Can syndromic surveillance help forecast winter hospital bed pressures in England?

Morbey, Roger; Charlett, André; Lake, Iain; Mapstone, James; Pebody, Richard; Sedgwick, James; Smith, Gillian; Elliot, Alex J

doi:10.1371/journal.pone.0228804

Cited by 8 publications

(6 citation statements)

References 24 publications

(43 reference statements)

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“…Therefore, syndromic surveillance (such as ILI rates, which in Italy are provided weekly by the National Health Service Sentinel System) may be able to provide early warning of hospital bed capacity strain caused by seasonal respiratory disease. 26 To our knowledge, there is no study linking all this information together to ED visits.…”

Section: Introductionmentioning

confidence: 99%

“…In this work, they found that predicting ED visits by considering both seasonality and ILI rates, compared with a model taking into account only seasonality, notably increased the fitting of the model. Therefore, syndromic surveillance (such as ILI rates, which in Italy are provided weekly by the National Health Service Sentinel System) may be able to provide early warning of hospital bed capacity strain caused by seasonal respiratory disease 26. To our knowledge, there is no study linking all this information together to ED visits.…”

Section: Introductionmentioning

confidence: 99%

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Time-series cohort study to forecast emergency department visits in the city of Milan and predict high demand: a 2-day warning system

Murtas¹,

Tùnesi²,

Andreano³

et al. 2022

BMJ Open

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ObjectivesThe emergency department (ED) is one of the most critical areas in any hospital. Recently, many countries have seen a rise in the number of ED visits, with an increase in length of stay and a detrimental effect on quality of care. Being able to forecast future demands would be a valuable support for hospitals to prevent high demand, particularly in a system with limited resources where use of ED services for non-urgent visits is an important issue.DesignTime-series cohort study.SettingWe collected all ED visits between January 2014 and December 2019 in the five larger hospitals in Milan. To predict daily volumes, we used a regression model with autoregressive integrated moving average errors. Predictors included were day of the week and year-round seasonality, meteorological and environmental variables, information on influenza epidemics and festivities. Accuracy of prediction was evaluated with the mean absolute percentage error (MAPE).Primary outcome measuresDaily all-cause EDs visits.ResultsIn the study period, we observed 2 223 479 visits. ED visits were most likely to occur on weekends for children and on Mondays for adults and seniors. Results confirmed the role of meteorological and environmental variables and the presence of day of the week and year-round seasonality effects. We found high correlation between observed and predicted values with a MAPE globally smaller than 8.1%.ConclusionsResults were used to establish an ED warning system based on past observations and indicators of high demand. This is important in any health system that regularly faces scarcity of resources, and it is crucial in a system where use of ED services for non-urgent visits is still high.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Time-series cohort study to forecast emergency department visits in the city of Milan and predict high demand: a 2-day warning system

Murtas¹,

Tùnesi²,

Andreano³

et al. 2022

BMJ Open

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“…Seasonal periodicity of hospital admissions has been described, 9,12 especially for influenza. 13,14 Seasonal periodicity has also been described for cerebrovascular, 15 cardiovascular, 16,17 and gastrointestinal 18 pathologies and may relate to weather patterns, the availability of services, and patient behaviours. 19 Weekly and daily periodicity might also relate to pathogenesis of the diseasefor example demand distribution of cardiovascular disease peaks in the late morning and there are greater case numbers on Mondays [15][16][17] .…”

Section: Discussionmentioning

confidence: 99%

Rhythmicity of patient flow in an acute medical unit: relationship to hospital occupancy, 7-day working and the effect of COVID-19

