Estimating the impact of Tiny Targets in reducing the incidence of gambiense sleeping sickness in the North West Uganda focus

doi:10.21203/rs.3.rs-362729/v1

Download PDF

Research

Estimating the impact of Tiny Targets in reducing the incidence of gambiense sleeping sickness in the North West Uganda focus

https://doi.org/10.21203/rs.3.rs-362729/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 18 Aug, 2021

Read the published version in Parasites & Vectors →

You are reading this latest preprint version

Background Riverine species of tsetse (Glossina) transmit Trypanosoma brucei gambiense which causes Gambian human African trypanosomiasis (gHAT), a neglected tropical disease. Uganda aims to eliminate gHAT as a public health problem through detection and treatment of human cases and vector control. The latter is being achieved through the deployment of ‘Tiny Targets’, insecticide-impregnated panels of material which attract and kill tsetse. We analysed the spatial and temporal distribution of cases of gHAT in Uganda during the period 2010-2019 to assess whether Tiny Targets have had an impact on disease incidence.

Methods To quantify the deployment of Tiny Targets, we mapped the rivers and their associated watersheds in the intervention area. We then categorised each of these on a 0-3 scale according to whether Tiny Targets were absent (0), present only in neighbouring watersheds (1), present in the watersheds but not all neighbours (2) or present in the watershed and all neighbours (3). We overlaid all cases that were diagnosed between 2000 and 2020 and assessed whether the probability of finding cases in a watershed changed following the deployment of targets. We also estimated the number of cases averted by tsetse control.

Results We found that following the deployment of Tiny Targets in a watershed there are fewer cases of HAT (p=0.007). We estimate that during the intervention period 2012-2019 we should have expected 48 cases (95% confidence intervals = 40-57) compared to the 36 cases observed. The results are robust to a range of sensitivity analyses.

Conclusions Tiny Targets have reduced the incidence of gHAT by 25% in North Western Uganda.

Parasitology

Human African Trypanosomiasis

Tsetse Control

Tiny Targets

Uganda

Disease control

Elimination

Human African trypanosomiasis (HAT) is a fatal neglected tropical disease that occurs in rural areas of sub-Saharan Africa. The disease exists in two forms, Gambian HAT (gHAT) caused by Trypanosoma brucei gambiense and Rhodesian HAT (rHAT), caused by T. b. rhodesiense, both forms are transmitted by tsetse flies (Glossina spp). The Gambian form of the disease is found in West and Central Africa and is transmitted by riverine species of tsetse, it causes a chronic infection and is responsible for greater than 95% of HAT cases reported annually [1]. The Rhodesian form is an acute infection which occurs in East and Southern Africa where it is transmitted by savanna species of tsetse. Uganda is the only country in the world with both forms of the disease, however, there is no known geographic overlap of disease transmission.

The first half of the 20^th century saw devastating epidemics of HAT including one epidemic in Uganda which is estimated to have killed more than 200,000 people [2]. Large scale screening and treatment campaigns successfully brought the numbers of cases down so that by the mid-1960s the disease was close to elimination. This low endemicity led to a shift in priorities and as a consequence there was a resurgence of the disease [3]. By 1998 the global number of reported gHAT cases was greater than 37,000 but estimates suggest that 300,000 cases were either missed or misdiagnosed and therefore were not treated [3]. Renewed efforts to control the disease saw the number of cases decline again and by 2009 fewer than 10,000 cases were reported annually [1,4]. The disease is targeted for elimination of transmission by 2030 [5].

The mainstay of gHAT control has historically been mass screening and treatment of populations with little involvement of vector control as the available methods were considered to not be cost-effective and were logistically challenging to implement. However, as prevalence of HAT decreases the cost-effectiveness of mass active screening for HAT decreases exponentially [6]. This can be addressed through passive screening [7] and through vector control with Tiny Targets, a novel, cost-effective technology comprising insecticide treated panels of material which attract and kill riverine tsetse [8–11]. Tiny Targets have been shown to reduce tsetse fly densities by more than 80% in Chad [12,13], Côte d’Ivoire [14], Democratic Republic of Congo (DRC) [15] and Uganda [9], but by a lower amount in Guinea where the deployment methodology is different [16]. In Guinea and Chad the use of Tiny Targets to reduce transmission has been shown to reduce the incidence of gHAT cases [12,17]. In DRC which is the country that contributes the greatest number of cases, modelling studies have demonstrated that in some foci of the DRC, relying on screening and treatment alone will not be sufficient to achieve elimination goals on time but that the addition of vector control will accelerate progress [18].

In Uganda, the historical gHAT foci are in the West Nile region in the north west of the country where the disease is transmitted by Glossina fuscipes fuscipes. In 2011 Tiny Targets were introduced in two Districts as a trial to assess the feasibility and effectiveness of using the technology at scale [9]. The success of the trial led to the scaling up into full tsetse control in 2014 to contribute to the gHAT elimination effort in Uganda, with Tiny Targets deployed in five Districts. Tsetse control efforts subsequently expanded in 2017 from five to seven Districts covering around 3,900km² [19]. As a consequence of these measures, tsetse densities across the region have been reduced by greater than 90% [9]. Here we quantify the impact of vector control on the spatial and temporal distribution of cases and infer the impact on disease incidence [9].

