Cluster randomized test-negative design (CR-TND) trials: a novel and efficient method to assess the efficacy of community level dengue interventions

Cluster randomized trials are the gold standard for assessing efficacy of community-level interventions, such as vector control strategies against dengue. We describe a novel cluster randomized trial methodology with a test-negative design, which offers advantages over traditional approaches. It utilizes outcome-based sampling of patients presenting with a syndrome consistent with the disease of interest, who are subsequently classified as test-positive cases or test-negative controls on the basis of diagnostic testing. We use simulations of a cluster trial to demonstrate validity of efficacy estimates under the test-negative approach. This demonstrates that, provided study arms are balanced for both test-negative and test-positive illness at baseline and that other test-negative design assumptions are met, the efficacy estimates closely match true efficacy. We also briefly discuss analytical considerations for an odds ratio-based effect estimate arising from clustered data, and outline potential approaches to analysis. We conclude that application of the test-negative design to certain cluster randomized trials could increase their efficiency and ease of implementation

Disease prevention versus data privacy : using landcover maps to inform spatial epidemic models

The availability of epidemiological data in the early stages of an outbreak of an infectious disease is vital for modelers to make accurate predictions regarding the likely spread of disease and preferred intervention strategies. However, in some countries, the necessary demographic data are only available at an aggregate scale. We investigated the ability of models of livestock infectious diseases to predict epidemic spread and obtain optimal control policies in the event of imperfect, aggregated data. Taking a geographic information approach, we used land cover data to predict UK farm locations and investigated the influence of using these synthetic location data sets upon epidemiological predictions in the event of an outbreak of foot-and-mouth disease. When broadly classified land cover data were used to create synthetic farm locations, model predictions deviated significantly from those simulated on true data. However, when more resolved subclass land use data were used, moderate to highly accurate predictions of epidemic size, duration and optimal vaccination and ring culling strategies were obtained. This suggests that a geographic information approach may be useful where individual farm-level data are not available, to allow predictive analyses to be carried out regarding the likely spread of disease. This method can also be used for contingency planning in collaboration with policy makers to determine preferred control strategies in the event of a future outbreak of infectious disease in livestock

Vaccination against Foot-and-mouth disease : do initial conditions affect its benefit?

When facing incursion of a major livestock infectious disease, the decision to implement a vaccination programme is made at the national level. To make this decision, governments must consider whether the benefits of vaccination are sufficient to outweigh potential additional costs, including further trade restrictions that may be imposed due to the implementation of vaccination. However, little consensus exists on the factors triggering its implementation on the field. This work explores the effect of several triggers in the implementation of a reactive vaccination-to-live policy when facing epidemics of foot-and-mouth disease. In particular, we tested whether changes in the location of the incursion and the delay of implementation would affect the epidemiological benefit of such a policy in the context of Scotland. To reach this goal, we used a spatial, premises-based model that has been extensively used to investigate the effectiveness of mitigation procedures in Great Britain. The results show that the decision to vaccinate, or not, is not straightforward and strongly depends on the underlying local structure of the population-at-risk. With regards to disease incursion preparedness, simply identifying areas of highest population density may not capture all complexities that may influence the spread of disease as well as the benefit of implementing vaccination. However, if a decision to vaccinate is made, we show that delaying its implementation in the field may markedly reduce its benefit. This work provides guidelines to support policy makers in their decision to implement, or not, a vaccination-to-live policy when facing epidemics of infectious livestock disease

Chronic obstructive pulmonary disease in sub-Saharan Africa.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide and an important cause of death in sub-Saharan Africa (SSA). We conducted a systematic review and meta-analysis on the prevalence of and risk factors for COPD in SSA.METHODS: We conducted a protocol-driven systematic literature search in MEDLINE, EMBASE, CINAHL and Global Health, supplemented by a manual search of the abstracts from thoracic conference proceedings from 2017 to 2020. We did a meta-analysis of COPD prevalence and its association with current smoking.RESULTS: We identified 831 titles, of which 27 were eligible for inclusion in the review and meta-analysis. The population prevalence of COPD ranged from 1.7% to 24.8% (pooled prevalence: 8%, 95% CI 6-11). An increased prevalence of COPD was associated with increasing age, smoking and biomass smoke exposure. The pooled odds ratio for the effect of current smoking (vs. never smoked) on COPD was 2.20 (95% CI 1.62-2.99).CONCLUSION: COPD causes morbidity and mortality in adults in SSA. Smoking is an important risk factor for COPD in SSA, and this exposure needs to be reduced through the combined efforts of clinicians, researchers and policymakers to address this debilitating and preventable lung disease