Dauncey

Kelly

Baykov

et al. 2021

QJM: An International Journal of Medicine

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Summary Background The Acute Medical Unit (AMU) provides care for unscheduled hospital admissions. Seven-day consultant presence and morning AMU discharges have been advocated to improve hospital bed management. Aims To determine whether a later time of daily peak AMU occupancy correlates with measures of hospital stress; whether 7-day consultant presence, for COVID-19, abolished weekly periodicity of discharges. Design Retrospective cohort analysis. Methods : Anonymised AMU admission and discharge times were retrieved from the Profile Information Management System (PIMS), at a large, urban hospital from 14 April 2014 to 31 December 2018 and 20 March to 2 May 2020 (COVID-19 peak). Minute-by-minute admission and discharge times were combined to construct a running total of AMU bed occupancy. Fourier transforms were used to determine periodicity. We tested association between (i) average AMU occupancy and (ii) time of peak AMU occupancy, with measures of hospital stress (total medical bed occupancy and ‘medical outliers’ on non-medical wards). Results : Daily, weekly and seasonal patterns of AMU bed occupancy were evident. Timing of AMU peak occupancy was unrelated to each measure of hospital stress: total medical inpatients (Spearman’s rho, rs = 0.04, P = 0.24); number of medical outliers (rs = −0.06, P = 0.05). During COVID-19, daily bed occupancy was similar, with continuation of greater Friday and Monday discharges than the weekend. Conclusions : Timing of peak AMU occupancy did not alter with hospital stress. Efforts to increase morning AMU discharges are likely to have little effect on hospital performance. Seven-day consultant presence did not abolish weekly periodicity of discharges—other factors influence weekend discharges.

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“…Additionally, data on general practice (GP) physician in-hours and out-of-hours consultations are collected, among others. These existing syndromic data sources [ 15 ] were previously used in studies focused on defining early warning indicators and algorithms for gastrointestinal illness [ 16 ] and norovirus [ 17 ] and also real-time predictions informing forecasts of hospital admissions due to respiratory illness [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Predicting Norovirus in England Using Existing and Emerging Syndromic Data: Infodemiology Study

Ondrikova¹,

Harris²,

Douglas³

et al. 2023

J Med Internet Res

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Background Norovirus is associated with approximately 18% of the global burden of gastroenteritis and affects all age groups. There is currently no licensed vaccine or available antiviral treatment. However, well-designed early warning systems and forecasting can guide nonpharmaceutical approaches to norovirus infection prevention and control. Objective This study evaluates the predictive power of existing syndromic surveillance data and emerging data sources, such as internet searches and Wikipedia page views, to predict norovirus activity across a range of age groups across England. Methods We used existing syndromic surveillance and emerging syndromic data to predict laboratory data indicating norovirus activity. Two methods are used to evaluate the predictive potential of syndromic variables. First, the Granger causality framework was used to assess whether individual variables precede changes in norovirus laboratory reports in a given region or an age group. Then, we used random forest modeling to estimate the importance of each variable in the context of others with two methods: (1) change in the mean square error and (2) node purity. Finally, these results were combined into a visualization indicating the most influential predictors for norovirus laboratory reports in a specific age group and region. Results Our results suggest that syndromic surveillance data include valuable predictors for norovirus laboratory reports in England. However, Wikipedia page views are less likely to provide prediction improvements on top of Google Trends and Existing Syndromic Data. Predictors displayed varying relevance across age groups and regions. For example, the random forest modeling based on selected existing and emerging syndromic variables explained 60% variance in the ≥65 years age group, 42% in the East of England, but only 13% in the South West region. Emerging data sets highlighted relative search volumes, including “flu symptoms,” “norovirus in pregnancy,” and norovirus activity in specific years, such as “norovirus 2016.” Symptoms of vomiting and gastroenteritis in multiple age groups were identified as important predictors within existing data sources. Conclusions Existing and emerging data sources can help predict norovirus activity in England in some age groups and geographic regions, particularly, predictors concerning vomiting, gastroenteritis, and norovirus in the vulnerable populations and historical terms such as stomach flu. However, syndromic predictors were less relevant in some age groups and regions likely due to contrasting public health practices between regions and health information–seeking behavior between age groups. Additionally, predictors relevant to one norovirus season may not contribute to other seasons. Data biases, such as low spatial granularity in Google Trends and especially in Wikipedia data, also play a role in the results. Moreover, internet searches can provide insight into mental models, that is, an individual’s conceptual understanding of norovirus infection and transmission, which could be used in public health communication strategies.

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Can syndromic surveillance help forecast winter hospital bed pressures in England?

Cited by 8 publications

References 24 publications

Time-series cohort study to forecast emergency department visits in the city of Milan and predict high demand: a 2-day warning system

Time-series cohort study to forecast emergency department visits in the city of Milan and predict high demand: a 2-day warning system

Rhythmicity of patient flow in an acute medical unit: relationship to hospital occupancy, 7-day working and the effect of COVID-19

Predicting Norovirus in England Using Existing and Emerging Syndromic Data: Infodemiology Study

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