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Allison, J., Kiefe, C.I., and Weissman, N.W. (1999, January). "Can data-driven benchmarks be used to set the goals of Healthy People 2010?" (AHCPR grant HS09446). American Journal of Public Health 89, pp. 61-65.

These researchers discuss a process for determining quantitative targets for health objectives that can be used in setting goals for Healthy People 2010. They developed a "pared-mean" method to define from data the best achievable health care practices. They calculated the pared-mean benchmark for screening mammography from the 1994 National Health Interview Survey by using metropolitan statistical areas as the "provider" unit. The researchers then established the minimum provider subset that included at least 10 percent of all women surveyed about receipt of mammography. The pared-mean benchmark becomes the proportion of women in this subset who received mammography, which was 71 percent compared with the Healthy People 2000 goal of 60 percent. The researchers conclude that benchmarks derived from data reflecting the best available care provide viable alternatives to consensus-derived targets.

Bazzoli, G.J., Shortell, S.M., Dubbs, N., and others. (1999, February). "A taxonomy of health networks and systems: Bringing order out of chaos." (AHCPR grant HS09524). Health Services Research 33(6), pp. 1683-1717.

These authors disagree with the widespread perception that organizational change in health care has been chaotic. They conclude that some important and meaningful similarities can be found across many newly evolving hospital-led organizations. Using a conceptual framework focused on differentiation, integration, and centralization, they were able to classify about 70 percent of health networks and about 90 percent of health systems into well-defined organizational clusters. Their analysis suggested that some organizational clusters had high degrees of differentiation of hospital, physician, and insurance activities (for example, decentralized health networks and systems), whereas other clusters had low differentiation (for example, independent health networks and systems). Some clusters had extensive centralization in all three service/product dimensions (for example, centralized health networks and systems). Health networks and systems typically engaged in both ownership-based and contract-based integration or they had no integration at all.

Clark, D.O., Kroenke, K., Callahan, C.M., and McDonald, C.J. (1999). "Validity and utility of patient-reported health measures on hospital admission." (AHCPR grant HS07719). Journal of Clinical Epidemiology 52(1), pp. 65-71.

These investigators developed a 15-question interview to assess several domains of health status relevant to hospitalized patients—including symptoms, functional status, mood, and perceived health—to determine the validity of patient-reported health status upon hospital admission. They delivered the structured interview to 88 percent of 2,672 eligible patients shortly after hospital admission from July 1996 through June 1997 and calculated the patients' acute physiology score (APS, indicator of illness severity). The correlation of the patient-reported measures with the APS was 0.01 to 0.13. Overall perceived health was correlated 0.20 to 0.45 with symptoms and functional status and 0.07 with the APS. The patient-reported measures also performed comparably to the APS in predicting length of hospital stay. The researchers conclude that patient-reported health measures upon hospital admission are valid.

Crystal, S., Sasso, A.T., and Sambamoorthi, U. (1999, February). "Incidence and duration of hospitalizations among persons with AIDS: An event history approach." (AHCPR grant HS06339). Health Services Research 33(6), pp. 1611-1638.

Considerable socioeconomic and geographic variability exists both in the incidence and duration of hospitalization among people with AIDS in New Jersey, based on event history analysis. These authors modeled both the incidence and duration of hospitalizations among 1,401 Medicaid beneficiaries with AIDS in New Jersey by using a multi-State/multi-episode continuous time duration model that frames hospital use as a series of transitions among States. They used simulations to translate race, geographic location, and other parameters into estimates of length of stay, the probability that a hospitalization would end in death, and the probability that a nonhospitalized person would be hospitalized within 90 days. The authors found, for example, that black race and Hispanic ethnicity were associated with hospital stays 1.2 days and 1 day longer, respectively, than stays for non-Hispanic whites; blacks also experienced more frequent hospital admissions. Residents of the high-HIV-prevalence areas of the State had more frequent admissions and stays that were 2 days longer than people residing elsewhere in the State.

Heckerling, P.S., Verp, M.S., and Albert, N. (1999). "Patient or physician preferences for decision analysis." (AHCPR grant HS06945). Medical Decision Making 19, pp. 66-77.

A woman choosing chorionic villus sampling (CVS) over amniocentesis is trading off a small increased risk associated with CVS to her pregnancy for the psychologic benefits of decreased anxiety, increased maternal-fetal bonding, and the option for a first-trimester instead of second-trimester therapeutic abortion in the event of a genetic abnormality. Another woman may find the increased risk of miscarriage associated with CVS unacceptable and opt for later amniocentesis. The authors of this study found that patient but not physician preferences when incorporated into decision models corresponded with the prenatal test choice made by the patient. They examined the relationship between prenatal test choices made by women and the choices prescribed by decision-analytic models based on their preferences and separate models based on the preferences of their physicians. Preferences were assessed using written scenarios describing testing outcomes and were recorded on linear rating scales.

Kell, S.H., Allison, J.J., Brown, K.C., and others. (1999). "Measurement of mammography rates for quality improvement." (AHCPR grant HS09446). Quality Management in Health Care 7(2), pp. 11-19.

Health insurance claims data capture a higher percentage of mammograms than chart audit data, finds this study. Thus quality improvement projects should consider using claims data only to ascertain mammography rates, conclude the researchers. They compared mammography rates from abstracted chart data and claims data for 1,096 female Medicare beneficiaries with diabetes and their 74 physicians participating in the Ambulatory Care Quality Improvement Project. Chart audit showed that 26 percent of women had received a mammogram, and claims data showed that 35 percent had done so during the 18-month period studied. The mammography rate from claims data over the 2-year period as opposed to 18 months was 42 percent.

Rosenberg, M.A., Fryback, D.G., and Lawrence, W.F. (1999). "Computing population-based estimates of health-adjusted life expectancy." (AHCPR grant HS06491). Medical Decision Making 19, pp. 90-97.

There are many different data sources and many different methods to compute health-adjusted life expectancy (HALE), an index used to measure the current health of a population and to track it over time. These authors present a Bayesian approach—a method of calculating mortality rates using a blend of regional and local data—to computing HALE for a local population, in this case, older adults in the community of Beaver Dam, WI. They used quality of well-being measures from 1,430 participants in the Beaver Dam Health Outcomes Study as weights. The authors conclude that the Bayesian method of computing population-based estimates of HALE creates a smooth set of rates, retains the local flavor of the community, and gives a measure of variability of the estimated HALE.

Schwartz, C.E., Vollmer, T., Lee, H., and others. (1999, January). "Reliability and validity of two self-report measures of impairment and disability for MS." (AHCPR grant HS08582). Neurology 52, pp. 63-70.

Two new patient self-report measures, the Symptom Inventory (which measures symptoms of neurologic impairment) and the Performance Scales (which measure eight areas of disability), were better able to distinguish multiple sclerosis (MS) patients with moderate and severe levels of disability than were generic measures such as the Health Status Questionnaire and the Quality of Well-Being Index, finds this study. Thus, if the total score on the new measures increases more than 1 SD for a given patient's subgroup (that is, minimally, moderately, or severely disabled), it might indicate that the patient's condition is worsening and would merit an imminent evaluation and intervention by a neurologist, conclude the authors. They describe results of a multicenter study that validated these patient-reported measures, which they developed for use in MS clinical research. Participants included 274 MS patients and 296 healthy controls who were matched to patients on age, sex, and education.

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AHCPR Publication No. 99-0028
Current as of April 1999

The information on this page is archived and provided for reference purposes only.


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