Study area

The study was conducted in the gHAT endemic area of north-west Uganda in the districts of Adjumani, Amuru, Arua, Koboko, Maracha, Moyo and Yumbe [20] (Figure 1).

Geographical data processing

In Uganda, Tiny Targets are deployed along the larger permanent rivers and streams. However, to define an intervention area, it is necessary to define an area around the watersheds that is controlled and so to do this we identified the watersheds that had been controlled. To identify watersheds we used NASA Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) imagery, produced at the spatial resolution of 1 arc second (~30m × 30m) [21] to extract HydroSHEDS [22] in ESRI ArcGIS (v10.5). The HyrdoSHEDS method identifies rivers and streams and orders them according to their number of tributaries (Figure 1) so an order 1 river has no tributaries, order 2 has one or more order 1 tributaries, etc.

Watersheds are defined as the area of land that drains into a single waterbody. To define watersheds, we extracted all order 4 or greater rivers (rivers with 2 or more third order tributaries [23]), and from these identified pour points at the intersections of rivers. Pour points define the end point of the watershed. The pour points were used to define watersheds using the methodology described by ESRI ArcGIS (support article 000012346). The resulting watersheds for the area are shown in Figure 2 (mean area 9.8km², range 1.02-57.4km²). The resulting number of watersheds that are defined exceeds the number of rivers that were controlled using Tiny Targets, as the defined watersheds include some non-permanent streams, or simply drainage features that do not have any sort of flowing water. Consequently, the density of watersheds is typically greater than the density of rivers defined by Tiny Target deployments.

Tiny Target deployment

We used Tiny Target deployment data in Uganda between December 2011 and 2019, conducted at approximately six-month intervals, giving a total of 15 deployments. For each deployment, the locations of targets were recorded using global positioning systems (GPSs). For the first two deployments (2011 – 2012), Tiny Targets were deployed in five separate 7 x 7 km blocks [9]. Thereafter, (2013-2019) deployments were along continuous stretches of river. The coverage of each deployment was overlain onto the watershed polygons to identify polygons where targets were present. For the first two deployments (2011-12), watershed polygons were regarded as treated if they had at least 500m of river that were deployed in the watershed area. Thereafter, a polygon was considered as controlled if targets were deployed on at least two occasions during 2011 to 2019, with control being deemed to have started at the earliest deployment, for nearly all (98%) deployed areas once an area was deployed it was deployed in all subsequent years. If deployment was between January and June then deployment was deemed to have started that year. If deployment first occurred in the second half of the year, control was deemed to have started at the beginning of the next year. The rationale behind this is that the reduction in tsetse numbers following deployment of Tiny Targets is gradual and takes several months to reach the reductions of up to 90%. Thus for each year between 2012 (the deployment in 2011 was in the second half of the year, so defined as 2012) and 2019 we are able to identify where a watershed had a deployment in that year (Figure 3). As our definition of watersheds includes polygons that are smaller than the deployed rivers, we identified watersheds that did not contain a deployment but were surrounded entirely by polygons containing deployments. Such polygons were marked as controlled for the purpose of analysis as it could be assumed that there was not a permanent drainage feature there or would be controlled by local dispersal of tsetse [23].

To allow for the mobility of tsetse and humans, we classified each watershed polygon in each year on a 0 to 3 scale according to the presence/absence of targets in neighbouring polygons (Table 1, Figure S1).

Table 1. Table to describe the deployment zone score classification system based on the deployment status of the watershed and its neighbours.

Classification score	Watershed	Neighbouring watersheds
0	Not deployed	No neighbours are deployed
1	Not deployed	One or more neighbours is deployed
2	Deployed	One or more neighbours is not deployed
3	Deployed	All neighbours are deployed

HAT case data

HAT case data were obtained from the WHO HAT Atlas for the period 2000 – 2011 [24] and from the Ugandan Ministry of Health through the Trypa-No Programme for the years 2012-2020. Data from the WHO HAT Atlas provided point geolocations for 4,165 cases between 2000 and 2011 but 30 cases were excluded because they could not be georeferenced. The Ugandan Ministry of Health supplied data on a further 56 cases from 2012-2020. Data from 2012-2020 were geolocated by mapping the villages using and cross-checking publicly available datasets, principally Open Street Maps, Google Maps and Geonames [19]. Where these were not able to provide a location for a village, the case was mapped to the school (the school being an identifiable place that reflective of the centre of population) of the patient’s parish. Four of the cases that were reported since 2016 were identified as being refugees from South Sudan, it is estimated that >650,000 refugees from South Sudan have settled in northern Uganda since 2016 (https://data2.unhcr.org/en/situations/southsudan/location/1925). South Sudan is a gHAT focus [25], the provenance of infection of these cases cannot be determined with certainty and so the cases were excluded from further analysis. A further case of a Ugandan who was resident in South Sudan was also excluded. This gives a total of 4,186 georeferenced cases between 2000-2020.

