Results SF-36 (scores 0 ± 100; 100 = perfect health) PURPOSE: The complications of type 2 diabetes result in substantial morbidity and mortality. Racial disparities in clinical outcomes of diabetes care are well documented; however, racial variations in disability rates due to diabetes have not been studied. We sought to evaluate the impact of higher complication rates in minorities on health status and rates of disability. METHODS: We used data from the 1992 wave of the Health and Retirement Study (HRS), a national household sample of people age 50 or older in the United States, to provide estimates of prevalence of disability in patients with type 2 diabetes. Logistic regression models were constructed to evaluate predictors of disability. Self-reported disability was the dependent variable. Independent variables included race and other sociodemographic characteristics and the presence or absence of common diabetes complications including stroke, heart failure, coronary artery disease, visual loss, and renal failure. Costs of disability were calculated by applying the median reported income in the HRS to the lost work time associated with disability. Results were extrapolated to the US population using survey weights. RESULTS: In bivariate analyses, we found that African-Americans with diabetes were substantially more likely to be disabled than non-Hispanic whites with diabetes (unadjusted OR = 2.38; 95% CI 1.79, 3.16), as were Hispanic-Americans (OR = 1.54, 95% CI 1.05, 2.26). However, after adjustment for age, education, and gender, only African-Americans were at higher risk of disability (adjusted OR = 2.09; 95% CI 1.78, 2.45). These effects persisted when adjusting for the microvascular and macrovascular complications of diabetes (OR 2.74, 95% CI 1.89, 4.00). There were no significant interaction effects between race and other demographic or clinical variables. In the US population, the total lost income since the onset of disability for African-Americans with type 2 diabetes is $26 billion, which represents an incremental lost income of $12 billion compared to non-Hispanic whites with diabetes. CONCLUSION: African-Americans with diabetes have substantially elevated risks of disability and loss of income compared to non-Hispanic whites. However, the reasons for this disability remain unclear, and do not appear to be solely related to the increased risk of microvascular and cardiovascular complications. More research is needed to better define the causes and effects of increased rates of disability in minorities with type 2 diabetes. PURPOSE: The complications of type 2 diabetes, such as cardiovascular disease, stroke, visual loss, and renal failure, result in high rates of morbidity and mortality. Disability from these complications is substantial and leads to dramatic losses in income and productivity. We sought to quantify the rates, causes, and costs of disability in patients with type 2 diabetes in the United States, which have not been well delineated. METHODS: We used data from the 1992 wave of the Health and Retirement Study (HRS), a national household sample of people 50 and older in the United States, to estimate risks of disability in patients with type 2 diabetes. Self-reported disability was the primary outcome variable; duration of disability was a secondary outcome. Predictors of disability in the population of patients with type 2 diabetes (n=1330) were evaluated using logistic regression models. Independent variables included sociodemographic characteristics, health status, and presence or absence of common diabetes complications including stroke, heart failure, coronary artery disease, visual loss, and renal failure. Costs of disability were calculated by applying the median reported income in the HRS to the lost work time associated with disability; these were estimated for the entire US population using survey weights. RESULTS: 23.1% of patients with diabetes were disabled, compared to 7.7% of subjects without diabetes (unadjusted OR = 3.62; 95% CI 3.14 ± 4.18). Although more patients with diabetes were disabled, they were not disabled for longer than patients without diabetes (2550 days versus 2847 days; p=.1663). In multivariate analyses, the strongest predictors of disability in patients with diabetes were the presence of poor visual acuity (OR = 3.48; 95% CI 1.23, 9.81), congestive heart failure (OR = 3.47; 95% CI 1.69 ± 7.14), coronary artery disease (OR = 2.41; 95% CI 1.26,4.64), or renal failure (OR = 2.41, 95% CI 1.25, 4.62); stroke was not a significant independent predictor of disability. After adjustment for these outcomes, diabetes was still an independent predictor of disability (adjusted OR=2.29; 95% CI 2.61, 3.55). Based upon a median reported income of $26,000 in this sample, the lost income in the current United States cohort of patients with type 2 diabetes is approximately $14 billion annually; the total incremental lost income attributable to diabetes is $9.3 billion annually. To date, the total lost income over the lifetime of the current US diabetes population is nearly $100 billion. CONCLUSION: Type 2 Diabetes is associated with a substantially increased risk of disability, and diabetes-related disability leads to staggering losses in income and productivity. Some, but not all of this increased risk is explained by their rates of microvascular and cardiovascular diabetes complications. Both health care and societal costs should be considered when setting health care policy. 