Skip to main content
  • Loading metrics

A Literature Review of Economic Evaluations for a Neglected Tropical Disease: Human African Trypanosomiasis (“Sleeping Sickness”)

  • C. Simone Sutherland ,

    ‡ CSS and FT contributed equally to this work. JY and RG also contributed equally to this work.

    Affiliations Swiss Tropical and Public Health Institute, Basel, Switzerland, University of Basel, Basel, Switzerland

  • Joshua Yukich ,

    ‡ CSS and FT contributed equally to this work. JY and RG also contributed equally to this work.

    Affiliation Department of Global Health Systems and Development, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, United States of America

  • Ron Goeree ,

    ‡ CSS and FT contributed equally to this work. JY and RG also contributed equally to this work.

    Affiliations Programs for Assessment of Technology in Health (PATH) Research Institute, St. Joseph’s Healthcare Hamilton, Hamilton, Ontario, Canada, Department of Clinical Epidemiology & Biostatistics, McMaster University, Hamilton, Ontario, Canada

  • Fabrizio Tediosi

    ‡ CSS and FT contributed equally to this work. JY and RG also contributed equally to this work.

    Affiliations Swiss Tropical and Public Health Institute, Basel, Switzerland, University of Basel, Basel, Switzerland, Centre for Research on Health and Social Care Management (CERGAS), Università Bocconi, Milano, Italy


Human African trypanosomiasis (HAT) is a disease caused by infection with the parasite Trypanosoma brucei gambiense or T. b. rhodesiense. It is transmitted to humans via the tsetse fly. Approximately 70 million people worldwide were at risk of infection in 1995, and approximately 20,000 people across Africa are infected with HAT. The objective of this review was to identify existing economic evaluations in order to summarise cost-effective interventions to reduce, control, or eliminate the burden of HAT. The studies included in the review were compared and critically appraised in order to determine if there were existing standardised methods that could be used for economic evaluation of HAT interventions or if innovative methodological approaches are warranted. A search strategy was developed using keywords and was implemented in January 2014 in several databases. The search returned a total of 2,283 articles. After two levels of screening, a total of seven economic evaluations were included and underwent critical appraisal using the Scottish Intercollegiate Guidelines Network (SIGN) Methodology Checklist 6: Economic Evaluations. Results from the existing studies focused on the cost-effectiveness of interventions for the control and reduction of disease transmission. Modelling was a common method to forecast long-term results, and publications focused on interventions by category, such as case detection, diagnostics, drug treatments, and vector control. Most interventions were considered cost-effective based on the thresholds described; however, the current treatment, nifurtomix-eflornithine combination therapy (NECT), has not been evaluated for cost-effectiveness, and considerations for cost-effective strategies for elimination have yet to be completed. Overall, the current evidence highlights the main components that play a role in control; however, economic evaluations of HAT elimination strategies are needed to assist national decision makers, stakeholders, and key funders. These analyses would be of use, as HAT is currently being prioritized as a neglected tropical disease (NTD) to reach elimination by 2020.


Human African trypanosomiasis (HAT) is a disease caused by infection with the parasite Trypanosoma brucei gambiense or T. b. rhodesiense and is transmitted to humans via the tsetse fly. Approximately 70 million people worldwide were at risk of infection in 1995 [1], and although 7,216 cases were reported in 2012 [2], it is estimated that approximately 20,000 people across Africa are infected with HAT [2]. According to the Global Burden of Disease, recent estimates of years lived with disability (YLDs) for HAT annually range from 2,000 to 25,000 [3]. There are approximately 30 African countries affected by this disease, and it has been identified by the World Health Organization (WHO) as a neglected tropical disease (NTD) [4].

WHO describes the disease as a neurological breakdown that is caused by the trypanosome parasite in the brain, which eventually leads to a coma or death if a patient is not treated [5]. Patients are identified by self-reporting to health care centres (referred to as “passive case detection”), while active screening by trained professionals in mobile teams continues in high- and moderate-transmission areas. Active screening campaigns are carried out in remote villages, and a series of tests are used for the diagnosis of the disease. The current diagnostic algorithms for HAT include the card agglutination test for trypanosomiasis (CATT) followed by full blood assays to identify the parasite microscopically. Lumbar puncture with parasitological confirmation is then used for staging of the disease. Patients that are diagnosed with HAT are then referred to HAT treatment centres. Limited active screening is done for T. b. rhodesiense because there is no serological test available to facilitate easy identification. Hence, most T. b. rhodesiense cases are detected by clinical signs and symptoms. The subsequent diagnostic steps are similar to T. b. gambiense in that parasite detection is done using chancre aspirate or blood, and staging of the disease again uses cerebrospinal fluid obtained from lumbar puncture. The treatments for T. b. gambiense and T. b. rhodesiense also differ. Treatment for T. b. gambiense includes a 7-day intramuscular injection treatment of pentamidine for patients in stage 1 of the disease that is generally well tolerated, with minor adverse events. Nifurtimox-eflornithine combination therapy (NECT) is a 14-day in-hospital chemotherapy treatment that is required for patients suffering from stage 2 of HAT. The adverse events commonly seen in patients treated with NECT are considered to be mild to moderate in severity. For HAT T. b. rhodesiense, the treatment for stage 1 includes weekly intravenous injections of suramin over the course of 5 weeks [5]. Negative reactions to suramin coincide with the patient’s health status, but overall, it is a well-tolerated treatment. Stage 2 treatment for T. b. rhodesiense is a 10-day treatment of melarsoprol. Melarsoprol is the most toxic of the HAT treatments, leading to encephalopathic syndrome in 5% to 18% of patients treated and often resulting in death. Vector control methods for prevention of HAT T. b. rhodesiense are commonly used, as the disease is well-known to have an animal reservoir that contributes to transmission in both human and animal populations [5]. In regards to HAT T. b. gambiense, historically, vector control has not been suggested. However, evidence of an animal reservoir for T. b. gambiense has been discussed [6,7], and vector control was recently encouraged by WHO as an integrated strategy for HAT [5].

