Environmental Tobacco Smoke Revisited: The Reliability of the Data Used for Risk Assessment

Several epidemiological studies have found a weak, but consistent association between lung cancer in nonsmokers and exposure to environmental tobacco smoke (ETS). In addition, a purported link between such exposure and coronary heart disease (CHD) has been of major concern. Although it is biologically plausible that ETS has a contributory role in the induction of lung cancer in nonsmoking individuals, dose-response extrapolation-supported by the more solid database for active smokers-gives an additional risk for lung cancer risk that is more than one order of magnitude lower than that indicated by major positive epidemiological studies. The discrepancy between available epidemiological data and dosimetric estimates seems, to a major part, to reflect certain systematic biases in the former that are difficult to control by statistical analysis when dealing with risks of such low magnitudes. These include, most importantly, misclassification of smoking status, followed by inappropriate selection of controls, as well as certain confounding factors mainly related to lifestyle, and possibly also hereditary disposition. A significant part of an association between lung cancer and exposure to ETS would disappear, if, on the average, 1 patient out of 20 nonsmoking cases had failed to tell the interviewer that he had, in fact, recently stopped smoking. In the large International Agency for Research on Cancer (IARC) multicenter study even lower misclassification rates would abolish the weak, statistically nonsignificant associations that were found. In the former study an apparent significant protective effect from exposure to ETS in childhood with respect to lung cancer later in life was reported, a most surprising finding. The fact that the mutation spectrum of the p53 tumor suppressor gene in lung tumors of ETS-exposed nonsmokers generally differs from that found in tumors of active smokers lends additional support to the notion that the majority of tumors found in ETS-exposed nonsmokers have nothing to do with tobacco smoke. The one-sided preoccupation with ETS as a causative factor of lung cancer in nonsmokers may seriously hinder the elucidation of the multifactorial etiology of these tumors. Due to the high prevalence of cardiovascular disease in the population, even a modest causal association with ETS would, if valid, constitute a serious public health problem. By pooling data from 20 published studies on ETS and heart disease, some of which reported higher risks than is known to be caused by active smoking, a statistically significant association with spousal smoking is obtained. However, in most of these studies, many of the most common confounding risk factors were ignored and there appears to be insufficient evidence to support an association between exposure to ETS and CHD. Further, it seems highly improbable that exposure to a concentration of tobacco smoke at a level that is generally much less than 1% of that inhaled by a smoker could result in an excess risk for CHD that-as has been claimed-is some 30% to 50% of that found in active smokers. There are certainly valid reasons to limit exposure to ETS as well as to other air pollutants in places such as offices and homes in order to improve indoor air quality. This goal can be achieved, however, without the introduction of an extremist legislation based on a negligible risk of lung cancer as well as an unsupported and highly hypothetical risk for CHD.     

Potency Factors for Risk Assessment at Libby,Montana

We reanalyzed the Libby vermiculite miners’ cohort assembled by Sullivan to estimate potency factors for lung cancer, mesothelioma, nonmalignant respiratory disease (NMRD), and all‐cause mortality associated with exposure to Libby fibers. Our principal statistical tool for analyses of lung cancer, NMRD, and total mortality in the cohort was the time‐dependent proportional hazards model. For mesothelioma, we used an extension of the Peto formula. For a cumulative exposure to Libby fiber of 100 f/mL‐yr, our estimates of relative risk (RR) are as follows: lung cancer, RR = 1.12, 95% confidence interval (CI) =[1.06, 1.17]; NMRD, RR = 1.14, 95% CI =[1.09, 1.18]; total mortality, RR = 1.06, 95% CI =[1.04, 1.08]. These estimates were virtually identical when analyses were restricted to the subcohort of workers who were employed for at least one year. For mesothelioma, our estimate of potency is K_M = 0.5 × 10^?8, 95% CI =[0.3 × 10^?8, 0.8 × 10^?8]. Finally, we estimated the mortality ratios standardized against the U.S. population for lung cancer, NMRD, and total mortality and obtained estimates that were in good agreement with those reported by Sullivan. The estimated potency factors form the basis for a quantitative risk assessment at Libby.     

The Use of Reality Orientation in Adult Foster Care Homes:

The emphasis in recent years on providing long-term care in community-based alternatives to the institutional setting can help to facilitate the development of new and exciting opportunities for enrichment in the lives of individuals whose educational needs often go unmet. Reality orientation has been demonstrated to be an effective approach to the treatment and prevention of loss of memory, confusion, and disorientation among institutionalized adults. However, certain problems can arise that may limit the potential benefits of reality orientation programs in institutions. This paper explores the rationale for considering the use of reality orientation in one alternative living arrangement, the adult foster care home. The personalized, family-like environment of the adult foster care setting might help to minimize or even eliminate many of the problems that detract from reality orientation programs in larger institutions.     

In life and in death: the story of people living with metastatic cancer

ABSTRACT

Advances in the treatment of metastatic cancers such as melanoma enable patients to live for many years. However, melanoma patients are under constant threat of a recurrence or a new growth, are under intensive follow-up, and must avoid exposure to the sun. These factors engender anxiety, a constant fear of recurrence, and a reduction in routine activity, thus requiring patients to develop mechanisms for coping simultaneously with the illness and the threat of death. This study uses content analysis of the documentation of a support group for metastatic melanoma patients to examine how they cope with both. The findings suggest that they are able to cope with both simultaneously. However, perhaps because they recognise their total lack of control over the illness, they exercise control in how they cope with death. The findings suggest that support groups like the one documented in this article could serve as sheltered and effective therapeutic spaces for coping with the threat of death.     

