Using remote sensing and geographic information systems to identify villages at high risk for rhodesiense sleeping sickness in Uganda

Enhanced passive screening and diagnosis for gambiense human African trypanosomiasis in north-western Uganda - Moving towards elimination

Introduction

The incidence of gambiense human African trypanosomiasis (gHAT) in Uganda has been declining, from 198 cases in 2008, to only 20 in 2012. Interruption of transmission of the disease by early diagnosis and treatment is core to the control and eventual elimination of gHAT. Until recently, the format of available screening tests had restricted screening and diagnosis to central health facilities (passive screening). We describe a novel strategy that is contributing to elimination of gHAT in Uganda through expansion of passive screening to the entire population at risk.

Methodology / Principal findings

In this strategy, patients who are clinically suspected of having gHAT at primary health facilities are screened using a rapid diagnostic test (RDT), followed by parasitological confirmation at strategically located microscopy centres. For patients who are positive with the RDT and negative by microscopy, blood samples undergo further testing using loop-mediated isothermal amplification (LAMP), a molecular test that detects parasite DNA. LAMP positive patients are considered strong suspects, and are re-evaluated by microscopy. Location and upgrading of facilities to perform microscopy and LAMP was informed by results of georeferencing and characterization of all public healthcare facilities in the 7 gHAT endemic districts in Uganda. Three facilities were upgraded to perform RDTs, microscopy and LAMP, 9 to perform RDTs and microscopy, and 200 to screen patients with RDTs. This reduced the distance that a sick person must travel to be screened for gHAT to a median distance of 2.5km compared to 23km previously. In this strategy, 9 gHAT cases were diagnosed in 2014, and 4 in 2015.

Conclusions

This enhanced passive screening strategy for gHAT has enabled full coverage of the population at risk, and is being replicated in other gHAT endemic countries. The improvement in case detection is making elimination of the disease in Uganda an imminent possibility.

The Role of Spatial Statistics in the Control and Elimination of Neglected Tropical Diseases in Sub-Saharan Africa: A Focus on Human African Trypanosomiasis, Schistosomiasis and Lymphatic Filariasis

Disease control and elimination programmes can benefit greatly from accurate information on the spatial variability of disease risk, particularly when risk is highly spatially heterogeneous. Due to advances in statistical methodology, coupled with the increased availability of geospatial technology, this information is becoming increasingly accessible. In this chapter we describe recent advancements in spatial methods associated with the analysis of disease data measured at the point-level and demonstrate their application to the control and elimination of neglected tropical diseases (NTDs). We further provide information on spatially referenced data sources and software that can be used to create NTD risk maps, concentrating on those that can be freely obtained. Examples relating to three NTDs affecting populations in sub-Saharan Africa are presented throughout the chapter, i.e., human African trypanosomiasis, schistosomiasis and lymphatic filariasis. These three diseases, with differing routes of transmission, control methods and level of spatial heterogeneity, demonstrate the flexibility and applicability of the methods described.

The burden and spatial distribution of bovine African trypanosomes in small holder crop-livestock production systems in Tororo District, south-eastern Uganda

Background

African animal trypanosomiasis (AAT) is considered to be one of the greatest constraints to livestock production and livestock-crop integration in most African countries. South-eastern Uganda has suffered for more than two decades from outbreaks of zoonotic Human African Trypanosomiasis (HAT), adding to the burden faced by communities from AAT. There is insufficient AAT and HAT data available (in the animal reservoir) to guide and prioritize AAT control programs that has been generated using contemporary, sensitive and specific molecular techniques. This study was undertaken to evaluate the burden that AAT presents to the small-scale cattle production systems in south-eastern Uganda.

Methods

Randomised cluster sampling was used to select 14% (57/401) of all cattle containing villages across Tororo District. Blood samples were taken from all cattle in the selected villages between September-December 2011; preserved on FTA cards and analysed for different trypanosomes using a suite of molecular techniques. Generalized estimating equation and Rogen-Gladen estimator models were used to calculate apparent and true prevalences of different trypanosomes while intra cluster correlations were estimated using a 1-way mixed effect analysis of variance (ANOVA) in R statistical software version 3.0.2.

