An exact test to detect geographic aggregations of events

<p>Abstract</p> <p>Background</p> <p>Traditional approaches to statistical disease cluster detection focus on the identification of geographic areas with high numbers of incident or prevalent cases of disease. Events related to disease may be more appropriate for analysis than disease cases in some contexts. Multiple events related to disease may be possible for each disease case and the repeated nature of events needs to be incorporated in cluster detection tests.</p> <p>Results</p> <p>We provide a new approach for the detection of aggregations of events by testing individual administrative areas that may be combined with their nearest neighbours. This approach is based on the exact probabilities for the numbers of events in a tested geographic area. The test is analogous to the cluster detection test given by Besag and Newell and does not require the distributional assumptions of a similar test proposed by Rosychuk et al. Our method incorporates diverse population sizes and population distributions that can differ by important strata. Monte Carlo simulations help assess the overall number of clusters identified. The population and events for each area as well as a nearest neighbour spatial relationship are required. We also provide an alternative test applicable to situations when only the aggregate number of events, and not the number of events per individual, are known. The methodology is illustrated on administrative data of presentations to emergency departments.</p> <p>Conclusions</p> <p>We provide a new method for the detection of aggregations of events that does not rely on distributional assumptions and performs well.</p

Adaptations for finding irregularly shaped disease clusters

<p>Abstract</p> <p>Background</p> <p>Recent adaptations of the spatial scan approach to detecting disease clusters have addressed the problem of finding clusters that occur in non-compact and non-circular shapes – such as along roads or river networks. Some of these approaches may have difficulty defining cluster boundaries precisely, and tend to over-fit data with very irregular (and implausible) clusters shapes.</p> <p>Results & Discussion</p> <p>We describe two simple adaptations to these approaches that can be used to improve the effectiveness of irregular disease cluster detection. The first adaptation penalizes very irregular cluster shapes based on a measure of connectivity (non-connectivity penalty). The second adaptation prevents searches from combining smaller clusters into large super-clusters (depth limit). We conduct experiments with simulated data in order to observe the performance of these adaptations on a number of synthetic cluster shapes.</p> <p>Conclusion</p> <p>Our results suggest that the combination of these two adaptations may increase the ability of a cluster detection method to find irregular shapes without affecting its ability to find more regular (i.e., compact) shapes. The depth limit in particular is effective when it is deemed important to distinguish nearby clusters from each other. We suggest that these adaptations of adjacency-constrained spatial scans are particularly well suited to chronic disease and injury surveillance.</p

Gender Differences in the Relationships between Research Impact and Compensation and Promotion: A Case Study Among PhD/PharmD

We examine whether the effects of research impact on faculty compensation and promotion to full professor differ for male and female associate and full professors in the Faculty of Medicine &amp; Dentistry at the University of Alberta. We exclude faculty with MDs and DDSs and proxy for research impact using the faculty member’s h-index, where h represents the number of publications that have been cited at least h times. We find that while the compensation of male faculty members increases by 0.6% for every one-unit increase in the h-index, the compensation of female faculty is essentially uncorrelated with their h-indices. We likewise find that for female faculty to be promoted to full professor they have to have higher research impact proxies than their male peers. Our findings highlight the urgent need for more research on the gendered relationships between research impact and career rewards among faculty.Nous avons évalué l’impact de la recherche sur la rémunération du corps professoral et la promotion au rang de professeur titulaire, entre les professeurs agrégés et titulaires hommes et femmes de la Faculté de médecine et de dentisterie de l’Université de l’Alberta. Nous avons exclu les professeurs cliniciens ayant un doctorat en médecine (MD) ou un doctorat en médecine dentaire (DDSs). L’impact de la recherche a été évalué à l’aide de l’indice h de chaque membre du corps professoral, oùh représente le nombre de publications qui ont été citées au moins h fois. Nous constatons ainsi que tandis que la rémunération des hommes augmente en moyenne de 0,6 % pour chaque augmentation d’une unité de l’indice h, la rémunération des femmes est essentiellement non corrélée avec leurs indices h. Nous constatons également que les professeures atteignent la parité pour la  promotion au poste de professeur titulaire avec les professeurs masculins équivalents à des indicateursd’impact de la recherche considérablement supérieurs aux valeurs médianes normalement attendues pour une promotion au poste de professeur titulaire. Nos résultats mettent en évidence le besoin urgent de plus de recherches sur les relations entre le genre et l’impact de la recherche sur l’avancement de la carrière chez les professeurs-chercheurs

Medical Students and Pandemic Influenza

To assess knowledge of pandemic influenza, we administered a questionnaire to all medical students at the University of Alberta; 354 (69%) of 510 students responded. Data from questionnaires such as this could help determine the role of medical students during a public health emergency

Study of Natural Health Product Adverse Reactions (SONAR): Active Surveillance of Adverse Events Following Concurrent Natural Health product and Prescription Drug Use in Community Pharmacies

