Rapid Diagnostic Testing (RDT) has the potential to identify many millions of people infected with tropical diseases, but even more is possible when diagnostic identification of patients is combined with the infrastructure and expertise offered by laboratory networks. Luiz Gustavo Guedes Corrêa PhD, Director, Diaceutics, looks at the possibilities.
In the year 2000, the United Nations established the Millennium Development Goals (MDGs)1, in which the fight against HIV/AIDS, malaria, tuberculosis and neglected tropical diseases (NTDs) was one of the focus points. In 2012, a collaborative programme under the London declaration2 was founded with the aim to eliminate or control ten NTDs3, which together affect over 1.4 billion people. Despite the fact that huge investment has been made and significant progress reached on the road to control or elimination, most control programs for these diseases share a common feature – a suboptimal diagnostic strategy.
Detection of leprosy
Let’s take leprosy as an example. By 2000, leprosy was considered ‘eliminated’ on a global scale (with a prevalence of less than 1 case per 10,000 persons). World Health Organisation (WHO) fact sheets published in May 2015 still recorded a prevalence rate for leprosy of less than 1 case per 10,000 persons at the global level, which is within WHO ‘global elimination’ definitions. Notably, a decrease in the reported prevalence of the disease comes sharply after the years following the elimination statement, as surveillance and elimination efforts are reduced and reach a constant of approximately 200,000 detected cases in recent years (Figure 1).
Figure 1: Trend in new detected leprosy cases 1985-2014, Cairns Smith, University of Aberdeen.
However, since 2000, field research and population surveys have repeatedly shown that the official reported numbers presented by governments vary from the actual numbers observed on the ground, with a significant number of cases going undetected and untreated. Two regional examples support this under-reporting:
|City (Para, Brazil)||Individuals examined||New cases diagnosed (%)|
|Oriximiná||500||25 = 5.0%|
|Redencão||600||30 = 5.0%|
|Breves||650||62 = 9.5%|
|Santarém||556||45 = 8.1%|
|Mosqueiro Island||1,000||110 = 11.0%|
|Senador José Porfirio||700||94 = 13.4%|
|Acara||611||21 = 3.4%|
|Total||4,617||387 = 8.4%|
Table 1: New cases of leprosy detected in a field screen in the state of Para (Brazil) 2013-2014 using PGL-1 ELISA test via targeted campaigns. Source: Dr John Spencer, Colorado University.
Experts globally believe this reservoir of undetected disease could be as high as three million cases, suggesting the actual annual infection rate to be three times higher than WHO reported levels. This in turn has renewed calls for better diagnostic strategies to support field work and treatment strategies (Figure 2).
Figure 2: Reservoir of 3 million missed leprosy cases 1985-2014, Cairns Smith, University of Aberdeen.
Identification of these new cases has largely been made by a range of molecular tools and performed in general by a forgotten stakeholder in the diagnostic strategy – the laboratory. The yellow section of Figure 2 shows results gathered by lab initiative rather than current programs. The labs are performing different analyses, not just using microscopy, and new tests with greater sensitivity can show a higher incidence of disease. Using different diagnostic and laboratory tools can also identify different stages of a disease but this means that many cases can be identified at a lower burden, when there are no surveillance programs in place and no follow-up with family contacts.
In the classic scenario, identification of infection via microscopy has a limit of detection of 10 alcohol-acid resistant bacilli per gram of tissue5, but if someone has a lower bacillary load, that person is still infected, but not detected. For example, in 2015 CREDESH (a reference centre for leprosy in Brazil) identified that 42 contacts developed a symptomatic form of leprosy, but just one was identified as positive via microscopy, the gold standard process. Twenty-five out of the 42 could only be identified via qPCR.
Making the most of a lab network
The testing methodology and assays available in labs are certainly a factor in identifying previously unreported cases of tropical diseases, but another, perhaps less recognised factor, is the laboratory network infrastructure. The use of the installed infrastructure and knowledge offered by labs in different regions has certainly been part of the current success of diagnostic testing for TB in India, where quality affordable tests can be available to the population using an existing lab network managed through the IPAQT initiative6.
Using an established network to support a testing program means the expertise and infrastructure is already in place. Investment here could make an immediate difference to testing for NTDs, as seen with TB in India.
If labs are pivotal players in the diagnostic equation for TB, the same scenario should be seen for other diseases. With this aim, Diaceutics has embarked on lab mapping for malaria. So far, the project has uncovered over 60 labs in 19 tropical or developing countries that have the capability to complement other diagnostic strategies in malaria7, 8. These labs are not currently part of mainstream testing programs as they only test at research levels but they have the capability and knowledge to do this on a widespread basis. These labs could play an important role in:
Lab mapping for malaria
Malaria can be tested for in the field using rapid diagnostics. People may have symptoms but the infection cannot be detected. Labs, however, use technologies other than microscopy that can reveal a lower level of parasitemia. A similar issue is observed during pregnancy, when the bug infects not the mother, but the developing foetus, making the diagnosis very challenging.
An identified and organized lab network could standardize the approach to improving current diagnostics for NTDs, malaria and TB as it can deliver a very cost-effective diagnostic via the infrastructure and expertise already in place. As many of these diseases appear in overlapping regions, the use of an existing lab infrastructure could affect multiple diseases at the same time. This could have a major impact on the current scenario and its potential should be considered by the therapy companies targeting these diseases and the organizations employed in disease control.
The work done on malaria testing by Diaceutics has highlighted how laboratories can make a significant difference right now. Additionally, this is a model that can be used for other NTDs. Pharma companies and international organizations working in tropical disease would do well to consider laboratories as a much more important stakeholder. Improving diagnostics is not just about having a better test – it’s about using it in a better way and making it more available to a larger population. Laboratories can play a significant role in this plan.
We would like to thank Dr Isabela Goulart (CREDESH, Uberlâdia, Brazil), Dr Cairns Smith (University of Aberdeen, UK) and Dr John Spencer (Colorado University, USA) for the field data shared here.