In resource poor rural areas there is a lack of proper laboratory facilities and also a lack of clinical expertise to make accurate diagnoses. When healthcare workers in these rural settings are not able to rely on laboratory based diagnostics, there are usually two paths they take:
- Those who do not have sufficient clincal experience will not know the clinical signs of TB infection and patients with TB will be left undiagnosed (according to the WHO, every year 3 million people with TB are left undiagnosed)
- If the healthcare worker knows some clinical signs of TB and knows that there is a high incidenc in their area, they will prescribe TB medications to patients who have some symptoms that match TB; a significant number of these patients will not actually have TB and they end up receiving medications they do not need and which have side effects.
The TB LAM test is a first step in addressing the needs of those in resource poor settings because it is inexpensive and can be used at the point of care without the need for laboratory facilities. However, because of the interpretive difficulties with the test, up to 50% of TB positive patients are misdiagnosed as being TB negative. Our diagnostic strip reader app and data platform can address this problem and preliminary results indicate a much greater accuracy in being able to positively identify patients who have TB; thus helping to mitigate the problems of leaving TB patients undiagnosed and overuse of medication in patients who do not have TB. Improving diagnosis rates by just 1% could result in tens of thousands of lives being saved per year. We are using technology to help increase the diagnostic accuracy of TB LAM.
(1) Lawn, SD et al Lancet Infect Dis