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

Additional file 1: of Using the hierarchical ordinal regression model to analyse the intensity of urinary schistosomiasis infection in school children in Lusaka Province, Zambia

Multilingual abstract in the five official working languages of the United Nations. (PDF 656 kb

The epidemiology and small-scale spatial heterogeneity of urinary schistosomiasis in Lusaka province, Zambia

In line with the aims of the “National Bilharzia Control Programme” and the “School Health and Nutrition Programme” in Zambia, a study on urinary schistosomiasis was conducted in 20 primary schools of Lusaka province to further our understanding of the epidemiology of the infection, and to enhance spatial targeting of control. We investigated risk factors associated with urinary schistosomiasis, and examined small-scale spatial heterogeneity in prevalence, using data collected from 1,912 schoolchildren, 6 to 15-year-old, recruited from 20 schools in Kafue and Luangwa districts. The risk factors identified included geographical location, altitude, normalized difference vegetation index (NDVI), maximum temperature, age, sex of the child and intermediate host snail abundance. Three logistic regression models were fitted assuming different random effects to allow for spatial structuring. The mean prevalence rate was 9.6%, with significance difference between young and older children (odds ratio (OR) = 0.71; 95% confidence interval (CI) = 0.51-0.96). The risk of infection was related to intermediate host snail abundance (OR = 1.03; 95% CI = 1.00-1.05) and vegetation cover (OR = 1.04; 95% CI = 1.00-1.07). Schools located either on the plateau and the valley also differed in prevalence and intensity of infection for moderate infection to none (OR = 1.64; 95% CI = 1.36- 1.96). The overall predictive performance of the spatial random effects model was higher than the ordinary logistic regression. In addition, evidence of heterogeneity of the infection risk was found at the micro-geographical level. A sound understanding of small-scale heterogeneity, caused by spatial aggregation of schoolchildren, is important to inform health planners for implementing control schistosomiasis interventions

Survival of people on antiretroviral treatment in Zambia: a retrospective cohort analysis of HIV clients on ART

Introduction: provision of free anti-retroviral therapy in Zambia started in June 2004. There were only 15,000 people on treatment as at December that year, mainly due to lack of access. This number rose to 580,000 people as at December 2013. The general objective of this study was to determine survival of people on ART and to examine associated predictors for survival. Methods: the study included ART patients enrolled between the year 2002 and 2013 (n=10,395) in 285 health facilities in Zambia. Patient files were analyzed retrospectively. The study used Kaplan Meier and Cox-proportional hazard models to describe the relationship between lost to follow up and age, sex, baseline CD4 cell count and weight. Results: results showed that lost to follow up accounted for 90% of the clients that had dropped out, while 10% was to deaths. Low baseline CD4 count (p-value 0.001, HR 0.9994, (95% CI 0.9993, 0.9996) at initiation was associated with lost to follow up together with weight at initiation (p-value 0.031, HR 0.9987 at 95% CI (0.9975, 0.9998)) of ART. Conclusion: this study has demonstrated that lost to follow up is a substantial contributing factor to drop outs among HIV patients on treatment. Strengthening of community treatment supporters especially immediate family members in emphasizing to the client the need to continue treatment is necessary. The health facility could do more in emphasizing the importance of treatment especially in the initial stages. Further, in order to reduce opportunistic infections and probable deaths during treatment, cotrimoxazole prophylaxis should be maintained so as to raise the CD4 levels. Improved nutritional assessment and counseling to boost the nutritional status of the clients throughout should be encouraged.The Pan African Medical Journal 2016;2

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