The year scheduled for HAT elimination is 2020 [8], and as this deadline approaches, research groups are currently developing new drug treatments and diagnostic tools [911] for HAT. Additionally, experts in vector control methods are also seeking interventions that would be more cost-effective and feasible for communities at risk for the disease. Even traditional teams that have gone out via trucks are now being reconsidered in combination with newer drug treatments using motorbike teams. Although some screening programs include a component of community sensitization, community involvement within control and elimination campaigns and knowledge of how this “disease awareness” is translated into behavioural changes and attitudes within affected populations need to be considered. There is now a need to evaluate not only the possibility of control and elimination for HAT but also how these new interventions and approaches may contribute to the grand scheme of such endeavours.

WHO has provided recommendations to improve certain factors likely to achieve elimination [2], and decision makers have also committed to funding the elimination of the disease [12]; yet, a clear path to the achievement of this goal is not available, nor is it clear what the most efficient pathway towards elimination would be. In addition, thus far there has been no synthesis of the current costs and effectiveness of all strategies that could intervene in the transmission of the disease. The objective of this review was to identify existing economic evaluations in order to summarise cost-effective interventions to reduce, control, or eliminate the burden of HAT. The studies included in the review were compared and critically appraised in order to determine if there were standardised methods that could be used for economic evaluations of HAT interventions or if innovative methodological approaches are warranted.


Literature Search Strategy

A literature search was conducted via the OvidSp interface on January 22, 2014 using keywords for HAT specific to the Medical Subject Headings (MeSH) terms required for Medical Literature Analysis and Retrieval System Online (MEDLINE) and Embase databases. An economic filter developed by Scottish Intercollegiate Guidelines Network (SIGN) was also applied. (Refer to S1 Supporting Information) The Journal Storage (JSTOR) database was also searched using the following key words: African trypanosomiasis OR trypanosom& OR “sleeping sickness” AND cost& AND economics. In addition, the following keywords were also searched in the Database of Abstracts of Reviews of Effects (DARE), National Health Service Economic Evaluation Database Health Technology Assessment (NHSEED HTA), and Cochrane databases: “African” AND “Trypanosomiasis” OR “sleeping sickness”. All citations were downloaded into Mendeley, where duplicates were identified and removed.

Literature Screening & Inclusion/Exclusion Criteria

Screening of the articles was done in two stages. At the first level, all titles and abstracts were screened. Articles that were considered potentially relevant were then assessed at the second level, in which the full text was read. After reading the full text, articles that still met the inclusion criteria were considered. A full description of the inclusion and exclusion criteria is available in S2 Supporting Information. Data were screened on both levels according to the outline of population-intervention-comparators-outcomes-setting (PICOS) criteria, in which the population pertained to humans. Evaluations regarding strains of both HAT T. b. gambiense and T. b. rhodesiense were reviewed, although outcomes only pertaining to humans impacted by the disease were taken into consideration (no animal implications). Interventions (I) and comparators (C) included any intervention that could lead to prevention or reduction of disease in human populations (including vector control). The outcomes (O) that were considered for review were costs, consequences (life-years saved [LYS], disability-adjusted life years [DALYs], etc.), and the incremental cost-effectiveness ratio (ICER), while the setting (S) included any African country. For the purpose of this analysis, an economic evaluation was defined by the Drummond et al. definition of a “full economic evaluation,” and therefore, both costs and consequences of two or more alternatives had to be present in the analyses evaluated [13]. In cases in which an incremental analysis was not performed, articles were not excluded. Instead, if there was sufficient information in the publication to calculate the ICER, it was calculated during the review process. If there was insufficient information to calculate the ICER, it was noted in the critical appraisal that an incremental analysis was not present. No time constraints were added to the search.

Quality Assessment and Critical Appraisal

The quality of the included studies was assessed using the SIGN Methodology Checklist 6: Economic Evaluations Version 3.0 [14], which was composed of two parts. The first portion contained questions regarding the internal and external validity of the publication. Items in the sections were assessed using answers of “Yes,” “No,” or “Can’t say.” The second portion of the checklist addressed the reviewers overall assessment of the study and also provided the reviewer with an area to judge if the article was “unacceptable,” “acceptable,” or of “high quality.” Studies that received a “Yes” on 65% or more of the questions in Section 1 were considered acceptable to the authors.


Literature Search Results

The NHSEED, JSTOR, MEDLINE, and Embase searches yielded a total of seven articles, 1,000 articles, 595 articles, and 673 articles, respectively. An additional eight articles from the grey literature, reference lists, and referrals from subject matter experts were also included. There were a total of 2,283 studies found, and after the removal of duplicates, 2,095 were chosen for primary screening (title and abstracts). A total of 41 publications were then selected for full-text screening. Thirty-four studies were excluded after full-text review, and reasons for exclusion were recorded. (Refer to Table 1.) Seven full texts [1521] were included for full critical appraisal and data abstraction for analysis. (Refer to Fig. 1.)

Table 1. Characteristics of excluded studies at second-level screening.

Fig 1. Preferred reporting items for systematic reviews and meta-analyses (PRISMA) diagram.

JSTOR, Journal Storage; MEDLINE, Medical Literature Analysis and Retrieval System Online; NHSEED, National Health Service Economic Evaluation Database. 1000*: Although 1,490 articles were found using JSTOR, only 1,000 articles were accessible due to limitations of the JSTOR database.

Quality Assessment and Critical Appraisal

The quality scores for the seven included studies [1521] displayed in Table 2 (SIGN Methodology Checklist 6: Economic Evaluations) demonstrated that on average 81% (67%–89%) of the items stipulated by the SIGN checklist were addressed. Economic theory suggests that individuals have a time preference in regards to gains, and hence, costs and outcomes in the future are less valuable than those in the present [22]. This concept is referred to as “discounting” and is standard methodology in economic evaluation; however, five out the seven studies in this review did not address it [1619,21]. Each publication considered the cost and consequence compared to more than one intervention for HAT; however, three of the publications [15,17,18] did not include an incremental analysis to examine the marginal benefit of adopting one intervention compared to the next best option. A single study [19] did not have a clear objective, and Shaw’s study did not justify the study design or clearly describe the cost sources [15]. All but one study [20] completed a sensitivity analysis in addition to the base results. All studies discussed the economic importance of the question and had outcomes that could be relevant for decision makers. Overall, all studies were judged to be “acceptable” for this review.

