Biomedical subjects

J A López del Val

Publications and source records attributed to J A López del Val.

3 recordsLinked to original sources

PubMed1993

[Principal components analysis].

OBJECTIVE: This study wants to identify factors or components latent besides health indicators from Spanish regions, and its graphic output. DESIGN: Observational study. SETTING: Twenty eight indicators from regions were used: mortality, morbidity, communicable and no-communicable diseases, diet, dwelling and sanitary resources. Measurement was made between 1980-1988. INTERVENTIONS: Principal component analysis has been applied to the indicators, reducing data dimension. MEASUREMENT AND MAIN RESULTS: Eight factors have been extracted, which explain 90% of the original information. This analysis, as can be seen from communnalities, represents correctly the set of original variables. The factors with more easily interpretation were: shortage sanitary resources, develop diet, mortality, chronic diseases and accidental. CONCLUSIONS: Only reduction data dimension could be justify the use of principal component analysis. Behind the agrupation of variables is mostly the socioeconomic background.

Health Status Indicators↗

PubMed1992

[Time-series analysis applied to nosocomial infection].

BACKGROUND: In this study we introduce a new view of hospital infection, to apply time series techniques to it. Our objective is to complement hospital infection's epidemiological surveillance by means of obtaining alert and alarm thresholds that make easy to the epidemiologist the decision of intervention, in case they are exceeded. METHODS: We have used the classic time series analysis described by Rumeau-Rouquette, and ARIMA (Autoregresive Integrated Moving Average) models developed by Box and Jenkins. The study focus on three hospital units: one intensive care, one long term care and one surgical unit. The nosocomial infection intervals have been calculated with a 68% (1SD) and 95% (2SD) confidence levels. RESULTS: We detect an ascending general trend in the last two units, without the detection of seasonal variations. Two ARIMA (1, 0, 0) models we obtained for surgery and long term care, discarding other better adjusted models, more complex and difficult to obtain, but with no real advantage in prediction power. Confidence intervals were calculated with both methods. We did not find general trend and seasonal variations for intensive care unit. No model was considered valid, because of its high random component. The nosocomial infection intervals have been calculated with mean +/- 1SD and mean +/- 2SD. CONCLUSIONS: We think that more precise knowledge of hospital infection, with a high random component in our study, can be in addition useful to assign priority to human and material resources.

Analysis of Variance↗

PubMed1991

[Effect of atmospheric factors on the presentation of cerebrovascular accidents].

The concept of meteoropathy implicates the atmospheric properties as determining or at least provoking causes of different disease conditions. Thus, we know that changes in temperature, atmospheric pressure and humidity rate are associated with a higher incidence of stroke presentation. Considered as a whole, these three atmospheric factors have been associated with the several clinical diagnoses of stroke. A higher presentation rate of stroke has been found when the three following facts concurred: a reduction of 10 degrees (C) in the temperature of the preceding day, an increase of the atmospheric humidity rate from 20% to 60% on the day of stroke, and an increase in atmospheric pressure of 6 mlbs from the preceding day (s = 0.0000). In addition, a higher rate of presentation of stroke was found when the difference in atmospheric pressure was 16 mlbs between the day of stroke and the preceding one (s = 0.049). Also, when the atmosphere humidity rate increased from 20% to 60%, the difference in mean temperature between the day of stroke and the preceding one was 3 degrees (C), and the atmospheric pressure increased in 6 mlbs, the incidence of stroke was higher (s = 0.007). We compare these findings with those from other authors.

Atmospheric Pressure↗