Assessment of Health Risk from Historical Use of Cosmetic Talcum Powder

This study's objective is to assess the risk of asbestos‐related disease being contracted by past users of cosmetic talcum powder.  To our knowledge, no risk assessment studies using exposure data from historical exposures or chamber simulations have been published. We conducted activity‐based sampling with cosmetic talcum powder samples from five opened and previously used containers that are believed to have been first manufactured and sold in the 1960s and 1970s.  These samples had been subject to conflicting claims of asbestos content; samples with the highest claimed asbestos content were tested.  The tests were conducted in simulated‐bathroom controlled chambers with volunteers who were talc users.  Air sampling filters were prepared by direct preparation techniques and analyzed by phase contrast microscopy (PCM), transmission electron microscopy (TEM) with energy‐dispersive x‐ray (EDX) spectra, and selective area diffraction (SAED).  TEM analysis for asbestos resulted in no confirmed asbestos fibers and only a single fiber classified as “ambiguous.”  Hypothetical treatment of this fiber as if it were asbestos yields a risk of 9.6 × 10^?7 (under one in one million) for a lifetime user of this cosmetic talcum powder.  The exposure levels associated with these results range from zero to levels far below those identified in the epidemiology literature as posing a risk for asbestos‐related disease, and substantially below published historical environmental background levels.  The approaches used for this study have potential application to exposure evaluations of other talc or asbestos‐containing materials and consumer products.     

Sequential Monte Carlo methods for mixtures with normalized random measures with independent increments priors

Normalized random measures with independent increments are a general, tractable class of nonparametric prior. This paper describes sequential Monte Carlo methods for both conjugate and non-conjugate nonparametric mixture models with these priors. A simulation study is used to compare the efficiency of the different algorithms for density estimation and comparisons made with Markov chain Monte Carlo methods. The SMC methods are further illustrated by applications to dynamically fitting a nonparametric stochastic volatility model and to estimation of the marginal likelihood in a goodness-of-fit testing example.     

The Effects of Community Pluralism on Press Coverage of Health Risks from Local Environmental Contamination

Based on the conflict/consensus model of Tichenor, Donohue and Olien, we proposed that mass mediated information signalling that local agents are contaminating the local environment and posing health risks is conflict-generating information and, therefore, will be controlled in the interest of community stability. We expected such control to vary by community structure. A content analysis of nine months of coverage by 19 newspapers supported the hypothesis that papers in more pluralistic communities were more likely than papers in less pluralistic communities to link contamination from local agents to threats to human health in the community and to frame such stories as problems. Newspapers in less pluralistic communities were more likely to frame local contamination in the context of solutions to the problem and were more likely to link contamination to health risks if the contamination were in a distant community.     

Research records and the resolution of misconduct allegations at research universities

Accurate record keeping is an important part of the responsible conduct of research. However, there is very little empirical research on scientific record keeping. No one knows the incidence of serious problems with research records, the types of problems that occur, nor their consequences. In this study, we examined the role of research records in the resolution of misconduct allegations as a useful barometer for the incidence and types of problems that occur with records. We interviewed Research Integrity Officers (RIOs) at 90 major research universities and conducted focus groups with active research faculty. RIOs reported problems with research records in 38% of the 553 investigations they conducted. Severe problems with research records often prevented completion of investigations while problems that are more typical lengthened them by 2 to 3 weeks. Five types of poor record keeping practices accounted for 75 % of the problems with incomplete/inadequate records being the most common (30%). The focus groups concurred with the findings from the interviews with RIOs, stressed the importance of the research group leader in setting and maintaining record practices, and offered additional insights. While university officials and faculty members have suspected for many years that there are serious problems with research record keeping, our study provides empirical evidence for this belief. By documenting some of the problems with record keeping in university-based research, the results of our study provide information that will be useful for policy development at academic institutions.     