Table 2. Critical appraisal (Scottish Intercollegiate Guidelines Network (SIGN) Methodology Checklist 6: Economic Evaluations).

Characteristics of Included Economic Evaluations

Each of the seven included publications had varying characteristics, as summarised in Table 3. The first publication of a full economic evaluation for HAT identified was completed in 1989 by Alexandra Shaw [15], with the next publication coming in 1995 [16]. The remaining five publications were published from 2005 to 2008 [1721]. The evaluations covered four African countries: Democratic Republic of the Congo (DRC), Uganda, Côte d’Ivoire, and Angola. Most (3/7) evaluations (n = 3) came from DRC [1820], with one study from Côte d’Ivoire [15], one study from Uganda [16], one study from Angola [21], and finally one study that included an analysis from both Uganda and Côte d’Ivoire [17]. Economic evaluations concerning HAT in human populations looked almost exclusively at the disease T. b. gambiense (71%), although in two instances the disease strain was not specified explicitly [15,21]. A total of four economic evaluations [15,18,19,21] were considered cost-effectiveness analyses (CEA) in which the cost for a desired effect or consequence (e.g., lives saved, years of infection avoided, etc.) was measured. Two studies [16,20] included both a CEA and cost utility analysis (CUA) in which the utility was measured in DALYs. One study exclusively completed a CUA in which cost per DALY averted was measured as the main outcome [17]. Overall, there was only one publication that was found in an “economic” journal, as the remaining articles were published in journals pertaining to tropical medicine and infectious diseases. Funding for the research was often not mentioned. However, WHO was referred to as a means of support in two publications [16,18], and support from the Belgian Directorate General for Development Cooperation was also mentioned [18].

Table 3. Characteristics of included economic evaluations.


The majority (5/7) of the publications evaluated interventions that included case detection and diagnosis, while two of the articles evaluated treatment interventions of melarsoprol and eflornithine (difluoromethlyornithine [DFMO]) for stage 2, as the treatment for stage 1 was always considered to be pentamidine [16,21]. Two publications by Lutumba [18,19] looked exclusively at sensitivity and specificity of diagnostic algorithms and staging algorithms, while one study also looked at the differences between treatment and vector control interventions in addition to case detection and diagnosis [15]. The study by Shaw in 1989 was the only publication that included a comparative economic analysis for vector control as an intervention to control HAT in a human population.

Economic Evaluation Description

Key insights regarding the details of the included economic evaluations are described below and also summarised in Table 4.

Methods and Software

Six of the seven included studies used modelling to measure outcomes for the economic evaluation. Only one study completed an economic evaluation alongside a clinical trial. The most common form of modelling was decision tree modelling; the structure of the remaining models was not described in detail although they were all described as being implemented with spreadsheets. For decision tree models, TreeAge software (TreeAge Software, Williamstown, Massachusetts, United States) was used for three of four studies [18,19,21], and one publication did not mention which software was used. The two spreadsheet models that were reviewed [15,17] used Super-Calc 4 (Sorcim, Silicon Valley, California, US) and Microsoft Excel (Microsoft Corp., Redmond, Washington, US) software, while the economic evaluation alongside clinical trial (EEACT) [20] relied on Microsoft Access (Microsoft Corp., Redmond, Washington, US), Microsoft Excel (Microsoft Corp., Redmond, Washington, US), and Epi Info 2002 (Centers for Disease Control and Prevention, Atlanta, Georgia, US).

Model Structure, Assumptions & Validation

A visual diagram of the model was provided for five of the six studies that included models [1619,21]. Although descriptions of the six models were available, no details of the assumptions or justification for the inputs used in the modelling were addressed in any of the included literature. None of the articles reported completing an internal validation of the models, but the authors of one article [19] did compare their outcomes to other literature in similar areas for external validity.

Population, Setting, and Perspective

In one of the modelling studies, the number of patients modelled was not mentioned, while the remaining studies included 690 to 1,000,000 hypothetical patients. The clinical trial included a total of 57 patients from 47 households [20]. As mentioned previously, the populations were based on four countries (DRC, Côte D’Ivoire, Angola, and Uganda), with different settings including: rural communities, health centres, and a sleeping-sickness hospital ward.

In one case [15], the perspective of the analysis was not mentioned, but two articles approached the economic evaluation from a societal perspective [16,20], and the remaining four articles used the provider perspective (e.g., a donor or national health service) [1719,21].

Additional Inputs, Outcomes, and Features of Included Economic Evaluations

Data sources for the economic evaluations came from clinical trials, primary data collection from national programmes (e.g., Programme National de Lutte contre la Trypanosomiase Humaine Africaine [PNTHLA], Médecins Sans Frontières [MSF], and National Sleeping Sickness Programme Uganda), reports from WHO, available literature, and from speaking with experts in the arena of HAT. Prevalence values were not mentioned in two studies and ranged from 0.1% to 70% in the remaining literature.

