Search PubMed⌕ Search

PubMed · 6536945

Measuring passive smoking: methods, problems, and perspectives.

Abstract

The definition of passive smoking used by T. Hirayama [Brit. Med. J. 282, 183-185 (1981)] and other authors has at least three major shortcomings: it is only applicable to a highly selective sub-population; varying lifestyle patterns cannot be taken into account; and exposure outside the home is neglected. To overcome these shortcomings, a preliminary qualitative and classificatory definition of passive smoking that includes the total population as well as any smoke exposure, regardless of its location, was developed. This definition does not include the quantity of exposure or changes over time. Based on a representative survey, 17.8% of the population over 35 years are potential passive smokers according to this definition. The questionnaire developed by E.L. Wynder and associates (American Health Foundation, New York) presents difficulties in scoring the results for different lifestyles. Therefore a quantitative concept was developed for estimating exposure over any period of time as a generalized assessment instrument. The concept of maximum exposed M-time for an individual (TMI) as well as for a group (TMG) is introduced. The results are presented graphically by a cumulative standardized exposed M-time diagram. Results obtained so far lean toward plausibility and stability of the data and the concept. The interview form has still to be validated.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

L C Johnson, H W Letzel. 1984. Measuring passive smoking: methods, problems, and perspectives.. https://doi.org/10.1016/s0091-7435(84)80020-1

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Development of an ecologic marine classification in the new zealand region.

We describe here the development of an ecosystem classification designed to underpin the conservation management of marine environments in the New Zealand region. The classification was defined using multivariate classification using explicit environmental layers chosen for their role in driving spatial variation in biologic patterns: depth, mean annual solar radiation, winter sea surface temperature, annual amplitude of sea surface temperature, spatial gradient of sea surface temperature, summer sea surface temperature anomaly, mean wave-induced orbital velocity at the seabed, tidal current velocity, and seabed slope. All variables were derived as gridded data layers at a resolution of 1 km. Variables were selected by assessing their degree of correlation with biologic distributions using separate data sets for demersal fish, benthic invertebrates, and chlorophyll-a. We developed a tuning procedure based on the Mantel test to refine the classification's discrimination of variation in biologic character. This was achieved by increasing the weighting of variables that play a dominant role and/or by transforming variables where this increased their correlation with biologic differences. We assessed the classification's ability to discriminate biologic variation using analysis of similarity. This indicated that the discrimination of biologic differences generally increased with increasing classification detail and varied for different taxonomic groups. Advantages of using a numeric approach compared with geographic-based (regionalisation) approaches include better representation of spatial patterns of variation and the ability to apply the classification at widely varying levels of detail. We expect this classification to provide a useful framework for a range of management applications, including providing frameworks for environmental monitoring and reporting and identifying representative areas for conservation.

Classification↗

SINEs of progress: Mobile element applications to molecular ecology.

Mobile elements represent a unique and under-utilized set of tools for molecular ecologists. They are essentially homoplasy-free characters with the ability to be genotyped in a simple and efficient manner. Interpretation of the data generated using mobile elements can be simple compared to other genetic markers. They exist in a wide variety of taxa and are useful over a wide selection of temporal ranges within those taxa. Furthermore, their mode of evolution instills them with another advantage over other types of multilocus genotype data: the ability to determine loci applicable to a range of time spans in the history of a taxon. In this review, I discuss the application of mobile element markers, especially short interspersed elements (SINEs), to phylogenetic and population data, with an emphasis on potential applications to molecular ecology.

Classification↗