Abstract
We aimed to develop a conceptual framework of diabetes mellitus (DM) effects on tuberculosis (TB) natural history and treatment outcomes, and to assess the impact of these effects on TB-transmission dynamics. The model was calibrated using TB data for India. A conceptual framework was developed based on a literature review, and then translated into a mathematical model to assess the impact of the DM-on-TB effects. The impact was analyzed using TB-disease incidence hazard ratio (HR) and population attributable fraction (PAF) measures. Evidence was identified for 10 plausible DM-on-TB effects. Assuming a flat change of 300% (meaning an effect size of 3.0) for each DM-on-TB effect, the HR ranged between 1.0 (Effect 9-Recovery) and 2.7 (Effect 2-Fast progression); most effects did not have an impact on the HR. Meanwhile, TB-disease incidence attributed directly and indirectly to each effect ranged between −4.6% (Effect 7-TB mortality) and 34.5% (Effect 2-Fast progression). The second largest impact was for Effect 6-Disease infectiousness at 29.9%. In conclusion, DM can affect TB-transmission dynamics in multiple ways, most of which are poorly characterized and difficult to assess in epidemiologic studies. The indirect (e.g. onward transmission) impacts of some DM-on-TB effects are comparable in scale to the direct impacts. While the impact of several effects on the HR was limited, the impact on the PAF was substantial suggesting that DM could be impacting TB epidemiology to a larger extent than previously thought.
Highlights
diabetes mellitus (DM) appears to increase the risk of TB disease by about three-fold[5,6,8], and to have profound adverse impact on TB-treatment outcomes (e.g. DM appears to increase the risk of TB death by two to four-fold, and TB disease relapse and recurrence by two-fold, among others)[6,9,10,11,12,13]
We investigated the mechanisms by which DM can affect TB natural history and treatment outcomes, and can impact TB-transmission dynamics
Seven epidemiologically-relevant plausible effects for DM on TB natural history, and three for DM on TB treatment outcomes, were identified based on literature review. Informed by this empirical evidence, we developed a conceptual framework of DM’s effects on TB (Fig. 2), and translated it into a mathematical model to investigate the impact of these effects on TB-transmission dynamics
Summary
DM appears to increase the risk of TB disease by about three-fold[5,6,8], and to have profound adverse impact on TB-treatment outcomes (e.g. DM appears to increase the risk of TB death by two to four-fold, and TB disease relapse and recurrence by two-fold, among others)[6,9,10,11,12,13]. While conventional population attributable fraction (PAF) approaches (such as Levin’s formula23) can estimate the direct population impact of DM on TB disease, they do not account for the indirect impacts The latter, can be captured and estimated through mathematical modeling of TB-transmission dynamics in the population. Against this background, we aimed first to develop a conceptual framework that describes the different possible pathways by which DM could affect TB natural history and treatment outcomes. We aimed first to develop a conceptual framework that describes the different possible pathways by which DM could affect TB natural history and treatment outcomes We translated this conceptual framework into a population-based mathematical TB-DM model incorporating these effects and their direct and indirect impacts. It does not aim to provide precise information on the absolute actual impact of each pathway on TB epidemiology
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