Systematic literature review and network meta-analysis of lurasidone, brexpiprazole and cariprazine for schizophrenia.

A systematic review was undertaken to identify randomized controlled trials (RCTs) comparing the efficacy and safety of lurasidone, brexpiprazole and cariprazine (selected because of a shared safety profile) with each other or placebo in adult patients with schizophrenia. Key outcomes included: Positive and Negative Syndrome Scales (PANSS), Clinical Global Impression-Severity (CGI-S) scores and cardiovascular and metabolic parameters. A feasibility assessment evaluated the trials' suitability for inclusion in a Bayesian network meta-analysis (NMA). Random effects models were used. In total, 1138 records were identified and 19 RCTs contributed to the NMA. Lurasidone doses of 160 mg performed best in terms of change in PANSS and CGI-S scores at 6 weeks, with stronger evidence when compared with brexpiprazole than cariprazine. The safety outcomes were variable; for all treatments, the 95% credible intervals usually contained 'no difference'. Active treatments were associated with lower odds of discontinuation due to any cause, and higher odds of experiencing any adverse event. Lurasidone was comparable to brexpiprazole and cariprazine for efficacy and safety outcomes assessed at 6 weeks, with the 160 mg dose being superior for the change in PANSS and CGI-S outcomes. The lurasidone results were relatively consistent across doses compared with brexpiprazole and cariprazine.

Evaluating Diagnostic Tests With Near-Perfect Specificity: Use of a Hui-Walter Approach When Designing a Trial of a DIVA Test for Bovine Tuberculosis.

Active surveillance of rare infectious diseases requires diagnostic tests to have high specificity, otherwise the false positive results can outnumber the true cases detected, leading to low positive predictive values. Where a positive result can have economic consequences, such as the cull of a bovine Tuberculosis (bTB) positive herd, establishing a high specificity becomes particularly important. When evaluating new diagnostic tests against a "gold standard" reference test with assumed perfect sensitivity and specificity, calculation of sample sizes are commonly done using a normal approximation to the binomial distribution, although this approach can be misleading. As the expected specificity of the evaluated diagnostic test nears 100%, the errors arising from this approximation are appreciable. Alternatively, it is straightforward to calculate the sample size by using more appropriate confidence intervals, while precisely quantifying the effect of sampling variability using the binomial distribution. However, regardless of the approach, if specificity is high the sample size required becomes large, and the gold standard may be prohibitively costly. An alternative to a gold standard test is to use at least two imperfect, conditionally independent tests, and to analyse the results using a variant of the approach initially proposed by Hui and Walter. We show how this method performs for tests with near-perfect specificity; in particular we show that the sample size required to deliver useful bounds on the precision becomes very large for both approaches. We illustrate these concepts using simulation studies carried out to support the design of a trial of a bTB vaccine and a diagnostic that is able to "Differentiate Infected and Vaccinated Animals" (DIVA). Both test characteristics and the efficacy of the bTB vaccine will influence the sample size required for the study. We propose an improved methodology using a two stage approach to evaluating diagnostic tests in low disease prevalence populations. By combining an initial gold standard pilot study with a larger study analyzed using a Hui-Walter approach, the sample size required for each study can be reduced and the precision of the specificity estimate improved, since information from both studies is combined.

Animal Health Surveillance in Scotland in 2030: Using Scenario Planning to Develop Strategies in the Context of "Brexit".

