Parkerson Gr

The Duke Health Profile. A 17-item measure of health and dysfunction.

The Duke Health Profile (DUKE) is a 17-item generic self-report instrument containing six health measures (physical, mental, social, general, perceived health, and self-esteem), and four dysfunction measures (anxiety, depression, pain, and disability). Items were derived from the 63-item Duke-UNC Health Profile, based upon face validity and item-remainder correlations. The study population included 683 primary care adult patients. Reliability was supported by Cronbachs alphas (0.55 to 0.78) and test-retest correlations (0.30 to 0.78). Convergent and discriminant validity were demonstrated by score correlations between the DUKE and the Sickness Impact Profile, the Tennessee Self-Concept Scale, and the Zung Self-Rating Depression Scale. Clinical validity was supported by differences between the health scores of patients with clinically different health problems. Patients with painful physical problems had a DUKE physical health mean score of 58.1, while patients with only health maintenance problems had a mean score of 83.9 (scale: 0.0=poorest health and 100.0=best health). Patients with mental health problems had a DUKE mental health mean score of 49.2, in contrast to 75.7 for patients with painful physical problems and 79.2 for those with health maintenance. The DUKE is presented as a brief technique for measuring health as an outcome of medical intervention and health promotion.

The Duke-UNC Health Profile: an adult health status instrument for primary care.

The Duke–UNC Health Profile (DUHP) was developed as a brief 63-item instrument designed to measure adult health status in the primary care setting along four dimensions: symptom status, physical function, emotional function and social function. Reliability and validity were tested on a group of 395 ambulatory patients in a family medicine center. Temporal stability Spearman correlations ranged from 0.52 to 0.82 for the four dimensions. Cronbachs alpha for internal consistency was 0.85 for emotional function. Guttmans reproducibility coefficients were 0.98 for physical function and 0.93 for social function, and the scalability coefficients were 0.89 for physical and 0.71 for social. Observed relationships between DUHP scores and demographic characteristics of the respondents correlated well with those predicted by the investigators (overall Spearman correlation 0.79). Convergent and discriminant validity was supported by strong associations between components of DUHP and those on the Sickness Impact Profile (SIP), the Tennessee Self-Concept Scale (Tennessee), and the Zung Self-Rating Depression Scale (Zung). DUHP with SIP monocomponent–heteromethod Spearman correlations ranged from 0.34 to 0.45, and those for DUHP with Tennessee ranged from 0.68 to 0.81. DUHP with Zung monoitem–heteromethod correlations ranged from 0.54 to 0.57. It is concluded that this evidence supports the reliability and validity of the DUHP as an instrument suitable for studying the impact of primary health care on the health outcomes of patients.

The Duke severity of illness checklist (DUSOI) for measurement of severity and comorbidity

The Duke Severity of Illness Checklist (DUSOI) was evaluated on 414 primary care adult patients using data collected both by medical providers at the time of the patient visit and later by a chart auditor. Severity scores for individual diagnoses were determined by summing the ratings for four non-disease-specific parameters: symptom level, complications, prognosis without treatment, and expected response to treatment. Mean diagnosis severity scores (scale 0-100) among the 21 most prevalent diagnoses varied from a low of 13.9 for menopausal syndrome to a high of 43.0 for sprains and strains. An overall severity score was calculated by combining diagnosis severity scores and giving highest weights to the most severe diagnoses. Provider-generated overall severity scores (mean = 43.3) and auditor-generated overall severity scores (mean = 38.9) were significantly correlated (coefficient of agreement = 0.59, p < 0.0001). Diagnoses varied in their individual contribution to the overall severity score, from 8.9% for lipid disorder to 90.0% for sprains and strains. Separate comorbidity severity scores were calculated to measure the severity of all of each patients health problems except the diagnosis under study. For example, patients with menopausal syndrome had co-existing health problems which generated a high mean comorbidity severity score of 43.2, while patients with sprains and strains had a low mean comorbidity score of 4.7. The DUSOI Checklist can be used in the clinical setting by both providers and auditors to produce quantitative severity scores (by diagnosis, overall, and for comorbidity) which are based entirely upon clinical judgment. This method should be useful in controlling for severity of illness in clinical studies and indicating the outcome of medical care in terms of reduction in severity of illness following medical interventions.

Disease-specific versus generic measurement of health-related quality of life in insulin-dependent diabetic patients

The health-related quality of life of 170 adult insulin-dependent diabetic patients was measured cross-sectionally to compare a disease-specific instrument, Diabetes Quality of Life DQOL) questionnaire, and two generic instruments, the Duke Health Profile (DUKE) and the General Health Perceptions Questionnaire (GHP). The generic measures provided as much or more information about health-related quality of life as the disease-specific instrument. This was demonstrated both by comparison of the DQOL with the DUKE and GHP and by comparison of the disease-specific with the generic components of a modified version of the DQOL. Patients with the diabetic complication of nephropathy had increased worry over their health and lower general health perceptions. Neither the duration of diabetes nor the intensity of insulin therapy, however, was found to have a statistically significant effect on any of the health-related quality of life scores. Nondiabetic factors, such as the comorbidity, nondiabetic medications, marital status, social relationships, and family arguments were found to be predictors of health-related quality of life more often than the diabetic factors duration of diabetes, complications, and intensity of insulin therapy. These analyses suggest the clinical value of using generic questionnaires to measure health-related quality of life and psychosocial factors to identify nondiabetic problems that might respond to intervention, thereby potentially enhancing the effect of diabetes-specific therapy.

The Health Status and Life Satisfaction of First-Year Medical Students.