Results

The prevalence of all trypanosome species in cattle was 15.3% (95% CI; 12.2-19.1) while herd level trypanosome species prevalence varied greatly between 0-43%. Trypanosoma vivax (17.4%, 95% CI; 10.6-16.8) and Trypanosoma brucei rhodesiense (0.03%) were respectively, the most, and least prevalent trypanosome species identified.

Conclusions

The prevalence of bovine trypanosomes in this study indicates that AAT remains a significant constraint to livestock health and livestock production. There is need to implement tsetse and trypanosomiasis control efforts across Tororo District by employing effective, cheap and sustainable tsetse and trypanosomiasis control methods that could be integrated in the control of other endemic vector borne diseases like tick-borne diseases.

Epidemiology of human African trypanosomiasis

Human African trypanosomiasis (HAT), or sleeping sickness, is caused by Trypanosoma brucei gambiense, which is a chronic form of the disease present in western and central Africa, and by Trypanosoma brucei rhodesiense, which is an acute disease located in eastern and southern Africa. The rhodesiense form is a zoonosis, with the occasional infection of humans, but in the gambiense form, the human being is regarded as the main reservoir that plays a key role in the transmission cycle of the disease. The gambiense form currently assumes that 98% of the cases are declared; the Democratic Republic of the Congo is the most affected country, with more than 75% of the gambiense cases declared. The epidemiology of the disease is mediated by the interaction of the parasite (trypanosome) with the vectors (tsetse flies), as well as with the human and animal hosts within a particular environment. Related to these interactions, the disease is confined in spatially limited areas called “foci”, which are located in Sub-Saharan Africa, mainly in remote rural areas. The risk of contracting HAT is, therefore, determined by the possibility of contact of a human being with an infected tsetse fly. Epidemics of HAT were described at the beginning of the 20th century; intensive activities have been set up to confront the disease, and it was under control in the 1960s, with fewer than 5,000 cases reported in the whole continent. The disease resurged at the end of the 1990s, but renewed efforts from endemic countries, cooperation agencies, and nongovernmental organizations led by the World Health Organization succeeded to raise awareness and resources, while reinforcing national programs, reversing the trend of the cases reported, and bringing the disease under control again. In this context, sustainable elimination of the gambiense HAT, defined as the interruption of the transmission of the disease, was considered as a feasible target for 2030. Since rhodesiense HAT is a zoonosis, where the animal reservoir plays a key role, the interruption of the disease’s transmission is not deemed feasible.

The dispersal ecology of Rhodesian sleeping sickness following its introduction to a new area

Tsetse-transmitted human and animal trypanosomiasis are constraints to both human and animal health in sub-Saharan Africa, and although these diseases have been known for over a century, there is little recent evidence demonstrating how the parasites circulate in natural hosts and ecosystems. The spread of Rhodesian sleeping sickness (caused by Trypanosoma brucei rhodesiense) within Uganda over the past 15 years has been linked to the movement of infected, untreated livestock (the predominant reservoir) from endemic areas. However, despite an understanding of the environmental dependencies of sleeping sickness, little research has focused on the environmental factors controlling transmission establishment or the spatially heterogeneous dispersal of disease following a new introduction. In the current study, an annually stratified case-control study of Rhodesian sleeping sickness cases from Serere District, Uganda was used to allow the temporal assessment of correlations between the spatial distribution of sleeping sickness and landscape factors. Significant relationships were detected between Rhodesian sleeping sickness and selected factors, including elevation and the proportion of land which was "seasonally flooding grassland" or "woodlands and dense savannah." Temporal trends in these relationships were detected, illustrating the dispersal of Rhodesian sleeping sickness into more 'suitable' areas over time, with diminishing dependence on the point of introduction in concurrence with an increasing dependence on environmental and landscape factors. These results provide a novel insight into the ecology of Rhodesian sleeping sickness dispersal and may contribute towards the implementation of evidence-based control measures to prevent its further spread.

