Search

PURPOSE: There is growing interest in incorporating area indicators into epidemiologic analyses. Using data from the 1990 U.S. Census linked to individual-level data from three epidemiologic studies, we investigated how different area indicators are interrelated, how measures for different sized areas compare, and the relation between area and individual-level social position indicators. METHODS: The interrelations between 13 area indicators of wealth/income, education, occupation, and other socioenvironmental characteristics were investigated using correlation coefficients and factor analyses. The extent to which block-group measures provide information distinct from census tract measures was investigated using intraclass correlation coefficients. Loglinear models were used to investigate associations between area and individual-level indicators. RESULTS: Correlations between area measures were generally in the 0.5–0.8 range. In factor analyses, six indicators of income/wealth, education, and occupation loaded on one factor in most geographic sites. Correlations between block-group and census tract measures were high (correlation coefficients 0.85–0.96). Most of the variability in block-group indicators was between census tracts (intraclass correlation coefficients 0.72–0.92). Although individual-level and area indicators were associated, there was evidence of important heterogeneity in area of residence within individual-level income or education categories. The strength of the association between individual and area measures was similar in the three studies and in whites and blacks, but blacks were much more likely to live in more disadvantaged areas than whites. CONCLUSIONS: Area measures of wealth/income, education, and occupation are moderately to highly correlated. Differences between using census tract or block-group measures in contextual investigations are likely to be relatively small. Area and individual-level indicators are far from perfectly correlated and provide complementary information on living circumstances. Differences in the residential environments of blacks and whites may need to be taken into account in interpreting race differences in epidemiologic studies.

We sought to evaluate the predictive accuracy of four bypass surgery mortality clinical risk models and to examine the extent to which hospitals' risk-adjusted surgical outcomes vary depending on which risk-adjustment method is applied.Cardiovascular "report cards" often compare risk-adjusted surgical outcomes; however, it is unclear to what extent the risk-adjustment process itself may affect these metrics.As part of the Cooperative Cardiovascular Project's Pilot Revascularization Study, we compared the predictive accuracy of four bypass clinical risk models among 3,654 Medicare patients undergoing surgery at 28 hospitals in Alabama and Iowa. We also compared the agreement in hospital-level risk-adjusted bypass outcome performance ratings depending on which of the four risk models was applied.Although the four risk models had similar discriminatory abilities (C-index, 0.71 to 0.74), certain models tended to overpredict mortality in higher-risk patients. There was high correlation between a hospital's risk-adjusted mortality rates regardless of which of the four models was used (correlation between risk-adjusted rating, 0.93 to 0.97). In contrast, there was limited agreement in which hospitals were identified as "performance outliers" depending on which risk-adjustment model was used and how outlier status was defined.A hospital's risk-adjusted bypass surgery mortality rating, relative to its peers, was consistent regardless of the risk-adjustment model applied, supporting their use as a means of provider performance feedback. Designation of performance outliers, however, can vary significantly depending on the benchmark and methods used for this determination.

The prevalence of obesity increased in the United States through the 1980s. The authors examined 10-year aging and secular (time-related) trends in the Coronary Artery Risk Development in Young Adults (CARDIA) cohort for indications of whether these trends are continuing and for ages of peak weight gain in young adults. CARDIA is a population-based, prospective study of 5,115 African-American and White men and women aged 18-30 years at baseline. Body weight and overweight prevalence were measured at five time points from 1985-1986 to 1995-1996. Linear, mixed-model regression was used to partition weight gain into that due to secular trends and that due to aging. Prevalence of overweight (body mass index (BMI)or = 25.0 kg/m2) increased markedly, and prevalence of severe obesity (BMIor = 40.0 kg/m2) doubled in all race-sex groups. Each race-sex group experienced significant secular weight gains, ranging from 0.96 kg/year (95% confidence interval: 79, 1.13) in African-American women to 0.55 kg/year (95% confidence interval: 0.41, 0.69) in White women. Significant secular gains were present during each follow-up period. Each race-sex group also experienced significant weight increases related to aging during their early to midtwenties. Secular trends for weight gain are continuing in CARDIA, but the magnitude of weight gain differed among the four race-sex groups.

Webster's Dictionary defines a benchmark as 'something that serves as a standard by which others can be measured'. Benchmarking pervades the health care quality improvement literature, and benchmarks are usually based on subjective assessment rather than on measurements derived from data. As such, benchmarks may fail to yield an achievable level of excellence that can be replicated under specific conditions. In this paper, we provide an overview of benchmarking in health care. We then describe the evolution of our data-driven method for identifying an Achievable Benchmark of Care (ABC) on the basis of process-of-care indicators. Here, our experience leads us to postulate the following premises for sound benchmarks: (i) benchmarks should represent a level of excellence; (ii) benchmarks should be demonstrably attainable; (iii) providers with high performance should be selected from among all providers in a predefined way using reliable data; (iv) all providers with high performance levels should contribute to the benchmark level; and (v) providers with high performance levels but small numbers of cases should not unduly influence the level of the benchmark. An example of an ABC applied to the cooperative cardiovascular project leads the reader through the computation of an ABC. Finally, we consider several refinements of the original ABC concept that are in progress, e.g. how to approach the special problems posed by very small denominators. The ABC methodology has been well accepted in multiple quality improvement projects. This approach lends objectivity and reliability to benchmarks that have been a widely used, but until now, arbitrarily defined tool.