4) and a survey that asked about socio-demographic and clinical characteristics and health status. Hearing was tested with a hand-held audioscope during the study's physical examination. Hearing impairment was defined as better ear threshold at 40dB at the 1kHz or 2kHz frequencies. Logistic regression models were constructed to evaluate the influence of hearing impairment on depressive symptoms. All models were adjusted for patient socio-demographic and clinical characteristics, health status, and system of care. RESULTS: There were 484 study participants (response rate 68%). Mean age was 75+6 years; 47% were women; 52% were white, 20% Hispanic, 18% African-American, 6% Asian/Pacific Islander, and 5% Other/Multiracial; 48% had annual income under $20,000 per year; 9% received Medicaid; 24% had not graduated from high school; and 24% were enrolled in FFS. Depressive symptoms were reported by 25%. Hearing aids were worn by 9%, 17% had hearing loss at 1 kHz, and 23% demonstrated hearing loss at 2 kHz. In the multivariate analysis, hearing impairment was significantly associated with depression (OR, 1.8; P=0.05). Although there was an association between depression and hearing impairment at a frequency of 2kHz, it was not statistically significant (OR=0.67; p=.09). Other characteristics that were significantly associated with depression were higher comorbidity score (OR=1.2; p=.005); worse physical well-being (OR=1.04; p=.009); low income (OR=1.8; p=.02); and male gender (OR=1.6; p=.05). CONCLUSION: Hearing impairment is significantly associated with symptoms of depression among older persons with diabetes, a group that is already at high risk for sensory impairment due to visual loss and diabetic neuropathy, and points to a potentially correctable cause of depression. BACKGROUND: Although traditional teaching has insisted the sensitivity and specificity of a diagnostic test should not change as the pre-test probability of disease changes, several authors have suggested this may not be true in practice as patients are referred from a setting of undifferentiated problems with lower pre-test probabilities in primary care to tentative diagnoses with higher pre-test probabilities in secondary and tertiary care. The purpose of this paper is to demonstrate how and understand why sensitivity and specificity change as a patient population is divided by the physician's decision whether to refer. METHODS: Using MEDLINE, a collection of published studies with various inclusion criteria was assembled{216}. Studies were included when a) the study provided enough information about the studied population to determine its position along the referral algorithm b) the article provided history or physical examination component data that could be combined with data from other articles, and c) the article provided enough information to construct 2 Â 2 tables to calculate sensitivity and specificity for those components of the clinical examination. The studies were divided into two groups, depending on whether the patient population was studied before or after referral for surgery. Group A consists of those studies evaluating all patients in whom appendicitis was considered as a diagnosis, regardless of whether they were referred for surgery. Group B consists of those studies evaluating all patients who had been referred to the operating suite for suspected appendicitis. RESULTS: In the metanalysis of the two-by-two table data, there were several trends observed as the population studied changed from group A to group B. Sensitivity tended to rise and specificity tended to fall across most of the exam components reported. These changes were found to be statistically significant for the specificity of migration and rebound pain. When all components of the examination were combined, the fall in specificity was statistically significant for the aggregate data. CONCLUSION: This study provides the first evidence that sensitivity and specificity change in populations of patients evaluated at different positions along the referral spectrum. These changes in the operating characteristics of the finding on clinical examination are important to recognize. These changes have implications for teaching the clinical examination as well as interpreting their presence or absence in varying patient populations. For example, a finding that is felt to be non-specific in tertiary care settings may be quite specific in primary care settings. To determine the rate of hip fracture and the risk factors associated with hip fractures in disabled elderly persons who choose to live in the community rather than a nursing home. METHODS: We assessed potential risk factors in 5,187 persons who enrolled 1/90 ± 12/97 in 12 nationwide sites of the Program of All-Inclusive Care for the Elderly (PACE), which provides comprehensive care to nursing-home-eligible men and women living in the