The self-reported health status and life satisfaction of 286 first-year Duke University medical students in four consecutive classes were measured at the beginning and end of the school year and compared statistically with relevant sociodemographic and behavioral factors. Health status, quantitated in terms of Duke Health Profile scores, was generally lower for women than for men. Although there was a definite trend of worsening along all parameters of health and satisfaction during the year for both women and men, the most marked change was the increase in depressive symptoms. The students who were very satisfied with life had fewer symptoms of depression and anxiety; higher self-esteem, better physical, mental, and social health; stronger social ties; more physical activity; more sleep; and fewer stressful life events. Strong social ties was the factor most positively related to better health and life satisfaction.

Assessing health and quality of life outcomes in dialysis: A report on an institute of medicine workshop

I N DECEMBER 1994, the Institute of Medicine (IOM) convened a workshop to evaluate instruments for the measurement of functional status, health status, and health-related quality of life (QOL) for use in the end-stage renal disease (ESRD) clinical setting, especially with dialysis patients. The workshop had been preceded by a 1991 IOM report, Kidney Failure and the Federal Government,’ and by a 1993 IOM conference on “Measuring, Managing, and Improving Quality in the End-Stage Renal Disease Treatment Setting.“2-4 That conference had recommended that a workshop be convened “to develop criteria for evaluation of QOL measurement tools; to generate a guide to the various measurements of functional outcome, health status, and health-related QOL; and to report this information back to ESRD treatment units.“4’5* This article reports on that workshop.

Improving hypertension control: impact of computer feedback and physician education.

Two interventions designed to help physicians manage hypertensive patients were evaluated in a controlled trial: 1) computer-generated feedback to facilitate identification of poorly controlled patients; and 2) a physician education program on clinical management strategies, emphasizing patient compliance. Four physician practice teams received either computer feedback, the education program, both, or neither. Feedback team physicians received seven monthly listings of the latest visits and blood pressures of their hypertensive patients. The self-administered learning program included written clinical simulations and associated didactic material. Experimental and control physicians were similar in baseline knowledge, patient mix and level of training. All feedback team physicians requested appointments for listed patients, and their patients made twice as many visits as control patients during the intervention period (p < 0.05). Education team physicians showed significant gains on a content-specific post-intervention test: mean score 84 per cent compared with 74 per cent for the control group (p < 0.005). All patient groups showed improvement in blood pressure over the study period. However, no differences between intervention teams could be detected (p > 0.20). The probability of missing a 10 mm interteam difference in outcome diastolic pressure was 1 per cent (power of 0.99). Strategies for further improvement in outpatient hypertension management may need to come from outside the traditional medical model.

Health status and severity of illness as predictors of outcomes in primary care.

Two measures of health status and severity of illness were tested as indicators of patient case-mix to predict health-related outcomes in a rural primary care community health clinic, using a convenience sample of 413 ambulatory adults (mean age = 40.4 years: 58.6% women, and 47.2% black). At baseline; patients completed the Duke Health Profile, and providers completed the Duke Severity of Illness Checklist. During the 18-month follow-up study, patients experienced the following outcomes: at least one follow-up visit (74.3%), more than six visits (20.6%), at least one referral or hospital admission (17.3%), upper tertile severity scores (24.9%), and upper tertile office charges (24.9%). Baseline physical health, perceived health, and severity scores were statistically significantly predictive of all five outcomes. Predictive accuracy (i.e., area under the receiver operating characteristic curves) for outcome probabilities estimated from a case-mix model of physical health, severity, age, gender, and race was 72.3% for follow-up, 69.7% for frequent follow-up, 70.5% for referral and/or hospital stay, 65.7% for high follow-up severity of illness, and 67.6% for high follow-up charges. These data support health status and severity of illness as case-mix indicators and outcome predictors of follow-up utilization, severity of illness, and cost in the primary care setting.

Quality of life and functional health of primary care patients

Quality of life and functional health were measured cross-sectionally for 314 adult ambulatory primary care patients in a rural clinic and found to be much better for patients with low severity of illness who required no confinement to home because of health problems, than for patients with high severity of illness who required confinement. Severity of illness was the strongest predictor for patient-reported physical health function and for patient quality of life when assessed by the health provider. Confinement was the strongest predictor for patient quality of life when assessed by the patient. There was very little agreement between patient-assessed and provider-assessed quality of life. Family stress was the strongest predictor of function in terms of mental health, social health, general health, self-esteem, anxiety, and depression. These data suggest that clinicians should direct increased attention to patient-assessed quality of life, patient-reported functional health status, and psychosocial factors such as family stress in an effort to improve medical outcomes.

Characteristics of adult primary care patients as predictors of future health services charges.

Background.Utilization risk assessment is potentially useful for allocation of health care resources, but precise measurement is difficult. Objective.Test the hypotheses that health-related quality of life (HRQOL), severity of illness, and diagnoses at a single primary care visit are comparable case-mix predictors of future 1-year charges in all clinical settings within a large health system, and that these predictors are more accurate in combination than alone. Research Design. Longitudinal observational study in which subjects’ characteristics were measured at baseline, and their outpatient clinic visits and charges and their inpatient hospital days and charges were tracked for 1 year. Subjects.Adult primary care patients. Measures.Duke Health Profile for HRQOL, Duke Severity of Illness Checklist for severity of illness, and Johns Hopkins Ambulatory Care Groups for diagnostic groups classification. Results.Of 1,202 patients, 84.4% had follow up in the primary care clinic, 63.2% in subspecialty clinics, 14.8% in the emergency room, and 9.6% in the hospital. Of