Browsing Articles and other publications by RIVM employees by Authors
Can we predict tuberculosis cure? What tools are available?Goletti, Delia; Lindestam Arlehamn, Cecilia S; Scriba, Thomas J; Anthony, Richard; Cirillo, Daniela Maria; Alonzi, Tonino; Denkinger, Claudia M; Cobelens, Frank (2018-11)Antibiotic treatment of tuberculosis takes ≥6 months, putting a major burden on patients and health systems in large parts of the world. Treatment beyond 2 months is needed to prevent tuberculosis relapse by clearing remaining, drug-tolerant Mycobacterium tuberculosis bacilli. However, the majority of patients treated for only 2-3 months will cure without relapse and do not need prolonged treatment. Assays that can identify these patients at an early stage of treatment may significantly help reduce the treatment burden, while a test to identify those patients who will fail treatment may help target host-directed therapies.In this review we summarise the state of the art with regard to discovery of biomarkers that predict relapse-free cure for pulmonary tuberculosis. Positron emission tomography/computed tomography scanning to measure pulmonary inflammation enhances our understanding of "cure". Several microbiological and immunological markers seem promising; however, they still need a formal validation. In parallel, new research strategies are needed to generate reliable tests.
IP-10 Kinetics in the First Week of Therapy are Strongly Associated with Bacteriological Confirmation of Tuberculosis Diagnosis in HIV-Infected Patients.García-Basteiro, Alberto L; Mambuque, Edson; den Hertog, Alice; Saavedra, Belén; Cuamba, Inocencia; Oliveras, Laura; Blanco, Silvia; Bulo, Helder; Brew, Joe; Cuevas, Luis E; Cobelens, Frank; Nhabomba, Augusto; Anthony, Richard (2017-10-30)Simple effective tools to monitor the long treatment of tuberculosis (TB) are lacking. Easily measured host derived biomarkers have been identified but need to be validated in larger studies and different population groups. Here we investigate the early response in IP-10 levels (between day 0 and day 7 of TB therapy) to identify bacteriological status at diagnosis among 127 HIV-infected patients starting TB treatment. All participants were then classified as responding or not responding to treatment blindly using a previously described IP-10 kinetic algorithm. There were 77 bacteriologically confirmed cases and 41 Xpert MTB/RIF® and culture negative cases. Most participants had a measurable decline in IP-10 during the first 7 days of therapy. Bacteriologically confirmed cases were more likely to have high IP-10 levels at D0 and had a steeper decline than clinically diagnosed cases (mean decline difference 2231 pg/dl, 95% CI: 897-3566, p = 0.0013). Bacteriologically confirmed cases were more likely to have a measurable decline in IP-10 at day 7 than clinically diagnosed cases (48/77 (62.3%) vs 13/41 (31.7%), p < 0.001). This study confirms the association between a decrease in IP-10 levels during the first week of treatment and a bacteriological confirmation at diagnosis in a large cohort of HIV positive patients.
Latent tuberculosis infection in foreign-born communities: Import vs. transmission in The Netherlands derived through mathematical modelling.Korthals Altes, Hester; Kloet, Serieke; Cobelens, Frank; Bootsma, Martin (2018)While tuberculosis (TB) represents a significant disease burden worldwide, low-incidence countries strive to reach the WHO target of pre-elimination by 2035. Screening for TB in immigrants is an important component of the strategy to reduce the TB burden in low-incidence settings. An important option is the screening and preventive treatment of latent TB infection (LTBI). Whether this policy is worthwhile depends on the extent of transmission within the country, and introduction of new cases through import. Mathematical transmission models of TB have been used to identify key parameters in the epidemiology of TB and estimate transmission rates. An important application has also been to investigate the consequences of policy scenarios. Here, we formulate a mathematical model for TB transmission within the Netherlands to estimate the size of the pool of latent infections, and to determine the share of importation-either through immigration or travel- versus transmission within the Netherlands. We take into account importation of infections due to immigration, and travel to the country of origin, focusing on the three ethnicities most represented among foreign-born TB cases (after exclusion of those overrepresented among asylum seekers): Moroccans, Turkish and Indonesians. We fit a system of ordinary differential equations to the data from the Netherlands Tuberculosis Registry on (extra-)pulmonary TB cases from 1995-2013. We estimate that about 27% of Moroccans, 25% of Indonesians, and 16% of Turkish, are latently infected. Furthermore, we find that for all three foreign-born communities, immigration is the most important source of LTBI, but the extent of within-country transmission is much lower (about half) for the Turkish and Indonesian communities than for the Moroccan. This would imply that contact investigation would have a greater yield in the latter community than in the former. Travel remains a minor factor contributing LTBI, suggesting that targeting returning travelers might be less effective at preventing LTBI than immigrants upon entry in the country.