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To examine the association between lead exposure and both individual and geographic area indicators of socioeconomic position, the authors measured tibia lead concentration, a biomarker of cumulative lead exposure, using K x-ray fluorescence in a cross-sectional survey of 538 white males aged 50-92 years who were healthy when enrolled in the Normative Aging Study (Boston, Massachusetts) in the 1960s. Data on individual risk factors, education, occupation, and income were collected by questionnaire. Using subjects' residential addresses at the time of the tibia lead measurements, the authors obtained geographic area-specific measures of education, social class, and poverty by linking records to 1990 US Census block group data. In multivariate linear regression analysis controlling for age and cumulative smoking, tibia lead concentrations were 10.39 microg/g (95% confidence interval (CI) 7.80-12.97) higher in men who did not graduate from high school than in men with > or =4 years of college. Among the former men (non-high school graduates), living in an undereducated area was associated with a 9.28 microg/g (95% CI 1.59-16.97) increase in tibia lead level compared with living in a non-undereducated area; among the latter men (college graduates), no difference existed by residential area education (beta = 0.72, 95% CI -5.35 to 6.78). The authors conclude that the influence of individual socioeconomic position on cumulative lead exposure is modified by geographic area conditions.

Characterization of relationships between time-varying drug exposures and adverse events (AEs) related to health outcomes represents the primary objective in postmarketing drug safety surveillance. Such surveillance increasingly utilizes large-scale longitudinal observational databases (LODs), containing time-stamped patient-level medical information including periods of drug exposure and dates of diagnoses for millions of patients. Statistical methods for LODs must confront computational challenges related to the scale of the data, and must also address confounding and other biases that can undermine efforts to estimate effect sizes. Methods that compare on-drug with off-drug periods within patient offer specific advantages over between patient analysis on both counts. To accomplish these aims, we extend the self-controlled case series (SCCS) for LODs. SCCS implicitly controls for fixed multiplicative baseline covariates since each individual acts as their own control. In addition, only exposed cases are required for the analysis, which is computationally advantageous. The standard SCCS approach is usually used to assess single drugs and therefore estimates marginal associations between individual drugs and particular AEs. Such analyses ignore confounding drugs and interactions and have the potential to give misleading results. In order to avoid these difficulties, we propose a regularized multiple SCCS approach that incorporates potentially thousands or more of time-varying confounders such as other drugs. The approach successfully handles the high dimensionality and can provide a sparse solution via an L1 regularizer. We present details of the model and the associated optimization procedure, as well as results of empirical investigations.

A theoretical model was developed for the optimization of filter characteristics that would minimize the dose from the inhalation of Rn decay products. Modified forms of the Jacobi-Porstendorfer room model and the Jacobi-Eisfeld lung dose model were chosen for use in the mathematical simulation. Optimized parameters of the filter were the thickness, solidity, and fiber diameter. For purposes of the calculations, the room dimensions, air exchange rate, particle-size distribution and concentration, and the Rn concentration were specified. The resulting computer-aided optimal design was a thin filter (the minimum thickness used in the computer model was 0.1 mm) having low solidity (the minimum solidity used was 0.5%) and large diameter fibers (the maximum diameter used was 100 microns). The simulation implies that a significant reduction in the dose rate can be achieved using a well-designed recirculating filter system. The theoretical model, using the assumption of ideal mixing, predicts an 80% reduction in the dose rate, although inherent in this assumption is the movement of 230 room volumes per hour through the fan.

Evidence derived from existing health-care data, such as administrative claims and electronic health records, can fill evidence gaps in medicine. However, many claim such data cannot be used to estimate causal treatment effects because of the potential for observational study bias; for example, due to residual confounding. Other concerns include P hacking and publication bias. In response, the Observational Health Data Sciences and Informatics international collaborative launched the Large-scale Evidence Generation and Evaluation across a Network of Databases (LEGEND) research initiative. Its mission is to generate evidence on the effects of medical interventions using observational health-care databases while addressing the aforementioned concerns by following a recently proposed paradigm. We define 10 principles of LEGEND that enshrine this new paradigm, prescribing the generation and dissemination of evidence on many research questions at once; for example, comparing all treatments for a disease for many outcomes, thus preventing publication bias. These questions are answered using a prespecified and systematic approach, avoiding P hacking. Best-practice statistical methods address measured confounding, and control questions (research questions where the answer is known) quantify potential residual bias. Finally, the evidence is generated in a network of databases to assess consistency by sharing open-source analytics code to enhance transparency and reproducibility, but without sharing patient-level information. Here we detail the LEGEND principles and provide a generic overview of a LEGEND study. Our companion paper highlights an example study on the effects of hypertension treatments, and evaluates the internal and external validity of the evidence we generate.