Global late Quaternary megafauna extinctions linked to humans, not climate change

The late Quaternary megafauna extinction was a severe global-scale event. Two factors, climate change and modern humans, have received broad support as the primary drivers, but their absolute and relative importance remains controversial. To date, focus has been on the extinction chronology of individual or small groups of species, specific geographical regions or macroscale studies at very coarse geographical and taxonomic resolution, limiting the possibility of adequately testing the proposed hypotheses. We present, to our knowledge, the first global analysis of this extinction based on comprehensive country-level data on the geographical distribution of all large mammal species (more than or equal to 10 kg) that have gone globally or continentally extinct between the beginning of the Last Interglacial at 132 000 years BP and the late Holocene 1000 years BP, testing the relative roles played by glacial–interglacial climate change and humans. We show that the severity of extinction is strongly tied to hominin palaeobiogeography, with at most a weak, Eurasia-specific link to climate change. This first species-level macroscale analysis at relatively high geographical resolution provides strong support for modern humans as the primary driver of the worldwide megafauna losses during the late Quaternary

Improving coverage measurement for reproductive, maternal, neonatal and child health: gaps and opportunities.

BACKGROUND: Regular monitoring of coverage for reproductive, maternal, neonatal, and child health (RMNCH) is central to assessing progress toward health goals. The objectives of this review were to describe the current state of coverage measurement for RMNCH, assess the extent to which current approaches to coverage measurement cover the spectrum of RMNCH interventions, and prioritize interventions for a novel approach to coverage measurement linking household surveys with provider assessments. METHODS: We included 58 interventions along the RMNCH continuum of care for which there is evidence of effectiveness against cause-specific mortality and stillbirth. We reviewed household surveys and provider assessments used in low- and middle-income countries (LMICs) to determine whether these tools generate measures of intervention coverage, readiness, or quality. For facility-based interventions, we assessed the feasibility of linking provider assessments to household surveys to provide estimates of intervention coverage. RESULTS: Fewer than half (24 of 58) of included RMNCH interventions are measured in standard household surveys. The periconceptional, antenatal, and intrapartum periods were poorly represented. All but one of the interventions not measured in household surveys are facility-based, and 13 of these would be highly feasible to measure by linking provider assessments to household surveys. CONCLUSIONS: We found important gaps in coverage measurement for proven RMNCH interventions, particularly around the time of birth. Based on our findings, we propose three sets of actions to improve coverage measurement for RMNCH, focused on validation of coverage measures and development of new measurement approaches feasible for use at scale in LMICs

Syndromic surveillance of influenza-like illness in Scotland during the influenza A H1N1v pandemic and beyond

Syndromic surveillance refers to the rapid monitoring of syndromic data to highlight and follow outbreaks of infectious diseases, increasing situational awareness. Such systems are based upon statistical models to described routinely collected health data. We describe a working exception reporting system (ERS) currently used in Scotland to monitor calls received to the NHS telephone helpline, NHS24. We demonstrate the utility of the system to describe the time series data from NHS24 both at an aggregated Scotland level and at the individual health board level for two case studies, firstly during the initial phase of the 2009 Influenza A H1N1v and secondly for the emergence of seasonal influenza in each winter season from 2006/07 and 2010/11. In particular, we focus on a localised cluster of infection in the Highland health board and the ability of the system to highlight this outbreak. Caveats of the system, including the effect of media reporting of the pandemic on the results and the associated statistical issues, will be discussed. We discuss the adaptability and timeliness of the system and how this continues to form part of a suite of surveillance used to give early warnings to public health decision makers

Evaluation and use of surveillance system data toward the identification of high-risk areas for potential cholera vaccination: a case study from Niger.

In 2008, Africa accounted for 94% of the cholera cases reported worldwide. Although the World Health Organization currently recommends the oral cholera vaccine in endemic areas for high-risk populations, its use in Sub-Saharan Africa has been limited. Here, we provide the principal results of an evaluation of the cholera surveillance system in the region of Maradi in Niger and an analysis of its data towards identifying high-risk areas for cholera

Performance of Small Cluster Surveys and the Clustered LQAS Design to estimate Local-level Vaccination Coverage in Mali

<p>Abstract</p> <p>Background</p> <p>Estimation of vaccination coverage at the local level is essential to identify communities that may require additional support. Cluster surveys can be used in resource-poor settings, when population figures are inaccurate. To be feasible, cluster samples need to be small, without losing robustness of results. The clustered LQAS (CLQAS) approach has been proposed as an alternative, as smaller sample sizes are required.</p> <p>Methods</p> <p>We explored (i) the efficiency of cluster surveys of decreasing sample size through bootstrapping analysis and (ii) the performance of CLQAS under three alternative sampling plans to classify local VC, using data from a survey carried out in Mali after mass vaccination against meningococcal meningitis group A.</p> <p>Results</p> <p>VC estimates provided by a 10 × 15 cluster survey design were reasonably robust. We used them to classify health areas in three categories and guide mop-up activities: i) health areas not requiring supplemental activities; ii) health areas requiring additional vaccination; iii) health areas requiring further evaluation. As sample size decreased (from 10 × 15 to 10 × 3), standard error of VC and ICC estimates were increasingly unstable. Results of CLQAS simulations were not accurate for most health areas, with an overall risk of misclassification greater than 0.25 in one health area out of three. It was greater than 0.50 in one health area out of two under two of the three sampling plans.</p> <p>Conclusions</p> <p>Small sample cluster surveys (10 × 15) are acceptably robust for classification of VC at local level. We do not recommend the CLQAS method as currently formulated for evaluating vaccination programmes.</p

