Nadine Schur

Time to set the agenda for schistosomiasis elimination

It is time to raise global awareness to the possibility of schistosomiasis elimination and to support endemic countries in their quest to determine the most appropriate approaches to eliminate this persistent and debilitating disease. The main interventions for schistosomiasis control are reviewed, including preventive chemotherapy using praziquantel, snail control, sanitation, safe water supplies, and behaviour change strategies supported by information, education and communication (IEC) materials. Differences in the biology and transmission of the three main Schistosoma species (i.e. Schistosoma haematobium, S. mansoni and S. japonicum), which impact on control interventions, are considered. Sensitive diagnostic procedures to ensure adequate surveillance in areas attaining low endemicity are required. The importance of capacity building is highlighted. To achieve elimination, an intersectoral approach is necessary, with advocacy and action from local communities and the health community to foster cooperative ventures with engineers, the private sector, governments and non-governmental organizations specialized in water supply and sanitation. Examples of successful schistosomiasis control programmes are reviewed to highlight what has been learnt in terms of strategy for control and elimination. These include St. Lucia and other Caribbean islands, Brazil and Venezuela for S. mansoni; Saudi Arabia and Egypt for both S. mansoni and S. haematobium; Morocco, Tunisia, Algeria, Mauritius and the Islamic Republic of Iran for S. haematobium; Japan and the Peoples Republic of China for S. japonicum. Additional targets for elimination or even eradication could be the two minor human schistosome species S. guineenisis and S. intercalatum, which have a restricted distribution in West and Central Africa. The examples show that elimination of schistosomiasis is an achievable and desirable goal requiring full integration of preventive chemotherapy with the tools of transmission control. An agenda for the elimination of schistosomiasis would aim to identify the gaps in knowledge, and define the tools, strategies and guidelines that will help national control programmes move towards elimination, including an internationally accepted mechanism that allows verification/confirmation of elimination.

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

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. Methodology With an emphasis on schistosomiasis in Africa, we systematically searched the literature (peer-reviewed journals and ‘grey literature’), contacted Ministries of Health and research institutions in schistosomiasis-endemic countries for location-specific prevalence data and survey details (e.g., study population, year of survey and diagnostic techniques). The data were extracted, georeferenced, and stored in a MySQL database with a web interface allowing free database access and data management. Principal Findings At the beginning of 2011, our database contained more than 12,000 georeferenced schistosomiasis survey locations from 35 African countries available under http://www.gntd.org. Currently, the database is expanded to a global repository, including a host of other NTDs, e.g. soil-transmitted helminthiasis and leishmaniasis. Conclusions An open-access, spatially explicit NTD database offers unique opportunities for disease risk modeling, targeting control interventions, disease monitoring, and surveillance. Moreover, it allows for detailed geostatistical analyses of disease distribution in space and time. With an initial focus on schistosomiasis in Africa, we demonstrate the proof-of-concept that the establishment and running of a global NTD database is feasible and should be expanded without delay.

Geostatistical Model-Based Estimates of Schistosomiasis Prevalence among Individuals Aged ≤20 Years in West Africa

Background Schistosomiasis is a water-based disease that is believed to affect over 200 million people with an estimated 97% of the infections concentrated in Africa. However, these statistics are largely based on population re-adjusted data originally published by Utroska and colleagues more than 20 years ago. Hence, these estimates are outdated due to large-scale preventive chemotherapy programs, improved sanitation, water resources development and management, among other reasons. For planning, coordination, and evaluation of control activities, it is essential to possess reliable schistosomiasis prevalence maps. Methodology We analyzed survey data compiled on a newly established open-access global neglected tropical diseases database (i) to create smooth empirical prevalence maps for Schistosoma mansoni and S. haematobium for individuals aged ≤20 years in West Africa, including Cameroon, and (ii) to derive country-specific prevalence estimates. We used Bayesian geostatistical models based on environmental predictors to take into account potential clustering due to common spatially structured exposures. Prediction at unobserved locations was facilitated by joint kriging. Principal Findings Our models revealed that 50.8 million individuals aged ≤20 years in West Africa are infected with either S. mansoni, or S. haematobium, or both species concurrently. The country prevalence estimates ranged between 0.5% (The Gambia) and 37.1% (Liberia) for S. mansoni, and between 17.6% (The Gambia) and 51.6% (Sierra Leone) for S. haematobium. We observed that the combined prevalence for both schistosome species is two-fold lower in Gambia than previously reported, while we found an almost two-fold higher estimate for Liberia (58.3%) than reported before (30.0%). Our predictions are likely to overestimate overall country prevalence, since modeling was based on children and adolescents up to the age of 20 years who are at highest risk of infection. Conclusion/Significance We present the first empirical estimates for S. mansoni and S. haematobium prevalence at high spatial resolution throughout West Africa. Our prediction maps allow prioritizing of interventions in a spatially explicit manner, and will be useful for monitoring and evaluation of schistosomiasis control programs.