Expert knowledge sourcing for public health surveillance: national tsetse mapping in Uganda

In much of sub-Saharan Africa, availability of standardized and reliable public health data is poor or negligible. Despite continued calls for the prioritization of improved health datasets in poor regions, public health surveillance remains a significant global health challenge. Alternate approaches to surveillance and collection of public health data have thus garnered increasing interest, though there remains relatively limited research evaluating these approaches for public health. Herein, we present a case study applying and evaluating the use of expert knowledge sources for public health dataset development, using the case of vector distributions of Human African Trypanosomiasis (HAT) in Uganda. Specific objectives include: 1) Review the use of expert knowledge sourcing methods for public health surveillance, 2) Review current knowledge on tsetse vector distributions of public health importance in Uganda and the methods used for tsetse mapping in Africa; 3) Quantify confidence of the presence or absence of tsetse flies in Uganda based on expert informant reports, and 4) Assess the reliability and potential utility of expert knowledge sourcing as an alternative or complimentary method for public health surveillance in general and tsetse mapping in particular. Information on tsetse presence or absence, and associated confidence, was collected through interviews with District Entomologist and Veterinary Officers to develop a database of tsetse distributions for 952 sub-counties in Uganda. Results show high consistency with existing maps, indicating potential reliability of modeling approaches, though failing to provide evidence for successful tsetse control in past decades. Expert-sourcing methods provide a novel, low-cost and rapid complimentary approach for triangulating data from prediction modeling where field-based validation is not feasible. Data quality is dependent, however, on the level of expertise and documentation to support confidence levels for data reporting. Results highlight the need for increased evaluation of alternate approaches and methods to data collection.

An exploratory GIS-based method to identify and characterise landscapes with an elevated epidemiological risk of Rhodesian human African trypanosomiasis

BACKGROUND

Specific land cover types and activities have been correlated with Trypanosoma brucei rhodesiense distributions, indicating the importance of landscape for epidemiological risk. However, methods proposed to identify specific areas with elevated epidemiological risk (i.e. where transmission is more likely to occur) tend to be costly and time consuming. This paper proposes an exploratory spatial analysis using geo-referenced human African trypanosomiasis (HAT) cases and matched controls from Serere hospital, Uganda (December 1998 to November 2002) to identify areas with an elevated epidemiological risk of HAT.

METHODS

Buffers 3 km from each case and control were used to represent areas in which village inhabitants would carry out their daily activities. It was hypothesised that the selection of areas where several case village buffers overlapped would enable the identification of locations with increased risk of HAT transmission, as these areas were more likely to be frequented by HAT cases in several surrounding villages. The landscape within these overlap areas should more closely relate to the environment in which transmission occurs as opposed to using the full buffer areas. The analysis was carried out for each of four annual periods, for both cases and controls, using a series of threshold values (number of overlapping buffers), including a threshold of one, which represented the benchmark (e.g. use of the full buffer area as opposed to the overlap areas).

RESULTS

A greater proportion of the overlap areas for cases consisted of seasonally flooding grassland and lake fringe swamp, than the control overlap areas, correlating well with the preferred habitat of the predominant tsetse species within the study area (Glossina fuscipes fuscipes). The use of overlap areas also resulted in a greater difference between case and control landscapes, when compared with the benchmark (using the full buffer area).

CONCLUSIONS

These results indicate that the overlap analysis has enabled the selection of areas more likely to represent epidemiological risk zones than similar analyses using full buffer areas. The identification of potential epidemiological risk zones using this method requires fewer data than other proposed methods and further development may provide vital information for the targeting of control measures.

Estimating and mapping the population at risk of sleeping sickness

BACKGROUND

Human African trypanosomiasis (HAT), also known as sleeping sickness, persists as a public health problem in several sub-Saharan countries. Evidence-based, spatially explicit estimates of population at risk are needed to inform planning and implementation of field interventions, monitor disease trends, raise awareness and support advocacy. Comprehensive, geo-referenced epidemiological records from HAT-affected countries were combined with human population layers to map five categories of risk, ranging from "very high" to "very low," and to estimate the corresponding at-risk population.

RESULTS

Approximately 70 million people distributed over a surface of 1.55 million km(2) are estimated to be at different levels of risk of contracting HAT. Trypanosoma brucei gambiense accounts for 82.2% of the population at risk, the remaining 17.8% being at risk of infection from T. b. rhodesiense. Twenty-one million people live in areas classified as moderate to very high risk, where more than 1 HAT case per 10,000 inhabitants per annum is reported.

