Abstract
Most models of infectious diseases, including tuberculosis (TB), do not provide results customized to local conditions. We created a dynamic transmission model to project TB incidence, TB mortality, multidrug-resistant (MDR) TB prevalence, and incremental costs over 5 years after scale-up of nine alternative diagnostic strategies. A corresponding web-based interface allows users to specify local costs and epidemiology. In settings with little capacity for up-front investment, same-day microscopy had the greatest impact on TB incidence and became cost-saving within 5 years if delivered at $10/test. With greater initial investment, population-level scale-up of Xpert MTB/RIF or microcolony-based culture often averted 10 times more TB cases than narrowly-targeted strategies, at minimal incremental long-term cost. Xpert for smear-positive TB had reasonable impact on MDR-TB incidence, but at substantial price and little impact on overall TB incidence and mortality. This user-friendly modeling framework improves decision-makers' ability to evaluate the local impact of TB diagnostic strategies.
Highlights
Infectious disease transmission models are important tools for translating the best current knowledge of the natural history and epidemiology of infectious diseases into projections of epidemiological impact and costs under alternative strategies for disease control (Garnett et al, 2011)
We evaluated each of these diagnostic strategies in four emblematic epidemiological settings, defined by TB incidence, MDR-TB prevalence among new cases, and adult human immunodeficiency virus (HIV) prevalence: 1. ‘Reference/High-Incidence Setting’ (e.g., Southeast Asia): TB incidence 250 per 100,000/year, MDR-TB prevalence of 3.7% in new TB cases, adult HIV prevalence of 0·83%; 2
Our model estimated that MDR-TB prevalence in previously treated cases was 15.4% (WHO estimate 20% [World Health Organization, 2012]), but unlike our model, WHO notifications often count failure and recurrence after default as two separate cases
Summary
Infectious disease transmission models are important tools for translating the best current knowledge of the natural history and epidemiology of infectious diseases into projections of epidemiological impact (e.g., incidence, mortality) and costs under alternative strategies for disease control (Garnett et al, 2011). Most published transmission models are either loosely calibrated to reflect global/regional outcomes or more tightly fit to specific epidemiological settings; in either case, model results may be difficult for local decision-makers in the majority of public health settings to utilize. Estimates from the Spectrum models are routinely incorporated into official global and country-level estimates of HIV disease burden (Brown et al, 2010) and intervention impact (Farnham et al, 2013). Other simplified models are readily available for impact projections related to non-infectious diseases, where transmission assumptions are less important (Betz Brown et al, 2000; Walker et al, 2013). To date, simple, user-friendly transmission models have not been widely used for decision-making related to many infectious diseases other than HIV. Diagnosis of active tuberculosis (TB) is an example of a public health intervention for which transmission models
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