PURPOSE: To explore the association of major depressive symptoms with advancing age, sex, and self-rated health among older adults.

DESIGN AND METHODS: We analyzed 10 years of annual assessments in a longitudinal cohort of 5888 Medicare recipients in the Cardiovascular Health Study. Self-rated health was assessed with a single question, and subjects categorized as healthy or sick. Major depressive symptoms were assessed using the Center for Epidemiologic Studies Short Depression Scale, with subjects categorized as nondepressed (score < 10) or depressed (> or =10). Age-, sex-, and health-specific prevalence of depression and the probabilities of transition between depressed and nondepressed states were estimated.

RESULTS: The prevalence of a major depressive state was higher in women, and increased with advancing age. The probability of becoming depressed increased with advancing age among the healthy but not the sick. Women showed a greater probability than men of becoming depressed, regardless of health status. Major depressive symptoms persisted over one-year intervals in about 60% of the healthy and 75% of the sick, with little difference between men and women.

IMPLICATIONS: Clinically significant depressive symptoms occur commonly in older adults, especially women, increase with advancing age, are associated with poor self-rated health, and are largely intransigent. In order to limit the deleterious consequences of depression among older adults, increased attention to prevention, screening, and treatment is warranted. A self-rated health item could be used in clinical settings to refine the prognosis of late-life depression.

As a "thought experiment," the authors used a modified stages of change model for smoking to define homogeneous segments within various hypothetical populations. The authors then estimated the population effect of public health interventions that targeted the different segments. Under most assumptions, interventions that emphasized primary and secondary prevention, by targeting the Never Smoker, Maintenance, or Action segments, resulted in the highest nonsmoking life expectancy. This result is consistent with both social marketing and public health principles. Although the best thing for an individual smoker is to stop smoking, the greatest public health benefit is achieved by interventions that target nonsmokers

BACKGROUND: Older adults are often advised to walk more, but randomized trials have not conclusively established the benefits of walking in this age group. Typical analyses based on observational data may have biased results. Here, we propose a "limited-bias," more interpretable estimate of the health benefits to sedentary healthy older adults of walking more, using longitudinal data from the Cardiovascular Health Study.

METHODS: The number of city blocks walked per week, collected annually, was classified as sedentary (<7 blocks per>week), somewhat active, or active (>or=28). Analysis was restricted to persons sedentary and healthy in the first 2 years. In Year 3, some became more active (the treatment groups). Self-rated health at Year 5 (follow-up) was regressed on walking at Year 3, with additional covariates from Year 2, when all were sedentary.

RESULTS: At follow-up, 83.5% of those active at baseline had excellent, very good, or good self-rated health, as compared with 63.9% of the sedentary, an apparent benefit of 19.6 percentage points. After covariate adjustment, the limited-bias estimate of the benefit was 11.2 percentage points (95% confidence interval 3.7-18.6). Ten different outcome measures showed a benefit, ranging from 5 to 11 percentage points. Estimates from other study designs were smaller, less interpretable, and potentially more biased.

CONCLUSIONS: In longitudinal studies where walking and health are ascertained at every wave, limited-bias estimates can provide better estimates of the benefits of walking. A surprisingly small increase in walking was associated with meaningful health benefits.

Background: Women with ovarian cancer have reported abdominal=pelvic pain, bloating, difficulty eating or feeling full quickly, and urinary frequency=urgency prior to diagnosis. We explored these findings in a general population using a dataset of insured women aged 40–64 and investigated the potential effectiveness of a routine review of claims data as a prescreen to identify women at high risk for ovarian cancer.

Methods: Data from a large Washington State health insurer were merged with the Seattle-Puget Sound Surveillance, Epidemiology and End Results (SEER) cancer registry for 2000–2004. We estimated the prevalence of symptoms in the 36 months prior to diagnosis for early and late-stage ovarian cancer cases and for two comparison groups. The potential performance of a passive screener that would flag women with two or more visits for any of the symptoms in the previous 2-month period was examined.

