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[Time-series analysis applied to nosocomial infection]
J A López del Val1, H I Calvete Fernández, C A Carreter Oróñez
1Servicio de Medicina Preventiva, Hospital Miguel Servet, Zaragoza.
Medicina Clinica
|June 6, 1992
Summary
Time series analysis, including ARIMA models, can enhance hospital infection surveillance by establishing alert thresholds. This aids epidemiologists in timely intervention decisions for patient safety.
Area of Science:
- Epidemiology
- Biostatistics
- Healthcare Management
Background:
- Hospital-acquired infections (HAIs) pose a significant challenge to patient safety and healthcare systems.
- Current epidemiological surveillance methods for HAIs can be enhanced with advanced analytical techniques.
- Establishing reliable alert and alarm thresholds is crucial for timely intervention.
Purpose of the Study:
- To introduce a novel application of time series analysis for hospital infection surveillance.
- To develop alert and alarm thresholds for epidemiological intervention.
- To complement existing HAI surveillance systems.
Main Methods:
- Utilized classic time series analysis and Autoregressive Integrated Moving Average (ARIMA) models (Box-Jenkins).
- Focused on three distinct hospital units: intensive care, long-term care, and surgical.
- Calculated nosocomial infection intervals using 68% (1SD) and 95% (2SD) confidence levels.
Main Results:
- Detected an ascending trend in surgical and long-term care units, with no seasonal variations.
- Developed ARIMA (1,0,0) models for surgical and long-term care, prioritizing predictive power over complexity.
- Intensive care unit showed no significant trend or seasonality; models were deemed invalid due to high randomness.
Conclusions:
- Time series analysis provides valuable insights into hospital infection dynamics.
- The high random component in some units necessitates further investigation.
- Improved understanding of HAI patterns can optimize resource allocation for infection control.
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