Spontaneous baroreceptor reflex sensitivity for risk stratification of heart failure patients: optimal cut-off and age effects.

Baroreceptor reflex sensitivity (BRS) is an important prognostic factor because a reduced BRS has been associated with an adverse cardiovascular outcome. The threshold for a 'reduced' BRS was established by the ATRAMI study at BRS <3 ms/mmHg in patients with a previous myocardial infarction, and has been shown to improve risk assessment in many other cardiac dysfunctions. The successful application of this cut-off to other populations suggests that it may reflect an inherent property of baroreflex functioning, so our goal is to investigate whether it represents a 'natural' partition of BRS values. As reduced baroreflex responsiveness is also associated with ageing, we investigated whether a BRS estimate <3 ms/mmHg could be the result of a process of physiological senescence as well as a sign of BRS dysfunction. This study involved 228 chronic heart failure patients and 60 age-matched controls. Our novel method combined transfer function BRS estimation and automatic clustering of BRS probability distributions, to define indicative levels of different BRS activities. The analysis produced a fit clustering (cophenetic correlation coefficient 0.9 out of 1) and identified one group of homogeneous patients (well separated from the others by 3 ms/mmHg) with an increased BRS-based mortality risk [hazard ratio (HR): 3.19 (1.73, 5.89), P<0.001]. The age-dependent BRS cut-off, estimated by 5% quantile regression of log (BRS) with age (considering the age-matched controls), provides a similar mortality value [HR: 2.44 (1.37, 4.43), P=0.003]. In conclusion, the 3 ms/mmHg cut-off identifies two large clusters of homogeneous heart failure (HF) patients, thus supporting the hypothesis of a natural cut-off in the HF population. Furthermore, age was found to have no statistical impact on risk assessment, suggesting that there is no need to establish age-based cut-offs because 3 ms/mmHg optimally identifies patients at high mortality risk.

Wavelets-based clustering of air quality monitoring sites.

This paper aims at providing a variance/covariance profile of a set of 36 monitoring stations measuring ozone (O3) and nitrogen dioxide (NO2) hourly concentrations, collected over the period 2005-2013, in Portugal mainland. The resulting individual profiles are embedded in a wavelet decomposition-based clustering algorithm in order to identify groups of stations exhibiting similar profiles. The results of the cluster analysis identify three groups of stations, namely urban, suburban/urban/rural, and a third group containing all but one rural stations. The results clearly indicate a geographical pattern among urban stations, distinguishing those located in Lisbon area from those located in Oporto/North. Furthermore, for urban stations, intra-diurnal and daily time scales exhibit the highest variance. This is due to the more relevant chemical activity occurring in high NO2 emissions areas which are responsible for high variability on daily profiles. These chemical processes also explain the reason for NO2 and O3 being highly negatively cross-correlated in suburban and urban sites as compared with rural stations. Finally, the clustering analysis also identifies sites which need revision concerning classification according to environment/influence type.

Prediction of extreme ozone levels in Barcelona, Spain.

Barcelona is one of the most polluted cities in Western Europe, although our levels of air pollution are within the World Health Organisation air quality guidelines. However, high concentrations of air pollution have not been studied yet. Ground ozone levels is a topic of considerable environmental concern, since excessive level of ozone are taken as indicative of high pollution. In terms of the air quality guidelines ozone levels higher than 100 microg m(-3) can start to be health-hazards for human health. Our objective is to report a detailed analysis of ozone data exceeding the thresholds established by the air quality guidelines. Data analysed were collected in two measurement stations in Barcelona, for the reference period 1991--1996. Applying statistical techniques commonly used in the analysis of extreme values, mainly the Peak Over Threshold method was used for in this study. The analysis reveal that the ozone threshold values for the protection of human health has exceeded many times in both stations. The estimated return values for 3, 10, and 40 yr exceed the threshold value for information to the public of almost once in both stations, also it seems to be unlikely that the threshold value for warning to the public will be exceeded in 40 yr.

[Accurate interpretation of point estimates, confidence intervals, and hypothesis contrasts in public health]. / Interpretando correctamente en salud pública estimaciones puntuales, intervalos de confianza y contrastes de hipótesis.

This essay reviews some statistical concepts frequently used in public health research that are commonly misinterpreted. These include point estimates, confidence intervals, and hypothesis tests. By comparing them using the classical and the Bayesian perspectives, their interpretation becomes clearer. The English version of this paper is available at:http://www.insp.mx/salud/index.html.

Interpretando correctamente en salud pública estimaciones puntuales, intervalos de confianza y contrastes de hipótesis / Accurate interpretation of point estimates, confidence intervals, and hypothesis tests in public health

El presente ensayo trata de aclarar algunos conceptos utilizados habitualmente en el campo de investigación de la salud pública, que en numerosas situaciones son interpretados de manera incorrecta. Entre ellos encontramos la estimación puntual, los intervalos de confianza, y los contrastes de hipótesis. Estableciendo un paralelismo entre estos tres conceptos, podemos observar cuáles son sus diferencias más importantes a la hora de ser interpretados, tanto desde el punto de vista del enfoque clásico como desde la óptica bayesiana.

This essay reviews some statistical concepts frequently used in public health research that are commonly misinterpreted. These include point estimates, confidence intervals, and hypothesis tests. By comparing them using the classical and the Bayesian perspectives, their interpretation becomes clearer.