Designing programs for eliminating canine rabies from islands: Bali, Indonesia as a case study

<p>Background: Canine rabies is one of the most important and feared zoonotic diseases in the world. In some regions rabies elimination is being successfully coordinated, whereas in others rabies is endemic and continues to spread to uninfected areas. As epidemics emerge, both accepted and contentious control methods are used, as questions remain over the most effective strategy to eliminate rabies. The Indonesian island of Bali was rabies-free until 2008 when an epidemic in domestic dogs began, resulting in the deaths of over 100 people. Here we analyze data from the epidemic and compare the effectiveness of control methods at eliminating rabies.</p> <p>Methodology/Principal Findings: Using data from Bali, we estimated the basic reproductive number, R0, of rabies in dogs, to be ~1·2, almost identical to that obtained in ten–fold less dense dog populations and suggesting rabies will not be effectively controlled by reducing dog density. We then developed a model to compare options for mass dog vaccination. Comprehensive high coverage was the single most important factor for achieving elimination, with omission of even small areas (<0.5% of the dog population) jeopardizing success. Parameterizing the model with data from the 2010 and 2011 vaccination campaigns, we show that a comprehensive high coverage campaign in 2012 would likely result in elimination, saving ~550 human lives and ~$15 million in prophylaxis costs over the next ten years.</p> <p>Conclusions/Significance: The elimination of rabies from Bali will not be achieved through achievable reductions in dog density. To ensure elimination, concerted high coverage, repeated, mass dog vaccination campaigns are necessary and the cooperation of all regions of the island is critical. Momentum is building towards development of a strategy for the global elimination of canine rabies, and this study offers valuable new insights about the dynamics and control of this disease, with immediate practical relevance.</p&gt

Determining research knowledge infrastructure for healthcare systems: a qualitative study

<p>Abstract</p> <p>Background</p> <p>This study examines research knowledge infrastructures (RKIs) found in health systems. An RKI is defined as any instrument (<it>i.e</it>., programs, interventions, tools) implemented in order to facilitate access, dissemination, exchange, and/or use of evidence in healthcare organisations. Based on an environmental scan (17 key informant interviews) and scoping review (26 studies), we found support for a framework that we developed that outlines components that a health system can have in its RKI. The broad domains are climate for research use, research production, activities used to link research to action, and evaluation.</p> <p>The objective of the current study is to profile the RKI of three types of health system organisations--regional health authorities, primary care practices, and hospitals--in two Canadian provinces to determine the current mix of components these organisations have in their RKI, their experience with these components, and their views about future RKI initiatives.</p> <p>Methods</p> <p>This study will include semistructured telephone interviews with a purposive sample region of a senior management team member, library/resource centre manager, and a 'knowledge broker' in three regional health authorities, five or six purposively sampled hospitals, and five or six primary care practices in Ontario and Quebec, for a maximum of 71 interviewees. The interviews will explore (a) which RKI components have proven helpful, (b) barriers and facilitators in implementing RKI components, and (c) views about next steps in further development of RKIs.</p> <p>Discussion</p> <p>This is the first qualitative examination of potential RKI efforts that can increase the use of research evidence in health system decision making. We anticipate being able to identify broadly applicable insights about important next steps in building effective RKIs. Some of the identified RKI components may increase the use of research evidence by decision makers, which may then lead to more informed decisions.</p

Mimotope ELISA for Detection of Broad Spectrum Antibody against Avian H5N1 Influenza Virus