Data analysis

If Tiny Targets reduce the incidence of gHAT, then the incidence of cases will be lower in areas where Tiny Targets were continuously deployed. Accordingly, we proposed the following hypothesis:

Null hypotheses (H₀) = The number of cases in a polygon was not significantly affected by the deployment of targets in the polygon and/or its neighbouring polygons.

Alternative hypothesis (H₁) = Following the deployment of Tiny Targets in a polygon and/or its neighbours, the relative number of cases within that area declined.

For each year with vector control we define two populations of cases by overlaying the case locations onto the deployment polygons:

Cases are gHAT cases in the NW Uganda focus that were reported following the start of the Tiny Target intervention (taken as 2012 as the intervention commenced in December 2011) and broken down by the year in which we assume that they were infected. The date of infection we infer from the reporting date, for stage 1 cases, we conservatively assume that the infection date is the same as the reporting date. For stage 2 cases, we assume that the case was infected 263 days before the reporting date this being the lower limit of the duration of stage 1 duration in passive screening from Checchi et al [26].
Case-controls are all gHAT cases that were infected between 2000 and 2020.

Using these categories, we conduct two analyses, one is the impact of the deployments and the second is of the number of cases prevented.

Deployment impact analysis

For each year from 2012-2020 the cases and matched case-controls are overlain onto the vector control deployment zones for that year and we determine the vector control zone class score (Table 1) for the location of each case and each case-control. Thus, starting with 2012 we take the cases with a putative infection date in that year, and extract the deployment scores for those cases. As the comparison controls, we extract the deployment scores for all cases between 2000 and 2020 and randomly sample a number of controls equal to the number of cases for that year.

The above procedure was implemented using in R version 3.6.0 [27] with the following algorithm:

Starting with 2012 as the analysis year:
1. Take the cases that were estimated to have been infected in that year.
2. From the case-controls, we randomly sample a number equal to the number of cases for that year. So if there are 4 cases in a year then we randomly sample 4 from the 4187 case-controls for 2000-2020 and extract their deployment score based on where it would be relative to the deployments in 2012 (and then for subsequent years).
3. We sum the deployment zone scores for both the cases and the sampled case-controls.
Repeat a-c for each year (i.e. 2013, 2014….2019).
Across the years we sum the scores for the cases versus the sampled case-controls. If the total deployment score for the case-controls is lower than for the cases, that would suggest that the cases are more likely to be in the deployment area compared to the overall distribution of cases. If the scores of the case-controls are greater than for the cases, then the cases are less likely to be in the deployment area than would be expected.
Repeat steps 1-3 for 1,000,000 replicates.
The data are analysed by the proportion of iterations for which the cases had a higher score compared to the sampled case-controls. A proportion below 5% would represent statistical significance at the 95% level.

Cases prevented analysis

We also estimate the number of cases that were averted by the intervention. This is based on the premise that there is a case incidence rate in the controlled areas and a separate case incidence rate in the uncontrolled area. For example, suppose that prior to control there may have been an incidence rate of 4/10,000 inside the controlled area and 2/10,000 outside, so the controlled area has twice the incidence rate of the uncontrolled area. We can continue to measure the incidence rate of the uncontrolled area following the start of the intervention and if the intervention has reduced the incidence rate then there should be a drop in the incidence rate in the controlled area relative to the uncontrolled area. So if following the start of the intervention the incidence rate outside the controlled area fell from 2/10,000 to 1/10,000 and the incidence rate inside the controlled area fell from 4/10,000 to 2/10,000 this would indicate that the control had no impact on case numbers, but a fall in the controlled area to 1/10,000 would indicate a 50% drop in the incidence rate as a result of the intervention. Thus, we can use this relative difference to estimate the number of cases that were prevented in the controlled area by the vector control measures.

To carry out this analysis we must invert the deployment zone scores so that a score of 3 indicates that the case is completely outside the deployment area and 0 indicates that the case is completely inside the deployment area. This methodology is then implemented using the following algorithm:

