Systematic review and meta-analysis. small intestinal bacterial overgrowth in chronic pancreatitis

BACKGROUND: Evidence on small intestinal bacterial overgrowth (SIBO) in patients with chronic pancreatitis (CP) is conflicting. AIM: The purpose of this study was to perform a systematic review and meta-analysis on the prevalence of SIBO in CP and to examine the relationship of SIBO with symptoms and nutritional status. METHODS: Case-control and cross-sectional studies investigating SIBO in CP patients were analysed. The prevalence of positive tests was pooled across studies, and the rate of positivity between CP cases and controls was calculated. RESULTS: In nine studies containing 336 CP patients, the pooled prevalence of SIBO was 36% (95% confidence interval (CI) 17-60%) with considerable heterogeneity (I2 = 91%). A sensitivity analysis excluding studies employing lactulose breath test gave a pooled prevalence of 21.7% (95% CI 12.7-34.5%) with lower heterogeneity (I2 = 56%). The odds ratio for a positive test in CP vs controls was 4.1 (95% CI 1.6-10.4) (I2 = 59.7%). The relationship between symptoms and SIBO in CP patients varied across studies, and the treatment of SIBO was associated with clinical improvement. CONCLUSIONS: One-third of CP patients have SIBO, with a significantly increased risk over controls, although results are heterogeneous, and studies carry several limitations. The impact of SIBO and its treatment in CP patients deserve further investigation

The Aetiology of Pneumonia Associated with Measles in Bantu Children

Antemortem and postmortem lung puncture aspiration was performed in Bantu children with pneumonia associated with measles. The superinfecting organisms were commonly Staphylococcus pyogenes, but from one-third of the patients Gramnegative organisms were cultured. These organisms were rarely sensitive to ampicillin or streptomycin. Antibiotic therapy should be tailored accordingly.S. Afr. Med. J., 45, 1402 (1971

Hierarchical network meta-analysis models to address sparsity of events and differing treatment classifications with regard to adverse outcomes

This is the accepted version of the article, which has been published in final form at DOI: 10.1002/sim.6131.Meta-analysis for adverse events resulting from medical interventions has many challenges, in part due to small numbers of such events within primary studies. Furthermore, variability in drug dose, potential differences between drugs within the same pharmaceutical class and multiple indications for a specific treatment can all add to the complexity of the evidence base. This paper explores the use of synthesis methods, incorporating mixed treatment comparisons, to estimate the risk of adverse events for a medical intervention, while acknowledging and modelling the complexity of the structure of the evidence base. The motivating example was the effect on malignancy of three anti-tumour necrosis factor (anti-TNF) drugs (etanercept, adalimumab and infliximab) indicated to treat rheumatoid arthritis. Using data derived from 13 primary studies, a series of meta-analysis models of increasing complexity were applied. Models ranged from a straightforward comparison of anti-TNF against non-anti-TNF controls, to more complex models in which a treatment was defined by individual drug and its dose. Hierarchical models to allow 'borrowing strength' across treatment classes and dose levels, and models involving constraints on the impact of dose level, are described. These models provide a flexible approach to estimating sparse, often adverse, outcomes associated with interventions. Each model makes its own set of assumptions, and approaches to assessing goodness of fit of the various models will usually be extremely limited in their effectiveness, due to the sparse nature of the data. Both methodological and clinical considerations are required to fit realistically complex models in this area and to evaluate their appropriateness.Partially supported by a National Institute for Health Research Senior Investigator Awar

Quantitative evidence synthesis methods for the assessment of the effectiveness of treatment sequences for clinical and economic decision-making: a review and taxonomy of simplifying assumptions