All costs were evaluated in US dollars (USD} [1518,20,21] except for one study by Lutumba et al. [19] that estimated cost-effectiveness in euros. Three studies reported only one outcome, while the remaining studies reported two outcomes in terms of cost per outcome. Cost per DALY averted was reported in three studies, while cost per LYS was reported in four studies. Cost per years of life lost (YLL), cost per patient/control case detected or patient cured, and cost per infection prevented were also examples of cost-effectiveness reported in the literature reviewed. Shaw (1989) and Shaw and Catt and reported time horizons of 20 years and one year, respectively [15,17]. Studies that used decision tree modelling did not report time horizons as decision trees have no time-related component [16,18,19]. The two remaining studies did not report a discrete time horizon for the analysis [20,21]. Two publications reported using discount rates of 10% [15,21], while one publication reported using a discount rate of 3% [20]. The remaining publications did not mention any discounting [1619], which was probably due to the fact that decision trees were used and therefore had no time horizon that or the time span modelled was one year or less. Two of the seven articles made explicit references to willingness-to-pay (WTP) thresholds for the cost-effectiveness of HAT as US$25/DALY [16,17]. One article mentioned that the WHO-CHOICE (CHOosing Interventions that are Cost-Effective) considered the gross domestic product (GDP) per capita of a country to be used as the WTP threshold for choosing between competing interventions [21,23]. The remaining publications [15,1821] made no reference to a WTP threshold for the economic analysis under evaluation.

Base Case and Sensitivity Analyses

A full description of the economic outcomes for each study is outline in Table 5. The results from the sensitivity analyses conducted for the included publications are provided in Table 4.

A total of 5 studies [16,1821] discussed cost-effectiveness results by calculating incremental cost-effectiveness ratios (ICERs), which are summarised in Table 5. Lutumba and colleagues published cost-effectiveness analyses of varying diagnostic algorithms for HAT [18,19]. Their results in 2005 demonstrated that lymph node puncture (LNP) in addition to CATT was more cost-effective ($20/LYS) relative to CATT alone or LNP alone [18]. In 2007, LNP followed by capillary tube centrifugation (CTC) and mini-anion exchange centrifugation technique (mAECT) (€76/LYS); LNP followed by thick blood film (TBF), CTC, and mAECT (€200/LYS); and LNP followed by TBF, CTC, mAECT, and CATT titration (€2,618/LYS) were deemed cost-effective relative to four other diagnostic algorithms. Although the strengths of these cost-effective algorithms were noted, Lutumba and colleagues noted that some of these algorithms may not be feasible to carry out in the field [19]. In regards to treatment regimens, Politi’s analysis [16] in 1995 demonstrated that based on a WTP of US$25/DALY, melarsoprol alone (initial treatment and relapses) was cost-effective at US$8/DALY (US$209/LYS) compared to no treatment. Politi’s analysis also demonstrated that a treatment pathway of melarsoprol with treatment relapses on Eflornithine (difluoro-methylornithine [DMFO]) (US$41/DALY and US$1,033/LYS) or DMFO for both treatment and relapses (US$167/DALY and US$4,444/LYS) would not have been considered cost-effective based on the aforementioned cost-effectiveness threshold of US$25/DALY [16]. A more recent publication by Robays demonstrated that DFMO was more cost-effective than melarsoprol (US$1,596/LYS and US$58/control case detected) when donated drug costs were not included; the analysis of cost-effectiveness was based on WHO-CHOICE’s suggestion that interventions at a cost of GDP per capita are very cost-effective and interventions at three times GDP per capita are cost-effective [24]. When donated drug costs were included, Robays found that DFMO was more cost-effective than melarsoprol at US$8,169/LYS and US$299/control case detected. Lutumba et al. [20] found that active screening (case detection) in addition to treatment was more cost-effective than treatment alone at $17/DALY averted and $301/control case detected or patient cured.

Two studies [15,17] did not report cost and effect results incrementally. Although Shaw et al. [15,17] conducted several analyses exploring combinations of case detection, diagnostics, treatment, and vector control, outcomes were not compared incrementally; consequently, ICERs were not attained. They did calculate $/patient detected with varying prevalence for five strategies and found that lower prevalence rates were associated with higher $/DALY and higher prevalence rates with lower $/DALY; these were based on average cost-effectiveness ratios, not ICERs.

All but one study included some form of one-way sensitivity analysis (OWSA). No studies completed subgroup analyses or conducted probabilistic sensitivity analyses (PSA), and hence, results were not presented using cost-effectiveness acceptability curves (CEAC). Additional measures of uncertainty were not explored in the form of a value of information (VOI) analysis in any of the reviewed publications.


A review of previous evidence has demonstrated that there have been only a few economic evaluations conducted to assess the cost-effectiveness of interventions to control HAT and reduce disease burden. From this evidence alone, it would prove difficult for decision makers to strategize on which interventions would be most cost-effective for elimination; however, the results do provide some insights into the key components of HAT disease control and how these components could be translated into HAT elimination strategies, which could then be assessed through economic evaluation.

Overall the strengths of this review are that it highlights the components that play a role in disease control and reduction of transmission and emphasizes that these are the components that should be incorporated into elimination strategies. Case detection, diagnosis, treatment, and vector control are the four categories of interventions that have been considered thus far in the literature. Strategies towards elimination should continue to consider the impact of these components but also aim to highlight their individual and collective use within a formal strategy for reaching elimination. This was highlighted in the study by Lutumba et al. [20] in which case-detection with treatment was compared to treatment alone and also in the work by Shaw and colleagues in 1989 in which essentially all four categories were evaluated with varying incidence. Within diagnostics, algorithms for CATT showed that the addition of tests led to more efficient outcomes [19]. However, there is still a gap in cost-effectiveness knowledge of the current treatment for HAT, NECT. As global investors, partners, and academic groups [10,11,2529] are now working together not only to control and treat this disease but also to develop novel diagnostic tools [9,11] and drug treatments [10], it would be useful to compare NECT to interventions that have recently come or are near entry to the market (e.g., fexinidazole [10] and rapid diagnostic tests [9,11]). Shaw et al. [15,17] and Lutumba [20] both made reference to the benefits of combining interventions for treatment, and it would be wise for stakeholders to move beyond this and develop more complicated and time-sensitive strategies with interventions not only on their own but in combination to identify the most cost-effective pathways towards elimination.

There are still some additional considerations that have not been considered as components in HAT economic evaluations. Although T. b. gambiense HAT contributes to 95% of the HAT disease [5], separate strategies for T. b. rhodesiense could also be considered. Cultural beliefs and attitudes towards HAT will also play a role in the effectiveness of interventions [30], and although education and community sensitization programs for HAT have been evaluated in terms of their societal benefit and impact on changing knowledge and behaviour [3133], no studies have shown their benefit in terms of cost-effectiveness. Methods of delivery and integration of health systems should also be further explored in terms of accessibility and availability, as resource constraints and lack of access in remote areas may delay elimination timelines if not considered beforehand [34,35].

