Prevalence and genetic profiles of isoniazid resistance in tuberculosis patients: A multicountry analysis of cross-sectional data.

BACKGROUND: The surveillance of drug resistance among tuberculosis (TB) patients is central to combatting the global TB epidemic and preventing the spread of antimicrobial resistance. Isoniazid and rifampicin are two of the most powerful first-line anti-TB medicines, and resistance to either of them increases the risk of treatment failure, relapse, or acquisition of resistance to other drugs. The global prevalence of rifampicin resistance is well documented, occurring in 3.4% (95% CI 2.5%-4.4%) of new TB patients and 18% (95% CI 7.6%-31%) of previously treated TB patients in 2018, whereas the prevalence of isoniazid resistance at global and regional levels is less understood. In 2018, the World Health Organization (WHO) recommended a modified 6-month treatment regimen for people with isoniazid-resistant, rifampicin-susceptible TB (Hr-TB), which includes rifampicin, pyrazinamide, ethambutol, and levofloxacin. We estimated the global prevalence of Hr-TB among TB patients and investigated associated phenotypic and genotypic drug resistance patterns. METHODS AND FINDINGS: Aggregated drug resistance data reported to WHO from either routine continuous surveillance or nationally representative periodic surveys of TB patients for the period 2003-2017 were reviewed. Isoniazid data were available from 156 countries or territories for 211,753 patients. Among these, the global prevalence of Hr-TB was 7.4% (95% CI 6.5%-8.4%) among new TB patients and 11.4% (95% CI 9.4%-13.4%) among previously treated TB patients. Additional data on pyrazinamide and levofloxacin resistance were available from 6 countries (Azerbaijan, Bangladesh, Belarus, Pakistan, the Philippines, and South Africa). There were no cases of resistance to both pyrazinamide and levofloxacin among Hr-TB patients, except for the Philippines (1.8%, 95% CI 0.2-6.4) and Belarus (5.3%, 95% CI 0.1-26.0). Sequencing data for all genomic regions involved in isoniazid resistance were available for 4,563 patients. Among the 1,174 isolates that were resistant by either phenotypic testing or sequencing, 78.6% (95% CI 76.1%-80.9%) had resistance-conferring mutations in the katG gene and 14.6% (95% CI 12.7%-16.8%) in both katG and the inhA promoter region. For 6.8% (95% CI 5.4%-8.4%) of patients, mutations occurred in the inhA promoter alone, for whom an increased dose of isoniazid may be considered. The main limitations of this study are that most analyses were performed at the national rather than individual patient level and that the quality of laboratory testing may vary between countries. CONCLUSIONS: In this study, the prevalence of Hr-TB among TB patients was higher than the prevalence of rifampicin resistance globally. Many patients with Hr-TB would be missed by current diagnostic algorithms driven by rifampicin testing, highlighting the need for new rapid molecular technologies to ensure access to appropriate treatment and care. The low prevalence of resistance to pyrazinamide and fluoroquinolones among patients with Hr-TB provides further justification for the recommended modified treatment regimen

Exposure Patterns Driving Ebola Transmission in West Africa:A Retrospective Observational Study

BackgroundThe ongoing West African Ebola epidemic began in December 2013 in Guinea, probably from a single zoonotic introduction. As a result of ineffective initial control efforts, an Ebola outbreak of unprecedented scale emerged. As of 4 May 2015, it had resulted in more than 19,000 probable and confirmed Ebola cases, mainly in Guinea (3,529), Liberia (5,343), and Sierra Leone (10,746). Here, we present analyses of data collected during the outbreak identifying drivers of transmission and highlighting areas where control could be improved.Methods and findingsOver 19,000 confirmed and probable Ebola cases were reported in West Africa by 4 May 2015. Individuals with confirmed or probable Ebola ("cases") were asked if they had exposure to other potential Ebola cases ("potential source contacts") in a funeral or non-funeral context prior to becoming ill. We performed retrospective analyses of a case line-list, collated from national databases of case investigation forms that have been reported to WHO. These analyses were initially performed to assist WHO's response during the epidemic, and have been updated for publication. We analysed data from 3,529 cases in Guinea, 5,343 in Liberia, and 10,746 in Sierra Leone; exposures were reported by 33% of cases. The proportion of cases reporting a funeral exposure decreased over time. We found a positive correlation (r = 0.35, p ConclusionsAchieving elimination of Ebola is challenging, partly because of super-spreading. Safe funeral practices and fast hospitalisation contributed to the containment of this Ebola epidemic. Continued real-time data capture, reporting, and analysis are vital to track transmission patterns, inform resource deployment, and thus hasten and maintain elimination of the virus from the human population

Diffusion of global policies for management of multidrug-resistant tuberculosis in high-burden countries: A secondary data analysis

