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Review
. 2020 Feb 15;395(10223):524-533.
doi: 10.1016/S0140-6736(19)33019-3.

Improving the prognosis of health care in the USA

Affiliations
Review

Improving the prognosis of health care in the USA

Alison P Galvani et al. Lancet. .

Abstract

Although health care expenditure per capita is higher in the USA than in any other country, more than 37 million Americans do not have health insurance, and 41 million more have inadequate access to care. Efforts are ongoing to repeal the Affordable Care Act which would exacerbate health-care inequities. By contrast, a universal system, such as that proposed in the Medicare for All Act, has the potential to transform the availability and efficiency of American health-care services. Taking into account both the costs of coverage expansion and the savings that would be achieved through the Medicare for All Act, we calculate that a single-payer, universal health-care system is likely to lead to a 13% savings in national health-care expenditure, equivalent to more than US$450 billion annually (based on the value of the US$ in 2017). The entire system could be funded with less financial outlay than is incurred by employers and households paying for health-care premiums combined with existing government allocations. This shift to single-payer health care would provide the greatest relief to lower-income households. Furthermore, we estimate that ensuring health-care access for all Americans would save more than 68 000 lives and 1·73 million life-years every year compared with the status quo.

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Figures

Figure 1.
Figure 1.. Single-Payer Healthcare Interactive Financing Tool (SHIFT) interface (http://shift.cidma.us/).
This tool, available at: http://shift.cidma.us/, allows users to adjust input parameters and assumptions, including expansion in utilization, to determine the healthcare budget. The user is also able to select revenue generation options to cover the projected budget. Here, we provide a modified static image of the tool, displaying all adjustable parameters set to their default values. Within the online tool, the Healthcare Budget, Expansion in Utilization and Revenue Generation are individual tabs. Panel 1 details parameter defaults and bounds.
Figure 2.
Figure 2.. Influence of key parameters on national healthcare expenditure.
(A) Impact of reduction in physician and clinical fees (base case: 7.38%, range: 0 to 19.23%) and hospital fees (5.54%, 0 to 18.74%) on the total budget. (B) Impact of fraud reduction (4%, 0 to 10%) and overhead (2.2%, 2.2 to 12.4%) on the total budget. (C) Impact of pharmaceutical price reduction (40%, 0 to 60%) and projected healthcare utilization by the uninsured upon becoming insured, compared to those who are already adequately insured (50.1%, 50.1 to 100%) on the total budget. For example, if hospital and clinical fees are each reduced by 5% the total budget becomes $3054 billion. Furthermore, the total budget becomes $3144 billion if an overhead rate of 2.2% and a fraud reduction of 0% are enacted. Lastly, if pharmaceutical costs are reduced by 40% and healthcare utilization among the uninsured upon becoming insured rises to 50% of those who are adequately insured, then the total budget would be $3034 billion.
Figure 3:
Figure 3:. Overview of Single-Payer Healthcare Interactive Financing Tool Calculations.
Arrows indicate changes in total National Healthcare Expenditure upon implementation of each step. Subtotals and changes in National Healthcare Expenditure have been rounded to the nearest billion. Additional details on steps in the enactment of the MAA and relevant calculations are provided in the Appendix and Appendix Tables.
Figure 4:
Figure 4:
The life-saving potential of Medicare for All compared to the present.
Figure 5:
Figure 5:
Lives saved by Medicare for All as a function of increased mortality among the uninsured. The number of uninsured Americans, and therefore the estimated lives saved, would be higher if the Affordable Care Act is repealed (blue line), compared to the current status quo (tan line). Vertical lines indicate studies which found a statistically significant relationship between insurance status and mortality, among those identified in a recent review: Franks, Kronick, Sommers 1, Sommers 2, Sorlie, and Wilper.

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References

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