DISCUSSION

Updated estimates of the population at risk of sleeping sickness were made, based on quantitative information on the reported cases and the geographic distribution of human population. Due to substantial methodological differences, it is not possible to make direct comparisons with previous figures for at-risk population. By contrast, it will be possible to explore trends in the future. The presented maps of different HAT risk levels will help to develop site-specific strategies for control and surveillance, and to monitor progress achieved by ongoing efforts aimed at the elimination of sleeping sickness.

Spatial epidemiology of hospital-diagnosed brucellosis in Kampala, Uganda

BACKGROUND

A retrospective case-control study was undertaken to examine the spatial risk factors for human brucellosis in Kampala, Uganda.

METHODS

Information on age, sex and month of diagnosis was derived from records from plate agglutination tests undertaken at Mulago Hospital, Kampala. Information on Parishes (LC2s) where patients reside was sourced from the outpatient registration book. In-patient fracture cases were selected for use as controls using 1:1 matching based on the age, sex and month of diagnosis. The locations of cases and controls were obtained by calculating Cartesian coordinates of the centroids of Parish level (LC2) polygons and a spatial scan statistic was applied to test for disease clustering. Parishes were classified according to the level of urbanization as urban, peri-urban or rural.

RESULTS

Significantly more females than males were found to show sero-positivity for brucellosis when compared with the sex ratio of total outpatients, in addition female brucellosis patients were found to be significantly older than the male patients. Spatial clustering of brucellosis cases was observed including around Mulago Hospital (radius = 6.8 km, p = 0.001). The influence of proximity to the hospital that was observed for brucellosis cases was not significantly different from that observed in the controls. The disease cluster was confounded by the different catchment areas between cases and controls. The level of urbanization was not associated with the incidence of brucellosis but living in a slum area was a significant risk factor among urban dwellers (odds ratio 1.97, 95% CI: 1.10-3.61).

CONCLUSIONS

Being female was observed to be a risk factor for brucellosis sero-positvity and among urban dwellers, living in slum areas was also a risk factor although the overall risk was not different among urban, peri-urban and rural areas of the Kampala economic zone.

Spatial epidemiology of suspected clinical leptospirosis in Sri Lanka

Leptospirosis is one of the most widespread zoonoses in the world. A large outbreak of suspected human leptospirosis began in Sri Lanka during 2008. This study investigated spatial variables associated with suspected leptospirosis risk during endemic and outbreak periods. Data were obtained for monthly numbers of reported cases of suspected clinical leptospirosis for 2005–2009 for all of Sri Lanka. Space–time scan statistics were combined with regression modelling to test associations during endemic and outbreak periods. The cross-correlation function was used to test association between rainfall and leptospirosis at four locations. During the endemic period (2005–2007), leptospirosis risk was positively associated with shorter average distance to rivers and with higher percentage of agriculture made up of farms <0·20 hectares. Temporal correlation analysis of suspected leptospirosis cases and rainfall revealed a 2-month lag in rainfall-case association during the baseline period. Outbreak locations in 2008 were characterized by shorter distance to rivers and higher population density. The analysis suggests the possibility of household transmission in densely populated semi-urban villages as a defining characteristic of the outbreak. The role of rainfall in the outbreak remains to be investigated, although analysis here suggests a more complex relationship than simple correlation.

Bayesian geostatistical analysis and prediction of Rhodesian human African trypanosomiasis

Background

The persistent spread of Rhodesian human African trypanosomiasis (HAT) in Uganda in recent years has increased concerns of a potential overlap with the Gambian form of the disease. Recent research has aimed to increase the evidence base for targeting control measures by focusing on the environmental and climatic factors that control the spatial distribution of the disease.

Objectives

One recent study used simple logistic regression methods to explore the relationship between prevalence of Rhodesian HAT and several social, environmental and climatic variables in two of the most recently affected districts of Uganda, and suggested the disease had spread into the study area due to the movement of infected, untreated livestock. Here we extend this study to account for spatial autocorrelation, incorporate uncertainty in input data and model parameters and undertake predictive mapping for risk of high HAT prevalence in future.

