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Molecular data are now widely used in epidemiological studies to investigate the transmission, distribution, biology, and diversity of pathogens. Our objective was to establish recommendations to support good scientific reporting of molecular epidemiological studies to encourage authors to consider specific threats to valid inference. The statement Strengthening the Reporting of Molecular Epidemiology for Infectious Diseases (STROME-ID) builds upon the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) initiative. The STROME-ID statement was developed by a working group of epidemiologists, statisticians, bioinformaticians, virologists, and microbiologists with expertise in control of infection and communicable diseases. The statement focuses on issues relating to the reporting of epidemiological studies of infectious diseases using molecular data that were not addressed by STROBE. STROME-ID addresses terminology, measures of genetic diversity within pathogen populations, laboratory methods, sample collection, use of molecular markers, molecular clocks, timeframe, multiple-strain infections, non-independence of infectious-disease data, missing data, ascertainment bias, consistency between molecular and epidemiological data, and ethical considerations with respect to infectious-disease research. In total, 20 items were added to the 22 item STROBE checklist. When used, the STROME-ID recommendations should advance the quality and transparency of scientific reporting, with clear benefits for evidence reviews and health-policy decision making.

\"Futurism & Europe: The Aesthetics of a New World examines for the first time the many interconnections between Futurism and other European avant-gardes as varied as the Bauhaus in Germany, De Stijl in the Netherlands, Omega Workshops in Britain, Constructivism in Russia and Esprit Nouveau in France. Featuring over twenty essays by an international team of experts, this expansive book covers a range of topics and mediums including painting, sculpture, architecture, interior and stage designs, graphic work, fashion, theatre and cinema, as well as a diverse variety of functional objects from furniture and carpets to ceramics and toys. Spanning various avant-gardes from 1912 to 1939, artists featured include Italian futurists such as Giacomo Balla, Umberto Boccioni and Fortunato Depero, alongside other European artists including Sonia Delaunay, Le Corbusier, Fernand Léger, Walter Gropius, Alexander Rodchenko, Fritz Lang, László Moholy-Nagy, Wassily Kandinsky, Hans Arp, Duncan Grant, Natalia Goncharova and Vladimir Tatlin. Broad in scope, this pioneering book examines the intersections between Futurism and other European avant-garde movements in their shared quest for a new aesthetic, triggering a lively exchange of new ideas, friction and rivalry.\"-- Page 4 of cover.

Sarah Fortune and colleagues report that Mycobacterium tuberculosis strains from lineage 2 acquire drug resistance in vitro more rapidly than strains from lineage 4 and show that this correlates with a higher in vivo mutation rate, as estimated from whole-genome sequencing of clinical isolates. They develop a stochastic mathematical model of the within-host evolution of drug resistance, using these mutation rate estimates to predict the rates of emergence of resistance in individuals with tuberculosis. A key question in tuberculosis control is why some strains of M. tuberculosis are preferentially associated with resistance to multiple drugs. We demonstrate that M. tuberculosis strains from lineage 2 (East Asian lineage and Beijing sublineage) acquire drug resistances in vitro more rapidly than M. tuberculosis strains from lineage 4 (Euro-American lineage) and that this higher rate can be attributed to a higher mutation rate. Moreover, the in vitro mutation rate correlates well with the bacterial mutation rate in humans as determined by whole-genome sequencing of clinical isolates. Finally, using a stochastic mathematical model, we demonstrate that the observed differences in mutation rate predict a substantially higher probability that patients infected with a drug-susceptible lineage 2 strain will harbor multidrug-resistant bacteria at the time of diagnosis. These data suggest that interventions to prevent the emergence of drug-resistant tuberculosis should target bacterial as well as treatment-related risk factors.

Background People successfully completing treatment for tuberculosis remain at elevated risk for recurrent disease, either from relapse or reinfection. Identifying risk factors for recurrent tuberculosis may help target post-tuberculosis screening and care. Methods We enrolled 500 patients with smear-positive pulmonary tuberculosis in South Africa and collected baseline data on demographics, clinical presentation and sputum mycobacterial cultures for 24-loci mycobacterial interspersed repetitive unit-variable number tandem repeat (MIRU-VNTR) typing. We used routinely-collected administrative data to identify recurrent episodes of tuberculosis occurring over a median of six years after successful treatment completion. Results Of 500 patients initially enrolled, 333 (79%) successfully completed treatment for tuberculosis. During the follow-up period 35 patients with successful treatment (11%) experienced a bacteriologically confirmed tuberculosis recurrence. In our Cox proportional hazards model, a 3+ AFB sputum smear grade was significantly associated with recurrent tuberculosis with a hazard ratio of 3.33 (95% CI 1.44–7.7). The presence of polyclonal M. tuberculosis infection at baseline had a hazard ratio for recurrence of 1.96 (95% CI 0.86–4.48). Conclusion Our results indicate that AFB smear grade is independently associated with tuberculosis recurrence after successful treatment for an initial episode while the association between polyclonal M. tuberculosis infection and increased risk of recurrence appears possible.