Potential Use of Cost-Effective Modelling for HAT Control and Elimination

It was quite evident from the literature review that modelling will play a role in the economic evaluation of HAT. Most of the previous economic evaluations conducted were based on models, and modelling is known to assist with forecasting future economic consequences [13]. Decision makers would benefit from the use of whole disease modelling of alternative elimination scenarios because it would allow them to consider the implications and incremental benefits of each potential strategy. Previous economic evaluation studies reliant on modelling have addressed how individual interventions reduced transmission but not how these interventions, or combinations of them, could lead to eventual elimination or interruption of disease transmission. Current modelling techniques for economic evaluation, including those used to evaluate the impact of uncertainty related to model parameters, would also be useful for decision makers in communicating the consequences of choosing non-cost-effective strategies [36]. Additionally, modelling the feasibility of interventions through health service delivery is also necessary. For example, the results from an economic evaluation regarding diagnostic algorithms [19] showed that sometimes even the most cost-effective tools may not be affordable or feasible in some of the locations where HAT occurs [19].

Potential Use of Economic Evaluation Methodology in HAT Control and Elimination

A few considerations of cost-effective interventions could be gleaned from the few economic evaluations found. This was highlighted in the scenario described by Lutumba et al. [20] in which case-detection with treatment was more cost-effective than treatment alone, and an economic evaluation of diagnostic algorithms showed that the addition of tests to CATT could increase cost-effectiveness [19]. Treatment regimens including melarsoprol and eflornithine were considered cost-effective [16,21] for patients with HAT T. b. gambiense, and Politi’s analysis in 1995 also demonstrated a good understanding of economic outcomes because dominance was assessed and the importance of the efficiency frontier was illustrated [16]. Dominance refers to the economic concept that an intervention that costs less and has better outcomes relative to its comparator is considered dominant [13]. In regards to budgeting, sensitivity analyses [15,17,18] demonstrated that prevalence is related to costs. This will be important to consider because the cost per patient will increase towards the end goal of HAT elimination, but the overall cost per benefit still needs to be ascertained.

The economic evaluations reviewed presented some methodological inconsistencies. For example, there was a lack of clarity in reporting costs and consequences incrementally to a base-case scenario or relative to the next-best intervention. Historically calculations may have been done this way because of the “generalized cost-effectiveness” method [37], but if incremental and net benefits are always compared to “do nothing” instead of to the next-best option available, then the consequences of this methodology could lead to error [38]. Furthermore, when multiple strategies are being considered, dominance needs to be examined. Although four out of seven studies had more than two competing strategies, dominance was only addressed once. Evaluations that ignore dominance could lead to decision errors in which the health utility is not maximised at a societal level [13,39]. Cost-effectiveness was also referred to by the authors without making reference to a cost-effectiveness threshold. WHO-CHOICE [24] has defined thresholds previously; however, it is not clear if these thresholds values are acceptable for all global stakeholders because the authors did not always refer to a threshold value to determine cost-effectiveness.

The methodology of CEA with different interventions permits one to compare varying strategies across a disease, but the outcomes need to be unified so that decision makers can assess these comparators with ease and clarity. It is evident from this review that although CEA research may be conducted, the results are hard to interpret without standardization or reporting in a common metric (e.g., cost per DALY). Following existing guidelines for economic evaluation such as the SIGN Guidelines [14] and the more recent Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement [40] or developing guidelines that stakeholders feel acceptable for an elimination strategy would allow for consistency of analyses for HAT and other neglected tropical diseases. Formal economic evaluation guidelines and even a standard reference case have been developed by various public health funders [4143], and researchers should consider these standards to further the future of CEA within tropical disease and disease elimination decision-making. In addition, traditional CEA measures two outcomes (cost and effects), but programs for elimination also need to consider time. Health economists will need to consider how to make recommendations to stakeholders for strategy prioritization considering all three elements for elimination.


This review has demonstrated that previous research highlights the main components that play a role in elimination. Furthermore, cost-effective modelling and economic evaluation have been used and could address future economic concerns regarding elimination. Researchers interested in evaluating economic concerns regarding HAT elimination should think about modelling elimination strategies to assess cost-effectiveness using standardized methodology in order to assist stakeholder and key funders. These analyses would be of use since HAT is currently being prioritized as a NTD to reach elimination by 2020.


Box 1. Key Learning Points from Economic Evaluations for HAT

  • Most interventions assessed to date to reduce and control HAT are fairly cost-effective.
  • Previous publications have focused on case detection, diagnostics, drug treatments, and vector control; however, examination of combinations of interventions have not yet been assessed for HAT elimination.
  • No studies to date have explored the CE of the current first-line treatment for stage one HAT, NECT.
  • The feasibility of deployment of current and new interventions for HAT also should be taken into consideration in future economic evaluations.
  • Previous economic evaluations demonstrate that this method can play a role in assessing the cost-effectiveness of interventions for a disease in the developing world.