Results: Of the 223,903 insured women, 161 had incident cases of ovarian cancer. Both early and late-stage patients had a higher prevalence of abdominal=pelvic pain and bloating than the comparison groups, primarily in the 3 months before diagnosis. The passive screener had a sensitivity of 0.31 and specificity of 0.83 and usually identified women right before diagnosis. Assuming an average cost of $500 per false positive, the screener would be considered cost-effective if the true positives had an average increase of 8.5 years of life expectancy.

Conclusions: These results support previous findings that ovarian cancer symptoms were reported in health insurance claims and were more prevalent before diagnosis, but the symptoms may occur too close to the diagnosis date to provide useful diagnostic information. The passive screening approach should be reevaluated in the future using electronic medical records; if found to be effective, the method may be potentially useful for other incident diseases.

The goal of small area analysis is often to demonstrate that hospital admission rates or procedure rates vary greatly among regions, suggesting the occurrence of unnecessary admissions or procedures in some regions. Recent articles have shown that such variation may be largely due to chance, even if no underlying differences exist among the small areas; thus, it is important to test if the observed variation is larger than expected by chance. In this article we discuss how the appropriate method for testing the null hypothesis depends on the distribution of the number of admissions at the person level. If it is not possible for an individual to have more than one admission for a given procedure, the appropriate test is a simple chi-square test. If multiple admissions are possible, a modified chi-square test can be used to account for the excess variability due to multiple admissions. Failure to make the correct modification to the chi-square test in this latter case can result in spurious results. This underscores the importance of collecting data on multiple admissions in order to estimate the distribution of the number of admissions at the individual-patient level.

OBJECTIVES: In national and local discussions of health care reform, there is disagreement about whether a national health insurance plan should be mandatory or voluntary. This study describes characteristics of low- income people who were more likely or less likely to be covered by a voluntary plan. METHODS: Survey data were available from an evaluation of Washington State's Basic Health Plan, which offered subsidized health insurance to low-income residents. For those subjects who were eligible and uninsured at baseline, those who joined were compared with those who did not join on a variety of demographic and health-related characteristics. RESULTS: There were substantial differences between those who did and did not join the Basic Health Plan. Those who did not enroll were generally less well-off, with less education, lower income, and worse health. Many had never had health insurance. CONCLUSIONS: If health care reform results in a voluntary plan, additional measures may be needed to ensure that less advantaged citizens have adequate access to health care.

Headache classification has evolved through clinical experience because there are no objective findings which define causation or headache type. The resulting groupings are inconsistently defined, and may not be optimal for the study or treatment of headache patients. To determine whether the same headache types would have resulted if today's sophisticated statistical methods had been applied to standard data collected on unselected patients, 21 symptoms collected from 726 patients with acute headaches were analyzed. Five "natural' groupings or syndromes of symptoms were found in the data. Three were similar to traditional groupings: tension, migraine and cluster. Approximately one fourth of the patients had both tension and migraine symptoms, which suggests that the division of headaches into "tension' vs "migraine' has a large overlap. An "OCULAR' and a "URI' syndrome were also detected. Contrary to expectations, vascular headaches were not usually unilateral and patients with cluster headache symptoms were not usually males. The discrepancy between these findings and current perceptions may have been caused by differences in the populations or from biased patient selection in previous studies.

