Racial and ethnic diversity in senior centers: comparing participant characteristics in more and less multicultural settings

The 2008 Health Indicators Project surveyed a probability sample (N = 1,870) of New York City senior center participants. Attendees of racially and ethnically diverse and nondiverse senior centers were compared across 5 domains: demographics; health and quality of life; social support networks; neighborhood perceptions and engagement; health service access/utilization. Although homogeneous and diverse center participants demonstrate similar health and quality-of-life outcomes, those from diverse centers demonstrate greater risk of social isolation, receive less family support, and more likely seek medical care from hospitals or community clinics. Implications and future directions for research, practice and policy are discussed.     

Forecast uncertainty reduction in nonlinear models

Summary In spite of widespread criticism, macroeconometric models are still most popular for forecasting and policy, analysis. When the most recent data available on both the exogenous and the endogenous variable are preliminaryestimates subject to a revision process, the estimators of the coefficients are affected by the presence of the preliminary data, the projections for the exogenous variables are affected by the presence of data uncertainty, the values of lagged dependent variables used as initial values for, forecasts are still subject to revisions. Since several provisional estimates of the value of a certain variable are available before the data are finalized, in this paper they are seen as repeated predictions of the same quantity (referring to different information sets not necessarily overlapping with one other) to be exploited in a forecast combination framework. The components of the asymptotic bias and of the asymptotic mean square prediction error related to data uncertainty can be reduced or eliminated by using a forecast combination technique which makes the deterministic and the Monte Carlo predictors not worse than either predictor used with or without provisional data. The precision of the forecast with the nonlinear model can be improved if the provisional data are not rational predictions of the final data and contain systematic effects. Economics Department European University Institute Thanks are due to my Ph. D. thesis advisor Bobby Mariano for his guidance and encouragment at various stages of this research. The comments of the participants in the Europan Meeting of the Econometric Society in Maastricht, Aug. 1994, helped in improving the presentation,. A grant from the NSF (SES 8604219) is gratefully acknowledged.     

The Fallacy of Ranking Possible Carcinogen Hazards Using the TD50

Ames et al. have proposed a new model for evaluating carcinogenic hazards in the environment. They advocate ranking possible carcinogens on the basis of the TD50, the estimated dose at which 50% of the test animals would get tumors, and extrapolating that ranking to all other doses. We argue that implicit in this methodology is a simplistic and inappropriate statistical model. All carcinogens are assumed to act similarly and to have dose-response curves of the same shape that differ only in the value of one parameter. We show by counterexample that the rank order of cancer potencies for two chemicals can change over a reasonable range of doses. Ames et al.'s use of these TD50 ranks to compare the hazards from low level exposures to contaminants in our food and environment is wholly inappropriate and inaccurate. Their dismissal of public health concern for environmental exposures, in general, based on these comparisons, is not supported by the data.