Quality of life at the end of life

<p>Abstract</p> <p>Background</p> <p>Little is known about self-perceived quality of life (QOL) near the end of life, because such information is difficult to collect and to interpret. Here, we describe QOL in the weeks near death and determine correlates of QOL over time, with emphasis on accounting for death and missing data.</p> <p>Methods</p> <p>Data on QOL were collected approximately every week in an ongoing randomized trial involving persons at the end of life. We used these data to describe QOL in the 52 weeks after enrollment in the trial (prospective analysis, N = 115), and also in the 10 weeks just prior to death (retrospective analysis, N = 83). The analysis consisted of graphs and regressions that accounted explicitly for death and imputed missing data.</p> <p>Results</p> <p>QOL was better than expected until the final 3 weeks of life, when a terminal drop was observed. Gender, race, education, cancer, and baseline health status were not significantly related to the number of “weeks of good-quality life” (WQL) during the study period. Persons younger than 60 had significantly higher WQL than older persons in the prospective analysis, but significantly lower WQL in the retrospective analysis. The retrospective results were somewhat sensitive to the imputation model.</p> <p>Conclusion</p> <p>In this exploratory study, QOL was better than expected in persons at the end of life, but special interventions may be needed for persons approaching a premature death, and also for the last 3 weeks of life. Our descriptions of the trajectory of QOL at the end of life may help other investigators to plan and analyze future studies of QOL. Methodology for dealing with death and the high amount of missing data in longitudinal studies at the end of life needs further investigation.</p

Overestimating Outcome Rates: Statistical Estimation When Reliability Is Suboptimal

To demonstrate how failure to account for measurement error in an outcome (dependent) variable can lead to significant estimation errors and to illustrate ways to recognize and avoid these errors. Data Sources . Medical literature and simulation models. Study Design/Data Collection . Systematic review of the published and unpublished epidemiological literature on the rate of preventable hospital deaths and statistical simulation of potential estimation errors based on data from these studies. Principal Findings . Most estimates of the rate of preventable deaths in U.S. hospitals rely upon classifying cases using one to three physician reviewers (implicit review). Because this method has low to moderate reliability, estimates based on statistical methods that do not account for error in the measurement of a “preventable death” can result in significant overestimation. For example, relying on a majority rule rating with three reviewers per case (reliability ∼0.45 for the average of three reviewers) can result in a 50–100 percent overestimation compared with an estimate based upon a reliably measured outcome (e.g., by using 50 reviewers per case). However, there are statistical methods that account for measurement error that can produce much more accurate estimates of outcome rates without requiring a large number of measurements per case. Conclusion . The statistical principles discussed in this case study are critically important whenever one seeks to estimate the proportion of cases belonging to specific categories (such as estimating how many patients have inadequate blood pressure control or identifying high-cost or low-quality physicians). When the true outcome rate is low (<20 percent), using an outcome measure that has low-to-moderate reliability will generally result in substantially overestimating the proportion of the population having the outcome unless statistical methods that adjust for measurement error are used.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/74896/1/j.1475-6773.2006.00661.x.pd

Using the Stages of Change Model to Choose an Optimal Health Marketing Target

Background: In the transtheoretical model of behavior change, “stages of change” are defined as Precontemplation (not even thinking about changing), Contemplation, Preparation, Action, and Maintenance (maintaining the behavior change). Marketing principles suggest that efforts should be targeted at persons most likely to “buy the product.” Objectives: To examine the effect of intervening at different stages in populations of smokers, with various numbers of people in each “stage of change.” One type of intervention would increase by 10% the probability of a person moving to the next higher stage of change, such as from Precontemplation to Contemplation. The second type would decrease by 10% the probability of relapsing to the next lower stage, such as from Maintenance to Action, and also of changing from Never Smoker to Smoker. Nine hypothetical interventions were compared with the status quo, to determine which type of intervention would provide the most improvement in population smoking. Methods: Three datasets were used to estimate the probability of moving among the stages of change for smoking. Those probabilities were used to create multi-state life tables, which yielded estimates of the expected number of years the population would spend in each stage of change starting at age 40. We estimated the effect of each hypothetical intervention, and compared the intervention effects. Several initial conditions, time horizons, and criteria for success were examined. Results: A population of 40-year-olds in Precontemplation had a further life expectancy of 36 years, of which 26 would be spent in the Maintenance stage. In a population of former and current smokers, moving more persons from the Action to the Maintenance stage (a form of relapse prevention) decreased the number of years spent smoking more than the any other intervention. In a population of 40-year-olds that included Never Smokers, primary smoking prevention was the most effective. The results varied somewhat by the choice of criterion, the length of follow-up, the initial stage distribution, the data, and the sensitivity analyses. Conclusions: In a population of 40-year-olds, smokers were likely to achieve Maintenance without an intervention. On the population basis, targeting quitters and never-smokers was more effective than targeting current smokers. This finding is supported by some principles of health marketing. Additional research should target younger ages as well as other health behaviors

