Doyo Gragn Enki

An improved algorithm for outbreak detection in multiple surveillance systems.

Objective To improve the performance of the England and Wales large scale multiple statistical surveillance system for infectious disease outbreaks with a view to reducing the number of false reports, while retaining good power to detect genuine outbreaks. Introduction There has been much interest in the use of statistical surveillance systems over the last decade, prompted by concerns over bioterrorism, the emergence of new pathogens such as SARS and swine flu, and the persistent public health problems of infectious disease outbreaks. In the United Kingdom (UK), statistical surveillance methods have been in routine use at the Health Protection Agency (HPA) since the early 1990s and at Health Protection Scotland (HPS) since the early 2000s (1,2). These are based on a simple yet robust quasi-Poisson regression method (1). We revisit the algorithm with a view to improving its performance. Methods We fit a quasi-Poisson regression model to baseline data. One of the limitations of the current algorithm is the small number of baseline weeks used. We propose a simple seasonal adjustment using factors. We extend the model to include a 10-level factor. We fit the trend component always irrespective of its statistical significance. We are concerned that the existing weighting procedure is too drastic. The baseline at a certain week is down-weighted if the standardized Anscombe residual for that week is greater than 1. This condition was chosen empirically to avoid reducing the sensitivity of the system in the presence of large outbreaks in the baselines, but may be increasing the FPR unduly when there are no or only small outbreaks in the baselines. We investigate several other options, including reducing the down-weighting to cases where the Anscombe residuals are greater than 2 or 3. We evaluate a new re-weighting scheme informed by past decisions. Using this adaptive scheme, baseline data where an alarm was flagged are down-weighted to reduce their effect on current predictions. The criterion we use for re-weighting, here, is the value of the exceedance score. Finally, we investigate the validity of the upper threshold values based on the quasi-Poisson model when the data are generated using known negative binomial distributions. Results Our evaluation of the existing algorithm showed that the false positive rate (FPR) is too high. A novel feature of our new models is that they make use of much more baseline data. This resulted in a better estimation of the trend and variance and decreased the FPR. In addition, we found that the trend should always be fitted even when non-significant (or extreme). This decreases the discrepancies in the results when moving from one week to another. The adaptive reweighting scheme was found to give broadly equivalent results to the reweighting method based on scaled Anscombe residuals. Using the latter as in the original HPA method, but with much higher threshold for reweighting decreased the FPR further. Our investigations also suggest that the negative binomial model is a reasonable one, though not ideal in all circumstances. Thus, there is a good case for replacing the quasi-Poisson model with the negative binomial. One of the unusual features of the HPA system is that it is run every week on a database of more than 3300 distinct organisms, which is likely to produce a large number of aberrances. We found that retaining the exceedance score approach based on the 0.995 quantile is perfectly reasonable. This involves ranking aberrant organisms in order of exceedance. Conclusions We have undertaken a thorough evaluation of the HPA’s outbreak detection system based on simulated and real data. The main conclusion from this evaluation is that the FPR is too high, owing to a combination of factors notably excessive down-weighting of high baselines and reliance on too few baseline weeks.

Automated Biosurveillance Data from England and Wales, 1991–2011

Twenty years of data provide valuable insights for the design of large automated outbreak detection systems.

A clustering approach to interpretable principal components

A new method for constructing interpretable principal components is proposed. The method first clusters the variables, and then interpretable (sparse) components are constructed from the correlation matrices of the clustered variables. For the first step of the method, a new weighted-variances method for clustering variables is proposed. It reflects the nature of the problem that the interpretable components should maximize the explained variance and thus provide sparse dimension reduction. An important feature of the new clustering procedure is that the optimal number of clusters (and components) can be determined in a non-subjective manner. The new method is illustrated using well-known simulated and real data sets. It clearly outperforms many existing methods for sparse principal component analysis in terms of both explained variance and sparseness.

Coital incontinence: what can we learn from urodynamic assessment?

OBJECTIVE To evaluate the association of coital incontinence (CI) with lower urinary tract symptoms (LUTS) and to understand the pathophysiology of CI. METHODS A database of all women who underwent urodynamic testing in a tertiary referral center in the United Kingdom, from January 1991 to December 2009, was retrospectively analyzed. All women reporting CI were included in the study. Urodynamic testing and interpretation of results were performed in accordance with the recommendations of the International Continence Society. RESULTS The prevalence of CI in women with LUTS undergoing urodynamics was 11.8%, and they were significantly younger (mean age = 45.3 years; P <.001) than the rest of the group (mean age = 53.4 years). Obesity (body mass index >30 kg/m(2)) and parity were significantly associated with CI (P <.001). Women reporting CI significantly smoked cigarettes and used antidepressants (P <.001). There were fewer postmenopausal women (P <.001) with CI, and previous hysterectomy had a negative association with CI (P = .005). The majority of women had overactive bladder symptoms and stress urinary incontinence (P <.001). CI was significantly associated with urodynamic stress incontinence (UDSI; odds ratio = 2.35) and detrusor overactivity (DO; odds ratio = 1.22) but not DO incontinence (P <.001). Parity, overactive bladder symptoms, and UDSI reached statistical significance when analysis was performed for age-matched controls. CONCLUSION CI is not uncommon in women with LUTS, and they present earlier than women with LUTS and no CI. CI is significantly associated with risk factors like parity, obesity, cigarette smoking, and antidepressant usage. CI is multifactorial and associated with UDSI and DO but not DO incontinence.