Science and Technology Foundation of Fujian Province [2009YZ0002]; National Natural Science Foundation of China [30901077]; National High Technology Research and Development Program [2010AA022801]Background: We have raised a panel of broad spectrum neutralizing monoclonal antibodies against the highly pathogenic H5N1 avian influenza virus, which neutralize the infectivity of, and afford protection against infection by, most of the major genetic groups of the virus evolved since 1997. Peptide mimics reactive with one of these broad spectrum H5N1 neutralizing antibodies, 8H5, were identified from random phage display libraries. Method: The amino acid residues of the most reactive 12mer peptide, p125 (DTPLTTAALRLV), were randomly substituted to improve its mimicry of the natural 8H5 epitope. Result: 133 reactive peptides with unique amino acid sequences were identified from 5 sub-libraries of p125. Four residues (2,4,5.9) of the parental peptide were preserved among all the derived peptides and probably essential for 8H5 binding. These are interspersed among four other residues (1,3,8,10), which exhibit restricted substitution and probably could contribute to binding, and another four (6,7,11,12) which could be randomly substituted and probably are not essential for binding. One peptide, V-1b, derived by substituting 5 of the latter residues is the most reactive and has a binding constant of 3.16x10(-9) M, which is 38 fold higher than the affinity of the parental p125. Immunoassay produced with this peptide is specifically reactive with 8H5 but not also the other related broad spectrum H5N1 avian influenza virus neutralizing antibodies. Serum samples from 29 chickens infected with H5N1 avian influenza virus gave a positive result by this assay and those from 12 uninfected animals gave a negative test result. Conclusion: The immunoassay produced with the 12 mer peptide, V1-b, is specific for the natural 8H5 epitope and can be used for detection of antibody against the broad spectrum neutralization site of H5N1 avian influenza virus

Spatial Re-Establishment Dynamics of Local Populations of Vectors of Chagas Disease

Chagas disease is transmitted by blood-sucking bugs (vectors) and presents a severe public health threat in the Americas. Worldwide there are approximately 10 million people infected with Chagas disease, a disease for which there is currently no effective cure. Vector suppression is the main strategy to control the spread of this disease. Unfortunately, the vectors have been resurgent in some areas. It is important to understand the dynamics of reinfestation where it occurs. Here we show how different models fitted to patch-level bug infestation data can elucidate different aspects of re-establishment dynamics. Our results demonstrated a 6-month time lag between detection of a new infestation and dispersal events, seasonality in dispersal rates and effects of previous vector infestation on subsequent vector establishment rates. In addition we provide estimates of dispersal distances and the effect of insecticide spraying on rates of vector re-establishment. While some of our results confirm previous findings, the effects of season and previous infestation on bug establishment challenge our current understanding of T. infestans ecology and highlight important gaps in our knowledge of T. infestans dispersal

Vulnerability of the British swine industry to classical swine fever

Classical swine fever (CSF) is a notifiable, highly contagious viral disease of swine which results in severe welfare and economic consequences in affected countries. To improve preparedness, it is critical to have some understanding of how CSF would spread should it be introduced. Based on the data recorded during the 2000 epidemic of CSF in Great Britain (GB), a spatially explicit, premises-based model was developed to explore the risk of CSF spread in GB. We found that large outbreaks of CSF would be rare and generated from a limited number of areas in GB. Despite the consistently low vulnerability of the British swine industry to large CSF outbreaks, we identified concerns with respect to the role played by the non-commercial sector of the industry. The model further revealed how various epidemiological features may influence the spread of CSF in GB, highlighting the importance of between-farm biosecurity in preventing widespread dissemination of the virus. Knowledge of factors affecting the risk of spread are key components for surveillance planning and resource allocation, and this work provides a valuable stepping stone in guiding policy on CSF surveillance and control in GB

A mechanistic spatio-temporal framework for modelling individual-to-individual transmission—With an application to the 2014-2015 West Africa Ebola outbreak

In recent years there has been growing availability of individual-level spatio-temporal disease data, particularly due to the use of modern communicating devices with GPS tracking functionality. These detailed data have been proven useful for inferring disease transmission to a more refined level than previously. However, there remains a lack of statistically sound frameworks to model the underlying transmission dynamic in a mechanistic manner. Such a development is particularly crucial for enabling a general epidemic predictive framework at the individual level. In this paper we propose a new statistical framework for mechanistically modelling individual-to-individual disease transmission in a landscape with heterogeneous population density. Our methodology is first tested using simulated datasets, validating our inferential machinery. The methodology is subsequently applied to data that describes a regional Ebola outbreak in Western Africa (2014-2015). Our results show that the methods are able to obtain estimates of key epidemiological parameters that are broadly consistent with the literature, while revealing a significantly shorter distance of transmission. More importantly, in contrast to existing approaches, we are able to perform a more general model prediction that takes into account the susceptible population. Finally, our results show that, given reasonable scenarios, the framework can be an effective surrogate for susceptible-explicit individual models which are often computationally challenging