Synchrony of change in depressive symptoms, health status, and quality of life in persons with clinical depression

BACKGROUND: Little is known about longitudinal associations among measures of depression, mental and physical health, and quality of life (QOL). We followed 982 clinically depressed persons to determine which measures changed and whether the change was synchronous with change in depressive symptoms. METHODS: Data were from the Longitudinal Investigation of Depression Outcomes (LIDO). Depressive symptoms, physical and mental health, and quality of life were measured at baseline, 6 weeks, 3 months, and 9 months. Change in the measures was examined over time and for persons with different levels of change in depressive symptoms. RESULTS: On average, all of the measures improved significantly over time, and most were synchronous with change in depressive symptoms. Measures of mental health changed the most, and physical health the least. The measures of change in QOL were intermediate. The 6-week change in QOL could be explained completely by change in depressive symptoms. The instruments varied in sensitivity to changes in depressive symptoms. CONCLUSION: In clinically depressed persons, measures of physical health, mental health, and quality of life showed consistent longitudinal associations with measures of depressive symptoms

Trajectories of self-rated health in people with diabetes: Associations with functioning in a prospective community sample

© 2013 Schmitz et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Background: Self-rated health (SRH) is a single-item measure that is one of the most widely used measures of general health in population health research. Relatively little is known about changes and the trajectories of SRH in people with chronic medical conditions. The aims of the present study were to identify and describe longitudinal trajectories of self-rated health (SRH) status in people with diabetes. Methods: A prospective community study was carried out between 2008 and 2011. SRH was assessed at baseline and yearly at follow-ups (n=1288). Analysis was carried out through trajectory modeling. The trajectory groups were subsequently compared at 4 years follow-up with respect to functioning. Results: Four distinct trajectories of SRH were identified: 1) 72.2% of the participants were assigned to a persistently good SRH trajectory; 2) 10.1% were assigned to a persistently poor SRH trajectory; 3) mean SRH scores changed from good to poor for one group (7.3%); while 4) mean SRH scores changed from poor to medium/good for another group (10.4%). Those with a persistently poor perception of health status were at higher risk for poor functioning at 4 years follow-up than those whose SRH scores decreased from good to poor. Conclusions: SRH is an important predictor for poor functioning in diabetes, but the trajectory of SRH seems to be even more important. Health professionals should pay attention to not only SRH per se, but also changes in SRH over time.This work was supported by Operating Grant MOP-84574 from the Canadian Institutes of Health Research (CIHR). GG was supported by a doctoral fellowship from the CIHR. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Examining the BMI-mortality relationship using fractional polynomials

