Local and Marginal Control Charts Applied to Methicillin Resistant<i>Staphylococcus Aureus Bacteraemia</i>Reports in UK Acute National Health Service Trusts

Grigg, O. A.; Spiegelhalter, D; Jones, Hayley E

doi:10.1111/j.1467-985x.2008.00553.x

Cited by 20 publications

(23 citation statements)

References 37 publications

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“…We used a Monte Carlo approach to estimate uncertainty around threshold and slope of city-specific effects of heat 23 24. For each city, we sampled 1000 values from the city-specific posterior distribution of the slope ( b c , c =1,2…1000) and from the city-specific posterior distribution of the threshold ( h c ) obtained through the Bayesian meta-analysis.…”

Section: Methodsmentioning

confidence: 99%

Impact of heat on mortality in 15 European cities: attributable deaths under different weather scenarios

Baccini

Kosatsky²,

Analitis³

et al. 2009

Journal of Epidemiology & Community Health

180

103

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Section: Methodsmentioning

confidence: 99%

Impact of heat on mortality in 15 European cities: attributable deaths under different weather scenarios

Baccini

Kosatsky²,

Analitis³

et al. 2009

Journal of Epidemiology & Community Health

180

103

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“…Alternatives could be the adoption of other count transformations. 24,25 Notice that the multivariate spatially correlated specification lead to a borrowing strength of information between parasites in making inference on posterior quantities of interest, which is particularly important when data on a single response are sparse.…”

Section: Discussionmentioning

confidence: 99%

Statistical approaches for farm and parasitic risk profiling in geographical veterinary epidemiology

Catelan

Rinaldi

Musella

et al. 2012

Stat Methods Med Res

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We address the problem of farm and parasitic risk profiling in the context of Veterinary Epidemiology. We take advantage of a cross-sectional study carried out in the Campania Region in order to study the spatial distribution of 16 parasites in 121 ovine farms. We propose a tri-level hierarchical Bayesian model, which account for multivariate spatially structured overdispersion, to obtain estimate of posterior classification probabilities, that is for each parasite and farm the probability to belong to the set of the null hypothesis. We explore four decision rules based on either posterior probabilities or posterior means and compare the results in terms of the number of false discoveries/non-discoveries or the rate of false discovery/non-discovery. Our approach proved useful for parasitological risk profiling and we show that decision rules can be easily handled.

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“…As an alternative, a signalling procedure has been proposed by [59] to control the FDR. SPC critical thresholds depend on the data and thus change over time, “which can make the technique difficult to justify to a general audience” [60].…”

Section: Methodsmentioning

confidence: 99%

Methodological challenges to multivariate syndromic surveillance: a case study using Swiss animal health data

2016

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BackgroundIn an era of ubiquitous electronic collection of animal health data, multivariate surveillance systems (which concurrently monitor several data streams) should have a greater probability of detecting disease events than univariate systems. However, despite their limitations, univariate aberration detection algorithms are used in most active syndromic surveillance (SyS) systems because of their ease of application and interpretation. On the other hand, a stochastic modelling-based approach to multivariate surveillance offers more flexibility, allowing for the retention of historical outbreaks, for overdispersion and for non-stationarity. While such methods are not new, they are yet to be applied to animal health surveillance data. We applied an example of such stochastic model, Held and colleagues’ two-component model, to two multivariate animal health datasets from Switzerland.ResultsIn our first application, multivariate time series of the number of laboratories test requests were derived from Swiss animal diagnostic laboratories. We compare the performance of the two-component model to parallel monitoring using an improved Farrington algorithm and found both methods yield a satisfactorily low false alarm rate. However, the calibration test of the two-component model on the one-step ahead predictions proved satisfactory, making such an approach suitable for outbreak prediction. In our second application, the two-component model was applied to the multivariate time series of the number of cattle abortions and the number of test requests for bovine viral diarrhea (a disease that often results in abortions). We found that there is a two days lagged effect from the number of abortions to the number of test requests. We further compared the joint modelling and univariate modelling of the number of laboratory test requests time series. The joint modelling approach showed evidence of superiority in terms of forecasting abilities.ConclusionsStochastic modelling approaches offer the potential to address more realistic surveillance scenarios through, for example, the inclusion of times series specific parameters, or of covariates known to have an impact on syndrome counts. Nevertheless, many methodological challenges to multivariate surveillance of animal SyS data still remain. Deciding on the amount of corroboration among data streams that is required to escalate into an alert is not a trivial task given the sparse data on the events under consideration (e.g. disease outbreaks).

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Local and Marginal Control Charts Applied to Methicillin ResistantStaphylococcus Aureus BacteraemiaReports in UK Acute National Health Service Trusts

Cited by 20 publications

References 37 publications

Impact of heat on mortality in 15 European cities: attributable deaths under different weather scenarios

Impact of heat on mortality in 15 European cities: attributable deaths under different weather scenarios

Statistical approaches for farm and parasitic risk profiling in geographical veterinary epidemiology

Methodological challenges to multivariate syndromic surveillance: a case study using Swiss animal health data

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