The diffusion of global policies for detection of multidrug-resistant tuberculosis (MDR-TB) and its treatment in high-burden countries is not yet fully described in the literature. Evaluating the status of policy diffusion from the global to the national level can be helpful in planning for future policy development and strategies for vulnerable settings. Policies for management and care of MDR-TB have been issued and disseminated through a multitude of channels. However, the uptake of such policies has been slow, despite the urgent need for public health programmes and national authorities to provide MDR-TB patients with evidence-based interventions that may improve their quality of life. The adoption of policies or their key components appears to be falling behind in many countries. Understanding the status of policy diffusion, especially in high MDR-TB burden settings, can provide some knowledge about the challenges for diffusion of global MDR-TB policies. This study used the diffusion of innovation theory as a lens to understand and illustrate the diffusion of global policies for diagnosis and treatment of MDR-TB from 2010 to 2015. The aim was to evaluate the current status of the diffusion process, and serve as a proxy to estimate the diffusion of newer policies such as the introduction of the World Health Organization (WHO)-recommended shorter MDR-TB regimen and implementation of new TB drugs. This study did not examine a government’s or an adopter’s decision to implement, but instead focused on the status, time-to-adopt and determinants of policy adoption. Multilevel and Cox proportional hazards modelling were used to assess policy diffusion of drug-susceptibility testing (DST) and provision of treatment for MDR-TB cases. Overall, the findings of this study indicate that the diffusion of these policy components is occurring at a slow pace; however, although the time-to-adoption was difficult to estimate, the diffusion rate of MDR-TB treatment appears to be increasing over time. Estimated number of HIV-associated TB and regional components appeared to be determinants of the diffusion of DST and MDR-TB treatment

Prevalence and genetic profiles of isoniazid resistance in tuberculosis patients: A multicountry analysis of cross-sectional data.

BackgroundThe surveillance of drug resistance among tuberculosis (TB) patients is central to combatting the global TB epidemic and preventing the spread of antimicrobial resistance. Isoniazid and rifampicin are two of the most powerful first-line anti-TB medicines, and resistance to either of them increases the risk of treatment failure, relapse, or acquisition of resistance to other drugs. The global prevalence of rifampicin resistance is well documented, occurring in 3.4% (95% CI 2.5%-4.4%) of new TB patients and 18% (95% CI 7.6%-31%) of previously treated TB patients in 2018, whereas the prevalence of isoniazid resistance at global and regional levels is less understood. In 2018, the World Health Organization (WHO) recommended a modified 6-month treatment regimen for people with isoniazid-resistant, rifampicin-susceptible TB (Hr-TB), which includes rifampicin, pyrazinamide, ethambutol, and levofloxacin. We estimated the global prevalence of Hr-TB among TB patients and investigated associated phenotypic and genotypic drug resistance patterns.Methods and findingsAggregated drug resistance data reported to WHO from either routine continuous surveillance or nationally representative periodic surveys of TB patients for the period 2003-2017 were reviewed. Isoniazid data were available from 156 countries or territories for 211,753 patients. Among these, the global prevalence of Hr-TB was 7.4% (95% CI 6.5%-8.4%) among new TB patients and 11.4% (95% CI 9.4%-13.4%) among previously treated TB patients. Additional data on pyrazinamide and levofloxacin resistance were available from 6 countries (Azerbaijan, Bangladesh, Belarus, Pakistan, the Philippines, and South Africa). There were no cases of resistance to both pyrazinamide and levofloxacin among Hr-TB patients, except for the Philippines (1.8%, 95% CI 0.2-6.4) and Belarus (5.3%, 95% CI 0.1-26.0). Sequencing data for all genomic regions involved in isoniazid resistance were available for 4,563 patients. Among the 1,174 isolates that were resistant by either phenotypic testing or sequencing, 78.6% (95% CI 76.1%-80.9%) had resistance-conferring mutations in the katG gene and 14.6% (95% CI 12.7%-16.8%) in both katG and the inhA promoter region. For 6.8% (95% CI 5.4%-8.4%) of patients, mutations occurred in the inhA promoter alone, for whom an increased dose of isoniazid may be considered. The main limitations of this study are that most analyses were performed at the national rather than individual patient level and that the quality of laboratory testing may vary between countries.ConclusionsIn this study, the prevalence of Hr-TB among TB patients was higher than the prevalence of rifampicin resistance globally. Many patients with Hr-TB would be missed by current diagnostic algorithms driven by rifampicin testing, highlighting the need for new rapid molecular technologies to ensure access to appropriate treatment and care. The low prevalence of resistance to pyrazinamide and fluoroquinolones among patients with Hr-TB provides further justification for the recommended modified treatment regimen

Priority-Setting for Novel Drug Regimens to Treat Tuberculosis: An Epidemiologic Model

