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Cover -- Title Page -- Copyright Page -- Contents -- List of Figures -- List of Tables -- Preface -- Acknowledgments -- 1 Qualitative Overview -- 1.1 Introduction -- 1.2 Forecasting Mortality -- 1.2.1 The Data -- 1.2.2 The Patterns -- 1.2.3 Scientific versus Optimistic Forecasting Goals -- 1.3 Statistical Modeling -- 1.4 Implications for the Bayesian Modeling Literature -- 1.5 Incorporating Area Studies in Cross-National Comparative Research -- 1.6 Summary -- Part I: Existing Methods for Forecasting Mortality -- 2 Methods without Covariates -- 2.1 Patterns in Mortality Age Profiles -- 2.2 A United Statistical Framework -- 2.3 Population Extrapolation Approaches -- 2.4 Parametric Approaches -- 2.5 A Nonparametric Approach: Principal Components -- 2.5.1 Introduction -- 2.5.2 Estimation -- 2.6 The Lee-Carter Approach -- 2.6.1 The Model -- 2.6.2 Estimation -- 2.6.3 Forecasting -- 2.6.4 Properties -- 2.7 Summary -- 3 Methods with Covariates -- 3.1 Equation-by-Equation Maximum Likelihood -- 3.1.1 Poisson Regression -- 3.1.2 Least Squares -- 3.1.3 Computing Forecasts -- 3.1.4 Summary Evaluation -- 3.2 Time-Series, Cross-Sectional Pooling -- 3.2.1 The Model -- 3.2.2 Postestimation Intercept Correction -- 3.2.3 Summary Evaluation -- 3.3 Partially Pooling Cross Sections via Disturbance Correlations -- 3.4 Cause-Specific Methods with Microlevel Information -- 3.4.1 Direct Decomposition Methods Modeling -- 3.4.2 Microsimulation Methods -- 3.4.3 Interpretation -- 3.5 Summary -- Part II: Statistical Modeling -- 4 The Model -- 4.1 Overview -- 4.2 Priors on Coefficients -- 4.3 Problems with Priors on Coeffcients -- 4.3.1 Little Direct Prior Knowledge Exists about Coefficients -- 4.3.2 Normalization Factors Cannot Be Estimated -- 4.3.3 We Know about the Dependent Variable, Not the Coefficients -- 4.3.4 Difficulties with Incomparable Covariates

To avoid changes in current health coverage, the Patient Protection and Affordable Care Act exempts existing plans from some regulations. These exemptions may lead to higher employer-sponsored insurance enrollment and lower government spending.

The study assesses seven cases of nation-building operations following major conflicts: Germany and Japan immediately after World War II; Somalia, Haiti, and Kosovo in the 1990s; and Afghanistan and Iraq since 2001. It concludes that two factors increase the likelihood of successful health outcomes: planning and coordination, and infrastructure and resources. In addition, the study argues that health can have an independent impact on broader political, economic, and security objectives during nation-building operations

A number of small area geographies are used in Australia to investigate primary care relevant outcomes/behaviours and to manage the supply of Primary Care Providers (PCP) that influence these outcomes. However, very little research exists on the choice of a small area geography suitable for these purposes. We evaluated a large basket of Australian small area geographies to determine which geography is optimal for investigating relationships between PCP supply and the use of PCP services. We used linked data to evaluate the relationship between PCP supply and the likelihood of a patient visiting a PCP, after adjusting for individual level covariates. PCP supply was measured at different geographies including Local Government Areas (LGAs), Primary Health Networks (PHNs), Statistical Areas-1/2/3 and Remoteness Areas. Overall, the strongest relationships between PCP density and PCP use were found when LGAs were used to measure PCP density. Large geographies such as PHNs also detected strong relationships while custom built geographies such as Primary Care Service Areas were not significantly better than the rest. Existing geographies such as LGAs may be suitable for investigating the effect of PCP supply at state or national scales.