Starting in 2012 we generate a random sample of 1,000,000 putative controls from the population of 4,186 case-controls by sampling with replacement. We extract the 2012 deployment score for the location of the sampled controls. Subsequently:
1. We take the cases that were infected during that year and sum their inverted deployment scores. The resulting summed score gives us our benchmark score.
2. For the 1,000,000 samples we take the cumulative sum of the scores of the sampled case-controls
3. We perform 10,000 iterations, where we:
  1. Count how many controls are required until the cumulative score of the sampled controls is greater than the benchmark identified above. This number of controls is our sample index. So if our benchmark score is 5 and the cumulative sum of our controls is 0, 2, 3, 6, 7, 9, 9, 12 then we take the fourth score of 6 as our sampled score and the sample index is four.
  2. We evaluate the values above and below the sample index to determine which is closer to the total of our cases. So from the example above we take the third value of 3 and fourth value of 6. As our benchmark of 5 is nearer to the fourth value than the third we take the fourth value as our number of controls. Had the benchmark been 5 and the sequence 0, 2, 4, 6 then we select at random between 4 and 6.
  3. Record how many sampled case-controls were required to match the total of the cases. In the example above this is four.
  4. From the 1,000,000 samples we remove all values up to and including our sample index and repeat the cumulative sum. So in the example above with the sequence 0, 2, 3, 6, 7, 9, 9, 12, we remove the first 4 values and subtract 6 from the remainder, so our sample now starts 1, 3, 3, 6.
  5. Repeat i-iv until we have completed 10,000 iterations

Repeat 1 for each subsequent year 2013, 2104…2019

We analyse this by comparing the number of cases from each year to the numbers of controls that were sampled in each iteration in each year above. In a given year if more than 95% of iterations had a number of controls that was greater than the number of cases for that year then it would indicate that there was a significant difference between the number of cases and the random sample. The difference between the median of the samples and the number of cases gives the median number of excess cases.

Sensitivity analysis

We tested the results using a number of sensitivity analyses:

The earlier years of Tiny Target deployments had a smaller extent of deployment and a greater number of cases, so we test the robustness of the results by excluding these years.
We adjust the deployment scores using three methodologies. The first increases the scores of 2 and 3 by 1 point, so the scale is 0,1,3,4. The second is multiplicative, by two, so the revised scale is 0,2,4,6. The third is a square transformation, so the scale is 0,1,4,9.
We conducted two separate leave one out analyses on the deployment impact analysis. In the first instance we repeat the analysis and resulting probability by dropping in turn each case infected between 2011 and 2019. The second analysis takes the ranks of these probabilities and drops first the highest ranked case, then also the 2^nd, 3^rd, …nth ranked case repeating the analysis after each drop to re-evaluate the remaining probability. A further analysis evaluates the resulting probability after adding further cases in the controlled zone (deployment score 3) in 2019.

The deployment scores increase year-on-year but the greatest increase was between 2014 and 2015 when the deployment expanded in the core area (Table 2, Table 3, Figure 3, Figure 4, Supplementary Figures S1 & S2). The deployment zone scores for cases were lower than those for putative controls in all years apart from 2014 (Figure 5). The decrease was particularly pronounced from 2015 onwards when the extent of target deployment was at its greatest and mean scores for controls were greatest (Figure 5).

Table 2. (Left) the area and (right) number of watersheds under different deployment scores during each year of the intervention.

	Watershed areas (km²)				Number of watersheds
	Deployment score				Deployment score
Year	0	1	2	3	0	1	2	3
2012	20,381	659	335	2	2,048	55	29	1
2013	20,249	523	403	201	2,032	47	32	22
2014	19,529	937	675	235	1,966	83	58	26
2015	16,923	1,894	1,745	814	1,733	174	143	83
2016	16,780	1,974	1,709	913	1,718	181	144	90
2017	15,145	2,914	2,331	987	1,560	276	201	96
2018	14,357	3,105	2,773	1,141	1,486	295	240	112
2019	14,357	3,105	2,773	1,141	1,486	295	240	112

Table 3. The Number of case-controls (denominator) in each deployment score category by year (left) and (Right) the number of cases infected in each year by the case’s deployment score category of that year.

	Denominator				Case data
	Deployment score				Deployment score
Year	0	1	2	3	0	1	2	3
2012	3,528	349	309	0	10	0	1	0
2013	3,453	157	304	272	11	0	0	0
2014	2,012	434	1,422	318	3	0	0	2
2015	997	340	1,955	894	3	0	0	1
2016	968	359	1,808	1,051	0	1	0	0
2017	651	458	1,833	1,244	0	0	1	0
2018	520	502	1,810	1,354	1	1	0	0
2019	505	514	1,810	1,357	1	0	0	0

Taking the sum of the results over the entire period, the mean sampled case-control deployment score is 1.86x that of the deployment scores for cases infected between 2012-2019 and in only 0.7% of iterations were the sampled control deployment scores lower than for the cases (Table 4 row 1). Furthermore, when we adjust the length of time that we consider changing the duration from 2012-2019 by removing the first year at a time the results remain the same, thus it is insensitive to the period that we analyse (Table 4 rows 2-4).

Table 4. Summary of the statistical model results for different truncations to the study periods. Results are summed across the study years.