The Functional Effects of Cigarette Smoking in Women on the Lower Urinary Tract

The aim of the study was to evaluate the urodynamic findings in women who smoke cigarettes, with bothersome lower urinary tract symptoms, to help develop an understanding of potential impact of smoking on the lower urinary tract function. Methods: A database of 11,678 women who underwent urodynamic testing in a tertiary referral centre in the United Kingdom, from January 1991 to December 2009 was retrospectively analysed. All women reporting cigarette smoking were included in the study group. Urodynamic testing and interpretation of results were done in accordance with the recommendations of the International Continence Society. Results: Overall, 2,476 (21.2%) women reported smoking cigarettes. Overactive bladder symptoms (OAB) were more common in smokers (OR 1.14, p = 0.006). Female smokers significantly complained of secondary nocturnal enuresis (OR 2.26, p < 0.001) and coital incontinence (CI; OR 1.14, p < 0.001). Detrusor overactivity (DO; OR 1.42, p < 0.001) and detrusor overactivity incontinence (DOI; OR 1.42, p < 0.001) were the most significant urodynamic findings. Smoking was not shown to be significantly associated with SUI (OR 1.08, p = 0.213) or urodynamic stress incontinence (OR 0.86, p = 0.001). Conclusion: Cigarette smoking is associated with OAB, secondary nocturnal enuresis and CI. DO and DOI are the most significant urodynamic findings.

Activation of multiple growth factor signalling pathways is frequent in meningiomas.

A minority of meningiomas are difficult to treat with surgery or radiotherapy, and chemotherapeutic alternatives are limited. This study aims to better understand pathways that are active in meningiomas, in order to direct future treatment strategies. We investigated the expression and activation of multiple growth factor receptors, their ligands and downstream signalling pathways in 30 meningiomas using immunohistochemistry. Expression was correlated with chromosome 22q loss. Membrane expression of VEGF receptor (VEGFR) and platelet‐derived growth factor receptor (PDGFR)β was seen in 83% of tumors, Axl in 70%, EGFR in 50% and insulin‐like growth factor receptor in 47%. Expression was similar in low‐ and high‐grade tumors, but membrane EGFR expression was not seen in tumors showing chromosome 22q loss (P < 0.05). Expression of ligands (IGF, NRG, VEGF, Gas 6), and signalling proteins (Mek, Erk, Jnk, Akt) and pS6RP, was widespread. Western blot confirmed widespread Axl expression and supported selective expression of EGFR in NF2‐intact meningiomas. The majority of meningiomas express and show activation of multiple growth factor receptors and their signalling pathways, irrespective of tumor grade. In addition to previously reported receptors, Axl offers a new therapeutic target. The findings also suggest that anti‐EGFR based therapies may be less effective in meningiomas with 22q loss.

Risk factors and functional abnormalities associated with adult onset secondary nocturnal enuresis in women

The study aims to evaluate bothersome lower urinary tract symptoms (LUTS), risk factors, and associated functional abnormalities in women reporting adult onset secondary nocturnal enuresis (SNE), to help understand factors associated with SNE.

Detection of Infectious Disease Outbreaks From Laboratory Data With Reporting Delays

ABSTRACT Many statistical surveillance systems for the timely detection of outbreaks of infectious disease operate on laboratory data. Such data typically incur reporting delays between the time at which a specimen is collected for diagnostic purposes, and the time at which the results of the laboratory analysis become available. Statistical surveillance systems currently in use usually make some ad hoc adjustment for such delays, or use counts by time of report. We propose a new statistical approach that takes account of the delays explicitly, by monitoring the number of specimens identified in the current and past m time units, where m is a tuning parameter. Values expected in the absence of an outbreak are estimated from counts observed in recent years (typically 5 years). We study the method in the context of an outbreak detection system used in the United Kingdom and several other European countries. We propose a suitable test statistic for the null hypothesis that no outbreak is currently occurring. We derive its null variance, incorporating uncertainty about the estimated delay distribution. Simulations and applications to some test datasets suggest the method works well, and can improve performance over ad hoc methods in current use. Supplementary materials for this article are available online.

A time-varying shared frailty model with application to infectious diseases

We propose a new parametric time-varying shared frailty model to represent changes over time in population heterogeneity, for use with bivariate current status data. The model uses a power transformation of a time-invariant frailty

Personal well-being networks, social capital and severe mental illness: exploratory study

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