Mid-Arm Point in PAEDiatrics (MAPPAED): An effective procedural aid for safe pleural decompression in trauma.

OBJECTIVE: Life-threatening thoracic trauma requires emergency pleural decompression and thoracostomy and chest drain insertion are core trauma procedures. Reliably determining a safe site for pleural decompression in children can be challenging. We assessed whether the Mid-Arm Point (MAP) technique, a procedural aid proposed for use with injured adults, would also identify a safe site for pleural decompression in children. METHODS: Children (0-18 years) attending four EDs were prospectively recruited. The MAP technique was performed, and chest wall skin marked bilaterally at the level of the MAP; no pleural decompression was performed. Radio-opaque markers were placed over the MAP-determined skin marks and corresponding intercostal space (ICS) reported using chest X-ray. RESULTS: A total of 392 children participated, and 712 markers sited using the MAP technique were analysed. Eighty-three percentage of markers were sited within the 'safe zone' for pleural decompression (4th to 6th ICSs). When sited outside the 'safe zone', MAP-determined markers were typically too caudal. However, if the site for pleural decompression was transposed one ICS cranially in children ≥4 years, the MAP technique performance improved significantly with 91% within the 'safe zone'. CONCLUSIONS: The MAP technique reliably determines a safe site for pleural decompression in children, albeit with an age-based adjustment, the Mid-Arm Point in PAEDiatrics (MAPPAED) rule: 'in children aged ≥4 years, use the MAP and go up one ICS to hit the safe zone. In children <4 years, use the MAP.' When together with this rule, the MAP technique will identify a site within the 'safe zone' in 9 out of 10 children.

Cattle movements in Northern Ireland form a robust network: implications for disease management.

The movements of undetected infected animals can facilitate long-distance pathogen spread, making control and eradication difficult by (re)infecting disease-free populations. Characterising movement patterns is essential in understanding pathogen spread and how potential interventions, particularly animal movement restrictions, could help as a control mechanism. In Northern Ireland (NI), cattle movements are important contributors to a significant portion of agricultural trade. They can be disrupted due to statutory interventions, for example, during bovine tuberculosis (bTB) control. Identifying populations at risk of becoming infected would allow for improved resource allocation. This could be through targeting herds with an above-average risk of becoming infected or spreading (amplifying) infection, and restricting their movement to manage future outbreaks. In this study, cattle movements were investigated using social network analysis (SNA) at the monthly temporal scale across NI during 2010-2015. Targeted and random herd restrictions were compared and their impact on the structure and connectivity of the networks' was assessed (e.g. connected component subgraphs). This work was contextualised in relation to bTB, the most persistent infectious disease currently impacting agriculture in NI, where reduced connectivity would represent potential reduced vulnerability from infection introduction. There was seasonal variation in network size and level of connectivity with spring and autumn being the largest and most connected due to common farming practices in NI. Across the study period, there was limited inter-annual variation in global network metrics. On average there were 6.28 movements between each pair of nodes each month, low reciprocity (mean of 0.155) and the networks were moderately accessible with an average path length of 4.28. Movements were not confined to within each disease management area but frequently occurred between these areas (mean assortativity of -0.0731) and herds with high degree interacted with herds of low degree (mean assortativity of -0.351). The Giant Weakly Connected Component (GWCC) spanned most of the networks (between 75% and 100% of nodes); however the Giant Strongly Connected Component (GSCC) included, at most, 23% of the network. There was heterogeneous contributions across NI with little participation in the GSCC from some disease management areas, and the GSCC was comprised predominantly of 'beef breeders', 'beef rearers', and 'other/mixed' type herds. Targeted restrictions were more effective at fragmenting the network than randomly restricting movements when 25% of nodes or more were removed. Cattle networks in NI are extremely interconnected and robust to movement restrictions, suggesting potential vulnerability to movement-facilitated pathogen spread, such as bTB.

A two-stage approach to the joint analysis of longitudinal and survival data utilising the Coxian phase-type distribution.

The Coxian phase-type distribution is a special type of Markov model which can be utilised both to uncover underlying stages of a survival process and to make inferences regarding the rates of flow of individuals through these latent stages before an event of interest occurs. Such models can be utilised, for example, to identify individuals who are likely to deteriorate faster through a series of disease states and thus require more aggressive medical intervention. Within this paper, a two-stage approach to the analysis of longitudinal and survival data is presented. In Stage 1, a linear mixed effects model is first used to represent how some longitudinal response of interest changes through time. Within this linear mixed effects model, the individuals' random effects can be considered as a proxy measure for the effect of the individuals' genetic profiles on the response of interest. In Stage 2, the Coxian phase-type distribution is employed to represent the survival process. The individuals' random effects, estimated in Stage 1, are incorporated as covariates within the Coxian phase-type distribution so as to evaluate their effect on the individuals' rates of flow through the system represented by the Coxian. The approach is illustrated using data collected on individuals suffering from chronic kidney disease, where focus is given to an emerging longitudinal biomarker of interest - an individual's haemoglobin level.