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

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

LAMP for Human African Trypanosomiasis: A Comparative Study of Detection Formats

Loop-mediated isothermal amplification (LAMP) is at the forefront of the search for innovative diagnostics for human African trypanosomiasis (HAT). Several simple endpoint detection methods have been developed for LAMP and here we compare four of these: (i) visualization of turbidity; (ii) addition of hydroxynaphthol blue before incubation; (iii) addition of calcein with MnCl2 before incubation and (iv) addition of Quant-iT PicoGreen after incubation. These four methods were applied to four LAMP assays for the detection of human African trypanosomiasis, including two Trypanozoon specific and two Trypanosoma brucei rhodesiense specific reactions using DNA extracted from cryo-preserved procyclic form T. b. rhodesiense. A multi-observer study was performed to assess inter-observer reliability of two of these methods: hydroxynapthol blue and calcein with MnCl2, using DNA prepared from blood samples stored on Whatman FTA cards. Results showed that hydroxynaphthol blue was the best of the compared methods for easy, inexpensive, accurate and reliable interpretation of LAMP assays for HAT. Hydroxynapthol blue generates a violet to sky blue colour change that was easy to see and was consistently interpreted by independent observers. Visible turbidity detection is not possible for all currently available HAT LAMP reactions; Quant-iT PicoGreen is expensive and addition of calcein with MnCl2 adversely affects reaction sensitivity and was unpopular with several observers

LAMP primer sequences for the <i>SRA</i>2 assay for <i>T. b. rhodesiense</i>.

<p>LAMP primer sequences for the <i>SRA</i>2 assay for <i>T. b. rhodesiense</i>.</p

Detection limit with different assays and detection methods with a 10 fold dilution series of 13.6 nM <i>T. b. rhodesiense</i> DNA.

<p>Detection limit with different assays and detection methods with a 10 fold dilution series of 13.6 nM <i>T. b. rhodesiense</i> DNA.</p

Examples of colour changes seen with hydroxynaphthol blue, calcein with MnCl₂ and Quant-iT PicoGreen.

<p>Panel A shows the colour change seen with hydroxynaphthol blue, panel B shows the colour change seen with calcein and MnCl₂ and panel C shows the colour change seen with Quant-iT PicoGreen.</p

Costs associated with the methods investigated in this study, per 100 reactions (prices are based on UK reagent prices at the time of the study, and are converted to US $).

<p>Costs associated with the methods investigated in this study, per 100 reactions (prices are based on UK reagent prices at the time of the study, and are converted to US $).</p

Results of one-step regression analysis using prevalence outcome variable and all villages.

<p>Results of one-step regression analysis using prevalence outcome variable and all villages.</p

Covariates collected for analysis, indicating variables used for model development.

1<p>Advanced Very High Resolution Radiometer.</p>2<p>normalised difference vegetation index.</p>3<p>middle-infrared.</p>4<p>land surface temperature.</p

Difference in predicted prevalence between first and second analysis.

<p>Difference in predicted prevalence between first and second analysis.</p