OBJECTIVES: To determine whether weight categories predict subsequent mortality and morbidity in older adults. DESIGN: Multistate life tables, using data from the Cardiovascular Health Study, a longitudinal population-based cohort of older adults. SETTING: Data were provided by community-dwelling seniors in four U.S. counties: Forsyth County, North Carolina; Sacramento County, California; Washington County, Maryland; and Allegheny County, Pennsylvania. PARTICIPANTS: Five thousand eight hundred eighty-eight adults aged 65 and older at baseline. MEASUREMENTS: The age- and sex-specific probabilities of transition from one health state to another and from one weight category to another were estimated. From these probabilities, future life expectancy, years of healthy life, active life expectancy, and the number of years spent in each weight and health category after age 65 were estimated. RESULTS: Women who are healthy and of normal weight at age 65 have a life expectancy of 22.1 years. Of that, they spend, on average, 9.6 years as overweight or obese and 5.3 years in fair or poor health. For both men and women, being underweight at age 65 was associated with worse outcomes than being normal weight, whereas being overweight or obese was rarely associated with worse outcomes than being normal weight and was sometimes associated with significantly better outcomes. CONCLUSION: Similar to middle-aged populations, older adults are likely to be or to become overweight or obese, but higher weight is not associated with worse health in this age group. Thus, the number of older adults at a "healthy" weight may be much higher than currently believed. PMID: 18031486 [PubMed - as supplied by publisher] Related LinksSurveillance for certain health behaviors among states and selected local areas--behavioral risk factor surveillance system, United States, 2004. [MMWR Surveill Summ. 2006]Weight-modification trials in older adults: what should the outcome measure be? [Curr Control Trials Cardiovasc Med. 2002]Obesity in adulthood and its consequences for life expectancy: a life-table analysis. [Ann Intern Med. 2003]Body mass index in middle age and health-related quality of life in older age: the Chicago heart association detection project in industry study. [Arch Intern Med. 2003]Age-related trends in cardiovascular morbidity and physical functioning in the elderly: the Cardiovascular Health Study.

Consider a 3-state system with one absorbing state, such as Healthy, Sick, and Dead. Over time, the prevalence of the Healthy state will approach an 'equilibrium' value that is independent of the initial conditions. We derived this equilibrium prevalence (Prev:Equil) as a function of the local transition probabilities. We then used Prev:Equil to estimate the expected number of years spent in the healthy state over time. This estimate is similar to the one calculated by multi-state life table methods, and has the advantage of having an associated standard error. In longitudinal data for older adults, the standard error was accurate when a valid survival table was known from other sources, or when the available data set was sufficient to estimate survival accurately. Performance was better with fewer waves of data. If validated in other situations, these estimates of prevalence and years of healthy life (YHL) and their standard errors may be useful when the goal is to compare YHL for different populations.

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

To see if the number of sick persons in a cohort was approximately constant over time, we calculated the number of sick persons in a “research” cohort of older adults followed for up to 14 years, and also in a synthetic birth cohort.

Methods: In the research cohort, we calculated the actual number of persons in each health state over time, using eight different definitions of “sick”. For the birth cohort, we estimated the number of sick persons each year after birth.

Results: The number of sick persons in the research cohort was approximately constant for 14 years, for all definitions of “sick”. The number sick in the birth cohort was approximately constant from ages 55-80, and then declined.

Conclusions: The relative excess of sick persons in later life is caused by a decline in the number of healthy persons rather than an increase in the number who are sick. The number of current Medicare enrollees who are sick may be approximately constant for 14 years. These insights may help in planning for the aging population.

BACKGROUND: Choosing cost-effective strategies for improving the health of the public is difficult because the relative effects of different types of interventions are not well understood. The benefits of one-shot interventions may be different from the benefits of interventions that permanently change the probability of getting sick, recovering, or dying. Here, we compare the benefits of such types of public health interventions. METHODS: We used multi-state life table methods to estimate the impact of five types of interventions on mortality, morbidity (years of life in fair or poor health), and years of healthy life (years in excellent, very good, or good health). RESULTS: A one-shot intervention that makes all the sick persons healthy at baseline would increase life expectancy by 3 months and increase years of healthy life by 6 months, in a cohort beginning at age 65. An equivalent amount of improvement can be obtained from an intervention that either decreases the probability of getting sick each year by 12%, increases the probability of a sick person recovering by 16%, decreases the probability that a sick person dies by 15%, or decreases the probability that a healthy person dies by 14%. Interventions aimed at keeping persons healthy increased longevity and years of healthy life, while decreasing morbidity and medical expenditures. Interventions focused on preventing mortality had a greater effect on longevity, but had higher future morbidity and medical expenditures. Results differed for older and younger cohorts and depended on the value to society of an additional year of sick life. CONCLUSION: Interventions that promote health and prevent disease performed well, but other types of intervention were sometimes better. The value to society of interventions that increase longevity but also increase morbidity needs further research. More comprehensive screening and treatment of new Medicare enrollees might improve their health and longevity without increasing future medical expenditures.

