Biomaterial Database

Multivariate time series analysis in nosocomial infection surveillance: a case study. 1998

C Fernández-Pérez, and J Tejada, and M Carrasco

Servicio de Medicina Preventiva, Hospital Universitario San Carlos, Madrid, Spain.

BACKGROUND The present study describes the use of time series analysis in the evaluation of the incidence of nosocomial infection. The main hypothesis analysed was that monthly occurrence of nosocomial infection in a hospital may be related to work-related factors such as the control and training of personnel imposed by a surveillance system, strikes supported by medical personnel and movement of personnel. Time series analysis was used to quantify, model and statistically evaluate these interventions. METHODS The data employed (March 1982-December 1990) were supplied by the nosocomial infection surveillance system of a primary-care general hospital. The monthly time series incidence of nosocomial infections (measured as percentage cumulative incidence) was analysed by curve fitting, autoregressive, integrated and moving average (ARIMA) modelling (Box-Jenkins) and intervention and dynamic regression analysis. RESULTS The imposed control and training of personnel by the surveillance system was associated with a 3.63% decrease in the accumulated monthly incidence of nosocomial infection from 7.82% to a baseline level of 4.19%. There was a strong indication that an increase of infection incidence of 4.34% corresponded to a medical strike. This increase was maintained over the following months raising the baseline level to 4.84%. An increase of 0.18% was associated with each new nursing contract. Evidence was obtained for the possible relationship between incidence of nosocomial infection and vacation periods. CONCLUSIONS The results suggest the need for strict control of the activities of hospital personnel and for the adoption of certain preventative measures during vacation periods to avoid an undesirable increase in the incidence of nosocomial infections.

UI	MeSH Term	Description	Entries
D011159	Population Surveillance	Ongoing scrutiny of a population (general population, study population, target population, etc.), generally using methods distinguished by their practicability, uniformity, and frequently their rapidity, rather than by complete accuracy.	Surveillance, Population
D011786	Quality Control	A system for verifying and maintaining a desired level of quality in a product or process by careful planning, use of proper equipment, continued inspection, and corrective action as required. (Random House Unabridged Dictionary, 2d ed)	Control, Quality,Controls, Quality,Quality Controls
D003428	Cross Infection	Any infection which a patient contracts in a health-care institution.	Hospital Infections,Nosocomial Infections,Health Care Associated Infection,Health Care Associated Infections,Healthcare Associated Infections,Infection, Cross,Infections, Hospital,Infections, Nosocomial,Cross Infections,Healthcare Associated Infection,Hospital Infection,Infection, Healthcare Associated,Infection, Hospital,Infection, Nosocomial,Infections, Cross,Infections, Healthcare Associated,Nosocomial Infection
D006769	Hospitals, General	Large hospitals with a resident medical staff which provides continuous care to maternity, surgical and medical patients.	General Hospital,General Hospitals,Hospital, General
D006801	Humans	Members of the species Homo sapiens.	Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D013030	Spain	Country located between France on the northeast and Portugal on the west and bordered by the Atlantic Ocean and the Mediterranean Sea. The capital is Madrid.	Balearic Islands,Canary Islands
D013997	Time Factors	Elements of limited time intervals, contributing to particular results or situations.	Time Series,Factor, Time,Time Factor
D015233	Models, Statistical	Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.	Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model
D015994	Incidence	The number of new cases of a given disease during a given period in a specified population. It also is used for the rate at which new events occur in a defined population. It is differentiated from PREVALENCE, which refers to all cases in the population at a given time.	Attack Rate,Cumulative Incidence,Incidence Proportion,Incidence Rate,Person-time Rate,Secondary Attack Rate,Attack Rate, Secondary,Attack Rates,Cumulative Incidences,Incidence Proportions,Incidence Rates,Incidence, Cumulative,Incidences,Person time Rate,Person-time Rates,Proportion, Incidence,Rate, Attack,Rate, Incidence,Rate, Person-time,Rate, Secondary Attack,Secondary Attack Rates
D015999	Multivariate Analysis	A set of techniques used when variation in several variables are studied simultaneously. In statistics, multivariate analysis is interpreted as any analytic method that allows simultaneous study of two or more dependent variables.	Analysis, Multivariate,Multivariate Analyses