Conjoint Measurement of Base Station Siting Preferences

Mobile communication has become a ubiquitous part of today's life. The ongoing growth of this technology, however, involves the construction of new mobile phone base stations in order to assure network coverage. The selection of a new base station site often results in conflicts between providers and public authorities, on the one hand, and residents on the other. The aim of the present study was to examine public preferences regarding base station sites. A random sample of 503 persons from the German speaking part of Switzerland was interviewed face-to-face in their homes. Conjoint analysis was used to evaluate participants’ preferences for various attributes of base stations (appearance, location, building, decision process). The results show that location plays the most important role in participants’ acceptance of base stations. The findings also indicate that most people would prefer a covered or camouflaged base station to a freely visible one. By means of a cluster analysis, several segments were distinguished, showing that base station siting preferences were not homogeneous. Implications for risk communication are discussed.     

Laypeople's Health Concerns and Health Beliefs in Regard to Risk Perception of Mobile Communication

The consensus scientific view is that there is an absence of convincing scientific evidence for health risks of exposures to electromagnetic fields (EMF) at levels below those recommended in international guidelines. Nevertheless, some citizens are worried about EMF emitted by mobile communication and its consequences for health. The present study explored, by means of a mail survey, health concerns and the prevalence of health beliefs related to EMF in the general population. A random sample (n = 765, response rate 41%) of the German-speaking population in Switzerland was asked to assess various health beliefs. Results suggest that health concerns are widespread but lower than health concerns in regard to other hazards. About two-thirds of the respondents believed that some people suffer from electromagnetic hypersensitivity (EHS). Health beliefs items were analyzed using the Mokken scale. This scale was related to respondents' health concerns and showed that health beliefs differed in regard to sociodemographic variables. For example, analyses showed that females, younger respondents, and respondents who believed that some people are affected by EHS endorsed significantly more health beliefs than males, older respondents, and non-EHS respondents. Results indicate that it is important for policy-makers to develop a clear understanding of the possible effects of health beliefs on health concerns and risk perception. These findings may provide guidance for the further development of information materials and strategies.     

A Bayesian Two‐Part Latent Class Model for Longitudinal Medical Expenditure Data: Assessing the Impact of Mental Health and Substance Abuse Parity

Summary In 2001, the U.S. Office of Personnel Management required all health plans participating in the Federal Employees Health Benefits Program to offer mental health and substance abuse benefits on par with general medical benefits. The initial evaluation found that, on average, parity did not result in either large spending increases or increased service use over the four‐year observational period. However, some groups of enrollees may have benefited from parity more than others. To address this question, we propose a Bayesian two‐part latent class model to characterize the effect of parity on mental health use and expenditures. Within each class, we fit a two‐part random effects model to separately model the probability of mental health or substance abuse use and mean spending trajectories among those having used services. The regression coefficients and random effect covariances vary across classes, thus permitting class‐varying correlation structures between the two components of the model. Our analysis identified three classes of subjects: a group of low spenders that tended to be male, had relatively rare use of services, and decreased their spending pattern over time; a group of moderate spenders, primarily female, that had an increase in both use and mean spending after the introduction of parity; and a group of high spenders that tended to have chronic service use and constant spending patterns. By examining the joint 95% highest probability density regions of expected changes in use and spending for each class, we confirmed that parity had an impact only on the moderate spender class.     

Data representativeness in LCA: A framework for the systematic assessment of data quality relative to technology characteristics

A shortcoming in current data quality assessment schemes is that the data quality information is not used systematically to identify the critical data in a life cycle inventory (LCI) model. In addition, existing criteria employed to evaluate representativeness lack relevance to the specific context of a study. A novel framework is proposed herein for the evaluation of the representativeness of LCI data, including an analysis of the importance of the data and a modification of quality criteria based on unit process characteristics. Temporal characteristics are analyzed by identifying the technology shift, because data generated before this time are considered outdated. Geographical and technological characteristics are analyzed by defining a “related area” and a “related technology,” which is done by identifying a number of relevant geographical and technical factors, and then comparing the collected data with these factors. The framework was illustrated in a case study on household waste incineration in Denmark. The results demonstrated the applicability of the method in practice, and they provided data quality criteria unique to waste incineration unit processes, for example, different time intervals to evaluate temporal representativeness. However, the proposed method is time demanding, and thus sector‐level characteristic analyses are feasible instead of the user having to do the analyses.