Comparison of Statistical Algorithms for the Detection of Infectious Disease Outbreaks in Large Multiple Surveillance Systems

A large-scale multiple surveillance system for infectious disease outbreaks has been in operation in England and Wales since the early 1990s. Changes to the statistical algorithm at the heart of the system were proposed and the purpose of this paper is to compare two new algorithms with the original algorithm. Test data to evaluate performance are created from weekly counts of the number of cases of each of more than 2000 diseases over a twenty-year period. The time series of each disease is separated into one series giving the baseline (background) disease incidence and a second series giving disease outbreaks. One series is shifted forward by twelve months and the two are then recombined, giving a realistic series in which it is known where outbreaks have been added. The metrics used to evaluate performance include a scoring rule that appropriately balances sensitivity against specificity and is sensitive to variation in probabilities near 1. In the context of disease surveillance, a scoring rule can be adapted to reflect the size of outbreaks and this was done. Results indicate that the two new algorithms are comparable to each other and better than the algorithm they were designed to replace

Detection of Infectious Disease Outbreaks From Laboratory Data With Reporting Delays

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

Comparison of statistical algorithms for daily syndromic surveillance aberration detection

Motivation: Public health authorities can provide more effective and timely interventions to protect populations during health events if they have effective multi-purpose surveillance systems. These systems rely on aberration detection algorithms to identify potential threats within large datasets. Ensuring the algorithms are sensitive, specific and timely is crucial for protecting public health. Here, we evaluate the performance of three detection algorithms extensively used for syndromic surveillance: the ‘rising activity, multilevel mixed effects, indicator emphasis’ (RAMMIE) method and the improved quasi-Poisson regression-based method known as ‘Farrington Flexible’ both currently used at Public Health England, and the ‘Early Aberration Reporting System’ (EARS) method used at the US Centre for Disease Control and Prevention. We model the wide range of data structures encountered within the daily syndromic surveillance systems used by PHE. We undertake extensive simulations to identify which algorithms work best across different types of syndromes and different outbreak sizes. We evaluate RAMMIE for the first time since its introduction. Performance metrics were computed and compared in the presence of a range of simulated outbreak types that were added to baseline data. Results: We conclude that amongst the algorithm variants that have a high specificity (i.e. ¿90%), Farrington Flexible has the highest sensitivity and specificity, whereas RAMMIE has the highest probability of outbreak detection and is the most timely, typically detecting outbreaks 2-3 days earlier. Availability and Implementation: R codes developed for this project are available through https://github.com/FelipeJColon/AlgorithmCompariso

A Methodological Framework for the Evaluation of Syndromic Surveillance Systems: A Case Study of England

Background: Syndromic surveillance complements traditional public health surveillance by collecting and analysing health indicators in near real time. The rationale of syndromic surveillance is that it may detect health threats faster than traditional surveillance systems permitting more timely, and hence potentially more effective public health action. The effectiveness of syndromic surveillance largely relies on the methods used to detect aberrations. Very few studies have evaluated the performance of syndromic surveillance systems and consequently little is known about the types of events that such systems can and cannot detect. Methods: We introduce a framework for the evaluation of syndromic surveillance systems that can be used in any setting based upon the use of simulated scenarios. For a range of scenarios this allows the time and probability of to be determined and uncertainty is fully incorporated. In addition, we demonstrate how such a framework can model the benefits of increases in the number of centres reporting syndromic data and also determine the minimum size of outbreaks that can or cannot be detected. Here, we demonstrate its utility using simulations of national influenza outbreaks and localised outbreaks of cryptosporidiosis. Results: Influenza outbreaks are consistently detected with larger outbreaks being detected in a more timely manner. Small cryptosporidiosis outbreaks (<1000 symptomatic individuals) are unlikely to be detected. We also demonstrate the advantages of having multiple syndromic data streams (e.g. emergency attendance data, telephone helpline data, general practice consultation data) as different streams are able to detect different types outbreaks with different efficacy (e.g. emergency attendance data are useful for the detection of pandemic influenza but not for outbreaks of cryptosporidiosis). We also highlight that for any one disease, the utility of data streams may vary geographically, and that the detection ability of syndromic surveillance varies seasonally (e.g. an influenza outbreak starting in July is detected sooner than one starting later in the year). We argue that our framework constitutes a useful tool for public health emergency preparedness in multiple settings. Conclusions: The proposed framework allows the exhaustive evaluation of any syndromic surveillance system and constitutes a useful tool for emergency preparedness and response

Challenges in developing methods for quantifying the effects of weather and climate on water-associated diseases: A systematic review