Frequentist and Bayesian approaches for a joint model for prostate cancer risk and longitudinal prostate-specific antigen data

The paper describes the use of frequentist and Bayesian shared-parameter joint models of longitudinal measurements of prostate-specific antigen (PSA) and the risk of prostate cancer (PCa). The motivating dataset corresponds to the screening arm of the Spanish branch of the European Randomized Screening for Prostate Cancer study. The results show that PSA is highly associated with the risk of being diagnosed with PCa and that there is an age-varying effect of PSA on PCa risk. Both the frequentist and Bayesian paradigms produced very close parameter estimates and subsequent 95% confidence and credibility intervals. Dynamic estimations of disease-free probabilities obtained using Bayesian inference highlight the potential of joint models to guide personalized risk-based screening strategies.     

EPA Underestimates,Oversimplifies, Miscommunicates,and Mismanages Cancer Risks by Ignoring Human Susceptibility

If exposed to an identical concentration of a carcinogen, every human being would face a different level of risk, determined by his or her genetic, environmental, medical, and other uniquely individual characteristics. Various lines of evidence indicate that this susceptibility variable is distributed rather broadly in the human population, with perhaps a factor of 25‐ to 50‐fold between the center of this distribution and either of its tails, but cancer risk assessment at the EPA and elsewhere has always treated every (adult) human as identically susceptible. The National Academy of Sciences “Silver Book” concluded that EPA and the other agencies should fundamentally correct their mis‐computation of carcinogenic risk in two ways: (1) adjust individual risk estimates upward to provide information about the upper tail; and (2) adjust population risk estimates upward (by about sevenfold) to correct an underestimation due to a mathematical property of the interindividual distribution of human susceptibility, in which the susceptibility averaged over the entire (right‐skewed) population exceeds the median value for the typical human. In this issue of Risk Analysis, Kenneth Bogen disputes the second adjustment and endorses the first, though he also relegates the problem of underestimated individual risks to the realm of “equity concerns” that he says should have little if any bearing on risk management policy. In this article, I show why the basis for the population risk adjustment that the NAS recommended is correct—that current population cancer risk estimates, whether they are derived from animal bioassays or from human epidemiologic studies, likely provide estimates of the median with respect to human variation, which in turn must be an underestimate of the mean. If cancer risk estimates have larger “conservative” biases embedded in them, a premise I have disputed in many previous writings, such a defect would not excuse ignoring this additional bias in the direction of underestimation. I also demonstrate that sensible, legally appropriate, and ethical risk policy must not only inform the public when the tail of the individual risk distribution extends into the “high‐risk” range, but must alter benefit‐cost balancing to account for the need to try to reduce these tail risks preferentially.     

Building multivariable prognostic and diagnostic models: transformation of the predictors by using fractional polynomials

To be useful to clinicians, prognostic and diagnostic indices must be derived from accurate models developed by using appropriate data sets. We show that fractional polynomials, which extend ordinary polynomials by including non-positive and fractional powers, may be used as the basis of such models. We describe how to fit fractional polynomials in several continuous covariates simultaneously, and we propose ways of ensuring that the resulting models are parsimonious and consistent with basic medical knowledge. The methods are applied to two breast cancer data sets, one from a prognostic factors study in patients with positive lymph nodes and the other from a study to diagnose malignant or benign tumours by using colour Doppler blood flow mapping. We investigate the problems of biased parameter estimates in the final model and overfitting using cross-validation calibration to estimate shrinkage factors. We adopt bootstrap resampling to assess model stability. We compare our new approach with conventional modelling methods which apply stepwise variables selection to categorized covariates. We conclude that fractional polynomial methodology can be very successful in generating simple and appropriate models.     

Bayesian assessment of times to diagnosis in breast cancer screening

Breast cancer is one of the diseases with the most profound impact on health in developed countries and mammography is the most popular method for detecting breast cancer at a very early stage. This paper focuses on the waiting period from a positive mammogram until a confirmatory diagnosis is carried out in hospital. Generalized linear mixed models are used to perform the statistical analysis, always within the Bayesian reasoning. Markov chain Monte Carlo algorithms are applied for estimation by simulating the posterior distribution of the parameters and hyperparameters of the model through the free software WinBUGS.     

Cancer Dose-Response Modeling of Epidemiological Data on Worker Exposures to Aldrin and Dieldrin

The paper applies classical statistical principles to yield new tools for risk assessment and makes new use of epidemiological data for human risk assessment. An extensive clinical and epidemiological study of workers engaged in the manufacturing and formulation of aldrin and dieldrin provides occupational hygiene and biological monitoring data on individual exposures over the years of employment and provides unusually accurate measures of individual lifetime average daily doses. In the cancer dose-response modeling, each worker is treated as a separate experimental unit with his own unique dose. Maximum likelihood estimates of added cancer risk are calculated for multistage, multistage-Weibull, and proportional hazards models. Distributional characterizations of added cancer risk are based on bootstrap and relative likelihood techniques. The cancer mortality data on these male workers suggest that low-dose exposures to aldrin and dieldrin do not significantly increase human cancer risk and may even decrease the human hazard rate for all types of cancer combined at low doses (e.g., 1 g/kg/day). The apparent hormetic effect in the best fitting dose-response models for this data set is statistically significant. The decrease in cancer risk at low doses of aldrin and dieldrin is in sharp contrast to the U.S. Environmental Protection Agency's upper bound on cancer potency based on mouse liver tumors. The EPA's upper bound implies that lifetime average daily doses of 0.0000625 and 0.00625 g/kg body weight/day would correspond to increased cancer risks of 0.000001 and 0.0001, respectively. However, the best estimate from the Pernis epidemiological data is that there is no increase in cancer risk in these workers at these doses or even at doses as large as 2 g/kg/day.