Sequential use of alternative treatments for chronic conditions represents a complex intervention pathway; previous treatment and patient characteristics affect both the choice and effectiveness of subsequent treatments. This paper critically explores the methods for quantitative evidence synthesis of the effectiveness of sequential treatment options within a health technology assessment (HTA) or similar process. It covers methods for developing summary estimates of clinical effectiveness or the clinical inputs for the cost-effectiveness assessment and can encompass any disease condition. A comprehensive review of current approaches is presented, which considers meta-analytic methods for assessing the clinical effectiveness of treatment sequences and decision-analytic modelling approaches used to evaluate the effectiveness of treatment sequences. Estimating the effectiveness of a sequence of treatments is not straightforward or trivial and is severely hampered by the limitations of the evidence base. Randomised controlled trials (RCTs) of sequences were often absent or very limited. In the absence of sufficient RCTs of whole sequences, there is no single best way to evaluate treatment sequences; however, some approaches could be re-used or adapted, sharing ideas across different disease conditions. Each has advantages and disadvantages, and is influenced by the evidence available, extent of treatment sequences (number of treatment lines or permutations), and complexity of the decision problem. Due to the scarcity of data, modelling studies applied simplifying assumptions to data on discrete treatments. A taxonomy for all possible assumptions was developed, providing a unique resource to aid the critique of existing decision-analytic models

Do Economic Evaluations in Primary Care Prevention and the Management of Hypertension Conform to Good Practice Guidelines? A Systematic Review

Background: Results of previous research have identified the need for further investigation into the compliance with good practice guidelines for current decision-analytic modeling (DAM). Objective: To identify the extent to which recent model-based economic evaluations of interventions focused on lowering the blood pressure (BP) of patients with hypertension conform to published guidelines for DAM in health care using a five-dimension framework developed to assess compliance to DAM guidelines. Methods: A systematic review of English language articles was undertaken to identify published model-based economic evaluations that examined interventions aimed at lowering BP. The review covered the period January 2000 to March 2015 and included the following electronic bibliographic databases: EMBASE and Medline via Ovid interface and the Centre for Reviews and Dissemination’s (CRD) NHS-EED. Data were extracted based on different components of good practice across five dimensions utilizing a framework to assess compliance to DAM guidelines. Results: Thirteen articles were included in this review. The review found limited compliance to good practice DAM guidelines, which was most frequently justified by the lack of data. Conclusions: The assessment of structural uncertainty cannot yet be considered common practice in primary prevention and management of hypertension, and researchers seem to face difficulties with identifying sources of structural uncertainty and then handling them correctly. Additional guidelines are needed to aid researchers in identifying and managing sources of potential structural uncertainty. Adherence to guidelines is not always possible and it does pose challenges, in particular when there are limitations due to data availability that restrict, for example, a validation process

Estimating the cost-effectiveness of detecting cases of chronic hepatitis C infection on reception into prison

Background In England and Wales where less than 1% of the population are Injecting drug users (IDUs), 97% of HCV reports are attributed to injecting drug use. As over 60% of the IDU population will have been imprisoned by the age of 30 years, prison may provide a good location in which to offer HCV screening and treatment. The aim of this work is to examine the cost effectiveness of a number of alternative HCV case-finding strategies on prison reception Methods A decision analysis model embedded in a model of the flow of IDUs through prison was used to estimate the cost effectiveness of a number of alternative case-finding strategies. The model estimates the average cost of identifying a new case of HCV from the perspective of the health care provider and how these estimates may evolve over time. Results The results suggest that administering verbal screening for a past positive HCV test and for ever having engaged in illicit drug use prior to the administering of ELISA and PCR tests can have a significant impact on the cost effectiveness of HCV case-finding strategies on prison reception; the discounted cost in 2017 being £2,102 per new HCV case detected compared to £3,107 when no verbal screening is employed. Conclusion The work here demonstrates the importance of targeting those individuals that have ever engaged in illicit drug use for HCV testing in prisons, these individuals can then be targeted for future intervention measures such as treatment or monitored to prevent future transmission

