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We outline the implementation of a new method of measuring the quality of medical care that counts cases of unnecessary disease and disability and unnecessary untimely deaths. First of all, conditions are listed in which the occurrence of a single case of disease or disability or a single untimely death would justify asking, "Why did it happen?" Secondly, we have selected conditions in which critical increases in rates of disease, disability, or untimely death could serve as indexes of the quality of care. Finally, broad categories of illness are noted in which redefinition and intensive study might reveal characteristics that could serve as indexes of health. We describe how these inth of the general population and the effects of economic, political, and other environmental factors upon it, and to evaluate the quality of medical care provided both within and without the hospital to maintain health and to prevent and treat disease.

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OBJECTIVES: This study examined differences among obstetricians, family physicians, and certified nurse-midwives in the patterns of obstetric care provided to low-risk patients. METHODS: For a random sample of Washington State obstetrician-gynecologists, family physicians, and certified nurse-midwives, records of a random sample of their low-risk patients beginning care between September 1, 1988, and August 31, 1989, were abstracted. RESULTS: Certified nurse-midwives were less likely to use continuous electronic fetal monitoring and had lower rates of labor induction or augmentation than physicians. Certified nurse-midwives also were less likely than physicians to use epidural anesthesia. The cesarean section rate for patients of certified nurse-midwives was 8.8% vs 13.6% for obstetricians and 15.1% for family physicians. Certified nurse-midwives used 12.2% fewer resources. There was little difference between the practice patterns of obstetricians and family physicians. CONCLUSIONS: The low-risk patients of certified nurse-midwives in Washington State received fewer obstetrical interventions than similar patients cared for by obstetrician-gynecologists or family physicians. These differences are associated with lower cesarean section rates and less resource use.

Currently, there is considerable interest in studies that use the community as the experimental unit. Health promotion programmes are one example. Because such activities are expensive, the number of experimental units (communities) is usually very small. Investigators often match communities on demographic variables in order to improve the power of their studies. Matching is known to improve power in certain circumstances. However, we show here that if the number of communities is small, the matched design will probably have less power than the unmatched design. This is due primarily to the loss of degrees of freedom in the matched design, which outweighs the benefits of matching on any but the strongest correlates of changes in behaviour. In the community intervention situation, even small differences in sample size between the matched and unmatched analyses can have expensive consequences.