@article {657, title = {Area characteristics and individual-level socioeconomic position indicators in three population-based epidemiologic studies.}, journal = {Ann Epidemiol}, volume = {11}, year = {2001}, month = {2001 Aug}, pages = {395-405}, abstract = {

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.

}, keywords = {Adult, Black or African American, Cardiovascular Diseases, Demography, Educational Status, Factor Analysis, Statistical, Humans, Income, Linear Models, Occupations, Risk Factors, Social Class, Social Environment, Socioeconomic Factors, Statistics, Nonparametric, United States, White People}, issn = {1047-2797}, doi = {10.1016/s1047-2797(01)00221-6}, author = {Diez-Roux, A V and Kiefe, C I and Jacobs, D R and Haan, M and Jackson, S A and Nieto, F J and Paton, C C and Schulz, R} } @article {658, title = {Transforming self-rated health and the SF-36 scales to include death and improve interpretability.}, journal = {Med Care}, volume = {39}, year = {2001}, month = {2001 Jul}, pages = {670-80}, abstract = {

BACKGROUND: Most measures of health-related quality of life are undefined for people who die. Longitudinal analyses are often limited to a healthier cohort (survivors) that cannot be identified prospectively, and that may have had little change in health.

OBJECTIVE: To develop and evaluate methods to transform a single self-rated health item (excellent to poor; EVGGFP) and the physical component score of the SF-36 (PCS) to new variables that include a defensible value for death.

METHODS: Using longitudinal data from two large studies of older adults, health variables were transformed to the probability of being healthy in the future, conditional on the current observed value; death then has the value of 0. For EVGGFP, the new transformations were compared with some that were published earlier, based on different data. For the PCS, how well three different transformations, based on different definitions of being healthy, discriminated among groups of patients, and detected change in time were assessed.

RESULTS: The new transformation for EVGGFP was similar to that published previously. Coding the 5 categories as 95, 90, 80, 30, and 15, and coding dead as 0 is recommended. The three transformations of the PCS detected group differences and change at least as well as the standard PCS.

CONCLUSION: These easily interpretable transformed variables permit keeping persons who die in the analyses. Using the transformed variables for longitudinal analyses of health when deaths occur, either for secondary or primary analysis, is recommended. This approach can be applied to other measures of health.

}, keywords = {Aged, Data Interpretation, Statistical, Death, Decision Making, Female, Health Status, Humans, Logistic Models, Longitudinal Studies, Male, Models, Statistical, Quality of Life, ROC Curve, Surveys and Questionnaires}, issn = {0025-7079}, doi = {10.1097/00005650-200107000-00004}, author = {Diehr, P and Patrick, D L and Spertus, J and Kiefe, C I and McDonell, M and Fihn, S D} }