Evidence in Practice – A Pilot Study Leveraging Companion Animal and Equine Health Data from Primary Care Veterinary Clinics in New Zealand

Veterinary practitioners have extensive knowledge of animal health from their day-to-day observations of clinical patients. There have been several recent initiatives to capture these data from electronic medical records for use in national surveillance systems and clinical research. In response, an approach to surveillance has been evolving that leverages existing computerized veterinary practice management systems to capture animal health data recorded by veterinarians. Work in the United Kingdom within the VetCompass program utilizes routinely recorded clinical data with the addition of further standardized fields. The current study describes a prototype system that was developed based on this approach. In a 4-week pilot study in New Zealand, clinical data on presentation reasons and diagnoses from a total of 344 patient consults were extracted from two veterinary clinics into a dedicated database and analyzed at the population level. New Zealand companion animal and equine veterinary practitioners were engaged to test the feasibility of this national practice-based health information and data system. Strategies to ensure continued engagement and submission of quality data by participating veterinarians were identified, as were important considerations for transitioning the pilot program to a sustainable large-scale and multi-species surveillance system that has the capacity to securely manage big data. The results further emphasized the need for a high degree of usability and smart interface design to make such a system work effectively in practice. The geospatial integration of data from multiple clinical practices into a common operating picture can be used to establish the baseline incidence of disease in New Zealand companion animal and equine populations, detect unusual trends that may indicate an emerging disease threat or welfare issue, improve the management of endemic and exotic infectious diseases, and support research activities. This pilot project is an important step toward developing a national surveillance system for companion animals and equines that moves beyond emerging infectious disease detection to provide important animal health information that can be used by a wide range of stakeholder groups, including participating veterinary practices

The effect of scientific evidence on conservation practitioners' management decisions.

A major justification of environmental management research is that it helps practitioners, yet previous studies show it is rarely used to inform their decisions. We tested whether conservation practitioners focusing on bird management were willing to use a synopsis of relevant scientific literature to inform their management decisions. This allowed us to examine whether the limited use of scientific information in management is due to a lack of access to the scientific literature or whether it is because practitioners are either not interested or unable to incorporate the research into their decisions. In on-line surveys, we asked 92 conservation managers, predominantly from Australia, New Zealand, and the United Kingdom, to provide opinions on 28 management techniques that could be applied to reduce predation on birds. We asked their opinions before and after giving them a summary of the literature about the interventions' effectiveness. We scored the overall effectiveness and certainty of evidence for each intervention through an expert elicitation process-the Delphi method. We used the effectiveness scores to assess the practitioners' level of understanding and awareness of the literature. On average, each survey participant changed their likelihood of using 45.7% of the interventions after reading the synopsis of the evidence. They were more likely to implement effective interventions and avoid ineffective actions, suggesting that their intended future management strategies may be more successful than current practice. More experienced practitioners were less likely to change their management practices than those with less experience, even though they were not more aware of the existing scientific information than less experienced practitioners. The practitioners' willingness to change their management choices when provided with summarized scientific evidence suggests that improved accessibility to scientific information would benefit conservation management outcomes.W.J.S. is funded by Arcadia, and L.V.D. is funded by the Natural Environment Research Council, UK (Grant code NE/K015419/1).This is the final published version. It is also available from Wiley at http://onlinelibrary.wiley.com/doi/10.1111/cobi.12370/abstract

Estimating cancer incidence using a Bayesian back-calculation approach

We propose a Bayesian hierarchical model for the calculation of incidence counts from mortality data by a convolution equation that expresses mortality through its relationship with incidence and the survival probability density. The basic idea is to use mortality data together with an estimate of the survival distribution from cancer incidence to cancer mortality to reconstruct the numbers of individuals who constitute previously incident cases that give rise to the observed pattern of cancer mortality. This model is novel because it takes into account the uncertainty from the survival distribution; thus, a Bayesian-mixture cure model for survival is introduced. Furthermore, projections are obtained starting from a Bayesian age-period-cohort model. The main advantage of the proposed approach is its consideration of the three components of the model: the convolution equation, the survival mixture cure model and the age-period-cohort projection within a directed acyclic graph model. Furthermore, the estimation are obtained through the Gibbs sampler. We applied the model to cases of women with stomach cancer using six age classes [15–45], [45–55], [55–65], [65–75], [75–85] and [85–95] and validated it by using data from the Tuscany Cancer Registry. The model proposed and the program implemented are convenient because they allow different cancer disease to be analysed because the survival time is modelled by flexible distributions that are able to describe different trends