Nonparametric inference and uniqueness for periodically observed progressive disease models

In many studies examining the progression of HIV and other chronic diseases, subjects are periodically monitored to assess their progression through disease states. This gives rise to a specific type of panel data which have been termed “chain-of-events data”; e.g. data that result from periodic observation of a progressive disease process whose states occur in a prescribed order and where state transitions are not observable. Using a discrete time semi-Markov model, we develop an algorithm for nonparametric estimation of the distribution functions of sojourn times in a J state progressive disease model. Issues of uniqueness for chain-of-events data are not well-understood. Thus, a main goal of this paper is to determine the uniqueness of the nonparametric estimators of the distribution functions of sojourn times within states. We develop sufficient conditions for uniqueness of the nonparametric maximum likelihood estimator, including situations where some but not all of its components are unique. We illustrate the methods with three examples.     

Deriving the Normal and Exponential Densities Using EDA Techniques

This paper shows how students in an introductory statistics course discover mathematical expressions for the normal and exponential density functions. Techniques of exploratory data analysis (EDA) are applied to large data sets, the key idea being a straight-line fit to an appropriate reexpression of the histogram.     

Cross-validation prior choice in Bayesian probit regression with?many covariates

This paper examines prior choice in probit regression through a predictive cross-validation criterion. In particular, we focus on situations where the number of potential covariates is far larger than the number of observations, such as in gene expression data. Cross-validation avoids the tendency of such models to fit perfectly. We choose the scale parameter c in the standard variable selection prior as the minimizer of the log predictive score. Naive evaluation of the log predictive score requires substantial computational effort, and we investigate computationally cheaper methods using importance sampling. We find that K-fold importance densities perform best, in combination with either mixing over different values of c or with integrating over c through an auxiliary distribution.     

The Injurious Relationship Between Racial Microaggressions and Physical Health: Implications for Social Work

ABSTRACT

In recent years, the study of racial microaggressions (or subtle forms of racial discrimination) has increased significantly in the social sciences, particularly highlighting the negative impact of racial microaggressions on individuals’ mental health. Despite this, there is a dearth of literature that has examined the relationship between racial microaggressions and physical and psychological health. Using two self-reported measures—the Racial and Ethnic Microaggressions Scale (REMS) and the RAND 36-Item Short Form Health Survey—with a diverse group of participants (N = 277), results suggest that racial microaggressions are significantly correlated with poorer health conditions. Furthermore, racial microaggressions were found to predict various types of physical health conditions, such as general health problems, pain, lower energy levels, and fatigue. Finally, different types of microaggressions (e.g., microaggressions in school or the workplace, environmental microaggressions) were found to be predictors of specific health issues. Implications for social work are discussed.     

Landmark estimation of survival and treatment effects in observational studies

Clinical studies aimed at identifying effective treatments to reduce the risk of disease or death often require long term follow-up of participants in order to observe a sufficient number of events to precisely estimate the treatment effect. In such studies, observing the outcome of interest during follow-up may be difficult and high rates of censoring may be observed which often leads to reduced power when applying straightforward statistical methods developed for time-to-event data. Alternative methods have been proposed to take advantage of auxiliary information that may potentially improve efficiency when estimating marginal survival and improve power when testing for a treatment effect. Recently, Parast et al. (J Am Stat Assoc 109(505):384–394, 2014) proposed a landmark estimation procedure for the estimation of survival and treatment effects in a randomized clinical trial setting and demonstrated that significant gains in efficiency and power could be obtained by incorporating intermediate event information as well as baseline covariates. However, the procedure requires the assumption that the potential outcomes for each individual under treatment and control are independent of treatment group assignment which is unlikely to hold in an observational study setting. In this paper we develop the landmark estimation procedure for use in an observational setting. In particular, we incorporate inverse probability of treatment weights (IPTW) in the landmark estimation procedure to account for selection bias on observed baseline (pretreatment) covariates. We demonstrate that consistent estimates of survival and treatment effects can be obtained by using IPTW and that there is improved efficiency by using auxiliary intermediate event and baseline information. We compare our proposed estimates to those obtained using the Kaplan–Meier estimator, the original landmark estimation procedure, and the IPTW Kaplan–Meier estimator. We illustrate our resulting reduction in bias and gains in efficiency through a simulation study and apply our procedure to an AIDS dataset to examine the effect of previous antiretroviral therapy on survival.