Automated Detection of External Ventricular and Lumbar Drain-Related Meningitis Using Laboratory and Microbiology Results and Medication Data

OBJECTIVE: Monitoring of healthcare-associated infection rates is important for infection control and hospital benchmarking. However, manual surveillance is time-consuming and susceptible to error. The aim was, therefore, to develop a prediction model to retrospectively detect drain-related meningitis (DRM), a frequently occurring nosocomial infection, using routinely collected data from a clinical data warehouse. METHODS: As part of the hospital infection control program, all patients receiving an external ventricular (EVD) or lumbar drain (ELD) (2004 to 2009; n = 742) had been evaluated for the development of DRM through chart review and standardized diagnostic criteria by infection control staff; this was the reference standard. Children, patients dying <24 hours after drain insertion or with <1 day follow-up and patients with infection at the time of insertion or multiple simultaneous drains were excluded. Logistic regression was used to develop a model predicting the occurrence of DRM. Missing data were imputed using multiple imputation. Bootstrapping was applied to increase generalizability. RESULTS: 537 patients remained after application of exclusion criteria, of which 82 developed DRM (13.5/1000 days at risk). The automated model to detect DRM included the number of drains placed, drain type, blood leukocyte count, C-reactive protein, cerebrospinal fluid leukocyte count and culture result, number of antibiotics started during admission, and empiric antibiotic therapy. Discriminatory power of this model was excellent (area under the ROC curve 0.97). The model achieved 98.8% sensitivity (95% CI 88.0% to 99.9%) and specificity of 87.9% (84.6% to 90.8%). Positive and negative predictive values were 56.9% (50.8% to 67.9%) and 99.9% (98.6% to 99.9%), respectively. Predicted yearly infection rates concurred with observed infection rates. CONCLUSION: A prediction model based on multi-source data stored in a clinical data warehouse could accurately quantify rates of DRM. Automated detection using this statistical approach is feasible and could be applied to other nosocomial infections

Strategies for tropical forest protection and sustainable supply chains: challenges and opportunities for alignment with the UN sustainable development goals

Governance for sustainable development increasingly involves diverse stakeholder groups, with the promise of enhanced legitimacy and effectiveness in decision-making and implementation. The UN sustainable development goals (SDGs) emphasise the important role of multiple (non-state) actors, including businesses and non-governmental organisations, including in efforts to ensure the sustainability of supply chains, and to reduce tropical deforestation and forest degradation. This paper critically analyses sustainability strategies to examine how the UN SDG agendas related to ‘sustainable supply chains’ and ‘tropical forest protection’ are framed and enacted by two contrasting non-state actors: (1) Instituto Centro de Vida (ICV), an NGO in Brazil working to address deforestation, including by supporting farmers to produce commodities, and (2) Unilever, a global consumer goods manufacturer and major buyer of such commodities. By identifying areas of variability in the discursive techniques used by ICV and Unilever, we unearth particular power dynamics that can shape the processes and outcomes of sustainability strategies. This paper finds that the two organisations use diverse strategies at different levels of governance, both participate actively in multi-stakeholder forums to advance their organisations’ goals, but have divergent framings of ‘sustainability’. Despite being considered ‘non-state’ actors, the strategies of the two organisations examined both reflect, and influence, the structural effects of the state in the implementation of non-state organisations’ strategies, and progress towards the SDGs. Although there is alignment of certain strategies related to tropical forest protection, in some cases, there is a risk that more sustainable, alternative approaches to governing forests and supply chains may be excluded

Quantitative PCR reveals strong spatial and temporal variation of the wasting disease pathogen, Labyrinthula zosterae in northern European eelgrass (Zostera marina) beds

