderate infection rates (10.0–49.9%) and high infection rates (≥50%) indicated by a red scale from light red to dark red. Cut-offs follow WHO recommendations <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001404#pntd.0001404-WHO3" target="_blank">[35]</a>.</p

Observed prevalence of <i>S. mansoni</i> based on current progress of the GNTD database in Africa.

<p>The data included 4604 georeferenced survey locations. Prevalence equal to 0% in yellow dots, low infection rates (0.1–9.9%) in orange dots, moderate infection rates (10.0–49.9%) in light brown dots and high infection rates (≥50%) in brown dots. Cut-offs follow WHO recommendations <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001404#pntd.0001404-WHO3" target="_blank">[35]</a>.</p