End-of-life studies are likely to have missing data because sicker persons are less likely to provide information and because measurements cannot be made after death. Ignoring missing data may result in data that are too favorable, because the sickest persons are effectively dropped from the analysis. In a comparison of two groups, the group with the most deaths and missing data will tend to have the most favorable data, which is not desirable. Results based on only the available data may not be generalizable to the original study population. If most of the missing data are absent because of death, methods that account for the deaths may remove much of the bias. Imputation methods can then be used for the data that are missing for other reasons. An example is presented from a randomized trial involving frail veterans. In that dataset, only two thirds of the subjects had complete data, but 60% of the "missing" data were missing because of death. The available data alone suggested that health improved significantly over time. However, after accounting for the deaths, there was a significant decline in health over time, as had been expected. Imputation of the remaining missing data did not change the results very much. With and without the imputed data, there was never a significant difference between the treatment and control groups, but in two nonrandomized comparisons the method of handling the missing data made a substantive difference. These sensitivity analyses suggest that the main results were not sensitive to the death and missing data, but that some secondary analyses were sensitive to these problems. Similar approaches should be considered in other end-of-life studies.

STUDY AIMS: (1) To develop indexes measuring the degree of managedness and the covered benefits of health insurance plans, (2) to describe the variation in these indexes among plans in one health insurance market, (3) to assess the validity of the health plan indexes, and (4) to examine the association between patient characteristics and the health plan indexes. Measures of the "managedness" and covered benefits of health plans are requisite for studying the effects of managed care on clinical practice and health system performance, and they may improve people's understanding of our complex health care system. DATA SOURCES/STUDY SETTING: As part of our larger Physician Referral Study, we collected health insurance information for 189 insurance product lines and 755 products in the Seattle, Washington metropolitan area, which we linked with the study's data for 2,277 patients recruited in local primary care offices. STUDY DESIGN: Managed care and benefit variables were constructed through content analysis of health plan information. Principal component analysis of the variables produced a managedness index, an in-network benefits index, and an out-of-network benefits index. Bivariable analyses examined associations between patient characteristics and the three indexes. PRINCIPAL FINDINGS: From the managed care variables, we constructed three provider-oriented indexes for the financial, utilization management, and network domains of health plans. From these, we constructed a single managedness index, which correlated as expected with the individual measures, with the domain indexes, with plan type (FFS, PPO, POS, HMO), with independent assessments of local experts, and with patients' attitudes about their health insurance. For benefits, we constructed an in-network benefits index and an out-of-network benefits index, which were correlated with the managedness index. The personal characteristics of study patients were associated with the managed care and benefit indexes. Study patients in more managed plans reported somewhat better health than patients in less managed plans. CONCLUSIONS: Indexes of the managedness and benefits of health plans can be constructed from publicly available information. The managedness and benefit indexes are associated with the personal characteristics and health status of study patients. Potential uses of the managed care and benefits indexes are discussed.

BACKGROUND: There have been few long-term follow-up studies of older adults who follow different dietary patterns. METHODS: We cluster-analyzed data on dietary fat, fiber, protein, carbohydrate, and calorie consumption from the U.S. Cardiovascular Health Study (mean age=73), and examined the relationship of the dietary clusters to outcomes 10 years later. RESULTS: The five clusters were named "Healthy diet" (relatively high in fiber and carbohydrate and low in fat), "Unhealthy diet" (relatively high in protein and fat, relatively low in carbohydrates and fiber); "High Calorie," "Low Calorie," and "Low 4," which was distinguished by higher alcohol consumption. The clusters were strongly associated with demographic factors, health behaviors, and baseline health status. The Healthy diet cluster had the most years of life and years of healthy life, and the Unhealthy diet cluster had the fewest. The Low 4 cluster had the best cardiovascular outcomes. Differences were not usually large. CONCLUSIONS: Older adults who followed the healthy eating pattern had somewhat longer and healthier lives, and the cluster with more alcohol consumption was associated with fewer cardiovascular events. The unhealthy eating pattern had the worst outcomes.

