Spatial and Temporal Patterns of Demand for Hotel Accommodation—Time Series Analysis in Yorkshire and Humberside, UK. Douglas Jeffrey. Tourism Management, vol. 6, no. 1, March 1985, pp. 8-22. Butterworth Scientific Ltd., P.O. Box 63, Westbury House, Bury Street, Guildford GU2 5BH, United Kingdom. $80 annual subscription

OBJECTIVETo describe the effect of universal methicillin-resistant Staphylococcus aureus (MRSA) decolonization therapy in a large intensive care unit (ICU) on the rates of MRSA cases and acquisitions in a UK hospital.DESIGNDescriptive study.SETTINGUniversity Hospitals Birmingham (UHB) NHS Foundation Trust is a tertiary referral teaching hospital in Birmingham, United Kingdom, that provides clinical services to nearly 1 million patients every year.METHODSA break-point time series analysis and kernel regression models were used to detect significant changes in the cumulative monthly numbers of MRSA bacteremia cases and acquisitions from April 2013 to August 2016 across the UHB system.RESULTSPrior to 2014, all ICU patients at UHB received universal MRSA decolonization therapy. In August 2014, UHB discontinued the use of universal decolonization due to published reports in the United Kingdom detailing the limited usefulness and cost-effectiveness of such an intervention. Break-point time series analysis of MRSA acquisition and bacteremia data indicated that break points were associated with the discontinuation and subsequent reintroduction of universal decolonization. Kernel regression models indicated a significant increase (P<.001) in MRSA acquisitions and bacteremia cases across UHB during the period without universal decolonization.CONCLUSIONWe suggest that routine decolonization for MRSA in a large ICU setting is an effective strategy to reduce the spread and incidence of MRSA across the whole hospital.Infect Control Hosp Epidemiol 2017;38:430–435

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Effects of Data Aggregation on Time Series Analysis of Seasonal Infections

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17165887 ◽

2020 ◽

Vol 17 (16) ◽

pp. 5887 ◽

Cited By ~ 2

Author(s):

Tania M. Alarcon Falconi ◽

Bertha Estrella ◽

Fernando Sempértegui ◽

Elena N. Naumova

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Data Aggregation ◽

Temporal Patterns ◽

Epidemiological Studies ◽

Monthly Data ◽

Minimal Impact ◽

Series Analysis ◽

Enteric Infections ◽

Thinking Process

Time series analysis in epidemiological studies is typically conducted on aggregated counts, although data tend to be collected at finer temporal resolutions. The decision to aggregate data is rarely discussed in epidemiological literature although it has been shown to impact model results. We present a critical thinking process for making decisions about data aggregation in time series analysis of seasonal infections. We systematically build a harmonic regression model to characterize peak timing and amplitude of three respiratory and enteric infections that have different seasonal patterns and incidence. We show that irregularities introduced when aggregating data must be controlled during modeling to prevent erroneous results. Aggregation irregularities had a minimal impact on the estimates of trend, amplitude, and peak timing for daily and weekly data regardless of the disease. However, estimates of peak timing of the more common infections changed by as much as 2.5 months when controlling for monthly data irregularities. Building a systematic model that controls for data irregularities is essential to accurately characterize temporal patterns of infections. With the urgent need to characterize temporal patterns of novel infections, such as COVID-19, this tutorial is timely and highly valuable for experts in many disciplines.

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