Materials and Methods

Using a spatial analysis in which a generalised linear geostatistical model is used in a Bayesian framework to account explicitly for spatial autocorrelation and incorporate uncertainty in input data and model parameters we are able to demonstrate a more rigorous analytical approach, potentially resulting in more accurate parameter and significance estimates and increased predictive accuracy, thereby allowing an assessment of the validity of the livestock movement hypothesis given more robust parameter estimation and appropriate assessment of covariate effects.

Results

Analysis strongly supports the theory that Rhodesian HAT was imported to the study area via the movement of untreated, infected livestock from endemic areas. The confounding effect of health care accessibility on the spatial distribution of Rhodesian HAT and the linkages between the disease's distribution and minimum land surface temperature have also been confirmed via the application of these methods.

Conclusions

Predictive mapping indicates an increased risk of high HAT prevalence in the future in areas surrounding livestock markets, demonstrating the importance of livestock trading for continuing disease spread. Adherence to government policy to treat livestock at the point of sale is essential to prevent the spread of sleeping sickness in Uganda.

The tsetse transmitted parasites, Trypanosoma brucei rhodesiense and Trypanosoma brucei gambiense, cause the fatal disease human African trypanosomiasis (HAT); the clinical progression, as well as the preferred diagnostic and treatment methods differ between the two types. Currently, the two do not overlap, although recent spread of Rhodesian HAT in Uganda has raised concerns over a potential future overlap. A recent study using geo-referenced HAT case records suggested that the most recent spread of Rhodesian HAT may have been due to movements of infected, untreated livestock (the main reservoir of the parasite). Here, the initial analysis has been extended by explicitly accounting for spatial locations and their proximity to one another, providing improved accuracy. The results provide strengthened evidence of the significance of livestock movements for the continued spread of Rhodesian HAT within Uganda, despite the introduction of cattle treatment regulations which were implemented in an effort to curb the disease's spread. The application of predictive mapping indicates an increased risk of HAT in areas surrounding livestock markets, demonstrating the importance of livestock trading for continuing disease spread. This robust evidence can be used for the targeting of disease control efforts within Uganda to prevent further spread of Rhodesian HAT.

Review of methods for space-time disease surveillance

A review of some methods for analysis of space-time disease surveillance data is presented. Increasingly, surveillance systems are capturing spatial and temporal data on disease and health outcomes in a variety of public health contexts. A vast and growing suite of methods exists for detection of outbreaks and trends in surveillance data and the selection of appropriate methods in a given surveillance context is not always clear. While most reviews of methods focus on algorithm performance, in practice, a variety of factors determine what methods are appropriate for surveillance. In this review, we focus on the role of contextual factors such as scale, scope, surveillance objective, disease characteristics, and technical issues in relation to commonly used approaches to surveillance. Methods are classified as testing-based or model-based approaches. Reviewing methods in the context of factors other than algorithm performance highlights important aspects of implementing and selecting appropriate disease surveillance methods.

Characterization of sleeping sickness transmission sites in rural and periurban areas of Kinshasa (République Démocratique du Congo)

To characterize the potential transmission sites of sleeping sickness in Kinshasa, two entomologic surveys were carried out during the dry and the rainy seasons in rural and periurban areas of Kinshasa in 2005. About 610 pyramidal traps were set up, and 897 Glossina fuscipes quanzensis were captured. Environmental and biologic factors were reported, and relationships between these factors were evaluated using logistic regression and multiple correspondence analysis. The biologic factors (the presence of tsetse flies, human blood meals, and teneral flies) were progressively accumulated at each capture site to permit the characterization of the sleeping sickness transmission risk. The dry season was found to be a more favorable period for the disease transmission than the rainy season. Moreover, the landscapes characterized by the presence of argillaceous soils, raised ground cover with forest residues and rivers, were identified as types of environments with greater risk of sleeping sickness transmission. Pig breeding appeared as an important factor increasing the disease transmission. If vector control is continuously performed along rivers segments at high risk, the transmission of sleeping sickness in rural and periurban areas of Kinshasa will considerably decrease.