Year range (year of infection)	Total cases in the focus	Case score	Mean case-control score	Case-control score / case score	Proportion of iterations case scores > case-control scores
2012-2019	36	15	27.8	1.86	0.007
2013-2019	25	13	25.2	1.94	0.006
2014-2019	14	13	21.1	1.62	0.027
2015-2019	9	7	16.0	2.29	0.004

In the analysis of cases prevented, the model shows that by 2019 there would have been a median of 48 infected cases in the focus (95% CIs = 40-57) if no Tiny Targets had been deployed whilst there were 36 observed cases (Figure 6). Our analysis therefore suggests that Tiny Targets prevented 12 cases of gHAT between 2012 and 2019.

Sensitivity analysis

The results in Table 2 from 2012-2019 are insensitive to the scoring system that is used (Table 5).

Table 5. Sensitivity analysis of different scoring systems and the resulting probabilities.

Deployment scores	Probability
Baseline: Outside = 0 Out-In = 1 In-Out = 2 Inside = 3	0.007
S1 (additive): Outside = 0 Out-In = 1 In-Out = 4 Inside = 5	0.004
S2 (multiplicative): Outside = 0 Out-In = 2 In-Out = 4 Inside = 6	0.007
S3 (non-linear): Outside = 0 Out-In = 1 In-Out = 4 Inside = 9	0.003

The leave-one-out analysis showed a strong influence of certain cases and specifically those that were infected outside the controlled zones and infected more recently (Figure 7). Consequently if we remove the three most influential cases (infected in 2019, 2018 and 2015) then our result becomes non-significant (Figure 7).

Tiny Targets are a technology that has been demonstrated to be effective at reducing the numbers of riverine tsetse [9]. However, the purpose of this technology is to control numbers of cases rather than to eradicate the vector and this has been demonstrated for hyperendemic foci [17] and high incidence riverine swamp ecosystems [12] but not previously in a large low incidence (< 1 case / 100,000) riverine ecosystem as found in Uganda. This is made more complicated by the delay between the deployment of the technology and realisation of any impact on case numbers due to the chronic nature of gHAT. As Tiny Target deployments are being carried out in a greater number of foci with different agro-ecological settings it is important to establish whether Tiny Targets have an impact on gHAT case numbers in different ecosystems.

The strategy in Uganda has not been for blanket coverage of vector control over the entire area, rather control that is targeted at those areas with the greatest numbers of cases. Hence, the extent of vector control increased, but never covered the entire area, or the entirety of any district (Tables 2 & 3, Figure S1). These analyses show that despite the limited extent of vector control a substantial reduction in case incidence can be achieved.

In Uganda the decline in numbers of HAT cases has been ongoing for several years prior to the commencement of vector control which were associated with active screening campaigns during the decade prior to the commencement of vector control [26,28], By reducing transmission the implementation of Tiny Targets has increased the rate of decline and thus accelerated progress towards elimination. Based on data to the end of 2018, Uganda would be in a position of meeting the gHAT elimination criteria [1], and this low level was maintained in 2019 with only 2 cases.

The deployment in Uganda presents a number of specific challenges:

There was no clear definition to the area of intervention with vector control. Arbitrary boundaries have been drawn for illustrative purposes, but these are not grounded in the epidemiology or entomology of the disease or its vectors. Additionally, whilst an intervention area was defined there were some variations in the extent of the deployment for certain time points, and this variation between deployments must be accounted for. Here we developed a novel way of defining the area of intervention using the watersheds. This works because Tiny Targets are deployed along the rivers which are the habitat of the tsetse. Thus by defining the area of intervention in terms of watersheds we account for the ecology of the tsetse fly. Alternatives include defining buffers or grid cells for the interventions, but watersheds more accurately reflect the tsetse ecology and logistics of the intervention.
We don’t know the true date of infection of the cases, only the reporting date. To overcome this we use a conservative approach by setting the date of infection for stage 2 cases as the lower bound on the published range of duration of stage 1 infection. An alternative and less conservative approach would be to estimate the dates of infection from all cases by sampling from the distributions described in [26].
People and vectors are non-stationary but the case locations are point locations, this does not represent the true extent of potential exposures to infection. We overcome this by basing our analysis on the vector control status of the watershed of the case as well as its neighbours.

However, for these analyses, Uganda also has a number of advantages over other geographies:

It has one of the longest histories of Tiny Target deployment with the first deployments at the end of 2011
In the controlled areas the deployment is total (every permanent river is deployed)
There have been no gross interruptions to deployment

We have shown that there were fewer cases than would have been expected inside the intervention area and that a median of 12 cases are estimated to have been averted by the vector control intervention. There have been other interventions in this area running concurrently, not least a programme of enhanced passive screening which started in 2013 and had different coverages over the period in question [20]. However, the broad coverage was the entire area and the programme did not result in an increase in detection of cases which might be expected when screening is enhanced. A similar passive screening programme in a HAT endemic area of the DRC with no vector control did result in an increase in numbers of cases detected [29]. Additionally to this was an active screening campaign in the vector control area which did not detect any HAT cases [30]. So, whilst this study does not have the power of a randomised control trial it does add further evidence of the impact of VC on HAT. Whilst the impact of Tiny Targets on case numbers looks modest, the benefits of cessation of transmission must be considered against the other strategies for breaking transmission in a population of 2 million, or the costs that would be amassed if disease were reintroduced.

