Estimating Preventable Fractions of Disease Caused by a Specified Biological Mechanism: PAHs in Smoking Lung Cancers as an Example

Epidemiology textbooks often interpret population attributable fractions based on 2 x 2 tables or logistic regression models of exposure-response associations as preventable fractions, i.e., as fractions of illnesses in a population that would be prevented if exposure were removed. In general, this causal interpretation is not correct, since statistical association need not indicate causation; moreover, it does not identify how much risk would be prevented by removing specific constituents of complex exposures. This article introduces and illustrates an approach to calculating useful bounds on preventable fractions, having valid causal interpretations, from the types of partial but useful molecular epidemiological and biological information often available in practice. The method applies probabilistic risk assessment concepts from systems reliability analysis, together with bounding constraints for the relationship between event probabilities and causation (such as that the probability that exposure X causes response Y cannot exceed the probability that exposure X precedes response Y, or the probability that both X and Y occur) to bound the contribution to causation from specific causal pathways. We illustrate the approach by estimating an upper bound on the contribution to lung cancer risk made by a specific, much-discussed causal pathway that links smoking to a polycyclic aromatic hydrocarbon (PAH) (specifically, benzo(a)pyrene diol epoxide-DNA) adducts at hot spot codons at p53 in lung cells. The result is a surprisingly small preventable fraction (of perhaps 7% or less) for this pathway, suggesting that it will be important to consider other mechanisms and non-PAH constituents of tobacco smoke in designing less risky tobacco-based products.     

A Critical Evaluation of the Web‐Based Version of the Career Key

The authors assessed the reliability and validity of the Web‐based version of the Career Key (L. K. Jones, 1997). Ninety‐nine undergraduates completed the Web‐based version of the Career Key and the Self‐Directed Search‐Form R (J. L. Holland, 1994) in counterbalanced order and completed a second Career Key administration 2 weeks after completing the first test administration. Test—retest reliability ranged between .75 and .84 With the exception of the Conventional scale (.47), all concurrent validity coefficients were at or above .65.     

Technology, skills, and health care labor markets

Conclusion I examine the role of technological change in health care labor markets. One of the biggest issues in the U.S. economy over the recent past has been rapidly rising health care costs. Conventional wisdom holds the main factor driving these costs increases has been technological change. These changes in technology have lead to a direct transformation in the delivery of health care and have also lead to indirect transformations through the reshaping of the private and public insurance industry. My findings are consistent with the idea that technological change has resulted in an increase in demand for higher skilled workers in the industry, while being labor-saving among low-skill workers. The earnings of RNs and health therapists rose rapidly over the 1983 to 1993 period, declined between 1993 and 1996, but then began to rise again after 1996. It is generally believed that managed care has had a one-time cost reducing effect in the industry by eliminating some of the inefficiencies associated with fee-for-service health insurance (Newhouse, 1992). The results here suggest a similar finding in the labor market. I appreciate helpful comments from Laurence Baker, James Bennett, Ann Frost, Barry Hirsch, Joanne Spetz, and Daphne Taras.     

Building trust through promises and performance. Can your organization achieve the stellar results and employee loyalty found at Southwest Airlines?

Establishing a trusting relationship with your employees can help you transform your organization into a top performer. Examine how well-defined visions and goals are some of the keys to building that trust.     

Depth‐Oriented Values Extraction

The author examines the role of values in the career decision‐making process and describes Depth‐Oriented Values Extraction (DOVE) as a process that translates various types of psychological data (e.g., Holland‐type themes) into values‐based terms and language that facilitate career decision making. The author presents a case study to demonstrate the implications of DOVE for career counseling and makes suggestions for counselors.     

Policy, management, and political activities: A current evaluation of the time allocations of mayors and managers in small cities and towns

This study examines executive time allocations in cities and towns with populations between 2,500 and 25,000. The majority of previous research has focused on municipalities with populations greater than 50,000. Do mayors and managers in small municipalities spend the same amount of time on policy, management, and political activities as their counterparts in larger municipalities? Four-hundred-sixty-seven surveys from mayors and city managers are evaluated, and the data reveal that a significant relationship between form of government and the time these individuals devote to policy, management, and political activities does exist. This analysis confirms that managers in small cities and towns do spend more time on policy and management activities, and less time on political activities than mayors in small cities and towns. Also, the research demonstrates that differences in time devoted to these three activities can be seen when small and large municipalities are compared.     

SPACE–TIME MODELLING OF SYDNEY HARBOUR WINDS

This paper develops a space‐time statistical model for local forecasting of surface‐level wind fields in a coastal region with complex topography. The statistical model makes use of output from deterministic numerical weather prediction models which are able to produce forecasts of surface wind fields on a spatial grid. When predicting surface winds at observing stations , errors can arise due to sub‐grid scale processes not adequately captured by the numerical weather prediction model , and the statistical model attempts to correct for these influences. In particular , it uses information from observing stations within the study region as well as topographic information to account for local bias. Bayesian methods for inference are used in the model , with computations carried out using Markov chain Monte Carlo algorithms. Empirical performance of the model is described , illustrating that a structured Bayesian approach to complicated space‐time models of the type considered in this paper can be readily implemented and can lead to improvements in forecasting over traditional methods.