The Patient Protection and Affordable Care Act (ACA) contains substantial new requirements aimed at increasing rates of health insurance coverage. Because many of these provisions impose additional costs on the states, officials need reliable estimates of the likely impact of the ACA in their state. To demonstrate the usefulness of modeling for state-level decisionmaking, RAND undertook a preliminary analysis of the impact of the ACA on five states—California, Connecticut, Illinois, Montana, and Texas—using the RAND COMPARE microsimulation model. For California, the model predicts that, in 2016 (the year that all of the provisions in the ACA related to coverage expansion will be fully implemented), the uninsured rate in California will fall to 4 percent; without the law, it would remain at 20 percent. The model projects that total state government spending on health care will be 7 percent higher for the combined 2011–2020 period because of the ACA.

The Patient Protection and Affordable Care Act (ACA) contains substantial new requirements aimed at increasing rates of health insurance coverage. Because many of these provisions impose additional costs on the states, officials need reliable estimates of the likely impact of the ACA in their state. To demonstrate the usefulness of modeling for state-level decisionmaking, RAND undertook a preliminary analysis of the impact of the ACA on five states—California, Connecticut, Illinois, Montana, and Texas—using the RAND COMPARE microsimulation model. For Connecticut, the model predicts that, in 2016 (the year that all of the provisions in the ACA related to coverage expansion will be fully implemented), the uninsured rate in Connecticut will fall to 5 percent; without the law, it would remain at 11 percent. The model projects that total state government spending on health care will be 10 percent lower for the combined 2011–2020 period than it would be without the ACA, mostly because of federal subsidies for residents who would have been covered by Connecticut's state-run health insurance program (State-Administered General Assistance).

The Patient Protection and Affordable Care Act (ACA) contains substantial new requirements aimed at increasing rates of health insurance coverage. Because many of these provisions impose additional costs on the states, officials need reliable estimates of the likely impact of the ACA in their state. To demonstrate the usefulness of modeling for state-level decisionmaking, RAND undertook a preliminary analysis of the impact of the ACA on five states—California, Connecticut, Illinois, Montana, and Texas—using the RAND COMPARE microsimulation model. For Montana, the model predicts that, in 2016 (the year that all of the provisions in the ACA related to coverage expansion will be fully implemented), the uninsured rate in Montana will fall to 3 percent; without the law, it would remain at 18 percent. The model projects that total state government spending on health care will be 3 percent higher for the combined 2011–2020 period because of the ACA.

The Patient Protection and Affordable Care Act (ACA) contains substantial new requirements aimed at increasing rates of health insurance coverage. Because many of these provisions impose additional costs on the states, officials need reliable estimates of the likely impact of the ACA in their state. To demonstrate the usefulness of modeling for state-level decisionmaking, RAND undertook a preliminary analysis of the impact of the ACA on five states—California, Connecticut, Illinois, Montana, and Texas—using the RAND COMPARE microsimulation model. For Texas, the model predicts that, in 2016 (the year that all of the provisions in the ACA related to coverage expansion will be fully implemented), the uninsured rate in Texas will fall to 6 percent; without the law, it would remain at 28 percent, the highest in the nation. The model projects that total state government spending on health care will be 10 percent higher for the combined 2011–2020 period because of the ACA.

The Patient Protection and Affordable Care Act (ACA) contains substantial new requirements aimed at increasing rates of health insurance coverage. Because many of these provisions impose additional costs on the states, officials need reliable estimates of the likely impact of the ACA in their state. To demonstrate the usefulness of modeling for state-level decisionmaking, RAND undertook a preliminary analysis of the impact of the ACA on five states—California, Connecticut, Illinois, Montana, and Texas—using the RAND COMPARE microsimulation model. For Illinois, the model predicts that, in 2016 (the year that all of the provisions in the ACA related to coverage expansion will be fully implemented), the uninsured rate in Illinois will fall to 3 percent; without the law, it would remain near 15 percent. The model projects that total state government spending on health care will be 10 percent higher for the combined 2011–2020 period because of the ACA.