The balance between screening activities and the impacts of vector control as estimated here must be further evaluated using economic analysis to estimate the relative cost-effectiveness of vector control as an intervention.

These analyses have demonstrated and quantified a clear impact of the Tiny Targets programme on incidence of gHAT cases which will contribute to the elimination of gHAT in this focus. Whilst the reduction in case numbers appears modest it makes a critical contribution to elimination in a population too large for systematic mass screening and that has a large transboundary flow of people from neighbouring South Sudan.

Ethical approval

Not applicable

Consent for publication

Not applicable

Availability of data and materials

The datasets on Tiny Target deployments used during the current study are available from the corresponding author on reasonable request. Data on HAT incidence are partially based on the WHO HAT Atlas and are available by application to the WHO.

Competing interests

The authors declare that they have no competing interests

Funding

This work has been funded by the Bill & Melinda Gates Foundation (BMGF), Grant Nos. 1017770, OPP1104516, OPP1154033 and INV-001785. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Authors' contributions

PRB, SJT and AH conceived and designed the study, JE, AM, RS, IT, CWai, SJT and AH undertook the interventions and collected the vector data, CWam collected medical data, JL collated vector data. PRB analysed the data, PRB, SJT and AH wrote the manuscript and all authors read and contributed to the drafting.

Acknowledgements

HAT screening activities in Uganda since 2013 have been implemented by the Ugandan Ministry of Health, and supported by the Foundation for Innovative New Diagnostics (FIND) with support from the Bill & Melinda Gates Foundation, the Republic and Canton of Geneva, the German Federal Ministry of Education and Research through KfW, the Swiss Government, UK aid from the UK government and the Medical Research Council. We are grateful to all health facility staff, district leadership and district coordinators in Uganda that enabled this project. The World Health Organization (WHO) provide drugs for treating HAT cases and have supported targeted active screening in affected villages and kindly provided case data through the HAT Atlas.

The authors would like to thank the District Entomologists where the Tiny Target programme is implemented for their efforts in managing deployment and entomological monitoring. The authors also thank the LSTM field team based in Uganda for their support to deployment activities and conducting entomological monitoring.

