Reducing the numbers of the uninsured: policy implications from state-level data analysis.

The objective of the study described in this article was to identify a model that best predicts state uninsurance rates and quantifies the contribution of socio-economic factors to enable targeted state programs to reduce uninsurance. Linear regression analysis was carried out using state uninsurance rate as the dependent variable and state-level data on demographic, employment, income, and health care environment data (independent variables). For 2000 data, the model R is 0.77, indicating that 77% of the variation in uninsurance rates is explained by the percentage of immigrant population, the workforce in very small businesses, the Black population, the state's median income, and the Medicare-aged population (model R = 0.77 for 1999 and 0.68 for 1998 data). A 1% increase in immigrant population is associated with 0.18% increase in uninsurance rate. A 1% increase in workforce employed in very small businesses associates with 0.79% increase in uninsurance. The findings indicate substantial potential for reducing uninsurance through targeted state policies. Policy recommendations are made to alleviate the insurance hurdles faced by immigrant and small business employee populations.