Diagnostic accuracy of TB-LAMP for pulmonary tuberculosis: a systematic review and meta-analysis.

BACKGROUND:The need for a rapid, molecular test to diagnose tuberculosis (TB) has prompted exploration of TB-LAMP (Eiken; Tokyo, Japan) for use in resource-limited settings. We conducted a systematic review to assess the accuracy of TB-LAMP as a diagnostic test for pulmonary TB. METHODS:We analyzed individual-level data for eligible patients from all studies of TB-LAMP conducted between Jan 2012 and October 2015 to compare the diagnostic accuracy of TB-LAMP with that of smear microscopy and Xpert MTB/RIF® using 3 reference standards of varying stringency. Pooled sensitivity and specificity and pooled differences in sensitivity and specificity were estimated using random effects meta-analysis. Study quality was evaluated using QUADAS-2. RESULTS:Four thousand seven hundred sixty individuals across 13 studies met eligibility criteria. Methodological quality was judged to be low for all studies. TB-LAMP had higher sensitivity than sputum smear microscopy (pooled sensitivity difference + 13·2, 95% CI 4·5-21·9%) and similar sensitivity to Xpert MTB/RIF (pooled sensitivity difference - 2·5, 95% CI -8·0 to + 2·9) using the most stringent reference standard available. Specificity of TB-LAMP was similar to that of sputum smear microscopy (pooled specificity difference - 1·8, 95% CI -3·8 to + 0·2) and Xpert MTB/RIF (pooled specificity difference 0·5, 95% CI -0·9 to + 1·8). CONCLUSIONS:From the perspective of diagnostic accuracy, TB-LAMP may be considered as an alternative test for sputum smear microscopy. Additional factors such as cost, feasibility, and acceptability in settings that continue to rely on sputum smear microscopy should be considered when deciding to adopt this technology. Xpert MTB/RIF should continue to be preferred in settings where resource and infrastructure requirements are adequate and where HIV co-infection or drug-resistance is of concern

Using the weighted area under the net benefit curve for decision curve analysis

Supplementary Material.docx. Includes the Appendix and two supplementary figures referred in the manuscript. (DOCX 327 kb

Comparison of multilevel modeling and the family-based association test for identifying genetic variants associated with systolic and diastolic blood pressure using Genetic Analysis Workshop 18 simulated data

Identifying genetic variants associated with complex diseases is an important task in genetic research. Although association studies based on unrelated individuals (ie, case-control genome-wide association studies) have successfully identified common single-nucleotide polymorphisms for many complex diseases, these studies are not so likely to identify rare genetic variants. In contrast, family-based association studies are particularly useful for identifying rare-variant associations. Recently, there has been some interest in employing multilevel models in family-based genetic association studies. However, the performance of such models in these studies, especially for longitudinal family-based sequence data, has not been fully investigated. Therefore, in this study, we investigated the performance of the multilevel model in the family-based genetic association analysis and compared it with the conventional family-based association test, by examining the powers and type I error rates of the 2 approaches using 3 data sets from the Genetic Analysis Workshop 18 simulated data: genome-wide association single-nucleotide polymorphism data, sequence data, and rare-variants-only data. Compared with the univariate family-based association test, the multilevel model had slightly higher power to identify most of the causal genetic variants using the genome-wide association single-nucleotide polymorphism data and sequence data. However, both approaches had low power to identify most of the causal single-nucleotide polymorphisms, especially those among the relatively rare genetic variants. Therefore, we suggest a unified method that combines both approaches and incorporates collapsing strategy, which may be more powerful than either approach alone for studying genetic associations using family-based data

Outlook for tuberculosis elimination in California: An individual-based stochastic model.

