Research Briefs

Franks, P., Clancy, C.M., and Nutting, P.A. (1997). "Defining primary care: Empirical analysis of the national ambulatory medical care survey." Medical Care 35(7), pp. 655-668.

In this paper, Carolyn M. Clancy, M.D., Acting Director of the Agency for Health Care Policy and Research's Center for Primary Care Research, and her colleagues identified 10 elements of primary care to determine whether the recent Institute of Medicine (IOM) definition of primary care could be operationalized. These elements addressed comprehensiveness, coordination, continuity, and accessibility of care. They used data from the 1985 to 1991 National Ambulatory Medical Care Surveys to examine the activities of over 6,000 physicians in 29 physician specialty groups, according to IOM's definition of primary care. They found that, in addition to the well-defined primary care specialties, office-based emergency medicine specialists, general surgeons, and nephrologists had high primary care scores. Dr. Clancy and her colleagues call for more data to explicitly capture the functions of primary care in the health care system. Minor modifications to existing national surveys would go a long way to accomplishing this goal.

Reprints (AHCPR Publication No. 97-R090) are available from the AHCPR Publications Clearinghouse.

Fryback, D.G., and Lawrence, W.F. (1997, July). "Dollars may not buy as many QALYs as we think: A problem with defining quality-of-life adjustments." (AHCPR grant HS06491). Medical Decision Making 17, pp. 276-284.

Cost-utility analysis (CUA) is important because of its ability to make broad comparisons of the dollars per quality-adjusted life year ($/QALY) ratio across a range of interventions for different medical conditions and in differing types of people. CUA allows us to infer whether the intervention at hand is a particularly expensive or an inexpensive way to "produce" health. The Q scale that is used to compute QALYs ranges from 0 (death) to 1 (excellent health). But many CUAs use the upper anchor of the scale to denote only the absence of the particular condition under investigation and weight the disease state proportional to this endpoint. These are called q scales. This approach ignores the fact that the average patient is still subject to chronic and acute conditions that coexist with the condition being analyzed. The absence of a particular condition is not in general the same as excellent health, note these authors. CUAs based on q scales yield qALYs. Incremental $/qALY ratios are generally lower than $/QALY ratios. Other CUAs correctly weight disease states using the Q scale but erroneously assign a quality weight of 1 to the absence of disease in the CUA computations. The authors suggest that analysts doing CUAs without access to primary data from treated patients use average age-specific, health-related quality-of-life weights from population-based studies to represent the absence of a particular disease.

Lee, D., and Lopez, L. (1997, June). An invitational workshop on collaboration between quality improvement organizations and business coalitions. (AHCPR grant HS09360). Journal on Quality Improvement 23(6), pp. 334-341.

This paper summarizes a workshop, supported by the Agency for Health Care Policy and Research, of federally designated peer review/quality improvement organizations and business coalitions from nine States. The objectives of the workshop were to encourage public- and private-sector organizations to collaborate on community-based quality improvement projects; involve and encourage other groups—such as specialty societies, health associations, consumer groups, and Federal agencies—to seek other major stakeholders in their communities to expand on these activities; advance the use of valid and reliable performance measures; highlight current public- and private-sector quality initiatives; and develop a framework to help promote community-based quality improvement that cuts across populations and involves a multitude of community health partners.

Stansfield, S.A., Roberts, R., and Foot, S.P. (1997). "Assessing the validity of the SF-36 general health survey." (AHCPR grant HS06516). Quality of Life Research 6, pp. 217-224, 1997.

The researchers assessed the validity of the SF-36 General Health Survey against the Social Maladjustment Schedule (SMS) and two questionnaire measures, the Social Problem Questionnaire and the Nottingham Health Profile (NHP), in a random subsample of 206 men and women from a longitudinal survey of health and disease among 10,308 London-based civil servants. They found that social functioning on the SF-36 correlated significantly with social contacts, total satisfaction, and total management scores on the SMS and with social isolation and emotional reactions on the NHP. General mental health on the SF-36 was associated with marriage, social contacts, leisure scores, total satisfaction, and total management scores on the SMS and with emotional reactions, energy level, and social isolation on the NHP. Conversely, physical functioning and physical role limitations were generally not associated with the SMS but were associated with physical abilities and pain on the NHP. This analysis provides supportive evidence for the validity of several of the SF-36 subscales, particularly those concerned with social and psychological functioning.

Stineman, M.G. (1997). "Measuring casemix, severity, and complexity in geriatric patients undergoing rehabilitation." (AHCPR grant HS07595). Medical Care 35(6), pp. JS90-JS105.

Geriatric rehabilitation is intended to maintain or restore function, maximize life satisfaction, enhance psychologic well-being, and maintain the social status of older persons. This article presents a theoretical basis for casemix measurement in medical rehabilitation, contrasts structure of the functional independent measure-function-related groups (FIM-FRGs) intended for casemix measurement to the diagnosis-related groups (DRGs) and resource utilization groups (RUG) III systems designed for acute and long-term care settings. It also focuses on special issues of relevance to the rehabilitation of older persons.

The author points out that not all rehabilitation patients require the intensive 3 hours of daily therapy traditionally provided by rehabilitation hospitals and inpatient units. Medical rehabilitation is challenged to identify ways of ensuring equitable access to the most cost-effective level of care appropriate to each patient.

Stineman, M.G., Jette, A., Fiedler, R., and Granger, C. (1997, June). "Impairment-specific dimensions within the functional independence measure." (AHCPR grant HS07595). Archives of Physical Medicine and Rehabilitation 78, pp. 636-643.

In this paper, the authors analyzed the impairment-specific dimensions within the Functional Independence Measure (FIM) to seek more refined dimensions beyond the motor and cognitive dimensions of the FIM. They used factor analysis within 20 categories of impairment to test whether FIM items can be grouped according to functional areas of the body, using data from the Uniform Data System for Medical Rehabilitation on nearly 94,000 patients discharged in 1992 from 252 free-standing rehabilitation hospitals and units. They found that in 18 of 20 impairment categories, factor analyses of patients' admission FIM scores showed impairment-specific FIM dimensions. The impairment-specific dimensions were always nested within the motor-FIM subscale. The subscales appear to cluster FIM items by the area of the body involved, neurological level, or relative energy consumption. The researchers conclude that the FIM can be viewed as a multilayered, multidimensional measure of human function. The impairment-specific dimensions provide insight about the causal linkage between the impairment and resulting patterns of disability.

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Current as of October 1997