Spatial predictions of Rhodesian Human African Trypanosomiasis (sleeping sickness) prevalence in Kaberamaido and Dokolo, two newly affected districts of Uganda

The continued northwards spread of Rhodesian sleeping sickness or Human African Trypanosomiasis (HAT) within Uganda is raising concerns of overlap with the Gambian form of the disease. Disease convergence would result in compromised diagnosis and treatment for HAT. Spatial determinants for HAT are poorly understood across small areas. This study examines the relationships between Rhodesian HAT and several environmental, climatic and social factors in two newly affected districts, Kaberamaido and Dokolo. A one-step logistic regression analysis of HAT prevalence and a two-step logistic regression method permitted separate analysis of both HAT occurrence and HAT prevalence. Both the occurrence and prevalence of HAT were negatively correlated with distance to the closest livestock market in all models. The significance of distance to the closest livestock market strongly indicates that HAT may have been introduced to this previously unaffected area via the movement of infected, untreated livestock from endemic areas. This illustrates the importance of the animal reservoir in disease transmission, and highlights the need for trypanosomiasis control in livestock and the stringent implementation of regulations requiring the treatment of cattle prior to sale at livestock markets to prevent any further spread of Rhodesian HAT within Uganda.

Statistical modeling of holding level susceptibility to infection during the 2001 foot and mouth disease epidemic in Great Britain

BACKGROUND

An understanding of the factors that determine the risk of members of a susceptible population becoming infected is essential for estimating the potential for disease spread, as opposed to just focusing on transmission from an infected population. Furthermore, analysis of the risk factors can reveal important characteristics of an epidemic and further develop understanding of the processes operating.

METHODS

This paper describes the development of a mixed effects logistic regression model of susceptibility of holdings to foot and mouth disease (FMD) during the 2001 epidemic in Great Britain following the imposition of a national ban on the movements of susceptible animals (NMB).

RESULTS

The principal risk factors identified in the model were shorter distances to the nearest infectious seed (a holding infected before the NMB) and the county of the holding (principally Cumbria). Additional risk factors included holdings that are mixed species rather than single species, the surface area of the holding, and the number of cattle within 10km (all p<0.001), but not surrounding sheep densities (p>0.1). The fit of the model was evaluated using the area under the receiver operator characteristic curve (ROC) and the Hosmer and Lemeshow Chi-squared statistic; the fit was good with both tests (area under the ROC=0.962 and Hosmer and Lemeshow Chi-squared statistic=49.98 (p>0.1)).

CONCLUSIONS

Holdings at greatest risk of infection can be identified using simple readily available risk factors; this information could be employed in the control of future FMD epidemics.

Analysis of risk factors for T. brucei rhodesiense sleeping sickness within villages in south-east Uganda

Background

Sleeping sickness (HAT) caused by T.b. rhodesiense is a major veterinary and human public health problem in Uganda. Previous studies have investigated spatial risk factors for T.b. rhodesiense at large geographic scales, but none have properly investigated such risk factors at small scales, i.e. within affected villages. In the present work, we use a case-control methodology to analyse both behavioural and spatial risk factors for HAT in an endemic area.

Methods

The present study investigates behavioural and occupational risk factors for infection with HAT within villages using a questionnaire-based case-control study conducted in 17 villages endemic for HAT in SE Uganda, and spatial risk factors in 4 high risk villages. For the spatial analysis, the location of homesteads with one or more cases of HAT up to three years prior to the beginning of the study was compared to all non-case homesteads. Analysing spatial associations with respect to irregularly shaped geographical objects required the development of a new approach to geographical analysis in combination with a logistic regression model.

Results

The study was able to identify, among other behavioural risk factors, having a family member with a history of HAT (p = 0.001) as well as proximity of a homestead to a nearby wetland area (p < 0.001) as strong risk factors for infection. The novel method of analysing complex spatial interactions used in the study can be applied to a range of other diseases.

Conclusion

Spatial risk factors for HAT are maintained across geographical scales; this consistency is useful in the design of decision support tools for intervention and prevention of the disease. Familial aggregation of cases was confirmed for T. b. rhodesiense HAT in the study and probably results from shared behavioural and spatial risk factors amongmembers of a household.