Box 2. Key Papers in Economic Evaluation of HAT Interventions

  1. Shaw AP (1989) Comparative analysis of the costs and benefits of alternative disease control strategies: vector control versus human case finding and treatment. Ann Soc Belg Med Trop 69 Suppl 1: 237–253.
  2. Politi C, Carrin G, Evans D, Kuzoe FA, Cattand PD (1995) Cost-effectiveness analysis of alternative treatments of African gambiense trypanosomiasis in Uganda. Health Econ 4: 273–287.
  3. Shaw AP, Cattand P (2001) Analytical tools for planning cost-effective surveillance in Gambiense sleeping sickness. Med Trop 61: 412–421.
  4. Lutumba P, Robays J, Miaka C, Kande V, Simarro PP, Shaw APM, et al. (2005) [The efficiency of different detection strategies of human African trypanosomiasis by T. b. gambiense]. Trop Med Int Health 10: 347–356.
  5. Lutumba P, Meheus F, Robays J, Miaka C, Kande V, Buscher P, et al. (2007) Cost-effectiveness of Algorithms for Confirmation Test of Human African Trypanosomiasis. Emerg Infect Dis 13: 1484–1490.
  6. Lutumba P, Makieya E, Shaw A, Meheus F, Boelaert M (2007) Human African trypanosomiasis in a rural community, Democratic Republic of Congo. Emerg Infect Dis 13: 248–254.
  7. Robays J, Raguenaud ME, Josenando T, Boelaert M (2008) Eflornithine is a cost-effective alternative to melarsoprol for the treatment of second-stage human West African trypanosomiasis in Caxito, Angola. Trop Med Int Health 13: 265–271.

Supporting Information

S2 Supporting Information. Inclusion-exclusion criteria legend.

Abbreviations: LYG, life-years gained; HRQoL, health-related quality of life; BIA, budget impact analysis; BOI, burden of illness.



