Spatial patterns and determinants of malaria infection during pregnancy in Zambia.

BACKGROUND: Malaria in pregnancy (MiP) is a major concern in Zambia. Here we aim to determine the burden and risk factors of MiP. METHODS: Monthly reported district-level malaria cases among pregnant women (count data) from January 2009 to December 2014 were obtained from the Zambian District Health Information System. Negative binomial regression model was used to investigate the associations between vector control tools, coverage of health care facilities, transportation networks and population density. Data on MiP treatment were obtained from the 2012 Zambian Malaria Indicator Survey. Yearly clusters of MiP were investigated using spatial statistics in ArcGIS v 10.1. RESULTS: The results indicated that MiP decreased in Zambia between 2010 and 2013. MiP was observed throughout the year, but showed a strong seasonal pattern. Persistent hotspots of MiP were reported in the southeast and northeast regions of Zambia, with districts that had better access to rail road and presence of water bodies associated with decreased prevalence of MiP. Better indoor residual spraying and long-lasting insecticide-treated nets coverage was demonstrated to be protective against MiP. CONCLUSIONS: Mapping the distribution of MiP to track the future requirements for scaling up essential disease-prevention efforts in stable hotspots can help the Zambian National Malaria Control Center to further develop strategies to reduce malaria prevalence in this vulnerable sub-population.

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.

High burden of malaria following scale-up of control interventions in Nchelenge District, Luapula Province, Zambia.

BACKGROUND: Malaria control interventions have been scaled-up in Zambia in conjunction with a malaria surveillance system. Although substantial progress has been achieved in reducing morbidity and mortality, national and local information demonstrated marked heterogeneity in the impact of malaria control across the country. This study reports the high burden of malaria in Nchelenge District, Luapula Province, Zambia from 2006 to 2012 after seven years of control measures. METHODS: Yearly aggregated information on cases of malaria, malaria deaths, use of malaria diagnostics, and malaria control interventions from 2006 to 2012 were obtained from the Nchelenge District Health Office. Trends in the number of malaria cases, methods of diagnosis, malaria positivity rate among pregnant women, and intervention coverage were analysed using descriptive statistics. RESULTS: Malaria prevalence remained high, increasing from 38% in 2006 to 53% in 2012. Increasing numbers of cases of severe malaria were reported until 2010. Intense seasonal malaria transmission was observed with seasonal declines in the number of cases between April and August, although malaria transmission continued throughout the year. Clinical diagnosis without accompanying confirmation declined from 95% in 2006 to 35% in 2012. Intervention coverage with long-lasting insecticide-treated nets and indoor residual spraying increased from 2006 to 2012. CONCLUSIONS: Despite high coverage with vector control interventions, the burden of malaria in Nchelenge District, Zambia remained high. The high parasite prevalence could accurately reflect the true burden, perhaps in part as a consequence of population movement, or improved access to care and case reporting. Quality information at fine spatial scales will be critical for targeting effective interventions and measurement of progress.

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.

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.

Virtual globes and geospatial health: the potential of new tools in the management and control of vector-borne diseases.

The rapidly growing field of three-dimensional software modeling of the Earth holds promise for applications in the geospatial health sciences. Easy-to-use, intuitive virtual globe technologies such as Google Earth enable scientists around the world to share their data and research results in a visually attractive and readily understandable fashion without the need for highly sophisticated geographical information systems (GIS) or much technical assistance. This paper discusses the utility of the rapid and simultaneous visualization of how the agents of parasitic diseases are distributed, as well as that of their vectors and/or intermediate hosts together with other spatially-explicit information. The resulting better understanding of the epidemiology of infectious diseases, and the multidimensional environment in which they occur, are highlighted. In particular, the value of Google Earth, and its web-based pendant Google Maps, are reviewed from a public health view point, combining results from literature searches and experiences gained thus far from a multidisciplinary project aimed at optimizing schistosomiasis control and transmission surveillance in sub-Saharan Africa. Although the basic analytical capabilities of virtual globe applications are limited, we conclude that they have considerable potential in the support and promotion of the geospatial health sciences as a userfriendly, straightforward GIS tool for the improvement of data collation, visualization and exploration. The potential of these systems for data sharing and broad dissemination of scientific research and results is emphasized.