Distribution of quality adjusted life expectancy: A systems approach

Statistical methods are developed which assess survival data for two attributes; (1) prolongation of life, (2) quality of life. Health state transition probabilities correspond to prolongation of life and are modeled as a discrete-time semi-Markov process. Imbedded within the sojourn time of a particular health state are the quality of life transitions. They reflect events which differentiate perceptions of pain and suffering over a fixed time period. Quality of life transition probabilities are derived from the assumptions of a simple Markov process. These probabilities depend on the health state currently occupied and the next health state to which a transition is made. Utilizing the two forms of attributes the model has the capability to estimate the distribution of expected quality adjusted life years (in addition to the distribution of expected survival times). The expected quality of life can also be estimated within the health state sojourn time making more flexible the assessment of utility preferences. The methods are demonstrated on a subset of follow-up data from the Beta Blocker Heart Attack Trial (BHAT). This model contains the structure necessary to make inferences when assessing a general survival problem with a two dimensional outcome. ^

Perceived risk and outcome expectancy on the practice of home infusion technique in a hemophilia population

This study described home infusion techniques and practices, measured the perceived risk of HIV and hepatitis transmission to self and others, and measured the outcome expectancy of following risk reduction guidelines for 90 hemophilia patients and/or their infusion assistants. It also assessed general knowledge of HIV and hepatitis information for the same population.^ The study subjects were hemophilia patients or their infusion assistants from the Gulf States Hemophilia Center in Houston, the El Paso Satellite Hemophilia Clinic in El Paso, or Texas members of the Women Outreach Network of the National Hemophilia Foundation (WONN) group. Each subject was interviewed either by telephone or in person. The questionnaire used was developed for the study and consisted of 60 items. These items assessed general demographics for the patients and assistants, including questions about their training to do infusions as well as the actual practices, measured perceived personal risk for the transmission of HIV or hepatitis to the assistants, perceived risk of transmission of HIV or hepatitis to others for assistants and self-infusers, and the outcome expectancy for following recommended risk reduction guidelines also for both groups.^ The theoretical framework used assumed that perceived risk and outcome expectancy would be predictive of behavior. The findings did not support this theory. Instead, the findings suggest that infusion behavior is habitual in nature; most respondents perform exactly the same behavior for every infusion. Since none of the variables selected were predictive of the compliance behavior for home infusion the teaching method should be directed towards mastery learning, or learning that will incorporate the correct behavior into a habitual pattern of home infusion. ^

FAMILY MEDICINE RESIDENT SURVEY ON HEALTH PROMOTION (PERSONAL EXPECTATIONS, BEHAVIORS, PERCEIVED SELF-EFFICACY, COGNITIVE ASSESSMENT, OUTCOME EXPECTANCY)

This study was designed to test the theoretical predictors of personal efficacy expectations among family medicine resident physicians for helping their patients change thirteen high risk health behaviors. A survey questionnaire was sent to 781 family medicine residents in the six state south central region. The response rate was 60 percent. The hypothesized relationship between lower levels of difficulty and higher personal efficacy expectations was supported by the data. Effort was a significant predictor of perceived self efficacy for health behaviors considered less difficult to change. Situational support did not prove to be a significant predictor for many of the health behaviors. Rate and pattern of success were consistent and significant predictors of perceived self efficacy for helping patients change all thirteen of the health behaviors. Modeling of effective methods by faculty was a significant predictor of efficacy expectations for several but not all of the behaviors. Personal modeling was a significant predictor of perceived efficacy for helping patients change behaviors related to alcohol misuse and exercise. The respondents personally modeled positive health behaviors more consistently than their older colleagues or the general population.^ The results of this study lend substantially to the usefulness of the cognitive-behavioral theory of perceived self efficacy and provide a mechanism for assessing the predictors of personal efficacy expectations of family medicine resident physicians. The findings are expected to have direct implications for faculty to institute systematic programs of interventions designed to increase residents' perceptions of efficacy in facilitating more positive health behaviors among their patients. ^

Estimates of life expectancy gains from reducing/eliminating major causes of death in the USA: An update analysis for population in 2001--2008

Evaluation of the impact of a disease on life expectancy is an important part of public health. Potential gains in life expectancy (PGLE) that can properly take into account the competing risks are an effective indicator for measuring the impact of the multiple causes of death. This study aimed to measure the PGLEs from reducing/eliminating the major causes of death in the USA from 2001 to 2008. To calculate the PGLEs due to the elimination of specific causes of death, the age-specific mortality rates for heart disease, malignant neoplasms, Alzheimer disease, kidney diseases and HIV/AIDS and life table constructing data were obtained from the National Center for Health Statistics, and the multiple decremental life tables were constructed. The PGLEs by elimination of heart disease, malignant neoplasms or HIV/AIDS continued decreasing from 2001 to 2008, but the PGLE by elimination of Alzheimer's disease or kidney diseases revealed increased trends. The PGLEs (by years) for all race, male, female, white, white male, white female, black, black male and black female at birth by complete elimination of heart disease 2001–2008 were 0.336–0.299, 0.327–0.301, 0.344–0.295, 0.360–0.315, 0.349–0.317, 0.371–0.316,0.278–0.251, 0.272–0.255, and 0.282–0.246 respectively. Similarly, the PGLEs (by years) for all race, male, female, white, white male, white female, black, black male and black female at birth by complete elimination of malignant neoplasms, Alzheimer's disease, kidney disease or HIV/AIDS 2001–2008 were also uncovered, respectively. Most diseases affect specific population, such as, HIV/AIDS tends to have a greater impact on people of working age, heart disease and malignant neoplasms have a greater impact on people over 65 years of age, but Alzheimer's disease and kidney diseases have a greater impact on people over 75 years of age. To measure the impact of these diseases on life expectancy in people of working age, partial multiple decremental life tables were constructed and the PGLEs were computed by partial or complete elimination of various causes of death during the working years. Thus, the results of the study outlined a picture of how each single disease could affect the life expectancy in age-, race-, or sex-specific population in USA. Therefore, the findings would not only assist to evaluate current public health improvements, but also provide useful information for future research and disease control programs.^

Analysis of life expectancy using linear and nonlinear models

Life expectancy has consistently increased over the last 150 years due to improvements in nutrition, medicine, and public health. Several studies found that in many developed countries, life expectancy continued to rise following a nearly linear trend, which was contrary to a common belief that the rate of improvement in life expectancy would decelerate and was fit with an S-shaped curve. Using samples of countries that exhibited a wide range of economic development levels, we explored the change in life expectancy over time by employing both nonlinear and linear models. We then observed if there were any significant differences in estimates between linear models, assuming an auto-correlated error structure. When data did not have a sigmoidal shape, nonlinear growth models sometimes failed to provide meaningful parameter estimates. The existence of an inflection point and asymptotes in the growth models made them inflexible with life expectancy data. In linear models, there was no significant difference in the life expectancy growth rate and future estimates between ordinary least squares (OLS) and generalized least squares (GLS). However, the generalized least squares model was more robust because the data involved time-series variables and residuals were positively correlated. ^