Probabilistic bias analysis of epidemiological results

Classification errors, selection bias, and uncontrolled confounding are likely to be present in most epidemiological studies, but the uncertainty introduced by this type of biases is seldom quantified. The authors present a simple yet easy-to-use method to adjust the relative risk of a disease for misclassification of a binary exposure, selection bias, and unmeasured confounding variable. The accompanying Stata tool implements both ordinary and probabilistic sensitivity analysis. It allows the user to specify a variety of probability densities for the bias parameters, and use these densities to obtain simulation limits for the bias adjusted exposure-disease relative risk. The authors illustrate the method by applying it to a published positive association between occupational resin exposure and lung-cancer deaths in a case-control study. By employing plausible probability distributions for the bias parameters, investigators can report results that incorporate their uncertainties regarding unmeasured or uncontrolled confounding, and thus avoid overstating their certainty about the effect under study. These results can usefully supplement standard data descriptions and conventional results.

Multivariate Dose-Response Meta-Analysis: The dosresmeta R Package

An increasing number of quantitative reviews of epidemiological data includes a doseresponse analysis. Aims of this paper are to describe the main aspects of the methodology and to illustrate the novel R package dosresmeta developed for multivariate dose-response meta-analysis of summarized data. Specific topics covered are reconstructing covariances of correlated outcomes; pooling of study-specific trends; flexible modeling of the exposure; testing hypothesis; assessing statistical heterogeneity; and presenting in either a graphical or tabular way the overall dose-response association

Multiplicative models for survival percentiles: estimating percentile ratios and multiplicative interaction in the metric of time

Evaluating percentiles of survival was proposed as a possible method to analyze time-to-event outcomes. This approach sets the cumulative risk of the event of interest to a specific proportion and evaluates the time by which this proportion is attainedIn this context, exposure-outcome associations can be expressed in terms of differences in survival percentiles, expressing the difference in survival time by which different subgroups of the study population experience the same proportion of events, or in terms of percentile ratios, expressing the strength of the exposure in accelerating the time to the event. Additive models for conditional survival percentiles have been introduced, and their use to estimate multivariable-adjusted percentile differences, and additive interaction on the metric of time has been described. On the other hand, the percentile ratio has never been fully described, neither statistical methods have been presented for its models-based estimation. To bridge this gap, we provide a detailed presentation of the percentile ratio as a relative measure to assess exposure-outcome associations in the context of time-to-event analysis, discussing its interpretation and advantages. We then introduce multiplicative statistical models for conditional survival percentiles, and present their use in estimating percentile ratios and multiplicative interactions in the metric of time. The introduction of multiplicative models for survival percentiles allows researchers to apply this approach in a large variety of context where multivariable adjustment is required, enriching the potentials of the percentile approach as a flexible and valuable tool to evaluate time-to-event outcomes in medical research

Risk of childhood leukemia and exposure to outdoor air pollution. Updated review and dose-response meta-analysis

Leukemia is the most frequent malignant disease of childhood. Most epidemiologic studies have suggested that exposure to traffic pollutants may increase the risk of childhood leukemia. We updated our previous review and metaanalysis as some recent studies have now available, and we also performed a dose-response metaanalysis using traffic estimators

Contribución al estudio de los fenómenos de absorción y adsorción : fijación de materias colorantes por las zeolitas

Fil: Nicola, Orsini F. F.. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina

Correlates of total physical activity among middle-aged and elderly women

Information on correlates of total physical activity (PA) levels among middle-aged and elderly women is limited. This article aims to investigate whether total daily PA levels are associated with age, body mass index, smoking, drinking status, and sociodemographic factors

Circular economy in the water and wastewater sector: Tariff impact and financial performance of SMARTechs

This paper proposes a financial evaluation of the investment in SMARTechs in wastewater companies. SMAR Techs are innovative technologies that enable companies to work toward the circular economy approach, thanks to allowing the development of by-products from wastewater. A simulation of the financial impact of the SMARTech introduction was conducted based on the Italian tariff system. It is performed assuming two different scenarios. These relate to a market’s presence (or absence) for the by-products resulting from the application of SMARTechs. The results show that investing in these technologies provides both financial and environmental benefits

Meta-Analysis of Potassium Intake and the Risk of Stroke

Background-—The possibility that lifestyle factors such as diet, specifically potassium intake, may modify the risk of stroke has been suggested by several observational cohort studies, including some recent reports. We performed a systematic review and meta-analysis of existing studies and assessed the dose–response relation between potassium intake and stroke risk. Methods and Results-—We reviewed the observational cohort studies addressing the relation between potassium intake, and incidence or mortality of total stroke or stroke subtypes published through August 6, 2016. We carried out a meta-analysis of 16 cohort studies based on the relative risk (RR) of stroke comparing the highest versus lowest intake categories. We also plotted a pooled dose–response curve of RR of stroke according to potassium intake. Analyses were performed with and without adjustment for blood pressure. Relative to the lowest category of potassium intake, the highest category of potassium intake was associated with a 13% reduced risk of stroke (RR=0.87, 95% CI 0.80–0.94) in the blood pressure–adjusted analysis. Summary RRs tended to decrease when original estimates were unadjusted for blood pressure. Analysis for stroke subtypes yielded comparable results. In the spline analysis, the pooled RR was lowest at 90 mmol of potassium daily intake (RRs=0.78, 95% CI 0.70–0.86) in blood pressure–adjusted analysis, and 0.67 (95% CI 0.57–0.78) in unadjusted analysis. Conclusions-—Overall, this dose–response meta-analysis confirms the inverse association between potassium intake and stroke risk, with potassium intake of 90 mmol (!3500 mg)/day associated with the lowest risk of stroke