RationaleAs part of the End TB Strategy, the World Health Organization calls for low-tuberculosis (TB) incidence settings to achieve pre-elimination (<10 cases per million) and elimination (<1 case per million) by 2035 and 2050, respectively. These targets require testing and treatment for latent tuberculosis infection (LTBI).ObjectivesTo estimate the ability and costs of testing and treatment for LTBI to reach pre-elimination and elimination targets in California.MethodsWe created an individual-based epidemic model of TB, calibrated to historical cases. We evaluated the effects of increased testing (QuantiFERON-TB Gold) and treatment (three months of isoniazid and rifapentine). We analyzed four test and treat targeting strategies: (1) individuals with medical risk factors (MRF), (2) non-USB, (3) both non-USB and MRF, and (4) all Californians. For each strategy, we estimated the effects of increasing test and treat by a factor of 2, 4, or 10 from the base case. We estimated the number of TB cases occurring and prevented, and net and incremental costs from 2017 to 2065 in 2015 U.S. dollars. Efficacy, costs, adverse events, and treatment dropout were estimated from published data. We estimated the cost per case averted and per quality-adjusted life year (QALY) gained.Measurements and main resultsIn the base case, 106,000 TB cases are predicted to 2065. Pre-elimination was achieved by 2065 in three scenarios: a 10-fold increase in the non-USB and persons with MRF (by 2052), and 4- or 10-fold increase in all Californians (by 2058 and 2035, respectively). TB elimination was not achieved by any intervention scenario. The most aggressive strategy, 10-fold in all Californians, achieved a case rate of 8 (95% UI 4-16) per million by 2050. Of scenarios that reached pre-elimination, the incremental net cost was $20 billion (non-USB and MRF) to$48 billion. These had an incremental cost per QALY of $657,000 to$3.1 million. A more efficient but somewhat less effective single-lifetime test strategy reached as low as $80,000 per QALY.ConclusionsSubstantial gains can be made in TB control in coming years by scaling-up current testing and treatment in non-USB and those with medical risks

The Immunoepidemiology of Hookworm Infection in the Peruvian Amazon

Hookworm infection caused by Ancylostoma duodenale or Necator americanus is a significant global health threat, causing chronic anemia, malnutrition, developmental delay, enteritis, and increased susceptibility to non-parasitic diseases. One of the most prevalent of the neglected tropical diseases, hookworm infection affects almost 1 billion people worldwide, particularly in developing countries. Current methods for diagnosis and treatment of hookworm infection are largely the same as they have been for the past century. However, several recent advances in the molecular characterization of hookworm virulence factors now provide researchers with an improved understanding of disease pathogenesis, potential targets for treatment and novel antigens for vaccine development. In order to better understand hookworm pathophysiology and immunology in human populations, a comprehensive, cross-sectional immunoepidemiologic survey of approximately 200 villagers in a remote area of the Peruvian Amazon was conducted. Hookworm prevalence rates were found to approach 40% by microscopic diagnosis. Additionally, molecular speciation techniques showed that both A. duodenale and N. americanus are endemic to this region. Reagents from a laboratory model of hookworm disease were then utilized to characterize human immune responses to hookworm specific antigens. By studying the immunoepidemiology of an endemic community we have found that a laboratory strain of hookworm, Anyclostoma ceylanicum, is a useful tool for describing species specific immune responses to disease. This work lays the foundation for future development of improved hookworm diagnostic techniques by molecular and immunologic methods

Modelling the impact of social protection on tuberculosis: the S-PROTECT project.

BACKGROUND: Tackling the social determinants of Tuberculosis (TB) through social protection is a key element of the post-2015 End TB Strategy. However, evidence informing policies are still scarce. Mathematical modelling has the potential to contribute to fill this knowledge gap, but existing models are inadequate. The S-PROTECT consortium aimed to develop an innovative mathematical modelling approach to better understand the role of social protection to improve TB care, prevention and control. METHODS: S-PROTECT used a three-steps approach: 1) the development of a conceptual framework; 2) the extraction from this framework of three high-priority mechanistic pathways amenable for modelling; 3) the development of a revised version of a standard TB transmission model able to capture the structure of these pathways. As a test case we used the Bolsa Familia Programme (BFP), the Brazilian conditional cash transfer scheme. RESULTS: Assessing one of these pathways, we estimated that BFP can reduce TB prevalence by 4% by improving households income and thus their nutritional status. When looking at the direct impact via malnutrition (not income mediated) the impact was 33%. This variation was due to limited data availability, uncertainties on data transformation and the pathway approach taken. These results are preliminary and only aim to serve as illustrative example of the methodological challenges encountered in this first modelling attempt, nonetheless they suggest the potential added value of integrating TB standard of care with social protection strategies. CONCLUSIONS: Results are to be confirmed with further analysis. However, by developing a generalizable modelling framework, S-PROTECT proved that the modelling of social protection is complex, but doable and allowed to draw the research road map for the future in this field