Franco JR, Cecchi G, Priotto G, Paone M, Diarra A, Grout L, et al. Monitoring the elimination of human African trypanosomiasis at continental and country level: Update to 2018. Matovu E, editor. PLoS Negl Trop Dis [Internet]. Public Library of Science; 2020 [cited 2020 Sep 9];14:e0008261. Available from: https://dx.plos.org/10.1371/journal.pntd.0008261
Berrang-Ford L, Odiit M, Maiso F, Waltner-Toews D, McDermott J. Sleeping sickness in Uganda: Revisiting current and historical distributions. Afr Health Sci [Internet]. Makerere University Medical School; 2006 [cited 2020 Sep 9];6:223–31. Available from: /pmc/articles/PMC1832067/?report=abstract
Steverding D. The history of African trypanosomiasis. Parasites and Vectors [Internet]. BioMed Central; 2008 [cited 2020 Sep 9];1:3. Available from: http://parasitesandvectors.biomedcentral.com/articles/10.1186/1756-3305-1-3
Franco JR, Cecchi G, Priotto G, Paone M, Diarra A, Grout L, et al. Monitoring the elimination of human African trypanosomiasis: Update to 2014. Boelaert M, editor. PLoS Negl Trop Dis [Internet]. World Health Organization; 2017 [cited 2017 Jun 2];11:e0005585. Available from: http://dx.plos.org/10.1371/journal.pntd.0005585
WHO. Report of a WHO meeting on elimination of African trypanosomiasis (Trypanosoma brucei gambiense) [Internet]. Geneva; 2010. Available from: https://scholar.googleusercontent.com/scholar?q=cache:WaCAwRmLeqQJ:scholar.google.com/&hl=en&as_sdt=0,5
Bessell PR, Lumbala C, Lutumba P, Baloji S, Biéler S, Ndung’u JM. Cost-effectiveness of using a rapid diagnostic test to screen for human African trypanosomiasis in the Democratic Republic of the Congo. Arez AP, editor. PLoS One [Internet]. Public Library of Science; 2018 [cited 2018 Oct 9];13:e0204335. Available from: http://dx.plos.org/10.1371/journal.pone.0204335
Wamboga C, Matovu E, Bessell PR, Picado A, Biéler S, Ndung’u JM. Enhanced passive screening and diagnosis for gambiense human African trypanosomiasis in north-western Uganda – Moving towards elimination. PLoS One. 2017;12:e0186429.
Lehane M, Alfaroukh I, Bucheton B, Camara M, Harris A, Kaba D, et al. Tsetse Control and the Elimination of Gambian Sleeping Sickness. Bates PA, editor. PLoS Negl Trop Dis [Internet]. Public Library of Science; 2016 [cited 2020 Sep 9];10:e0004437. Available from: https://dx.plos.org/10.1371/journal.pntd.0004437
Tirados I, Esterhuizen J, Kovacic V, Mangwiro TNC, Vale GA, Hastings I, et al. Tsetse Control and Gambian Sleeping Sickness; Implications for Control Strategy. PLoS Negl Trop Dis [Internet]. 2015 [cited 2016 Jan 11];9:e0003822. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4580652&tool=pmcentrez&rendertype=abstract
Solano P, Torr SJ, Lehane MJ. Is vector control needed to eliminate gambiense human African trypanosomiasis? Front Cell Infect Microbiol [Internet]. Frontiers; 2013 [cited 2020 Sep 9];3:33. Available from: http://journal.frontiersin.org/article/10.3389/fcimb.2013.00033/abstract
Shaw APM, Tirados I, Mangwiro CTN, Esterhuizen J, Lehane MJ, Torr SJ, et al. Costs of using “tiny targets”; to control Glossina fuscipes fuscipes, a vector of gambiense sleeping sickness in Arua District of Uganda. PLoS Negl Trop Dis [Internet]. 2015 [cited 2016 Jul 25];9:e0003624. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25811956
Mahamat MH, Peka M, Rayaisse JB, Rock KS, Toko MA, Darnas J, et al. Adding tsetse control to medical activities contributes to decreasing transmission of sleeping sickness in the Mandoul focus (Chad). PLoS Negl Trop Dis [Internet]. Public Library of Science; 2017 [cited 2020 Sep 9];11. Available from: /pmc/articles/PMC5549763/?report=abstract
Rayaisse JB, Courtin F, Mahamat MH, Chérif M, Yoni W, Gadjibet NMO, et al. Delivering “tiny targets” in a remote region of southern Chad: A cost analysis of tsetse control in the Mandoul sleeping sickness focus [Internet]. Parasites and Vectors. BioMed Central; 2020 [cited 2020 Sep 30]. Available from: /pmc/articles/PMC7427721/?report=abstract
Berté D, De Meeûs T, Kaba D, Séré M, Djohan V, Courtin F, et al. Population genetics of Glossina palpalis palpalis in sleeping sickness foci of Côte d’Ivoire before and after vector control. Infect Genet Evol [Internet]. Elsevier B.V.; 2019 [cited 2020 Sep 30];75. Available from: https://pubmed.ncbi.nlm.nih.gov/31301424/
Tirados I, Hope A, Selby R, Mpembele F, Miaka EM, Boelaert M, et al. Impact of tiny targets on Glossina fuscipes quanzensis, the primary vector of human African trypanosomiasis in the Democratic Republic of the Congo. Matovu E, editor. PLoS Negl Trop Dis [Internet]. Public Library of Science (PLoS); 2020 [cited 2020 Nov 2];14:e0008270. Available from: https://dx.plos.org/10.1371/journal.pntd.0008270
Courtin F, Camara M, Rayaisse J-B, Kagbadouno M, Dama E, Camara O, et al. Reducing Human-Tsetse Contact Significantly Enhances the Efficacy of Sleeping Sickness Active Screening Campaigns: A Promising Result in the Context of Elimination. Aksoy S, editor. PLoS Negl Trop Dis [Internet]. Public Library of Science; 2015 [cited 2020 Sep 9];9:e0003727. Available from: https://dx.plos.org/10.1371/journal.pntd.0003727
Kagabadouno M, Camara O, Camara M, Ilboudo H, Camara M, Rayaisse J-B, et al. Ebola outbreak brings to light an unforeseen impact of tsetse control on sleeping sickness transmission in Guinea. bioRxiv [Internet]. Cold Spring Harbor Laboratory; 2018 [cited 2020 Sep 9];202762. Available from: https://doi.org/10.1101/202762
Rock KS, Torr SJ, Lumbala C, Keeling MJ. Quantitative evaluation of the strategy to eliminate human African trypanosomiasis in the Democratic Republic of Congo. Parasit Vectors [Internet]. 2015 [cited 2015 Dec 3];8:532. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4618948&tool=pmcentrez&rendertype=abstract
Ndung’u JM, Boulangé A, Picado A, Mugenyi A, Mortensen A, Hope A, et al. Trypa-NO! contributes to the elimination of gambiense human African trypanosomiasis by combining tsetse control with “screen, diagnose and treat” using innovative tools and strategies. Caljon G, editor. PLoS Negl Trop Dis [Internet]. Public Library of Science; 2020 [cited 2020 Nov 16];14:e0008738. Available from: https://dx.plos.org/10.1371/journal.pntd.0008738
Wamboga C, Matovu E, Bessell PR, Picado A, Biéler S, Ndung’u JM. Enhanced passive screening and diagnosis for gambiense human African trypanosomiasis in north-western Uganda – Moving towards elimination. PLoS One. 2017;12.
Farr TG, Rosen PA, Caro E, Crippen R, Duren R, Hensley S, et al. The shuttle radar topography mission. Rev Geophys. 2007;45.
Lehner B, Verdin K, US AJ-WWF, 2006 undefined. HydroSHEDS technical documentation, version 1.0. hydrosheds.cr.usgs.gov [Internet]. [cited 2020 Apr 21]; Available from: https://hydrosheds.cr.usgs.gov/HydroSHEDS_doc_v1_draft_dec7.doc
Scheidegger AE. Stream orders. Geomorphology [Internet]. Kluwer Academic Publishers; 2006 [cited 2020 Nov 12]. p. 1064–6. Available from: https://link.springer.com/referenceworkentry/10.1007/3-540-31060-6_355
Simarro PP, Cecchi G, Paone M, Franco JR, Diarra A, Ruiz JA, et al. The Atlas of human African trypanosomiasis: a contribution to global mapping of neglected tropical diseases. Int J Health Geogr [Internet]. 2010 [cited 2015 Jan 14];9:57. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2988709&tool=pmcentrez&rendertype=abstract
Simarro PP, Cecchi G, Franco JR, Paone M, Diarra A, Priotto G, et al. Monitoring the Progress towards the Elimination of Gambiense Human African Trypanosomiasis. Matovu E, editor. PLoS Negl Trop Dis [Internet]. Food and Agriculture Organization of the United Nations; 2015 [cited 2017 Jun 1];9:e0003785. Available from: http://dx.plos.org/10.1371/journal.pntd.0003785
Checchi F, Filipe JAN, Haydon DT, Chandramohan D, Chappuis F. Estimates of the duration of the early and late stage of gambiense sleeping sickness. BMC Infect Dis [Internet]. 2008 [cited 2015 Apr 18];8:16. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2259357&tool=pmcentrez&rendertype=abstract
R Core Team. R: A Language and Environment for Statistical Computing [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2019. Available from: http://www.r-project.org/
Checchi F, Cox AP, Chappuis F, Priotto G, Chandramohan D, Haydon DT. Prevalence and under-detection of gambiense human African trypanosomiasis during mass screening sessions in Uganda and Sudan. Parasit Vectors [Internet]. 2012 [cited 2015 Apr 13];5:157. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3430581&tool=pmcentrez&rendertype=abstract
Lumbala C, Kayembe S, Makabuza J, Lutumba P, Van Geertruyden J-P, Bessell PR, et al. Development and implementation of a strategy for intensified screening for gambiense human African trypanosomiasis in Kongo Central province, DRC. Werneck GL, editor. PLoS Negl Trop Dis [Internet]. Public Library of Science; 2020 [cited 2020 Dec 9];14:e0008779. Available from: https://dx.plos.org/10.1371/journal.pntd.0008779
Selby R, Wamboga C, Erphas O, Mugenyi A, Jamonneau V, Waiswa C, et al. Gambian human African trypanosomiasis in North West Uganda. Are we on course for the 2020 target? Matovu E, editor. PLoS Negl Trop Dis [Internet]. Public Library of Science; 2019 [cited 2020 Sep 9];13:e0007550. Available from: https://dx.plos.org/10.1371/journal.pntd.0007550

