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The initial models developed for Tanzania demonstrated how new diagnostic technologies used in an appropriate diagnostic algorithm could cost-effectively increase detection of TB [1]. These results were shown to be robust in Ethiopia and South Africa [2, 3], but when similar algorithms were modeled in the Philippines it was shown the same technology was unlikely to increase detection of drug sensitive TB. Importantly, however, detection of multidrug resistant TB would be improved substantially and would justify the implementation of the new tools on its own.
Patients queueing for MDR-TB treatment in Philippines
In the Philippines the models were extended to look at access to patients for diagnosis and the impact on the costs incurred by patients. These model runs showed one of the key benefits for patients in the Philippines would be reduced costs through less travel and reduced lost income. A key part of the project in the Philippines was to build capacity in the Philippines to use models, which has been successfully achieved through training programs and new funding.
Early in the development of the WITNESS models, it was decided to link the simulation models to transmission models to understand how transmission of the disease might be impacted by these new technologies. In Tanzania the models revealed the impact was small, as most patients would have already infected close friends and family by the time they sort treatment.
Along with this and the World Health Organisations (WHO) new ‘ End-TB Strategy’ there is now a new focus on the TB models created in WITNESS. The objective is to look at active case finding strategies for TB, so that patients can be diagnosed earlier and thus receive appropriate treatment earlier, reducing the risk of transmission. New models are currently being developed to look at this in the Philippines and Nepal.
WITNESS model of patient pathways in the Philippines
The last 9 years has shown how WITNESS models applied to health projects in low - and middle - income countries can lead to interventions that enhance society through helping countries and global organisations develop strategies that increase detection of killer infectious diseases, such as tuberculosis, while at the same time reducing the burden to individuals through reduced patient costs and lower transmission.
1.Langley I, Lin H-H, Egwaga S, et al. (2014). Assessment of the patient, health system, and population effects of Xpert MTB/RIF and alternative diagnostics for tuberculosis in Tanzania: an integrated modelling approach. Lancet. Glob. Heal. 2014;2(10):e581-91. doi:10.1016/S2214-109X(14)70291-8.
2.Tesfaye A, Fiseha D, Assefa D, Klinkenberg E, Balanco S, Langley I. (2017). Modeling the patient and health system impacts of alternative Xpert® MTB/RIF algorithms for the diagnosis of pulmonary tuberculosis in Addis Ababa, Ethiopia. BMC Infect Dis. 2017 May 2;17(1):318. doi: 10.1186/s12879-017-2417-6.
3.Dunbar R, Naidoo P, Beyers N, Langley I. (2017). High laboratory cost predicted per tuberculosis case diagnosed with increased case finding without a triage strategy. Int J Tuberc Lung Dis. 2017 Sep 1;21(9):1026-1034. doi: 10.5588/ijtld.17.0156.
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