<p>Abstract</p> <p>Background</p> <p>Many previous studies estimating the relationship between body mass index (BMI) and mortality impose assumptions regarding the functional form for BMI and result in conflicting findings. This study investigated a flexible data driven modelling approach to determine the nonlinear and asymmetric functional form for BMI used to examine the relationship between mortality and obesity. This approach was then compared against other commonly used regression models.</p> <p>Methods</p> <p>This study used data from the National Health Interview Survey, between 1997 and 2000. Respondents were linked to the National Death Index with mortality follow-up through 2005. We estimated 5-year all-cause mortality for adults over age 18 using the logistic regression model adjusting for BMI, age and smoking status. All analyses were stratified by sex. The multivariable fractional polynomials (MFP) procedure was employed to determine the best fitting functional form for BMI and evaluated against the model that includes linear and quadratic terms for BMI and the model that groups BMI into standard weight status categories using a deviance difference test. Estimated BMI-mortality curves across models were then compared graphically.</p> <p>Results</p> <p>The best fitting adjustment model contained the powers -1 and -2 for BMI. The relationship between 5-year mortality and BMI when estimated using the MFP approach exhibited a J-shaped pattern for women and a U-shaped pattern for men. A deviance difference test showed a statistically significant improvement in model fit compared to other BMI functions. We found important differences between the MFP model and other commonly used models with regard to the shape and nadir of the BMI-mortality curve and mortality estimates.</p> <p>Conclusions</p> <p>The MFP approach provides a robust alternative to categorization or conventional linear-quadratic models for BMI, which limit the number of curve shapes. The approach is potentially useful in estimating the relationship between the full spectrum of BMI values and other health outcomes, or costs.</p

Assessing, treating and preventing community acquired pneumonia in older adults: findings from a community-wide survey of emergency room and family physicians

BACKGROUND: Respiratory infections, like pneumonia, represent an important threat to the health of older Canadians. Our objective was to determine, at a community level, family and emergency room physicians' knowledge and beliefs about community acquired pneumonia (CAP) in older adults and to describe their self-reported assessment, management and prevention strategies. METHODS: All active ER and family physicians in Brant County received a mailed questionnaire. An advance notification letter and three follow-up mailings were used to maximize physician participation rate. The questionnaire collected information about physicians' assessment, management, and prevention strategies for CAP in older adults (≥60 years of age) plus demographic, training, and practice characteristics. The analysis highlights differences in approaches between office-based and emergency department physicians. RESULTS: Seventy-seven percent of physicians completed and returned the survey. Although only 16% of physicians were very confident in assessing CAP in older adults, more than half reported CAP to be a very important health concern in their practices. In-service training for family physicians was associated with increased confidence in CAP assessment and more frequent use of diagnostic tests. Family physicians who reported always requesting chest x-rays were also more likely to request pulse oximetry (OR 5.6, 95% CI 1.40 to 22.5) and recommend both follow-up x-rays (OR 5.4, 95% CI 1.7 to 16.6) and pneumococcal vaccination (OR 3.4, 95% CI 1.1 to 10.0). CONCLUSION: The findings of this study provide a snapshot of how non-specialists from a non-urban Ontario community assess, manage and prevent CAP in older adults and highlight differences between office-based and emergency department physicians. This information can guide researchers and clinicians in their efforts to improve the management and prevention of CAP in older adults

Relative contribution of various chronic diseases and multi-morbidity to potential disability among Dutch elderly

BACKGROUND: The amount of time spent living with disease greatly influences elderly people’s wellbeing, disability and healthcare costs, but differs by disease, age and sex. METHODS: We assessed how various single and combined diseases differentially affect life years spent living with disease in Dutch elderly men and women (65+) over their remaining life course. Multistate life table calculations were applied to age and sex-specific disease prevalence, incidence and death rates for the Netherlands in 2007. We distinguished congestive heart failure, coronary heart disease (CHD), breast and prostate cancer, colon cancer, lung cancer, diabetes, COPD, stroke, dementia and osteoarthritis. RESULTS: Across ages 65, 70, 75, 80 and 85, CHD caused the most time spent living with disease for Dutch men (from 7.6 years at age 65 to 3.7 years at age 85) and osteoarthritis for Dutch women (from 11.7 years at age 65 to 4. 8 years at age 85). Of the various co-occurrences of disease, the combination of diabetes and osteoarthritis led to the most time spent living with disease, for both men (from 11.2 years at age 65 to 4.9 -years at age 85) and women (from 14.2 years at age 65 to 6.0 years at age 85). CONCLUSIONS: Specific single and multi-morbid diseases affect men and women differently at different phases in the life course in terms of the time spent living with disease, and consequently, their potential disability. Timely sex and age-specific interventions targeting prevention of the single and combined diseases identified could reduce healthcare costs and increase wellbeing in elderly people