SupplementaryFiguresS1.pdf
Supplementary Figure S1. Maps showing the vector control intervention in each year form 2012-2019.
SupplementaryFiguresS2.pdf
Supplementary Figure S2. Maps showing the vector control intervention in each year form 2012-2019 with cases overlain. The first figure shows just the baseline cases (2000-2020).
TinyTargetsAnyaurvUgandaJohanEsterhuizen.jpg

Download PDF

Journal Publication

published 18 Aug, 2021

Read the published version in Parasites & Vectors →

Reviewer #2 agreed at journal
05 Jun, 2021
Review #2 received at journal
05 Jun, 2021
Editorial decision: Major Revision
05 Jun, 2021
Review #1 received at journal
22 May, 2021
Reviews received at journal
02 May, 2021
Reviewer #1 agreed at journal
02 May, 2021
Reviewers invited by journal
31 Mar, 2021
Editor invited by journal
28 Mar, 2021
Editor assigned by journal
28 Mar, 2021
Submission checks completed at journal
28 Mar, 2021
First submitted to journal
24 Mar, 2021

You are reading this latest preprint version

Estimating the impact of Tiny Targets in reducing the incidence of gambiense sleeping sickness in the North West Uganda focus

Status:

Journal Publication

Version 1

Abstract

Figures

Background

Methods

Study area

Geographical data processing

Tiny Target deployment

HAT case data

Data analysis

Deployment impact analysis

Cases prevented analysis

Sensitivity analysis

Results

Sensitivity analysis

Discussion

Conclusions

Declarations

Ethical approval

Consent for publication

Availability of data and materials

Competing interests

Funding

Authors' contributions

Acknowledgements

References

Supplementary Files

Status:

Journal Publication

Version 1