community (mean age = 79, 71% women, 49% white, 47% with dementia). Functional status on enrollment was assessed by each site's nursing staff and included degree of dependence in walking and 5 Activities of Daily Living (ADL): bathing, dressing, toileting, transferring and eating. Cognitive status was assessed using a 10-item mental status questionnaire (SPMSQ). Demographics and comorbidities were also recorded on enrollment. The main outcome measure was new hip fracture that resulted in hospital admission. Average follow-up was 2 years. RESULTS: A total of 238 hip fractures (4.6%) occurred during follow-up. The rate of hip fracture was 2.3% per person-year. Four independent predictors of hip fracture were identified using Cox proportional hazard analysis: age !75 years (adjusted hazard ratio (HR) = 2.0, 95% CI 1.4 ± 2.8); white ethnicity (HR= 2.1, 95% CI 1.6 ± 2.8); ability to transfer independently from bed to chair (HR= 3.0, 95% CI 1.2 ± 7.2); and SPMSQ errors !5 (HR= 1.6, 95% CI 1.3 ± 2.1). Several risk factors, including gender and history of stroke, were not independently associated with hip fracture after adjustment for the above risk factors. PURPOSE: Missed clinic visits interfere with good medical care and have adverse effects on clinic efficiency. Explanations for patient non-compliance in keeping appointments generally emphasize practical physical issues such as finance, transportation and socioeconomic ones. We evaluated other factors, as well, in a community hospital's medical clinic in order to define ways to improve compliance with appointments. METHODS: A 10-item questionnaire covering demographic, medical conditions, socioeconomic and support conditions, missed visits, and reasons for missed visits, were filled out by 24 clinic visitors. All charts were separately analyzed for kept and missed appointments in the previous year. 72 patients were compliant ( < 20% appointments missed) and 36 were noncompliant ( > 30% appointment missed; range=30.7 ± 85.7% median=42.3%). RESULTS: Contrary to expectations non-compliant and compliant patients did not differ by medical insurance, availability of social support, transportation, education, age, or ethnicity. The sole significant difference in medical conditions was the presence of depression. The odds ration for depression was 3.18 (95% confidence interval(1.7 ± 8.7); p=.016) times greater among noncompliers, 39% of whom admitted to depression, than compliers. The non-compliers were aware of their noncompliance. The most common reason cited for missed appointments were forgetting (n=13), inadequate money for transportation (n=12), personal problems (n=9), and too long a wait (n=9). However, these did not differ significantly from the reasons cited by the others. CONCLUSION: The most striking, and we believe important, finding in our survey was the significant association of depression with noncompliance far outstripped all other characteristics, including financial and social ones. This observation suggests a very important problem, as well as a potential solution to the problem of noncompliance. We conclude that it is essential to address the possibility of depression actively in such patients as both a therapeutic goal in itself, and a potential means to improve compliance with medical care. PURPOSE: Although intentional weight(wt) loss is recommended to many patients, whether it improves survival, particularly among men, is unclear. We examined the association between intentional wt loss and mortality in a national sample of men. METHODS: We used data from the 1989 National Health Interview Survey where 20,131 adults (91% response rate) were asked about sociodemographic and basic health information including self-reported health and smoking status, health care utilization, height, current and maximum(max) wt, and whether they were trying to lose wt. Date of death was obtained by linking to the National Death Index through 12/31/95. We determined wt loss by subtracting wt in 1989 from max wt. We classified wt loss as intentional if respondents reported that they were trying to lose wt in 1989; otherwise, it was considered of unclear intention. We used Cox models to examine if intentional wt loss was associated with time to death. We limited our analyses to men only to avoid misclassification and confounding due to pregnancy. We weighted all percentages to reflect population estimates and adjusted standard errors to account for the complex sampling. RESULTS: Among 8395 eligible men, 604 had died by the end of follow-up. Among men who lost wtintentionally,thosewholost5 ± 10lbshadthelowestunadjustedmortality(2.3%)whencompared to those who lost up to 5 lbs (5.2%), p=.004. After adjusting for age, max. BMI, race, education, smoking, health status, number of doctor visits, hospitalized days, and days in bed, and intent to lose wt, intentional wt loss was associated with improved survival among men who lost 5 ± 10 lbs (see Table) . The benefits of weight loss were most consistent among men with BMIs of 25 ± 29.9 kg/m 2 . CONCLUSIONS: Modest intentional weight loss appears to be associated with mortality benefit among men. The benefit of greater weight loss is less clear.