Animal health surveillance is necessary to protect human and animal health, rural economies, and the environment from the consequences of large-scale disease outbreaks. In Scotland, since the Kinnaird review in 2011, efforts have been made to engage with stakeholders to ensure that the strategic goals of surveillance are better aligned with the needs of the end-users and other beneficiaries. The aims of this study were to engage with Scottish surveillance stakeholders and multidisciplinary experts to inform the future long-term strategy for animal health surveillance in Scotland. In this paper, we describe the use of scenario planning as an effective tool for the creation and exploration of five plausible long-term futures; we describe prioritization of critical drivers of change (i.e., international trade policy, data-sharing philosophies, and public versus private resourcing of surveillance capacity) that will unpredictably influence the future implementation of animal health surveillance activities. We present 10 participant-developed strategies to support 3 long-term visions to improve future resilience of animal health surveillance and contingency planning for animal and zoonotic disease outbreaks in Scotland. In the absence of any certainty about the nature of post-Brexit trade agreements for agriculture, participants considered the best investments for long-term resilience to include data collection strategies to improve animal health benchmarking, user-benefit strategies to improve digital literacy in farming communities, and investment strategies to increase veterinary and scientific research capacity in rural areas. This is the first scenario planning study to explore stakeholder beliefs and perceptions about important environmental, technological, societal, political, and legal drivers (in addition to epidemiological "risk factors") and effective strategies to manage future uncertainties for both the Scottish livestock industry and animal health surveillance after Brexit. This insight from stakeholders is important to improve uptake and implementation of animal heath surveillance activities and the future resilience of the livestock industry. The conclusions drawn from this study are applicable not only to Scotland but to other countries and international organizations involved in global animal health surveillance activities.

Estimation of the impact of Fasciola hepatica infection on time taken for UK beef cattle to reach slaughter weight.

Fasciolosis is common in UK beef cattle, but it is unclear at what levels liver fluke burdens cause production losses. This study aimed to address these uncertainties by estimating the impact of liver fluke infection on UK beef cattle productivity and investigating the use of diagnostic tests in a quantitative manner. We built three linear regression models for slaughter age by weight and different measures of liver fluke status, while accounting for sex, breed, season, year and farm of origin. Data were sourced from Scotland's largest red meat abattoir throughout 2013 and 2014. Our Meat Hygiene Service model estimated that cattle classified as having liver fluke damage had on average 10 days greater slaughter age than animals with no evidence of fasciolosis. Our liver fibrosis model estimated that the increase in slaughter age was more severe for higher fibrosis scores. Similarly, our burden model showed an increase in slaughter age for animals with as few as 1 to 10 parasites found in their livers. Lastly, we used receiver operating characteristic curves to show that serum antibody ELISA, copro-antigen ELISA and faecal egg counting can be useful in distinguishing between animals with and without production limiting levels of fasciolosis.

Drivers of Live Cattle Price in the Livestock Trading System of Central Cameroon.

Livestock production and trade are critical for the food security and welfare of rural households in sub-Saharan Africa. In Cameroon, animal trade consists mainly of live cattle commercialized through livestock markets. Identifying the factors contributing to cattle price formation is critical for designing effective policies for sustainable production and for increasing food availability. In this study, we evaluated the influence of a range of individual- and market-level factors on the price of cattle that were sold in all transactions (n = 118,017) recorded over a 12-month period from 31 livestock markets in the main cattle production area of the country. An information-theoretic approach using a generalized additive mixed-effect model was implemented to select the best explanatory model as well as evaluate the robustness of the identified drivers and the predictive ability of the model. The age and gender of the cattle traded were consistently found to be important drivers of the price (p < 0.01). Also, strong, but complex, relationships were found between cattle prices and both local human and bovine population densities. Finally, the model highlighted a positive association between the number of incoming trading connections of a livestock market and the price of the traded live cattle (p < 0.01). Although our analysis did not account for factors informing on specific phenotypic traits nor breed characteristics of cattle traded, nearly 50% of the observed variation in live cattle prices was explained by the final model. Ultimately, our model gives a large scale overview of drivers of cattle price formation in Cameroon and to our knowledge is the first study of this scale in Central Africa. Our findings represent an important milestone in designing efficient and sustainable animal health management programme in Cameroon and ensure livelihood sustainability for rural households.

Using Combined Diagnostic Test Results to Hindcast Trends of Infection from Cross-Sectional Data.