Designing a Predictive Coding System for Electronic Discovery

Not long ago, the concept of using predictive coding and other technologies to assist with the electronic discovery process seemed revolutionary. Da Silva Moore and Global Aerospace stand as the first major cases where judges strongly supported predictive coding.1-2 A recent Indiana case recognized it as a useful method for reducing the amount of potentially relevant evidence that has to be searched and culled.3 Within just a few short years, using predictive coding as part of an electronic discovery process is now considered acceptable and perhaps even expected. It is not difficult to appreciate the advantages of predictive coding and its superiority over a manual process at various steps of electronic discovery, particularly during the review step.4-11 However, questions still remain about the efficacy of the predictive coding process and the tools that are available.12-13 Because the use of predictive coding systems in law is still in its infancy, it presents us with an opportunity to design something that will not only take advantage of the power of big data and computational algorithms, but that will also incorporate design and usability principles to provide an attractive and easy-to-use interface for lawyers to interact with. Predictive coding uses natural language processing and other mathematical models to enhance search results, but the essence of these systems is that they actually learn and the precision of the retrieval improves as additional collections of evidence are entered. Behind-the-scenes will be a repository where all of the evidence for a case resides. Our system will assist the lawyers in reducing the time and cost of an electronic discovery process as well as minimize the chances for mistakes in determining which evidence is relevant to a case and which evidence can be withheld under attorney-client privilege, as attorney work-product or another confidentiality doctrine. 1. Da Silva Moore v. Publicis Groupe & MSL Group, No. 11 Civ. 1279, 2012 WL 607412 (ALC) (AJP) (S.D.N.Y. Feb. 24, 2012). 2. Global Aerospace, Inc. v. Landow Aviation, L.P., No. CL 61040 (Vir. Cir. Ct. Apr. 23, 2012). 3. In re Biomet, 2013 WL 1729682 (N.D. Ind. Apr. 18, 2013). 4. Alison Silverstein and Geoffrey Vance. E-Discovery Myth Busters: Why Predictive Coding is Safe, Successful and Smart. Peer to Peer, Vol. 29, No. 4, December 2013, pp. 66-69. 5. John Papageorge. Predictive Coding Gaining Support in Courts. Indiana Lawyer, January 29-February 11, 2014, p. 8. 6. Adam M. Acosta. Predictive Coding: The Beginning of a New E-Discovery Era. Res Gestae, October 2012, pp. 8-14. 7. Ajith (AJ) Samuel. Analytics Driving the E-Discovery Process. Peer to Peer, Vol. 28, No. 2, June 2012. 8. Richard Acello. Beyond Prediction: Technology-Assisted Review Enters the Lexicon. ABA Journal, August 2012, pp. 37, 70. 9. Barry Murphy. The Rise of Technology-Assisted Review (TAR). Peer to Peer, Vol. 28, No. 2, June 2012, pp. 10. Brian Ingram. Controlling E-Discovery Costs in a Big Data World. Peer to Peer, Vol. 29, No. 1, March 2013. 11. Hal Marcus and Susan Stone. Beyond Predictive Coding - The True Power of Data Analytics [webinar]. International Legal Technology Association, May 19, 2015. 12. Jessica Watts and Gareth Evans. Predictive Coding in the Real World [webinar]. International Legal Technology Association, August 5, 2015. 13. Danielle Bethea. Predictive Coding: Revolutionizing Review or Still Gaining Momentum? Litigation and Practice Support: ITLA White Paper, International Legal Technology Association, June 2014

Integrating Qualitative Techniques in Model Development: A Case Study Using the Framework Approach

Background Despite their potential, there is limited uptake of formal qualitative methods in model development by modellers and health economists. The aim of this case study was to highlight in a real-world context how a qualitative approach has been applied to gain insight into current practice (delineating existing care pathways) for typhoid fever in Ghana, which can then assist in model structure conceptualisation in a model-based cost-effectiveness analysis. Methods The perspectives of a range of healthcare professionals working in different settings and across different practices in the Eastern region of Ghana were captured with a self-administered survey using open-ended questions and analysed using the framework method. Results A total of 51 completed questionnaires were retrieved representing a 73% response rate. It was found that two main care pathways for typhoid fever exist in Ghana and there was no consensus on how a new test might be applied to the existing pathways. Conclusion The two settings in Ghana have different care pathways and any cost-effectiveness analysis should consider the alternative pathways separately. This study demonstrated that framework analysis is a qualitative methodology that is likely to be accessible and feasible across a wide range of health economic settings