<div><p>Background</p><p>Novel drug regimens are needed for tuberculosis (TB) treatment. New regimens aim to improve on characteristics such as duration, efficacy, and safety profile, but no single regimen is likely to be ideal in all respects. By linking these regimen characteristics to a novel regimen’s ability to reduce TB incidence and mortality, we sought to prioritize regimen characteristics from a population-level perspective.</p><p>Methods and Findings</p><p>We developed a dynamic transmission model of multi-strain TB epidemics in hypothetical populations reflective of the epidemiological situations in India (primary analysis), South Africa, the Philippines, and Brazil. We modeled the introduction of various novel rifampicin-susceptible (RS) or rifampicin-resistant (RR) TB regimens that differed on six characteristics, identified in consultation with a team of global experts: (1) efficacy, (2) duration, (3) ease of adherence, (4) medical contraindications, (5) barrier to resistance, and (6) baseline prevalence of resistance to the novel regimen. We compared scale-up of these regimens to a baseline reflective of continued standard of care.</p><p>For our primary analysis situated in India, our model generated baseline TB incidence and mortality of 157 (95% uncertainty range [UR]: 113–187) and 16 (95% UR: 9–23) per 100,000 per year at the time of novel regimen introduction and RR TB incidence and mortality of 6 (95% UR: 4–10) and 0.6 (95% UR: 0.3–1.1) per 100,000 per year. An optimal RS TB regimen was projected to reduce 10-y TB incidence and mortality in the India-like scenario by 12% (95% UR: 6%–20%) and 11% (95% UR: 6%–20%), respectively, compared to current-care projections. An optimal RR TB regimen reduced RR TB incidence by an estimated 32% (95% UR: 18%–46%) and RR TB mortality by 30% (95% UR: 18%–44%). Efficacy was the greatest determinant of impact; compared to a novel regimen meeting all minimal targets only, increasing RS TB treatment efficacy from 94% to 99% reduced TB mortality by 6% (95% UR: 1%–13%, half the impact of a fully optimized regimen), and increasing the efficacy against RR TB from 76% to 94% lowered RR TB mortality by 13% (95% UR: 6%–23%). Reducing treatment duration or improving ease of adherence had smaller but still substantial impact: shortening RS TB treatment duration from 6 to 2 mo lowered TB mortality by 3% (95% UR: 1%–6%), and shortening RR TB treatment from 20 to 6 mo reduced RR TB mortality by 8% (95% UR: 4%–13%), while reducing nonadherence to the corresponding regimens by 50% reduced TB and RR TB mortality by 2% (95% UR: 1%–4%) and 6% (95% UR: 3%–10%), respectively. Limitations include sparse data on key model parameters and necessary simplifications to model structure and outcomes.</p><p>Conclusions</p><p>In designing clinical trials of novel TB regimens, investigators should consider that even small changes in treatment efficacy may have considerable impact on TB-related incidence and mortality. Other regimen improvements may still have important benefits for resource allocation and outcomes such as patient quality of life.</p></div

Relative mortality impact of different individual characteristics of novel regimens for the treatment of RS or RR TB.

<p>Characteristics and levels are defined in <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1002202#pmed.1002202.t001" target="_blank">Table 1</a>. Impact is measured as a relative change in TB mortality (RS TB regimen, A and B) or RR TB mortality (RR TB regimen, C and D) 10 y after introduction of the novel regimen, as illustrated in Fig 3. In A and C, the benefit of partially (striped bars) or fully (solid bars) optimizing only one aspect of a regimen, with the remaining characteristics meeting only minimal targets, is compared to the impact of a regimen that is fully optimized in all aspects. In B and D, the mortality reduction achievable by a regimen that fails to meet only one optimistic target (relative to mortality projections using standard regimens) is compared to mortality reduction with a regimen that meets all optimistic targets. Percentages need not sum to 100% due to synergy between multiple characteristics of the regimen. Error bars show the 95% UR for the impact of each fully optimized characteristic.</p

Model structure.

<p>The model (panel A) includes infection, rapid or slow progression to active TB, and initiation of treatment with a standard regimen or novel regimen (the transition from Active TB to Treatment, shown in more detail in panels B and C). (Also included in model but not shown in Fig 1: parallel structure for eight different drug resistance phenotypes; parallel structure for HIV infected/uninfected and treatment naïve/experienced; and death/spontaneous resolution.) Six novel drug regimen characteristics were evaluated within this transmission model; improved novel regimen (a) efficacy increases the probability of durable cure. A high barrier to resistance (b) prevents acquisition of resistance to drugs in the novel regimen. Less preexisting resistance to components of the novel regimen (c) and fewer medication contraindications or treatment-limiting toxicities associated with the novel regimen (d) increase the number of patients for whom the novel regimen is prescribed. Shorter regimen duration (e) and greater ease of adherence (f) both increase treatment completion, and shortened duration also reduces the probability of cure after loss to follow-up at any given time point.</p

Illustration of resulting mortality trends and comparisons for different novel RS and RR TB regimens.

<p>Trajectories illustrate the median impact of novel regimens on the median projections of TB mortality. The impact of variation in each individual characteristic (such as efficacy, illustrated here) was evaluated as a fraction of the total impact of regimen optimization (distance between solid red and green trend lines). This evaluation was performed by optimizing the characteristic in question with an otherwise minimal baseline (difference between solid and dashed red lines, corresponding to the results shown in <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1002202#pmed.1002202.g003" target="_blank">Fig 3A and 3C</a>) and then by removing the characteristic from an otherwise optimized novel regimen (difference between solid and dashed green lines, corresponding to <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1002202#pmed.1002202.g003" target="_blank">Fig 3B and 3D</a>). Scale-up of the novel regimen was assumed to occur over 3 y following regimen introduction, and analyses were performed over the 10 y following the novel regimen’s introduction (including the 3 y of scale-up).</p