Buruli ulcer disease prevalence in Benin, West Africa: associations with land use/cover and the identification of disease clusters

BACKGROUND

Buruli ulcer (BU) disease, caused by infection with the environmental mycobacterium M. ulcerans, is an emerging infectious disease in many tropical and sub-tropical countries. Although vectors and modes of transmission remain unknown, it is hypothesized that the transmission of BU disease is associated with human activities in or around aquatic environments, and that characteristics of the landscape (e.g., land use/cover) play a role in mediating BU disease. Several studies performed at relatively small spatial scales (e.g., within a single village or region of a country) support these hypotheses; however, if BU disease is associated with land use/cover characteristics, either through spatial constraints on vector-host dynamics or by mediating human activities, then large-scale (i.e., country-wide) associations should also emerge. The objectives of this study were to (1) investigate associations between BU disease prevalence in villages in Benin, West Africa and surrounding land use/cover patterns and other map-based characteristics, and (2) identify areas with greater and lower than expected prevalence rates (i.e., disease clusters) to assist with the development of prevention and control programs.

RESULTS

Our landscape-based models identified low elevation, rural villages surrounded by forest land cover, and located in drainage basins with variable wetness patterns as being associated with higher BU disease prevalence rates. We also identified five spatial disease clusters. Three of the five clusters contained villages with greater than expected prevalence rates and two clusters contained villages with lower than expected prevalence rates. Those villages with greater than expected BU disease prevalence rates spanned a fairly narrow region of south-central Benin.

CONCLUSION

Our analyses suggest that interactions between natural land cover and human alterations to the landscape likely play a role in the dynamics of BU disease. For example, urbanization, potentially by providing access to protected water sources, may reduce the likelihood of becoming infected with BU disease. Villages located at low elevations may have higher BU disease prevalence rates due to their close spatial proximity to high risk environments. In addition, forest land cover and drainage basins with variable wetness patterns may be important for providing suitable growth conditions for M. ulcerans, influencing the distribution and abundance of vectors, or mediating vector-human interactions. The identification of disease clusters in this study provides direction for future research aimed at better understanding these and other environmental and social determinants involved in BU disease outbreaks.

A geographic information system to study trauma epidemiology in India

BACKGROUND

Geographic Information Systems (GIS) describe the topography and chronology of events in a defined vector space. GIS may also be used for an integrated analysis of environmental and road-related risk factors for traffic accidents.

METHODS

In a retrospective study, various features of 165 road crashes were linked to a GIS-generated digital map of an area close to a national highway in India. By overlay tools, clusters of accidents were identified, and color-coded according to accident mechanisms and injury patterns.

RESULTS

Spatial analysis revealed a cluster with a high incidence of motorbike injuries resulting in fractures. Examination of the spot demonstrated the risky combination of a speed breaker and a broken traffic light. After fixing the light, no further accidents occurred at the site.

CONCLUSION

GIS is a promising technology for geo-referencing accident data, and may be a valuable tool to identify areas of priority for injury prevention in India.

Spatial and temporal variability of the Glossina palpalis palpalis population in the Mbini focus (Equatorial Guinea)

Background

Human African Trypanosomiasis is a vector-borne parasitic disease. The geographical distribution of the disease is linked to the spatial distribution of the tsetse fly. As part of a control campaign using traps, the spatial and temporal variability is analysed of the glossina populations present in the Mbini sleeping sickness foci (Equatorial Guinea).

Results

A significant drop in the annual mean of the G. p. palpalis apparent density was noted from 2004 to 2005, although seasonal differences were not observed. The apparent density (AD) of G. p. palpalis varies significantly from one biotope to another. The fish dryers turned out to be zones with the greatest vector density, although the AD of G. p. palpalis fell significantly in all locations from 2004 to 2005.

Conclusion

Despite the tsetse fly density being relatively low in fish dryers and jetties, the population working in those zones would be more exposed to infection. The mono-pyramidal traps in the Mbini focus have been proven to be a useful tool to control G. p. palpalis, even though the activity on the banks of the River Wele needs to be intensified. The application of spatial analysis techniques and geographical information systems are very useful tools to discriminate zones with high and low apparent density of G. p. palpalis, probably associated with different potential risk of sleeping sickness transmission.