Seagrass beds are the foundation species of functionally important coastal ecosystems worldwide. The world’s largest losses of the widespread seagrass Zostera marina (eelgrass) have been reported as a consequence of wasting disease, an infection with the endophytic protist Labyrinthula zosterae. During one of the most extended epidemics in the marine realm, ~90% of East and Western Atlantic eelgrass beds died-off between 1932 and 1934. Today, small outbreaks continue to be reported, but the current extent of L. zosterae in European meadows is completely unknown. In this study we quantify the abundance and prevalence of the wasting disease pathogen among 19 Z. marina populations in northern European coastal waters, using quantitative PCR (QPCR) with primers targeting a species specific portion of the internally transcribed spacer (ITS1) of L. zosterae. Spatially, we found marked variation among sites with abundances varying between 0 and 126 cells mg−1 Z. marina dry weight (mean: 5.7 L. zosterae cells mg−1 Z. marina dry weight ±1.9 SE) and prevalences ranged from 0–88.9%. Temporarily, abundances varied between 0 and 271 cells mg−1 Z. marina dry weight (mean: 8.5±2.6 SE), while prevalences ranged from zero in winter and early spring to 96% in summer. Field concentrations accessed via bulk DNA extraction and subsequent QPCR correlated well with prevalence data estimated via isolation and cultivation from live plant tissue. L. zosterae was not only detectable in black lesions, a sign of Labyrinthula-induced necrosis, but also occurred in green, apparently healthy tissue. We conclude that L. zosterae infection is common (84% infected populations) in (northern) European eelgrass populations with highest abundances during the summer months. In the light of global climate change and increasing rate of marine diseases our data provide a baseline for further studies on the causes of pathogenic outbreaks of L. zosterae

Cross-National Analysis of the Associations among Mental Disorders and Suicidal Behavior: Findings from the WHO World Mental Health Surveys

Using data from over 100,000 individuals in 21 countries participating in the WHO World Mental Health Surveys, Matthew Nock and colleagues investigate which mental health disorders increase the odds of experiencing suicidal thoughts and actual suicide attempts, and how these relationships differ across developed and developing countries

Sources of potential bias when combining routine data linkage and a national survey of secondary school-aged children: a record linkage study

Background Linking survey data to administrative records requires informed participant consent. When linkage includes child data, this includes parental and child consent. Little is known of the potential impacts of introducing consent to data linkage on response rates and biases in school-based surveys. This paper assessed: i) the impact on overall parental consent rates and sample representativeness when consent for linkage was introduced and ii) the quality of identifiable data provided to facilitate linkage. Methods Including an option for data linkage was piloted in a sub-sample of schools participating in the Student Health and Wellbeing survey, a national survey of adolescents in Wales, UK. Schools agreeing to participate were randomized 2:1 to receive versus not receive the data linkage question. Survey responses from consenting students were anonymised and linked to routine datasets (e.g. general practice, inpatient, and outpatient records). Parental withdrawal rates were calculated for linkage and non-linkage samples. Multilevel logistic regression models were used to compare characteristics between: i) consenters and non-consenters; ii) successfully and unsuccessfully linked students; and iii) the linked cohort and peers within the general population, with additional comparisons of mental health diagnoses and health service contacts. Results The sub-sample comprised 64 eligible schools (out of 193), with data linkage piloted in 39. Parental consent was comparable across linkage and non-linkage schools. 48.7% (n = 9232) of students consented to data linkage. Modelling showed these students were more likely to be younger, more affluent, have higher positive mental wellbeing, and report fewer risk-related behaviours compared to non-consenters. Overall, 69.8% of consenting students were successfully linked, with higher rates of success among younger students. The linked cohort had lower rates of mental health diagnoses (5.8% vs. 8.8%) and specialist contacts (5.2% vs. 7.7%) than general population peers. Conclusions Introducing data linkage within a national survey of adolescents had no impact on study completion rates. However, students consenting to data linkage, and those successfully linked, differed from non-consenting students on several key characteristics, raising questions concerning the representativeness of linked cohorts. Further research is needed to better understand decision-making processes around providing consent to data linkage in adolescent populations