No abstract

RV detection was greatest when diarrhea, vomiting and fever occurred together and lowest when each symptom occurred alone. The spectrum of symptoms of rotavirus disease in children at the time of admission to the hospital or short stay unit may be broader than previously recognized.

There are socioeconomic and environmental factors and aspects of the child's medical and dietary history that identify children at risk for hospitalization with rotavirus AGE.

Measurement of rotavirus-coded hospital discharges alone seems to greatly underestimate the true burden of rotavirus-associated hospitalizations. The numbers of national rotavirus hospitalization discharges may be substantially greater than previously estimated.

This field experiment explored the use of informational brochures to encourage home radon testing. Homeowners (N= 271) received a radon brochure, a questionnaire, and a form for ordering a $20 radon test kit. The brochures differed in their presentations of the magnitude of the threat (varying risk likelihood and severity) and the difficulty of reducing radon levels. Some also included a detailed list of home radon risk factors. Combinations of these three variables yielded a 2 × 2 × 2 factorial design. Although 19.2% of the sample ordered tests, the percentage was constant across brochures. Data from the questionnaire showed that self‐reported risk likelihood, risk seriousness, and concern were strongly correlated with intentions to test and with actual test orders. Calculations revealed that although the threat manipulation had a highly significant effect on these risk perceptions, the effect was too small to produce different rates of test orders. Confirming previous radon studies, perceived mitigation difficulty proved unrelated to interest in radon testing.

Currently, the most widely used method in the disease management industry for evaluating program effectiveness is the "total population approach." This model is a pretest-posttest design, with the most basic limitation being that without a control group, there may be sources of bias and/or competing extraneous confounding factors that offer plausible rationale explaining the change from baseline. Survival analysis allows for the inclusion of data from censored cases, those subjects who either "survived" the program without experiencing the event (e.g., achievement of target clinical levels, hospitalization) or left the program prematurely, due to disenrollement from the health plan or program, or were lost to follow-up. Additionally, independent variables may be included in the model to help explain the variability in the outcome measure. In order to maximize the potential of this statistical method, validity of the model and research design must be assured. This paper reviews survival analysis as an alternative, and more appropriate, approach to evaluating DM program effectiveness than the current total population approach.

Tested in a field experiment (N = 647) the hypothesis that perceptions of personal susceptibility are important in decisions to test one's home for radioactive radon gas. Experimental group subjects received a personal telephone call to tell them they lived in a high-risk area and a personal letter to reinforce the telephone message. After the intervention, experimental subjects were significantly more likely than minimal-treatment subjects to acknowledge the possibility of high radon levels in their homes. Perceptions of susceptibility and illness severity were significantly correlated with orders of radon test kits and with testing intentions. Nevertheless, there were no differences between groups in test orders or intentions. Results are discussed in terms of the difficulty of getting people to acknowledge susceptibility and the factors other than risk perceptions that influence self-protective behavior.

Currently, the most widely used method in the disease management (DM) industry for evaluating program effectiveness is referred to as the "total population approach." This model is a pretest-posttest design, with the most basic limitation being that without a control group, there may be sources of bias and/or competing extraneous confounding factors that offer a plausible rationale explaining the change from baseline. Furthermore, with the current inclination of DM programs to use financial indicators rather than program-specific utilization indicators as the principal measure of program success, additional biases are introduced that may cloud evaluation results. This paper presents a non-technical introduction to time-series analysis (using disease-specific utilization measures) as an alternative, and more appropriate, approach to evaluating DM program effectiveness than the current total population approach.