The Xpert MTB/RIF test enables rapid detection of tuberculosis (TB) and rifampicin resistance. The World Health Organization recommends Xpert for initial diagnosis in individuals suspected of having multidrug-resistant TB (MDR-TB) or HIV-associated TB, and many countries are moving quickly toward adopting Xpert. As roll-out proceeds, it is essential to understand the potential health impact and cost-effectiveness of diagnostic strategies based on Xpert. We evaluated potential health and economic consequences of implementing Xpert in five southern African countries--Botswana, Lesotho, Namibia, South Africa, and Swaziland--where drug resistance and TB-HIV coinfection are prevalent. Using a calibrated, dynamic mathematical model, we compared the status quo diagnostic algorithm, emphasizing sputum smear, against an algorithm incorporating Xpert for initial diagnosis. Results were projected over 10- and 20-y time periods starting from 2012. Compared to status quo, implementation of Xpert would avert 132,000 (95% CI: 55,000-284,000) TB cases and 182,000 (97,000-302,000) TB deaths in southern Africa over the 10 y following introduction, and would reduce prevalence by 28% (14%-40%) by 2022, with more modest reductions in incidence. Health system costs are projected to increase substantially with Xpert, by US$460 million (294-699 million) over 10 y. Antiretroviral therapy for HIV represents a substantial fraction of these additional costs, because of improved survival in TB/HIV-infected populations through better TB case-finding and treatment. Costs for treating MDR-TB are also expected to rise significantly with Xpert scale-up. Relative to status quo, Xpert has an estimated cost-effectiveness of US$959 (633-1,485) per disability-adjusted life-year averted over 10 y. Across countries, cost-effectiveness ratios ranged from US$792 (482-1,785) in Swaziland to US$1,257 (767-2,276) in Botswana. Assessing outcomes over a 10-y period focuses on the near-term consequences of Xpert adoption, but the cost-effectiveness results are conservative, with cost-effectiveness ratios assessed over a 20-y time horizon approximately 20% lower than the 10-y values. Introduction of Xpert could substantially change TB morbidity and mortality through improved case-finding and treatment, with more limited impact on long-term transmission dynamics. Despite extant uncertainty about TB natural history and intervention impact in southern Africa, adoption of Xpert evidently offers reasonable value for its cost, based on conventional benchmarks for cost-effectiveness. However, the additional financial burden would be substantial, including significant increases in costs for treating HIV and MDR-TB. Given the fundamental influence of HIV on TB dynamics and intervention costs, care should be taken when interpreting the results of this analysis outside of settings with high HIV prevalence.

Multidrug-resistant tuberculosis threatens to reverse recent reductions in global tuberculosis incidence. Although children younger than 15 years constitute more than 25% of the worldwide population, the global incidence of multidrug-resistant tuberculosis disease in children has never been quantified. We aimed to estimate the regional and global annual incidence of multidrug-resistant tuberculosis in children. We developed two models: one to estimate the setting-specific risk of multidrug-resistant tuberculosis among child cases of tuberculosis, and a second to estimate the setting-specific incidence of tuberculosis disease in children. The model for risk of multidrug-resistant tuberculosis among children with tuberculosis needed a systematic literature review. We multiplied the setting-specific estimates of multidrug-resistant tuberculosis risk and tuberculosis incidence to estimate regional and global incidence of multidrug-resistant tuberculosis disease in children in 2010. We identified 3403 papers, of which 97 studies met inclusion criteria for the systematic review of risk of multidrug-resistant tuberculosis. 31 studies reported the risk of multidrug-resistant tuberculosis in both children and treatment-naive adults with tuberculosis and were used for evaluation of the linear association between multidrug-resistant disease risk in these two patient groups. We identified that the setting-specific risk of multidrug-resistant tuberculosis was nearly identical in children and treatment-naive adults with tuberculosis, consistent with the assertion that multidrug-resistant disease in both groups reflects the local risk of transmitted multidrug-resistant tuberculosis. After application of these calculated risks, we estimated that around 999 792 (95% CI 937 877–1 055 414) children developed tuberculosis disease in 2010, of whom 31 948 (25 594–38 663) had multidrug-resistant disease. Our estimates underscore that many cases of tuberculosis and multidrug-resistant tuberculosis disease are not being detected in children. Future estimates can be refined as more and better tuberculosis data and new diagnostic instruments become available. US National Institutes of Health, the Helmut Wolfgang Schumann Fellowship in Preventive Medicine at Harvard Medical School, the Norman E Zinberg Fellowship at Harvard Medical School, and the Doris and Howard Hiatt Residency in Global Health Equity and Internal Medicine at the Brigham and Women's Hospital.