Background: Many consumers use natural health products (NHPs) concurrently with prescription medications. As NHP-related harms are under-reported through passive surveillance, the safety of concurrent NHP-drug use remains unknown. To conduct active surveillance in participating community pharmacies to identify adverse events related to concurrent NHP-prescription drug use. Methodology/Principal Findings: Participating pharmacists asked individuals collecting prescription medications about (i) concurrent NHP/drug use in the previous three months and (ii) experiences of adverse events. If an adverse event was identified and if the patient provided written consent, a research pharmacist conducted a guided telephone interview to gather additional information after obtaining additional verbal consent and documenting so within the interview form. Over a total of 112 pharmacy weeks, 2615 patients were screened, of which 1037 (39.7%; 95% CI: 37.8% to 41.5%) reported concurrent NHP and prescription medication use. A total of 77 patients reported a possible AE (2.94%; 95% CI: 2.4% to 3.7%), which represents 7.4% of those using NHPs and prescription medications concurrently (95%CI: 6.0% to 9.2%). Of 15 patients available for an interview, 4 (26.7%: 95% CI: 4.3% to 49.0%) reported an AE that was determined to be “probably” due to NHP use. Conclusions/Significance: Active surveillance markedly improves identification and reporting of adverse events associated with concurrent NHP-drug use. Although not without challenges, active surveillance is feasible and can generate adverse event data of sufficient quality to allow for meaningful adjudication to assess potential harms

Plasma matrix metalloproteinases in neonates having surgery for congenital heart disease

During cardiopulmonary-bypass matrix-metalloproteinases released may contribute to ventricular dysfunction. This study was to determine plasma matrix-metalloproteinases in neonates after cardiopulmonary-bypass and their relation to post-operative course. A prospective observational study included 18 neonates having cardiac surgery. Plasma matrix-metalloproteinases-2 and 9 activities were measured by gelatin-zymography pre-operatively, on starting cardiopulmonarybypass, 7–8 min after aortic cross-clamp release, and 1h, 4h, 24h, and 3d after cardiopulmonary-bypass. Plasma concentrations of their tissue inhibitors 1 and 2 were determined by enzyme-linked immunosorbent assay. Cardiac function was assessed by serial echocardiography. Paired t-tests and Wilcoxon tests were used to assess temporal changes, and linear correlation with simultaneous clinical and cardiac function parameters were assessed using Pearson's product-moment correlation coefficient. Plasma matrix-metalloproteinases activities and their tissue inhibitor concentrations decreased during cardiopulmonary-bypass. Matrix-metalloproteinase-2 plasma activity increased progressively starting 1h after cardiopulmonarybypass and returned to pre-operative levels at 24h. Matrix-metalloproteinase-9 plasma activity increased significantly after release of aortic cross-clamp, peaked 7–8min later, and returned to baseline at 24h. Plasma tissueinhibitor 1 and 2 concentrations increased 1h after cardiopulmonary-bypass. Cardiac function improved from 4h to 3d after surgery (p<0.05). There was no evidence of significant correlations between matrix-metalloproteinases or their inhibitors and cardiac function, inotrope scores, organ dysfunction scores, ventilation days, or hospital days. The temporal profile of plasma matrix-metalloproteinases and their inhibitors after cardiopulmonary-bypass in neonates are similar to adults. In neonates, further study should determine whether circulating matrix-metalloproteinases are useful biomarkers of disease activity locally within the myocardium, and hence of clinical outcomes

Identifying geographic areas with high disease rates: when do confidence intervals for rates and a disease cluster detection method agree?

<p>Abstract</p> <p>Background</p> <p>Geographic regions are often routinely monitored to identify areas with excess cases of disease. Further epidemiological investigations can be targeted to areas with higher disease rates than expected. Surveillance strategies typically include the calculation of sub-regional rates, and their associated confidence intervals, that are compared with the rate of the entire geographic region. More sophisticated approaches use disease cluster detection methods that require specialized software. These approaches are not the same but may lead to similar results in specific situations. A natural question arises as to when these different approaches lead to the same conclusions. We compare the Besag and Newell <abbrgrp><abbr bid="B1">1</abbr></abbrgrp> cluster detection method, suitable for geographic areas with diverse population sizes, with confidence intervals for crude and directly standardized rates. The cluster detection method tests each area at a pre-specified cluster size. Conditions when these methods agree and disagree are provided. We use a dataset on self-inflicted injuries requiring medical attention as an illustration and give power comparisons for a variety of situations.</p> <p>Results</p> <p>Three conditions must be satisfied for the confidence interval and cluster detection methods to both provide statistically significant higher rates for an individual administrative area. These criteria are based on observed and expected cases above specific thresholds. In our dataset, two areas are significant with both methods and one additional area is identified with the cluster detection method. Power comparisons for different scenarios suggest that the methods have similar power for detecting rates that are twice as large as the overall rate and when the overall rate and sample sizes are not too small. The cluster detection method has better power when the size of the cluster is relatively small.</p> <p>Conclusion</p> <p>The cluster size plays a key role in the comparability of methods. The cluster detection method is preferred when the cluster size exceeds the number of cases in an administrative area or when the expected number of cases exceeds a threshold.</p