Infectious disease surveillance is key to limiting the consequences from infectious pathogens and maintaining animal and public health. Following the detection of a disease outbreak, a response in proportion to the severity of the outbreak is required. It is thus critical to obtain accurate information concerning the origin of the outbreak and its forward trajectory. However, there is often a lack of situational awareness that may lead to over- or under-reaction. There is a widening range of tests available for detecting pathogens, with typically different temporal characteristics, e.g. in terms of when peak test response occurs relative to time of exposure. We have developed a statistical framework that combines response level data from multiple diagnostic tests and is able to 'hindcast' (infer the historical trend of) an infectious disease epidemic. Assuming diagnostic test data from a cross-sectional sample of individuals infected with a pathogen during an outbreak, we use a Bayesian Markov Chain Monte Carlo (MCMC) approach to estimate time of exposure, and the overall epidemic trend in the population prior to the time of sampling. We evaluate the performance of this statistical framework on simulated data from epidemic trend curves and show that we can recover the parameter values of those trends. We also apply the framework to epidemic trend curves taken from two historical outbreaks: a bluetongue outbreak in cattle, and a whooping cough outbreak in humans. Together, these results show that hindcasting can estimate the time since infection for individuals and provide accurate estimates of epidemic trends, and can be used to distinguish whether an outbreak is increasing or past its peak. We conclude that if temporal characteristics of diagnostics are known, it is possible to recover epidemic trends of both human and animal pathogens from cross-sectional data collected at a single point in time.

GET WELL: an automated surveillance system for gaining new epidemiological knowledge.

BACKGROUND: The assumption behind the presented work is that the information people search for on the internet reflects the disease status in society. By having access to this source of information, epidemiologists can get a valuable complement to the traditional surveillance and potentially get new and timely epidemiological insights. For this purpose, the Swedish Institute for Infectious Disease Control collaborates with a medical web site in Sweden. METHODS: We built an application consisting of two conceptual parts. One part allows for trends, based on user specified requests, to be extracted from anonymous web query data from a Swedish medical web site. The second conceptual part permits tailored analyses of particular diseases, where more complex statistical methods are applied to the data. To evaluate the epidemiological relevance of the output, we compared Google search data and search data from the medical web site. RESULTS: In the paper, we give concrete examples of the output from the web query-based system. We also present results from the comparison between data from the search engine Google and search data from the national medical web site. CONCLUSIONS: The application is in regular use at the Swedish Institute for Infectious Disease Control. A system based on web queries is flexible in that it can be adapted to any disease; we get information on other individuals than those who seek medical care; and the data do not suffer from reporting delays. Although Google data are based on a substantially larger search volume, search patterns obtained from the medical web site may still convey more information from an epidemiological perspective. Furthermore we can see advantages with having full access to the raw data.

Web queries as a source for syndromic surveillance.

In the field of syndromic surveillance, various sources are exploited for outbreak detection, monitoring and prediction. This paper describes a study on queries submitted to a medical web site, with influenza as a case study. The hypothesis of the work was that queries on influenza and influenza-like illness would provide a basis for the estimation of the timing of the peak and the intensity of the yearly influenza outbreaks that would be as good as the existing laboratory and sentinel surveillance. We calculated the occurrence of various queries related to influenza from search logs submitted to a Swedish medical web site for two influenza seasons. These figures were subsequently used to generate two models, one to estimate the number of laboratory verified influenza cases and one to estimate the proportion of patients with influenza-like illness reported by selected General Practitioners in Sweden. We applied an approach designed for highly correlated data, partial least squares regression. In our work, we found that certain web queries on influenza follow the same pattern as that obtained by the two other surveillance systems for influenza epidemics, and that they have equal power for the estimation of the influenza burden in society. Web queries give a unique access to ill individuals who are not (yet) seeking care. This paper shows the potential of web queries as an accurate, cheap and labour extensive source for syndromic surveillance.