Influence of dietary total antioxidant capacity on the association between smoking and hypertension in Brazilian graduates (CUME project).

BACKGROUND AND AIMS: Hypertension (HTN) is a chronic non-communicable disease influenced by non-modifiable risk factors, such as sex and age, as well as modifiable risk factors such as lifestyle, including diet and smoking. Moreover, diet quality among smokers is worse than that of non-smokers, mainly in terms of antioxidant content. Thus, the current study aimed to investigate whether dietary total antioxidant capacity (dTAC) influences the association between smoking and HTN. METHODS AND RESULTS: This cross-sectional study included 4303 graduates (69.35% women) from the Cohort of Minas Gerais Universities (CUME) project. An online food frequency questionnaire was administered to participants, and dTAC was estimated using the ferric reducing antioxidant power method. In the questionnaires, individuals reported smoking status, systolic and diastolic blood pressure values, previous HTN diagnosis, and use of antihypertensive drugs. Logistic regression models were used to estimate the odds ratio and 95% confidence interval between smoking and HTN, stratified by the median dTAC. Current and former smokers had higher dTAC values despite their lower fruit intake. Moreover, coffee was the main contributor to dTAC among them. Smoking was associated with a higher likelihood of HTN, mainly among individuals with a higher dTAC. However, after exclusion of coffee antioxidant capacity, there was an association between only smoking and HTN in individuals with lower dTAC. CONCLUSIONS: The controversial association between higher dTAC and HTN can result from high coffee intake. Higher dTAC without coffee intake may mitigate the association between smoking and HTN in this population.

Border analysis for spatial clusters.

BACKGROUND: The spatial scan statistic is widely used by public health professionals in the detection of spatial clusters in inhomogeneous point process. The most popular version of the spatial scan statistic uses a circular-shaped scanning window. Several other variants, using other parametric or non-parametric shapes, are also available. However, none of them offer information about the uncertainty on the borders of the detected clusters. METHOD: We propose a new method to evaluate uncertainty on the boundaries of spatial clusters identified through the spatial scan statistic for Poisson data. For each spatial data location i, a function F(i) is calculated. While not a probability, this function takes values in the [0, 1] interval, with a higher value indicating more evidence that the location belongs to the true cluster. RESULTS: Through a set of simulation studies, we show that the F function provides a way to define, measure and visualize the certainty or uncertainty of each specific location belonging to the true cluster. The method can be applied whether there are one or multiple detected clusters on the map. We illustrate the new method on a data set concerning Chagas disease in Minas Gerais, Brazil. CONCLUSIONS: The higher the intensity given to an area, the higher the plausibility of that particular area to belong to the true cluster in case it exists. This way, the F function provides information from which the public health practitioner can perform a border analysis of the detected spatial scan statistic clusters. We have implemented and illustrated the border analysis F function in the context of the circular spatial scan statistic for spatially aggregated Poisson data. The definition is clearly independent of both the shape of the scanning window and the probability model under which the data is generated. To make the new method widely available to users, it has been implemented in the freely available SaTScan[Formula: see text] software www.satscan.org .

Anti-helminthic activity of Momordica charantia L. against Fasciola hepatica eggs after twelve days of incubation in vitro.

Fasciolosis, a parasitic disease caused by the trematode Fasciola hepatica underreported is expanding both in human and animal population, throughout the world. The constant use of synthetic drugs to treat this condition has led to the natural selection of resistant strains of the parasite. Hence, there is a growing focus on the potential anti-helminthic properties of medicinal plants and phytopharmaceuticals. The current study assessed the potential anti-fasciolicide action of Momordica charantia leaf extracts and fractions on the eggs of F. hepatica parasites. The lyophilized crude extract (CE) of M. charantia leaves and its sub-fractions, obtained from liquid-liquid partitioning with organic solvents, were analysed by High Performance Liquid Chromatography (HPLC), suspended in 1% DMSO and used in in vitro tests. Quadruplicates of 50F. hepatica eggs were incubated at 23°C with M. charantia leaf CE in different concentrations. After 12days no larvae were formed in eggs incubated with CE concentrations above 12.5mg/mL. Eggs incubated with CE sub-fractions at concentrations of 1000, 100, 10, 1, 0.1, 0.01µg/mL affected embryonic development, with n-butanol presenting the strongest inhibition of miracidia formation. In contrast, on the 12th day, 90% of the miracidia hatched in the control experiments using 0.03% DMSO whereas embryogenesis was completely abolished with any concentration of albendazole sulphoxide ABZ(SO). Chemical analysis of the CE and sub-fractions revealed a prominent presence of flavonoids. HPLC-MS confirmed Quercetin to be one of the main flavonoids present in the CE and the n-butanol subfraction. This is the first study to analyse the potential anti-fasciolicide action of M. charantia leaf CE and subfractions.

Data-driven inference for the spatial scan statistic.

BACKGROUND: Kulldorff's spatial scan statistic for aggregated area maps searches for clusters of cases without specifying their size (number of areas) or geographic location in advance. Their statistical significance is tested while adjusting for the multiple testing inherent in such a procedure. However, as is shown in this work, this adjustment is not done in an even manner for all possible cluster sizes. RESULTS: A modification is proposed to the usual inference test of the spatial scan statistic, incorporating additional information about the size of the most likely cluster found. A new interpretation of the results of the spatial scan statistic is done, posing a modified inference question: what is the probability that the null hypothesis is rejected for the original observed cases map with a most likely cluster of size k, taking into account only those most likely clusters of size k found under null hypothesis for comparison? This question is especially important when the p-value computed by the usual inference process is near the alpha significance level, regarding the correctness of the decision based in this inference. CONCLUSIONS: A practical procedure is provided to make more accurate inferences about the most likely cluster found by the spatial scan statistic.

Nonparametric intensity bounds for the delineation of spatial clusters.

BACKGROUND: There is considerable uncertainty in the disease rate estimation for aggregated area maps, especially for small population areas. As a consequence the delineation of local clustering is subject to substantial variation. Consider the most likely disease cluster produced by any given method, like SaTScan, for the detection and inference of spatial clusters in a map divided into areas; if this cluster is found to be statistically significant, what could be said of the external areas adjacent to the cluster? Do we have enough information to exclude them from a health program of prevention? Do all the areas inside the cluster have the same importance from a practitioner perspective? RESULTS: We propose a method to measure the plausibility of each area being part of a possible localized anomaly in the map. In this work we assess the problem of finding error bounds for the delineation of spatial clusters in maps of areas with known populations and observed number of cases. A given map with the vector of real data (the number of observed cases for each area) shall be considered as just one of the possible realizations of the random variable vector with an unknown expected number of cases. The method is tested in numerical simulations and applied for three different real data maps for sharply and diffusely delineated clusters. The intensity bounds found by the method reflect the degree of geographic focus of the detected clusters. CONCLUSIONS: Our technique is able to delineate irregularly shaped and multiple clusters, making use of simple tools like the circular scan. Intensity bounds for the delineation of spatial clusters are obtained and indicate the plausibility of each area belonging to the real cluster. This tool employs simple mathematical concepts and interpreting the intensity function is very intuitive in terms of the importance of each area in delineating the possible anomalies of the map of rates. The Monte Carlo simulation requires an effort similar to the circular scan algorithm, and therefore it is quite fast. We hope that this tool should be useful in public health decision making of which areas should be prioritized.