BACKGROUND: Overweight older adults are often counseled to lose weight, even though there is little evidence of excess mortality in that age group. Overweight and underweight may be more associated with health status than with mortality, but few clinical trials of any kind have been based on maximizing years of healthy life (YHL), as opposed to years of life (YOL). OBJECTIVE: This paper examines the relationship of body mass index (BMI) to both YHL and YOL. Results were used to determine whether clinical trials of weight-modification based on improving YHL would be more powerful than studies based on survival. DESIGN: We used data from a cohort of 4,878 non-smoking men and women aged 65-100 at baseline (mean age 73) and followed 7 years. We estimated mean YHL and YOL in four categories of BMI: underweight, normal, overweight, and obese. RESULTS: Subjects averaged 6.3 YOL and 4.6 YHL of a possible 7 years. Both measures were higher for women and whites. For men, none of the BMI groups was significantly different from the normal group on either YOL or YHL. For women, the obese had significantly lower YHL (but not YOL) than the normals, and the underweight had significantly lower YOL and YHL. The overweight group was not significantly different from the normal group on either measure. CONCLUSIONS: Clinical trials of weight loss interventions for obese older women would require fewer participants if YHL rather than YOL was the outcome measure. Interventions for obese men or for the merely overweight are not likely to achieve differences in either YOL or YHL. Evaluations of interventions for the underweight (which would presumably address the causes of their low weight) may be conducted efficiently using either outcome measure.

OBJECTIVES: This study assesses the relationship of body mass index to 5-year mortality in a cohort of 4317 nonsmoking men and women aged 65 to 100 years. METHODS: Logistic regression analyses were conducted to predict mortality as a function of baseline body mass index, adjusting for demographic, clinical, and laboratory covariates. RESULTS: There was an inverse relationship between body mass index and mortality; death rates were higher for those who weighed the least. Inclusion of covariates had trivial effects on these results. People who had lost 10% or more of their body weight since age 50 had a relatively high death rate. When that group was excluded, there was no remaining relationship between body mass index and mortality. CONCLUSIONS: The association between higher body mass index and mortality often found in middle-aged populations was not observed in this large cohort of older adults. Over-weight does not seem to be a risk factor for 5-year mortality in this age group. Rather, the risks associated with significant weight loss should be the primary concern.

Knowledge of the characteristics of survey non-respondents is important to determine generalizability to the population of interest. In a recent random-digit dialling survey of health behaviours only 73 per cent of the households contacted provided any information about household composition, and only 74 per cent of those actually completed the extended interview, for an overall response rate of 54 per cent. To identify possible biases we grouped all attempted phone numbers by their prefix, and looked for the association between the response rate for that prefix and other summary variables known about the prefix. A simulation study showed that the method can identify non-response biases if certain assumptions are correct. The analysis suggested that our survey data under-represent older people and those with a college education. We found no significant biases in health behaviours, possibly because the basic assumptions did not hold. This method may assist in identification of non-response bias in other studies.

County data on the percentage of people without health insurance are seldom available, although state program planning requires such information. As part of an evaluation of Washington's Basic Health Plan (BHP), we conducted a telephone survey in nine Washington counties to estimate the percentage of people under the age of 65 who were uninsured. We used regression analysis to estimate the percentage uninsured in a county as a function of the percentage unemployed. Two validation approaches yielded very good results, suggesting that the equation could be used to estimate the percentage uninsured in unsurveyed counties. The variation ranged from 15% to 23% uninsured in the 9 surveyed counties, and was estimated to range from 9% to 35% among the state's 39 counties. With proper caution, estimates based on this equation can probably be used in other states if better data are unavailable.

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.