  1. 1. WHO (n.d.) WHO | WHO to roll out implementation strategy to eliminate sleeping sickness. Accessed 4 December 2013.
  2. 2. World Health Organization (WHO) (2013) WHO Technical Report Series 984: Control and surveillance of human African trypanosomiasis. WHO. Accessed 14 January 2014.
  3. 3. Vos T, Flaxman AD, Naghavi M, Lozano R, Michaud C, et al. (2012) Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 380: 2163–2196. Accessed 23 May 2013. pmid:23245607
  4. 4. WHO (2013) The 17 neglected tropical diseases. World Heal Organ. Accessed 5 September 2014.
  5. 5. World Health Organization (WHO) (2013) Control and surveillance of human African trypanosomiasis. World Heal Organ Tech Rep Ser: 1–237. Accessed 21 February 2014. pmid:24552089
  6. 6. Simo G, Mbida JAM, Eyenga VE, Asonganyi T, Njiokou F, et al. (2014) Challenges towards the elimination of Human African Trypanosomiasis in the sleeping sickness focus of Campo in southern Cameroon. Parasit Vectors 7: 374. Accessed 20 October 2014. pmid:25129168
  7. 7. Funk S, Nishiura H, Heesterbeek H, Edmunds WJ, Checchi F (2013) Identifying transmission cycles at the human-animal interface: the role of animal reservoirs in maintaining gambiense human african trypanosomiasis. PLoS Comput Biol 9: e1002855. Accessed 16 October 2014. pmid:23341760
  8. 8. WHO (2012) Accelerating Work to overcome the global impact of neglected tropical diseases: A roadmap for implementation: 1–22. Accessed 5 September 2014.
  9. 9. Büscher P, Mertens P, Leclipteux T, Gilleman Q, Jacquet D, et al. (2014) Sensitivity and specificity of HAT Sero-K-SeT, a rapid diagnostic test for serodiagnosis of sleeping sickness caused by Trypanosoma brucei gambiense: a case-control study. Lancet Glob Heal 2: e359–e363. pmid:25103304
  10. 10. Drugs for Neglected Diseases initiative (2014) DNDi—Drugs for Neglected Diseases initiative. Accessed 5 September 2014.
  11. 11. Foundation for Innovative New Diagnostics (n.d.) FIND—Foundation for Innovative New Diagnostics. Accessed 5 September 2014.
  12. 12. London Declaration (2013) From promises to progress: The First Annual Report on the London Declaration on NTDs. Accessed 14 March 2014.
  13. 13. Drummond MF, Schulper MJ, Torrance GW, O’Brien BJ, Stoddart GL (2005) Methods for economic evaluation of health care programmes. Third. New York: Oxford University Press.
  14. 14. Scottish Intercollegiate Guidelines Network (SIGN) (n.d.). Accessed 26 November 2013.
  15. 15. Shaw AP (1989) Comparative analysis of the costs and benefits of alternative disease control strategies: vector control versus human case finding and treatment. Ann Soc Belg Med Trop (1920) 69 Suppl 1: 237–253.
  16. 16. Politi C, Carrin G, Evans D, Kuzoe FA, Cattand PD (1995) Cost-effectiveness analysis of alternative treatments of African gambiense trypanosomiasis in Uganda. Health Econ 4: 273–287. pmid:8528430
  17. 17. Shaw AP, Cattand P (2001) Analytical tools for planning cost-effective surveillance in Gambiense sleeping sickness. Med Trop (Mars) 61: 412–421. pmid:11803834
  18. 18. Lutumba P, Robays J, Miaka C, Kande V, Simarro PP, et al. (2005) [The efficiency of different detection strategies of human African trypanosomiasis by T. b. gambiense]. Trop Med Int Health 10: 347–356. pmid:15807799
  19. 19. Lutumba P, Meheus F, Robays J, Miaka C, Kande V, et al. (2007) Cost-effectiveness of Algorithms for Confirmation Test of Human African Trypanosomiasis. Emerg Infect Dis 13: 1484–1490. pmid:18257991
  20. 20. Lutumba P, Makieya E, Shaw A, Meheus F, Boelaert M (2007) Human African trypanosomiasis in a rural community, Democratic Republic of Congo. Emerg Infect Dis 13: 248–254. pmid:17479887
  21. 21. Robays J, Raguenaud ME, Josenando T, Boelaert M (2008) Eflornithine is a cost-effective alternative to melarsoprol for the treatment of second-stage human West African trypanosomiasis in Caxito, Angola. Trop Med Int Health 13: 265–271. pmid:18304274
  22. 22. Morris S, Devlin N, Parkin D (2007) Economic Analysis in Health Care. Glasgow: John Wiley & Sons, Ltd.
  23. 23. Evans DB, Lim SS, Adam T, Edejer TTT, Team WHOCI that are CEMDG (2005) Achieving the millennium development goals for health: evaluation of current strategies and future priorities for improving health in developing countries. Br Med J (Clinical Res Ed 331: 1457–1461. 16 ref. pmid:16282377
  24. 24. WHO (n.d.) WHO | Cost-effectiveness thresholds. Accessed 27 November 2013.
  25. 25. World Health Organization (WHO) (n.d.) Microsoft Word—A66_20-en.docx—A66_20_Eng.pdf. Accessed 5 September 2014.
  26. 26. Bill & Melinda Gates Foundation (n.d.) Bill & Melinda Gates Foundation. Accessed 5 September 2014.
  27. 27. Department for International Development (n.d.) Department for International Development—GOV.UK. Accessed 5 September 2014.
  28. 28. Swiss Tropical and Public Health Institute (Swiss TPH) (2014) Accessed 5 September 2014.
  29. 29. Liverpool School of Tropical Medicine (n.d.) LSTM—Liverpool School of Tropical Medicine. Accessed 5 September 2014.
  30. 30. Leygues M, Gouteux JP (1989) [A community battle against a tropical endemic disease: supernatural beliefs and tsetse fly traps in the Congo]. La lutte Communaut contre une Endem Trop croyances surnaturelles pieges a tsetse au Congo 28: 1255–1267. pmid:2734626
  31. 31. Kovacic V, Tirados I, Esterhuizen J, Mangwiro CTN, Torr SJ, et al. (2013) Community acceptance of tsetse control baits: a qualitative study in Arua District, North West Uganda. PLoS Negl Trop Dis 7: e2579. pmid:24349593
  32. 32. Palmer JJ, Kelly AH, Surur EI, Checchi F, Jones C (2014) Changing landscapes, changing practice: Negotiating access to sleeping sickness services in a post-conflict society. Soc Sci Med 120: 396–404. pmid:24679924
  33. 33. Waiswa C, Kabasa JD (2010) Experiences with an in-training community service model in the control of zoonotic sleeping sickness in Uganda. J Vet Med Educ 37: 276–281. pmid:20847337
  34. 34. Laveissiere C, Meda AH, Doua F, Sane B (1998) [Detecting sleeping sickness: comparative efficacy of mobile teams and community health workers]. Depist la Mal du sommeil Effic Comp des equipes mobiles des agents sante Communaut 76: 559–564. pmid:10191551
  35. 35. Simarro PP, Cecchi G, Franco JR, Paone M, Diarra A, et al. (2014) Mapping the capacities of fixed health facilities to cover people at risk of gambiense human African trypanosomiasis. Int J Health Geogr 13: 4. pmid:24517513
  36. 36. Claxton K (1999) Bayesian approaches to the value of information: implications for the regulation of new pharmaceuticals. Health Econ 8: 269–274. pmid:10348422
  37. 37. Hutubessy R, Chisholm D, Edejer TT-T (2003) Generalized cost-effectiveness analysis for national-level priority-setting in the health sector. Cost Eff Resour Alloc 1: 8. pmid:14687420
  38. 38. Weinstein MC (1990) Principles of cost-effective resource allocation in health care organizations. Int J Technol Assess Health Care 6: 93–103. pmid:2113893
  39. 39. Torrance GW, Thomas WH, Sackett DL (1972) A utility maximization model for evaluation of health care programs. Health Serv Res 7: 118–133. pmid:5044699
  40. 40. Husereau D, Drummond M, Petrou S, Carswell C, Moher D, et al. (2013) Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement. Eur J Heal Econ 14: 367–372. doi:10.1007/s10198-013-0471-6.