Spatial distribution of schistosomiasis and treatment needs in sub-Saharan Africa: a systematic review and geostatistical analysis

BACKGROUND Schistosomiasis affects more than 200 million individuals, mostly in sub-Saharan Africa, but empirical estimates of the disease burden in this region are unavailable. We used geostatistical modelling to produce high-resolution risk estimates of infection with Schistosoma spp and of the number of doses of praziquantel treatment needed to prevent morbidity at different administrative levels in 44 countries. METHODS We did a systematic review to identify surveys including schistosomiasis prevalence data in sub-Saharan Africa via PubMed, ISI Web of Science, and African Journals Online, from inception to May 2, 2014, with no restriction of language, survey date, or study design. We used Bayesian geostatistical meta-analysis and rigorous variable selection to predict infection risk over a grid of 1 155 818 pixels at 5 × 5 km, on the basis of environmental and socioeconomic predictors and to calculate the number of doses of praziquantel needed for prevention of morbidity. FINDINGS The literature search identified Schistosoma haematobium and Schistosoma mansoni surveys done in, respectively, 9318 and 9140 unique locations. Infection risk decreased from 2000 onwards, yet estimates suggest that 163 million (95% Bayesian credible interval [CrI] 155 million to 172 million; 18·5%, 17·6-19·5) of the sub-Saharan African population was infected in 2012. Mozambique had the highest prevalence of schistosomiasis in school-aged children (52·8%, 95% CrI 48·7-57·8). Low-risk countries (prevalence among school-aged children lower than 10%) included Burundi, Equatorial Guinea, Eritrea, and Rwanda. The numbers of doses of praziquantel needed per year were estimated to be 123 million (95% CrI 121 million to 125 million) for school-aged children and 247 million (239 million to 256 million) for the entire population. INTERPRETATION Our results will inform policy makers about the number of treatments needed at different levels and will guide the spatial targeting of schistosomiasis control interventions. FUNDING European Research Council, China Scholarship Council, UBS Optimus Foundation, and Swiss National Science Foundation.

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.

Mapping malaria risk among children in Côte d’Ivoire using Bayesian geo-statistical models

BackgroundIn Côte d’Ivoire, an estimated 767,000 disability-adjusted life years are due to malaria, placing the country at position number 14 with regard to the global burden of malaria. Risk maps are important to guide control interventions, and hence, the aim of this study was to predict the geographical distribution of malaria infection risk in children aged <16 years in Côte d’Ivoire at high spatial resolution.MethodsUsing different data sources, a systematic review was carried out to compile and geo-reference survey data on Plasmodium spp. infection prevalence in Côte d’Ivoire, focusing on children aged <16 years. The period from 1988 to 2007 was covered. A suite of Bayesian geo-statistical logistic regression models was fitted to analyse malaria risk. Non-spatial models with and without exchangeable random effect parameters were compared to stationary and non-stationary spatial models. Non-stationarity was modelled assuming that the underlying spatial process is a mixture of separate stationary processes in each ecological zone. The best fitting model based on the deviance information criterion was used to predict Plasmodium spp. infection risk for entire Côte d’Ivoire, including uncertainty.ResultsOverall, 235 data points at 170 unique survey locations with malaria prevalence data for individuals aged <16 years were extracted. Most data points (n = 182, 77.4%) were collected between 2000 and 2007. A Bayesian non-stationary regression model showed the best fit with annualized rainfall and maximum land surface temperature identified as significant environmental covariates. This model was used to predict malaria infection risk at non-sampled locations. High-risk areas were mainly found in the north-central and western area, while relatively low-risk areas were located in the north at the country border, in the north-east, in the south-east around Abidjan, and in the central-west between two high prevalence areas.ConclusionThe malaria risk map at high spatial resolution gives an important overview of the geographical distribution of the disease in Côte d’Ivoire. It is a useful tool for the national malaria control programme and can be utilized for spatial targeting of control interventions and rational resource allocation.

Determining Treatment Needs at Different Spatial Scales Using Geostatistical Model-Based Risk Estimates of Schistosomiasis