Infectious diseases attributable to unsafe water supply, sanitation and hygiene (e.g. Cholera, Leptospirosis, Giardiasis) remain an important cause of morbidity and mortality, especially in low-income countries. Climate and weather factors are known to affect the transmission and distribution of infectious diseases and statistical and mathematical modelling are continuously developing to investigate the impact of weather and climate on water-associated diseases. There have been little critical analyses of the methodological approaches. Our objective is to review and summarize statistical and modelling methods used to investigate the effects of weather and climate on infectious diseases associated with water, in order to identify limitations and knowledge gaps in developing of new methods. We conducted a systematic review of English-language papers published from 2000 to 2015. Search terms included concepts related to water-associated diseases, weather and climate, statistical, epidemiological and modelling methods. We found 102 full text papers that met our criteria and were included in the analysis. The most commonly used methods were grouped in two clusters: process-based models (PBM) and time series and spatial epidemiology (TS-SE). In general, PBM methods were employed when the bio-physical mechanism of the pathogen under study was relatively well known (e.g. Vibrio cholerae); TS-SE tended to be used when the specific environmental mechanisms were unclear (e.g. Campylobacter). Important data and methodological challenges emerged, with implications for surveillance and control of water-associated infections. The most common limitations comprised: non-inclusion of key factors (e.g. biological mechanism, demographic heterogeneity, human behavior), reporting bias, poor data quality, and collinearity in exposures. Furthermore, the methods often did not distinguish among the multiple sources of time-lags (e.g. patient physiology, reporting bias, healthcare access) between environmental drivers/exposures and disease detection. Key areas of future research include: disentangling the complex effects of weather/climate on each exposure-health outcome pathway (e.g. person-to-person vs environment-to-person), and linking weather data to individual cases longitudinally

Comparison of statistical algorithms for syndromic surveillance aberration detection

ObjectiveTo investigate whether alternative statistical approaches can improve daily aberration detection using syndromic surveillance in England.IntroductionSyndromic surveillance involves monitoring big health datasets to provide early warning of threats to public health. Public health authorities use statistical detection algorithms to interrogate these datasets for aberrations that are indicative of emerging threats. The algorithm currently in use at Public Health England (PHE) for syndromic surveillance is the ‘rising activity, multi-level mixed effects, indicator emphasis’ (RAMMIE) method (Morbey et al, 2015), which fits a mixed model to counts of syndromes on a daily basis. This research checks whether the RAMMIE method works across a range of public health scenarios and how it compares to alternative methods.MethodsFor this purpose, we compare RAMMIE to the improved quasi-Poisson regression-based approach (Noufaily et al, 2013), currently implemented at PHE for weekly infectious disease laboratory surveillance, and to the Early Aberration Reporting System (EARS) method (Rossi et al, 1999), which is used for syndromic surveillance aberration detection in many other countries. We model syndromic datasets, capturing real data aspects such as long-term trends, seasonality, public holidays, and day-of-the-week effects, with or without added outbreaks. Then, we compute the sensitivity and specificity to compare how well each of the algorithms detects synthetic outbreaks to provide recommendations for the most suitable statistical methods to use during different public health scenarios.ResultsPreliminary results suggest all methods provide high sensitivity and specificity, with the (Noufaily et al, 2013) approach having the highest sensitivity and specificity. We showed that for syndromes with long-term increasing trends, RAMMIE required modificaiton to prevent excess false alarms. Also, our study suggests further work is needed to fully account for public holidays and day-of-the-week effects.ConclusionsOur study will provide recommendations for which algorithm is most effective for PHE's syndromic surveillance for a range of different syndromes. Furthermore our work to generate standardised synthetic syndromic datasets and a range of outbreaks can be used for future evaluations in England and elsewhere.ReferencesNoufaily, A., Enki, D. G., Farrington, C. P., Garthwaite, P., Andrews, N. and Charlett, A. (2013). An Improved Algorithm for Outbreak Detection in Multiple Surveillance Systems. Statistics in Medicine, 32(7), 1206-1222.Morbey, R. A., Elliot, A. J., Charlett, A., Verlander, A. Q, Andrews, N. and Smith, G. (2013). The application of a novel ‘rising activity, multi-level mixed effects, indicator emphasis’ (RAMMIE) method for syndromic surveillance in England, Bioinformatics, 31(22), 3660-3665.Rossi, G, Lampugnani, L, Marchi, M. (1999), An approximate CUSUM procedure for surveillance of health events. Statistics in Medicine, 18, 2111–212

Uterine artery embolization for management of placenta accreta, a single-center experience and literature review

“Morbidly adherent placenta” is a term that describes the continuum of placenta accreta, increta, and percreta. Placenta accreta is the least invasive form, whereas placenta percreta represents a complete penetration of the trophoblast through the uterus that reaches the serosal surface and potentially invades the bladder, rectal wall, and pelvic vessels. Leaving the placenta in situ in the setting of abnormally invasive placenta is now widely practiced. We herein present three cases of abnormal placental implantation diagnosed by antenatal ultrasound and magnetic resonance imaging, in which uterine artery embolization was performed to induce placental infarction and eventually rapid regression but most importantly to minimize peripartum and postpartum bleeding. As we do this, we sought to review the risks of placenta accreta, increta, and percreta and evaluate the role of endovascular therapy to improve maternal outcomes when abnormal placental implantation occurs