PubMed HealthSearch

PubMed · 7246512

Techniques for detecting and determining risks from low-level radiation.

Abstract

Epidemiology is the study of disease in man. In evaluating radiation hazards, analytic studies have utilized the cohort type of investigation (where persons exposed and not exposed to radiation are followed forward in time for determination of disease experience) or case-control approaches (where persons with and without a specific disease are evaluated for previous exposure to radiation). Most radiation studies have evaluated cohorts (e.g., radiologists), although important case-control studies have been conducted (e.g., childhood leukemia as related to prenatal x ray). At its best, epidemiology is capable fo evaluating relative risks (RR) on the order of 1.4 (i.e., a 40% relative excess). However, the RRs of interest following low doses of radiation (1 rad) are on the order of 1.02-1.002. Thus, not much should be anticipated from direct observations at 1 rad, and indirect approaches must be taken to estimate low-dose effects. Such indirect approaches include evaluating 1) populations exposed to a range of doses, both low and high, where interpolation models can be reasonably applied to estimate low-dose effects; and 2) populations exposed to fractionated doses over a long period of time where the resulting dose-effect relationship theoretically should be linear and the estimation of low-level health effects facilitated.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

J D Boice. 1980. Techniques for detecting and determining risks from low-level radiation.. https://pubmed.ncbi.nlm.nih.gov/7246512/

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

KEEP EXPLORING

Related citations

How religion influences morbidity and health: reflections on natural history, salutogenesis and host resistance.

This paper surveys the field that has come to be known as the epidemiology of religion. Epidemiologic study of the impact of religious involvement, broadly defined, has become increasingly popular in recent years, although the existence, meaning and implications of an apparently salutary religious effect on health have not yet been interpreted in an epidemiologic context. This paper attempts to remedy this situation by putting the "epidemiology" into the epidemiology of religion through discussion of existing empirical findings in terms of several substantive epidemiologic concepts. After first providing an overview of key research findings and prior reviews of this field, the summary finding of a protective religious effect on morbidity is examined in terms of three important epidemiologic concepts: the natural history of disease, salutogenesis and host resistance. In addition to describing a theoretical basis for interpreting a religion-health association, this paper provides an enumeration of common misinterpretations of epidemiologic findings for religious involvement, as well as an outline of hypothesized pathways, mediating factors, and salutogenic mechanisms for respective religious dimensions. It is hoped that these reflections will serve both to elevate the status of religion as a construct worthy of social-epidemiologic research and to reinvigorate the field of social epidemiology.

Epidemiologic Methods

Standard error and sample size determination for estimation of probabilities based on a test variable.

A method of sample size determination for estimation of probabilities based on a test variable is presented. Applications to estimation of sensitivity and specificity of medical tests are the focus of this research, although the methods can be applied to other areas of study such as engineering reliability. Examples are given for determining sample sizes required for the classification of patients with cutaneous lupus erythematosus based on the incidence of several markers. In this example, the test variable is the number of markers present. The methodology employs a weighted average of model-based and non-model-based estimates of the probability with the weights determined by the closeness to or the confidence in the given model. Formulas and charts required for determining sample size are provided for test variables that can be modeled by the binomial, Poisson, or normal distributions, i.e., for the most commonly encountered distributions for counting events (binomial and Poisson) and for measurements (normal). However, the methods given can be applied to any distribution, including multivariate. Especially when relatively small probabilities (the rare events) are being estimated, the techniques provided assistance in safeguarding against undersampling brought on by unwarranted confidence in a test variable distribution and against oversampling required for high accuracy in non-model-based probability estimators.

Epidemiologic Methods

Behavior and interpretation of the kappa statistic: resolution of the two paradoxes.

Two apparent paradoxes have been identified for the kappa (kappa) statistic: (1) high levels of observer agreement with low kappa values; (2) lack of predictability of changes in kappa with changing marginals. The first paradox is a function of prevalence of the trait in the sample, while the second is related to symmetry of observations in the disagreement categories. While examining the behavior of kappa as a function of the distribution of responses in a contingency table, it was discovered that for any measured level of observer agreement (Po) there are three characteristic values of kappa: kappa max, kappa min, and kappa nor, each of which is a function only of Po. The characteristic values allow an observed kappa (kappa o) to be placed into perspective. By observing symmetry in agreement and disagreement categories, the behavior of kappa is readily understood and predictable. We define symmetry expressions for agreement (SA) and disagreement (SD) in order to represent and quantify these effects. Kappa alone has little interpretive value and we recommend that studies reporting kappa also report Po, SD, and P++ (agreement on the presence of the trait).

Epidemiologic Methods