Understanding how our use of antimicrobial drugs shapes future levels of drug resistance is crucial. Recently, there has been debate over whether an aggressive (i.e., high dose) or more moderate (i.e., lower dose) treatment of individuals will most limit the emergence and spread of resistant bacteria. In this study, we demonstrate how one can understand and resolve these apparently contradictory conclusions. We show that a key determinant of which treatment strategy will perform best at the individual level is the extent of effective competition between resistant and sensitive pathogens within a host. We extend our analysis to the community level, exploring the spectrum between strict inter-strain competition and strain independence. From this perspective as well, we find that the magnitude of effective competition between resistant and sensitive strains determines whether an aggressive approach or moderate approach minimizes the burden of resistance in the population. Antibiotics are chemical compounds used to treat bacterial infections. The discovery of antibiotics, starting with penicillin in 1929, revolutionized medicine, making it possible to cure or prevent life-threatening infections such as tetanus and pneumonia. However, many bacteria have become resistant to one or more antibiotics and so can no longer be killed by these drugs. The emergence of antibiotic resistance reflects an evolutionary process that occurs during antibiotic treatment. While the antibiotic will kill most bacteria, some bacteria may naturally have a feature or genetic mutation that allows them to survive in the presence of the antibiotic. These bacteria then reproduce and pass on their resistant traits, eventually leading to the emergence of a new antibiotic-resistant strain of bacteria. Once a resistant strain exists it may be able to spread from one person to another. There is conflicting evidence about how best to prevent antibiotic-resistant bacteria from evolving and spreading. The results of some experiments suggest that treating bacteria with large doses of antibiotics early in an infection is the most effective way to optimize treatment and minimize the risk of an antibiotic-resistant strain developing. However, other studies suggest that exposing bacteria to high levels of antibiotics more efficiently selects for resistance; in this case a more moderate approach should be used when treating bacterial infections. Here, Colijn and Cohen present a mathematical model that suggests that the natural competition between the antibiotic-resistant and antibiotic-sensitive strains of bacteria influence which treatment strategy should be taken. Strains were modeled both within individual hosts and spreading in a community of individuals. In the models, aggressive antibiotic treatment is most effective (in that it minimizes antibiotic resistance) when the antibiotic-resistant strain either does not experience strong competition from the non-resistant strains of bacteria or is not fit enough to be a good competitor. However, a more moderate treatment is appropriate when the two strains are competing and the antibiotic-resistant strain is a fit competitor. Competition may mean that moderate treatment is best to avoid resistance at the community level, even in situations when aggressive treatment is likely best for individuals. Two important future challenges are to better understand the diversity of strains in bacterial infections, and to develop tools to measure to what extent strains are effectively competing with each other.

Each year in the United States, influenza causes illness in 9.2 to 35.6 million individuals and is responsible for 12,000 to 56,000 deaths. The U.S. Centers for Disease Control and Prevention (CDC) tracks influenza activity through a national surveillance network. These data are only available after a delay of 1 to 2 weeks, and thus influenza epidemiologists and transmission modelers have explored the use of other data sources to produce more timely estimates and predictions of influenza activity. We evaluated whether data collected from a national commercial network of influenza diagnostic machines could produce valid estimates of the current burden and help to predict influenza trends in the United States. Quidel Corporation provided us with de-identified influenza test results transmitted in real-time from a national network of influenza test machines called the Influenza Test System (ITS). We used this ITS dataset to estimate and predict influenza-like illness (ILI) activity in the United States over the 2015-2016 and 2016-2017 influenza seasons. First, we developed linear logistic models on national and regional geographic scales that accurately estimated two CDC influenza metrics: the proportion of influenza test results that are positive and the proportion of physician visits that are ILI-related. We then used our estimated ILI-related proportion of physician visits in transmission models to produce improved predictions of influenza trends in the United States at both the regional and national scale. These findings suggest that ITS can be leveraged to improve “nowcasts” and short-term forecasts of U.S. influenza activity.