  41. 41. Stevens AJ, Longson C (2013) At the center of health care policy making: the use of health technology assessment at NICE. Med Decis Making 33: 320–324. pmid:23519880
  42. 42. Canadian Agency for Drugs and Technologies in Health (CADTH) (2006) Guidelines for the Economic Evaluation of Health Technologies, 3rd Edition. Ottawa. Accessed 22 August 2014.
  43. 43. National Institute for Health and Clinical Excellence (NICE) International (2014) Bill and Melinda Gates Foundation Methods for Economic Evaluation Project (MEEP). York, UK.
  44. 44. Abila PP, Okello-Onen J, Okoth JO, Matete GO, Wamwiri F, et al. (2007) Evaluation of the cost-effectiveness of pyramidal, modified pyramidal and monoscreen traps for the control of the tsetse fly, Glossina fuscipes fuscipes, in Uganda. J Insect Sci 7: 1–7. pmid:20345292
  45. 45. Brandl FE (1988) Costs of different methods to control riverine tsetse in West Africa. Trop Anim Health Prod 20: 67–77. pmid:3135642
  46. 46. Brightwell R, Dransfield RD, Kyorku C (1991) Development of a low-cost tsetse trap and odour baits for Glossina pallidipes and G. longipennis in Kenya. Med Vet Entomol 5: 153–164. pmid:1768909
  47. 47. Checchi F, Chappuis F, Karunakara U, Priotto G, Chandramohan D (2011) Accuracy of Five Algorithms to Diagnose Gambiense Human African Trypanosomiasis. PLoS Negl Trop Dis 5: 15.
  48. 48. Esterhuizen J, Rayaisse JB, Tirados I, Mpiana S, Solano P, et al. (2011) Improving the cost-effectiveness of visual devices for the control of riverine tsetse flies, the major vectors of human African trypanosomiasis. PLoS Negl Trop Dis 5: e1257. pmid:21829743
  49. 49. Etchegorry MG, Helenport JP, Pecoul B, Jannin J, Legros D (2001) Availability and affordability of treatment for Human African Trypanosomiasis. Trop Med Int Health 6: 957–959. pmid:11703853
  50. 50. Fevre EM, Wissmann BV, Welburn SC, Lutumba P (2008) The burden of human African trypanosomiasis. PLoS Negl Trop Dis 2: e333. pmid:19104653
  51. 51. Fevre EM, Odiit M, Coleman PG, Woolhouse MEJ, Welburn SC (2008) Estimating the burden of rhodesiense sleeping sickness during an outbreak in Serere, eastern Uganda. BMC Public Health 8: 96. pmid:18366755
  52. 52. Gouteux JP, Bansimba P, Noireau F, Frezil JL (1987) [Cost of the individual treatment of Trypanosoma brucei gambiense trypanosomiasis in a focus of infection in Niari (Congo)]. Le cout du Trait Individ la Trypanos a T.b gambiense dans le foyer du Niari (Congo) 47: 61–63.
  53. 53. JORDAN AM (1961) An assessment of the economic importance of the tsetse species of Southern Nigeria and the Southern Cameroons based on their trypanosome infection rates and ecology. Bull Entomol Res 52: 431–441. pmid:14452425
  54. 54. Kamuanga M, Swallow BM, Sigué H, Bauer B (2001) Evaluating contingent and actual contributions to a local public good: Tsetse control in the Yale agro-pastoral zone, Burkina Faso. Ecol Econ 39: 115–130.
  55. 55. Laveissiere C, Grebaut P (1990) [The trapping of tsetse flies (Diptera: Glossinidae). Improvement of a model: the Vavoua trap]. Rech sur les pieges a glossines (Diptera Glossinidae) Mise au point d’un Model Econ le piege “Vavoua” 41: 185–192.
  56. 56. Lutumba P, Robays J, Miaka mia Bilenge C, Mesu VKBK, Molisho D, et al. (2005) Trypanosomiasis control, Democratic Republic of Congo, 1993–2003. Emerg Infect Dis 11: 1382–1388. pmid:16229766
  57. 57. Lutumba P, Robays J, Miaka C, Kande V, Mumba D, et al. (2006) [Validity, cost and feasibility of the mAECT and CTC confirmation tests after diagnosis of African of sleeping sickness]. Validite, cout faisabilite la mAECT CTC comme tests confirmation dans la Detect la Trypanos Hum Africaine 11: 470–478. pmid:16553930
  58. 58. Matemba LE, Fevre EM, Kibona SN, Picozzi K, Cleaveland S, et al. (2010) Quantifying the burden of rhodesiense sleeping sickness in Urambo District, Tanzania. PLoS Negl Trop Dis 4: e868. pmid:21072230
  59. 59. McDermott JJ, Coleman PG (2001) Comparing apples and oranges—model-based assessment of different tsetse-transmitted trypanosomosis control strategies. Int J Parasitol 31: 603–609. pmid:11334949
  60. 60. Mitashi P, Hasker E, Lejon V, Kande V, Muyembe JJ, et al. (2012) Human African trypanosomiasis diagnosis in first-line health services of endemic countries, a systematic review. Int J Infect Dis 16: e400.
  61. 61. Mugasa CM, Adams ER, Boer KR, Dyserinck HC, Büscher P, et al. (2012) Diagnostic accuracy of molecular amplification tests for human African trypanosomiasis—systematic review. PLoS Negl Trop Dis 6: e1438. pmid:22253934
  62. 62. Okoth JO (1991) Description of a mono-screen trap for Glossina fuscipes fuscipes Newstead in Uganda. Ann Trop Med Parasitol 85: 309–314. pmid:1746979
  63. 63. Putt SN, Leslie J, Willemse L (1988) The economics of trypanosomiasis control in western Zambia. Acta Vet Scand Suppl 84: 394–397. pmid:3232644
  64. 64. Ruiz Postigo JA, Franco JR, Simarro PP, Bassets G, Nangouma A (2001) [Cost of a national program to control human African trypanosomiasis in the high Mbomou region, Central African Republic]. Cout d’un Proj d’appui au Program Natl lutte contre la Trypanos Hum africaine dans le foyer du Haut Mbomou, Repub Centrafr 61: 422–424. pmid:9525904
  65. 65. Shaw APM (2004) Economics of African Trypanosomiasis. In: Maudlin I, Holmes PH, Miles MA, editors. The Trypanosomiases. CAB International. pp. 369–401.
  66. 66. Shaw A, Hendrickx G, Gilbert M, Mattioli R, Codjia V, et al. (2006) Mapping the benefits: a new decision tool for tsetse and trypanosomiasis interventions. Rome, Italy.
  67. 67. Shaw A, Torr S, Waiswa C, Robinson T (2007) Comparable Costings of Alternatives for Dealing with Tsetse: Estimates for Uganda. Rome, Italy.
  68. 68. Shaw APM (2009) Assessing the economics of animal trypanosomosis in Africa—history and current perspectives. Onderstepoort J Vet Res 76: 27–32. pmid:19967925
  69. 69. Shaw APM, Torr SJ, Waiswa C, Cecchi G, Wint GRW, et al. (2013) Estimating the costs of tsetse control options: an example for Uganda. Prev Vet Med 110: 290–303. pmid:23453892
  70. 70. Simarro PP, Diarra A, Ruiz Postigo JA, Franco JR, Jannin JG (2011) The human African trypanosomiasis control and surveillance programme of the World Health Organization 2000–2009: the way forward. PLoS Negl Trop Dis 5: e1007. pmid:21364972
  71. 71. Simarro PP, Franco J, Diarra A, Postigo JAR, Jannin J (2012) Update on field use of the available drugs for the chemotherapy of human African trypanosomiasis. Parasitology 139: 842–846. pmid:22309684
  72. 72. Trowbridge M, McFarland D, Richer M, Adeoye M, Moore A (2000) Cost-effectiveness of programs for sleeping sickness control. (Meeting abstract No. 417). Am J Trop Med Hyg 62: 312.
  73. 73. Vale GA, Torr SJ (2005) User-friendly models of the costs and efficacy of tsetse control: application to sterilizing and insecticidal techniques. Med Vet Entomol 19: 293–305. pmid:16134978
  74. 74. World Health Organization (WHO) (1998) Control and surveillance of African trypanosomiasis. Report of a WHO Expert Committee. World Health Organ Tech Rep Ser 881: I—114. pmid:10070249
  75. 75. World Health Organization (WHO) (2003) Making choices in health: WHO guide to cost-effectiveness analysis. Geneva: World Health Organization. Accessed 16 January 2014.