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Higher mortality in Blacks than Whites has been consistently reported in the US, but previous investigations have not accounted for poverty at the individual level. The health of its population is an important part of the capital of a nation. We examined the association between individual level poverty and disability and racial mortality differences in a 5% Medicare beneficiary random sample from 2004 to 2010. Cox regression models examined associations of race with all-cause mortality, adjusted for demographics, comorbidities, disability, neighborhood income, and Medicare “Buy-in” status (a proxy for individual level poverty) in 1,190,510 Black and White beneficiaries between 65 and 99 years old as of January 1, 2014, who had full and primary Medicare Part A and B coverage in 2004, and lived in one of the 50 states or Washington DC. Overall, black beneficiaries had higher sex-and-age adjusted mortality than Whites (hazard ratio [HR] 1.18). Controlling for health-related measures and disability reduced the HR for Black beneficiaries to 1.03. Adding “Buy-in” as an individual level covariate lowered the HR for Black beneficiaries to 0.92. Neither of the residential measures added to the predictive model. We conclude that poorer health status, excess disability, and most importantly, greater poverty among Black beneficiaries accounts for racial mortality differences in the aged US Medicare population. Poverty fosters social and health inequalities, including mortality disparities, notwithstanding national health insurance for the US elderly. Controlling for individual level poverty, in contrast to the common use of area level poverty in previous analyses, accounts for the White survival advantage in Medicare beneficiaries, and should be a covariate in analyses of administrative databases.

The usefulness of the vector autoregression (VAR) approach to forecasting regional economies is explored. A VAR model and a Bayesian VAR (BVAR) model of selected New York State economic variables are constructed using monthly data. Their predictions about these variables are compared with ARIMA and transfer function model forecasts. Overall, the accuracy of BVAR matches or exceeds that of the other techniques. Thus, a previous suggestion that BVAR is promising, as a forecasting tool and as a benchmark for regional forecasts, is supported.

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In 1997, Grabowski and Mullins described why pharmacy benefit management use of pharmacoeconomics was limited. The current manuscript examines recent changes in health care payers' use of pharmacoeconomic information, payers' perceptions of that information's value for informing formulary decision making, and payers' attitudes toward the generation and dissemination of pharmacoeconomic evidence. Despite a perceived improvement in the scientific rigor and "real-world applicability" of pharmacoeconomic information, payers remain skeptical of pharmacoeconomic evidence generated or funded by pharmaceutical manufacturers. This skepticism is reinforced when transparency is limited and when the picture provided of the comparative effectiveness of alternative products is incomplete. Payers suggest that greater two-way communications between payers and drug manufacturers could improve the usefulness of pharmacoeconomic information.Insurance, Pharmacoeconomics, Cost-effectiveness Analysis, Decision Making,