Background After many years of neglect, schistosomiasis control is going to scale. The strategy of choice is preventive chemotherapy, that is the repeated large-scale administration of praziquantel (a safe and highly efficacious drug) to at-risk populations. The frequency of praziquantel administration is based on endemicity, which usually is defined by prevalence data summarized at an arbitrarily chosen administrative level. Methodology For an ensemble of 29 West and East African countries, we determined the annualized praziquantel treatment needs for the school-aged population, adhering to World Health Organization guidelines. Different administrative levels of prevalence aggregation were considered; country, province, district, and pixel level. Previously published results on spatially explicit schistosomiasis risk in the selected countries were employed to classify each area into distinct endemicity classes that govern the frequency of praziquantel administration. Principal Findings Estimates of infection prevalence adjusted for the school-aged population in 2010 revealed that most countries are classified as moderately endemic for schistosomiasis (prevalence 10–50%), while four countries (i.e., Ghana, Liberia, Mozambique, and Sierra Leone) are highly endemic (>50%). Overall, 72.7 million annualized praziquantel treatments (50% confidence interval (CI): 68.8–100.7 million) are required for the school-aged population if country-level schistosomiasis prevalence estimates are considered, and 81.5 million treatments (50% CI: 67.3–107.5 million) if estimation is based on a more refined spatial scale at the provincial level. Conclusions/Significance Praziquantel treatment needs may be over- or underestimated depending on the level of spatial aggregation. The distribution of schistosomiasis in Ethiopia, Liberia, Mauritania, Uganda, and Zambia is rather uniform, and hence country-level risk estimates are sufficient to calculate treatment needs. On the other hand, countries like Burkina Faso, Mali, Mozambique, Sudan, and Tanzania show large spatial heterogeneity in schistosomiasis risk, which should be taken into account for calculating treatment requirements.

Modelling age-heterogeneous Schistosoma haematobium and S. mansoni survey data via alignment factors

BackgroundReliable maps of the geographical distribution, number of infected individuals and burden estimates of schistosomiasis are essential tools to plan, monitor and evaluate control programmes. Large-scale disease mapping and prediction efforts rely on compiled historical survey data obtained from the peer-reviewed literature and unpublished reports. Schistosomiasis surveys usually focus on school-aged children, whereas some surveys include entire communities. However, data are often reported for non-standard age groups or entire study populations. Existing geostatistical models ignore either the age-dependence of the disease risk or omit surveys considered too heterogeneous.MethodsWe developed Bayesian geostatistical models and analysed existing schistosomiasis prevalence data by estimating alignment factors to relate surveys on individuals aged ≤ 20 years with surveys on individuals aged > 20 years and entire communities. Schistosomiasis prevalence data for 11 countries in the eastern African region were extracted from an open-access global database pertaining to neglected tropical diseases. We assumed that alignment factors were constant for the whole region or a specific country.ResultsRegional alignment factors indicated that the risk of a Schistosoma haematobium infection in individuals aged > 20 years and in entire communities is smaller than in individuals ≤ 20 years, 0.83 and 0.91, respectively. Country-specific alignment factors varied from 0.79 (Ethiopia) to 1.06 (Zambia) for community-based surveys. For S. mansoni, the regional alignment factor for entire communities was 0.96 with country-specific factors ranging from 0.84 (Burundi) to 1.13 (Uganda).ConclusionsThe proposed approach could be used to align inherent age-heterogeneity between school-based and community-based schistosomiasis surveys to render compiled data for risk mapping and prediction more accurate.

Cost-effectiveness of everolimus-eluting versus bare-metal stents in ST-segment elevation myocardial infarction: An analysis from the EXAMINATION randomized controlled trial

Background Use of everolimus-eluting stents (EES) has proven to be clinically effective and safe in patients with ST-segment elevation myocardial infarction but it remains unclear whether it is cost-effective compared to bare-metal stents (BMS) in the long-term. We sought to assess the cost-effectiveness of EES versus BMS based on the 5-year results of the EXAMINATION trial, from a Spanish health service perspective. Methods Decision analysis of the use of EES versus BMS was based on the patient-level clinical outcome data of the EXAMINATION trial. The analysis adopted a lifelong time horizon, assuming that long-term survival was independent of the initial treatment strategy after the end of follow-up. Life-expectancy, health-state utility scores and unit costs were extracted from published literature and publicly available sources. Non-parametric bootstrapping was combined with probabilistic sensitivity analysis to co-assess the impact of patient-level variation and parameter uncertainty. The main outcomes were total costs and quality-adjusted life-years. The incremental cost-effectiveness ratio was expressed as cost per quality-adjusted life-years gained. Costs and effects were discounted at 3%. Results The model predicted an average survival time in patients receiving EES and BMS of 10.52 and 10.38 undiscounted years, respectively. Over the life-long time horizon, the EES strategy was €430 more costly than BMS (€8,305 vs. €7,874), but went along with incremental gains of 0.10 quality-adjusted life-years. This resulted in an average incremental cost-effectiveness ratio over all simulations of €3,948 per quality-adjusted life-years gained and was below a willingness-to-pay threshold of €25,000 per quality-adjusted life-years gained in 86.9% of simulation runs. Conclusions Despite higher total costs relative to BMS, EES appeared to be a cost-effective therapy for ST-segment elevation myocardial infarction patients due to their incremental effectiveness. Predicted incremental cost-effectiveness ratios were below generally acceptable threshold values.