Treating and Preventing Influenza in Aged Care Facilities: A Cluster Randomised Controlled Trial

PMCID: PMC3474842This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Gene therapy for monogenic liver diseases: clinical successes, current challenges and future prospects

Over the last decade, pioneering liver-directed gene therapy trials for haemophilia B have achieved sustained clinical improvement after a single systemic injection of adeno-associated virus (AAV) derived vectors encoding the human factor IX cDNA. These trials demonstrate the potential of AAV technology to provide long-lasting clinical benefit in the treatment of monogenic liver disorders. Indeed, with more than ten ongoing or planned clinical trials for haemophilia A and B and dozens of trials planned for other inherited genetic/metabolic liver diseases, clinical translation is expanding rapidly. Gene therapy is likely to become an option for routine care of a subset of severe inherited genetic/metabolic liver diseases in the relatively near term. In this review, we aim to summarise the milestones in the development of gene therapy, present the different vector tools and their clinical applications for liver-directed gene therapy. AAV-derived vectors are emerging as the leading candidates for clinical translation of gene delivery to the liver. Therefore, we focus on clinical applications of AAV vectors in providing the most recent update on clinical outcomes of completed and ongoing gene therapy trials and comment on the current challenges that the field is facing for large-scale clinical translation. There is clearly an urgent need for more efficient therapies in many severe monogenic liver disorders, which will require careful risk-benefit analysis for each indication, especially in paediatrics

Towards effective outbreak detection: a qualitative study to identify factors affecting nurses’ early warning surveillance practice in Solomon Islands

Abstract Background Intelligence generated by a surveillance system is dependent on the quality of data that are collected. We investigated the knowledge, attitudes and practices of nurses responsible for outbreak early warning surveillance data collection in Solomon Islands to identify factors that influence their ability to perform surveillance-related tasks with rigour. Methods We interviewed 12 purposively selected surveillance nurses and conducted inductive analysis on resulting data. Results Interviewees were knowledgeable and willing to contribute to the surveillance system. Constraining factors included the perception that surveillance was less important than patient care and could be ‘deferred’ during busy periods and wide variability in the application of case definitions. Motivating factors were frequent in-clinic training, formal recognition for good performance, incentives and designation of a focal point. Nurses held mixed views about the effect of mobile technologies on surveillance practice. Conclusions This study identified several challenges to consistent and accurate data collection and reporting. Engagement of different parts of the health system, including human resources and health facilities’ management, is needed to address these challenges

Surveillance for outbreaks of influenza-like illness in the institutionalized elderly

Respiratory outbreaks are common in aged-care facilities (ACFs), are both underreported and frequently identified late, and are often associated with considerable burden of illness and death. There is emerging evidence that active surveillance coupled with early and systematic intervention can reduce this burden. Active surveillance for influenza-like illness and rapid diagnosis of influenza were established in 16 ACFs in Sydney, Australia, prior to the winter of 2006. A point-of-care influenza test and laboratory direct immunofluorescence tests for common respiratory viruses were used for diagnosis. We achieved early identification of seven respiratory disease outbreaks, two of which were caused by influenza. For the influenza outbreaks, antiviral treatment and prophylaxis were initiated 4-6 days from symptom onset in the primary case. A simple active surveillance system for influenza was successfully implemented and resulted in early detection of influenza and other respiratory disease outbreaks. This enabled earlier implementation of prevention and control measures and increased the potential effectiveness of anti-influenza chemoprophylaxis

Epidemic surveillance in a low resource setting: lessons from an evaluation of the Solomon Islands syndromic surveillance system, 2017

Abstract Background Solomon Islands is one of the least developed countries in the world. Recognising that timely detection of outbreaks is needed to enable early and effective response to disease outbreaks, the Solomon Islands government introduced a simple syndromic surveillance system in 2011. We conducted the first evaluation of the system and the first exploration of a national experience within the broader multi-country Pacific Syndromic Surveillance System to determine if it is meeting its objectives and to identify opportunities for improvement. Methods We used a multi-method approach involving retrospective data collection and statistical analysis, modelling, qualitative research and observational methods. Results We found that the system was well accepted, highly relied upon and designed to account for contextual limitations. We found the syndromic algorithm used to identify outbreaks was moderately sensitive, detecting 11.8% (IQR: 6.3–25.0%), 21.3% (IQR: 10.3–36.8%), 27.5% (IQR: 12.8–52.3%) and 40.5% (IQR: 13.5–65.7%) of outbreaks that caused small, moderate, large and very large increases in case presentations to health facilities, respectively. The false alert rate was 10.8% (IQR: 4.8–24.5%). Rural coverage of the system was poor. Limited workforce, surveillance resourcing and other ‘upstream’ health system factors constrained performance. Conclusions The system has made a significant contribution to public health security in Solomon Islands, but remains insufficiently sensitive to detect small-moderate sized outbreaks and hence should not be relied upon as a stand-alone surveillance strategy. Rather, the system should sit within a complementary suite of early warning surveillance activities including event-based, in-patient- and laboratory-based surveillance methods. Future investments need to find a balance between actions to address the technical and systems issues that constrain performance while maintaining simplicity and hence sustainability

Number of incident cases (confirmed+probable) per 100 people (residents & staff) in T and T&P ACFs.

<p>Number of incident cases (confirmed+probable) per 100 people (residents & staff) in T and T&P ACFs.</p

Mathematical modeling.

<p>Efficacy of Treatment only (“T”) vs Treatment and Prophylaxis (“T&P”) strategies for preventing transmission of influenza, assuming a latent period of 1 day and a serial interval of 2.5 days. Efficacy is expressed as the percentage of secondary cases prevented by the intervention.</p><p>“Fraction with prior immunity” is necessarily lower than fraction vaccinated, as the vaccine is not 100% effective and non-vaccine strains may be responsible for outbreaks. In our modeling, as 45% of ACF C residents were infected, our data can only be used to model up to 55% assumed fraction with prior immunity.</p

A comparison of the baseline characteristics of consented staff who were involved in the nine influenza outbreaks compared to those in facilities with no confirmed influenza outbreaks.

<p>A comparison of the baseline characteristics of consented staff who were involved in the nine influenza outbreaks compared to those in facilities with no confirmed influenza outbreaks.</p

Baseline characteristics of consented residents in outbreak versus non-outbreak facilities.

<p>Baseline characteristics of consented residents in outbreak versus non-outbreak facilities.</p